esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Awareness in Software-Defined Multi-Domain 5G Networks
## 5 Implementation
### Trust Metric Enabler  
The Trust Metric Enabler is a Python application that implements the Trust Level Agreement Mechanism, specified in Section 3.5. It simplifies the security monitoring from the client's perspective. The enabler is requested to follow particular 5G measurements and events and determine how well they match a trust model specified by the client. The enabler notifies clients only if changes occur in the network that affect client's trust towards it. The implementation utilizes a Kafka framework to collect measurements as well as to exchange trust models and trust metric information with clients at run-time.  
Trust models specify requirements that the 5G network must fulfil to provide the required trust level. All requirements stated in the model must be fulfilled to confirm that the network is trusted. The current implementation supports three types of measurements:  
* _Service_--Stating that a particular service is running. \"The service must be running for the network to be trusted\".
* _Max_level_--Setting an upper boundary for particular aggregated events. \"There must be at most x number of events y occurrences for the network to be trusted.\"
* _Min_level_--Setting a lower boundary for particular aggregated events. \"There must be at least x number of events y occurrences for the network to be trusted.\"  
Trust models are presented using JavaScript Object Notation (JSON) files. A simple example is given in Figure 8. The example contains an identifier of the model and a requirement that two particular enablers must be running, as well as requirements that the number of devices that have been authenticated using MD5-based AKA is at most 50, and that the anomaly level is 1.  
Trust models are sent to the Trust Metric Enabler, which provides metrics as a reply, first when the request is made and then again if the metric changes. Metrics are presented as a simple JSON file, with the model's identifier (as specified by the Trust Model Creator) and its value. The enabler counts the amount of rules in each trust model and can be used to filter models that provide only few rules and hence may, for example, leak privacy critical information.
## 5 Applied Magnetic Linkage Estimation Methods
### The Used Digital Method
Considering technical limitations, it is necessary to modify the numerical integration algorithm in such a way that during subsequent iterations, both the constant component in the input signal and the constant component (offset) in the output signal resulting from the assumed zero initial conditions are eliminated. A simplified block diagram of the proposed integration algorithm with offset elimination is shown in \[\Delta t=h \tag{56}\]8  
The algorithm uses Walsh filters of order 0 (rectangular filters of the average value with a window width of 25 Hz). Subtracting the average value calculated from the input signal eliminates the problem of the presence of a constant component of the input signal, resulting in a linear rise/fall of the solution. The elimination of the constant component at the output is ensured by filtering the constant component from the solution, the value of which is then subtracted in the feedback loop from the signal entering the integrator. The calculated average value is also multiplied by an appropriately selected gain/attenuation coefficient \(\beta\). This coefficient should always be less than 0.5. Otherwise, in certain conditions, problems with the obtained stability (convergence) of the solution may occur. Adopting values of the \(\beta\) coefficient closer to 0.5 results in faster elimination of the constant component but at the cost of increasing the decaying oscillations of the solution. The adoption of smaller values, closer to 0.1, extends the elimination time of the DC component but allows obtaining an asymptotically stabilising solution. In the proposed algorithm, the value of the gain/attenuation coefficient was assumed to be \(\beta=0.25\). The sampling frequency of the analogue signal was \(f_{\text{p}}=3200\) Hz.
## 5 Methodology, Experiments and Results
### Proposed Methodology
#### 5.1.1 Data Acquisition  
Data were acquired for two hardware architectures \(A1\) and \(A2\) (Table 2) with different number of containers (1; 5; 10). A total of 27 tests cases \(P1\)-\(P27\) for the \(A1\) architecture (Figure 2) and 27 test cases \(P28\)-\(P54\) for the \(A2\) architecture (with three different test scenarios (\(S1\); \(S2\); \(S3\)) and three different numbers of customer requests (100,000; 400,000; 700,000) for a particular test case) were executed.
