esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Tests
### Test #2  
Test #2 examines the system capability of setting up a peer-to-peer subnetwork. Throughout these trials, only two NB-IoT Nodes were employed since NB-IoT technology is far more suitable than LoRaWAN whenever the necessity of data interchanging between nodes arises. Indeed, while LoRaWAN uplinks can occur at any time they are needed provided that regulations on ISM bands temporal occupancy are concerned, downlinks can only take place at specific moments just after the uplinks. Indeed, LoRaWAN protocol imposes that nodes can receive downlinks in two temporal windows, which are opened only after each of the uplinks. Once such windows expire, the node is virtually unreachable unless it performs an additional uplink. On the other hand, NB-IoT is not subject to such a functioning scheme, thus resulting in being more appropriate in this context. In particular, the experiment was carried out by resorting to the following methodology. One of the nodes acted as the master, while the remaining one was the slave. The master sent an NB-IoT packet, which contained a progressive counter as the payload, to the slave via MQTT over NB-IoT. Then, the master waited for 30 s. No sooner had the slave received the packet, than it sent back such data to the master by making use of the same technique (i.e., MQTT over NB-IoT). If the master received the response from the slave within the timeout, then the test turned out to be successful; otherwise, it did not. In the former case, the RTT was measured since it was considered a significant test metric along with the success rate of packets received by the master from the slave. RTTs were measured by exploiting a PC. In particular, the master was connected to a PC USB port that was monitored by the PC itself. Indeed, the master logged through the events resulting from the process of sending data to the slave and from the process of receiving the slave response, if it occurred, to the serial port. Each of the logs had the incremental counter within the payload since it was logged too. Then, the PC recorded the respective timestamps associated with such events to calculate RTTs by performing a subtraction, along with the logs coming from the master. The USB port monitoring as well as the recording of the timestamps were accomplished via a Python script, while RTT identification, calculation and analysis were carried out by resorting to MATLAB. Specifically, RTTs had to be identified by making use of the progressive counter in the packet payload, since if the timeout expired, then no RTT was measured and the relative packet was considered lost. This testing scenario reproduces all of those contexts in which two nodes assiduously require interactions with each other on the basis, for instance, of the sensed environment to act on a remote part of the latter. In addition, such a framework recreates those settings in which both the nodes alternatively behave as sensors and actuators.
# An Approach to Knowledge Base Completion
## 6 Experiments  
### Experimental Settings  
Two benchmark data sets of FB15K and WN18 are used to evaluate the proposed model, where FB15K is a subset of Freebase [1] that contains general facts about the world and WN18 is originated from WordNet [38] which provides the semantic relations among words. Table 4 gives simple statistics on these data sets. In this table, '# of Entities' and '# of Relations' denote the number of entities and relation, respectively. '# of Training Triples', '# of Validation Triples', and '# of Test Triples' are the number of triples in the training, validation, and test set. FB15K has 592,213 triples while WN18 has 151,441 triples. The total number of triples in FB15K is larger than that in WN18, but the number of entities in FB15K is smaller than that in WN18. In addition, the number of relations in FB15K is about ten times larger than that of WN18.  
Four different knowledge graph embeddings: TransE [6], TransR [24], DistMult [29] and ComplEx [32] are adopted as members of the proposed embedding committee. Each member embedding is pre-trained with a training set from the data sets to initialize the committee. In TransE and TransR, entities and relation are embedded into a 50-dimensional space with 512 batch size. On the other hand, DistMult and ComplEx represent them as 100-dimensional vectors with 32 batch size. Margin and learning rate are set to 1 and 0.1 for all member embeddings.  
In training the committee, 512 and 0.001 are used for batch size and learning rate, respectively, for WN18. The size of negative samples is double of the batch size. For FB15K, 1024 batch size is used, and the learning rate is equal to that for WN18. Adam [39] is adopted as an optimizer.  
Hits@k, a commonly-used evaluation metric for knowledge base completion task is used to measure the performance of the models. This metric returns the proportion of correct triples in the top-\(k\) ranked triples. In all experiments, we report Hits@k's following _raw_ settings [6].
