esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Solution  
The third equation in (4) can be immediately integrated to give  
\[\sigma_{\theta\varphi}=\frac{C_{1}k_{0}}{\sin^{2}\theta} \tag{23}\]  
where \(C_{1}\) is constant. It follows from (3) that \(C_{1}>0\). Equations (15) and (23) combine to yield  
\[\frac{\xi_{\theta\varphi}}{\xi_{0}}=\Lambda\left(\frac{C_{1}}{\sin^{2}\theta}\right) \tag{24}\]  
where \(\Lambda\) is the function inverse to \(\Phi\). Using (6) and (11) the shear strain rate is expressed as  
\[\xi_{\theta\varphi}=-\frac{\omega}{2}\left(\frac{dw}{d\theta}-w\cot\theta \right). \tag{25}\]  
Eliminating \(\xi_{\theta\varphi}\) in (24) by means of (25) results in  
\[\frac{dw}{d\theta}-w\cot\theta=-\mu\Lambda\left(\frac{C_{1}}{\sin^{2}\theta}\right) \tag{26}\]  
where \(\mu=2\xi_{0}/\omega\). The solution of this equation satisfying the boundary condition (21) is  
\[w=\mu\sin\theta\int\limits_{\theta}^{\pi/2}\frac{1}{\sin\chi}\Lambda\left( \frac{C_{1}}{\sin^{2}\chi}\right)d\chi. \tag{27}\]  
The equation for \(C_{1}\) follows from (22) and (27) in the form  
\[1=\mu\int\limits_{\theta_{0}}^{\pi/2}\frac{1}{\sin\theta}\Lambda\left(\frac{C _{1}}{\sin^{2}\theta}\right)d\theta. \tag{28}\]  
Equations (27) and (28) are valid if there is no rigid region. It is seen from (23) that if a rigid region exists then it is adjacent to the contact surface \(\theta=\pi/2\). It is assumed that the rigid/plastic boundary is determined by the equation \(\theta=\theta_{c}\) (Figure 2). In this case, the boundary condition (21) should be replaced with  
\[w=0 \tag{29}\]  
for \(\theta=\theta_{c}\). Since \(\sigma_{\theta\varphi}=k_{0}\) at the rigid/plastic boundary, it follows from (23) that  
\[C_{1}=\sin^{2}\theta_{c}. \tag{30}\]Then, Equations (27) and (28) become  
\[w=\mu\sin\theta\int\limits_{\theta}^{\theta_{c}}\frac{1}{\sin\chi}\Lambda\bigg{(} \frac{\sin^{2}\theta_{c}}{\sin^{2}\chi}\bigg{)}d\chi \tag{31}\]  
and  
\[1=\mu\int\limits_{\gamma}^{\theta_{c}}\frac{1}{\sin\theta}\Lambda\bigg{(}\frac {\sin^{2}\theta_{c}}{\sin^{2}\theta}\bigg{)}d\theta. \tag{32}\]  
Equation (32) serves for determining \(\theta_{c}\).  
It is seen from the definition for \(\mu\) that \(\mu\to 0\) as \(\omega\to\infty\). Therefore, no solution to Equation (28) or (32) may exist.
# A B5G Non-Terrestrial-Network (NTN) and Hybrid Constellation Based Data Collection System (DCS)
## 6 DCS Equipment Design
### SDR Design  
A Gnu Radio Companion (GRC) software platform is implemented in the SDR. Various communication functions can be defined as flows in GRC. Each flow is configured by several blocks, as shown in Figure 8a. A lot of basic blocks are defined internally by GRC. Customized blocks can be self-defined by python 3.8 or C++.  
In the specific DCS equipment design, all communication modes can be satisfied by internally defined blocks. To optimize the performance of receivers, multiple design parameters are adjusted and iterated. Receive sensitivity and doppler frequency correction are the main optimization goals, as shown in Table 6.  
As shown in Figure 9, different signal modes can be compatible by relative flow designs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Item** & **AIS** & **ADS-B** & **LORA** & **NB-IoT** & **Beidou** & **Military** \\ \hline Receive sensitivity & \(-107\) dBm & \(-98\) dBm & \(-115\) dBm & \(-110\) dBm & \(-100\) dBm & \(-90\) dBm \\ Doppler frequency & \(15\) kHz & \(40\) kHz & \(100\) kHz & \(100\) kHz & \(40\) kHz & \(200\) kHz \\ \hline \hline \end{tabular}
\end{table}
Table 6: SDR receiver performance optimization requirements.  
