esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Review of Model Construction and Parameter Determination
### Task Constraints  
Application of forward-dynamics simulation models to predict individual-specific optimum technique has been mostly limited to \"closed\" skills, where environmental and task constraints remain relatively constant [1; 143]. Constraints relating to the task being represented by the forward-dynamics simulation model are often incorporated using penalty functions within optimization algorithms [10; 12; 21; 110; 129]. Importantly, penalty functions should ramp-up, rather than utilizing \"all or nothing\" penalties, increasing the likelihood of optimization algorithms converging to global maxima or minima (depending on the cost function), which must not incur any penalties. Examples have included linear, exponential, and logarithmic ramped penalty functions. Task constraints often involve the ability to perform subsequent movements, which may or may not be simulated. This has included kinematics linked to the ability to regrasp the bar in gymnastics [129], landing orientation for a subsequent phase of the triple jump [10; 21], board clearance in diving [12], and bar clearance in high jump [110]. Constraints representing the sporting laws of the movement have also been included, such as ensuring the elbow in cricket bowling does not extend by more than 15 degrees between the instant of a horizontal upper arm and ball release [13; 109]. Secondary performance outcomes have also been constrained within acceptable limits while optimizing for primary outcomes (e.g., constraining ball landing location while optimizing for ball release speed) [13; 109].
[14] explored the relationship between the MISR-observed width of the sun glint pattern over ocean waters and the near-surface wind speed, and the acquired data were then used to develop an algorithm for wind retrieval. [PERSON] et al. [15] conducted a detailed analysis of brightness reversal in the SG signature of a lake bed topographic feature observed within stereo ASTER data. [PERSON] and [PERSON] [16] developed a scheme to retrieve wind speed directly from a passive satellite sensor in the visible/near infrared bands by an iteration process. [PERSON] et al. [11] and [PERSON] et al. [17] studied the shallow sea sand wave topography based on multi-angle SG, and showed the difference in SG radiance along the scan line with different SG imaging geometries. They confirmed that SG is very sensitive to SVA. [PERSON] et al. [18; 19] developed a practical method for retrieving directional spectra of ocean surface waves quantitatively from near-multidirectional SG imagery from the staggered detectors of the Copernicus Sentinel-2 Multi-Spectral Instrument. [PERSON] et al. [20] constructed a multi-angle SG remote-sensing model using ASTER stereo multi-angle SG images, and established an SSR estimation model. To take better advantage of multi-angle SG remote sensing, multi-angle ocean sun glitter remote-sensing platforms can be designed concurrently with the development of unmanned aerial vehicle formations and satellite constellations [21]. However, multi-angle SG remote-sensing detection applications and SSR quantitative inversion studies can only use satellite remote-sensing platforms with fixed SVA combinations. Due to the sensitivity of the SG to SVA, studies must extensively explore the selection of the correct optimal imaging angle. Therefore, considering the SG radiation error propagation, this study constructed an error transfer simulation model based on the multi-angle SG remote-sensing radiation transmission and SSR estimation models and implemented the SSR inversion errors at different imaging geometry combinations to evaluate the optimal imaging angle combination. Finally, we discuss the design optimization potential that the imaging geometry has on the multi-angle remote-sensing platforms for satellite constellations and drone platforms.
## 1 Introduction
A similar problem has been explored in [18] that use Upper Confidence Bound learning for Greedy Maximal Matching (GMM) when the channel statistics are unknown. Since the subchannel and power allocation problem is a non-convex combinatorial problem, the optimal solution of the subchannel and power allocation problem requires an exhaustive search over all possible combinations of subchannels and power levels. In order to train the deep neural network (DNN) for an optimal solution, Ref. [19] utilizes the genetic algorithm to get the training data for DNN. It shows that the prediction accuracy increases with the size of dataset and the number of hidden layers. A four-step reinforcement learning based intercell interference coordination (ICIC) scheme is presented in [20]. The users selection, resource allocation, power allocation, and retransmit packet identification are handled by reinforcement learning to reduce the intercell interference. However, to the best of our knowledge no available literature discusses LTE-A with L3 relays for SE and EE consideration. In this work,  
* We present an energy efficient algorithm based on SAMM and BOPA for LTE-A system with a L3 relay. Performance evaluation in terms of throughput, fairness, power consumption, SE and EE is shown between two best performing schemes i.e., MT and SAMM considering equal power and BOPA. * Considering the practical deployment, where there may be more than one relay supporting the cell edge users, we devise a clustering strategy to obtain near optimal placement of L3 relays and users' association. * In a multiple relay scenario, to optimize EE and reduce computational complexity of running algorithm every TTI, we present a two step machine learning process that uses both the SAMM and BOPA approach for resource and power allocation of the cell users. The proposed approach is compared to MT equal power, MT BOPA and SAMM equal power in terms of users' throughput and EE. A complete list of notations used in this paper is given in Table 1.  
