esa-sceva/satcom-chunk-collection huggingface dataset:
# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
###### Abstract  
With the proliferation of smart devices and the emergence of high-bandwidth applications, Unmanned Aerial Vehicle (UAV)-assisted Device-to-Device (D2D) communications and Non-Orthogonal Multiple Access (NOMA) technologies are increasingly becoming important means of coping with the scarcity of the spectrum and with high data demand in future wireless networks. However, the efficient coordination of these techniques in complex and changing 3D environments still faces many challenges. To this end, this paper proposes a NOMA-based multi-UAV-assisted D2D communication model in which multiple UAVs are deployed in 3D space to act as airborne base stations to serve ground-based cellular users with D2D clusters. In order to maximize the system throughput, this study constructs an optimization problem of joint channel assignment, trajectory design, and power control, and on the basis of these points, this study proposes a joint dynamic hypergraph Multi-Agent Deep Q Network (DH-MDQN) algorithm. The dynamic hypergraph method is first used to construct dynamic simple edges and hyperedges and to transform them into directed graphs for efficient dynamic coloring to optimize the channel allocation process; subsequently, in terms of trajectory design and power control, the problem is modeled as a multi-agent Markov Decision Process (MDP), and the Multi-Agent Deep Q Network (MDQN) algorithm is used to collaboratively determine the trajectory design and power control of the UAVs. Simulation results show the following: (1) the proposed algorithm can achieve higher system throughput than several other benchmark algorithms with different numbers of D2D clusters, different D2D cluster communication spacing, and different UAV sizes; (2) the proposed algorithm designs UAV trajectory optimization with a 27% improvement in system throughput compared to the 2D trajectory; and (3) in the NOMA scenario, compared to the case of no decoding order constraints, the system throughput shows on average a 34% improvement.  
unmanned aerial vehicles; non-orthogonal multiple access; D2D; dynamic hypergraph; multi-agent reinforcement learning 2025  
62  
[PERSON], [PERSON], [PERSON]
# Low Complexity Deep Learning Framework for Greek Orthodox Church Hymns Classification
## 2 Materials and Methods
### Performance Metrics  
ML and DL algorithms are evaluated in terms of their performance and their generalization capabilities, using several different performance and statistical metrics. In a supervised learning framework, the most common performance metrics for multi-class classification are presented below [41; 42; 43]:  
* _Accuracy_ is defined as the fraction of correct predictions. It is expressed mathematically as \[\text{Accuracy}=\frac{1}{M}\sum_{m=0}^{M-1}\mathcal{I}(\hat{q}_{n}=q_{n})\] (7) \(M\) is the number of test classes, \(\hat{q}_{n}\) denotes the ML model class predicted label, \(q_{n}\) is the true class label, and \(\mathcal{I}(x)\) represents the indicator function.
* _Precision_ expresses the ratio of correctly predicted positive classes to all predicted positive classes and in multi-class classification problems is defined as \[\text{Precision}=\frac{\sum_{l=1}^{L}TP_{l}}{\sum_{l=1}^{L}(TP_{l}+FP_{l})}\] (8) \(L\) refers to the number of classes, \(TP_{l}\) is the number of true positive outcomes, and \(FP_{l}\) is the number of false positive outcomes for class label \(l\).
* _Recall_ expresses the ratio of correctly predicted positive classes to all existing positive classes. In mathematical terms, \[\text{Recall}=\frac{\sum_{l=1}^{L}TP_{l}}{\sum_{l=1}^{L}(TP_{l}+FN_{l})}\] (9) \(L\) is the number of classes, \(TP_{l}\) is the number of true positive, and \(FN_{l}\) is the number of false negative for class label \(l\), respectively.  
* \(F_{1}\)-score aggregates Precision and Recall metrics under the concept of harmonic mean. This is defined as \[F_{1}\text{-score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}}.\] (10) \(F_{1}\)-score can be viewed also as the weighted average between Precision and Recall.
# Evaluation of the Prospects for the Use of Microalgae in Functional Break Production
## 3 Results  
### Organoleptic Properties of Braad with Microalgae  
Through the identification of microalgae by sequencing, it was found that the algae isolated from the Baltic Sea belonged to the different species. The composition of microalgae collected and identified by sequencing is presented in Table 5.  
The microalgae samples (Table 5) collected in the Baltic Sea contained the largest amount of _C. vulgaris_ (36.4%) and _A. platensis_ (21.4%). The amounts of other microalgae (_P. carterae, D. salina, A. acicularis Korsch_, and _C. fusca_) were 1.5-2 times less (15.3%, 8.6%, 6.3%, and 12.0%, respectively).  
