esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussions
### Numerical Analysis on Benchmark Functions
#### 5.3.1 Numerical Results for SET-1

Table 6: Results of the [PERSON] statistical test with \(p=0.01\) for SET-2, comparison of COBRA-SHA with other approaches. Figure 4: Results of the [PERSON] statistical test with \(p=0.01\), comparison of COBRA-SHA with other approaches (SET-2, \(D=10\)). Figure 5: Results of the Mann-Whitney statistical test with \(p=0.01\), comparison of COBRA-SHA with other approaches (SET-2, \(D=30\)). It was again established that the results obtained by the proposed approach are statistically better according to the [PERSON] and Mann-Whitney tests than the results obtained by the mentioned biology-inspired algorithms (PSO, WPS, FSS, FFA, CSA and BA) and their modifications with the external archive (FFA-a, CSA-a, BA-a). Moreover, COBRA-SHA statically outperformed the fuzzy-controlled COBRA-f in 11 out of 18 cases. However, as for SET-1 there is almost no difference between the results obtained by COBRA-SHA and the similar modification COBRA-fas on functions from SET-2.
# MSPNet: Multi-Scale Strip Pooling Network for Road Extraction from Remote Sensing Images
###### Abstract  
Extracting roads from remote sensing images can support a range of geo-information applications. However, it is challenging due to factors such as the complex distribution of ground objects and occlusion of buildings, trees, shadows, etc. Pixel-wise classification often fails to predict road connectivity and thus produces fragmented road segments. In this paper, we propose a multi-scale strip pooling network (MSPNet) to learn the linear features of roads. Motivated by the strip pooling being more aligned with the shape of roads, which are long-span and narrow, we develop a multi-scale strip pooling (MSP) module that utilizes strip pooling layers with long but narrow kernel shapes to capture multi-scale long-range context from horizontal and vertical directions. The proposed MSP module focuses on establishing relationships along the road region to guarantee the connectivity of roads. Considering the complex distribution of ground objects, the spatial pyramid pooling is applied to enhance the learning ability of complex features in different sub-regions. In addition, to alleviate the problem caused by an imbalanced distribution of road and non-road pixels, we use binary cross-entropy and dice-coefficient loss functions to jointly train our proposed deep learning model. Then, we perform ablation experiments to adjust the loss contributions to suit the task of road extraction. Comparative experiments on a popular benchmark DeepGlobe dataset demonstrate that our proposed MSPNet establishes new competitive results in both IoU and F1-score.  
Keywords:road extraction; deep learning: strip pooling; remote sensing images; spatial pyramid pooling +
Footnote †: journal:
# English-Welsh Cross-Lingual Embeddings
###### Abstract  
Cross-lingual embeddings are vector space representations where word translations tend to be co-located. These representations enable learning transfer across languages, thus bridging the gap between data-rich languages such as English and others. In this paper, we present and evaluate a suite of cross-lingual embeddings for the English-Welsh language pair. To train the bilingual embeddings, a Welsh corpus of approximately 145 M words was combined with an English Wikipedia corpus. We used a bilingual dictionary to frame the problem of learning bilingual mappings as a supervised machine learning task, where a word vector space is first learned independently on a monolingual corpus, after which a linear alignment strategy is applied to map the monolingual embeddings to a common bilingual vector space. Two approaches were used to learn monolingual embeddings, including word2 vec and fastText. Three cross-language alignment strategies were explored, including cosine similarity, inverted softmax and cross-domain similarity local scaling (CSLS). We evaluated different combinations of these approaches using two tasks, bilingual dictionary induction, and cross-lingual sentiment analysis. The best results were achieved using monolingual fastText embeddings and the CSLS metric. We also demonstrated that by including a few automatically translated training documents, the performance of a cross-lingual text classifier for Welsh can increase by approximately 20 percent points.  
