esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Comparative Experiment
This is because one of the UAVs in the swarm ran out of battery, which prevented the other UAVs from establishing a stable network connection. However, the DPG algorithm and our proposed algorithm have a significant advantage in terms of lifetime, and they both maintained a stable working state even after 400 time slots. In the environment of this dataset, our proposed algorithm achieves a coverage score and fairness index of over 0.8, while the DPG algorithm only achieves 0.7, indicating that our algorithm performs better in coverage than the DPG algorithm. In summary, our algorithm performs better in coverage and lifetime than the other three comparison algorithms and has good performance.
# Solving the Urban Transit Routing Problem Using a Cat Swarm Optimization-Based Algorithm
## 3 The Proposed CSO-Based Algorithm
### The Proposed CSO-Based Approach
#### 3.1.5 Constructing the Best Route Procedure  
The algorithm starts by reading the input data, i.e.,:  
* Data concerning the road network as well as the transfer requests.
* Data concerning problem requirements (number of routes in the resulting route set, maximum number of nodes for each route, the maximum time length per route and the minimum nodes per rout).
* Transfer penalty.
* Test's seed.  
From the initialization phase, each cat acquires a property that determines its initial mode in terms of seeking mode or tracing mode. A small percentage of cats is put into seeking mode and the rest in tracing mode. This percentage is determined by the value of the mixture ratio (\(MR\)) parameter, which in this contribution was set to 0.04 (see Section 3.1.8 and Table 3). \(MR\) determines the proportion of cats in seeking mode in the total active cats' population [52]. An \(MR\) value equal to 0.04 means that about 4% of the population is in seeking mode.  
The main phase of the route set configuration algorithm runs for a predetermined number of iterations (see Section 3.1.8 and Table 3). It consists essentially in separating the active cats according to the mode they are in (seeking mode or tracing mode) and applying to them the corresponding seeking mode procedure (see Section 3.1.6) or tracing mode procedure (see Section 3.1.7). After completing these procedures, each cat's mode is redefined, and the process is repeated. When the predetermined number of iterations is completed, the algorithm ends its main phase and enters an improvement phase of the resulting solution. In this phase, a refinement procedure which seeks to improve the resulting solution by adding one node at the end of each route of the final route set is applied (see Section 3.2). The pseudocode in Figure 2 demonstrates the structure of the proposed algorithm. Steps 1-4 constitute the initialization phase; steps 5-19 comprise the main phase of the algorithm, while step 20 concerns the execution of the refinement procedure.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter.** & **Value** \\ Number of Iterations & 300 + nodes of the network \\ Initial Number of Agents & 1000, 1000, 648, 288, 135 \\ Number of Active Agents & 500, 500, 335, 135, 50 \\ \(MR\) & 0.04 \\ Size of \(SMP\) & 10 \\ \(x_{m}\) & 20.0 \\ \(K_{1}\) & 10.0 \\ \(b_{1}\) & \(-\)0.04 \\ \(A\) & 1.0 \\ \(b_{2}\) & 1.0 \\ \(A\) & 1.0 \\ \(B\) & 1.0 \\ \(C\) & 1.0 \\ \(K_{2}\) & 10.0 \\ \(b_{3}\) & \(-\)0.3 \\ \(K_{3}\) & 10.0 \\ \(\omega_{1}\) & 1.0 \\ \(\omega_{2}\) & 1.0 \\ \(\omega_{3}\) & \(\left\{\begin{array}{l}121.0,\text{if number of routes is less than 6}\\ 1.0,\text{ otherwise}\\ 10.0\\ \omega_{4}\) & 1.0 \\ \(b_{4}\) & \(-\)0.05 \\ \(x_{4m}\) & 20.0–200.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values of the algorithm parameters.
