esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Method
### Contrastive Learning
#### 3.5.1 Translation-Level Contrastive Learning  
In CLTC tasks, the Translation-Level Contrastive Learning (TL-CL) strategy effectively reduces translation errors by minimizing the distance between anchor samples and their translated counterparts, thereby enhancing model performance and generalization. Figure 3b shows how TL-CL applies contrastive learning within the proposed XCLHG framework. Specifically, a source, text \(x_{src}\), serves as the anchor, and its translated counterpart, \(x_{trans}\), is treated as the positive sample. Subsequently, negative samples are randomly selected from the dataset, which comprises source, translated, and target documents. Using the model described above, feature representations for each example are obtained. Thus, the loss function of TL-CL can be defined as:  
\[L_{TL-CL}=-\sum_{x_{i},x_{i}\in D}\log\frac{exp(sim^{+}(h(x_{i}),h(x_{i})/ \gamma)}{\sum_{x_{\varepsilon}\in D,x_{\varepsilon}\
eq x_{i}}exp(sim^{-}(h(x_ {i}),h(x_{\varepsilon})/\gamma)} \tag{9}\]  
where \(sim\) represents the similarity between examples, \(h(\cdot)\) represents the heterogeneous graph neural network, and \(+\) and \(-\) denote positive and negative examples, respectively. \(x_{t}\) represents the translated sample from \(x_{i}\) and \(x_{\varepsilon}\) denotes a document in dataset \(D\). Here, this  
Figure 3: Illustration of multi-view contrastive learning. Distinct shapes represent different stance labels; blue and yellow indicate source and target languages, respectively. (**a**) Document feature representations. (**b**) Example of training for Translation-Level Contrastive Learning (TL-CL). (**c**) Example of training for Label-Level Contrastive Learning (LL-CL). (**d**) Result of contrastive learning and Classification Alignment (CA).  
study simply employs the cosine similarity to compute the similarity. \(\gamma\) is the temperature coefficient, which regulates the challenge of distinguishing between positive and negative examples. Intuitively, the contrastive learning loss function aims to maximize the similarity of positive examples while minimizing the similarity of negative examples.
## 3 Mobile Communication Infrastructure and SMS  
In this work, a GSM network infrastructure is used to transmit power references through a wireless channel. GSM is the most widespread worldwide 2G mobile communication standard. Despite the high penetration of the new 3G and 4G standards, it is still widely employed to offer voice services  
Figure 2: Block diagram of the implemented DFIG controller.  
in remote locations and low and medium data rate services (e.g., wireless credit card machines) with wide coverage and low cost.  
GSM employs a Gaussian Minimum Shift Keying (GMSK) modulation scheme and multiplexes user transmissions in time and frequency by means of Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) techniques. In short, a user's voice call or SMS are sent and received at specific time slots (one of eight slots) and frequency channels (200 kHz of bandwidth) [38; 39].  
For data protection, GSM implements block interleaving and a Convolutional Coding (CONV) scheme [40], which is a Forward Error Correction (FEC) system extensively used in different wireless systems (e.g., cellular networks, wireless computer networks, space communication systems, and so on), due to its excellent relationship between error correction capability and encoding and decoding complexity [41; 42]. In comparison to Turbo Coding (TC) [43; 44] and Low Density Parity Check (LDPC) coding schemes [45; 46; 47; 48], the error correction capability of CC is lower; however, it has a much smaller decoding complexity and latency [41; 42; 49; 50], which is why CC has been used in most of the new wireless standards. The CC used on GSM varies on rates of 1/2, 1/3 and 244/456 [38; 40].  
The proposed wind DFIG control system uses a GSM network infrastructure to transmit the power references from the grid operator to the wind generator by means of SMS messages, which each contain up to 160 7-bit encoded characters. Since it can work on the reliable Standalone Dedicated Control Channel (SDCCH), retransmissions and acknowledges are performed in layer 2, which, in conjunction with the FEC and interleaving techniques already mentioned, assures the reliable delivery of the power references to the wind generator controller.
