esa-sceva/satcom-chunk-collection huggingface dataset:
# Pyramid Spatial-Temporal Graph Transformer for Skeleton-Based Action Recognition
## 3 Method
### Transformer Block  
Two kinds of transformer blocks are adopted in our pyramid architecture: spatial-temporal transformer and joint transformer blocks, as shown in Figure 4. The vision transformer uses qkv self-attention [31] for a sequence of tokens \(x_{n}\), which is computed as follows:  
\[[Q,K,V]=x_{n}\textbf{W}_{qkv},\textbf{W}_{qkv}\in\mathbb{R}^{C\times 3C_{h}} \tag{3}\]  
where \(Q\), \(K\) and \(V\) are the query, key and value of the token vector, respectively, and \(\textbf{W}_{qkv}\) is a learnable weight matrix. The difference between these two transformer blocks lies in the self-attention operation, as shown in Figure 4. As the number of tokens can be very large due to redundancy in the time domain, the computation cost is extremely high for the regular multi-head self-attention (MSA), which can be written as follows:  
\[\text{MSA}(Q,K,V)=[head_{1};head_{2};\cdots;head_{h}]\textbf{W}_{msa} \tag{4}\]  
where \(head_{i}\) denotes the output of the self-attention and \(\textbf{W}_{msa}\) is a learnable matrix. Note that the number of head \(h\) is set to 1, 2, 4 or 8 for four stages. The formula of each head is as follows:  
\[head_{i}=\text{Attention}\Big{(}QW_{i}^{Q},KW_{i}^{K},VW_{i}^{K}\Big{)} \tag{5}\]  
where \(W_{i}^{Q}\), \(W_{i}^{K}\) and \(W_{i}^{K}\) are learnable matrix projections for the query, key and value of the tokens. Furthermore, as denoted in [31], the single-head attention is as follows:  
\[\text{Attention}(Q,K,V)=\text{softmax}\Big{(}\frac{QK^{T}}{\sqrt{d_{k}}} \Big{)}V \tag{6}\]  
where \(1/\sqrt{d_{k}}\) denotes the scaling factor. The attention mechanism reweights the representation vectors by a global connection of all tokens.  
For the first two stages with a large time dimension, we propose a separate spatial-temporal attention architecture, shown in Figure 4. For the last two stages, we prefer to flatten the skeleton tensor in both the spatial and temporal dimensions and calculate the joint spatial-temporal correlations. The computations of the blocks are calculated as follows:  
\[\textbf{Joint}_{head}=\text{MLP}(\text{MSA}(\text{Flatten}(x_{n}))) \tag{7}\]  
\[\textbf{ST}_{head}=\text{MLP}(\text{TSA}(\text{SSA}(x_{n}))) \tag{8}\]  
Figure 4: The spatial-temporal transformer block (**left**) vs. the joint transformer block (**right**).  
where MLP represents the multi-layer perceptron layer. Different from the multi-head self-attention (MSA) in the last two stages, the first two stages are optimized using spatial self-attention (SSA) and temporal self-attention (TSA) separately.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 4 Results and Discussion
### Transaction Fee Distribution  
A total of 1000 transactions were executed to analyze the gas fee distribution in our blockchain-enabled coffee supply chain system. Figure 6 presents the relative frequency distribution of transaction fees along with a Cumulative Distribution Function (CDF) curve [54]. The X-axis segments transaction fees into 13 bins of approximately 0.00025 ETH each, while the left Y-axis displays the percentage of transactions within each bin. The right Y-axis reflects the accumulated percentage of transactions across the bins, enabling a clear view of how transaction fees build up cumulatively.  
As can be seen, the distribution is heavily right-skewed, indicating that most transactions incur very low fees. Specifically, 63.0% of transactions fall within the ETH 0-0.00025 range, and an additional 18.0% are within ETH 0.00025-0.0005, meaning that over 81% of all interactions cost less than ETH 0.0005. The third bin (ETH 0.0005-0.00075) accounts for 7.5%, while only 1.5% of transactions exceed ETH 0.0015. The final bin, covering all transactions above ETH 0.003, contains just 0.1%. This extremely small fraction corresponds to the initial smart contract deployment, which is typically the most gas-intensive operation. After deployment, the remaining contract interactions such as ownership transfers, status updates, and coffee sale logging incur significantly lower gas costs.  
