esa-sceva/satcom-chunk-collection huggingface dataset:
# Hydrogen from Depleted/Depleting Hydrocarbon Reservoirs: A Reservoir Engineering Perspective
## 5 H\({}_{2}\) Generation in DGRs
### Reservoir Engineering Aspects
#### 5.2.2 Economics  
Here, a preliminary economic assessment for the application of SF in a DGR is performed for optimistic conditions assuming 1 m\({}^{3}\) of reservoir rock with a porosity of 0.2, irreducible water saturation of 0.2, and a CH\({}_{4}\)/H\({}_{2}\) conversion factor of 50%, with a steam/H\({}_{2}\) ratio of 1. To heat the reservoir rock with a heat capacity of 1 kJ/kg K from its original T of 100 \({}^{\circ}\)C to 400 \({}^{\circ}\)C (a minimum required T from laboratory experiments), approximately 250 kg of steam is required. With a remaining gas amount of approximately 2.5 kg CH\({}_{4}\) in 1 m\({}^{3}\) of the reservoir and considering the given conversion factor, the corresponding H\({}_{2}\) yield is 80 moles (0.16 kg) of H\({}_{2}\). The levelized cost of energy (LCOE) of steam is 25 USD\({}_{2023}\)/t at the lowest, including costs of emissions [54, 68]. Thus, the LCOE required for the generation of 1 kg H\({}_{2}\) can be approximated with 40 USD\({}_{2023}\). Obviously, this amount is strongly dependent on the fuel prices; it can, however, be argued that an in situ H\({}_{2}\) generation price lower than this price cannot be realized as the best case while also considering the optimistic assumptions of the calculations. It should be noted that H\({}_{2}\) generation by the SMR process on the surface costs less than 5 USD\({}_{2023}\)/kg H\({}_{2}\) including both the capturing of the co-produced CO\({}_{2}\) as well as the subsequent transport and storage into the thematized DGR; this is also referred to as blue H\({}_{2}\) generation (Table 3). The maximum blue H\({}_{2}\) production costs are estimated at 11 USD/kg H\({}_{2}\), assuming the most unfavorable scenarios for natural gas prices [5]. The costs of drilling new wells, capturing CO\({}_{2}\) from the produced gas, and the separation and transportation of H\({}_{2}\) and other OPEX costs are not included in the calculations as it is no longer needed to conclude the non-feasibility from both the technical and economic points of view. Risk costs are also excluded, some of which are discussed below.
## 3 Datasets and Preparations
### xBD Dataset  
xBD [40; 56] is the largest optical satellite imagery benchmark dataset for building segmentation and damage assessment in the remote sensing community [55]. The xBD dataset offers very high-resolution images (1.2 m to 2.4 m) acquired before and after different natural disasters, such as earthquakes, wildfires, tsunamis, and floods. As one of the largest datasets in remote sensing, it includes 22,068 RGB images (11,034 pairs) with a size of 1024 \(\times\) 1024 \(\times\) 3 pixels acquired from different high-resolutionoptical satellites, e.g., Worldview-2 or GeoEye, in various off-nadir (5\({}^{\circ}\) to 40\({}^{\circ}\)) and sun elevation (30\({}^{\circ}\) to 80\({}^{\circ}\)) angles, causing the dataset to be more complicated in terms of spatial resolution or shadow length. xBD contains 850,736 building polygons with different areas and in multiple locations around the world, in various types of textures such as low/high density urban or rural regions. The georeferencing information and damage class of each building, along with the disaster type and sensor parameters, are included in a metadata file for each image. [PERSON] et al. (2019) provided the images in four groups: Train, Test, Holdout, and Tier3. The first three groups were specifically considered to be used for training, testing, and validation purposes, respectively, while the fourth group was released later and was designed to be merged with the Train set to increase the diversity of the dataset. Using the same set of images of a benchmark dataset for specific steps allows researchers to be able to fairly compare their methods. Figure 1 shows how the image pairs are distributed in these four groups. For instance, images from wildfire events can be found mainly in the Tier3 group. Furthermore, Figure 1 reveals that although the xBD dataset covers six types of disasters, it is highly unbalanced, such that fires, floods, and wind events have many more samples in the dataset compared to volcanic eruptions, tsunamis, or earthquakes.  
xBD is a dataset with four classes of building damage, which are (1) no-damage, (2) minor-damage, (3) major-damage, and (4) destroyed; but it is biased towards no-damage buildings, such that the number of no-damage buildings is approximately five times the number of other classes. Figure 2 visualizes the geographical distribution and type of disasters present in xBD and their corresponding number of buildings in each category of damage. It is obvious that most of the pie charts are dominated by green, i.e., the no-damage class, and other classes are in the minority. Even more, there are some disasters, such as the Mexico earthquake, where almost all of the buildings are in the no-damage category; only 3 out of 51,500 buildings are categorized as destroyed. On the other hand, from the geographical distribution point of view, most of the xBD's disaster events are located across the United States, and only a few events are in other countries, such as Nepal, Australia, Indonesia, and Portugal.  