## 2 Materials and Methods
### pol-ATR-FTIR  
Polarisation spectroscopy uses linearly polarised light instead of an unpolarised light beam. GladiATR attenuated total reflection (ATR) device was equipped with a polarising filter (pol) with an adjustable angle \(\theta\). Spectral measurements were carried out at different angles \(\theta\) between the plane of polarization of the light and the axis of the sample [33]. For materials with a partially ordered structure, quenching and amplifying some absorption bands may be observed during the change in the \(\theta\) angle of polarisation of light. This phenomenon is observed because the bond's radiation absorption probability is maximal at a certain angle (between the beam polarisation plane and the axis of the bond) [7]. Polar-ATR-FTIR spectra were gathered with the angle step of 45\({}^{\circ}\), in the range of 0-360\({}^{\circ}\), and 15\({}^{\circ}\) step, in the range of 0-90\({}^{\circ}\).
## 3 Validation
### Flat Plate  
The first application example is the zero-pressure gradient boundary layer developing over a flat plate. Optimal V-shape riblets, with \(l_{g}^{+}=10.5\) corresponding to \(h^{+}=\sqrt{2}l_{g}^{+}\), are placed everywhere along the plate, immersed in a uniform external flow.  
Figure 2 (left) shows how riblets influence the streamwise evolution of the friction coefficient, demonstrating the correct amount of drag reduction. On the entire plate, the integrated percentage drag reduction is \(\Delta C_{D}/C_{D,0}=6.5\%\), in agreement with existing experimental [3] information. Local changes of \(C_{f}\), descending from the imposed slip, are consistent with those by ref. [15]. The evolution of the physical dimensions of the grooves is shown in Figure 2 (right), together with the analogous evolution of the slip length. Once the boundary layer becomes fully turbulent, i.e., for \(Re_{x}>10^{5}\), changes of the slip length with the streamwise coordinate are rather mild: there is a small increase with \(Re_{x}\) to reach the largest value of 50 microns at the downstream end of the plate. By construction, as explained earlier in Section 2, this corresponds to one viscous length. At the plate end, the predicted size of optimal riblets is approximately \(h=0.8\) mm.  
The correctness of the model is directly checked in Figure 3, which provides graphical evidence that, regardless of the streamwise location, the upward shift of the mean velocity profile is of unitary value, confirming that \(\Delta U^{+}=\lambda^{+}=1\).  
Figure 1: CAD model of the simplified UAV.
# Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
## 5 Algorithm for the Energy Saved Maximization Problem
### DEEC Protocol Description  
To best illustrate our proposed routing protocol, we provide a brief introduction of the DEEC protocol firstly. The DEEC is an efficient distributed clustering routing protocol [29]. The energy consumption of the CH is much higher than other normal nodes. To balance the energy load of nodes, in each round, the CH will be dynamically selected according to the ratio of the remaining energy to the average remaining energy of the network. In practice, there are many factors that influence the node energy consumption. That is, the energy consumption rates of different sensor nodes are different. Thus, the CH selection should consider not only remaining energy, but also the energy consumption rate. However, the DEEC only takes the remaining energy into consideration. We propose a new improved DEEC protocol by considering more factors that influence the lifetime of the sensor node.
# Strength Correlation and Prediction of Engineered Cementitious Composites with Microwave Properties
## 2 Methodologies  
The nondestructive microwave near-field technique and the conventional (destructive) mechanical compressive strength test were both employed in this study. The electrical conductance at a microwave frequency used in this study is the normalized conductance (dimensionless) known as the effective conductance, and it accounted for electromagnetic energy loss in cementitious material [18; 21]. Initial water content used during mixing plays a crucial role in the hydration reactions of cement binder system. Water acts as conductive media and then, after the dielectric loss at microwave frequencies, it combines the conductive loss effects associated with the acceleration of free ions in the interstitial pore. Furthermore, the effective conductance is partly attributed to the dipolar loss effects associated with molecular rotation of water, and the interstitial build-up of charges within the capillary pores. Therefore, in this study the authors measured electrical conductance via the near-field technique, in which decrease in conductance directly reflects reductions of water content during hydration of ECC specimens.  
