esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### LEO-Augmented Four-GNSS
The coverage of the five Schemes LEO constellations is shown in Figure 9. Obviously, for Schemes 1 and 2, the satellite visibility varies greatly with increasing latitude, from approximately 4 LEO satellites at low latitudes to more than 16 at high latitudes. Scheme 3 is the most abundant in terms of visible satellites in the middle latitude region, with the number of visible satellites being over 10; however, that value significantly decreases in high-altitude regions. With a lower orbit inclination, the number visible satellites for Scheme 5 LEO constellation are larger in the low-latitude regions (30\({}^{\circ}\)S-30\({}^{\circ}\)N), where more than 12 satellites are visible. LEO constellations with different orbit parameters have great differences in satellite visibility. To satisfy the equally distributed LEO satellites as much as possible on a global scale, we designed mixed-orbit LEO constellations (LEO-mix), which are presented as Scheme 6. Each orbit of the LEO-mixed constellation contains 40 satellites. The satellite  
Figure 8: Distribution of Schemes 1-5 LEO constellation satellite observation probabilities. Figure 9: Coverage performance of six LEO constellations schemes. visibility of the Scheme 6 LEO constellations ranges from 5 to 8 (average: 6.53) over all latitudes, which is shown in Figure 6 (red line). Figure 10a,c present the satellite visibility and GDOP of LEO-mix (Scheme 6) with the four-GNSS-NG, respectively, and the corresponding Figure 10b,d illustrate their improvement rate compared to the four-GNSS-NG. As shown in Figure 10a,c, LEO-mixed satellites can fill the coverage gap in the polar area, which is helpful for users at high latitudes (\(|B|>60^{\circ}\)). With more visible LEO satellites in this region (average: 6.86), the average improvement rate of the four-GNSS-NG+LEO-mix reaches 14.7%. Likewise, in the mid-low latitude region of the Western Hemisphere, approximately five more satellites can be observed, and the improvement rate is 14.4% on average. In general, the average visible satellites of the four-GNSS-NG+LEO-mix is 50.97. Ranging from 0.95-1.25 (average: 1.12), the GDOP of the four-GNSS-NG+LEO-mix decreases by about 4.1-11.0% (average: 6.2%) compared to four-GNSS-NG, and at the high latitudes, that value is almost more than 9%. Overall, new LEO satellites will provide the better signal coverage at high latitudes, which are not well-covered at the present. This contributes to ensuring the efficiency of all kinds of scientific research and to the safety of transportation in the Arctic region.
# A Smart Approach to Electric Vehicle Optimization via IoT-Enabled Recommender Systems
## 3 Infra-Structure Analysis
### IOT Framework for EV  
The automotive sector has the potential to undergo a transformation owing to the integration of Internet of Things (IoT) technologies within Electric Vehicle (EV) systems. Real-time data processing, effective communication protocols, security measures, and scalability are some of the issues to be considered when designing an IoT (Internet of Things) framework for electric vehicles (EVs). When considering an IoT (Internet of Things) approach specifically for electric vehicles (EVs), the focus typically revolves around optimizing vehicle performance, enhancing user experience, ensuring safety, and enabling efficient fleet management. Performance Monitoring and Telemetry: Install sensors in the EV to gather data in real-time on a variety of characteristics, including speed, energy usage, environmental conditions, temperature, and health of the battery. IoT protocols can send this data to a centralized system for monitoring and analysis. Remote Service and Troubleshooting: Provide IoT connectivity to facilitate troubleshooting, predictive maintenance, and remote diagnostics for electric vehicle components. Potential problems can be found early on by continuously monitoring vehicle data, which minimizes maintenance costs and downtime. Infrastructure management charges considering features like automatic billing, intelligent charging scheduling, and remote charging station monitoring, integrate Internet of Things (IoT) capabilities into EV charging infrastructure. This optimizes charging sessions according to grid demand, electricity rates, and specific vehicle necessities.  
Features for safety and security set IoT-based security measures to protect EVs from theft, illegal access, and cyber threats. This incorporates strong encryption and authentication methods along with features like geofencing, remote car locking and unlocking, and tamper detection. Implementing IoT solutions for driver behavior monitoring, fleet tracking, route optimization, and performance analytics for commercial electric vehicle fleets. Fleet managers benefit by increasing operational effectiveness, cutting expenses, and guaranteeing regulatory compliance.
# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
## 3 Methodology
### MCI Module  
In remote sensing images, objects are generally distributed across various scales. Precise object recognition hinges not only on visual appearance cues but also on their contextual information. Contemporary approaches enhance the receptive field through the integration of large-kernel convolutions and dilated convolutions within the backbone architecture. However, the adoption of large-kernel convolutions inevitably introduces extraneous background noise, while dilated convolutions inherently overlook fine-grained spatial details within the expanded receptive field. In order to capture multi-scale texture features when objects present various different scale situations, we introduce the MCI module. First, local features are extracted through a small kernel convolution. Following this, a parallel configuration of depthwise convolution layers is utilized to extract multi-scale contextual information. Ultimately, the locally extracted feature maps and multi-scale contextual representations are fused along the channel dimension, facilitating the aggregation of spatio-contextual semantics. In detail:  
Local Feature Extraction: First, a 3 \(\times\) 3 convolution is applied to the input feature map to extract fine-grained local features, laying the foundation for subsequent context modeling.  
Multi-Scale Context Extraction: Second, a cascade of convolutional layers with progressively increasing kernel sizes (5 \(\times\) 5, 7 \(\times\) 7, 9 \(\times\) 9, 11 \(\times\) 11) extracts hierarchical contextual representations. Each convolutional layer operates independently, ensuring that it can both cover the details of small targets and capture the overall context of large targets. The formulas are as follows:  
\[L_{l-1,n}=\text{Conv}_{k_{n}\times k_{n}}(X_{l-1,n}^{(2)}),n=0,\dots,N_{l}-1 \tag{1}\]  
\[Z_{l-1,n}^{(m)}=\text{DWConv}_{k^{(m)}\times k^{(m)}}(\mathbf{L}_{l-1,n}),m=1, \dots,4 \tag{2}\]  
Figure 2: Architecture of the proposed MIHE-Net.  
where \(L_{l-1,n}\) represents the local features extracted by a \(k_{s}\times k_{s}\) convolution, here set to \(k_{s}=3\), and \(Z_{l-1,n}^{(m)}\) denotes the contextual features extracted by the \(m\)-th \(k^{(m)}\times k^{(m)}\) depthwise convolution (DWConv), here set to \(k^{(m)}=2\times(m+1)+1\).  
Feature Fusion: Lastly, a \(1\times 1\) convolution is used as a channel fusion mechanism to integrate features with different receptive field sizes, which produces the fused feature \(P_{l-1,n}\). Through this methodology, our MCI module effectively captures comprehensive contextual dependencies while preserving the fidelity of local texture details. The formula is shown below.  
\[P_{l-1,n}=\text{Conv}_{1\times 1}(L_{l-1,n}+\sum_{m=1}^{4}Z_{l-1,n}^{(m)}) \tag{3}\]
## 3 Results and Discussion  
### SFC of Margarine  
SFC is a factor that determines the percentage of solid fatty acids at a particular temperature and is usually measured by nuclear magnetic resonance [1]. Since SFC affects spreadability, firmness, mouthfeel, and stability, this value is considered a qualitative parameter of margarine texture [23]. The solid fat content in the refrigerator, at room temperature, and at body temperature is related to the spreadability, product stability, and mouthfeel, respectively [24]. Table 2 shows the solid fat content of margarine formulated with beeswax deogel (BWO) and with hydrocolloid-based oleogel (HBO). As shown in the table, the highest amounts of solid fat at all temperatures were observed in the control sample, and SFC decreased with increasing replacement percentage. It has been reported that for spreadable margarine, the SFC at 10 \({}^{\circ}\)C should be not more than 32%, and to prevent any waxy sensation in the mouth, the SFC should be below 3.5% at 33.3 \({}^{\circ}\)C [24]. Therefore, given that the SFC of all samples was less than 21.59% at 10 \({}^{\circ}\)C and less than 2.13% at 35 \({}^{\circ}\)C, the margarine produced using these oleogels was spreadable and did not create a waxy taste in the mouth. [PERSON] et al. [25] also stated that more than 10% SFC at 20 \({}^{\circ}\)C indicates instability and oil separation. The SFC of all the samples at this temperature was less than 10%, so none of the samples had a phase separation at room temperature. Similar results have been reported by [PERSON] et al. [26], who reported that the solid fat content decreased with increasing replacement of palm oil with beeswax deogel in confectionery filling. Additionally, in a study conducted by [PERSON] et al. [27], wax deogels and palm-based shortening were used in gluten-free bakery products. The results showed that SFC decreased with increasing oleogel in the bread formulation. Several studies have shown that using wax deogels in products such as cakes [28] and bakery products [29] has reduced the content of solid fat compared with control samples. In the present study, although the control sample (1) had a higher SFC at all measurement temperatures than the samples containing oleogel, all the samples had the desired margarine characteristics.