# Research on the Efficient Space Debris Observation Method Based on Optical Satellite Constellations
## 5 Numerical Simulation Experiment  
### Experiment Overview  
The simulation model of the detection equipment was established by comprehensively considering the aperture of the observation telescope and limiting the detection magnitude; space object orbit data; sky, light, and Earth shadow conditions; the space environment model; and atmospheric influence, etc. The number of space objects, arc segments, and detection frequencies that can be detected were numerically simulated and analyzed. The observation ability of the space objects with a different number of observation satellites and different constellation configurations was simulated and calculated. The number of objects and arcs that could be detected under each constellation configuration, as well as the distribution of the arc length and detection frequency, were analyzed, and the detection performance for several constellation configurations was compared.  
The time of the simulation analysis was 26 February 2022, and the simulation duration was 24 h. The simulation was mainly for space objects with an orbital altitude less than 1000 km. It was assumed that the orientation of the detector on the satellite could be rotated according to the mission requirements during observation to realize the tracking and observation of space objects.  
The simulation was mainly divided into two parts. The first part simulated and calculated the situation of the space objects passing through the detection range of the satellite detectors under different constellation configurations. That is, it analyzed the basic information, such as the number of objects and arcs, the arc length distribution, detectable frequency, and so on in order to understand the influence of the orbital position of each constellation on the observation performance of the space objects. The second part was mainly based on the observation task scheduling algorithm of the space-based optical satellite constellation established above to allocate and schedule the observation tasks for the different constellation configurations. The various satellites in the constellation could coordinate, cooperate, and make full use of the observation resources and observation time. More space objects can be observed, and more observation data can be obtained, thereby providing support for the determination of the orbit of space objects and the establishment of a library of orbit catalogues of space objects.  
For convenience, each constellation was numbered, as shown in Table 1.
## 2 Materials and Methods
### Screening of Synthesized NDG for Antimicrobial Analysis  
The agar diffusion technique was adopted to test the antimicrobial activity of the synthesized NDG. The microbial strains were selected from the global priority pathogens (GPP) list as we have analyzed in our previous study. Selected microbial cultures were procured from the microbiology department, College of Pharmacy, King Saud University, Riyadh. The glycerol stock cultures of six bacterial strains (_Klebsiella pneumoniae_, _Pseudomonas aeruginosa_, methicillin-resistant _Staphylococcus aureus_ (MRSA), _Bacillus subtilis_, _Streptococcus pneumoniae_, and _Streptococcus mutans_) were taken and initially cultured on Muller Hilton agar media and monitored for isolating pure colonies for further antibacterial susceptibility testing [14]. In brief, pure colonies were selected from each microbial strain and cultured on Mullar Hilton broth at 37 \({}^{\circ}\)C for 24-48 h incubation. Next, 0.5 McFarland standard culture was prepared, and the broth was spread onto the Mullar Hilton agar plates by using the sterile spreader. A standard antibiotic stock and NDG concentrations were freshly prepared, and the working stock was prepared from the stock (ampicillin 100 ug/mL) for bacterial strains and NDG 100 ug/mL. Sterile blank discs were impregnated with a defined concentration of an antimicrobial agent and NDG and one blank broth disc as a positive control was placed on the surface of an agar plate. The plates were incubated and the zone of inhibition showing susceptible or resistance results was analyzed by measuring the diameter of the inhibition zone using a ruler. The entire analysis was performed in triplicate.
## 2 Wireless Sensor Network
### GSM Base Station and Remote Server  
The GSM (global system for mobile communications) node acts as a gateway between the local wireless sensor network and the remote server. As such, it has two communication modules, as depicted in Figure 7a. Figure 7b shows a photo of the gateway with its two antennas. The node has an internal memory buffer to transmit several data packets at once to the server via GSM.  
The remote server was a Raspberry Pi located at the Laboratory for Electrical Instrumentation, Department of Microsystems Engineering, University of Freiburg, Building 106 running a MySQL database. The main purpose of the database, was to store the data received from the wireless sensor network. However, another purpose was to reassemble the previously separated GNSS messages using their measurement timestamps as unique IDs, as will be introduced in following Section 2.3.