The CDF curve confirms that over 95% of all transactions incur gas fees below ETH 0.001, illustrating that the system is computationally efficient for the vast majority of use cases. The shape of the curve rises sharply in the first few bins and flattens thereafter, reinforcing the interpretation that only a negligible portion of interactions fall into high-cost categories.  
These findings validate the cost-effectiveness and operational scalability of the proposed framework. The system is not only capable of supporting frequent low-cost blockchain interactions, which is an essential characteristic for real-time supply chain logging, but also minimizes the financial burden of adopting blockchain technology in practical deployments. By isolating the high one-time cost of contract deployment and ensuring that all recurring operations remain lightweight, the architecture promotes both sustainability and extensibility for broader adoption.
# Dynamic Malware Analysis Based on API Sequence Semantic Fusion
## 4 Experiment
### Results and Analysis
#### 4.2.3 Comparison with API Frequency-Based Machine Learning Models  
The proposed Mal-ASSF model achieves an accuracy of 94.49%, a precision of 94.01%, a recall of 94.19%, and an F1-score of 0.9402 on the test set. Table 6 demonstrates that the classification performance is markedly superior to other machine learning models. This indicates that the classification based on the API frequency in the sequence fails to capture the features in the sequence adequately, resulting in a relatively low multi-classification performance. The classification accuracies of KNN, SVM, and DT are generally below 90%. RF and XGB employ the concept of ensemble learning to enhance accuracy to some degree, achieving 90.4% and 91.8%, respectively, which illustrates the effectiveness of feature integration. Mal-ASSF also leverages the concept of feature combinations. The integration of API correlation and implicit semantic features enables the implicit model to attain optimal classification outcomes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline KNN & 86.46\% & 87.85\% & 85.76\% & 0.8681 \\ SVM & 88.46\% & 89.85\% & 85.76\% & 0.8775 \\ DT & 89.78\% & 89.17\% & 89.13\% & 0.8914 \\ RF & 9 0.45\% & 88.95\% & 91.54\% & 0.9023 \\ XGB & 9 1.84\% & 91.95\% & 91.54\% & 0.9177 \\ Mal-ASSF & 94.49\% & 94.01\% & 94.19\% & 0.9402 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with machine learning model results.  
Figure 7: Accuracy with different sequence lengths.
## 2 Materials and Methods  
### Participants  
During the 15-day period between 26 February and 13 March 2015, 104 participants located in California, Lithuania, Saudi Arabia, New Zealand, and England participated in the study (note that only 11-days from 28 February to 11 March were chosen for further investigation as will be explained in further sections). The selection of those particular countries was based on the geographical locations where the magnetometers of the Global Coherence Monitoring Network [28] are installed. All the participants were healthy and either worked or attended classes during daytime hours. The Lithuanian group consisted of 20 medical students attending the Lithuanian University of Health Sciences. The California group consisted of 20 individuals, 16 were employees at the HeartMath Institute (HMI) in Boulder Creek, California and worked in offices at two separate locations about 5 miles apart. Two of these employees lived and worked remotely, one in in a nearby town and the other in southern California. The remaining four participants were acquaintances from the local community. The New Zealand group consisted of 22 individuals, located in Aotearoa and were from an array of different cultural origins and backgrounds. Eleven of the group were staff members of Freedom Farms who founded an organization called The Embassy of Peace. The other eleven participants had a connection with the organization and all the group member are reported to have longstanding and meaningful relationships with each other. The Saudi Arabia group consisted of 20 medical students attending the college of medicine at the King Faisal University. The group located in England consisted of 7 business leaders who worked in a shared office location. The other 7 were people known to the study coordinator with an interest in HeartMath and personal, social and global coherence and were located in different UK locations.  
Each location had a local site coordinator who was responsible for participant recruitment, logistics coordination and participant training in the study procedures and use of the HRV recorders. Study coordinators in the 5 countries recruited a total of 104 participants, one participant failed to return their HRV recorder at the end of the study so the number of participants dropped to 103 at the end of the study. Seven participants in New Zealand did not participate in the Heart Lock-In experiment and were not used in the current analysis. The mean age (SD) of the 96 participants in the analysis was, 36 (15.1) years consisting of 56 females and 40 males. Table 1 contains the age and gender breakdown for all participants by country.  