Figure 1: Number of image pairs in each disaster type per group in the xBD dataset.
# Principle and Implementation of Stokes Vector Polarization Imaging Technology
## 3 Implementation of Stokes Vector Polarization Imaging Technology
### Simultaneous Polarization Imaging Mode
#### 3.2.3 Split-Focal Plane Polarization Imaging  
The focal plane system includes micropolarizing plate array linearly polarized camera and microwave plate array fully polarized camera [30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41]. Is similar to the color camera buyer filter array, the array polarized micro polaroid camera each pixel covers different azimuth polaroid, when was the scene of the shooting light intensity spatial frequency and polarization spatial frequency is low, the neighbor of the covered with four different  
Figure 8: Schematic diagram of polarization imaging with sub-aperture [29].  
Figure 7: Schematic diagram of fractional amplitude polarization imaging [28].  
directions of each of the pixels of polaroid receives the Stokes vector can be considered to be consistent, The four pixels are regarded as one polarization pixel, which has four polarization channels, and the linearly polarized part of Stokes vector can be solved.  
With the development of Focal plane array (FPA) technology, it is possible to integrate micropolarized optical elements into FPA. One pixel of Focal plane can correspond to one micropolarized element to form a Focal plane simultaneous polarization imaging detector. Micropolarizations with different polarization directions are integrated in the four adjacent detector units, which can achieve single-exposure 4-polarization imaging without splitting. The focal plane polarization imaging system has the advantages of compact structure, small volume and high stability. However, each pixel corresponds to a polarization element, so the spatial resolution is low. Therefore, in the case of different polarization states, there is at least one pixel position matching error between each pixel. In 2018, Sony launched a polarization sensor IMX250 MZR [42]. As shown in Figure 9, each micropolarized plate has an anti-reflective layer and is placed between the microlens and the photodiode. This structure can minimize the crosstalk of polarized light in different directions.
# CCAIB: Congestion Control Based on Adaptive Integral Backstepping for Wireless Multi-Router Network
## 4 Performance Evaluation
### Results and Analysis
#### 4.2.7 Simulation Results for Different Routers  
In the above subsections, we only show the performance of router 1. In this subsection, comparison is taken between the five routers. Parameters are shown in Table 3. From Figure 8, it can be seen that router 1 has the best performance. This makes sense because the bandwidth of the router next to the receiver is determined by the receiver's capacity, which is a constant. Other routers' bandwidths are determined by the next-hop window size, which is constantly changing. Therefore, when routing protocols are designed, it should be with as few hops as possible.  
Figure 6: Simulation results of multiple routers using CCAIB for different downlink packet loss ratios. Columns from left to right show the performance of CCAIB in (**a**) queue length stability, (**b**) window size and (**c**) packet loss ratio in the queue.  
Figure 7: Simulation results of multiple routers using CCAIB for different desired queue lengths. Columns from left to right show the performance of CCAIB in (**a**) queue length stability, (**b**) window size and (**c**) packet loss ratio in the queue.  
The main conclusion obtained from the above simulation results is that the CCAIB has a superior performance than the H\(\infty\) algorithm congestion control algorithm in queue length, convergence time, queue packet loss ratio and window size. This is basically due to the packet loss ratios of wireless links having been estimated and taken into account when determining the packet loss ratio in queue, which improves the networks performance obviously.  
Through the simulation results, some technical suggestions can be provided for the deployment of the wireless networks with multiple bottleneck routers. For example, when routing protocols are designed, the number of hops between the sender and the receiver should be reduced if possible. Since a larger propagation delay decreases the effectiveness of the communication, the distance between routers should be carefully considered when the network is deployed. In addition, increasing the bandwidth and buffer size can effectively relieve the network congestion. In particular, since the quality of the uplink has a more significant impact on the performance of the entire network than the downlink, when the CCAIB is deployed, priority should be given to ensuring the quality of the uplink.
