esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Multi-Access Edge Computing (MEC) into Open 5G Core
## 4 A New Service Proposed
The response is sent back to _postData_, relaying it to the UE. In addition to these two methods, the service includes a third called _makeRegistry_ to register with the SR. This function contains all the service details, such as IP, port, and endpoints. This information is sent to the SR for registration, allowing interested applications to check and obtain these details. Figure 2: Proposal’s general architecture. For this whole flow to work with a higher level of security, a registry service called _ServiceRegistry_ will be implemented. This registry focuses on applications and services on the MEC platform that wish to allow internal or external access through the integration API. To this end, an application/service must register its IP, port, and endpoints with the respective methods. Interested applications will then be able to check this information. A client application in the UE will be able to check whether its respective server is available and, if it is, all the information for communication will be passed on. In this work, it will be implemented as an application. However, this service can be incorporated into the MEC platform as desired. In this scenario, the service would also be accessible via the MEP and communicate via the N6 interface. This application will be implemented as a RESTful API. By implementing it as an application, there is no need for any communication with the core CP. Therefore, all data traffic will occur through the DP, UPF. Communications take place via HTTP requests. In addition to the MEC reference architecture, the ETSI technical documents also deal with two APIs: Multi-Access Traffic Steering (MTS) and BandWidth Management (BWM) [13]. BWM is the API that will handle the allocation of bandwidth between applications, as well as the prioritization of specific traffic. The MTS API, on the other hand, is responsible for managing traffic, routing it, splitting it up, and replicating it on various access networks to meet the network requirements requested by the applications [14]. Recognizing that the objectives of the service that is the subject of this work and the MTS API are partially compatible, it is proposed that the MTS API be expanded to provide a traffic-forwarding service for the data network external to the MEC.
## 4 Simulation and Optimization
This corresponds to a link margin that exceeds 10 dB relative to the 5G receiver sensitivity threshold of \(-98\) dBm (based on a 100 MHz bandwidth and a BLER \(\leq\) 10%) [15], thereby ensuring reliable communication even under multipath fading conditions. Moreover, the spatial diversity and multiplexing capabilities inherent in 4 \(\times\) 4 MIMO systems further reduce the impact of individual port inefficiencies. Comparable efficiency margins have recently been reported and validated in studies on compact sub-6 GHz MIMO antennas for mobile platforms [1], reinforcing the soundness of the efficiency targets adopted in the present work. In brief, the proposed specifications are grounded in terms of both theory and practical implementation.
## 5 ACLR Classification  
In conventional strategies, such as discrimination and classification strategies (and sometimes in multiple regression), the objective is to classify observations into different groups (populations). In contrast, the main goal of the technique proposed in this research is to supply measurements and to measure the degree of abnormalities on a continuous scale. This can help to determine the correct actions to take predicated by the degree of abnormality [42].  
However, in the MTS methods, there are simply no populations [19; 24; 38; 48; 49]. It simply requires several observations, called a 'healthy' group, to acquire a correlation structure and to define the reference indication for the measurement scale MS. Selection of this healthy group is completely at the discretion of clinicians. In MTS strategies, every irregular condition (ACLR individuals) is considered exclusively, since the occurrence of such conditions differ. The degree of abnormality can be measured in reference to the healthy group [40; 43; 50; 51].To produce the referenced area, MS feature data units from the HG were utilised. The MDs of HG range from 0.560 to 1.180, calculated from the methods introduced in Table 7. The 10 ACLR subjects were used for MS validation [19; 24].  