natural language processing; distributional semantics; machine learning; language model; word embeddings; machine translation; sentiment analysis +
Footnote †: journal: Applied Sciences
# Hierarchical Federated Learning-Based Intrusion Detection for In-Vehicle Networks
## 2 Background and Related Work
### Cloud-Based FL and Hierarchical FL  
The standard cloud-based FL architecture comprises a cloud server and numerous clients, as shown in Figure 1. Clients (vehicles) download a global model from the cloud server, undergo multiple rounds of local training, and then send the model weights back to the server for aggregation. The iteration continues until the model achieves the desired level of accuracy. In cloud-based FL, the number of participating clients can reach into the millions [16], which can cause communication with the cloud server to become slow and unpredictable due to factors like network congestion, ultimately leading to inefficiencies in the training process [16; 17]. In contrast, H-FL architecture consists of a cloud server, multiple edge servers, and numerous clients. In H-FL, clients update their local parameters and send them to the nearest edge server for edge aggregations, following a similar approach as in cloud-based FL. The difference is that after several rounds of edge aggregations, multiple edge servers send their parameters to a cloud server for cloud aggregation. This design allows more clients to participate in the framework. Moreover, H-FL significantly reduces the costly communication with the cloud by leveraging efficient client-edge interactions, leading to considerable decreases in both runtime and the number of necessary local iterations [18].
# Fragility Assessment and Reinforcement Strategies for Transmission Towers Under Extreme Wind Loads
## 5 Concurrent Multi-Scale Reinforcement Modeling
### Evaluation of Bearing Capacity After Reinforcement  
To evaluate the effectiveness of the proposed reinforcement strategy, a structural analysis was conducted under a wind speed of 33 m/s. The resulting tower top displacements at various wind angles were calculated and are shown in Figure 21. Compared to the unreinforced results presented in Figure 8, the reinforced model exhibits a reduction in top displacement of approximately 10%, indicating an improvement in global structural stiffness and lateral stability.  
Figures 22-25 display the stress contours of both main and auxiliary structural components under four different wind directions. The results reveal a clear reduction in stress levels in the primary load-bearing members following reinforcement. The inclusion of bolt preload in the simulation introduces localized stress concentrations around the bolt holes in both the main and auxiliary components. Furthermore, the stress distribution in the auxiliary members is found to be non-uniform, with comparatively lower stress values observed along the edges of the angle steel. This indicates that the main structural stresses are effectively transferred to the ends of the auxiliary components via filler plates, after which the stresses gradually diffuse toward the central regions of the auxiliary materials.  
Figure 20: Local stress contrast diagram under 0\({}^{\circ}\) wind angle.  
Figure 23: The 45\({}^{\circ}\) stress contour.  
Figure 21: Displacement at different wind angle.  
Figure 22: The 0\({}^{\circ}\) stress contour.  
Despite these improvements, the stress levels within the auxiliary components remain relatively low, suggesting suboptimal force transmission and underutilization of reinforcement materials. To improve load distribution efficiency in practical engineering applications, it is recommended to increase the number of connecting plates used in the localized reinforcement of tower legs. This adjustment could enhance the uniformity of stress distribution within the auxiliary materials, thereby improving the overall structural efficiency and utilization of the reinforcement system.
## 5 Discussion
### Relationship Between Day-Night Anomalies and Magnatic Heat Sources  
The geothermal anomaly areas identified through day-night joint analysis exhibit high spatial consistency with potential magma heat sources derived from MODIS, particularly in known geothermal fields, like Yangbajing and Yangyi (Figure 13a). These high LST areas are strongly correlated with known hotspot locations, especially concentrated in the central and southern parts of the basin, suggesting proximity to subsurface heat sources and show-casing significant geothermal exploration potential (Figure 13b-d). This consistency further reinforces the intrinsic link between geothermal anomalies and deep heat sources, while also highlighting the effectiveness of thermal infrared remote sensing data in capturing geothermal activity features [70].  
However, some geothermal anomaly areas exhibit a spatial offset relative to deep heat source locations, likely due to differences in data types, processing methods, and research objectives [55]. Day-night joint analysis covers a wider area, emphasizing the overall distribution of surface temperature anomalies, whereas MODIS data focuses on the core areas with the highest temperatures, highlighting significant thermal anomalies in deep, high-heat zones. Moreover, the surface manifestations of hotspots, such as hot springs, are often offset from the locations of deep heat sources [71]. This may be due to the lateral migration of subsurface thermal fluids, controlled by fracture structures during vertical migration, causing geothermal anomaly areas not to be directly above deep heat sources [69].  