## 4 Discussion
Without a quantitative understanding of this relationship, the use of the SCE model in this field is limited, particularly in the design of pulse-setting parameters. To bridge this gap, we adopted PAR as an index to reflect the overall degree of electroporation of a cell. It was found that certain combinations of pulse-setting parameters can induce a particularly high PAR, i.e., the cumulative effect. Therefore, the study into the exact mechanism by which pulse-setting parameters can trigger the cumulative effect is necessary for the optimal design of pulsing protocols. This is the first attempt to quantitatively connect the SCE model with the field of tumor ablation, and it is believed that this study provides a good hint towards further research in this area. Specifically, our methodology allows for the identification of specific combinations of four pulse-setting parameters (electric field strength, pulse width, pulse delay, and pulse burst duration) that have a high probability of cell death. For example, in the pulsing protocol 1-1-1, it is supposed that increasing the pulse burst duration beyond 300 us and the electric field strength above 500 V/cm can result in a noticeable increase in cell death probability compared to pulsing protocols where the pulse burst duration or electric field strength do not meet the minimum requirements.
## 4 Real-Time Implementation of an Auralization System  
An interactive auralization system based on the plane wave expansion was developed. The system allows for real-time acoustic rendering of enclosures by using synthesized directional impulse responses calculated in advance. Due to the pre-computation of the PWE, the proposed auralization system enables interactive features such as translation and rotation of the listener within the enclosure. However, changes in the boundary conditions or modifications of the acoustic source in terms of its directivity and spatial location are not included at this stage without recalculating the PWE.  
Figure 14: Comparison of the complex amplitude of the plane wave expansion.  
Figure 12: Normalized amplitude PWE at 63 Hz (**a**) and 250 Hz (**b**), non-regularized, \(\text{q}_{\text{total}}(63)=26.11\), \(\text{q}_{\text{total}}(250)=7\times 10^{-4}\).  
The maximum frequency simulated was 447 Hz, which is sufficient to auralize the modal behaviour of the enclosure. The time required to recalculate the PWE depends on the volume of the enclosure and the maximum frequency computed in the FE solution. For this reference case, the computation time is about one day using six nodes per wavelength on a standard desktop computer with 32 GB of RAM and Intel i7 processor. A high-pass filter (cut-off at 20 Hz) and a low-pass filter (cut-off 355 Hz) were applied to reduce the ripples produced by the truncation of the data. The proposed auralization system combines a real-time acoustic rendering with a graphical interface based on a video game environment.  
Figure 15 shows the general architecture of the signal processing chain. It is composed of four main blocks. The first block is the convolution of anechoic audio material with a plane wave expansion. The second module refers to the implementation of the translation operator in the plane wave domain. The third block corresponds to the application of a rotation operator in the spherical harmonic domain, and finally, the last stage is the sound reproduction using a headphone-based binaural system with non-individual equalization. The implementation has been made using the commercial packages Max v.7.2 and Unity v.5.0. Max is a visual programming language oriented toward audio and video processing. In contrast, Unity is a programming language dedicated to video game development. It was used in the current research to create a graphical interface for the interactive auralization.
# Physicochemical Characterization and Antioxidant Activity of Jara Honey Produced in Western Georgia
## 3 Results and Discussion
### Phenolic Acids (Aromatic Carbonic Acids)  
Phenolic acids (aromatic carbonic acids) are a subclass of the most numerous and ubiquitous groups of secondary plant metabolites [78]. Phenolic acids are classified as derivatives of cinnamic and benzoic acids [70]. Their chemical structure is simple C6-C [55]. Honey contains a wide range of phenolic acids [26]. Phenolic acids are not only present in honey, but they also might distinguish some kinds of honey [78; 80].  
The phenolic acid content in the Jara honey samples ranged from 359.8 to 682.4 mg/kg and accounted for around 45-65% of the total phenolic content. The amount of phenolic acids (according to the average indicator) was much higher in the Shuakhevi samples, amounting to 573.40 units, while it was almost equal in the Keda (455.25 mg/kg) and Khulo (488.42 mg/kg) samples, similar to the content of the total phenols (Table 6).  
