esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 RF Communication Devices
### 4G/5G Mobile Technology  
In the mobile field, 4G Long Term Evolution (LTE) Technology is the most widely used and advanced mobile network technology with more than 100 billion connections [29; 30]. The current 5G technology is at least 100 times faster than 4G [31; 32]. 5G is incorporating new features, such as massive IoT connectivity, and offers much more functionalities than 4G by incorporating more and higher interconnectivity among smartphone devices, people, IoT, objects, and possibly drones. However, 5G, due its high bandwidth, has a serious disadvantage: it needs multiple antennas to serve an area. This is due to the fact that high bandwidth antennas have a limited range of 1 km. Thus, cities will need a vast array of antennas to serve 5G. 5G achieves a very high rate of broadband internet connection by using 100 antennas on each cell station, compared to just 10 antennas for 4G. Also, it has high interconnectivity among different devices and objects, smart homes, IoT, and computers. It also has the opportunity to incorporate a larger range of bandwidths (6 GHz to 100 GHz), compared to 4G that operates only around the 6 GHz area. 5G also incorporates machine learning and deep learning techniques to improve the quality of the telecommunication service in different areas, such as network slice admission control algorithm, 5G security, optimal 5G network slicing, resource allocation, network traffic prediction, and many others topics.  
5G is one of the most prominent technologies that is currently transforming the world. Certainly, it is the most prominent RF technology that is transforming and changing humanity, not only in a technological way, but also sociologically. 5G has already created competition between various companies over who is going to apply the fastest and most reliable 5G service, especially in the mobile technology sector. The most prominent companies in this field of technology, Ericsson and Huawei, have spent billions of Euros on 5G to create a wave of new technologies and global diplomacy policies.
## 4 Discussion
### Ionospheric Behavior
On the other hand, as a consequence of the geomagnetic activity (AE-index > 500 nT) in the polar regions from 5-15 UT on DoY 162; we can see an increase in ROTI activity (ROTI \(\geq\) 0.25 TECu/min) that starts at the northern polar region, propagating later the increment toward the equator (\(\sim\)50\({}^{\circ}\)N), which agrees with previous studies [29; 58; 59] (see Figure 6 (left panels)). In South America, the percentage of stations with ROTI activity increased from \(\sim\)12% to 24% (see Figure 6 (left panels), Figure 7 (upper panels), and Supplementary Materials, Table S4). The results obtained with the GNSS stations at 350 km (see Figures 3 and 8) were consistent with the ionospheric TEC behavior at 400 km above the Swarm-A (see Figure 4). At P1 time, we observe a TEC depletion (\(\sim\)\(-\)1.7 TECu, \(-\)35%) in the central Atlantic region, where the eclipse started and its conjugate. The greatest TEC reduction (\(\sim\)\(-\)2 TECu, \(-\)45%) occurred at GE time (see Figure 4 (upper panels)). This TEC value was similar to that reported by [PERSON] and [PERSON] (\(-\)2 to \(-\)1.5 TECu) [60]. From Figure 4 (bottom panels), we were also able to show that the disturbance remained in the North and South American regions (TEC \(\sim\)\(-\)30%) even though the eclipse was already over the northern European and Asian regions. Moreover, we can observe a close similarity in the behavior of in situ Ne and VTEC (see Figure 4). Furthermore, the results of ionospheric plasma depletion using Swarm-A LP were consistent with the findings presented in [60].
## 3 Results
It is easy to understand that the SPP mode on the top layer with t = 160 nm has a shorter cutoff incident wavelength. It should be noted that the imaginary part (\(\beta\)') of the propagation constant represents the propagation distance of this plasmonic mode, and from Figure 4, it is evident that the top dielectric layers affect the propagation distance. As an example, the propagation distances of the SPPs along the Ag NW placed on three multilayer substrates have been fitted and derived as shown in Figure 5. The incident wavelength was fixed at 590 nm, and the thickness of the top SiO\({}_{2}\) layer was selected as t = 160 nm, t = 180 nm, and t = 250 nm, respectively. The thicknesses of the other 13 layers were kept the same as shown in Figure 1a. It is clearly shown that, although the incident wavelength and diameter of the Ag NW were both the same on the three multilayer substrates, the propagation distances were different from each other. The propagation distances of the plasmonic mode were derived as 13 \(\upmu\)m (at t = 160 nm), 11 \(\upmu\)m (at t = 180 nm), and 10 \(\upmu\)m (at t = 250 nm), respectively.