The geothermal anomalies in the study area are primarily driven by subsurface geological processes. The subduction of the Indian Plate induces the upwelling of high-temperature materials from the asthenosphere along major deep fault zones, causing partial melting of the lower crust and generating strong heat flow [72; 73; 74]. Under compressive stress, partially molten magma intrudes into the upper crust, forming magma chambers that serve as the primary heat source for the geothermal system [75]. Fault structures act as pathways for heat flow migration, transmitting deep thermal energy to the surface, causing land surface temperature increases, which manifest as significant geothermal anomalies in thermal infrared remote sensing [76]. This mechanism further underscores the crucial application of LST remote sensing data in geothermal activity detection.
## 4 Results of Probabilistic Stability and Performance Assessment
### The Impact of PLL Bandwidth on System Stability and Performance
#### 4.3.2 Damping Ratio Analysis  
Based on Figure 10b and Table 5, PLL bandwidth had the most considerable effect on the damping ratio in the weak grid and the least effect in the strong one. In the weak grids, for lower and higher PLL bandwidths, fast (CC, HPF, delay) and slow (PLL, DVC, AVC) dynamics of the VSC had the highest contributions to damping ratio, respectively. Furthermore, for higher PLL bandwidths, PDFs became broader, and the critical mode moved to the unstable area.
# A Multi-Scale Feature Extraction Algorithm for Chinese Herbal Medicine Image Classification
## 2 Related Work
### Application of Convolutional Neural Networks in Herbal Classification  
Convolutional Neural Networks (CNNs) have shown significant advantages in herbal feature extraction and classification. [PERSON] et al. applied CNNs to feature learning of 44 plant images and demonstrated the superiority of CNN-learned features over traditional manual features [10; 11; 12; 13] through deconvolution network (Deconvnet) visualization [14]. [PERSON] et al. achieved breakthrough progress on a six-class Chinese herb dataset by improving CNN architecture and incorporating data augmentation techniques [5]. [PERSON] et al. successfully applied CNNs to the classification task of 11 Thai herbs, validating the universality of this method [6]. [PERSON] et al. innovatively introduced three-dimensional structural features, using PointNet to effectively classify a seven-class Chinese herb dataset [15]. [PERSON] et al. confirmed the superiority of the pre-trained ResNet-50 model in herbal classification tasks through comparative experiments [16]. [PERSON] et al. and [PERSON] et al. validated the effectiveness of ImageNet pre-trained CNNs in herbal classification [8; 18].  
The ConvNeXt series architecture has demonstrated excellent performance in various tasks. ConvNeXt achieved advanced results on ImageNet using only pure convolutional structures [21]. Inspired by Swin-Transformer [22] and ResNeXt [23], ConvNeXt adopted \(7\times 7\) large-kernel convolutions in its convolutional blocks, replaced common batch normalization layers [24] with layer normalization layers [25], and substituted ReLU activation functions with GeLU activation functions. ConvNeXt v2 [19] further developed this by introducing a new Global Response Normalization (GRN) layer [19] to enhance feature competition between channels within the original ConvNeXt convolutional module, capturing more discriminative channel features.  
Although progress has been made in Chinese herbal classification, some issues remain. Existing datasets often have small sample sizes, limited categories, and monotonous backgrounds, which affect model generalization. They also fail to reflect real-world diversity fully. In terms of methods, most studies only make simple improvements to CNNs or apply pre-trained models without considering the unique characteristics of herbal images.
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
## 4 Theoretical Background and Simulation Methodology  
The power spectral density (PSD) according to G.9964 recommendation [69] is implemented in the simulator. Figure 1 shows the PSD according to the relevant frequency band for BPL systems. The implementation allows variable setup of PSD in _f1-f13_ and _f11-f13_ spectrum fields. Maximum values are restricted by the recommendation.
