esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation of the HEMT Reliability  
To estimate the reliability of the component, many authors have studied the effect of the electrical characteristics on its structural performance [28; 29]. However, the thermal behavior also has a great impact on the HEMT's reliability, by different degradation forms such as gate burial, degradation of the feed metals interconnection and degradation of the Schottky contact [4].  
Thermal modeling has identified the parameters that have a great impact on the operating temperature, such as the power dissipation \(P_{diss}\) and the reference temperature \(T_{Ref}\). We consider that these two variables follow a normal law. If \(T_{max}\) is the maximum temperature that the system must not exceed, and \(T\) is the maximum temperature calculated by the finite element model, the performance function is written as:  
\[G=T_{\text{max}}-T(X). \tag{11}\]  
The temperature \(T(X)\) is evaluated by the FEM developed in the preceding section. It is calculated as a function of random variables vector \(X\). These variables represent the parameters of dissipated power and reference temperature. In this work, we have considered these parameters as random variables following a normal distribution. All parameters and their values are classified in Table 2.  
Our reliability analysis process is based on thermo-reliability coupling. The probability model of SORM and FORM methods coded on Matlab allows us to estimate the limit state function and calculate the reliability index and the failure probability. The FEM using Comsol multiphysics allows the calculation of the temperature as a function of the random variables. The process will stop if we obtain the best value of \(\beta\). The results obtained--the reliability index \(\beta\), failure probability, and reliability \(R\) of the transistor, calculated by both SORM and FORM approximation methods using the precedent process--are classified in Table 2. The reliability index obtained by the SORM method is 0.123 and by the FORM method is 0.124. Both methods allowed estimation of the reliability level but the SORM method is more accurate. From these results, we can see clearly that the HEMT's reliability level needs to be optimized to improve its performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **FORM** & **SORM** \\ \hline \(\mu_{\textit{{Pliss}}}\) (W/mm) & 5.5 & 5.5 \\ \(\sigma_{\textit{{Pliss}}}\) (W/mm) & 0.55 & 0.55 \\ \(\mu_{\textit{{Tref}}}\) (\({}^{\circ}\)C) & 150 & 150 \\ \(\sigma_{\textit{{Tref}}}\) (\({}^{\circ}\)C) & 15 & 15 \\ \(\beta\) & 0.124 & 0.123 \\ \(P_{f}\) & 48.35 & 48.35 \\ \(R\) & 51.64 & 51.64 \\ Temps(s) & 4609 & 4073 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter distribution.
## 5 Hardware Implementation and Experimental Results
### FPGA Placement and Routing Results of the Overall DFE Architecture  
Different than the ASIC synthesis results, the FPGA (XC6 VLX550T) P&R results show that FIR3 (Farrow interpolator), involving many multipliers, consumed resources most, which used the largest number of DSP slices. The resources used by the DFE building blocks are summarized in Figure 33. Since FIR1 (CIC filter) did not use any multiplier, it did not use any DSP slice. FIR3 (Farrow interpolator) used 24 coefficients for each of \(I\) and \(Q\) and hence 48 DSP slices were used. It also needs some conditions for carrying on operations, which increased complexity and also the count of LUTs and slice registers. The digital mixer also used many DSP slices. In the case of XC6 VLX550T, each DSP slice could support a 25-bit \(\times\) 18-bit multiplication. The digital mixer exceeded this bit range and needed an extra multiplier. Therefore, (1 + 1) \(\times\) 8 = 16 DSP slices were needed and since there are two carriers, carrier 1 and carrier 2, 32 DSP slices were used in total. FIR4 and FIR5 use 14-22 DSP slices but since they need \(I\) and \(Q\), 28-44 slices were used. Only 1% of the FPGA slice registers were used for all the building blocks in the DFE and only 2% of the LUTs were used while 24% of the DSP slices were used. Pie graphs for the use of slice registers, LUTs, and DSP slices are shown in Figures 34-36, respectively.  