Rest of the paper is organized as follows: system model is described in Section 2, algorithms and performance for MT, SAMM and BOPA with single relay network are given in Section 3. Multiple relay users' association and deployment with machine learning based power and RB allocation for SAMM is presented in Section 4. Complexity analysis is given in Section 5, followed by the conclusions in Section 6.
# Investigation of CuTi Alloy for Applications as Non-Sparking Material
## 3 Results and Discussions
### Structural, Chemical and Phase Analysis of Experimental CuTi Alloys
The chemical compositions determined by EDS analysis (Table 1) confirmed the proposed Cu\({}_{2}\)Ti and Cu\({}_{3}\)Ti alloys compositions, with a slight reduction in mass percentage of Ti from 2.9 to 2.7 after hot rolling. The copper matrix solubilized various amounts of Ti, and mass percentages between 0.9 and 2.6 Ti were determined for the Cu\({}_{2}\)Ti cast alloy and 2.3-3.7 for the Cu\({}_{3}\)Ti rolled alloy. Hot-rolled Cu-Ti alloys by plastic deformation were also subjected to an intermediate annealing heat treatment at a temperature that is lower than the solid solution formation temperature. In other words, during hot rolling, an intermediate annealing was also performed at a lower temperature than ordinary annealing to achieve a finer and uniform distribution and a precipitation of a secondary phase in the matrix.
## 2 Materials and Methods
### Deep Learning Approach
The same decoder structure was used for each of the architectures. Image segmentation is usually treated as a pixelwise classification problem. In many cases, it is a binary classification with the label \"0\" being background and the label \"1\" indicating object pixels. Segmentation task (especially if considered within the deep learning framework) presupposes a metric (loss function) for checking how well the prediction from the deep artificial neural network agrees with the true segmentation. Over the years, many metrics have been proposed, but the most frequently used are cross-entropy loss, Dice loss, intersection over union loss, focal loss, and Tversky loss, as well as their combinations or generalisations [29]. Suppose \(y\) refers to the true label, while \(\hat{y}\) defines the prediction from the deep network. Then, the cross-entropy loss \(CEL\) function is defined as  
\[CEL\left(y,\hat{y}\right)=-\left(y\log\left(\hat{y}\right)+\left(1-y\right) \log\left(1-\hat{y}\right)\right).\]  
A generalisation of cross-entropy is a focal loss, which tries to down-weight the contribution of examples that are easy to predict, while ensuring more focus on harder examples [30]. As such, focal loss \(FL\) is given by  
\[FL\left(y,\hat{y}\right)=-\left(\alpha\left(1-\hat{y}\right)^{\gamma}y\log \left(\hat{y}\right)+\left(1-\alpha\right)\hat{y}^{\gamma}\left(1-y\right) \log\left(1-\hat{y}\right)\right),\]  
where \(\gamma\geq 0\) is a focusing parameter that smoothly adjusts the rate at which easy examples are down-weighted; and \(\alpha\in[0;1]\) is a weighting factor. Note that the focal loss is equivalent to cross-entropy loss when \(\gamma=0\). The two most frequently used measures of overlap--Dice loss \(DL\) and intersection over union loss _IoUL_--allow us to determine how accurate a predicted segmentation is, compared to a known/ground-truth segmentation. These loss functions are given by  
\[DL\left(y,\hat{y}\right)=1-\frac{2y\hat{y}+1}{y+\hat{y}+1}\]  
and  
\[IoUL\left(y,\hat{y}\right)=1-\frac{y\hat{y}+1}{y+\hat{y}-y\hat{y}+1}.\]The value of these two measures ranges between 0 and 1, with 0 indicating complete disagreement and 1 complete agreement between the true segmentation and the prediction. Tversky loss \(TL\) is a generalisation of the Dice coefficient [31] given by  
\[TL\left(y,\hat{y}\right)=1-\frac{y\hat{y}+1}{y\hat{y}+\beta\left(1-y\right)\hat{ y}+\left(1-\beta\right)y\left(1-\hat{y}\right)+1},\]  
where \(\beta\in[0;1]\) is a weighting factor.
