esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Slab 3D-Printing, Assembly of Structures and Data Recording Setup
The reception coil (indicated by red arrows) is suspended above the strip position, J1 and J2 are indicated by red ellipses (dashed-solid, respectively) and the free end is supported/vibrated via the mini-exciter. The structure with two slabs involves only one bolted joint and is referred to as structure-A (Figure 2a), whereas that with three slabs and, hence, two bolted joints is referred as structure-B (Figure 2b). Both structures are clamped as cantilevers at one end, whereas the free end is vibrated by means of a mini-exciter with integrated power amplifier (K2004E01, The Modal Shop, Cincinnati, USA). The latter is capable of producing various profiles of vibratory force/motion (triangular, sinusoidal, pulse-like, etc.) supplied by an external waveform generator (SDG 5122, SIGLENT). Structure-A measures 300 mm, and the bolted join (referred to as J1 and indicated by red dashed ellipsis in Figure 2a) is located 150 mm from the clamped end. Structure-B measures 400 mm and the two joints are located 150 mm (J1, indicated in Figure 2b by a red dashed ellipsis) and 300 mm (J2, indicated in Figure 2b by a red solid ellipsis) from the clamped end, respectively. In both structures, the slab involving the sensing element (MetGlas(r) strip) is the one at the clamped end, as shown in Figure 2c. As in [19], the response of the vibrating structure is translated into electrical signals created by induction to a low-cost reception coil (Vishay IWAS) placed at 20 mm above the slab incorporating the sensing element, as shown in Figure 2c. According to the principle explained in [19], the vibrational loading provokes changes in the magnetoelastic strip's magnetization, which, in turn, causes induction of electrical signals in the reception coil circuits. Hence, this is a passive data transmission setup, in the sense that, unlike for instance [21; 22], no excitation coil is used. All such signals were recorded by means of a conventional digital oscilloscope at 1 MHz. Note that the coil's distance of 20 mm from the slab was defined by a trial-and-error process. In other words, the coil's position comes from a trade-off obtained via an empirical process for recording signal output with principal frequency components of magnitude +20 to +30 dB over those of the noise layer.
## 5 What Is C-RAN?
### Challenges and Solutions  
The huge drawback of C-RAN is the fronthaul network, connecting the RU to the DU. Originally, RUs used the CPRI protocol [119] for transport over the fronthaul network. However, the CPRI protocol induces a constant and very high load on the fronthaul network, since it is synchronous and scales by the number of antennas. Hence, the solution has been to add more functions to the RU, known as functional splitting. Various approaches to functional splits exist, proposed by enhanced Common Public Radio Interface (eCPRI) [119], small cell forum [117], O-RAN Alliance [120], and 3 GPP [110]. The more functions added to the RU, the more complex the RU and less shared processing in the data center. Conversely, having fewer functions added to the RU results in higher requirements to the fronthaul network in terms of capacity and latency. This is illustrated in Figure 16. So far, the O-RAN alliance and small cell forum are the only organizations focusing on only one or two functional split options. Small cell forums propose a division of the physical layer and the MAC layer. Hence, all of the physical layer processing must be handled in the RU, whereas the Media Access Control (MAC) and the above Radio Link Control (RLC) are handled in the DU. For the sake of O-RAN, they propose a functional split 7-2x category A and B, where the difference is that the category B radio unit includes beamforming and is more suitable for high-traffic areas [120].  
In the case of hardware for virtualized RAN functions, there is currently significant attention being paid to the CPUs handling physical layer processing [109]. This has already been described in the NFV Section 4.4.2 under Challenges and Solutions. This problem is also affected by the functional split since it is the physical layer functions that require heavy processing. Hence, using the PHY/MAC split proposed by the small cell forum will eliminate the discussion on in-line and look-aside acceleration.