Figure 31: Synthesis results of the building blocks in the proposed DFE.  
Figure 32: Area breakdown.  
After the FPGA implementation, speed performance was obtained, which reflects the routing effect as well. The P&R is carried out individually for each building block. An example (5 MHz + 5 MHz) CA case was tested and the P&R timing report is shown in Figure 37. ISE (integrated synthesis environment) delay is the delay obtained after the FPGA P&R. Sufficient timing margins or slacks are secured for the building blocks in the proposed LTE CA DFE.  
Figure 34: Breakdown for slice register use.  
Figure 35: Breakdown for look-up table (LUT) use.  
Figure 33: Resource use after the P&R of the proposed DFE.
# The Use of Educational Games in Programming Assignments: SQL Island as a Case Study
## 4 Results  
### Student Game Play Habits and Prior SQL Knowledge  
In the online questionnaire, nearly one out of four students stated that they play non-digital games on a weekly basis (18%) or even daily (5%), as shown in Figure 3a. When it comes to digital games (Figure 3b), more than half the students stated that they play such games on a weekly basis (30%) or daily (25%). Of course, this was not surprising since digital games are widely known to attract people of all ages. It is interesting, however, that although all the students stated that they play non-digital games, nearly one of ten students (9%) stated that they never play digital games and so it was possible that these students might not consider using a digital game in a course as a good idea.  
Apart from students' experience with entertainment games, 66% (\(N\) = 37) of the students had also used a game in the context of another course or on their own for practicing in various fields of knowledge.  
Finally, in a relevant 5-point Likert scale question, just 13% of the students stated that they had a good knowledge of SQL prior to playing the game, 46% of them had an average and 41% a poor knowledge (Mean = 2.7, Std.Dev. = 0.68, Median = 3).
## 4 Results
### Radar Charts and Boxplots Comparison
#### 4.2.2 Analysis of Real Signals  
The evolution in time of a real sag and an impulsive transient is shown through their radar charts per cycle in Figures 20 and 21. Since the SNR value is 40 dB, the presence of Gaussian noise causes a slight variation in the indicators with respect to the nominal values in state I. Despite this fact, states II and III are notable and distinguishable between them.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicator** & **Lower Limit** & **Upper Limit** \\ \hline Skewness & \(-\)0.0244 & 0.0266 \\ Kurtosis & 1.4771 & 1.5306 \\ Fifth-order statistic & \(-\)0.0144 & 0.0161 \\ Sixth-order statistic & 0.2854 & 0.3403 \\ Crest factor & 1.4027 & 1.5193 \\ SNR & 80 & 160 \\ THD & 0 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Tolerance bands for normal signal behavior in boxplots.  
Figure 22: Boxplots of (**a**) R-SAG signal; (**b**) R-IMP signal. State II values are circled. State III SOS values are also highlighted.  
Figure 22 shows the boxplots of the indicators' values obtained from R-SAG (Figure 22a) and R-IMP (Figure 22b) signals. The outliers of state II of Figures 20 and 21 have been circled. Unlike boxplots drawn from synthetic signals, for real signals, a box is obtained around the nominal values, and even outliers appear close to these values due to noise. The short sag duration in R-SAG signal versus the total signal duration causes the values acquired by SOS during the event to be marked as outliers (boxed in purple in Figure 22a).
###### Abstract  
Quantum Key Distribution (QKD) systems exploit the laws of quantum mechanics to generate secure keying material for cryptographic purposes. To date, several commercially viable decoy state enabled QKD systems have been successfully demonstrated and show promise for high-security applications such as banking, government, and military environments. In this work, a detailed performance analysis of decoy state enabled QKD systems is conducted through model and simulation of several common decoy state configurations. The results of this study uniquely demonstrate that the decoy state protocol can ensure Photon Number Splitting (PNS) attacks are detected with high confidence, while maximizing the system's quantum throughput at no additional cost. Additionally, implementation security guidance is provided for QKD system developers and users.  