Figure 8: Gnu Radio Companion functions: (**a**) Flow and block structures; (**b**) Block design in GRC.
## 2 Related Works
This involves using a deep learning algorithm to classify sitting posture based on data from eight pressure sensors installed on the chair. However, the classification accuracy was 91.68%. [PERSON] et al. proposed a Sitting Posture Monitoring System (SPMS) that measures six different sitting postures using a machine learning algorithm applied to weight ratios obtained from sensors attached to a chair [23]. The experiment was conducted with nine participants, and a support vector machine achieved an average accuracy of 97.2%. However, the sample size of the experiment is too small to generalize the results. Also, although chairs and cushions equipped with pressure sensors can classify the degree of body inclination, they may have lower accuracy in classifying the forward-head posture of the text neck where the back is rested on the chair and the neck is projected forward. In addition, sensor-equipped chairs are not cheap, making them difficult to use. In studies that classify posture by measuring the distance with sensors, a recent study has proposed using an ultrasonic sensor attached to the desktop computer monitor to measure the distance between a person's face and the monitor and classify whether they are in a good posture [24]. In that paper, they improved the accuracy by removing the noise that occurs during distance measurement and reducing error alerts. The proposed method has the advantage of being cost-effective and relatively convenient compared to other methods, as it can detect the distance between the computer monitor and the user's face with ease. However, distance-measuring sensors have inherent limitations in accuracy due to the nature of the sensor, resulting in inaccurate measurements. Therefore, a calibration procedure is necessary and there is a disadvantage of not being able to detect various postures. There have been studies proposed using a skeleton algorithm to determine posture from image data. [PERSON] proposed a study using a Microsoft Kinect sensor to obtain 3D skeleton coordinates for posture and classified good posture and text neck posture using machine learning techniques [25]. In the experiment, the random forest and support vector machine learning algorithms achieved an accuracy of 92.85%. Additionally, [PERSON] et al. presented a system that classifies between text neck and normal posture by calculating the angle of the neck using sensors and images [26]. However, most of the studies published so far have focused on investigating the occurrence of text neck posture when using smartphones. It is not only text neck posture but also the L-posture that can have negative effects on the user's body when using a desktop computer. Therefore, it is necessary to classify whether the user is in the L-posture when working on a desktop computer. However, there are relatively few studies on postures when using desktop computers, and there is a limitation that there are few studies with high accuracy that classify both the posture of the text neck and the L-posture, which are postures that have a negative impact on the neck and waist.
# Digital Health Technology for Stroke Rehabilitation in Canada: A Scoping Review
## 4 Discussion
### Determinants of DHT Adoption for Stroke Rehabilitation  
Understanding user experience is essential when implementing DHTS, as it can be context dependent. In Canada, several facilitators have supported the adoption of DHTs for stroke rehabilitation, including user motivation [31; 34; 37; 38], clinician support [31; 34; 37; 38], and technology performance [34; 37; 38]. Findings from the included studies revealed that motivation was enhanced through engaging activities, goal-setting, and social support. Programs that promoted autonomy and competence, such as exergames offering real-time feedback, helped improve adherence. Participants who observed progress were more likely to continue using the affected limb after the intervention [34; 37; 40]. Clinician support was also crucial, as therapists played a vital role in encouraging adoption and ensuring proper use of the technology [38]. On the other hand, barriers to adoption included technical challenges [25; 37], user inexperience [25; 28], and limited portability of devices [25; 28]. These challenges encompassed issues such as poor connectivity, interoperability, and limited computer literacy, all of which hindered uptake. Usability concerns were also highlighted, with participants stressing the importance of simple, easy-to-don devices for better adoption [25]. Miscommunication, particularly in cases of aphasia, further reduced motivation and engagement, diminishing the overall effectiveness of the program [37]. Therapists also faced barriers when incorporating social media and gaming technologies, such as concerns about age appropriateness, privacy issues, limited transfer of training, and accessibility to current systems [28]. Many of these factors are consistent with findings from other countries. Globally, ease of use, access to technologies, awareness of available options, and evidence of effectiveness are commonly reported facilitators [59]. Similarly, stakeholders, particularly therapists, have been recognized internationally as playing a crucial role in introducing and supporting the adoption of new rehabilitation technologies [60]. Furthermore, the Technology Acceptance Model highlights that computer self-efficacy and anxiety are key predictors of users' willingness to engage with telerehabilitation, a finding that aligns with observations in the Canadian context as well [61].