## 2 Probabilistic Approach for Cracked Concrete under Carbonation
### Probabilistic Approach for Service Life  
Uncertainties of carbonation include physical uncertainty, record uncertainty, model uncertainty and decision uncertainty [7]. Physical uncertainty refers to the natural random nature of the fundamental variables including concrete cover depth, CO\({}_{2}\) concentration, concrete quality and native condition. Record uncertainty originates from assumptions for the probability density function. Model uncertainty means the governing mechanism for carbonation including the simplified equation of carbonation, the assumption of the qualities of materials and the assumption of non-correlated variables. Decision uncertainty is the phrase durability failure qualifying criterion, like the period that the carbonation depth exceeds the concrete cover depth.  
Within this study, the durability failure qualifying criterion was understood to be carbonation depth exceeding the coverage depth. This durability failure probability can be calculated by utilizing the Monte Carlo method by following [3; 7]:  
\[g(t)=CV-x_{C} \tag{12}\]  
\[p_{f}=\frac{n\Big{(}g(t)<0\Big{)}}{N} \tag{13}\]  
where \(CV\) is the cover depth of concrete; \(g(t)\) is the durability failure criteria; and \(p_{f}\) is the probability of carbonation durability failure. \(n\Big{(}g(t)<0\Big{)}\) denotes the number of carbonation durability failures out of a total \(N\) trials. For the carbonation durability design of concrete, the time of failure probability of 10% was regarded as the service life of concrete [3; 7]. When the failure probability is 10%, the relatability index is 1.3. For other deterioration stages involved in rebar corrosion such as the formation of corrosion cracks and spalling of concrete cover, given that system failure will produce much more serious consequences than corrosion initiation, the failure probability should be lower than 10%.
# Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative Filtering
## 4 Analysis of Experimental Data and Results
### Preference Comparison Experiment and Result Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(K\) & \multicolumn{3}{c}{**Video Dominance**} & \multicolumn{3}{c}{**Text Dominance**} \\ \hline  & Precision & Recall & F1 & Precision & Recall & F1 \\ \(K=10\) & 0.7966 & 0.3599 & 0.4958 & 0.7858 & 0.3297 & 0.4645 \\ \(K=11\) & 0.7952 & 0.4011 & 0.5332 & 0.7843 & 0.3584 & 0.4919 \\ \(K=12\) & 0.7854 & 0.4261 & 0.5525 & 0.7815 & 0.3960 & 0.5256 \\ \(K=13\) & 0.7816 & 0.4528 & 0.5734 & 0.7784 & 0.4308 & 0.5546 \\ \(K=14\) & 0.7808 & 0.4878 & 0.6005 & 0.7775 & 0.4699 & 0.5858 \\ \(K=15\) & 0.7800 & 0.5136 & 0.6193 & 0.7770 & 0.5006 & 0.6089 \\ \(K=16\) & 0.7794 & 0.5473 & 0.6430 & 0.7759 & 0.5399 & 0.6367 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Preference comparison experiment results. recommendations provided by the system and begin to provide positive feedback on more courses suggested by the system. In future education and teaching, teachers can harness technology and educational innovations to integrate multimedia resources and online learning platforms into their teaching methodologies. Emphasizing the use of video resources can offer students immersive and engaging educational content and experiences. Schools can provide the necessary technical support and training to assist teachers in effectively incorporating video resources into their teaching methods. This approach not only enhances students' personalized learning experiences but also boosts the effectiveness of teaching for educators.