## 5 Challenges and Limitations
### Potential for Future Enhancements  
* Automated Dependency Recognition: Enhancing the framework by adding the capability to automatically detect and adapt to requirement dependencies using machine learning algorithms could significantly improve its effectiveness.
* Real-Time Data Integration: Incorporating real-time data analytics to continuously update and prioritize requirements based on ongoing feedback and system performance metrics would be of value.
* Advanced Simulation Tools: Developing simulation tools that can model the impact of different prioritization strategies on the overall system performance could help in fine-tuning the framework before full-scale implementation.  
Addressing these concerns through continuous research and development will be crucial in enhancing its applicability and effectiveness in real-world scenarios. Further studies could focus on integrating adaptive algorithms and real-time data processing to ensure the framework remains robust and flexible in the face of rapidly changing technology landscapes.
# Mechanical Assembly Monitoring Method Based on Semi-Supervised Semantic Segmentation
## 3 Overall Framework
### Structure of the Model
(2) A multibranch RFASPP module that imitates the human receptive field is proposed. It ensures that the target object gets near the center of the receptive field, and the receptive field is enlarged so that the capacity of the model to extract deep features is increased. (3) In G, the CoordConv module is introduced to allow the convolution to obtain spatial position information. This enables G to be position-sensitive. (4) In D, SN is introduced, and the number of convolutional layers of D is deepened. Based on this, the discriminant network satisfies the Lipschitz continuity, thereby improving the stability of network training. G performs operations such as convolution and pooling on the input assembly image to obtain a class probability map. This study realizes semi-supervised assembly semantic segmentation with G as the generator of the GAN. To obtain a class probability map with higher precision, this study proposes a new semantic segmentation network by adding the ASFF module, FRASPP module, and CoordConv module to ResNet101. In G, the multiscale features output by the ASFF module are fused to fully utilize the features of multiple scales, thereby improving the segmentation accuracy of small parts; the FRASPP module improves the ability to extract deep features; the CoordConv module enables G to be position-sensitive.
## 4 Methodologies and Implementation
### Comparison with Other Segmentation Methods
#### 4.2.4 Generalization across Different Imaging Modalities  
While deep learning models demonstrate remarkable performance, they might struggle with generalizing across different imaging modalities due to domain-specific variations in data distribution [82; 83].  
DeepSegDDPM, with its denoising capabilities and adaptive pre-processing, exhibits potential for improved generalization across different imaging modalities. The noise reduction contributes to a more consistent and robust segmentation approach [84; 85].
# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
## 2 Related Works
### Bounding Box Representation  
[PERSON] et al. [32] proposed the General Gaussian Heatmap Label Assignment method (GGHL), which employs an anchor-free target-adaptive label assignment (OLA) strategy based on 2D Gaussian heatmaps. This strategy assigns each label to Gaussian candidate positions in the feature map via a one-to-many mapping, enabling the assigned candidate objects to better align with the shape and orientation of real-world objects. R\({}^{2}\)PN [33] generates proposal regions in arbitrary directions by rotating anchor boxes to more orientations, and employs a rotated RoI pooling layer (R2 oI) as the max pooling operation on arbitrary-direction regions. This ensures that all detection steps can be integrated into a unified network and enables end-to-end training. [PERSON] et al. [34] constructed a semantic segmentation-guided RPN (sRPN) to suppress background clutter as much as possible. By introducing a semantic segmentation module, their approach obtains bounding box-based masks and semantic features; the masks guide the generation of horizontal candidate boxes, while the semantic features are fused during the ROI pooling process to enable a more accurate estimation of the bounding box. [PERSON] et al. [35] introduced a supeellipse-modulated 2D Gaussian function, termed the super-Gaussian distribution, which preserves angular information by maintaining anisotropy across all aspect ratios.  