# Experimental Study on Shear Creep Characteristics of Residual Soil with Different Stone Content
## 4 Test Results and Analysis
### Influence of Stone Content on Creep of Residual Soil  
Figure 6 illustrates the relationship between the final creep strain and the stone content of the specimen after each loading level. It is evident from the figure that, as the stone content increases, the total creep deformation decreases. This phenomenon can be attributed to two factors. Firstly, this is due to the fact that with the increase in stone content in the specimen, the density of the specimen increases, the number of block stones with large grain size on the shear surface increases, and the movement of large particles is more difficult during the shear process. Furthermore, as the content of rock fragments varies, there are significant changes in the soil-rock structure characteristics of the shear surface in the experiment. Figure 7 illustrates the particle distribution in samples with varying stone content. Specifically, Figure 7a shows the shear face under a consolidation pressure of 200 kPa when the stone content is 10%. At this stone content level, the influence of stones on the specimen is minimal, and particles with a size smaller than 5 mm dominate. During the shear creep process, the deformation is primarily governed by the dislocation between the fine particles. It is worth noting that specimens with 10% stone content have a lower density and relatively larger porosity. As a result, block stone particles located on the shear surface are more prone to rotational deformation, ultimately leading to a larger shear creep in the specimen. As the stone content ranges from 30% to 70%, the number of block stones on the shear surface gradually increases, leading to the formation of a skeleton structure through the filling and interlocking of fine particles. This process results in a significant increase in specimen density, making it challenging for rock particles to rotate during the shear creep process. During this stage, the specimen's deformation is primarily characterized by slight dislocation of both coarse and fine particles, as well as the breaking deformation of larger particles. As a result, shear creep is noticeably reduced. With further increases in  
stone content, the skeleton-embedding action becomes more pronounced. Particle rotation becomes increasingly difficult, leading to a further reduction in specimen shear creep.  
Figure 6: Final creep strain of specimens with different stone content.  
Figure 7: The schematic diagram of shear surface of samples with different stone content.
## 5 XAI Analysis for GNSS Signal Disruption Classification
### Local Explanations Results--SHAP Technique
The model's ability is determined based on the area under the curve and how the model distinguishes between different classification categories, as shown by the ROC curve in Figure 12. The clean signal has occupied the highest area for most of the ML algorithms. For the AdaBoost, RF, and SVM algorithms, the model assigned a lower probability to the positive values, flipping the labels of the negative class and resulting in a lower area for the categories of MP and spoofing. The ROC plots for all categories are shown in Figure 12.  
As per the frequency domain features, the classification of signal disruptions is evaluated in the form of a confusion matrix, as shown in Figure 13. The AdaBoost algorithm completely misclassifies the clean and multipath signals. The occurrence of this false classification between the MP and clean signals is due to similar kinds of signal characteristics in the frequency domain features, as the model should be able to capture any dissimilarities  
Figure 12: ROC one-vs-rest multiclass plot of machine learning models tested under different GNSS signal disruptions. in the spectra of both disruptions. This instance is handled using the DT, Random Forest, KNN and SVM algorithms with more than 40% error. Table 5 shows the results of feature selection using SHAP compared with other traditional feature selection methods; here, the optimal 'k' feature value is chosen as 4, based on the top features that are more trustworthy in classifying the instances correctly. Using the  
Figure 9:0 Confusion matrices (frequency domain features). SVM method, SHAP has shown the leading performance when classifying the different GNSS signal disruptions. Other than the chirp signal, most of the signals are correctly classified, but when other feature selection/elimination methods are applied, the clean and spoofing signals are not properly classified. The superiority of the SHAP-based novel feature selection method can be seen in Figure 9:1 The DT and KNN ML models, for most of the labels, classify the signal correctly, but when traditional methods are applied, the performance metrics are lower. For RF and KNN, our feature selection methods show the best performance, along with other feature reduction methods. Finally, for AdaBoost, the overall accuracy decreases under our methods; however, other performance metrics (precision, recall, and F1-score) increase for detecting the abnormalities in the signal classification.