## 4 Experiments and Results
### Model Comparison  
In this experiment using real world data, a comparative experiment was conducted considering three different artificial intelligence algorithms, MLP, LSTM, and GRU. The training was conducted in a GeForce 1660 TI environment, and Figure 14 shows the results of comparing the models according to these three algorithms. With a batch size of 40, the execution time for each epoch is less than 9 s on average. For accurate comparison of the three models, the early stop function was not used, and all models were run with 1000 epochs.  
As shown in Figure 14, as a result of model evaluation with the test data, the model with the fastest converging MAE values among the three AI algorithms was the GRU, which also had the lowest error value. The learning results of GRU are comparatively better starting from 100 epochs. This value is six times faster than for the MLP model, which produces the best results only after reaching 600 epochs, and four times faster than the LSTM, which exhibits meaningful results only after reaching 400 epochs. The difference in MAE between the GRU model and the LSTM model shows that the difference between the GRU model and the LSTM model is small, while the experimental results demonstrate that the GRU model is superior to LSTM in terms of the learning speed.  
Figure 14: Comparing training models.  
Figure 13: Comparison between train data and test data of the model using the GRU algorithm.  
Although the difference in MAE between the three models is not large, it can be seen from the following graph that GRU is the most effective in predicting the number of remaining cycles in the manufacturing industry.
# Analysis of Low Probability of Detection Capability for Chaotic Standard Map-Based FH-OFDMA System
## 2 FH Patterns for FH-OFDMA System
The users, \(k=1\), 2, and 5 are assigned their SBS-based FH patterns as \(\{L^{1}_{4,x}(j),L^{1}_{4,y}(j)\}\)=\(\{(1,1),(2,2),(3,3),(4,4),(5,5)\}\), \(\{L^{2}_{4,x}(j),L^{2}_{4,y}(j)\}=\{(5,5),(1,1),(2,2),(3,3),(4,4)\}\), and \(\{L^{5}_{4,x}(j),L^{5}_{4,y}(j)\}=\{(2,2),(3,3),(4,4),(5,5),(1,1)\}\) for \(j=1,\ldots,5\), respectively. As shown in Figure 2a as an example of the SBS-based FH pattern, the data index of each user is shown once at each cluster and are hopped to a different cluster for each symbol. In addition, the transmitting data order of every user is continuous in Figure 2a. In Figure 2b, we show an example of a cat map-based FH pattern for \(\{b_{11},b_{12},b_{21},b_{22}\}\)=\(\{1,1,1,2\}\) and seed number, \(\alpha=4\). The users, \(k=1\), 2, and 5 are assigned their cat map-based FH patterns as \(\{T^{1}_{4,x}(j),T^{1}_{4,y}(j)\}\)= \(\{(1,1),(3,4),(5,2),(2,5),(4,3)\}\), \(\{T^{2}_{4,x}(j),T^{2}_{4,y}(j)\}\)=\(\{(5,5),(2,3),(4,1),(1,4),(3,2)\}\), and \(\{T^{5}_{4,x}(j),T^{5}_{4,y}(j)\}\)=\(\{(2,2),(3,3),(4,4),(5,5),(1,1)\}\) for \(j=1,\ldots,5\), respectively. As shown in Figure 2b as an example of the cat map-based FH pattern, the first data set for \(k=2\) is transmitted at the symbol period of \(n=4\) and the first data set for \(k=5\) is transmitted at the symbol period of \(n=1\). In the cat map-based FH pattern, the transmitted data order is mixed as shown in Figure 2b. However, the data index of each user using the cat map-based FH pattern is shown once for each cluster and hopped to a different cluster for each symbol. In Figure 2c, we show the example of a CSM-based FH pattern for \(K_{\text{C}}=3000\), \(r_{x}=1\), \(r_{y}=3\) and seed number \(\alpha=2\). The users, \(k=1\), 2, and 5 are assigned their CSM-based FH patterns as \(\{C^{1}_{4,x}(j),C^{1}_{4,y}(j)\}\)=\(\{(5,5),(3,4),(1,1),(4,1),(2,1)\}\), \(\{C^{2}_{4,x}(j),C^{2}_{4,y}(j)\}=\{(3,3),(1,5),(4,5),(2,5),(5,4)\}\), and \(\{C^{5}_{4,x}(j),C^{5}_{4,y}(j)\}=\{(2,2),(5,1),(3,5),(1,2),(4,2)\}\) for \(j=1,\ldots,  
Figure 2: Comparison of symbol-by-symbol (SBS), cat map, and chaotic standard map (CSM)-based FH patterns.