The average values of measured microwave data from each measurement were curve-fitted and modelled by means of bi-exponential regression functions. In order to evaluate the strength prediction and strength evolution monitoring using microwave properties, the correlations between compressive strength and microwave conductance were established via the method of curve mapping. Figure 1 shows the block diagram of the parallel processes undertaken in this study. The mix of PVA-ECC materials and the casting of different shapes of specimens were performed on the same day, defined as Day-0. In consecutive experimental and computational processes, electromagnetic near-field and mechanical measurements were simultaneously performed for the same PVA-ECC materials with different initial water-to-binder (\(w/b\)) ratios. The daily decay of conductance at early ages (first 8 days) as well as the growing compressive strength of ECC specimens for 28 days were modelled using exponential regression functions. These two physical properties via respective early-age (conductance) and mature-age (strength) models were then correlated, and the ultimate goal was to predict the compressive strength by means of effective microwave conductance.
## 4 Results Achieved Following Our Pipeline
### Datasets and Models Used  
As mentioned in previous sections, our main datasets are composed, on the one hand, of the set of audios, both real and fake, provided by ASVspoof, and on the other hand, by a set of fake audios corresponding to persons of interest and a small compilation of fake audios found on social networks or received in the RTVE newsroom.  
The audio corpus generated by Monoceros Labs to train the fake audio classifier contains both real audio from public interventions and audio generated with synthetic voice models. For the latter, they adopted two different techniques: creating voice synthesis models (TTS) to obtain audio that imitates all the characteristics of the prosody of the voice, including intonation; and creating voice conversion (VC) models, to obtain audio that imitates only the timbre of the voice. We adopted these different techniques in order to obtain detection models that are able to identify different types of fakes.  
The cleaning and preparation of the data prior to the creation of the voice models has been the minimum necessary, as has been the training of the models, in order to obtain a result of similar quality to the possible results obtained by non-experts, which are the most common types of fakes that arrive to the newsrooms. In addition, Monoceros Labs has worked with these audio data and voice models in a private and secure manner, with its own and local infrastructure, without access to public clouds, to ensure that these results are not used for purposes other than those designed by the Chair.  
Although the audio provided by Monoceros Labs was already of high quality, a normalization process was carried out to standardize the sound with that of the authentic recordings. All audio was converted to FLAC format, with a 16 kHz specification and a mono channel, to meet the requirements of our detection model. Subsequently, the audios were tagged in files following specific guidelines to indicate its nature (real or false) to the detector in the training phase.  
Regarding the model for detecting fake audio, we selected FastAudio, a learnable audio front-end designed to improve fake audio detection. This technology differentiates itself from traditional fixed front-ends by being able to learn and adapt specifically to the characteristics of spoofing threats, resulting in greater effectiveness in identifying audio fakes.  
Technologically, FastAudio replaces fixed filter banks, which are typically used in other audio processing systems, with learnable filter layers. This allows the model to dynamically adjust the characteristics it extracts from the input audio during the training process, thus being able to focus on the most appropriate characteristics of the audio to classify them as fake or true. In addition, FastAudio uses advanced machine learning techniques such as short-time Fourier transforms (STFTs) to process the audio signal and apply logarithmic compression, imitating the non-linearity in the perception of sound intensity of the human ear, that is, techniques to simulate how the human ear perceives sound [48].
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 3 5G Coverage Enhancement Techniques
### 5G Optimization Algorithms  
Nowadays, cellular networks are becoming increasingly challenged to provide high data rates, low latency, enhanced coverage, and capacity. The user requirements are to be fulfilled with advanced technologies. These technologies might affect network complexity and also cost. Therefore, optimization algorithms are required to reduce the network complexity and to enhance the key parameters of 5G and beyond 5G. The benefits of optimization algorithms include reduction in interference and latency, enhanced SINR throughout the cell, spectral efficiency, throughput, coverage, and capacity [184]. In the literature, researches have proposed optimization techniques to enhance the key parameters of 5G [185; 186; 187]. In Ref. [185], a genetic optimization algorithm is used to identify the location of a mobile station in a cell and the location of the new BSs in the test regions. This method will enhance the coverage capacity and data rates and decrease latency and traffic density. In Ref. [186], a game theory based optimization algorithm is proposed to enhance energy efficiency and throughput based on link fairness. In Ref. [187], the authors proposed an innovative scheduling algorithm to reduce the delays and call drop rates in order to enhance the QoS. The position of an antenna, its tilt angle and height will affect the coverage of a cellular system. Therefore, optimization of antenna parameters will be considered to enhance the coverage of a network [188; 189; 190]. In a cellular system, directional antennas with higher order sectorization are considered to enhance the coverage. The antenna tilt will improve the coverage and also SINR [188]. In Ref. [189], a reinforcement learning (RL) algorithm is used to improve the spectrum efficiency and the sum rate of a network, using a simplified path loss model. In Ref. [190], a self-tuning algorithm is proposed to adjust the tilt of an antenna. Two fuzzy logic controllers are used for automatic adjustment of the antenna tilt, which will improve the SINR at the cell edge and spectrum efficiency. In Ref. [191], artificial neural network (ANN) and stochastic learning based optimization algorithms are used to optimise the antenna tilt. Network coverage and capacity are enhanced by usingthe ANN algorithm. In Ref. [192], two machine learning algorithms are proposed to reduce the interference and to enhance the coverage by optimizing the transmitted power and antenna tilt. The random distribution of the traffic across a cell and its uncertainty in the radio propagation needs dynamic antenna tilts and optimization algorithms to enhance the coverage and capacity. Different optimization algorithms have been proposed in recent research to enhance the coverage and capacity in 5G networks, as has been summarised in Table 16.