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 4 Proposed System
### Proposed Algorithm
```
1:Input: side number \(side\), sensor number \(sensor\), and timestamp \(t_{cur}\)
2:Output: A coordinate of predicted animal position \(\{x_{predict},y_{predict}\}\)
3:\(x_{prev}\)\(\triangleright\) x value of previous location
4:\(y_{prev}\)\(\triangleright\) y value of previous location
5:\(t_{prev}\)\(\triangleright\) Timestamp of previous reading
6:\(time\_threshold\)\(\triangleright\) Interval to refresh the collected data
7:\(time\_tolerance\)\(\triangleright\) Interval to define similar timestamp
8:latency\(\triangleright\) Time required from detection to camera pointing to the predicted position
9:\(pos\_mapping\leftarrow\{sensor:\{x:y\}\}\)
10:functionPredict(\(side\), \(sensor\), \(t_{cur}\))
11:\(x_{cur}\gets pos\_mapping[sensor][x]\)
12:\(y_{cur}\gets pos\_mapping[sensor][y]\)
13:if\(t_{cur}-t_{prev}>timing\_threshold\)then
14:Do nothing
15:elseif\(t_{cur}-t_{prev}\leq time\_tolerance\)then
16:Wait until all data received
17:else
18:\(dist\leftarrow\sqrt{(x_{cur}-x_{prev})^{2}+(y_{cur}-y_{prev})^{2}}\)
19:\(speed\gets dist/(t_{cur}-t_{prev})\)
20:\(\theta\leftarrow\text{ arctangent}(y_{cur}-y_{prev},x_{cur}-x_{prev})\)
21:\(d\gets latency*speed\)
22:\(x_{predict}\gets x_{cur}+d*math.cos(\theta)\)
23:\(y_{predict}\gets y_{cur}+d*math.sin(\theta)\)
24:return\(x_{predict},y_{predict}\)
25:endif
26:\(x_{prev}\gets x_{cur}\)
27:\(y_{prev}\gets y_{cur}\)
28:\(t_{prev}\gets t_{cur}\)
29:endfunction
30:while true do
31: Predict(\(side\), \(sensor\), \(t_{cur}\))
32:endwhile
```  
**Algorithm 1** Algorithm to Predict Animal Locations
# Unlocking Efficiency in Fine-Grained Compositional Image Synthesis: A Single-Generator Approach
## 4 Experiments and Results
### Results
#### 4.2.2 Compositional and Disentanglement Properties
While lowering the temperature can achieve better disentanglement, SSSGAN tended to generate femininity in males under lower temperatures. This outcome suggests a trade-off between disentanglement and diversity in SSSGAN, which can be controlled by the temperature variable. Higher temperatures increase the intensity of entanglement but yield more diverse images, while lower temperatures reduce entanglement intensity but also decrease the diversity of the generated images. To quantitatively assess the level of disentanglement in the SPC task, we propose a metric called Sequential Mean Square Error (SMSE), as defined in Equation (11). \[\text{SMSE}_{i}=\frac{\sum\limits_{h=1}^{H}\sum\limits_{w=1}^{W}\left[\left( \sum\limits_{j=1}^{i-1}M_{jh,w}^{i}\right)\left(I_{h,w}^{i}-I_{h,w}^{i-1}\right) \right]^{2}}{\sum\limits_{h=1}^{H}\sum\limits_{w=1}^{W}\sum\limits_{j=1}^{i-1}M_ {jh,w}^{i}}, \tag{11}\]  
where \(h,w\) represent the spatial height and width indices, respectively; \(i\) denotes the sequence index; \(M_{jh,w}^{i}\in\{0,1\}\) represents the mask value at location \((h,w)\) of the \(j\)th part in step \(i\); and \(I_{h,w}^{i}\) represents the pixel value at location \((h,w)\) in step \(i\). The SMSE metric quantifies the intensity of change in the original image when the \(i\)th part is added. A lower  
Figure 5: Illustration of the proposed entanglement mechanism. SMSE value indicates a better performance, in terms of disentanglement, meaning that the added part has a lesser impact on the rest of the image. Figure 6 displays the quartiles of the SMSE values at different temperatures for each semantic part. For quantitative comparison, Table 4 presents the SMSE values and their corresponding standard deviations. Each row in the table corresponds to a specific part, while each column represents the SMSE values at different temperatures. The lowest SMSE value for each part is highlighted in bold font in the table. From Figure 6, it can be observed that the skin and hair exhibited relatively larger SMSE values, indicating higher entanglement for larger parts. Additionally, SSSGAN with the default temperature of 1.0 exhibited higher SMSE values, compared to SemanticStyleGAN. However, by reducing the temperature in Equation (5) for SSSGAN, the SMSE values were effectively reduced. For instance, as shown in Table 4, the skin's SMSE value for SSSGAN with a temperature of 0.01 was 0.022 \(\pm\) 0.036, significantly lower than that for SemanticStyleGAN (0.078 \(\pm\) 0.101). This result indicates that the level of entanglement can be significantly reduced by lowering the temperature. This finding is in alignment with the proposed entanglement mechanism illustrated in Figure 5.