The PG data were normalised using the mean and standard deviation of the HG. Their MDs, which were calculated with the covariance coefficient matrix of the HG, range between 2.308 and 1509.811; these are outside of the MS. Therefore, the MS is certainly validated. Hence, Taguchi strategies were used to study the impact of every feature [43; 52; 53]. The 'Gain' of every feature was computed  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Run**} & \multicolumn{8}{c}{**Features**} & \multicolumn{8}{c}{**S/N**} \\ \cline{2-11}  & **X1** & **X2** & **X3** & **X4** & **X5** & **X6** & **X7** & **X8** & **X9** & **X10** & **X11** & **Ratio** \\ \hline
1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 8.49550614 \\
2 & 1 & 1 & 1 & 1 & 1 & 2 & 2 & 2 & 2 & 2 & 2 & 1.31702001 \\
3 & 1 & 1 & 2 & 2 & 2 & 1 & 1 & 1 & 2 & 2 & 2 & \(-\)1.4245509 \\
4 & 1 & 2 & 1 & 2 & 2 & 1 & 2 & 2 & 1 & 1 & 2 & 5.12525206 \\
5 & 1 & 2 & 2 & 1 & 2 & 2 & 1 & 2 & 1 & 2 & 1 & 3.6517743 \\
6 & 1 & 2 & 2 & 2 & 1 & 2 & 2 & 1 & 2 & 1 & 1 & 0.67514098 \\
7 & 2 & 1 & 2 & 2 & 1 & 1 & 2 & 2 & 1 & 2 & 1 & 2.99885419 \\
8 & 2 & 1 & 2 & 1 & 2 & 2 & 2 & 1 & 1 & 1 & 2 & 3.19462065 \\
9 & 2 & 1 & 1 & 2 & 2 & 2 & 1 & 2 & 2 & 1 & 1 & 3.29197259 \\
10 & 2 & 2 & 2 & 1 & 1 & 1 & 1 & 2 & 1 & 2 & 2.85909121 \\
11 & 2 & 2 & 1 & 2 & 1 & 2 & 1 & 1 & 1 & 2 & 2 & 3.71676119 \\
12 & 2 & 2 & 1 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2.65860164 \\ \hline \hline \end{tabular}
\end{table}
Table 10: L\({}_{12}\) (\(2^{11}\)) OA for the ACLR conditions.  
between these two conditions. As shown in DF, X2, and X8 were found to have an insignificant effect on the MD. Hence, the number of features decreased from 11 to 8.
# Enhancing Small Language Models for Graph Tasks Through Graph Encoder Integration
## 2 Related Works
### Graph Neural Networks  
Graph neural networks (GNNs) are a widely used approach for learning on graph-structured data and have been applied to various tasks. Early methods focused on leveraging the spectral and spatial properties of graphs. [PERSON] and [PERSON] [44] first introduced neural networks for directed acyclic graphs, laying the foundation for later developments. Graph convolutional networks (GCNs) [8] extended this idea by applying spectral-based convolution to capture node relationships, achieving strong performance in semi-supervised learning. This led to the development of more advanced models such as graph attention networks (GATs) [43] and GraphConv [45]. Additionally, GraphMAE [46] introduced an unsupervised learning framework for graph representation learning, further expanding the capabilities of GNNs.  
Recently, researchers have explored integrating transformers with GNNs to enhance graph learning. Transformers, with their multi-head attention mechanism, effectively model long-range dependencies, but they struggle with local structures in graphs. To address this, hybrid models combining GCNs and transformers have been introduced. [PERSON] et al. [47] proposed a model where transformers encode temporal patterns while GCNs capture spatial patterns, improving performance in time series and spatial analysis tasks such as COVID-19 spread prediction. Another key advancement is graph transformer networks (GTNs) from [PERSON] et al. [48], which learn meta-path-based connections in heterogeneous graphs. Unlike conventional GNNs, which operate on fixed graph structures, GTNs dynamically generate optimized graph structures tailored to specific tasks.  
Despite these advancements, applying standard transformers directly to graphs remains challenging. Transformers are designed for sequential or grid-like data, whereas graphs have irregular and dynamic structures. This makes it difficult for transformers to process graph connectivity effectively. To address this, [PERSON] et al. [9] proposed unified message passing (UniMP), a model which combines the benefits of GNNs and transformers while preserving the graph structure. Unlike standard transformers, which use attention to infer relationships between nodes, UniMP explicitly learns graph connections. It employs a message-passing mechanism to prevent the spread of unnecessary information, ensuring more meaningful graph representations. Additionally, UniMP introduces masked label prediction, a training strategy which masks a portion of node labels and predicts them, reducing overfitting and improving semi-supervised learning.  
Building on these studies, this paper proposes a new graph encoder which combines the structured learning of a GCN with the flexibility of a graph transformer (UniMP). By integrating the strengths of the GCN for local feature aggregation and the graph transformer for global pattern learning, our approach aims to improve representation learning for complex graph-based tasks.
## 4 Experiments, Results, and Analysis
### Model Interpretability  
Model interpretability is essential, especially in critical applications like aviation. To interpret the contributions of input features to the model's predictions, we employed SHAP (SHapley Additive exPlanations) [31], which assigns an importance value to each feature, thereby explaining the model's decisions. We grouped the input features into five primary categories: modulation order, data rate, power level, normalized frequency, and number of component signals.  
