esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents log likelihood ratio (LLR) based relay selection scheme for a cooperative amplify and forward relaying system. To evaluate the performance of the aforementioned system model, a three state Markov chain based fading environment has been presented to toggle among Rayleigh, Rician, and Nakagami-m fading environment. A simulation is carried out while assuming that there is no possibility of direct transmission from the source and destination terminal. Simulation results on the basis of Bit Error Rate (BER), Instantaneous Channel Capacity, and Outage probability have been presented and compared for different cases. In each case, the best performance of the proposed algorithm is obtained with a Binary Phase Shift Keying (BPSK) modulation scheme.  
Keywords:cooperative communication; optimal relay selection; finite state markov channel; multiple input multiple output (mimo); cooperative diversity; log-likelihood ratio +
Footnote †: journal:
## 4 Defense Against False Data Injection
### The Proposed Defense Strategy  
Since SoC values follow patterns linked to the daily electricity consumption habits of household residents, our defense strategy leverages machine learning to learn these patterns and detect any false SoC values reported by malicious users. The core idea is to train machine learning models on both benign and malicious SoC patterns, enabling them to identify discrepancies and flag false reports. In Section 3.1, we outline five subtle techniques attackers use to manipulate SoC values in ways designed to evade detection. To create a malicious class for training, we apply these attacks to real benign SoC data, simulating the altered SoC values. The detector takes as input 24 SoC reports of a consumer (covering 24 h), which should be sufficient to identify daily SoC patterns influenced by routines like sleeping, cooking, and working. We choose to train a single detector for all consumers, rather than one for each individual, because it is more efficient to have one model that applies to all. Training a separate detector for each consumer would require numerous models, consuming significant computational resources. Additionally, detection must occur at the control center level since, in our threat model, we assume that consumers may tamper with their meters, making meter-level detection unreliable.  
Selecting appropriate machine learning models is crucial for accurately distinguishing between benign and malicious SoC patterns. Given the time-series nature of SoC data, CNNs are well-suited for this task due to their effectiveness in extracting spatial features from sequential data. Additionally, CNN-GRU models are employed to capture temporal dependencies, as GRUs can effectively handle long-term dependencies within time-series data. The complementary capabilities of these models allow us to compare their performance and select the most appropriate one for the detection task. Logistic Regression serves as a baseline for comparison due to its simplicity and Interpretability. However, its linear nature makes it less suitable for detecting complex and non-linear attack patterns, highlighting the advantage of more advanced models like CNNs and CNN-GRUs.  
Once the detector flags a consumer as malicious, the RL-based charging coordination system excludes it from charging until a physical inspection confirms whether its smart meter has been compromised or if it is a false alarm.
# Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping
## 2 Material and Methodology
### Extreme Gradient Boosting Regressor  
XGBRs are an optimized distributed GBR, which are designed to be efficient, flexible, and portable [31]. XGBR provides additional regularization hyperparameters as shown in Equation (7), which can help reduce the chances of overfitting, decrease prediction variability and, therefore, improve accuracy. The predicted output \(\hat{y}_{i}\) is obtained by minimizing the regulation function \(\mathcal{L}\):  
\[\mathcal{L}=\sum_{i}l(\hat{y}_{i},y_{i})+\sum_{m}\Omega(f_{m})\;,\;\text{ where}\;\Omega(f)=\gamma T+\frac{1}{2}\lambda\|w\|^{2}+\alpha|w| \tag{7}\]  
Here, \(\Omega\) represents the regularization parameter that penalizes the complexity of the model like regression tree functions and smooth the final learned weights to avoid overfitting. \(T\) represents the number of leaf nodes and \(w\) is the score of the leaf node. \(\gamma\), \(\lambda\), and \(\alpha\) are used to define the level of regularization. \(\alpha\) and \(\lambda\) also known as L\({}_{1}\) and L\({}_{2}\) regularization, respectively, have different influences on weight; \(\alpha\) inspires sparsity, encouraging the weight to be zero, while \(\lambda\) inspires the weight to be small. \(\gamma\) is a commonly implemented pseudo-regularization hyperparameter known as a Lagrangian multiplier which controls the complexity of a given tree. \(\gamma\) specifies the minimum loss reduction required to make further partitions on a leaf node, which means that a higher value leads to fewer splits. In addition to the use of a regularization term, predictor _subsampling_ was used to prevent overfitting [30].  