Keywords:quantum key distribution; decoy state protocol; photon number splitting attack; implementation security +
Footnote †: journal: Applied sciences
###### Abstract  
The 2016 Ecuador \(M\) 7.8 earthquake ruptured the subduction zone boundary between the Nazca plate and the South America plate. This \(M\) 7.8 earthquake may have promoted failure in the surrounding crust, where six \(M\geq 6\) aftershocks occurred following this mainshock. These crustal ruptures were triggered by the high coulomb stress changes produced by the \(M\) 7.8 mainshock. Here, we investigate whether the six \(M\geq 6\) aftershocks are consistent with the positive coulomb stress region due to the mainshock. To explore the correlation between the mainshock and the aftershocks, we adopt a recently published high-quality finite fault model and focal mechanisms to study the coulomb stress triggers during the \(M\) 7.8 earthquake sequence. We compute the coulomb failure stress changes (\(\Delta\)CFS) on both of the focal mechanism nodal planes. We compare the \(\Delta\)CFS imparted by the \(M\) 7.8 mainshock on the subsequent aftershocks with the epicenter location of each aftershock. In addition, the shear stress, normal stress, and coulomb stress changes in the focal sources of each aftershock are also computed. Coulomb stress changes in the focal source for the six \(M\geq 6\) aftershocks are in the range of \(-2.17\)-7.564 bar. Only one computational result for the \(M\) 6.9 aftershock is negative; other results are positive. We found that the vast majority of the six \(M\geq 6\) aftershocks occurred in positive coulomb stress areas triggered by the \(M\) 7.8 mainshock. Our results suggest that the coulomb stress changes contributed to the development of the Ecuador \(M\) 7.8 earthquake sequence.  
Ecuador \(M\) 7.8 earthquake; finite fault model; coulomb failure stress; stress triggering; focal mechanisms +
Footnote †: journal: Applied Sciences
# A Space-Borne Ka-Band Tile-Type Phased Array Transmitter Module Based on HTCC Technology
## 5 Conclusions  
This work proposed a design for a Ka-band four-channel tile-type transmitter module based on the HTCC process. The presented SiP integrates multiple chips within a footprint of 12.2 \(\times\) 12.2 \(\times\) 4.3 mm. The test results show a good consistency among 20 samples. The gain, output power, and overall efficiency of the proposed module exceed 26 dB, 21 dBm, and 40%, respectively. The proposed modules exhibit an RMS accuracy of phase shift of 3\({}^{\circ}\) and an RMS accuracy of attenuation of 0.5 dB. The design achieves unique double-sided packaging with a reliable hermetic seal, which enables a compact footprint and conformal integration with systems. This design shows a high integration level and excellent performance, which is very promising in long-lifespan high-reliability space-borne applications.  
Conceptualization, H.X. and K.M.; methodology, H.X. and K.M.; validation, H.X., S.L. and G.L.; formal analysis, H.X., S.L. and G.L.; investigation, H.X., S.L. and G.L.; resources, H.X., S.L. and G.L.; data curation, H.X., S.L. and G.L.; writing--original draft preparation, H.X., S.L. and G.L.; writing--review and editing, H.X., K.M., S.L. and G.L.; visualization, H.X., S.L. and G.L.; supervision, K.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding authors upon reasonable request.  
The authors would like to thank colleagues at Tianjin University and CAST (Xi'an) for their kind help and useful discussion during the research.  
The authors declare no conflicts of interest.
# Skip token if not replaceable encoded_message.append(token)  
return \" \".join(encoded_message)  
Our implementation calculates semantic similarity using cosine distance between word embeddings derived from GPT-4-ommi's internal representation. This approach ensures that selected synonyms maintain semantic coherence while providing sufficient entropy for message encoding.  
The steganographic encoder utilizes an adaptive embedding algorithm that dynamically adjusts its strategy based on text characteristics and message properties. The algorithm first converts the input message into a binary representation optimized for the available synonym space. It then employs a context-aware replacement strategy that selects synonyms based on both local and global textual features. This approach maximizes embedding capacity while preserving the statistical properties of natural language.  