Local study coordinator in each country excluded candidates that had any of the following conditions; heart disease of any type, heart irregularities (arrhythmia, fibrillation etc.), hypertension, diabetes, severe anxiety or depression that requires prescription medication, and conditions like sleep apnea that requiring the use of a CPA.
## 4 Verification and Analysis  
In order to verify the performance of the two-sensors data fusion vehicle detection method proposed in the paper, we installed vehicle detectors with the proposed method on a road. For comparison, we also installed vehicle detectors with the traditional single magnetism-based method and inductance loop vehicle detectors in the same lane. The detectors installation diagram shown in Figure 1.  
The algorithm principle of the traditional single magnetism-based method has been described deeply in the literature [5; 19; 20]. Generally, the vehicle existence detected based on the change in the magnetism is greater than a specific threshold, the vehicle leaving detection is based on the change in the magnetism is less than a specific threshold within a certain time window. There is no \(S_{vehicle-learning}\) state in the traditional single magnetism-based vehicle detection method. In other word, if \(CNT_{mag-based-learning}\) is greater than \(Cn\), the state will jump to \(S_{non}\) and end a complete vehicle detection procedure.  
We used two methods to verify the performance of the proposed vehicle detection method: (i) We took the manual statistical results as a reference, compared the different types of vehicle detection accuracy of our proposed two-sensors fusion method and traditional single geomagnetic-based method at different time of a day. (ii)We took inductance loop vehicle detectors' statistical results as a reference and compared overall detection accuracy of the detectors with traditional single magnetism-based method and the proposed two-sensors data fusion method up to 30 days.
# High Reliable Uplink Transmission Methods in GEO-LEO Heterogeneous Satellite Network
## 3 Performance Evaluation
### Outage Probability of NOMA Method
\begin{array}{ll}Pr_{2}&=\mathbb{P}(SINR_{1}<T_{1}\;or\;SNR_{2}<T_{2})\\ &=\varepsilon_{1,J}\sum_{k=0}^{\infty}\frac{(m_{1,J})\kappa_{1,J}^{k}}{(k!)^{2} \kappa_{1,J}^{k+1}}\gamma(k+1,\frac{T_{2}}{K_{2}\alpha\beta}\kappa_{1,J})+ \frac{e^{\frac{-T_{2}\alpha\beta_{2,J}}{\alpha\beta\beta\beta}}}{\kappa_{2,J }}\sum_{i=1}^{n}\omega_{i}G(\frac{x_{i}}{\kappa_{2,J}}+\frac{T_{2}}{\alpha \beta R_{2}}),\end{array}  \tag{14}\]  
_where \(G(y)=\varepsilon_{1,J}\sum_{k=0}^{\infty}\frac{(m_{1,J})\kappa_{1,J}^{k}}{(k!) ^{2}\kappa_{1,J}^{k+1}}\gamma(k+1,\frac{T_{1}(y\kappa_{2}\alpha\beta+1)}{K_{1 }(1-\alpha)\beta}\kappa_{1,J})\varepsilon_{2,l1}F_{1}(m_{2,l};1;\
u_{2,l}y)\). Also, \(x_{i}\) and \(\omega_{i}\) denote the Gauss-Laguerre quadrature nodes and weights over \([0,+\infty]\)._  
Proof. : The definition of \(Pr_{2}\) can be further written as  
\[ \begin{array}{ll}Pr_{2}&=\mathbb{P}(SINR_{1}<T_{1}\;or\;SNR_{2}<T_{2})\\ &=1-\mathbb{P}(\frac{|h_{1}|^{2}(1-\alpha)\rho}{|h_{2}|^{2}\alpha\rho+\sigma ^{2}}\geq T_{1},\frac{|h_{2}|^{2}\alpha\rho}{\sigma^{2}}\geq T_{2})\\ &=1-\mathbb{P}(|h_{1}|^{2}\geq\frac{T_{1}(|h_{2}|^{2}\alpha\beta+1)}{(1-\alpha )\beta},|h_{2}|^{2}\geq\frac{T_{2}}{\alpha\beta})\\ &=1-\mathbb{P}(|\rho_{1,J}|^{2}\geq\frac{T_{1}(|\rho_{2,J}|^{2}\alpha\beta+1)}{ K_{1}(1-\alpha)\beta},|\rho_{2,J}|^{2}\geq\frac{T_{2}}{\alpha\beta R_{2}}).\end{array}  \tag{15}\]  
Following the distribution of Shadowed Rician, (15) can be calculated as  
\[\begin{array}{ll}Pr_{2}&=1-\int_{\frac{T_{2}}{\alpha\beta R_{2}}}^{\infty} \int_{\frac{T_{1}(y\kappa_{2}\alpha\beta+1)}{K_{1}(1-\alpha)\beta}}^{\infty}f_ {|\rho_{1,J}|^{2}}(x)dxf_{|\rho_{2,J}|^{2}}(y)dy\\ &=1-\int_{\frac{T_{2}}{\alpha\beta R_{2}}}^{\infty}f_{|\rho_{2,J}|^{2}}(y)dy+ \int_{\frac{T_{2}}{\alpha\beta R_{2}}}^{\infty}F_
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## 3 The Importance of Quantum Key Distribution (QKD)