The prediction process adds the results of each tree to obtain the final results in the XGBR model. The parameters of each tree (\(f_{t}\)), which includes the structure of the tree and the scores obtained by each leaf node, have to be determined. The additive training method adds the result of a tree to the model at a given time. The predicted value ( \(\hat{y}_{i}{}^{(t)}\)) obtained in step \(t\) can be used to obtain the algorithm process:  
\[\hat{y}_{i}{}^{(t)}=\sum_{m=1}^{M}f_{m}(\mathbf{x}_{i})=\hat{y}_{i}{}^{(t-1)}+ f_{t}(\mathbf{x}_{i}) \tag{8}\]  
In a scikit-learn implementation, the additional parameters in GBRs are \(\gamma\), \(\lambda\), and \(\alpha\), as mentioned above. These regularization parameters limit how extreme the weights (or influence) of the leaves in a tree can become.
# Query Processing in Blockchain Systems: Current State and Future Challenges
## 3 Application Domains Identified through Literature Review
### E-Voting  
Electronic voting systems (known as e-voting) are a means of strengthening democratic processes. By digitizing the election process, not only is bureaucracy reduced, but people can cast their votes much more efficiently. This is an advantage especially for elderly voters or voters with a disability, as e-voting enables them to participate in the election without having to leave home and rely on the help of others [84]. While in the past, mostly technical difficulties impeded the introduction of e-voting, in today's fully connected world, it is rather a matter of security concerns [85]. To this end, the transmission of votes must be trustworthy and secure [86], and the secrecy of the ballot has to be respected [87].  
However, one of the most important confidence-building measures is to ensure full transparency in e-voting and election results. This means, all voters must be able to verify that every vote is counted and that ballots are not manipulated retroactively. The use of blockchains is therefore particularly suitable to manage the votes. First of all, the community decides by consensus which data are included in the blockchain, i.e., which votes arevalid. Storing votes in a blockchain ensures that they are immutable, and tampering can be detected immediately. In addition, blockchains provide great transparency because each participant in the blockchain network keeps a complete copy of the blockchain--and thus all of the data--on their node [88]. Furthermore, the decentralized nature of blockchains ensures availability, as they are less susceptible to denial-of-service attacks than a centralized approach [89].  
There are multiple examples in literature in which blockchains are used to support secure and transparent e-voting, e.g., [PERSON] and [PERSON] [90], [PERSON] et al. [91], [PERSON] and [PERSON] [92], [PERSON] et al. [93], and [PERSON] et al. [94].  
Based on this research, we can conclude that there is only one type of data in e-voting:  
* The votes are stored in the blockchain as independent records. Once a vote has been cast, it must not be subsequently altered or deleted. Without any loss of generality, we assume that some kind of verification of whether a ballot is valid takes place before the votes are entered into the blockchain. Therefore, no extensions to the stored data are required.  
In the context of e-voting, statistical queries that aggregate the stored data are particularly relevant. Typical queries therefore include, but are not limited to:  
* Determine the final result of an election.
* Determine the voting behavior of different groups of voters.
* Determine which shifts of voters happened compared to the last election.