## 3 Results and Discussion
### Spectroscopy (FT-IR)  
A representative FT-IR spectrum of the synthesized nanoparticles and extract is shown in Figure 5. The leaves extract showed prominent peaks at 1074, 1723, 2927, and 3413 cm\({}^{-1}\).  
In ethyl acetate extract, O-H and aldehyde C-H stretching was observed at 3413 and 2927 cm\({}^{-1}\). For amide I, one absorption band was observed at 1635 cm\({}^{-1}\), whereas the \(\alpha\), \(\beta\) unsaturated aldehydes group showed an absorption band at 1709 cm\({}^{-1}\).  
The C-N stretching vibration of the amine group was observed at 1074 cm\({}^{-1}\). The carbonyl group of amino acid and protein showed correlation with metal and showed ability to bind at the active site to prevent agglomeration, thus stabilizing the nanoparticles. The bioactive constituents in the ethyl acetate fraction could possibly perform dual functions for gold nanoparticles, namely, their (1) synthesis and (2) stabilization. The adsorption band for aromatic groups (C=C), germinal methyls, and aromatic C-C for gold nanoparticles were observed at 1618, 1452, and 1056 cm\({}^{-1}\), respectively. The FT-IR spectra of _Spirulina platensis_ showed gold ion peaks at 1242, 1541, and 1653 cm\({}^{-1}\)[9].  
Figure 4: Effect of pH on GNPs.  
Figure 5: FTIR spectrum of extract and GNPs.
## 5 Experiments and Analysis
### Limitations  
While MATD3-TORA demonstrates promising performance in controlled UAV network scenarios, its scalability to large-scale deployments remains a critical limitation. The algorithm's computational complexity grows exponentially with increasing network size due to its multi-agent decision-making architecture, potentially leading to impractical training times and real-time inference delays. Furthermore, the centralized critic component may become a bottleneck in distributed UAV networks with hundreds of nodes, as it requires global state information that becomes increasingly difficult to collect and process efficiently. The current formulation also assumes perfect communication links between agents, which may not hold in real-world large-scale operations where packet loss and latency variations are prevalent. These scalability challenges suggest the need for hierarchical architectures or distributed learning paradigms to enable MATD3-TORA's application in massive UAV swarms while maintaining its coordination advantages.
## 3 The LOBIN Prototype: A Practical Study Case
### System Validation and Tests
#### 3.2.3 WCIS Tests
Thus, the reliability of the system is improved and the user mobility is supported without interruption. However, this WCI configuration presents also some drawbacks, such as the wired infrastructure is required and the number of duplicated packets in the Management Server increases dramatically, although this is not a problem since the Management Server handles packets much faster than the rate they arrive with. In others WHMS using IEEE 802.15.4 as communications technology, both stress and packet loss tests were also performed. In MEDISN [46], 3 patients sent monitoring information at the same time and the percentage of packets lost was of 0.65%. In MASN [38] the number of traffic sources was increased from 20 to 150, yielding a percentage of packets lost between 5% and 20%. In addition, the user mobility causes packet losses in most of the WHMS which support it. For instance, in MASN [38], several seconds (between 6 and 15 s) are lost from a source when it changes of cluster, and in MEDISN [46], the percentage of lost packets increases up to 3.3%. Figure 11: Gateway network test.