### The Challenges of QKD in 5G Implementation
#### 3.1.6 Real-Time Communication  
The sixth challenge is associated with real-time communication. A system similar to a smart grid that supports a myriad number of applications such as load shedding applications and dynamic pricing applications, demands a fast response application. Several of those applications that provide a substantial protection mechanism would deny any communication delay if it exceeded 4 ms [85]. In cases such as in Unmanned Aerial Vehicle (UAV) application, traditional networks' communication latency and dependability are unsuitable for UAVs deployed in warfare operations. In those kinds of scenarios, the system cannot tolerate even a 1 ms delay or system downtime, which results in big catastrophe for both persons and property. The challenge relies on the QKD protocols features that support real-time communication. The QKD protocols approximately take hundred milliseconds to distribute the secure key which depends on the length of that key.
# Marine Gelatine from Rest Raw Materials
## 3 Production Strategies for Gelatine
### Pre-Treatment and Extraction Strategies  
Differences in the available literature are seen between different pre-treatment procedures regarding the same type of fish material (skin, bones, offal). In general, during the production of gelatine, the pre-treatment steps are important for weakening the chemical bonds between collagen chains and make it more suitable for subsequent extraction. There are two main pre-treatments used in the gelatine industry today: (a) Acid pre-treatment, which is done by treatment of the material with diluted acids. It is suitable for materials with less cross-linked collagen, like pig skin, and results in the so called type A gelatine (with isoelectric point at pH 6-9) [41]. Acid pre-treatment is also necessary in the case of gelatine production from bones, where it ensures the removal of bone mineral components prior to extraction; (b) Alkali pre-treatment, which is achieved by soaking of the treated material with diluted alkali solutions (NaOH, KOH, Ca(OH)\({}_{2}\).). It is commonly used as a pre-treatment of materials with highly cross-linked collagen, such as bovine hides. Gelatine obtained by this type of pre-treatment is called type B, with an isoelectric point at pH 5 [41]. Various types of pre-treatment and extraction strategies for gelatine isolation from marine/freshwater sources are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors/Year** & **Material** & \multicolumn{1}{c}{**Pre-Treatment**} & **Extraction** \\ \hline  & & NaOH at various concentration and time range (0.15-0.35\%, & \\  & & (w/v) and 40-120 min). & & \\
[42] & African catfish (_Clurias Stripirus_) skin & Subtputer acid at various concentration and time range & Water at various temperature and time (0.08-0.35\% (w/v) and 40-120 min); & \\  & & Cit Citric acid at various concentration and time range (0.6-1.4\% & \\  & & (w/v) and 40-120 min) & \\ \hline
[43] & Swim bladders of centa (_Centa calls_) & 0.15\% NaOH (w/v) for 40 min; sulphuric acid (0.15\%, v/v) and & \\  & & citric acid (0.5\%, v/v) for 40 min (\(\times\)2) & \\ \hline
[44] & Dover sole (_Solata vulgaris_) skin & (a) Acetic acid 0.05 M & \\  & & (b) Acetic acid at various concentrations (0.01, 0.025, 0.05 M) & \\ \hline  & & Alkaline & \\
[45] & Tuna (_Thanus thymus_) head bones & protease from _Bellilus mojrestris_, 50 \({}^{\circ}\)C for 4 h; & \\  & & 0.4 M HCI for 7.5 h; & \\  & & 0.9\% Ca(OH) (2w/v) for 144 h & \\ \hline  & & Skins of several marine species (kerapu & \\
[4] & (_Epinetbus sezgscitats_), penahak (_Lutarians_ & 0.2\% NaOH (w/v) for 40 min; sulphuric acid (0.2\%, v/v) and \\  & _argretimic
## 3 CD, GFD and Metabolic Disorders
### CD, GFD, Glucose Intolerance and Type 2 Diabetes (T2D)  
To our knowledge, the prevalence of T2D in CD patients on a GFD [24, 25] and the glycemic level before and after the start of a GFD [9, 22] were reported, respectively, in two studies.  