## 3 Results  
In our analysis, we focused on two aspects, the primary of which concerned the impact of using GPS-based receiver antenna phase center corrections (G02) for the Galileo E5a frequency. At present, such a procedure applies to almost 90% of EPN stations providing Galileo observations. To evaluate this impact, we compared the coordinates of 25 stations that were modeled in three variants, namely E1, E2 and E3, separately for both positioning methods. It should be remembered here that antenna modeling impacts on all estimates, such as tropospheric delay or clocks. However, previously conducted studies on receiver antennas show that the greatest impact of using different PCCs is more visible in the height [23], and much less visible in the tropospheric parameters [34; 35]. Therefore, in our study, we focused only on the coordinates. Typically, the daily coordinates in the PPP approach are a sample-mean of 2760 samples. In the DD approach we have one set of coordinates per day from the least squares method. Based on the daily solutions, we prepared daily coordinate differences for each station--one per day. Then, we calculated the weekly weighted averages of the differences and their standard deviations. There were typically 7 daily differences recorded. We conducted a differential analysis based on comparable solutions to eliminate software- or approach-dependent issues. We did not compare directly the results from both approaches, since the models and algorithms are completely different, and the coordinate differences would be affected by the impact of other factors as well.  
Second, the analysis concerned the coordinate agreement between solutions derived from GPS and Galileo data. Here, we compared the corresponding solutions: E1-G1 (according to EPN guidelines), E2-G1 (in the absence of chamber calibrations), and E3-G2 (in the absence of any individual calibrations). The general quality of the Galileo solutions has already been proven by others [1; 6]. The coordinate repeatability for GPS and Galileo is almost on the same level for the EPN stations (Figure 2), despite the differences in constellation and number of observations. As both can provide different results [1], we focused only on how this agreement changed for different PCCs.
# Neural Network-Informed Lotka-Volterra Dynamics for Cryptocurrency Market Analysis
## 3 Deep Neural Network Model for Crypto Dynamics  
### Feed-Forward Neural Network Architecture  
A feed-forward neural network (NN) with three hidden layers as on Figure 1 was constructed to learn the temporal dynamics of the system. The architecture is described as follows:  
* Input layer: accepts a single input feature corresponding to time, denoted as \(t\).
* Hidden layers: Three fully connected hidden layers, with 10.6 and 9 neurons each and using the hyperbolic tangent (tanh) activation function. To promote stable learning, weights were initialized to small values and biases were set to zero.
* Output layer: A fully connected layer with three outputs, representing the estimated values of the variables \(x(t)\), \(y(t)\), and \(z(t)\). Each output is connected to a regression layer, which calculates the loss based on the deviation between predicted and actual values.  
Function of the neural network:  
1. Time-series prediction: The neural network is trained on datasets \((x,y,z)\) to learn and predict their evolution over time. Once trained, it produces predictions for the three variables based solely on time input.
2. Modeling underlying relationships: by learning from data, the NN approximates the hidden relationships between the variables and time, capturing complex and nonlinear behaviors that traditional models might overlook.
3. Preparing for optimization: After prediction, the outputs obtain continuous estimates of the state variables. These are then used to compute numerical derivatives \(\frac{dx}{dt}\), \(\frac{dy}{dt}\), and \(\frac{dz}{dt}\). These derivatives are essential for the next phase of analysis, where model parameters are estimated via optimization using lsqnonlin.  
In essence, the neural network plays a crucial role in approximating the dynamics of the system, bridging the gap between observed data and the differential equations required for parameter identification. The neural network architecture with three hidden layers consisting of 10, 6, and 9 neurons respectively was selected based on bootstrap validation performance. Using moving block bootstrap resampling to simulate multiple training and testing scenarios, we compared several candidate architectures. The 10-6-9 configuration consistently achieved the lowest average test error and exhibited minimal performance variance across bootstrap samples, indicating strong generalization capability and robustness to data perturbations. The network's depth allowed it to model nonlinear interactions among crypto market variables, while its compact structure minimized overfitting.