Although existing rotated feature extraction methods have made some progress, they focus more on redundancy reduction, lack explicit modeling of multi-scale contextual dependencies, treat scale and orientation as independent variables, and ignore the coupling relationship between them. Thus, they fail to completely solve the key issues of gradual scale changes, scale-orientation coupling, and stable rotation regression. Based on these outstanding issues, our proposed MIHE-Net focuses more on the collaborative perception of scale and orientation, enabling progressive scale modeling combined with orientation without modifying the existing detection framework.
## 5 Experimental Results
### Experimental Setup  
The proposed WBCA uses a web server installed on a CentOS-based Linux distribution. To achieve the stated goals, a CentOS-based virtual private server is deployed and tested in Almaty, Kazakhstan, where the program consistency, quality ratio, user response time, solution efficiency, and threat avoidance capacity are monitored and calculated during testing process. The proposed WBCA is compared with the existing the state-of-the-art methods: Framework competence development and assessment(FCDA) [34], Integration of self-determination and flow (ISF) [35], modified total interpretive structural model (M-TISM) [36], Cybersecurity knowledge and skills(CKS) [37], and Cyber security awareness (CSA) [38].  
These data are also calculated for the three most popular cybersecurity threats (email phishing, weak passwords, and virus). The minimal characteristics for the server hardware are Linux CentOS 7 x64 as the operating system, an Intel(R) Core (TM) i3-6100H with a frequency of 2.3 GHz and a cache size of 8192 kilobytes as the two-core processor, 4096 MB of random access memory, and a minimum of 50 gigabytes of hard disk space for collecting data. The required program component is Java SDK version 1.8.271u, and a monitor with a screen resolution of at least 920 \(\times\) 1080 is used to conduct the proper experiments. These necessary characteristics for the web server are summarized in Table 1.
## 2 Preliminaries and Problem Formulation
### Control Objective  
In Figure 1, a local path parallel reference frame is denoted as \(\{pp\}\). To arrive at\(\{pp\}\), \(\{i\}\) should be rotated an angle \(\gamma_{p}(\theta)=\text{atan2}\left(\text{y}_{d}^{\prime}(\theta),\text{x}_{d }^{\prime}(\theta)\right)\), where the notation \(\text{y}_{d}^{\prime}(\theta)=dy_{d}(\theta)/d\theta\) is used. Therefore, the position errors can be given as follows  
\[\left[\begin{array}{c}x_{e}\\ y_{e}\end{array}\right]=\underbrace{\left[\begin{array}{cc}\cos\gamma_{p}&- \sin\gamma_{p}\\ \sin\gamma_{p}&\cos\gamma_{p}\end{array}\right]^{T}}_{\text{{R}}^{\intercal}( \gamma_{p})}(P-P_{d}(\theta)) \tag{7}\]  
where \(x_{e}\) and \(y_{e}\) are the along-tracking error and cross-tracking error, respectively, and \(\text{{R}}(\gamma_{p})\) is the rotation matrix. Meanwhile, we also have  
\[\hat{P}_{d}(\theta)=\text{{R}}(\gamma_{p})\big{[}U_{pp},0\big{]}^{T} \tag{8}\]  
where \(\big{[}U_{pp},0\big{]}^{T}\) is the velocity of \(\{pp\}\) with respect to \(\{i\}\), represented in \(\{pp\}\).  
The path following control problem is concerned with designing control laws to reach and then keep following the desired path. Once the path is reached, the vessel can maintain a desired surge velocity assignment of \(u_{d}\). It is worth noting that a constant speed profile is frequently chosen in many cases. Therefore, the control objective is as follows:  
\[\sup_{t>0}|x_{e}|\leqslant\delta_{1},\sup_{t>0}|y_{e}|\leqslant\delta_{2}, \sup_{t>0}|u_{r}-u_{d}|\leqslant\delta_{3} \tag{9}\]  
where \(\delta_{1}\), \(\delta_{2}\), and \(\delta_{3}\) are small positive constants. Meanwhile, it is guaranteed that all error signals of the closed-loop system are locally UUB.  