Table 4 presents the normalized SHAP values (percentage importance) for each feature group across the estimated distribution parameters \(a\), \(b\), and \(s\), as well as the overall importance. These values indicate the relative contribution of each feature group to the model's predictions.  
The results demonstrate that modulation order and power level are the most influential features, each contributing approximately 44% to the overall model predictions. The number of component signals in a particular composite signal also has a notable impact, contributing around 10%. In contrast, data rate and normalized frequency have minimal impact on the overall estimation but are more influential when estimating the shape parameter \(a\).
# Adapting the GACT-X Aligner to Accelerate Minimap2 in an FPGA Cloud Instance
## 3 Results  
### Minimap2's Throughput and Profile  
To certify the appropriateness of accelerating Minimap2's alignment step, as indicated in Section 2.3, its original software version was executed and analyzed on a Dell PowerEdge R910 server with 4 Intel(r) Xeon(r) CPU E7-4870 processors, 80 cores, and 504 GB of memory, running Ubuntu 16.04.7 LTS (GNU/Linux 4.4.0-201-generic x86_64). All three read sets were mapped to the primary assembly of the GRCh38 reference. Minimap2 was executed to output the alignment in the \".sam\" format and with optimization for ONT or PacBio reads.  
For the throughput and total execution time measurements, the three datasets were mapped completely using 40 threads, and the results are presented from columns 2 to 5 in Table 3. The throughput was calculated based on the datasets' sizes and the CPU time. For the simulated reads with 61.99 Gheses of data, Minimap2 took 2:46 h to compute with 40 threads, corresponding to 42:41 CPU hours, which is a throughput of 403.46 kbases/s. For the real ONT reads with 52.2 Gheses of data, Minimap2 took 4:55 h to compute with 40 threads, corresponding to 88:21 CPU hours, which is a throughput of 164.11 kbases/s. Finally, for the real PacBio reads with 28.53 Gheses of data, Minimap2 took 2:14 h to compute with 40 threads, corresponding to 34:27 CPU hours, which is a throughput of 230.00 kbases/s. For the simulated, real ONT and the real PacBio reads, 6%, 27.5%, and 0.04% of the reads were not mapped, respectively.  
Each function's execution time was also collected to verify its contribution to Minimap2's total runtime. The gprof Linux tool [39] was used, and, for safety, each execution was done with one thread. Because of this, only the first 200,000 reads of each dataset were selected for use; otherwise, the execution would have taken too long. The results are presented from columns 6 to 8 in Table 3. For the simulated reads, chaining and extending (functions ksw_extd2_sse41 and mm_chain_dp) took 8% and 49% of the execution time, respectively. For the real ONT reads, chaining and extending took 50% and 25% of the execution time, respectively. For the real PacBio reads, chaining and extending took 27% and 42% of the execution time, respectively.
## 3 Results and Discussion  
### Trace Element Contents in Brassicaceae Species: Conventional Versus Organic  
The concentrations of Cu, Mn, and Zn for organic turnip greens (_Brassica rapa_) grown on Farm II were 7.4; 100.4 and 24.4 \(\upmu\)g/g d.w (see Table 3). The concentrations of these trace elements in ones conventionally grown on the same farm were practically the same, with values of 7.5; 110.8 and 22.9 \(\upmu\)g/g d.w. Conversely, there were statistically significant differences for Zn (\(p<0.05\)) and Cu, Mn (\(p<0.01\)) between the turnip greens grown on Farm I (conventional system) and those grown on Farm II (both conventional and organic systems). The highest concentrations of Cu and Zn (11.3; 38.9 \(\upmu\)g/g d.w) and the lowest one Mn (56.0 \(\upmu\)g/g d.w) found in turnip greens grown on Farm I compared to those analyzed on Farm II, demonstrate that, together with farming systems, the trace element concentrations in foodstuffs depend on many other factors, including soil characteristics, pollution from anthropogenic sources, genetic factors, seasonal influences and interactions between the elements [16]. The Cu, Mn and Zn in turnip tops grown under the three experimental conditions were Farm I, conventional, 8.1; 24.2 and 29.9 \(\upmu\)g/g d.w. respectively; Farm II, conventional 4.6; 25.5 and 25.1 \(\upmu\)g/g d.w.; Farm II, organic, 9.0; 24.8 and 31.2 \(\upmu\)g/g d.w. The concentrations in the turnip tops of the three trace elements were lower than those analyzed in the corresponding turnip greens. This could be explained by considering that turnip greens are the vegetative _Brassica rapa_ leaves, whereas the turnip tops are the fructiferous stems with flower buds and the surrounding leaves. Previous studies have indicated that metals tend to accumulate preferentially in roots rather than in storage organs or fruits [17; 18]. Moreover, unlike what happens to the turnip greens, statistically significant differences were found for Cu (\(p<0.05\)) and Zn (\(p<0.01\)) between the conventional and organic turnip tops grown on Farm II, the highest concentrations being found in the organic ones. These results are in agreement with those reported by [PERSON] and [PERSON] [9] for similar studies made with other vegetables species (tomatoes and lettuces)  