# Few-Shot Knowledge Graph Completion Model Based on Relation Learning
## 2 Related Work
### Translation-Based Methods  
The approach based on translation methods treats relations as translation operations between entity pairs, where modeling relations is viewed as a form of translation in a low-dimensional entity representation space. The existence of associations between entities and relations is determined using a distance scoring function. TransE [10] is a popular translation model that regards a relation as a translation from the head entity to the tail entity. It can handle large-scale knowledge graph completion tasks but struggles with complex relation types. Therefore, researchers have proposed several improved translation models to address the \"1-N\", \"N-1\" and \"N-N\" problems, such as TransH [11], TransR [22], TransD [23], and TransG [24]. Among them, TransH embeds each entity into different, relation-specific hyperplanes to address the complex relation representation problem; TransR first projects entities into the corresponding relation spaces and then establishes translation relations from head entities to tail entities. These improved methods have enhanced the performance and scalability of translation models.
## 3 Optimized Dual-Channel Data Storage and Access Method  
The conventional SAR data storage method will make data access in the azimuth direction inefficient extremely, and the processing engine will be in a waiting state when the reading/writing operation is performed, thereby affecting the real-time performance of the entire system. To solve this problem, we propose an optimized dual-channel SAR data storage and access method, which maps the logical addresses in the SAR data matrix to the physical addresses in the DDR3 chip by using a new mapping method instead of the conventional linear mapping method. With this method, both the range direction and azimuth direction can reach high data bandwidths, which are more suitable for the real-time processing of spaceborne SAR.
## 3 Results and Discussion
### Voltage Regulation and Current Regulation Accuracy Tests  
The most important part of the charging process is to control the charging voltage and charging current. As the stage charging method uses only the PI algorithm to maintain a stable charging voltage or charging current during each stage, voltage regulation and current regulation accuracy tests are required in order to verify whether the employed PI algorithm can satisfy the control requirements. In this study, we used a DC power supply to set the input voltage (bus voltage) to 380, 400, and 420 V, and an electronic load to set the output current (charging current) to 0, 10, and 20 A. Moreover, the charging voltage was set to 48, 50, and 52 V, respectively, to conduct the voltage regulation accuracy test. Figure 18 shows the test results.  
Figure 18 shows that at no load, the charging voltage is slightly higher than the set voltage; at a medium load, the charging voltage is closest to the set voltage; and at a high load, the charging voltage is lower than the set voltage. Figure 19 shows the voltage regulation accuracy results with respect to different set voltages.  
In Figure 19a, the voltage regulation accuracy at a set voltage of 48 V is between \(-0.27\%\) and \(0.29\%\). In Figure 19b, the voltage regulation accuracy at a set voltage of 50 V is between \(-0.18\%\) and \(0.32\%\). In Figure 19c, the voltage regulation accuracy at a set voltage of 52 V is between \(-0.13\%\) and \(0.31\%\). These results satisfy the design requirement, that is, a voltage regulation accuracy of \(\pm 0.5\%\).  
A DC power supply was used to set the input voltage (bus voltage) to 380, 400, and 420 V, and an electronic load was used to set the output current (charging current) to 0, 10, and 20 A. Moreover, the charging voltage was set to 48, 50, and 52 V in order to conduct the current regulation accuracy test. Figure 20 shows the test results.  
Figure 19: Voltage regulation accuracies with different set voltages. (**a**) 48 V set voltage; (**b**) 50 V set voltage; (**c**) 52 V set voltage.  
Figure 18: Measured charging voltages with different set voltages. (**a**) 48 V set voltage; (**b**) 50 V set voltage; (**c**) 52 V set voltage.  