# Social Network Sentiment Analysis Using Hybrid Deep Learning Models
## 4 Experiments and Results
### Optimum Hyperparameter Setting
Once the whole model tuning process is completed, the final architecture is obtained, as shown in Figure 2. In summary, this deep learning model corresponds to an LSTM-based RNN architecture consisting of five layers with their corresponding hyperparameters. The five layers are as follows: an embedding layer, a one-dimensional convolutional layer (Conv1D), a MaxPooling layer, an LSTM layer, and a dense layer with an Adam optimiser. The hyperparameters were carefully selected using a 10-fold cross-validation technique, where the batch size was set to 256. The embedding layer takes input tensors of size 33 and produces vectors of length 200. The convolutional layer comprises 192 filters with a dropout rate of 0.2, while the LSTM layer consists of 96 neurons with a dropout rate of 0.3 and a kernel size of 8. Finally, the dense layer consists of as many neurons as classes we want to classify, that is, three neurons in the case of classifying three polarity categories (P, N, or NONE) or two neurons in the case of classifying two polarity categories (P or N). In addition, this layer shall use the SoftMax activation function to transform the outputs into a representation in the form of probabilities. This detailed description of the deep learning model, as well as its layers and hyperparameters, will help other researchers reproduce and apply the model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dimension Size** & **Input Tensor Size** & **Cross-Validation Accuracy** \\ \hline
100 & 22 & 75.32\% \\
100 & 33 & 75.70\% \\
100 & 44 & 75.45\% \\
200 & 22 & 75.19\% \\
**200** & **33** & **75.82\%** \\
200 & 44 & 75.60\% \\
300 & 22 & 75.22\% \\
300 & 33 & 75.67\% \\
300 & 44 & 75.52\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of tests carried out to evaluate the hybrid model by modifying the size and maximum length of the embedding layer. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Batch Size** & **Time (min)** & **Cross-Validation Accuracy** \\ \hline
32 & 14 & 75.38\% \\
64 & 7 & 75.10\% \\
128 & 6 & 75.78\% \\
256 & 3 & 75.82\% \\
512 & 2 & 75.62\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of tests carried out to evaluate the hybrid model by modifying the batch size.
## 4 Enhancement of Energy Recovery (Biogas/Methane Yield) from the Anaerobic Digestion of Rice Straw
### Recycling Liquid Digestate (LD)
Higher recycling rates are recommended, but these are limited by the accumulation of VFAs which can inhibit the methanogenic process. The effect of LD recycling in various proportions (relative to recovered amount), with different frequencies and with varying post-treatments on the biogas/methane yield in the AD of various substrates is shown in Table 8. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  
**AD**, Anaerobic digestion; **LD**, Liquid digestate; **RS**, Rice straw; **PWW**, Piggery wastewater; **MS**, Maize stover; **Mixture**, PS and straw; **WS**, Wheat straw; **SS**, Sewage sludge; **MN**, Macronutrients (N and P); **LG**, Lawn grass; **DM**, Dairy manure; **AS**, Alfalfa silage; **CS**, Corn straw; **OFMSW**, Organic fraction of municipal solid waste.  
\end{table} 
Table 8: Effects of recycling LD on the biogas/methane yield in AD of various substrates. The studies presented in Table 8 show different results of the recycling of LD on the biogas or methane yield in the AD of various substrates. The results of the two studies show no increase or decrease in the biogas yield due to LD recycling, whereas the authors explained that there is no adverse impact of recycling on the process stability [94; 96]. Some authors obtained higher biogas yield with the recycling of LD. They attributed this to increase microbial growth, enhanced buffer capacity, higher alkalinity, increased methanogenic activities, higher nutrients availability, and improved hydrolysis [36; 98; 99; 100]. In some other studies, the biogas yield improved at a lower rate of LD recycling, and it reduced when more LD was recycled. The accumulation of VFAs and the dilution of essential nutrients were named as causes [100; 102; 103]. From these studies, it can be concluded that it is technically feasible to recycle part or all of the LD to the AD system. It is also possible that the process cannot be sustained for a longer time due to the dilution of nutrients. Co-digestion of straw with nutrients rich substrates combined with recycling LD is an excellent approach to achieve a stable AD system. In addition, an appropriate amount of LD is required because its excessive recycling may cause process failure due to the accumulation of VFAs. Therefore, LD recycling may enhance system stability and biogas yield.