## 8 Conclusions  
This study aimed to evaluate the interruption cost by interruption coordination modes and task types using user- and task-oriented measures. Many prior studies argued that interruptions may be disruptive and detrimental in complex multitasking environments. On the other hand, interruptions may be beneficial in initiating the reception of new information and alerts. Regardless of positive or negative, the reliable evaluation of interruption cost is valuable in interruption-ridden modern working environments. In this study, cognitive workload and task performance were taken as user-oriented and task-oriented cost components in the interruption cost evaluation approach. As specific measures of the cost components, pupil diameter changes and the NASA TLX questionnaire were used to assess cognitive workload, and task completion time and task accuracy were collected as task performance measures. As an application of the approach, three interruption coordination modes, namely the immediate, the negotiated, and the scheduled modes, were designed and tested, and task type depending on cognitive demands was considered as another factor for interruptive task environments. The results highlighted that the interruption cost in the negotiated mode is lower than that in other modes, and that primary task type is more critical in the evaluation of the cost. The results also validated the effects of task similarity of primary and peripheral tasks in interruptions. Moreover, the controllability of interruptions in the immediate mode and the interruption lags in scheduled mode were evaluated as the most cost-demanding processes. This study made several contributions toward understanding how the variations of workload and task performance with regards to the structure of interruptive task types and different interruption management modes are related. In the end, dissimilar types of tasks and optimal lag-time control can be suggested as potential strategies to reduce detrimental effects of unexpected and unnecessary interruptions.  
All authors conceived the research questions and the structure of this study. [PERSON] designed and conducted the experiment and data collection. All authors wrote the paper; [PERSON] wrote an original draft, and [PERSON] and [PERSON] revised the paper. [PERSON] secured funding to conduct the experiment and publish the paper.  
This research was funded by the National Research Foundation of Korea, grant number NRF-2017R1C1B5076926.  
The authors declare no conflicts of interest.
## 1 Introduction  
Currently, the most precise way of representing a person is through pulsed holographic portraitaire [1; 2; 3; 4]. A pulsed portrait is a hologram recorded using high-energy (several Joules) ruby (red) or neodymium doubled in frequency (green) pulsed lasers. The extremely short exposure time of approximately 20 ns allows successful recording of living subjects in a dark room. The originally recorded transmission master hologram (H1) is then transferred to obtain a second reflection hologram (H2) on a scale of 1:1 with a limited full parallax, usually at 40\({}^{\circ}\) and 30\({}^{\circ}\) with respect to the horizontal and vertical axes, respectively, which is observable with white light. In the 21 st century, this confidential technique, which requires heavy and expensive equipment and holographic skills, has almost disappeared. Indeed, if the three-dimensional (3D) rendition is spectacular, the main obstacle to success is the final monochromatic color of the holograms. Green or red colors often make holographic portraits cadaverous and ghostly for the public, which is used to seeing images of human beings in black-and-white or in color.  
In 1995, [PERSON] et al. [5] proposed the recording of holographic stereograms divided into a matrix of small elements or hogels [6]. In 2019, the third generation of holoprinters called CHIMERA [7]--based on three low-power, continuous, red, green, blue (RGB) lasers combined with the silver-halide material Ultimate U04 [8]--printed full-color and black-and-white digital holograms with a hogel resolution of 250 \(\upmu\)m, a speed of 60 Hz, and a full parallax of 120\({}^{\circ}\).  
Holographic stereogram recordings require the prior acquisition of a series of perspective images. In the case of a portrait, the recordings are usually conducted with a video camera rotating around the subject (Figure 1a) with an arc-curved rail guide, or by placing it on a turntable (Figure 1b) using a fixed video camera. The vertical rotation axis is the vertical axis of the final hologram, and everything in front of this axis is derived from the image. For aesthetic reasons, this axis is usually placed at eye level. For successful recordings, the model must keep their head still during shooting and ignore the relative movement of the camera.  