Figure 21 shows the current regulation accuracies at different set currents. The results show that the higher the set charging current, the more accurate the current regulation. In Figure 21a, the current regulation accuracy at a set current of 5 A is between \(-0.8\)% and 0.8%. In Figure 21b, the current regulation accuracy at a set current of 10 A is between \(-0.7\)% and 0.6%. In Figure 21c, the current regulation accuracy at a set current of at 20 A is between 0.3% and 0.6%. These results satisfy the design requirement, that is, a current regulation accuracy of \(\pm 1\)%.
# A High Throughput Hardware Architecture for Parallel Recursive Systematic Convolutional Encoders
## 2 Materials and Methods
### Analysis of the Tree Network Topology as a Function of the Parallelism Degree
In particular, since for sufficiently high values of \(k\) the contribution of the \(A^{\prime}\) and \(C^{\prime}\) matrices is roughly constant, the number of XOR gates necessary to realization of the entire RSC encoder can be estimated through the Equation (23). \[N_{XOR}[k]\approx N_{0}+N_{1}\cdot\Omega_{M^{\prime}i}[k]\cdot\lceil log2( \Omega_{M^{\prime}i}[k])\rceil, \tag{23}\]  
where \(N_{0}\) and \(N_{1}\) are constants to determine. We shall also consider that in FPGA implementations the number of XOR does not match in general the number of slice LUTs used. For such reason, in such conditions the model described in Equation (23) represents a worse estimation of source utilization.
# All-Optical Non-Inverted Parity Generator and Checker Based on Semiconductor Optical Amplifiers
## 2 Principle
### Parity Generator  
As shown parity generator in Figure 1, to generator an odd/even parity bit, the two data bits are added using XOR and XNOR gate, the parity bits _Pe_ and _Po_ are obtained, where _Pe_ is the even parity bit, _Po_ is the odd parity bit, and the expressions are, respectively,  
\[Pe=A\oplus B \tag{1}\]  
\[Po=\overline{A\oplus B} \tag{2}\]  
To verify whether there are errors or noise interference during the binary data transmitted from the sending end to the receiving end, the parity checker is applied to the sending end and receiving end of the data transmission to detect whether an error occurs (0 changed 1, 1 changed 0). On the one hand, the parity bit (0 or 1) is added to the data bit at the sending end so that total number of \"1\" codes is odd or even in the added data bit. On the other hand, the receiving end counts the number of \"1\" codes in the received data. If the total number of \"1\" codes is odd, it is called odd parity. If it is the opposite, it is called even parity. In odd or even parity, if the number of \"1\" codes are even or odd in the received data, it means that error has occurred.  
The 2-bit parity generator circuit can simultaneously generate parity bits _Pe_ and _Po_ using a single SOA. Compared with the scheme with a control circuit (control the parity generator to be odd or even), we achieve the expected purpose and make the circuit structure more compact, easier to integrate.  
As shown in Table 1, for all the truth of \(A\), \(B\), and _Pe_ in each row and the total number of \"1\" codes is even when the even parity generator is working. Similarly, for all the truth of \(A\), \(B\), and _Po_ in each row and the total number of \"1\" codes is odd when the odd parity generator is working. Parity bits _Pe_ and _Po_ of parity generator for the inputs \(A=[0111001]\) and \(B=[1100101]\) are shown in Table 1.
## 3 Methodology
### Random Forest Classification  
The random forest (RF) remote sensing image segmentation method is a classifier built based on multiple decision trees. Each decision tree classifies the sampled data set using the selected features and finally combines the classification results of each decision tree through a voting mechanism [49]. The entire construction process of the RF model uses randomly selected training samples and input features to generate and train each decision tree. After training, the test set samples are applied to the already trained classification model, and each decision tree will obtain the corresponding classification results. These results are recorded through voting, and the category with the most votes will be the final result.  
Glacier mapping using RF models to achieve target segmentation of glacier remote sensing images includes the following:  
1. Decompose the input Landsat 8 images and DEM data; extract the data's spectral features, index features, texture features, temperature features, topographic features, and glacier movement speed features to obtain a 24-dimensional data set.