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 3 Spatiotemporal Feature Enhancement Methods
### Other Spatiotemporal Feature Enhancement Methods
Due to its superiority in handling irregular data over CNN, graph convolution is used to learn more discriminative shape-based visual features from a graph adjacency matrix defined by the manifold distance between nodes between lip key points and their interrelationships [132]. In fact, in this model, such predefined lip connections, which are meticulously engineered rather than naturally occurring, greatly restrict the representational capacity of the complex variations and dynamics of lip movements, undermining its effectiveness in tasks such as lip-reading or facial expression analysis. Further advancements in graph convolution were made by [PERSON] et al. [130], where a three-branch feature enhancement method, consisting of global flow, local flow, and audio flow, was proposed to extract and fuse global spatial features from video sequences, and temporal-spatial and speech features from the lip graph sequence. For the dynamic lip shape modeling task, a graph convolution network is designed by embedding an adaptive semantic spatiotemporal graph convolution (ASST-GCN) module for processing eight subgraphs. By combining the multi-modal information, the model improved the accuracy in identifying dynamic lip movement. In the same year, studies by [PERSON] et al. [131] showcased the effectiveness of the graph structure and lip shape segmentation network across adjacent frames for extracting content-independent features. This innovative approach utilized a segmentation network to isolate the region of interest (ROI) corresponding to the lip shapes. They constructed a local feature extractor based on U-Net, alongside a graph-based extractor for neighboring features, effectively merging the outputs of these two distinct feature extraction mechanisms to establish enhanced features. Moreover, they devised a three-tier feature fusion strategy comprising feature-level, score-level, and decision-level integration. Experimental results  
Figure 14: The other typical spatiotemporal feature enhancement methods. (**a**) The architecture of Ref. [126]. (**b**) The architecture of Ref. [128]. (**c**) The architecture of Ref. [129]. (**d**) The architecture of Ref. [130]. (**e**) The architecture of Ref. [131]. demonstrated that this method possesses content-agnostic feature extraction capabilities, showcasing its robustness in diverse applications. Table 3 presents a comprehensive comparative analysis of various spatiotemporal feature enhancement methods, illuminating their unique characteristics and performance metrics to enhance understanding of their contributions to diverse fields. In the table, \"Structural Complexity\" refers to the model structural complexity of spatiotemporal feature extraction methods, which is used to measure the depth, width, and number of sub-modules of the model, and is usually related to the parameter count of the model. Models with complex structures usually have more parameters, while models with simple structures usually have fewer parameters.
# Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
## 2 Related Work
In all of these studies, the research was conducted by focusing on positioning accuracy rather than execution time. Nevertheless, the test for localization accuracy was omitted, or it was difficult to evaluate the performance of the system. Furthermore, these studies may not be suitable for practical environments using a small dataset, or it may be difficult to perform continuous positioning because additional sensor data are not used. Above all, these studies are focused only on typical indoor spaces; therefore, it is difficult to verify their performance in offshore environments.
# A Method for Building a Mixed-Reality Digital Twin of a Roadheader Monitoring System
## 6 Limitations and Future Research Directions
### Future Research Directions  
Future research will extend the digital twin monitoring system to real-world road-header operations to evaluate its performance under varying conditions, such as heterogeneous rock formations, uneven terrain, and complex underground environments. Experiments addressing environmental challenges like dust, electromagnetic interference, and lighting variations will be designed to ensure the system's robustness and reliability in harsh mining conditions. To further enhance the operator experience, the mixed-reality platform could integrate advanced sensing technologies, such as LiDAR and thermal imaging, providing a more comprehensive view of the operational environment and supporting real-time decision-making. User-centered studies will be conducted to assess the system's usability, focusing on its impact on operator efficiency, situational awareness, and decision-making accuracy. Additionally, future work will explore the cost-effectiveness of the system, addressing commercial implementation strategies, scalability, and long-term operational costs to ensure its practical viability.  