# Characterization of Volatile Substances in Pu-erh Tea (Raw Tea) at Different Storage Times
## 3 Results and Analysis  
### Sensory Evaluation of Pu-erh Tea During Storage  
Sensory evaluation results showed that storage time significantly affected the flavor quality of 14 Pu-erh tea (raw tea) samples (Table 2). In the storage process, the soup color of the tea becomes darker, yellow to orange-red, and the taste becomes more mellow. Especially, the aroma characteristics showed regular changes (Figure 1). Among them, the aroma of P1, P2, P4, and P6 is mainly fruity/floral, and the degree of the aroma gradually decreased; the P5, P9, and P10 are mainly smoky aroma, and the degree of the aroma gradually increased; the aroma of P7, P8, P12, and P13 are mainly fragrance, while the aroma of P11 and P14 are also fragrance, but to a stronger extent and purer. The aforementioned transformations suggest that the aroma profile of Pu-erh tea (raw tea) evolves progressively over time from a fruity/floral aroma to a smoky aroma and then to a fragrance aroma. Moreover, the intensity of the aroma also changes accordingly. According to the different aroma types and intensity, we divided P1, P2, P4, and P6 into the first group, P5, P9, and P10 into the second group, P7, P8, P12, and P13 into the third group, and P11 and P14 into the fourth group. Figure 1: Aroma evaluation radar chart. Volatile Substances Were Qualitatively and Quantitatively Determined by HS-SPME/GC\(\times\)GC-Q-TOF-MS  
To analyze the chemical mechanism of storage time on the aroma of Pu-erh tea (raw tea), HS-SPME/GC\(\times\)GC-Q-TOF-MS was further used for the qualitative and quantitative analysis of volatile components. A total of 290 volatile compounds were identified in 14 Pu-erh tea (raw tea) samples (Table S1). The volatiles are categorized into 11 major groups according to their functional groups (Figures 2 and 3), including 55 aromatic hydrocarbons, 51 olefins, 36 ketones, 34 alcohols, 32 aldehydes, 24 esters, 24 heterocyclic compounds, 20 ethers, 7 phenols, 6 alkanes, and 1 acid.
# Content and Privacy Protection in JPEG Images by Reversible Visual Transformation
## 4 Experimental Results
### Visual Effects, Image Quality and Reversibility  
In this part, the proposed algorithm is evaluated in terms of the invisibility of the protected regions, quality of the unprotected blocks and reversibility. Four testing JPEG images used in the experiments and those with the selected regions protected are shown in Figure 5. In each test image, one or more specific regions were selected and the protected content was made visually invisible while the quality of the unprotected regions remained unchanged. The images recovered with the correct key were identical to the original ones, while the regions obtained with the wrong key were meaningless, as shown in Figure 6.
## 5 Experiments
### Prefix Ablation Study  
For the QFeP, we designed a length-three prefix for the KV cache, including the BOS token, context token, and extra token for activation spike. Because the KV cache consumes the capacity of the pretrained sequence position, it raises a question about the length of the prefix. Therefore, we conducted an ablation study for different prefixes for the KV cache. For the prefixes, we prepared random, BOS only, and both QFeP without and with the context tokens. Notably, the BOS cache corresponds to \(IntactKV_{[B]}\)[38]. We illustrate the results of the ablation study in Figure 6. In all cases, the random prefix showcases the lowest performance. While the KV cache with the BOS token demonstrates inconsistent performance, our QFeP consistently shows significant improvements. Importantly, the results imply that the sufficient prefix for the models exhibits differences. However, we emphasize that our KV design for QFeP shows improvements by large margins across all models. Beyond our QFeP, \(IntactKV_{[P]}\)[38] extends the BOS token to the system prompt for supervised fine-tuned models and CushionCache [39] update prefixes through quantization-aware prefix tuning.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**\#Bits**} & \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Per-Token Quant.**} & \multicolumn{4}{c}{**Per-Tensor Quant.