2. Randomly scramble the data set and construct attribute sets separately. Select 70% of the samples as the labeled sample set, and the rest are unlabeled sample sets. Use the same loss function for the unlabeled and labeled samples to build the initial RF model.
3. Initialize the number of training iterations, select a certain number of label samples to train the classifier, take all the unlabeled samples with high confidence values, and give each label a value. Then, add it to the label set and update the label sample set. The latter label sample set is used as the training set for the semi-supervised training of the RF model to obtain a new RF model.
4. By introducing unlabeled data and adding all unlabeled sample data to the optimization goal, the initial value of the data misclassification rate of the overall model is given to control the optimization. When the misclassification rate of the entire out-of-bag data set is zero, the optimal RF model is obtained.
5. According to the category label matrix, each pixel of the glacier data based on remote sensing images and digital elevation models is classified; the classified image is output, and the classification accuracy rate is calculated.  
The technical process of debris-covered glacier classification based on the RF classifier is shown in Figure 5.
# Random Segmentation: New Traffic Obfuscation against Packet-Size-Based Side-Channel Attacks
###### Abstract  
Despite encryption, the packet size is still visible, enabling observers to infer private information in the Internet of Things (IoT) environment (e.g., IoT device identification). Packet padding obfuscates packet-length characteristics with a high data overhead because it relies on adding noise to the data. This paper proposes a more data-efficient approach that randomizes packet sizes without adding noise. We achieve this by splitting large TCP segments into random-sized chunks; hence, the packet length distribution is obfuscated without adding noise data. Our client-server implementation using TCP sockets demonstrates the feasibility of our approach at the application level. We realize our packet size control by adjusting two local socket-programming parameters. First, we enable the TCP_NODELAY option to send out each packet with our specified length. Second, we downsize the sending buffer to prevent the sender from pushing out more data than can be received, which could disable our control of the packet sizes. We simulate our defense on a network trace of four IoT devices and show a reduction in device classification accuracy from 98% to 63%, close to random guessing. Meanwhile, the real-world data transmission experiments show that the added latency is reasonable, less than 21%, while the added packet header overhead is only about 5%.  
device fingerprinting; IoT privacy; traffic analysis countermeasure; traffic shaping Article
## 2 Methodology
### Bayesian Network Improvement
#### 2.7.2 Result Correction Based on Observational Data  
To overcome the contradiction between the traditional DBN fixed transition rate assumption and the time-varying characteristics of equipment degradation and the uncertainty of maintenance effects in engineering practice, this paper proposes a correction method based on real-time component reliability. By introducing the real-time reliability of components as observational data and combining the bidirectional propagation characteristics of a DBN, the system's reliability prediction results are dynamically adjusted.  
Using the unreliable reliability observation values \(R_{i}(t)\) calculated by the method in Section 2.4, a time-varying correction factor is constructed, as follows:  
\[K_{i}(t)=\alpha\cdot(1-R_{i}(t)) \tag{19}\]  
where \(\alpha\) is the correction strength coefficient, controlling the extent of adjustment made to the prediction results based on observational data. In the previous DBN system reliability evaluation method, the state transition rate \(\lambda_{X_{t}\to X_{t+1}}\) is determined by historical data.  
Through the bidirectional propagation mechanism of forward and backward propagation, real-time reliability data dynamically correct the inference process. Specifically, forward propagation infers the future state of the system based on historical data and the corrected state transition rates, while backward propagation corrects the current state estimation based on real-time observational data. The forward propagation formula is:  
\[P(X_{t+1}|X_{t})=\prod_{i=1}^{N}P(X_{t+1,i}|\text{Pa}(X_{t+1,i})) \tag{20}\]  
The backward propagation formula is:  
\[P^{\prime}_{X_{t+1}|X_{t}}(t)=P(X_{t+1}|X_{t})\cdot(1+K_{i}(t)) \tag{21}\]  
A grid search strategy is used to determine the optimal correction strength coefficient \(\alpha\), and the root mean square error (RMSE) is used as the evaluation index. Experimental results show that \(\alpha=0.3\) is the most suitable choice.
