esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Several studies utilized pretraining language models to learn the semantic information contained in the knowledge graph, such as Bert [6] (Bidirectional Encoder Representations from Transformer), a multi-layer and bidirectional transformer encoder pretraining language model, which uses the semantic information contained in the text to embed representation learning through unlabeled corpus training. DKRL [7] (Description-embodied Knowledge Representation Learning) uses text description information as external information, using continuous word bags and deep convolutional neural network models to embed the semantics of entity descriptions. Inspired by relevant research, this paper proposes a knowledge embedding method based on an attention mechanism and incorporating entity types. It uses the convolution method to embed text description information to obtain the initialization vector, then assigns different weight values to entities under different roles according to different entity types, and then assigns different attention weight values to neighbors through the attention mechanism to complete neighborhood information aggregation. Our model is divided into three layers: initial embedding based on text information, entity fusion of type information under the current role, and fusion of the attention mechanism for graph representation learning. (1) We propose a knowledge representation model named KTAT, which integrates type information and the attention mechanism. This model simultaneously encodes entities and relationships, mapping them to type-specific weight matrices based on the different types by entities in different triplets, and obtaining their new entity embedding vectors that contain their own specific attribute information. Our model also utilizes text description information to initialize the entity vector, thereby improving the initial representation and enhancing the representation ability for rare entities. (2) We integrate the attention mechanism and type information into knowledge representation simultaneously, enabling the attention mechanism to be applied to knowledge graphs of heterogeneous neighborhoods, and we strengthen the node representation methods on this basis and embed entities and relationships. (3) We conducted link prediction experiments on the FB15K and FB15K-237 datasets, and conducted in-depth analysis of parameters on KTAT. Experiments on both datasetsshowed that the KTAT model outperformed existing advanced knowledge embedding models and performed well in most evaluation indicators.
# Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways
## 6 Performance Evaluation
### Simulation Results
#### 6.2.1 Algorithm Evaluation
For instance, when the number of nodes is 4000, the energy consumed using OPT-MAX and OPT-DELTA is three times higher than that obtained using EE-LoRa. The results in Figure 11 show the comparison in terms of energy efficiency. The energy efficiency of the network decreases as the number of nodes increases, but EE-LoRa always achieves the highest energy efficiency. When the number of nodes reaches 4000, the energy efficiency improvement of EE-LoRa becomes about 25%, 221%, and 222% compared to legacy LoRaWAN, OPT-MAX, and OPT-DELTA, respectively. To obtain a  
Figure 8: Percentage of nodes over different SFs when \(N_{t}=4000\). better improvement, we can increase the number of TP levels and reduce the idle current, as discussed hereafter. The complexity performance in our optimization problem does not depend on the number of nodes. In our proposed multi-objective problem, we find the optimal node distribution for each SF by computing their ratios \(p(s)\). Figure 12 illustrates the boxplot of the computation time to solve our proposed optimization problem from 30 simulation runs when the number of nodes is 4000 nodes. The computation time is evaluated on a 2.6 GHz intel i7 computer with 16 GB of memory. We can show that the convergence time of our optimization approach does not exceed 0.035 s, which is a fast computation time. In contrast, the complexity performance of ILP optimization used in OPT-MAX and OPT-DELTA is high and requires a long computation time, which increases exponentially with the number of nodes. With the minimum number of nodes 1000, the execution time of these algorithms (OPT-MAX and OPT-DELTA) exceeds 12 s, which is very high compared to the execution time of our algorithm. Figure 10: Comparison of energy consumption during period \(T\). Figure 9: Network throughput comparison.