The phenolic acid content was highest in the following honey samples: JH-2 (682.4 mg/kg), JH-13 (612.4 mg/kg), and JH-14 (648 mg/kg), whereas the sample JH-1 had the lowest content (453.3 mg/kg) (Table 6).
## 6 Results and Discussion
### Simulation Results  
As mentioned earlier, the simulations were run using the measured data. The initial step involved calculating the energy consumption of the propulsion system, utilizing the input data provided. This was followed by a similar calculation for the steering system and  
Figure 13: Trip 3 route.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Route ID** & **Trip.** & **Max.** & **Avg.** & **Avg.** & **Trip** & **Avg.** & **Battery** \\  & **Dist.** & **Veh. Spd.** & **Veh. Spd.** & **Consumption** & **Dur.** & **Amb. Temp.** & **SOC** \\ \hline Trip 1 & 9,09 km & 75.6 km/h & 14.35 km/h & 25 kWh/100 km & 2295 s & 13 °C & 94-91\% \\ Trip 2 & 8.71 km & 36.9 km/h & 3.4 km/h & 28 kWh/100 km & 2778 s & 19 °C & 91–88\% \\ Trip 3 & 8.32 km & 46.9 km/h & 21.26 km/h & 25.6 kWh/100 km & 1430 s & 19 °C & 96-94\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Trip summary.  
the HVAC system. The energy consumption of the lighting system was calculated using the simplified simulation approach introduced. Accordingly, the total power consumption equation is as follows:  
\[P_{trip}=P_{prop}+P_{steer}+P_{braac}+P_{light} \tag{39}\]  
where  
* \(P_{trip}\) is the total energy consumption of the entire trip,
* \(P_{prop}\) is the propulsion system energy consumption,
* \(P_{steer}\) is the steering system energy consumption,
* \(P_{trace}\) is the HVAC system energy consumption,
* \(P_{light}\) is the lighting system energy consumption.  
The results of the simulations are presented in Table 4. As expected, the vehicle's powertrain showed the greatest variation in energy consumption. It is also noteworthy that the energy consumption of the steering and HVAC systems showed the largest variation. In the case of the steering system, the consumption rate is a function of the specific route of the test route. The number of curves within the route is a determining factor in the energy consumption of the steering motor. It is important to note that in the context of the steering system, a reduction in speed results in an increase in electrical energy consumption due to the increased friction factor. The energy consumption of HVAC systems is affected by the difference between the ambient temperature and the desired passenger compartment temperature. During our validation runs, the cabin temperature was kept uniform at 21 degrees Celsius. However, the ambient temperature showed significant variations, as illustrated in Table 4. It can be seen that the lighting system consumption remained constant during the different trips. This can be attributed to the fact that the low-beam and tail lights in our system have the highest power consumption, and these loads were continuously operational. The minimal difference can be attributed to the various traffic scenarios in which the driver employed the turn indicator or the brake pedal.
# Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy
## 2 Related Work  
### Formulation  
Various factors can lead to the degradation of images due to noise during acquisition and transmission. Assuming the observed value \(y\) denotes a noisy image and its corresponding clean image is represented by \(x\), a common assumption regarding noise distribution is AWGN with variance \(\sigma^{2}\), denoted by \(N(0;\sigma^{2})\). The degradation process is expressed as:  
\[y=x+n \tag{1}\]  
Hence, the task of image denoising involves an inverse problem, requiring the restoration of the latent clean image, \(x\), from the observed data, \(y\). This problem is considered a classic ill-posed problem.  
Current methods for image denoising are broadly categorized into two groups: model-based [11; 12; 40; 41; 42; 43; 44; 45; 46] and learning-based methods [18; 19; 20; 47; 48; 49; 50; 51]. Model-based approaches can be conceptualized from a Bayesian perspective through a maximum a posteriori (MAP) estimation problem:  
\[x^{*}=\arg\max_{x}\{logP(y|x)+logP(x)\} \tag{2}\]  
Here, \(\log P(y|x)\) represents the log-likelihood of the observed noisy image, \(y\). It gauges the discrepancy between \(x\) and \(y\) and is linked to the data fidelity term \(F(x,y)\). To enhance denoising efficacy, traditional model-based methods need to incorporate a regularization term. This term confines the search space and imposes constraints on the characteristics of the resulting image. However, this approach involves intricate design complexities and demonstrates suboptimal execution efficiency, compared to the current mainstream supervised methods.