Conceptualization, X.H.; Methodology, X.H. and H.L.; Software, H.J.; Vali-dation, H.L. and H.J.; Formal analysis, X.H., H.L. and H.J.; Investigation, Y.C. and Y.G.; Resources, Y.C., S.W., Y.G. and J.G.; Data curation, S.W., J.G. and S.G.; Writing--original draft, H.L.; Writing--review and editing, X.H., H.J. and S.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (grant number: 2022 YFC2904105), National Key Research and Development Program of China (grant number: 2023 YFC3010400), and Key Research and Development Program of Hebei Province (grant number: 23311805D).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# An Efficient Routing Scheme Based on Node Attributes for Opportunistic Networks in Oceans
###### Abstract  
Along with the fast development of the marine economy and ever-increasing human activities, handy and reliable marine networking services are increasingly required in recent years. The ocean faces challenges to support cost-effective communication due to its special environments. Opportunistic networks with easy deployment and self-curing capability are expected to play an important role to adapt to such dynamic networking environments. In the literature, routing schemes for opportunistic networks mainly exploit node mobility and local relaying technologies. They did not take into account the impact of node behaviors on encountering opportunities and in case of no further relaying, network performance would be greatly degraded. To solve the problem, we propose an efficient routing scheme based on node attributes for opportunistic networks. We first construct delivery competency to predict the further relay nodes. Then a forwarding willingness mechanism is introduced to evaluate the relaying probability combining device capacity and movement behaviors of nodes. Finally, the utility metric is used to make decisions on message forwarding. The results show that the proposed scheme improves network performance in terms of delivery ratio, average latency, and overhead ratio as compared to other schemes.  
opportunistic networks; node attributes; delivery competency; forwarding willingness Article  
1
# A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection
## 1 Introduction
For the first time, we treat singular point detection as a problem of semantic segmentation of small singular point area completely and directly. Different from other methods based on deep learning, our method requires much less fingerprint data and easier training process. 2. A new customized network architecture called SinNet is designed for extracting the accurate singular point area. The training data labeling, the mixed loss function, the encoder-decoder architecture, the inception module, skip connections and the batch normalization are used together to ensure the efficient performance of the network. Our method achieves the best performance in terms of comprehensive evaluation on SPD2010 [2], especially in the correctly detection rate (CD) and the detection rate of cores. The rest of this paper is organized as follows. The literature review is given in Section 2. Details of the proposed method are introduced in Section 3. In Section 4, the experimental results are given, supported by the performance analysis. Finally, the conclusion is discussed in Section 5.
## 2 Materials and Methods
### WOFOST Model  
The WOFOST model, developed by the Wageningen University and Research Center (Wageningen, the Netherlands), is a mechanistic model for analyzing the interactions among soil, water, the atmosphere, and plants [35; 36]. The model's outputs are directly usable for crop-specific yield estimation and can provide estimates of biomass and grain yield at a daily time step for different crop types [1]. The WOFOST model can be run in potential mode, water-limited mode, and nutrient-limited mode. Soil moisture is an important crop growth parameter and is estimated from forcing data and soil properties in the WOFOST model. Therefore, SM was adopted as the state variable in the data-assimilation procedure in this study.  
We used the water-limited mode of WOFOST model in our research and assumed that the simulation of the soil water balance is performed for freely draining soils. The core of the crop model calibration is fitting the simulated values with the observed data [37]. Our previous studies have described the parameterization and calibration of the WOFOST model in detail [30; 31]. Here, we briefly show the main soil and crop parameters (Tables S1 and S2), which are of great importance to the water-limited mode. Each winter wheat pixel was considered as an assimilation grid. Each assimilation grid has its own unique folder, which contains a soil parameter file, a crop parameter file, and a meteorological parameter file. We used the Chinese soil database ([[http://www.soil.csdb.cn](http://www.soil.csdb.cn)]([http://www.soil.csdb.cn](http://www.soil.csdb.cn))) and followed the Saxton and Rawls method to derive soil moisture content at the wilting point (SMW), in saturated soil (SM0), and at field capacity (SMFCF) [38]. Other soil parameters were used as the default value in WOFOST. As for crop parameters, different assimilation grids have different values of IDEM, TSUM1, and TSUM2, and the remaining parameters are the same. The meteorological parameters are extracted from 10 m meteorological images interpolated from meteorological stations (Section 2.2).