## 5 Results and Discussion
### Economic Analysis  
#### 5.2.1 Cost Evaluation for Scenarios I and III  
To evaluate the economic feasibility of both CSP plants and compare them, the LCOE was calculated using the following equation:  
\[LCOE=\frac{\sum_{t=1}^{m}\frac{I_{\text{t}}+M_{\text{t}}+C_{\text{t}}}{(1+r)^{ t}}}{\sum_{t=1}^{m}\frac{E_{\text{t}}}{(1+r)^{t}}} \tag{13}\]  
where I\({}_{\text{t}}\) represents the initial investment in year t; M\({}_{\text{t}}\) corresponds to the operation and maintenance (O&M) expenses in year t; C\({}_{\text{t}}\) are electricity consumption costs in year t (if applicable); E\({}_{\text{t}}\) is the energy produced in year t; R is the discount rate, set at 5% [11]; and N is the system's lifespan, set at 25 years [11].  
To calculate the investment costs, reference values from [13] were used, accounting for the fact that these values are based on 2015 prices.  
Using (14), the different costs in Table 12 were updated to reflect their 2023 values:  
\[C_{2023}=C_{i}.\frac{Cost\ index\ in\ the\ year\ 2023}{Cost\ index\ in\ the\ year\ i} \tag{14}\]  
where C\({}_{2023}\) represents the updated cost for 2023, and C\({}_{\text{i}}\) is the cost for any given year i. The cost indices were derived from the average Consumer Price Index (CPI) for Spain [73].  
Table 13 presents the total costs for the plant. Additional costs, such as installation or contingencies, were taken as a reference from [13].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Equipment** & **Scenario I** & **Scenario III** & **Units** \\ \hline Total heliostat area & 200 & 200 & USD/m\({}^{2}\) \\ Receiver and tower & 173 & 195 & USD/kW\({}_{\text{th}}\) \\ Energy storage & 27 & 25 & USD/kW\({}_{\text{th}}\) \\ Power block/steam generator & 1550 & 1675 & USD/kW\({}_{\text{e}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 12: Cost of main equipment for Scenarios I and III.  
For Scenarios I and III, the annual fixed O&M costs are USD 4.8 M per year and USD 4.7 M per year, respectively [13]. By applying (13), the LCOE for Scenario I is calculated as 211 USD/MWh, while for Scenario III, it is 192 USD/MWh, representing a 9% reduction for the scenario using DPS.  
These values are slightly higher than those reported in the literature [11], which can be attributed to the current hyperinflationary context. However, the technical improvements of the DPS system significantly reduce production costs, offsetting the higher technological expenses.
## 2 Experimental Program
### Materials Specifications  
The fresh and hardened properties of GPC not only vary with the constituents but also changes with chemical composition of the source materials. The procedure of geopolymerization starts when source materials (FA and SG) react with the alkaline activators. This process is primarily based on the proportions of silica and alumina present in the source materials. It was made sure that industrial by-products, FA and SG, for all the mixes were obtained from the same source (power plant and steel industry respectively) to avoid error in results due to the variation in source materials. In this study, FA and SG were utilized as the source materials to produce geopolymer binder and FA was replaced by SG in different proportions to promote ambient curing conditions. The FA and SG used in the study are shown in Figures 2 and 3. The chemical composition and physical properties of FA and SG are shown in Table 1.  
The solutions of SH and SS were mixed in predetermined ratios to make alkaline activator solution (AAS). The solution of SH of required molarity was prepared by mixing 98-99% pure pellets of SH (with a specific gravity of 2.10) in water. The mass of SH pellets to prepare 1000 mL of solution depends on the molarity of solution stated in Molar, M. It was prepared 24 h before mixing with SS solution due to heat evolution resulting from exothermic reaction. The SS solution with a ratio of SiO2 to Na2O = 2.0, was used. It was composed of Na2O, SiO2, and H2O in 15.06%, 29.95%, and 54.99% by mass respectively. The properties of SS solution are shown in Table 2.  
Figure 3: Pictures of fly ash used in the study.  
Figure 2: Pictures of slag used in the study.  
The coarse aggregates (CA) with a maximum size of 20 mm were taken from the Margallah quarry source in Pakistan. They were prepared to surface dry condition conforming to ASTM C127-15 (2015). The natural river sand from Lawrencepur in Pakistan was used as fine aggregates in surface dry condition (SSD) conforming to ASTM C128-15. The properties of CA and sand are shown in Table 3.  
Since GPC mix is more cohesive and viscous due to high viscosity of SS and SH solutions [28], a naphthalene based superplasticizer (SP) was used to increase workability of the fresh mix.