The results of the study of the organoleptic properties of bread with microalgae are presented in Table 6. According to organoleptic parameters (Table 6), the best samples of bread were samples with the addition of a mixture of microalgae in the amount of 3%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Values**} & \multicolumn{3}{c}{**Samples**} \\ \cline{2-4}  & **1** & **2** & **3** & **4** \\ \hline \multicolumn{4}{c}{Appearance} \\ \hline Form & Corresponds to the bread & Corresponds to the bread & Corresponds to the bread & Corresponds to the bread form \\ Surface & Smooth & Smooth & Smooth \\ Color & Corresponds to wheat bread & Yellow & Greenish & Green \\ \hline \multicolumn{4}{c}{Crumb condition} \\ \hline Mising & Without lumps and traces of & Without lumps and traces of & Without lumps and traces of & A small number of lumps and \\ Mising & under-mixing & undermixing & undermixing & traces of undermixing \\ Doneness & Well-done, elastic & Well-done, elastic & Poorly done, inelastic \\ \multicolumn{4}{c}{Characteristics of this type of } \\ Taste and smell & & Characterisation of this type of bread, without foreign taste & Characteristics of this type of bread, with a slight “fishy” smell, with a sour taste \\ \multicolumn{4}{c}{and smell} \\ \hline \hline \end{tabular}  
* 1—control sample, without added microalgae; 2—bread with a microalgae content of 1.0%; 3—bread with a microalgae content of 3.0%; 4—bread with a microalgae content of 5.0%.  
\end{table}
Table 6: Organoleptic properties of bread with microalgae.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Microalgae**} & **Content in the Sample, \%** \\ \hline _Chlorella vulgaris_ & 36.4 \(\pm\) 1.1 \\ _Chlorella fusca_ & 12.0 \(\pm\) 0.3 \\ _Arthrospira platensis_ & 21.4 \(\pm\) 0.6 \\ _Pleurochrysis carterae_ & 15.3 \(\pm\) 0.5 \\ _Dunaliella salina_ & 8.6 \(\pm\) 0.4 \\ _Ankistrodesmus acicularis_ [PERSON] & 6.3 \(\pm\) 0.4 \\ \hline \hline \end{tabular}  
\end{table}
Table 5: Composition of microalgae in samples collected from the Baltic Sea.
## 4 Lifetime Improvement Mechanisms in Routing
### Node-Disjoint Multipath
Having this information, the nodes can compute the link cost function for their neighbors according to the following equation:  
\[\text{Next hop}=max_{\gamma\in N_{x}}\{\alpha E_{resd,y}+\beta B_{buffer,y}+ \gamma_{interference,xy}\} \tag{8}\]  
During the path discovery phase, each node utilizes this function to choose the best next hop. In Equation (8), node \(y\) is in the vicinity of node x and \(N_{x}\) is a set including all of the neighbors of node x. \(\text{E}_{\text{resd}}\) is the residual energy, \(\text{B}_{\text{buffer}}\) represents the buffer size, and \(\text{I}_{\text{interference}}\) is the interference between \(x\) and y based on signal-to-noise ratio (SNR). Moreover, \(a\), \(\beta\), and \(\gamma\) are the appropriate weights for these three factors. _Primary path discovery stage:_ now, the sink can send the route request (RREQ) message to the eligible neighbor node according to the mentioned cost function in order to construct the first path. This trend will be continued by the nodes until the source node is reached. _Alternative path discovery stage:_ the alternative paths are discovered with the same method but without any node sharing among the parallel paths. In order to guarantee node disjointness among multiple paths, each node only accepts one RREQ message and rejects the other ones. (2) _Path maintenance:_ in order to keep the paths and cost function factors updated, the source floods KEEPALIVE messages periodically over the multiple paths. (3) _Traffic allocation and data transmission:_ in this phase, the source node can select one of the following traffic allocation methods. _Data transfer through a single path (REER-1):_ in this state, the protocol chooses the best path among the available paths to relay data packets until the path-cost falls below a specific threshold. Then it can switch to the next alternative path. _Data transfer across multiple paths (REER-2):_ in delay-sensitive applications, REER can split up each packet to a number of equal size segments; append XOR-based error correction codes to each of them, and send the segments to the sink across multiple paths simultaneously. This can increase the reliability of data delivery and resiliency to path failures. Simulation results [15] have shown that REER-1 improves the energy consumption by 7% to 39% in comparison with directed diffusion. When compared to REER-2 and Directed Diffusion, the changes in network size have a little effect on the energy consumption of REER-1. The reason is that the route length does not increase as the number of nodes increases. In contrast with previous link and node disjoint protocols, the REER algorithm utilizes node available buffer size in the paths construction phase in order to define the next hop efficiently. However, the main drawback of this protocol is that the source should periodically flood KEEPALIVE messages over the multiple paths in path maintenance stage. This imposes a high overhead on the individual sensor nodes that causes unnecessary energy consumption across the network.