Unlike the pulsed laser technique, the recording of these portraits is not instantaneous and lasts for approximately 30 s, during which the subject has difficulty remaining motionless, mainly at eye level, yielding an unnatural result. Moreover, the portraits obtained had only a horizontal parallax. To obtain more realistic images, this study proposes a method for recording instant full-color and full-parallax holographic stereogram portraits using multiple synchronized cameras. Images were interpolated using a neural network [9] to limit the number of cameras used as much as possible.
## 2 Materials and Methods
### Kinetics (Batch) Experiments  
All kinetics experiments, excluding the pH effect tests, were carried out at the natural pH of the suspension initially measured, i.e., 7.74 for Cs\({}^{+}\); 7.42 for Rb\({}^{+}\); 7.37 for Sr\({}^{2+}\); and 7.29 for Co\({}^{2+}\). These experiments were conducted at 298 K. For this, with a a thermostat, we used a double-walled cell into which water maintained at a constant temperature flowed (model Thermofisher Polystat 71; Paris, France). A temperature sensor (Pt 1000 Metrohm, Paris, France) and a pH Methrom electrode (Metrohm 6.0262.100, Paris, France) were employed to accurately measure the temperature and pH of the suspension. These sensors were connected to an automatic titrator (model Metrohm Titrando 888, Paris, France), and the measuring device was controlled by the software Tiamo (version 2.3).  
Kinetics tests were performed at the minimum shaking speed above which the kinetics were independent of the degree of agitation, and therefore not influenced by film diffusion. The synthetic solutions which were employed for kinetics experiments were prepared by diluting 1000 mg\(\cdot\)L\({}^{-1}\) stock solutions of cations with distilled water. The course of the kinetics process was appraised by following the decrease in adsorbate with time in the liquid phase. For this, an element solution of 100 mL (2 mmol\(\cdot\)L\({}^{-1}\)) was put into contact with 0.5 g of alkali-activated brick in a 200 mL glass reactor until an equilibrium state was attained (\(\sim\)240 mn). Then, 0.5 mL of suspension solution was collected at different time intervals (ranged from 0.5 to 240 mn) and diluted with distilled water with the aim to determine the element concentration using either ICP-OES or ICP-MS.  
It is worth noting that the effects of element concentration (in the aqueous phase) on cation adsorption (on the brick) ought to be attributable preliminarily to the number of active sites of alkali-activated brick interacting with cationic species (i.e., the mass of brick grains in contact with solution). However, the extent of cationic removal ought also to be related to the affinity, reactivity, and diffusional/complexing characters of the adsorbate towards the brick surface.  
Note that the adsorptive behaviour of raw brick was studied in the lab, and data were afterwards compared to those obtained with brick activated either by acidic or basic treatment. Our investigations clearly showed the weak adsorption capacity of raw brick towards cations such as Fe\({}^{2+}\) and Pb\({}^{2+}\) because of the absence or negligible presence of negatively charged sites associated with extra-framework ions [59].
## 2 Research on UAV Path-Planning Problem
### Fitness Function
#### 2.4.1 Flight-Distance Cost  
The battery consumption of the drone is closely related to the flight distance; the shorter the path, the less power is used, and the more effectively the drone can complete logistics and distribution tasks per unit of power, which to a certain extent can reduce operating costs. In this paper, the flight path of the drone consists of \(k\) nodes. By calculating the Euclidean distance between two adjacent nodes and accumulating them, the flight-distance cost function \(f_{1}\) is obtained:  
\[f_{1}=\sum_{j=1}^{k-1}\sqrt{\left(x_{j+1}-x_{j}\right)^{2}+\left(y_{j+1}-y_{j} \right)^{2}+\left(z_{j+1}-z_{j}\right)^{2}} \tag{17}\]  
In this function, \(k\) represents the total number of path nodes including the start and end points; \(\left(x_{j},y_{j},z_{j}\right)\) and \(\left(x_{j+1},y_{j+1},z_{j+1}\right)\) represent the coordinates of the \(j\) and \(j+1\) node, respectively.