Figure 1: USV path following guidance information illustration.  
**Assumption 3**.: _The desired path should be sufficiently smooth such that its first derivative \(\dot{P}_{d}\) is bounded. In addition, the desired speed assignment \(u_{d}\) and its first derivative are bounded._
# Modeling the Submergence Depth of Oil Well States and Its Applications
## 1 Introduction
To this end, according to the law of fluid flow and the change law of the submergence depth during pumping, submergence depth models suitable for various pumping states, such as a full-pumping state model and a nonfull-pumping state model, are constructed in this paper. Corresponding application methods are given: the nonlinear-interpolation method and the least squares curve-fitting method.
## 6 Numerical Results
### Evaluation Scenario  
The evaluation is performed in two packet-switched transport network topologies, MESH-20 and MESH-38, presented in Figure 4, which were considered in the C-RAN studies in [27]. As mentioned in [12], mesh topologies are also foreseen for packet Xhaul transport networks. We assume that a random number of RUs (\(R\) in total) and one candidate PP node is connected to each switching node, and there is one hub node in the network. A cluster of RUs consists of the RUs linked with a given switch (SW). The network links have random lengths (in km) within the following limits: \([0.2\ldots 0.5]\) for RU and PP connections, \([1\ldots 3]\) between switches, and \([10\ldots 15]\) for the hub [20]. The link capacities are 50 Gbit/s, 100 Gbit/s, and 400 Gbit/s, respectively, for the RU-SW, SW-SW, and PP/hub-SW connections [20]. The PPs have capacity \(C^{\min}\times M\), where \(C^{\min}\) is the minimum capacity required to support the complete DU/CU processing of the largest cluster of RUs in the particular network scenario, and \(M\) is a capacity multiplier; \(M\) is equal to 1.5 if not mentioned otherwise. The switch store-and-forward delay is 5 \(\mu\)s [8].  
A radio system consisting of eight MIMO layers with 32 antenna ports, 100 MHz channels [57], and different numerologies \(\mu\in\{1,2,3\}\) is considered. Option 7.2 and Option 2 are assumed for the functional split [1] between the RU-DU and DU-CU, respectively. As in [4; 58], the DU and CU entities require 5 and 1 unit of PP resources, respectively; thus, the complete DU/CU load is 6 per a RU. The maximum bit rates generated by each RU, calculated using the model in [57], are shown in Table 4. The Ethernet frame size is 1542 bytes [8].  
Figure 4: Network topologies; MESH-20 is a subgraph of MESH-38.  
The network supports the network slices that realize the services eMBB and URLLC. The radio resources are divided between the slices, and parameter \(\gamma\) represents the ratio (percentage) of resources assigned to the URLLC slice. The bit rates of Xhaul flows of the URLLC and eMBB slices are calculated by multiplying the reference values in Table 4 by \(\gamma\) and \((1-\gamma)\), respectively. The same numerology is applied to the slices. The FH flows of URLLC and eMBB have 50 us and 100 \(\upmu\) one-way maximum latency limits, respectively, whereas 1 ms and 10 ms are assumed for the MH and BH flows [7]. By default, the packet priority is higher for the URLLC FH than for the eMBB FH, whereas the MH and BH packets have the lowest priority.  
The \(k\)-shortest path algorithm was used to generate candidate routing paths between the network nodes. The experiments were performed on a 3.7 GHz 32-core Threadripper-class machine with 128 GB of RAM. The MILP/LP models were solved using CPLEX v.12.9 [56]. The MILP and PBA methods were executed with the 1-h computation time limit.
## 3 Simulated Study of Different Decoupling Techniques
### Decoupling Structure (DS)  
To apply this DT, a vertical strip is added to the CG to increase isolation between two AEs. As shown in Figure 14, the radiation pattern becomes directional, and a complete UWB range is achieved. When comparing the isolation of the two-port UMA with DS to that of the antenna without DS, isolation is highly increased at the lower, middle, and higher frequency ranges.