## 2 Materials and Methods
### Quantification of Total MC in Bivalve Tissues by LC-MS/MS
#### 2.5.2 LC-MS/MS Analysis  
The LC-MS/MS analysis of the released MMPB from tissue extraction was performed according to [PERSON] et al. (2010). The system consisted of a Waters Synapt mass spectrometer (Waters, Milford, MA, USA) coupled with Alliance e2695 separation module (Waters, Milford, MA, USA) and MassLynx software (vs. 4.1, Micromass, Manchester, UK). An agilent ZORBAX SB-C18 5 \(\upmu\)\(3.0\times 250\) mm (Agilent Technologies, Palo Alto, CA, USA) column was used and kept at 30 \({}^{\circ}\)C. The mobile phase consisted of solvent A: 0.1% formic acid in water and B: methanol. The gradient programme for MMPB detection was from 25% to 90% B over 12 min, held at 90% B for 2 min, flow rate 0.3 mL/min. The injection volume was 10 \(\upmu\)L. The analysis was conducted in positive electrospray ionisation. The MS ion source was programmed as follows: desolvation gas flow 800 L/min, capillary voltage 4 kV, sampling cone voltage 5 V in MRM mode. The followed transition were m/z 209.2 to m/z 131.1 and 91.  
The standard curve was performed by spiking unexposed tissues (n = 3) of each species. Tissues were extracted as described above and, after being redissolved in 35% methanol, samples were spiked with 2, 4, 8, 10, 12, 50, 150, 300 and 800 ng of MMPB. To determine the percentage of recovery of total MC in digestive glands or whole individuals of each species, we spiked non-exposed tissues with 1, 100 and 1000 \(\upmu\)g of MC-LR before the oxidation. The mean percentages of extraction and detection of total MC on these freeze-dried tissues varied according to the range of doping concentrations used and was between \(32.9\pm 0.02\%\) and \(58.8\pm 16.1\%\) for the digestive glands of _A. anatina_ and between \(36.5\pm 1.2\%\) and \(67.0\pm 14.9\%\) for whole _D. polymorpha_. Total MC concentrations were calculated by applying an extraction yield depending on the MC concentration range. Free MC were expressed in \(\upmu\)g MC/g of dry weight (DW). Values obtained in tissues during this experiment were then expressed in \(\upmu\)g/g DW and corrected by the recovery rate. The quantity of bound MC was evaluated per pool of mussels, by subtracting the amount of free MC measured by ELISA to the amount of total MC measured after oxidation and analysis by LC-MS/MS.
## 5 Experiments  
### Experiments Settings  
**Implementation Details.** In our experiments, Meta-3-Llama-3-8B and Meta-3-Llama-3-70B were deployed on a server equipped with an L20 (48 GB) GPU, 20 vCPUs of Intel(R) Xeon(R) Platinum 8457C, and 100 GB of RAM for inference. The testing tasks for the remaining models, accessed via API, were conducted on a laptop equipped with an i9-13950 HX CPU, Nvidia GeForce RTX 4060 GPU, and 16 GB of RAM. The parameter settings of all the tested models are shown in Table 2. The temperature parameter determined whether the output was more random or more predictable. A lower temperature resulted in a higher probability, leading to a more predictable output. The top_p parameter affected the diversity of the output text generated by the LLMs; the larger the value, the greater the diversity of the generated text. For all Chinese MCQs, we tested using both Chinese prompts and English prompts. For all English MCQs, we tested using only English prompts. The max_output_tokens parameter specified the maximum number of tokens that the model could output. The parameters that we do not mention in the table were the default settings for the LLM creators.  