# Detecting Phishing URLs Based on a Deep Learning Approach to Prevent Cyber-Attacks
## 2 Literature Review
### Traditional Methods
#### 2.1.1 Whitelist Approach  
In 2021, an approach was proposed based on whitelist sites, in which they performed URL-based similarity checks to distinguish a phishing site by comparing the domain name system (DNS) query to overcome the phishing attack [9]. Later, in [10], proposed an approach in which an automated individual whitelist in the system maintained the user's previous login and warned them when unfamiliar access occurred. Although these techniques seem effective in phishing detection, there are many limitations faced by legitimate sites on the web.
## 4 Discussion
### Anteroposterior and Mediolateral Locations of COP  
Our findings demonstrated a significant interaction between foot type and walking condition for both the anteroposterior and mediolateral locations of the COP. The COP was located more anteriorly in the hyperpronated foot group across all but uphill walking at the maximum slope, compared to individuals with neutral foot type. To the best of our knowledge, no other studies have determined the COP based on a cyclograph, which involves connecting the COP trajectories from the forefoot of one side to the rearfoot of the contralateral side during selected gait cycles. Consequently, direct comparisons with previous research are not possible. However, the few available studies investigating this biomechanical feature have shown that the COP pathway of the flat feet group tends to be abnormal, moving straight from the heel to the toe without the medial shifting in the forefoot that is typically observed in individuals with a neutral foot type [27]. Otherinvestigators have shown that increased foot pronation displaces the COP posteriorly [33], which contrasts with the findings of the present study. Our findings indicate an anterior shift in the COP in the hyperpronated foot group across most tested gait conditions, coinciding with an increased exertion of vGRFs further toward the forefoot compared to the neutral foot type group, a response that has been observed in both current and previous findings [24; 26]. The forward location of the COP was further amplified during uphill walking, likely due to a more pronounced forward shift in the center of mass ahead of the foot's center. This shift may occur as the trunk leans forward in an attempt to enhance forward propulsion relative to the center of support against gravity, counterbalancing the increased gravitational forces acting on the body during uphill walking [62; 63].  
The results of the present study also showed significant mediolateral displacement of the COP in individuals with hyperpronation, but not in those with neutral feet. These results are aligned with recent data reported in healthy individuals tested under similar walking conditions [64]. Such responses likely reflect the need for greater lateral adaptations to maintain balance and stability on inclined surfaces in the hyperpronated foot type group. Individuals with hyperpronated feet are expected to demonstrate plastic deformation of the passive stabilizers of the subtalar joint due to the long-term effects of foot misalignment, with subsequent effects on joint proprioception [65; 66; 67]. This may potentially affect the mediolateral stability of the foot, particularly under more demanding conditions such as uphill and downhill walking.
## 3 Results
### Cell Viability and Protection in the Model of H\({}_{2}\)O\({}_{2}\)-Induced Oxidative Damage  
Cell viability, measured by the MTT assay, was reduced by 26.3% (\(p<0.001\), n = 3) compared to the control after 24 h incubation of U87 MG cells with H\({}_{2}\)O\({}_{2}\) (200 \(\upmu\)M) (Figure 3). The mRVT (1, 3, and 10 \(\upmu\)M) and RVT (10 \(\upmu\)M) pretreatment for 24 h restored cell viability near to the control levels. According to the post hoc test the mRVT treatment in concentrations of 1 and 3 \(\upmu\)M increased cell viability by the 28.9% (\(p<0.001\), n = 3) and 32.7% (\(p<0.001\), n = 3), respectively, compared to the H\({}_{2}\)O\({}_{2}\) treated group and by 14.4% (\(p<0.05\), n = 3) and 17.6% (\(p<0.05\), n = 3), respectively, compared to the pure RVT in the equivalent concentrations.  
Figure 3: Cell viability after 24 h pre-incubation with RVT or mRVT against 200 \(\upmu\)M H\({}_{2}\)O\({}_{2}\)-induced cell damage. Compared with the control: ***\(p<0.001\); compared with H\({}_{2}\)O\({}_{2}\) group: # \(p<0.05\), ## \(p<0.01\), ## \(p<0.001\); compared between RVT and mRVT groups: \(\blacklozenge p<0.05\).  