## 5 Conclusions
Technologies based on biocompatible, self-powered sensors are only beginning to emerge from the laboratory. Most AI models are still trained on small, demographically homogeneous datasets, and clinical trials are often single-center and rely on surrogate endpoints. Without large-scale, multicenter randomized clinical studies reporting hard clinical outcomes--such as patient independence and readmission rates--it is impossible to achieve the regulatory approval and widespread adoption of these technologies. Overcoming these limitations will require the development of interoperable standards, privacy-first system architectures, expanded and diverse training datasets, mandatory external model validation, and the creation of durable, self-powered sensors. Ethically aligned LLM interfaces must be implemented under human oversight, and economic models must ensure equitable access to these technologies. The miniaturization of devices and their integration with smartphones, laptops, and other interfaces provide both patients and clinicians with real-time access to physical activity and health monitoring. These technologies enable patients to report their condition, receive timely recommendations, and allow doctors to intervene immediately in case of adverse changes. Integrating such technologies into rehabilitation systems significantly improves patient satisfaction with care and facilitates the implementation of more convenient and individualized medical services. For the effective deployment of such systems, it is essential to continue improving the technology while adhering to ethical and legal standards. Only by addressing these challenges can the prototypes described in this review evolve into mature, safe, and scalable rehabilitation platforms capable of transforming recovery trajectories around the world. Conceptualization, A.B., Z.B. and K.O. ; methodology, B.I. and N.K. ; verification, K.O., T.A. and N.K. ; research, Z.B. and A.J.G. ; sources, A.B., B.I. and T.A. ; writing--original draft preparation, A.B., Z.B., A.J.G. and T.A. ; writing--review and editing, K.O., B.I. and N.K. ; supervision, B.I. and N.K. ; project administration, K.O. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministry of Science and Higher Education of the Republic of Kazakhstan, grant number BR24992820 \"Innovative medical technologies and devices to improve surgical interventions in prosthetics and rehabilitation in the field of orthopedics and medical rehabilitation\". Not applicable. We would like to express our sincere gratitude to [PERSON] and [PERSON] from the laboratory IoT, Al-Farabi Kazakh National University, for their essential contributions and support, which greatly facilitated the progress of this research. Conflicts of Interest: Author [PERSON] was employed by the company LLP \"Kazakhstan R&D Solutions\". The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Effects of Outlet Shrinkage on Hydraulics in Hyper-Concentrated Sediment-Laden Flow
## 3 Experimental Results
### Flow Regime  
#### 3.2.1 Water Nappe in Air  
The changes of overflow shape had an effect on the nappe surface and width. The flow regimes of water nappe are shown in Figure 5. Figure 5a-c depicts the water nappe in fresh water with contraction ratios of 0.3, 0.4, and 0.5 at an upstream water head of 2.20 m and a downstream water head of 0.15 m. The water nappe was smooth at the outlet and the air entrainment was obvious in the nappe as the water nappe dropped. The water nappe became deep due to the narrow width of the outlet. This caused air entrainment and dispersion of the water nappe. The air entrainment phenomenon of shapes 1 and 2 was not obvious, but in shape 3 it was obvious. The aerated part was mainly concentrated in the middle and lower part of water nappe. Figure 5d-f depicts the water nappe of sediment-laden flow with a sediment concentration of 250 kg/m\({}^{3}\) at an upstream water head of 2.20 m and downstream water head of 0.15 m. The results show that the nappe of sediment-laden flow in the outlet was similar to that of fresh water, the water nappe of sediment-laden flow was smoother than fresh water, and small waves appeared slightly in the lower part.  
Figure 5: _Cont._  
The water nappe became narrower with outlet shrinkage and as air entrainment increased. However, the sediment concentration caused attenuated air entrainment and dispersion of the nappe due to the viscosity of the fluid [21; 29]. The existence of sediment in the flow was beneficial to stabilize the regime of the water nappe.
# The Role of Fungi in Food Production and Processing
## 2 Understanding the Research Prospects
### Fungi as Food Spoilage Agents
#### 2.3.2 Fungal Species Responsible for Food Spoilage  
Numerous fungal species have been implicated in the spoilage of different food products, each exhibiting specific preferences for substrates and environmental conditions. Common spoilage fungi include species of _Aspergillus_, _Penicillium_, _Fusarium_, _Alternaria_, and _Mucor_[86; 87]. These fungi thrive in various food matrices, including grains, fruits, vegetables, dairy products, and processed foods, where they utilize the available nutrients and water to proliferate and produce metabolites that degrade the food quality.