**} \\ \cline{3-8}  & & & **Base** & **QFeM** & **QFeP** & **Base** & **QFeM** & **QFeP** \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} LLaMA-2-7B \\ (FP16: 5.268) \\ \end{tabular} } & W8A8 & SQ & 5.296 & **5.288** & 5.302 & 9.907 & **5.534** & 5.715 \\  & W8A8 & OSP & 5.293 & **5.288** & 5.303 & 38.490 & **5.493** & 5.642 \\  & W4A4 & OSP & 48.199 & **16.151** & 44.635 & 23491 & **4772** & 10940 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} LLaMA-2-13B \\ (FP16: 4.789) \\ \end{tabular} } & W8A8 & SQ & 4.809 & **4.808** & 4.818 & 34.869 & **5.118** & 6.551 \\  & W8A8 & OSP & 4.813 & **4.812** & 4.819 & 5.148 & **5.099** & 5.144 \\  & W4A4 & OSP & 21.037 & **11.535** & 11.860 & 9340 & **8680** & 9362 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Perplexity on Wikitext-2 for outlier alleviation methods with QFeM and QFeP under different activation quantization granularities. Activation quantization is applied at two levels: fine-grained (per-token) and coarse-grained (per-tensor). We employ per-channel granularity for weight quantization. For low-bit quantization (W6A6 and W4A4), we adopt asymmetric scale estimation. The bold values indicate the best performance within each group. “#Bits” denotes the bit-width configuration used for quantization, representing how much the weights and activations have been quantized.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 5 Different VANET Technologies Based on Transmission Range
### Short-Range Communication Technologies
#### 5.1.2 ZigBee Technology  
ZigBee technology was developed to address the need for low cost and low power wireless Internet-of-things (IoT) network [144]. The supported connectivity range is up to 100 m with a transfer data rate of about 250 kbps [145]. It is also recognised as IEEE 802.15.4 for Low Rate Wireless Personal Area Network (LRWPAN) and uses different frequency bands (868 MHz, 902-968 MHz and 2.4 GHz) for operation [146; 147]. ZigBee employs binary phase shift keying (BPSK) or orthogonal quadrature phase shift keying (OQPSK) for modulation.  
Similar to BLE, ZigBee also is suitable for intra-vehicular communication and to reduce the complex wiring within an AV. Recently, Volvo deployed a ZigBee based wireless sensor network in trucks to monitor the tire pressure [148]. It was concluded that ZigBee is sufficiently robust for non-safety and non-security critical applications requiring relaxed latency with update rates less than 10 Hz. [PERSON] et al. proposed the use of ZigBee for Advanced Driver Assistance Systems (ADAS) [149]. In [150], the performance of ZigBee was simulated in the QualNet simulator. It was observed that as the number of cars increased to 50, the packet delivery ratio dropped significantly and the average end-to-end delay was more than 200 s. As a consequence, it was concluded that ZigBee cannot be employed for urban or crowded places. Furthermore, ZigBee technology has successfully been implemented and tested for FCWS in vehicles [41]. ZigBee technology has been successfully implemented to locate monitored vehicles on the real time in [151]. However, the distance promised is based on received signal strength indicator (RSSI) which is not very accurate.
## 4 Algorithm Design
### Leak Detection Algorithm
#### 4.1.2 Leak Initial Recognition Based on SVM Multiple Classifiers
The specific algorithm is as follows: (i) we calculate the optimal \(\tilde{C}\) for maximum learning precision from linear SVM; (ii) for the SVM with RBF kernel function, we fix \(\tilde{C}\). If \(\tilde{C}\) satisfies \(\log\gamma=\log C-\log\tilde{C}\), we train the SVM for obtaining the optimal parameter values. (3) Probability Output of SVM Multiple Classifiers  
The standard SVM output is a hard decision output so that it is likely to cause excessive dependence on the voting method [40]. However, it needs a SVM with soft decision output in practical application. Therefore, in order to get posterior probability output, we utilize a sigmoid function to map the SVM output \(f(x)\) to the interval (0, 1). The expression of posterior probability output is:\[P(y=1\big{|}f(x))=\frac{1}{1+\exp(Gf(x)+H)} \tag{4}\]  
where the parameters \(G\) and \(H\) could be obtained from the following Equation:  
\[\min-(\sum_{i}t_{i}\log(p_{i})+(1-t_{i})\log(1-p_{i})) \tag{5}\]  
where \(p_{i}=\frac{1}{1+\exp\ (Gf(x)+H)}\), \(t_{i}=\frac{y_{i}+1}{2}\) and \(y_{i}\) is the classification label of sample \(i\).