## 2 Materials and Methods
### Methodology
#### 2.2.2 Thermophysical and Rheological Properties  
Thermal conductivities (\(k\)) of PG:W 30:70 and proposed nanofluids over the temperature range of 283 to 333 K were obtained by means of a Decagon Devices KD2 Pro thermal analyzer (Decagon Devices, Inc., Pullman, WA, USA) based on the transient hot wire (THW) technique with a KS-1 probe and uncertainty (\(k\) = 2) of 5%. The Polyscience 9510 thermostatic bath (Polyscience, Niles, IL, USA) was employed to ensure the temperature stability of the samples in the course of the measurements. Rheological characterisations of the analysed nanofluids and base fluid were conducted using an [PERSON] rheometer, model Physica MCR 101 (Anton Paar GmbH, Graz, Austria), featuring a CP50-1 cone-plate geometry (50 mm plate diameter and 1\({}^{\circ}\) cone angle) along with a Peltier plate system. A consistent 102 um gap between the Peltier and the CP50-1 was kept throughout the experiments. Initially, non-linear viscoelastic tests at 283 K were executed to evaluate the dependency of shear viscosity (\(\eta\)) on shear rate (\(\dot{\gamma}\)) over a range from 10 to 1000 s\({}^{-1}\)Afterward, the dynamic viscosity of all proposed samples was inferred from shear rate tests at a constant \(\dot{\gamma}\) of 100 s\({}^{-1}\) at various temperatures from 283 to 323 K with an increment of 10 K. An uncertainty (\(k\) = 2) estimation of 5% was selected for viscosity values. Density (\(\rho\)) measurements from 288 to 313 K were carried out using the oscillating U-tube method with an Anton Paar density meter DMA 501 (Graz, Austria), achieving 0.1% uncertainty (\(k\) = 2). Lastly, isobaric heat capacities (\(c_{p}\)) for the base fluid and proposed nanofluids were determined using a TA Instruments Q2000 heat-flux differential scanning calorimeter (TA Instruments, New Castle, DE, USA) equipped with a RSC90 cooling system. The analysis was conducted using the quasi-isothermal temperature-modulated differential scanning calorimetry method (TMDSC). Samples were enclosed in Tzero aluminum pans, which are hermetically sealed, and tests were performed up to 333 K. The \(c_{p}\) uncertainty (\(k\) = 2) is estimated to be 3%.
## 2 Background and Review of Related Works
### The Internet of Vehicles Structure and Need for XAI-IDS  
The astounding development in ITS has greatly spurred the invention of smart cars, resulting in the concept of \"The IoV\" which allows vehicles to initiate communication with accessible networks and the environment. Vehicles can exchange and collect data about other vehicles and roads in real-time, a concept known as the ICV [33]. It is an extended application of the IoT in intelligent transportation, designed as a data sensing and processing platform for the ITS [42]. A comprehensive report on the evolution and future prospects of the ICV can be found in [43], while the authors in [44] presented the global progress made on ICV, especially with a focus on China being one of the leading countries in ICV implementation [45]. The IoV architecture consists of three layers, as shown in Figure 1[46; 47], which includes the application (service platform), network (channel for V2X communication), and perception layers (device layer ensuring the incorporation of intelligent devices into the connected vehicles giving rise to the ICV).  
Since the IoV is a form of the IoT, it is faced with challenges related to efficient communication between thousands of integrated vehicles, the decision in data processing at the onboard unit, big data integration with IoV, intrusion detection and mitigation, etc. [48]. Additionally, the ability to process the vast amount of data to reduce road congestion, improve the management of traffic, and ensure road safety are parts of the issues in the future IoV trends [49; 50]. Hence, AI technology coupled with ML algorithms is capable of improving the network efficiency of the IoV [23]. With ML algorithms, data processing at the onboard units (OBUs), fog level, or cloud level can be resolved. Further issues such as the rapid topology of the IoV, channel modeling, optimization quality of experience, energy, and time can be solved with ML algorithms [50]. To resolve the intrusion challenges, several authors have developed AI and ML models [23; 32; 47; 49]. However, recent works have become devoted to the concept of XAI to resolve the inadequacies of traditional AI. To classify an AI as XAI, the following requirements should be present:  
1. _Fidelity_: For instance, it is not enough that AI-IDS for securing an ITS performed with high accuracy, it is now a research concern to know the details of the datasets and how it affected the systems. Is there any element of bias? How are individuals or persons affected by the decisions of AI-based decisions in response to the EU general data protection regulation [35]?
2. _Simulability_: Common questions that are now asked are \"can a third party check the correctness of the model?\" and \"Is it possible to repeat the simulation and arrive at similar results?\"
3. _Completeness_: \"Explainability\" is not enough. It is encouraging to have proper documentation of the model development for a sustainable enhancement of the system [17].
4. _Compactness_: Give human users the knowledge they need to comprehend, properly trust, and successfully manage the new generation of AI partners.