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 1 Introduction
However, currently, a handful of MPTCP schedulers and CCAs are available, making it difficult to set which combination of scheduler and CCA best suits to resolve the existing problems and yield a better throughput with reduced delay. Moreover, [PERSON] et al. has shown that applying MPTCP over 4G and  
Figure 1: An illustration of the non-standalone implementation of 5G network infrastructure where 5G and 4G base stations are located at the same tower. 5G does not always improve the overall performance [23]. Hence, a proper investigation is necessary before practical deployment. In this paper, we aim to develop a simulation testbed that will allow us to evaluate the performance of MPTCP in 4G and 5G network situations. Then, using extensive simulation tests, we intend to discover an appropriate combination of MPTCP scheduler and CCA to increase performance in terms of throughput and latency, particularly while using 5G and 4G network interfaces simultaneously. The following are the primary contributions of this paper:  
* We developed a simulation testbed using NS3-DCE [24] with MPTCP Linux Kernel v0.90 [25] and NS-3.33 mmWave module [26]. As a result, evaluating the performance of MPTCP on 3 GPP (e.g., 5G and 4G) and non-3 GPP (e.g., WiFi, WiMax, Ethernet, etc.) networks is possible. For a productive comparison, we also incorporated cutting-edge schedulers, such as redundant, earliest completion first (ECF), and blocking estimation (BLEST), in the MPTCP Linux Kernel v0.90 [25]. * We tested several MPTCP schedulers in combination with available CCAs in MPTCP Linux Kernel, compared their performance in terms of throughput and flow completion time (FCT), and outlined the best combination for real-world deployment. * During the experiments, we also found that the signal to interference and noise ratio (SINR) directly impacts the obtained throughput of each path. Through proper investigation, we outlined the relation between SINR, throughput, congestion window (CWND), and packet losses; how they impact the performance of different MPTCP schedulers and CCAs. The rest of the paper is organized as follows: Section 2 presents a summary of the tested MPTCP schedulers and CCAs, Section 3 briefly describes the testbed setup and illustrates the considered simulation scenario for the performance comparison, Section 4 provides a brief analysis of the results outlining the better candidates, and finally, Section 5 concludes the paper.
# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
## 5 Result and Analysis
### Communication Cost  
The data packet size directly affects communication costs. The proposed scheme transfers normal data as Boolean digit 0, considerably decreasing the packet size. The small packet lowers the communication cost and overall reduces the chances of packet loss. In FDAS and EHDA, data packets are also small in size, resulting in lower costs as compared  
Figure 5: The impact of the Number of Attributes on Control overhead is shown in (**a**) whereas Communication Cost is exhibited in (**b**).  
to larger data packets. The results of all schemes are depicted in Figure 5b. When a packet size of 1000 bytes is transmitted, FDAS, EHDA, DICA, and DICA_EXTENSION have costs of 6250 bytes, 5000 bytes, 7500 bytes, and 6100 bytes, respectively. However, among all the schemes, EE-FDAS has the lowest cost of 1990 bytes.