A study comparing 840 CD patients following GFD for approximately 84 months and 840 healthy controls found a lower prevalence of T2D and a lower BMI in the celiac group compared with controls [24]. These findings could be attributable to the alteration of pancreatic function, a lower nutrient absorption, and impairment of gastrointestinal, endocrine functions. Indeed, tissue transglutaminase drives inflammation in CD, impairing the expression of peroxisome proliferator-activated receptor \(\gamma\), which could, in turn, be implicated in a decreased risk of T2D [24]. In another large observational cohort study of CD patients and controls, the prevalence of T2D did not significantly differ [25].  
On the other hand, regarding glycemic levels, in a cohort observational study, including 98 CD patients, higher blood glucose levels were reported after initiating GFD compared with diagnosis [9]. Another study confirmed these results showing a significantly increased fasting blood glucose level in CD patients after the initiation of GFD compared with those at CD diagnosis [22].  
Thus, although the results of currently available studies remain controversial, overweight, obesity, dyslipidemia, and increase in glycemic levels intolerance could be possible consequences of GFD in CD patients, leading to potential risks of metabolic disease development.
## 4 Results and Discussions
### Analysis of Load Bearing Characteristics of Adjacent Pile Bases without Immersion
#### 4.1.2 Axial Force and Shaft Friction of Piles  
After measuring the pile strains during loading, the pile axial force and pile shaft friction were calculated by Formulas (4) and (5), respectively [52].  
\[N_{z}=\epsilon_{z}\times E\times A_{p} \tag{4}\]  
where \(N_{z}\) is the axial force of the pile at depth \(z\); \(\epsilon_{z}\) is the mean strain of 4 single pile shafts at depth \(z\); \(E\) is the elastic modulus of the pile; \(A_{p}\) is the cross-sectional area of the pile.  
\[\tau_{i}=\frac{N_{zi}-N_{z(i+1)}}{\pi\times D\times z_{i}} \tag{5}\]  
where \(\tau_{i}\) is the average shaft friction of the pile section \(i\); \(N_{zi}\) is the axial force of the pile section \(i\); \(D\) is the pile diameter; \(z_{i}\) is the length of the pile section \(i\). Figures 7 and 8 show the calculated axial force and shaft friction of the pile, respectively. It can be seen from the figures that with the increase in the load, the shaft friction and axial force of the pile at the same position of the model pile also increased gradually. The pile shaft friction can also be calculated by the analytical method proposed by [PERSON] et al. [53] for comparison (Figure 9), and the equation is shown in Formula (6).  
\[\tau_{i}=\frac{E\big{[}2(\epsilon_{i}-\epsilon_{i+1})+\big{(}\epsilon_{i}^{ \prime}-\epsilon_{i+1}^{\prime}\big{)}\big{]}}{3(A_{sw}+A_{sn})} \tag{6}\]  
where \(\epsilon_{i}\) and \(\epsilon_{i}^{\prime}\) are outside and inside strains of the upper end of pile section \(i\), respectively; \(\epsilon_{i+1}\) and \(\epsilon_{i+1}^{\prime}\) are outside and inside strains of the bottom end of pile section \(i\), respectively; \(A_{sw}\) and \(A_{sn}\) are outside and inside superficial areas of pile section \(i\).  
Figure 6: Load–settlement curve of pile bases without immersion.  
Figure 7: Mean axial force of 4 single piles without immersion.  
Under the action of the load, the pile shaft produced axial force, and the pile was compressed and began to settle down. The axial force of the pile was transferred to the surrounding soil layer through the side friction resistance, and the load on pile bases was gradually borne by the surrounding soil. The settlement of pile bases was smaller than that of the surrounding soil, so the shaft friction was positive under this circumstance. The axial force decreased along the depth due to the positive shaft friction.  
Figure 8: Mean shaft friction of 4 single piles without immersion.  
Figure 9: The calculated pile shaft friction without immersion calculated according to [PERSON] et al. [53].