Figure 2: Viability of U87 MG cells after 24 h of incubation with (**a**) empty micelles and (**b**) resveratrol-loaded micelles (mRVT) and pure resveratrol (RVT). Values are mean \(\pm\) SEM (n = 3).  
Effects of RVT and mRVT Treatment Against \(H_{2}\)O\({}_{2}\)-Induced Oxidative Stress in U87 MG Cells  
Our results showed that the H\({}_{2}\)O\({}_{2}\) treatment of U87 MG glioma cells increased LPO levels by 165% (\(p<\) 0.001, n = 3) and decreased SOD and CAT activity by 11% (\(p<\) 0.05, n = 3) and 25% (\(p<\) 0.01, n = 3), respectively, compared to the control (Figure 4a-c). The treatment with pure and micellar resveratrol decreased MDA levels in the samples, which indicates an antioxidant effect. The most effective concentration for both forms of resveratrol was 10 uM, at which the control levels of LPO were preserved. According to SOD activity analysis, the enzyme activity was reversed after mRVT treatment in concentration 1 uM (by 28%, \(p<\) 0.001, n = 3) and after pure RVT in concentration 10 uM (by 88.58%, \(p<\) 0.001, n = 3), compared to the H\({}_{2}\)O\({}_{2}\)-treated group. CAT activity was reversed only after mRVT treatment with the strongest effect at concentration 1 uM (by 94.44%, \(p<\) 0.001, n = 3), compared to the H\({}_{2}\)O\({}_{2}\)-treated group.
# Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites
## 3 Design and Implementation
### Transceiver Design
#### 3.5.5 Simulation Code  
In the simulation, the transmitted data are fixed at a size of 245 bytes. The received packets are counted by the receiver while being written in the dedicated file.
## 4 Deep Learning Methods for HDR Reconstruction
### GAN for HDR Reconstruction
#### 4.2.2 Multi LDR to HDR Reconstruction with GAN  
A significant amount of research has been carried out aiming to reconstruct HDR images from multiple images of the same reference with different exposures. In this context, GAN-based methods show remarkable results. Among these, the approach proposed for HDR-GAN [26] stands out, consisting of a multi-scale LDR encoder that processes each input with different exposures to extract features at various scales, enabling the network to learn LDR image features for subsequent HDR construction. Then, two processes happen simultaneously: one for merging aligned reference features and another for deep HDR supervision. In the first process, features extracted at multiple scales are fused and aligned with a residual structure in the feature domain. In the second, the process is reinforced by upsampling lower features and concatenating them with higher ones, improving alignment and the final HDR image quality. Lastly, a PatchGAN-based [141] discriminator evaluates the generated image.  
In the same perspective, the UPHDR-GAN [143] approach involves a generator that transforms the input image domain into the desired domain. The discriminator follows the PatchGAN [141] approach to distinguish between two images. Thus, a discriminator loss value is computed, and to obtain this value for the generator, a Min-patch module is applied, adding a pooling layer to the discriminator output.  
Likewise, MEF-GAN [144] uses a generator based on three blocks: a self-attention block applying the mechanism described in [145], a local detail block to retain details that might be lost in the first block, and a merge block that combines these results to produce the final HDR image. The generated HDR image is then evaluated by a discriminator, yielding a scalar value between 0 and 1 that determines how close the generator output is to the original HDR image.  
All the architectures above produce HDR images with good performance in both qualitative and quantitative results. Except for MEF-GAN, the other approaches handle the movement present in the utilized datasets, solving to some extent the misalignment of objects among different exposures. UPHDR-GAN stands out for training without needing paired datasets, while HDR-GAN [26] yields the best image metrics. Lastly, MEF-GAN is notable for its multi-exposure fusion approach that preserves crucial details and context.  
The models employing the methods described in the topics above and their metrics are presented in the following sections and in Table 4.