## 3 Results
### Osmotic Dehydration of PEF Pre-Treated Tomatoes
#### 3.4.5 Selection of the Appropriate Treatment Conditions  
The analysis provided above has explored a wide range of treatment conditions for PEF and HP as a pre-treatment to osmotic dehydration. From all the results presented, the most suitable PEF treatment conditions are 100 pulses (15 \(\upmu\)s pulse width, 1.2 kJ/kg specific energy input) at an electric field strength of 1.8 kV/cm. At these conditions dehydration is accelerated, and quality characteristics of the product are retained. In terms of the HP treatment conditions tested, the condition which leads to the best dehydration outcomes while preserving the quality parameters of the tomatoes is 600 MPa-5 min. Both treatments achieve a significant acceleration in water loss and can reduce the water activity of the OD treated products below 0.9. Also, this effect is achieved at significantly shorter dehydration times. Therefore, in terms of subsequent dehydration conditions, an OD duration of 90 min was selected for both HP and PEF treated samples with an osmotic solution containing 60% glycerol, while untreated samples require an OD time of 180 min. Although an in-depth cost and equipment analysis is beyond the scope of this work, both processes are expected to be applicable to industrial treatment of whole tomatoes, since there are industrial systems available that can process high product throughputs [27, 28]. The selected treatment conditions fall very well within the processing capabilities of such equipment. In an industrial setting, the nonthermal pre-treatment of the whole tomatoes would take place regardless of the subsequent dehydration steps. Both treatments can be performed on whole fruits after the washing step, as they both require the submersion of the fruits in processed water.
# Optimized Implementation of Argon2 Utilizing the Graphics Processing Unit
## 2 Related Work
### Graphics Processing Units  
The use of GPUs has become an integral and widespread component in modern computing systems. These GPUs are highly parallel processors with significant arithmetic and memory bandwidth capabilities that far surpass those of CPUs [12; 13; 14]. For our study, we utilized an Nvidia RTX 3060 Laptop GPU, which boasts an impressive 3840 cores and operates at a clock rate of 1702 MHz. It is important to note that clock rates may vary depending on the specific GPU manufacturer. The GPU we used is designed with the Ampere architecture, which has a Compute Capability (CC) of 8.3. CC refers to the device's ability to perform computations.  
To leverage the parallel processing power of the GPU, we employed the Compute Unified Device Architecture (CUDA), a GPGPU (General-Purpose Computing on Graphics Processing Units) technology. CUDA allows programmers to write parallel processing code using the C language. Developed and maintained by Nvidia, CUDA requires an Nvidia GPU and the corresponding stream processing driver. The CUDA GPU architecture comprises functional kernels, threads, blocks, grids, and warps (bundles of 32 threads) that run on the GPU. Multiple warps execute concurrently on a Streaming Multi-processor (SM), enabling efficient parallel computation [14; 15].  
The GPU provides several memory types, including register, shared memory, local memory, constant memory, texture memory, and global memory.  
* Global memory is the largest memory on the GPU and is commonly used for storing and accessing data. However, it is the slowest memory due to its off-chip location.
* Local memory is used to temporarily store register values when the number of registers used by a thread exceeds the available capacity. Excessive usage of local memory can impact performance as it relies on global memory.
* Texture memory is read-only memory designed for efficient data visualization. It allows for optimized texture access patterns.
* Constant memory is read-only memory that can be initialized before executing the kernel function. It utilizes a separate constant cache within global memory, resulting in faster access when multiple threads access the same address.
* Shared memory is memory shared among threads within a block. Although it provides a smaller memory space, it offers fast access speed. Shared memory employs a banking mechanism, allowing 32 threads executed in warp units to access it simultaneously, minimizing latency.  
CUDA manages GPU memory by dividing it into on-chip and off-chip memory. Register and shared memory are located on-chip for faster access, while other memory types reside off-chip. Maximizing on-chip memory can help reduce memory transmission delays and enhance performance. Efficient utilization of the available on-chip memory size is crucial.