# Prediction System for Overhead Cranes Based on Digital Twin Technology
## 5 Experimental Section
### Experimental Analysis of Life Prediction for the Overhead Crane  
The fatigue failure of the main beam of the overhead crane was the most important form of failure. Thus, the life prediction for the overhead-crane components was mainly the fatigue-life prediction for the main beam. The stress-strain gauges were arranged separately for the main beam of the bridge crane in the middle position, as shown in Figure 12. The collected stress data was transmitted back to the digital twin system through the IoT gateway for analysis. The collected stress changed with time, and the digital twin interface was controlled to make the crane hook move up and down. The stress changes are shown in Figure 13a. The collected data were subjected to finite element analysis, and the analysis results are shown in Figure 13b.  
The predictive maintenance system of the overhead crane based on digital twins could display the operation status of the crane in real time and display the status of the main girder, e.g., the finite element analysis result and the fault code, as shown in Figure 14.  
From the log records of the serial communication experiment data, the following observations can be made: When the overhead crane rose, its load-bearing stress value would reach the peak in 1.5 to 3 s, and it rapidly increased from \(-5\times 10^{-4}\) MPa to 0 MPa, then to 20-25 \(\times\) 10\({}^{-4}\) MPa, then remained at the same level. When the cart or trolley of the overhead crane moved in the horizontal direction to change its position, the stress value remained unchanged, and there were no obvious fluctuations in this period. When the overhead crane performed a descending operation, its load-bearing stress would fall back to about 0 MPa again in 3 to 5 s.  
Figure 12: The stress–strain gauges for the crane.  
Figure 13: The stress analysis of the main beam. (**a**) The results of stress-time curve analysis. (**b**) The results of finite-element analysis.  
Meanwhile, the digital twin program rendered corresponding color changes according to the actual movement instructions of the crane. The instrument panel on the right side of the simulation software highlighted the weight of the crane cargo and the length of the cable in real time. The true stress value at each time point was recorded on a linear graph, and the linear image had an almost consistent variation trend with the observed stress value of the overhead crane.  
During this experiment, when the crane had abnormal data and faults, the observed crane status was in line with the data recorded via serial communication and the prompt of the simulation software, which was consistent with the expected design judgment.
## 9 Perspectives for an AltiKa mission and conclusion  
The technical feasibility of a coupled altimeter / radiometer AltiKa payload has been demonstrated. The altimeter shall provide sea-surface measurements with a very high accuracy; improved performances over other types of surface shall also be reached, first thanks to the reduced size of the radar footprint compared to a Ku-band instrument, and second thanks to the implementation of new tracking algorithms. Complementary data will thus be acquired and made available above coastal areas, lakes, rivers: as a consequence, efforts should now be put on ground processing algorithms to better retrieve geophysical parameters in these zones (both for altimeter and radiometer data).  
As mentioned before, a preliminary definition phase of the instrument has been conducted with the support of ALCATEL SPACE from November 2001 to June 2003. Because an opportunity to embark the AltiKa payload on the OCEANSAT-3 platform has appeared in the frame of a cooperation between CNES and ISRO (Indian Space Research Organization), a delta phase B has started at the beginning of July 2005, with an expected start of the phase C/D during the first half of 2006. The launch of OCEANSAT-3 is presently planned in 2009-2010. This mission would thus be a demonstration of Ka  
Figure 13: 1 second range noise (cm) versus SWH: Ku (green diamonds), Ka (red triangles).  
band altimetry as a complement to the Jason-2 mission. The AltiKa payload on OCEANSAT-3 may then be considered as the post-ENVISAT altimetry gap filler.  