# Self-Organized Proactive Routing Protocol for Non-Uniformly Deployed Underwater Networks
## 4 Route Formation Process
### Forwarding the RR Packet  
1. GW broadcasts an RR packet three times with its node ID to ensure that all the neighbor nodes have received it;
2. The nodes that receive the RR packet form the path with the GW without considering RSS, because they are at one hop distance from the GW;
3. The nodes that receive the RR packet broadcast an RR packet at randomly selected time slots to avoid collision;
4. A node might receive the RR packet from only one node or multiple nodes: 1. If it receives the RR packet from just one node, then it forms its path to the GW through that node; 2. If it receives the RR from multiple nodes, then it compares the RSS values, hop counts to the GW, and number of neighbors of the candidate nodes. It will select the forwarding node according to the weights given to RSS, the least hop count, and least number of neighbors to form the path;
5. Once the forwarding node is selected, the acknowledgement of the RR packet (RR_ACK) is sent. An RR sending node receives acknowledgment from all nodes which are within its transmission range;  
Figure 7: A typical collision domain.  
Steps 1-5 (broadcast of RR packet) are repeated by all the nodes that received the RR packet earlier, until all the nodes in the network have received the RR packet.  
The RR packet is transmitted, at the most, three times, if acknowledgement is not received. If that occurs, then the node concludes that it is an end node and it will start sending a response (RR_RSP) packet that contains its own identification number and the identification numbers of the forwarding nodes. The RR_RSP packet is not broadcast, but sent to the node which was earlier selected by each node as a packet forwarding node.
# A Context-Aware Edge-Based VANET Communication Scheme for ITS +
## 3 Propose Scheme
### Calculation of Competency Value Based on Fuzzy Logic
#### 3.4.2 Calculation of Multiple Factors  
Upon reception of a hello message from a neighbor \(x\), node \(s\) calculates the following factors.  
**Velocity Factor (\(VF\)):** Node \(s\) extracts the velocity of node \(x\), \(v(x)\), and calculates \(VF(s,x)\) (the velocity factor for node \(x\) calculated at node \(s\)) as  
\[VF(s,x)=\frac{|v(x)|-\min_{y\in N_{s}}|v(y)|}{\max_{y\in N_{s}}|v(y)|} \tag{1}\]  
where \(N_{s}\) is the set of neighbors for node \(s\). A lower \(VF\) indicates a lower velocity. \(VF\) is updated for every hello interval using a weighted exponential moving average as  
\[VF_{i}(s,x)\ \leftarrow\ (1-\alpha)\times VF_{i-1}(s,x)+\alpha\times VF_{i}(s,x), \tag{2}\]  
where \(VF_{i-1}(s,x)\) and \(VF_{i}(s,x)\) denote the previous \(VF\) value and current \(VF\) value respectively. \(VF\) is initialized to 1. The coefficient \(\alpha\) is set to 0.7 which is the best value for most situations according to our simulation results [the same value is used in Equations (4) and (5)].  
**Follower Density Factor (\(FDF\)):** Node \(x\) announces the number of neighbor vehicles (\(c(x)\)) driving to the same direction by using hello messages. \(FDF\) of node \(x\) is calculated as  
\[FDF(s,x)=\frac{c(x)}{\max_{y\in N_{s}}c(y)}. \tag{3}\]\(FDF\) indicates the vehicle density moving towards the same direction. A higher \(FDF\) is preferable as it means that the node is more suitable for being an edge node. \(FDF\) is initialized to 0, and updated for each hello interval using a weighted exponential moving average as  
\[FDF_{i}(s,x)\ \leftarrow\ (1-\alpha)\times FDF_{i-1}(s,x)+\alpha\times FDF_{i}(s,x). \tag{4}\]  
**Channel Condition Factor (\(CCF\)):** For simplicity, the hello packet reception ratio is used as the channel Condition Factor (\(CCF\)). We only calculate the message reception ratio of the hello messages sent by the nodes located in \(R\) where \(R\) is the average transmission range. The hello messages are sent periodically with a predefined interval (1 s by default). If a vehicle has a better channel condition than other vehicles (for example, a truck with higher antenna), the \(CCF\) is larger. The \(CCF\) is initialized to 0, and updated as  
\[CCF_{i}(s)\leftarrow(1-\alpha)CCF_{i-1}(s)+\alpha\times CCF_{i}(s). \tag{5}\]
# Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications
## 4 Results and Discussion
Super-resolution image reconstruction will not only transform the low-resolution images into higher-resolution images but will also improve image interpretability. SPOT 5 is a good example of where SR techniques have used successfully to achieve a 2.5 m resolution image from a 5 m resolution sensor. A detailed overview of single-frame and multi-frame SR methods is provided by in many papers [49; 50; 51]. Notwithstanding the data downlink challenges imposed by a low bandwidth telemetry radio subsystem, a lossless compression was going to be more advantageous than the lossy compression implemented for nSight in terms of content retention and maintenance of image quality. Furthermore, while acknowledging the capacity constraints imposed by CubeSats in terms of size, weight and data downlinking, an additional near-red spectral band would have widened the applications offered by the satellite. A 64 km swath width available on nSight-1 gives it a competitive advantage over aerial photography due to its ability to cover vast tracks of land in one acquisition. The on-board image processing capability implemented on nSight-1 differentiates this mission from a range of operational satellites and future missions should inherit this novel capability. Whereas our study confirms the value of nSight-1 imagery, follow-up nSight missions will need to consider a wide range of operational remote sensing requirements such as data calibration and data policies. Ground segment operations such as data acquisition, archiving, cataloguing, curatorship, access, and distribution are also critical in ensuring the success of follow-up nSight missions. Potential benefits of developing a constellation of CubeSats whose primary mission objective is dedicated towards remote sensing of land cover parameters should be considered. As shown already by Planet Labs with more than 150 satellite in orbit, a constellation of nSight earth imaging CubeSats could also be used to shorten the revisit times and improve the temporal resolution of the nanosatellites. Such a constellation could leverage technological advancements in cloud computing, big data analytics, and future data architectures such as datacubes to maximize data usage.
## 1 Introduction
There are also Fisher-based linear manifold learning algorithms that improve class separability in the literature [27; 28; 29; 30] and an analysis on supervised nonlinear embeddings has been suggested in a study by [31]. One study explained manifold learning-based dimension reduction methods and the technique to make them supervised [32]. The supervised technique applied to LE shown in [32] was based on maximizing inter-class and minimizing intra-class distances. The method employed two different weight matrices for each task to optimize cost. However, the proposed method employed one weight matrix for optimization. Contrary to the existing method, it created class-specific variables considering scatter information of every class. They employed a heat kernel function with a supervised manner to create the weight matrix. This approach was applied to LPP and OLPP instead of LE and was more suitable for classification applications due to its out-of-sample applicability. The linear manifold learning methods used were LPP, OLPP, NPE, and ONPE. The study also included comparative analytical evaluations of the algorithm giving the best classification accuracy performance to understand the effect of the proposed method. Analytical evaluations were made with the help of visual reports of class specific accuracy enhancements and the coefficients proposed, visualizations of weight and correlation matrices with the \(p\)-values, and band information graphs. This study explores the relations between the analytical visuals and classification results. The application of the proposed method is expected to have better discrimination ability of extracted features as compared with a traditional version and to provide higher classification accuracy performance than the rivals mentioned. The experiments are conducted on HSI data. The organization of this paper is as follows: In Section 2, the linear manifold learning algorithms which are used in the proposed method and the experiments are briefly described; in Section 3, the proposed method is described clearly; in Section 4, datasets, experiments, and the results are presented; finally, in Section 5, we conclude the paper with some remarks.
# Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV
## 3 Overview of Image Recognition Technology
### Target Identification Applications
#### 3.3.2 Target Recognition Scheme for UAV Dropping  
To meet the requirements for target recognition, this study adopts the following approach: The study utilizes the YOLO series of deep-learning algorithms to train the target recognition model. The YOLO series includes various versions, such as YOLOv5n, YOLOv5s, YOLOv7, YOLOv8n, and YOLOv8s. This study sequentially employed these deep-learning algorithms to train the target recognition model and conducted model evaluations. As shown in Figure 25, the YOLOv8n model demonstrated superior recognition accuracy, recall rate, and recognition speed compared to the other models [66]. Table 8 is performance evaluation of each model in the YOLO series.  