**Evaluation Protocols.** Considering that we required the tested models to output only the correct answer option for the MCQs, we used _Accuracy_ as the sole evaluation metric. _Accuracy_ was defined as the number of correctly answered MAQs by the tested LLM divided by the total number of tested MCQs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **Prices (\$)/1 k Tokens** & & & \\ \cline{2-4}  & **Input** & **Output** & **Model Size** & **Version** & **Creators** \\ \hline Meta-Llama-3-8B & & 8B & Instruct & \\ \hline Meta-Llama-3-70B & Open Source & 70B & Instruct & Meta AI \\ \hline \multirow{2}{*}{Gamma-7B-it} & & 7B & Instruct & Google \\ \cline{2-4}  & & & & \\ \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **Temperature** & **Top\_p** & **\# Max Output Tokens** \\ \hline Qwen-turbo & 0.5 & 0.7 & 100 \\ \hline ERNIE-4.0-8k & 0.5 & 0.7 & 100 \\ \hline GPT-3.5-turbo & 0 & 1 & 100 \\ \hline CPT-4 & 0 & 1 & 100 \\ \hline GPT-4o & 0 & 1 & 100 \\ \hline GLM-3-turbo & 0.5 & 0.7 & 100 \\ \hline GLM-4-Air & 0.5 & 0.7 & 100 \\ \hline GLM-4 & 0.5 & 0.7 & 100 \\ \hline Qianfan-Chinese-Llama-2-7B & 0.5 & 0.7 & 100 \\ \hline Qianfan-Chinese-Llama-2-13B & 0.5 & 0.7 & 100 \\ \hline Qianfan-Chinese-Llama-2-70B & 0.5 & 0.7 & 100 \\ \hline Meta-Llama-3-8B & 0.5 & 0.7 & 100 \\ \hline Meta-Llama-3-70B & 0.5 & 0.7 & 100 \\ \hline Gemma-7B-it & 0.5 & 0.7 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameter settings of the tested LLMs.
## 4 Experiments
### Experimental Results
The figure reports class-based accuracy enhancements and \(\mu_{c}\) coefficients calculated for every class. The coefficient \(\mu_{c}\) is related to the scattering level of intra-class samples and specific for every class. Class-based success of the proposed method can be evaluated.for every dataset and compared with the coefficients proposed). Figure 5a-d shows the graphs that belong to the first two bands of low-dimensional data of hyperspectral images. According to Figure 5a-d which is obtained upon \"difficult classes\", the class discrimination information is increased predominantly by applying the proposed method for the Indian Pines and KSC datasets. Furthermore, the first two bands for KSC show a significant discriminative property for the classes marked with green and red circles. It helps to enhance classification accuracies for KSC. Additionally, the feature information of some classes belonging to the Indian Pines and KSC datasets is improved on randomly chosen Bands 11 and 12. This is shown in Figure 5e-h. The last evaluation metric is getting the visualizations of correlation matrices and \(p\)-values that give the relation between the samples for every dataset and their significance. If an off-diagonal value is smaller than 0.05, then, the correlation is considered to be significant. A smaller \(p\)-value is identified as a more significant correlation. The correlation matrices and \(p\)-values are given in Figures 6a-d and 6e-h, respectively. In Figure 6a-d, it can be seen that the correlation values of off-diagonal values are decreased, while there is no significant change around diagonals. Furthermore, \(p\)-values are increased for off-diagonal values, as observed in Figure 6e-h.  
Figure 4: Weight matrices obtained with HSI datasets: **(a)** LPP for Indian Pines; **(b)** Proposed + LPP for Indian Pines; **(c)** OLPP for KSC; **(d)** Proposed + OLPP for KSC. (LPP: Locality Preserving Projection, OLPP: Orthogonal Locality Preserving Projection). Figure 5: Information of Bands 1 and 2 for “difficult classes”: (**a**) LPP for Indian Pines; (**b**) Proposed + LPP for Indian Pines; (**c**) OLPP for KSC; (**d**) Proposed + OLPP for KSC. Information of Bands 11 and12 for empirically chosen classes: (**e**) LPP for Indian Pines; (**f**) Proposed + LPP for Indian Pines; (**g**) OLPP for KSC; (**h**) Proposed + OLPP for KSC. (LPP: Locality Preserving Projection, OLPP: Orthogonal Locality Preserving Projection). Figure 6: Correlation matrices obtained upon face and HSI datasets: (**a**) LPP for Indian Pines; (**b**) Proposed + LPP for Indian Pines; (**c**) OLPP for KSC; (**d**) Proposed + OLPP for KSC. Significance matrices (\(p\)-values) for the correlations: (**e**) LPP for Indian Pines; (**f**) Proposed + LPP for Indian Pines; (**g**) OLPP for KSC; (**h**) Proposed + OLPP for KSC. (LPP: Locality Preserving Projection, OLPP: Orthogonal Locality Preserving Projection).