On a longer time scale, the AltiKa payload could be the core of a permanent, operational, high-resolution altimetry system composed of a constellation of microsatellites embarking a recurrent payload, or as a combination of microsatellites and minisatellites. The AltiKa payload may then be a very good candidate to help constituting the altimetric component of the to be sustained GMES satellite ocean observation system.  
The work presented in this paper involved a large number of scientists who have been contributing through the AltiKa Mission Group: The authors would like to thank all these scientists for their continuous contribution to the AltiKa mission definition. The main author performed most part of his AltiKa work when he was a member of the CNES Space Agency. Last, the authors would like to thank [PERSON] and an anonymous reviewer for their fruitful comments which helped improving the paper.
## 4 Results  
### Shear Strain  
In Figure 6, typical examples of the damping ratio curve dependant on shear strain for three specimens, \(K2\), \(K6\), and \(K13\), from different test sites, tested in two similar mean effective stress are presented. The influence of shear stress on the damping ratio is clearly visible. The variability of the damping ratio dependant on shear strain is best described by the quadratic function, with the lowest coefficient of determination equal 0.92 for the \(K14\) sample.  
As was said in Section 2.2, based on the damping ratio curve, we can identify three zones of soil response for loading: linear elastic, nonlinear elastic, and plastic. Over the decades, researchers reported different threshold strains between soil response zones, for example, \(\gamma_{t}^{c}=0.0025\%\)[12], 0.001% [41; 42], and 0.002% [42] and \(\gamma_{t}^{c}\) around 0.04% [43] for cohesive soils. According to Figure 1, we estimated threshold strains for every tested soil. The results are presented in Table 3. It can be seen that both threshold strains are dependent on plasticity index (_PI_) and they are moving into the larger strains with _PI_ increasing. The exception is sample _K14_, which, despite much plasticity, obtained \(\gamma_{t}^{c}=0.007\%\) at \(p^{\prime}=95\) kPa. However, the general trend confirmed research presented in [30]. Moreover, degradation threshold strain is strongly dependant on effective stress, which is described in next section.
## 4 Experiments and Results
### The Resolution Preservation Ability Enhancement Brought by the Optimizations on Methodology
preservation ability enhancement brought by the proposed method. Classical and highly interpretable radiomics model was applied to better illustrate the superiority of the proposed method. First, the obtained images from glioma patients and their standardized results were segmented using the GrowCut method to obtain a glioma region [26]. Then, for the segmented regions, 555 features including intensity features (21), shape features (15), texture features (39), and wavelet features (480) were extracted using a self-adaptive feature extraction method [9]. The minimum-redundancy-maximum-relevance (mRMR) based genetic-algorithm (GA) was applied for feature selection. Finally, the selected features were used to train the SVM classifier. Table 6 show the classification result of the original and standardized test set. The accuracy of differential diagnosis between HGG and LGG was noticeably increased. Therefore, we believe that the proposed method can effectively improve the accuracy of differential diagnosis methods by increasing the preservation of image details and texture features as well as wavelet features, which highly rely on image details.
###### Abstract  
With developing technologies, network security is critical, predominantly active, and distributed ad hoc in networks. An intrusion detection system (IDS) plays a vital role in cyber security in detecting malicious activities in network traffic. However, class imbalance has triggered a challenging issue where many instances of some classes are more than others. Therefore, traditional classifiers suffer in classifying malicious activities and result in low robustness to unidentified glitches. This paper introduces a novel technique based on a modified conditional generative adversarial network (MCGAN) to address the class imbalance problem. The proposed MCGAN handles the class imbalance issue by generating oversamples to balance the minority and majority classes. Then, the Bi-LSTM technique is incorporated to classify the multi-class intrusion efficiently. This formulated model is experimented on using the NSL-KDD+ dataset with the aid of accuracy, precision, recall, FPR, and F-score to validate the efficacy of the proposed system. The simulation results of the proposed method are associated with other existing models. It achieved an accuracy of 95.16%, precision of 94.21%, FPR of 2.1%, and F1-score of 96.7% for the NSL-KDD+ dataset with 20 selected features.  
intrusion detection system; deep convolution generative adversarial network; class imbalance problem; NSL-KDD dataset; accuracy