## 4 Experiment and Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{\(\varepsilon\)=0.1} & \multicolumn{2}{c}{\(\varepsilon\)=0.01} & \multicolumn{2}{c}{\(\varepsilon\)=0.001} \\  & **After/Before** & & **After/Before** & & **After/Before** \\ \hline Step calculation times & 467 & 579 & 1318 & 2196 & 10,539 & 19,282 \\ Resolution times & 135 & 579 & 135 & 2196 & 135 & 19,282 \\ Run time & 97.5\% & 98.2\% & 95.0\% & 93.6\% & 76.2\% & 47.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the C11 satellite visibility solving efficiencies. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{\(\varepsilon\)=0.1} & \multicolumn{2}{c}{\(\varepsilon\)=0.01} & \multicolumn{2}{c}{\(\varepsilon\)=0.001} \\  & **After/Before** & & **After/Before** & & **After/Before** \\ \hline Step calculation times & 248 & 295 & 404 & 539 & 1104 & 1611 \\ Resolution times & 71 & 295 & 69 & 539 & 70 & 1611 \\ Run time & 98.34\% & 98.8\% & 98.03\% & 98.2\% & 96.5\% & 94.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the C01 satellite visibility solving efficiencies. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{\(\varepsilon\)=0.1} & \multicolumn{2}{c}{\(\varepsilon\)=0.01} & \multicolumn{2}{c}{\(\varepsilon\)=0.001} \\  & **After/Before** & & **After/Before** & **After/Before** \\ \hline Step calculation times & 248 & 295 & 404 & 539 & 1104 & 1611 \\ Resolution times & 71 & 295 & 69 & 539 & 70 & 1611 \\ Run time & 98.34\% & 98.8\% & 98.03\% & 98.2\% & 96.5\% & 94.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the C01 satellite visibility solving efficiencies. start time and the green stars indicate the \(PNE\) of the end time. The subgraph C01-0.1 in Figure 7 shows the \(PNE\) of the C01 satellite at the rise and set times relative to the Moon when \(\varepsilon=0.1\). Other subgraphs can also be interpreted in this way. The subgraph C01-0.1 in Figure 8 indicates the \(PNE\) values at the start and end times of the Earth occultation for the line of sight from satellite C01 to the Moon when \(\varepsilon=0.1\). Other subgraphs are also expressed in this way.
## Results
### Microscopic Evaluation of Wound Tissue  
The microscopic evaluation of wound tissue included an evaluation of wound width, the number of neutrophil cells, macrophages, fibrocytes, and fibroblasts, and the collagen density in each group. The results of the histopathological microscopic observations in all study groups are described in Figures 3 and 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **IC\({}_{\text{50}}\) (\(\upmu\)g/mL)** & **Classification** \\ \hline _S. illicitolium_ alginate & 120.88 & Moderate antioxidant \\ Mangosteen rind extract & 29.06 & Powerful antioxidant \\ Alginate–mangosteen rind extract & 52.72 & Strong antioxidant \\ \hline \hline \end{tabular}
\end{table}
Table 1: Antioxidant activity of alginate, mangosteen rind, and alginate–mangosteen rind combination.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group Treatment** & \multicolumn{4}{c}{**Blood Glucose Level (mg/dL)**} \\ \cline{2-5}  & **Day 1** & **Day 3** & **Day 7** & **Day 14** \\ \hline Normal (NC) & 118.0 \(\pm\) 4.2 & 117.0 \(\pm\) 2.3 & 114.0 \(\pm\) 2.8 & 110.0 \(\pm\) 1.4 \\ Diabetic (DC) & 256.0 \(\pm\) 4.9 & 250.0 \(\pm\) 11.3 & 242.0 \(\pm\) 2.1 & 250.0 \(\pm\) 10.6 \\ Alginate ointment (DA) & 250.0 \(\pm\) 8.5 & 230.0 \(\pm\) 12.0 & 225.0 \(\pm\) 2.8 & 209.0 \(\pm\) 11.3 \\ Alginate–mangosteen rind extract ointment (DAM) & 251.0 \(\pm\) 5.7 & 176.0 \(\pm\) 5.7 & 182.0 \(\pm\) 7.8 & 135.0 \(\pm\) 7.1 ** \\ \hline \hline \end{tabular} Asterisk notation in the table shows the result of the statistical test (\(\alpha\) < 0.05). ** notation shows insignificant differences with NC but significant differences with DC. ** = 0.01 \(\leq\)\(\alpha\) < 0.05. \(n\) = 3 animals per time point.  
\end{table}
Table 2: Mean and standard deviation of blood glucose.  
Based on microscopic observations, it can be seen that the DAM group revealed the best wound-healing process for diabetic mice compared to other diabetic treatment groups. The re-epithelialization of DA and DAM occurred on Day 7 and wound closing occurred on Day 14 for all groups. Figure 4 shows that the DAM group had the best re-epithelialization process among the groups.  
Figure 4 shows the wound parameters in microscopic observation, where neutrophils (green), macrophages (blue), fibrocytes (yellow), and fibroblasts (red) are shown by an arrow in a different color. The number of neutrophils increased at Day 3 and decreased at Day 7 and Day 14 for all groups. The number of macrophages increased at Day 3 to Day 7, and then declined at Day 14 for all groups. NC, DA, and DAM showed a significant rise in the number of fibrocytes and fibroblasts at Day 7 to indicate re-epithelial and a reduction at Day 14.  