# Real-Time Motion Blur Using Multi-Layer Motion Vectors
## 1 Introduction  
Motion blur is an effect created by the movement of objects between frames, blurring them in the direction of movement to produce realistic images. When this effect is applied to videos, the motion of objects is expressed in the image of each frame, enhancing realism. In general, sequences that contain a moderate amount of motion blur are perceived as natural. However, rendering motion blurred image requires significant computations, unlike cameras that generate motion blur automatically. As a result, much research has been done to reduce the resources required to generate motion blur. Although existing methods can achieve fast rendering speeds through post-processing, they generally rely on a single layer for motion blur generation [1; 2; 3; 4; 5; 6; 7]. If an object in the first layer moves, it cannot produce a color mixed with objects in the later layers. Another method to create motion blur is the accumulation method, which uses multiple rendering iterations with different times in a single frame to generate images that are subsequently averaged [8; 9]. Because this method uses an actual rendering process, the motion blur effect is correctly simulated. However, this method introduces artifacts at low rendering iterations and performs slowly at high rendering iterations. Another method to create motion blur outputs a single image with motion blur using images from the previous frame and the current frame with a trained AI model [10]. However, it requires training data and can cause artifacts in real-time rendering environments with many objects.  
Our proposed method employs multilayer motion vectors alongside color and depth images to calculate object movement, achieving a high-speed correct motion blur effect, as shown in Figure 1.  
The motion vector is the difference in the position of each pixel between the previous and current frames, as shown in Figure 2.  
All pixels between \(P_{T-1}\) and \(P_{T}\) in Figure 2 are averaged to obtain the motion-blurred color of pixel \(P_{T}\). Each pixel searches for colors that pass through it, and the motion-blurred color is calculated by averaging all these colors.  
This paper is organized as follows. Section 2 describes how motion blur is generated with multilayered images. Section 3 explains the generation of multilayered images, and Section 4 analyzes the rendering speed of each method. Section 5 discusses the research results and examines potential future directions of research. Section 6 concludes the study.
# Bioelectrical Signals for the Diagnosis and Therapy of Functional Gastrointestinal Disorders
## 4 Small Intestine  
Small intestinal disorders are a frequent cause for a variety of clinical symptoms or pathologies, such as chronic pain, dyspepsia, ischemia, irritable bowel syndrome (IBS), chronic intestinal pseudo-obstruction, bacterial overgrowth, or ileus [99; 100]. However, localization of such disorders can be challenging because endoscopically accessing the small intestine is physically difficult as it is over 7 m long in an adult. Therefore, such disorders are often difficult to diagnose and treat. Current techniques for assessment of small intestinal motility include tracing the passage of a food bolus, measuring propagation velocities with manometry, or imaging the frequencies of contractions [100]. However, transit time measures only represent sections of the gut or the entire gut, and do not reveal localized motility patterns. Manometry provides site-dependent quantitative pressure data, thus permitting an indirect quantification of bowel wall motion. However, it requires the uncomfortable insertion of a long catheter under sedation and is rarely performed. Capsule endoscopy is also commonly used to assess and quantify small intestinal function as it can measure the temperature, pH, and pressure of its surrounding environment while it travels passively through the gastrointestinal tract via gut peristalsis before being excreted [55].  
Classical imaging techniques such as ultrasound and x-ray fluoroscopy are not well suited to monitor bowel motility because of ionizing radiation, views hampered by intestinal gas, and a lack of reproducibility. MRI is emerging as a promising alternative with advantages including the ability to non-invasively image sections or the entire gastrointestinal tract in multiple planes or three-dimensions [57]. Initial attempts to image small intestine motility using dynamic MRI showed the ability to detect the peristaltic patterns of the gastric antrum and proximal small intestine in fasting and fed subjects and in subjects with pharmacologically stimulated peristalsis [101]. However, as the small intestine has thin walls and is often collapsed, bowel motility can only be visualized reliably when distended and filled with contents that provide sufficient contrast with the walls. Despite these challenges, MRI has been able to reliably extract the frequency of small intestine contractions and changes in intraluminal cross-sectional caliber [100].
## 3 Instrument Description
The receiver software also provides a way to calibrate the system over a short transmission path to remove any frequency dependencies in the signal chain that would otherwise bias the measurement.