Cu, Mn and Zn concentrations in _Sinapis alba_ were 6.0; 17.8 and 23.8 \(\upmu\)g/g d.w for the organic cropping system and 5.6; 18.7 and 25.9 \(\upmu\)g/g d.w for the conventional one. Similarly, concentrations for these trace elements in _Eruca veiscaria_ were 10.2; 48.1 and 33.0 \(\upmu\)g/g d.w for the organic system and 9.3; 33.2 and 34.2 \(\upmu\)g/g d.w for the conventional one. Zn
## 3 Results and Discussion
### Sensory Evaluation and Texture Profile Analysis  
Sensory characteristics of largemouth bass are shown in Figure 2. There were significant differences between HHP and HHP+SV in the sensory scores of colour, texture, taste and aroma, while the score of the taste attribute in HHP+SV was significantly higher than in HHP. The results of sensory analysis showed that HHP+SV would have a more intense taste attribute than HHP. The important quality characteristics of bass fillets include colour, texture, juiciness, aroma and taste. Processing technology and storage have important effects on sensory attributes of bass fillets. In the present study, aroma and taste were more significant than colour, texture and juiciness in discriminating the sensory characteristics of HHP and HHP+SV. It may be that SV treatment could help produce different aroma and taste components. Sensory characteristics tended to decrease as storage time increased, but sensory scores decreased more slowly in the HHP+SV treatment compared with the HHP group. In the later stages of storage, the fish in the HHP group became darker in colour, looser in texture and worse in smell.  
Figure 2: _Cont._Texture analysis is displayed in Figure 2. The springiness, hardness, cohesiveness and chewiness of HHP+SV were significantly higher than those in HHP (\(p<0.05\)). This showed a firm and elastic taste was obtained by SV pretreatment, which was popular with consumers. The initial hardness of the largemouth bass fillets from the CK group was 14,499 g. After treatment, the hardness showed a decreasing trend and gradually decreased as the storage time increased. The HHP+SV showed a lower elasticity value when compared with HHP. The springiness of bass fillets decreased with an increase in storage time and reached the minimum value at 30 days. Chewiness refers to the time required to chew food at a fixed speed to reduce it to a size suitable for swallowing [31]. Cohesiveness and chewiness exhibited the same change curves, which were complementary to hardness. This is consistent with the results of the sensory characteristics. HHP combined with SV treatment significantly reduced the hardness and chewability of fish and had a better texture, indicating that hardness and chewability are important components in this treatment.
###### Abstract  
The acceleration of microparticles to supersonic velocities is required for microscopic ballistic testing, a method for understanding material characteristics under extreme dynamic conditions, and for projectile gene and drug delivery, a needle-free administration technique. However, precise aerodynamic effects upon supersonic microsphere motion at sub-300 Reynolds numbers have not been quantified. We derive drag coefficients for microspheres traveling in air at subsonic, transonic, and supersonic velocities from the measured trajectories of microspheres launched by laser-induced projectile acceleration. Moreover, the observed drag effects on microspheres in atmospheric (760 Torr) and reduced pressure (76 Torr) are compared with existing empirical data and drag coefficient models. We find that the existing models adequately predict the drag coefficient for subsonic microspheres, while rarefaction effects cause a discrepancy between the model and empirical data in the supersonic regime. These results will improve microsphere flight modeling for high-precision microscopic ballistic testing and projectile gene and drug delivery.  
p 2  
stratum corneum and reach the viable epidermis [7, 9, 10, 11]. By improving the accuracy of \(C_{D}\) modeling, calibration of the instruments launching these projectiles may be improved so that the genes or drugs are delivered to the target tissue with greater efficacy.  