This study initially collected target image data in various scenarios and periods [67]. Subsequently, the EasyData data-processing platform was utilized to categorize and label the original images, ultimately yielding 12,004 labeled images. To enhance the speed of the model training, a PyTorch environment was configured. Utilizing both a central processing unit (CPU) and a graphics processing unit (GPU) for simultaneous training in the YOLOv8 deep-learning framework, the model underwent 300 training cycles, resulting in the most performant model. The exported precision and recall curves after the training completion are depicted in Figure 25. Recall is the ratio of correctly detected targets to the total actual target count, with a higher recall rate signifying more accurate target detection [68; 69].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Map@.5** & **S (m)** \\ \hline YOLOv5s & 0.823 & 0.672 & 0.726 & 5.5 \\ YOLOv5n & 0.823 & 0.606 & 0.687 & 3.6 \\ YOLOv7n & 0.876 & 0.721 & 0.785 & 3.5 \\ YOLOv8s & 0.896 & 0.856 & 0.854 & 3.4 \\ YOLOv8n & 0.910 & 0.925 & 0.869 & 2.8 \\ YOLOv8x & 0.904 & 0.910 & 0.814 & 3.4 \\ YOLOv8l & 0.890 & 0.897 & 0.809 & 3.8 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance evaluation of each model in the YOLO series.  
Figure 25: YOLOv8 training model’s precision and recall.  
From Figure 25, it can be deduced that the model developed in this study achieves a precision of 90% and a recall of 99.5%, indicating superior performance in target detection tasks. Through the model training described earlier, a highly accurate target recognition model was obtained, referred to in this study as the Open Neural Network Exchange (ONNX) target model. This model, based on deep learning, is capable for detecting and classifying targets within a single image [70]. By retrieving video streams from network cameras and employing OpenCV's built-in cv2. readNet function, the ONNX target model developed in this study is used to perform inference on video streams, achieving effective target detection. Figure 26 illustrates the OpenCV target detection process.
# TopoSinGAN: Learning a Topology-Aware Generative Model from a Single Image
## 3 Results
### Hyperparameter Tuning of \(\lambda_{3}\) for CREMI Experiment  
According to our experiments, the value of \(\lambda_{3}\), the coefficient of \(L_{\text{topo}}\) in Equation (1), depends on factors such as input image dimensions and the density of line features in the real mask. Therefore, hyperparameter tuning is crucial for each case study. Figure 8 shows the tuning process of the \(\lambda_{3}\) parameter for the CREMI experiment. In this experiment,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Dimensions** & **Pyramid Scales** & **SinGAN** & **TopoSinGAN** \\  & & **(Minutes)** & **(Minutes)** \\ \hline \(175\times 240\times 4\) & 8 & 20.93 & 22.90 \\ \(350\times 400\times 4\) & 11 & 65.13 & 69.88 \\ \(500\times 500\times 4\) & 12 & 108.11 & 114.58 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of comparative performance analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**SinGAN**} & \multicolumn{2}{c}{**TopoSinGAN**} \\  & _Mean_ & _STD_ & _Mean_ & _STD_ \\ \hline Agricultural & 0.2485 & 0.0086 & 0.1914 & 0.0083 \\ Dendrology & 0.0014 & 0.0013 & 0.0014 & 0.0018 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average FID values over 10 runs for 1000 synthetic images per model for each case study.  
we gradually increased \(\lambda_{3}\) by 0.2, starting from 0 (baseline SinGAN), while keeping the other two hyperparameters constant, and recorded the average FID and NTC values for each trial.  
In Figure 8, the blue line represents FID scores, which are initially low for SinGAN. As \(\lambda_{3}\) increases to 0.2, the FID worsens to 0.1801, while the NTC slightly improves to 14.9. The best balance of FID and NTC occurs at \(\lambda_{3}=0.4\), with FID at 0.1127 and NTC at 8.15. When \(\lambda_{3}\) is further increased to 0.6, NTC decreases to 6.91, but the FID jumps to 0.15, indicating a negative impact on the realism of the generated contexts. At \(\lambda_{3}=0.8\), this trend continues, with NTC dropping to 6.02 and FID rising sharply to 0.21. It is important to note that higher values of \(\lambda_{3}\) lead to model divergence.