## 5 Discussion  
At present, monitoring of slope stability focuses on monitoring the displacement of slope surface and horizontal displacement in deep areas of slopes. However, the index used for evaluating slope stability only evaluates the displacement of a slope [34; 35; 36]. The evaluation mode and evaluation index are relatively simplistic. In this study, it was found that the force on installed cables changed in the transition from no sliding to gradual sliding of the slope. In particular, the force in cables near the slope foot increased more, thus, the incremental force in such cables can be used as another index for evaluating slope stability. This has the following advantage: in engineering practices, owing to prestressed anchor cables being able to restrict the displacement of sliding masses in slopes, the incremental force in such cables can be found more readily than the displacement of the slope (in theory), therefore, this provides a better early-warning of imminent slope failure and instability.  
According to the aforementioned analysis, it can be seen that the incremental force in cables varied spatially, therefore, when monitoring these cable forces, the cables cannot be randomly selected but it is necessary to select specific cables at the lower part of such slopes. It is better to monitor the change in  
Figure 12: (**a**) The relationship between the displacement of the slope foot and the strength reduction factor under different schemes of prestressed anchor cables; (**b**) enlarged drawings of the enlarged sections in Figure 12b.  
force in those cables at the slope foot. Figure 13 shows the relationship between the displacement of the slope foot and the increment in cables at the slope foot. In the figure, although cable prestresses were different, there was a linear relationship between the displacement of the slope foot and the incremental cable force at the slope foot. Therefore, the corresponding relationship between the displacement of the slope foot and the increment in the force in such cables at the slope foot can be explored in detail, thus using the incremental cable force as the index and criterion for evaluating slope stability. Afterwards, by combining multiple evaluation indices and criteria such as the displacement of slopes and the incremental cable force, the slope stability can be assessed.  
In addition, through the above analysis, it can be seen that the prestressed anchor cable at the bottom of the slope plays a major role in strengthening the slope. In the process of slope sliding, the internal force of the anchor cable at lower part starts to increase at first and is very obvious. If the anchor cable at the bottom of the slope fails, the slope safety factor will be directly reduced. When the slope safety factor is less than 1, the slope will become unstable. The slope foot will start to slide, and the sliding surface will gradually run up, and finally the slope will be unstable. Therefore, it is important for the stability of the slope that the prestressed anchor cable at the bottom of the slope does not fail or break.
# Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD
## 2 Surface Defect Detection Method
### Image Pre-Processing
#### 2.1.1 Data Enhancement  
The defect images on the sealing surface of a container in the filling line were collected by a CCD camera. A total of 400 images were taken, covering defects like breaches, dents, burrs and abrasions. The images were converted to the size of 300 \(\times\) 300 as the training inputs. Such a size can reduce the computing load in training without losing too much information from the images. Then, data expansion was performed to increase the number of images and prepare the dataset for K-fold cross-validation. During data expansion, the following actions were targeted: rotation, horizontal migration, vertical migration, scaling, tangential transformation and horizontal flip. In this way, the CNN could learn more invariant image features and prevent over-fitting. The implementation method of data expansion is shown in Table 1:  
Figure 1: The flow of image pre-processing. NMS = non-maximum suppression.  
The K-fold cross validation method divides the expanded dataset C into K discrete subsets. During network training, a subset was selected as the test set, while the rest (K-1) subsets were combined into the training set. Each training outputs a classification accuracy of the network model on the selected test set. The same process was repeated K times to get the mean accuracy, i.e., the true accuracy of the model.
## 3 Experimental Setup
### Source Operations  
We use a USRP sink in the GNU Radio platform to transmit a binary bit stream. Messages are first encoded in the laptop to baseband signals. Then the USRP sink broadcasts the signal at 2.45 GHz with GMSK. A Python program is used to process the signals. We transmit 12,144 bits each time and repeat every situation 25 times to exclude an accident case. The signal's power is defined in the software part by limiting the signal amplitude to a certain voltage.