The steganographic encoder implements a practical binary representation optimization for message embedding. The process includes:  
1. Binary Message Encoding: The input message is converted to a binary sequence using 8-bit ASCII encoding: def binarize_message(message: str) -> str: \"\"\"Binarize an ASCII-encoded message.\"\"\" return \".join([bin(x)[2:].zfill(N_ASCII_BITS) for x in list (message.encode('ascii'))])
2. Chunking and Distribution: Binary messages are segmented based on embedding capacity: def divide_chunks(text: str | list, chunk_size: int) -> list[str]: \"\"\"Split string into chunks with specified chunk_size\"\"\" for i in range(0, len(text), chunk_size): yield text[i:i + chunk_size]
3. Synonym-Binary Mapping: The system maps binary segments to specific synonyms: def binarize_synonyms(base_token: str, synonyms: list[str]) -> dict[str, str]: \"\"\"Binarize a base token by assigning binary indices to synonyms.\"\"\" if not len(synonyms): return {\"@\": base_token, \"1\": base_token} return { \"\"\".join(index): synonym for index, synonym in zip(product(\"@1\", repeat=int (math.log2(len(synonyms)))), synonyms) }
4. Enhanced Capacity Mapping: For higher capacity requirements, the system implements partial binarization: def binarize_synonyms_partially(synonyms: list[str], include_ sequence: str): \"\"\"Binarize synonyms to include a specific binary sequence.\"\"\" # Implementation ensures include_sequence will be represented in the mappingThis binary optimization approach enables DeepStego to achieve up to 4 bits per word while maintaining detection resistance and semantic coherence.  
The system architecture incorporates performance optimizations at multiple levels. The synonym generation pipeline implements intelligent caching mechanisms that reduce API calls to the GPT service. Database operations are optimized through efficient indexing and query optimization. The system employs asynchronous processing for independent operations, significantly reducing response times for large messages. Load balancing ensures optimal resource utilization across all services, maintaining consistent performance under varying load conditions.
## 5 Challenges in Diagnosing Cachexia in ACHD  
One of the major challenges in the management of cachexia in HF, especially in ACHD, is underdiagnosis. Cachexia is often overlooked in clinical practice, partly due to the lack of standardized diagnostic criteria and its often insidious onset.  
Current general diagnostic criteria for cardiac cachexia include unintentional weight loss of more than 5% over a 12-month period, alongside symptoms such as fatigue, anorexia, and muscle weakness. These criteria may not capture the early stages of cachexia or account for the individual variability in body composition. However, it is unclear whether these parameters are appropriate for the ACHD population, which often has complex cardiopulmonary interactions and atypical clinical courses, necessitating a more tailored cachexia assessment.  
As underweight is a widely under-recognized complication in ACHD with HF, the Pathfinder Registry provides crucial real-world data on the relationship between weight status and the medical, surgical, and/or interventional treatment of the underlying cardiac abnormality in this population. This appears to be particularly true for patients with complex CHD or those with connective tissue diseases, who are particularly affected.  
In addition to the fact that the metabolic body weight (MBW) was significantly lower in women, there was interestingly no correlation between underweight and sex in the present study. However, unlike traditional metrics such as BMI, MBW may serve as a valuable metric for evaluating energy requirements and metabolic stress in this population, providing a more nuanced understanding of the metabolic demands for guiding nutritional and therapeutic strategies associated with varying body compositions. The lower MBW in women may indicate increased susceptibility to the catabolic effects of HF, necessitating sex-specific approaches to nutritional support.
# Analyzing and Predicting Students' Performance by Means of Machine Learning: A Review
## 3 Techniques
### Artificial Neural Networks  
An ANN consists of a set of highly interconnected entities, called Processing Elements. The structure and function of the network is inspired by the biological central nervous system, particularly the brain. Each Processing Element is designed to mimic its biological counterpart, the neuron [53], which accepts a weighted set of inputs and responds with the corresponding output.  