# The Mechanical Properties of Reinforced Concrete Columns with Longitudinal Pre-Embedded Holes
## 3 Results and Discussion
### Bearing Capacity Analysis
Increasing the stirrup ratio optimizes the internal force distribution, and reduces local stress concentrations and failure risks caused by the opening holes, thereby enhancing the bearing capacity of the column more effectively. Figure 19: Impact of stirrups on load–displacement curve of columns with longitudinal pre-embedded holes. Figure 18: Impact of concrete strength on load–displacement curve of columns with longitudinal pre-embedded holes. 2.4 Impact of Slederness Ratio on the Bearing Capacity of Reinforced Concrete Columns with Pre-Embedded Holes  
As shown in Figure 20, when the slenderness ratio of the longitudinal pre-embedded hole reinforced concrete column increases from 6.2 to 12.2, the axial compressive ultimate bearing capacity of the column exhibits only a minor fluctuation. Specifically, compared with \(\lambda\) = 8.2, the ultimate bearing capacity of the specimen with \(\lambda\) = 12.2 decreases by merely 0.41%, while that of the specimen with \(\lambda\) = 6.2 increases by only 0.31%. These findings suggest that within this range, the slenderness ratio has a negligible effect on the bearing capacity of the embedded hole column. This can primarily be attributed to the fact that, within a certain range of slenderness ratios, the bearing capacity is predominantly governed by the effective cross-sectional area and material strength. Given that the diameter of the embedded hole is 50 mm, the extent of section weakening is limited, thus keeping the effective bearing area of the cross-section essentially constant. Moreover, the failure mode of the embedded hole column remains unchanged as the slenderness ratio increases, characterized by typical axial compression failure without any instability-related failure. Consequently, variations in the slenderness ratio have minimal influence on the ultimate bearing capacity of the column.
# A Study of Model Iterations of Fitts' Law and Its Application to Human-Computer Interactions
## 2 Physiological Basis of Fitts' Law  
A basic idea is expressed by Fitts' law, which states that a target's size and closeness affect how quickly and correctly it may be reached. Larger, closer targets are typically reached more quickly than farther, smaller ones. Fitts' law's fundamentals are being studied in greater detail by experts, who are discovering fascinating linkages to the neural system that encourage more investigation into this complex relationship [8].
## 5 Research Findings
### RQ1: What Techniques Are Utilized to Abuse the CPLS as C&C Communication Channels?
#### 5.2.6 Emerging Threats  
The advent of AI-powered C&C attacks in 2022 highlights the continually increasing sophistication of cybercriminal tactics. While currently only observed on Twitter, it is conceivable that threat actors may leverage AI in abusing more platforms like Dropbox, Google Drive, Outlook, and OneDrive in the future. COM hijacking, though less prevalent currently, has been observed in several instances and poses a significant risk due to its ability to persist in a system undetected. This anticipatory threat underscores the need for continuous research and proactive defense strategies in cybersecurity.
## 2 Structure of Present and Future Detectors
### Present Seismic Attenuation Systems  
Seismic noise attenuation comes from the natural properties of a pendulum, which attenuates horizontal vibration transmission with a \(1/(f^{2}-{f_{0}}^{2})\) function that provides a cutoff starting above the pendulum resonant frequency \(f_{0}=\sqrt{(g/l)/(2\pi)}\) where \(l\) is the pendulum length and \(g\) is the gravitational acceleration. The mechanical attenuation is provided by a chain of pendulum wires alternated to massive vertical attenuation filters (discussed in Section 3.3) acting as pendulum mass. Each stage contributes a \(1/(f^{2}-{f_{0}}^{2})\) attenuation starting from its corresponding resonant frequency. With \(n\) stages a \(1/(f^{2}-{f_{0}}^{2})^{n}\) attenuation power is achieved [23]. Therefore, seismic attenuation performance on the low-frequency side is limited by the length of the pendulums used, which are limited by the height available above the test masses.  
The overall length of the seismic attenuation chains of present detectors ranges from a couple of meters in LIGO [17], to seven meters, in Virgo [18]. Most present detectors (except for KAGRA) rely on support structures extending up from the tunnel floor encroaching in the volume around the test masses. Tall structures need to be very stiff to avoid amplifying ground motion and their height is limited by the hall that hosts them. Individual pendulums in present detectors are limited to lengths of the order of a meter, with resonant frequencies (start of passive attenuation) distributed around 0.5 Hz. This is adequate for a detection threshold above 10 Hz but not for the lower detection frequency of the Einstein Telescope.