In both micro-ballistic characterization and projectile delivery of genes and drugs, microspheres often travel at supersonic Mach numbers (\(M\)) and low Reynolds numbers (\(Re<300\)). Prior methods of quantifying \(C_{D}\) for spheres within this high-\(M\), low-\(Re\) experimental space have included wind tunnel testing [12, 13, 14] and ballistics [15]. To access \(Re<300\), these methods have typically used macro-scale spheres (0.8 < diameter (\(D_{\text{p}}\)) < 25 mm) in low air pressure environments (\(P<5\) Torr) [12, 13, 14, 15]. However, quantifying \(C_{D}\) in near-vacuum conditions may suppress fluid flow characteristics that affect microsphere flight in the atmospheric pressure conditions of micro-ballistic characterization and projectile delivery of genes and drugs. In this work, we use laser-induced projectile acceleration to quantify \(C_{D}\) for microspheres (3.7 \(\leq\)\(D_{\text{p}}\)\(\leq\) 7.6 \(\upmu\)m) in \(P\) = 760 and 76 Torr at 0.3 \(<M<\) 2.2 and 5 \(<Re<300\) and compare the results with existing empirical data and \(C_{D}\) models. While the models agree with empirical data in the subsonic regime, we found their accuracy to diminish with increasing rarefaction in 1.2 \(<M<\) 4 and \(Re<300\) conditions. These findings will benefit future work seeking to improve modeling of microsphere velocity in order to increase the efficacy of micro-ballistic characterization and projectile gene and drug delivery, where the projectile's impact velocity and kinetic energy must be known with confidence.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 3 Materials and Methods
### Location Fingerprinting Method
For example, assuming normal distributions,  
\[\hat{\mathbf{u}}=\arg\max_{\mathbf{u}_{k}}\sum_{n=1}^{N}\log\Bigl{(}\mathcal{N}(p_{n} ^{\text{target}};\mu_{n,k}^{p},\sigma_{n,k}^{p})\cdot\mathcal{N}(\theta_{n}^{ \text{target}};\mu_{n,k}^{\theta},\sigma_{n,k}^{\theta})\cdot\mathcal{N}(\phi_{ n}^{\text{target}};\mu_{n,k}^{\phi},\sigma_{n,k}^{\phi})\Bigr{)} \tag{7}\]  
Here, \(\mu_{n,k}\) and \(\sigma_{n,k}\) denote the mean and standard deviation of each feature extracted from the fingerprint database. Several surveys have outlined the effectiveness of fingerprint-based methods. [PERSON] et al. [4] reviewed RSSI-based indoor techniques, while [PERSON] et al. [15] discussed wireless network positioning systems. [PERSON] et al. [7] proposed a UAV-assisted fingerprinting system that dynamically adjusts sensing positions via machine learning.
# Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering
## 4 The Proposed Scheme
### g2 NN Feature Matching  
After extracting feature points in the previous stage, g2 NN is used to perform feature matching on SURF feature \(f_{s}=\{f_{1},f_{2},\ldots,f_{n}\}\) and A-KAZE feature \(f_{A}=\{f_{1},f_{2},\ldots,f_{m}\}\) to search for similar feature vectors [35]. Assuming there are \(n\) features, \(f=\{f_{1},f_{2},\ldots,f_{n}\}\), the Euclidean distance is used to determine the similarity between two feature vectors. For the \(i\)th key point, its corresponding feature is represented by \(f_{i}\), and the Euclidean distance between \(f_{i}\) and the remaining features \(\{f_{1},f_{2},\ldots,f_{i-1},f_{i+1},\ldots,f_{n}\}\) is calculated. The results are sorted in ascending order to obtain \(D=\{d_{1},d_{2},\ldots,d_{n-1}\}\). In \(D\), the ratio of \(d_{j}\) and \(d_{j+1}\) is calculated and evaluated using threshold \(V_{t}\), as shown in the following formula:  
\[\frac{d_{j}}{d_{j+1}}<V_{t},V_{t}\in(0,1),j=1,2,\ldots,n-1 \tag{12}\]  
When the iteration reaches step \(k+1\), the process of this iteration will terminate, and key point \(k\) will be considered as a candidate similar point matching the \(i\)th key point. The value of \(V_{t}\) is 0.6. In the algorithm in this section, the k-d tree and nearest neighbor algorithm are used to search for the \(N_{bd}\) points closest to the key point. g2 NN feature matching is then used on these points to find similar feature vectors to obtain the matching key point pair matrix \(x_{w}\). However, there are still some mismatches in the obtained matrix \(x_{w}\), which requires removing the mismatched pairs.