ANNs have been applied to different prediction approaches, basically by considering the evaluation results of students, as the following cases show. A feedforward ANN was trained to predict the scores of evaluation tests considering partial scores during the course [54]. An ANN that uses the Cumulative Grade Point Average predicted the academic performance in the eighth semester [55]. Two models of ANN (Multilayer Perceptron and Generalized Regression Neural Network) were compared in order to identify the best model to predict academic performance of students [56]. Lastly, the potential of ANNs to predict learning results was compared to the multivariate LR model in the area of medical education [57].  
Not only mere evaluation results, but also additional information from students can improve prediction performed by ANNs. Thus, basic students' information, along with cognitive and non-cognitive measures, were used to design predictive models of students' performance by usingthree ANN models [58]. The non-linear relationship between cognitive and psychological variables that influence academic performance was analyzed by an ANN, which efficiently grouped students into different categories according to their level of expected performance [53]. Finally, an ELM (which is a particular type of ANN) predicted students' performance by considering the value of the subjects that focus on the final national exam [59].
# Eco-Friendly Preservation of Pharaonic Wooden Artifacts using Natural Green Products
## 2 Materials and Methods  
### Archaeological Objects  
Ten archaeological pharaonic wooden objects (Table 1 and Supplementary Materials, Figure S1) located at the Egyptian Museum (Cairo, Egypt) were selected for their biodegradation status. The objects belong to different ages (Old Kingdom: 2686-2181 BC; New Kingdom: 1550-1069 BC; Third Intermediate Period, Dynasty 21) and are currently stored at 24-26 \({}^{\circ}\)C and 50-55% humidity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Object No.** & **Object Name** & **Period/Dynasty** & **Dimensions** & **Provenance** & **ID** \\ \hline \multirow{3}{*}{\(1\)} & Wooden statue of a seated man & Old Kingdom (2686–2181 BC) & \
\begin{tabular}{c} Length 127 cm \\ Width (shoulders) \\ 47 cm \\ Base (length) 58 cm \\ \end{tabular} & Saqqara excavations site (Memphite region) & SR2/15969 \\ \hline \multirow{3}{*}{\(2\)} & Anthropoid wooden & \multirow{3}{*}{\(2\)} & Length 179 cm \\  & coffin with cartonnage & & & \\ \cline{1-1}  & mummy of & New Kingdom & Width (shoulders) & Deir el-Bahari, (Upper \\ \cline{1-1}  & [PERSON], son of & (1550–1069 BC) & 55 cm & Egypt, Luxor) & SR3/643 \\ \cline{1-1}  & Neskhonsupahred & & Width (foot) 32 cm & & \\ \hline \multirow{3}{*}{\(3\)} & Wooden box of & New Kingdom & \multirow{3}{*}{\(1550\)–1069 BC} & Length 43.5 cm & Deir el-Bahari (Upper \\ \cline{1-1}  & [PERSON]’s son & (1550–1069 BC) & Width 39 cm & Egypt, Luxor) & CG5028 \\ \hline \multirow{3}{*}{\(4\)} & Inner coffin of & \multirow{3}{*}{\(3\)rd intermediate \(\begin{array}{c}\text{period}\\ \text{Dynasty 21}\end{array}\)} & Length 195 cm & \multirow{3}{*}{\(2\)} & Deir el-Bahari (Upper \\ \cline{1-1}  & Khousuermenep (lid) & & Width 42 cm & Egypt, Luxor) & SR7/23476(b).2 \\ \hline \multirow{3}{*}{\(5\)} & Upper part of a wooden statue of a wooden (Wife & Old Kingdom (2686–2181 BC) &
\begin{tabular}{c} Length 61 cm \\ Width 25 cm \\ Depth cm \\ \end{tabular} & Saqqara excavations site (Memphite region) & SR 2/14958 \\ \cline{1-1}  & & & & \\ \cline{1-