# Empirical Study of Fully Homomorphic Encryption Using Microsoft SEAL
###### Abstract  
In the context of the increasing integration of Internet of Things technologies and the growing importance of data lakes, the need for robust cybersecurity measures to protect privacy without compromising data utility becomes key. Aiming to address the privacy-security challenge in such digital ecosystems, this study explores the application of Fully Homomorphic Encryption (FHE) using the Microsoft SEAL library. FHE allows for operations on encrypted data, offering a promising opportunity for maintaining data confidentiality during processing. Our research employs systematic experimental tests on datasets to evaluate the performance of homomorphic encryption in terms of CPU usage and execution time, executed across traditional PC configurations and a NVIDIA Jetson Nano device to assess the scalability and practicality of FHE in edge computing. The results reveal a performance disparity between computing environments, with the PC showing stable performance and the Jetson Nano revealing the limitations of edge devices in handling encryption tasks due to computational and memory constraints.  
anonymity; cybersecurity; data privacy; edge computing; IoT; homomorphic encryption +
Footnote †: journal: Applied  
1
Footnote †: journal: Applied  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model
## 5 Experimental Results and Analysis
### Evaluation of the ELi and IDSER Detector
#### 5.2.3 Comprehensive Evaluation Results of the ELi and IDSER Detector  
Since ELi and IDSER payloads are essentially Java code with similar malicious features, we need a unified machine learning model to detect them simultaneously. We combine the training data of ELi and IDSER to train a machine learning model for simultaneous detection. We evaluate trained machine learning models; the results are shown in Table 5, and ROC curves are shown in Figure 10. From the evaluation results, we can see that all the models, except naive Bayes, achieve a precision of 0.99. The recall and F1 score of the decision tree and random forest models are the same, both being the best among the six models, reaching 0.88 and 0.94, respectively. As for the ROC curves, the AUC of the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **TP** & **TN** & **FP** & **FN** & **Precision** & **Recall** & **F1** \\  & (\(\
earrow\)) & (\(\
earrow\)) \\ \hline Naive Bayes & 22 & **78** & **0** & 22 & **1** & 0.50 & 0.67 \\ Decision Tree & **37** & 77 & 1 & **7** & 0.97 & **0.84** & **0.90** \\ Logistic Regression & 34 & 75 & 2 & 10 & 0.94 & 0.77 & 0.85 \\ Random Forest & **37** & 77 & 1 & **7** & 0.97 & **0.84** & **0.90** \\ SVM & 34 & 76 & 1 & 10 & 0.97 & 0.77 & 0.86 \\ XGBoost & **37** & 75 & 3 & 7 & 0.93 & **0.84** & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation results of IDSER attack detection. The higher (\(\
earrow\)) or lower (\(\searrow\)) the value is, the better the model is. The best scores are shown in **bold**.  