Figure 3: Histopathology analysis of wound width was measured on the three variation times. A circle in the picture indicates the wound width in the specimen. NC: normal group; DA: alginate treatment group; DAM: alginate-magnosteen rind extract treatment group; and DC: diabetic group.
# Semantic Attention Guided Hierarchical Decision Network for Age Estimation
## 3 Hierarchical Age Estimation with Semantic Attention
### Swin Transformer Based Global Regression  
#### 3.1.1 Swin Transformer Architecture  
Swin Transformer introduces a hierarchical model that builds feature map at various scales, and the window-based self-attention mechanism allows it to capture long-range dependencies in images that significantly enhance modeling power. Its core structure includes patch partition, linear embedding, shifted window-based multi-head self-attention (SW-MSA), window-based multi-head self-attention (W-MSA), and patch merging modules.  
In this work, we adopt the Swin Transformer as the global feature extractor due to its great modeling power. Initially, the input image is partitioned into non-overlappingpatches (patch partition), and each patch is projected into a high-dimensional embedding through a linear layer. The network is composed of multiple Swin Transformer Blocks, each consisting of a window-based attention layer followed by a shifted window attention layer. This design significantly improves information exchange across neighboring windows without significantly increasing computational complexity.  
The Swin Transformer follows a four-stage hierarchical structure, with feature map resolutions progressively reduced from the input size of 224 \(\times\) 224 to 56 \(\times\) 56, 28 \(\times\) 28, 14 \(\times\) 14, and finally 7 \(\times\) 7. This pyramid-like architecture enables multi-scale feature extraction, which is advantageous for age estimation tasks that require capturing features such as wrinkles, facial contours, and skin texture.  
Compared with conventional convolutional models, Swin Transformer better models global contextual information and exhibits strong generalization capability in visual tasks, especially those requiring age-related reasoning across facial regions. In our model, the output of the fourth stage of the Swin Transformer serves as the input to the global regression branch, where label distribution learning and expectation regression are applied.  
However, inferior global age estimation will lead to worse refined age estimation. In order to utilize the correlation among adjacent labels and improve the global age estimation performance, one solution is to perform label distribution learning for global age estimation, which firstly converts real-value age label to a Gaussian distribution, then a softmax layer is employed to obtain the prediction age distribution. Finally, an expected value over the predicted distribution is taken as the final global estimation. The final global age prediction \(y_{i}^{(g)}\) is computed as the expected value of the predicted distribution \(p^{(i)}=\left\{p_{1}^{(i)},\ p_{2}^{(i)},\ \ldots,\ p_{L}^{(i)}\right\}\):  
\[y_{i}^{(g)}=\sum_{j=1}^{L}p_{j}^{(i)}\cdot j \tag{1}\]
# Enhanced IoT-Based End-To-End Emergency and Disaster Relief System
###### Abstract  
In this paper, we present a new enhancement for an emergency and disaster relief system called Critical and Rescue Operations using Wearable Wireless sensors networks (CROW\({}^{2}\)). We address the reliability challenges in setting up a wireless autonomous communication system in order to offload data from the disaster area (rescuers, trapped victims, civilians, media, etc.) back to a command center. The proposed system connects deployed rescuers to extended networks and the Internet. CROW\({}^{2}\) is an end-to-end system that runs the recently-proposed Optimized Routing Approach for Critical and Emergency Networks (ORACE-Net) routing protocol. The system integrates heterogeneous wireless devices (Raspberry Pi, smart phones, sensors) and various communicating technologies (WiFi IEEE 802.11n, Bluetooth IEEE 802.15.1) to enable end-to-end network connectivity, which is monitored by a cloud Internet-of-Things platform. First, we present the CROW\({}^{2}\) generic system architecture, which is adaptable to various technologies integration at different levels (i.e., on-body, body-to-body, off-body). Second, we implement the ORACE-Net protocol on heterogeneous devices including Android-based smart phones and Linux-based Raspberry Pi devices. These devices act as on-body coordinators to collect information from on-body sensors. The collected data is then pushed to the command center thanks to multi-hop device-to-device communication. Third, the overall CROW\({}^{2}\) system performance is evaluated according to relevant metrics including end-to-end link quality estimation, throughput and end-to-end delay. As a proof-of-concept, we validate the system architecture through deployment and extracted experimental results. Finally, we highlight motion detection and links' unavailability prevention based on the recorded data where the main factors (i.e., interference and noise) that affect the performance are analyzed.  
t  
t actical multi-hop routing protocol; Internet-of-Things; optimized routing approach for critical and emergency networks; disaster relief system; body-to-body communication