Figure 9: ROC curves of IDSER detection. (**a**) ROC curves of naive Bayes; (**b**) ROC curves of decision tree; (**c**) ROC curves of logistic regression; (**d**) ROC curves of random forest; (**e**) ROC curves of SVM; (**f**) ROC curves of XGBoost.  
decision tree is 0.91, while the AUC values of random forest, SVM, and XGBoost are all 0.9. Based on all the evaluation results, we find that both random forest and decision tree have advantages over other machine learning models, whether detecting ELi and IDSER separately or simultaneously.
# Advantages and Limitations of Current Microgravity Platforms for Space Biology Research
## 5 Orbiting Microgravity Facilities
### The International Space Station
#### 5.2.3 Time Constraints  
Usually, it takes about a week after delivery to the personnel conducting launch operations for a payload to be installed on the ISS. This includes pre-launch time both in the building and on the launch pad, vehicle ascent and dock with the ISS, and unloading. However, this time can vary from 4 days to longer than a week, marking a critical timeframe for many biological experiments [79]. To minimize the impact of delays, scientists are usually required to design payloads that remain viable for at least 2 weeks and to prepare a \"back up\" copy of the experiment in case of longer delays.  
Currently, payloads do not have access to electrical power during transit, and the payload environmental control is ensured by passive stowage bags. Temperature is thus either 4 \({}^{\circ}\)C or ambient temperature (18-29 \({}^{\circ}\)C). A payload, therefore, needs to be designed to be able to stay dormant and viable in these environmental conditions until its installation onboard the ISS.  
Once on board, most experimental procedures should be automated because crew time is not dedicated to any single experiment, but to several of them, and is thus very limited.  
For sample recovery, since the end of the Space Shuttle program, samples take a minimum time of 48 h from the landing to the laboratory, even in the case of the possibility to use nearby laboratories. Since the end of the shuttle program, samples take a minimum time of 48 h from the landing to the laboratory (in the case of the opportunity to use laboratory nearby the landing site). This is not a problem for fixed samples, but an extended exposure of living whole organisms to Earth gravity can trigger adaptive responses that can mask the previously achieved microgravity effects.
## 3 LIBS Spectral Pretreatment
### Model Validation  
After the collection of LIBS spectra, the database of 100 coal samples was randomly divided into training, test, and prediction sets. The training and test sets consist of 70% (70 samples), and the prediction set accounts for 30% (30 samples). The training set is used for model training, and the test set is used for cross-validation to find out the best performance of the model and avoid overfitting. The prediction set is composed of coal spectra, and the proximate analysis is only used to evaluate the prediction performance instead of model training and validation.  
The model validation and parameter selection were realized by cross-validation, which could evaluate the accuracy and robustness of the model and select the optimal parameter for modeling. Herein, the leave-one-out cross-validation (LOOCV) was used for the classification model and five-fold cross-validation was used for the quantification model. These two methods both belong to K-fold cross-validation, which divides the dataset into K parts, of which K \(-\) 1 parts are the training set, and the remaining parts are the test set. The difference is that the test set of LOOCV is 1 sample, and the test set of five-fold cross-validation is 14 samples. In addition, each part of the dataset will be as the test set in turn (K times), thus K model validation results can be obtained after the cross-validation. The average value of K model validation results can be considered as the basis for the model tuning.
# Finding Top-\(k\) Nodes for Temporal Closeness in Large Temporal Graphs
## 3 Approximating the Closeness
### Finding Top-K Nodes  
Theorem 3 states that we can approximate the closeness centrality of all nodes of a temporal graph by using a sample of size \(h\), which is logarithmic with respect to the number of nodes. In Section 5, we will show how this approximation method works particularly well for nodes with a high closeness. Based on this observation, a natural strategy for finding the top-\(k\) nodes consists of: (a) compute the approximated temporal closeness for all nodes, using a sample size \(h\); (b) rank the nodes according to this estimation and select the top-\(K\) nodes, with \(K>k\); and (c) compute the exact closeness of these \(K\) nodes, then rank them and select the top-\(k\) nodes. As we will see in Section 5, in practice choosing \(h=K=1024\) has worked in all cases we have investigated for finding the top-100 nodes. This leads to a total cost proportional to \(2048\cdot m\), which is a quite small (between \(1/10\) and \(1/100\)) fraction of the cost that would be needed to compute the exact closeness for all nodes.