## 4 Electrodes and Electrolytes for SSTFBs
### Thin-film Electrodes
#### 4.1.2 Cathodes  
LCO is one of the most widely studied cathode materials as discussed in the bulk electrode section [366]. Many efforts have been devoted to the usage of the LCO thin-film cathode fabricated by PLD [367; 368; 369; 370; 371; 372; 373], RF sputtering [374; 375; 376; 366], ALD [377; 378] and CVD [379; 380]. In contrast to the bulk materials, the physical and chemical properties of thin films can be controlled by the deposition conditions, which is beneficial for enhancing the performance of SSTFBs. Nishio et al. [373] demonstrated the growth of the (104)-oriented epitaxial LCO thin films on (100) STO substrates by PLD achieving a discharge capacity of 90 mAh/g at a low discharge rate of 0.01 C and a 23 mAh/g discharge capacity at a high discharge rate of 100 C. Compared to the previous work about polycrystalline thin films by Nishio et al. [381], epitaxial thin films showed a higher discharge capacity at 0.01 C (75 mAh/g for polycrystalline LCO thin film) and slightly lower discharge capacity at 100 C (26 mAh/g for polycrystalline LCO thin film). Polycrystalline thin films with (001) and (104) preferred orientations were also grown by Xie et al. [374] in which the (104)-oriented thin films were found to have the best discharge capacity of ~120 mAh/g, higher than the (001)-oriented thin films with a discharge capacity of ~110 mAh/g. Zhu et al. [375] demonstrated that the discharge capacity of the (001)-oriented LCO thin films depends on the deposition temperature. According to this work, the discharge capacity varied from 24.7 mAh/cm\({}^{2}\)\(\upmu\)m for the thin film deposited at room temperature to 60.5 \(\upmu\)Ah/cm\({}^{2}\)\(\upmu\)m for the thin film deposited at 500 \({}^{\circ}\)C. The authors also found that the performance of the film deposited at 500 \({}^{\circ}\)C compromised by crack formation during the deposition at high temperature was dropped from 60.5 \(\upmu\)Ah/cm\({}^{2}\)\(\upmu\)m to 23.1 \(\upmu\)Ah/cm\({}^{2}\)\(\upmu\)m after 50 cycles. Similarly, Donders et al. [377] used ALD to deposit LCO thin films on Si substrates. Interestingly, the authors observed that a post-annealing process at 700 \({}^{\circ}\)C could change the orientation of the LCO thin films, leading to a discharge capacity ranging from ~10 to 25 \(\upmu\)Ah/cm\({}^{2}\)\(\upmu\)m with different Li stoichiometry. Overall, polycrystalline LCO films with preferred orientations showed a better performance than epitaxial LCO thin films. Being a promising cathode material for SSTFBs, LMO thin films have been studied with different deposition techniques, including PLD [382; 383; 384; 385], RF sputtering [386; 387; 388], sol-gel [389; 390] and CVD [391]. Hendriks et al. [384] reported the growth of epitaxial LMO thin films with (100), (110) and (111) orientations with pyramidal, rooftop and flat surface morphology, respectively. According to this work, (100) oriented films were found to have the best discharge capacity and cycle
## 6 Limitations, Final Discussion and Conclusions
### Limitations
#### 6.1.3 Oversimplification of Infective Parameters and Individual Variability  
We remark that the ranges of numerical values we obtained of emitted droplets possibly emitted by vaping are rough average estimates gathered from outcomes reported in breathing studies (listed in Table 2) involving a wide variety of subjects, including both healthy subjects and individuals affected by respiratory conditions (not by SARS-CoV-2). We did not consider the small minority of outlier individuals who are super spreaders emitting significantly larger numbers of droplets [92]. We also simply considered droplet emission, disregarding the specification of a specific pathogen. Evidently, this oversimplification disregards important known facts, for example droplet characteristics vary among pathogens and between healthy and infected subjects [90,91].  
In fact, numerous aspects associated with the spreading and infection details of the SARS-CoV-2 virus remain uncertain and subject to large (often unexplained) individual and environmental variability (a good summary of these uncertainties is found in [13, 14, 15, 16]). However, in order to be able to model a possible (previously unexplored) route of droplet transmission and possible infection, it is necessary and unavoidable to simplify this complexity and lack of data to obtain plausible order of magnitude estimates that can be verified once empiric evidence is available.
# NIRS-EMG for Clinical Applications: A Systematic Review
## 2 Electromyography  
The surface electromyogram (EMG) is comprised of the sum of the electrical contributions made by the active motor units (MUs), as detected by electrodes placed on the skin overlying the muscle [10]. Thus, an EMG record includes the spatio-temporal summations of the action potentials originating from many motor units (MUAPs) located in the neighborhood of the recording electrode site. The recorded signal is influenced by some main factors, including the entity of the contraction, the number and type of involved fibers, and the distance of each MUAP from the site of signal detection. However, these aspects are only a relevant subset of the factors and sources of variability that impact on the temporal and frequency features of the EMG signal and may affect its interpretation. In previous studies, such factors have been sub-divided into three categories: causative, intermediate, and deterministic [35]. Causative factors include geometrical features of the muscle tissue, as well as the number of active motor unit potentials; intermediate factors include cross-talk, amplitude of the detection volume, and skin conductance, among others; deterministic factors include the measure MUAP firing rate, along with others. For an in-depth explanation, previous works have described these aspects in detail [35; 36].  
Despite the presence of the limitations and confounding factors previously discussed, the shapes and firing rates of MUAPs in EMG signals provide an important source of information for the diagnosis and evaluation of neuromuscular disorders [36]. For this reason, a wide range of algorithms and applications have been developed and applied in a wide variety of contexts, including, in particular, those related to clinical practice. More specifically, an EMG signal can be used with the aims briefly discussed as follows.
## 2 Related Work
### Multimodal Fusion Techniques: Transformers and Cross-Modal GANs  
Multimodal fusion has become a critical area of research, particularly in applications requiring the integration of heterogeneous data sources such as images, text, and time-series data. Transformer-based models and cross-modal generative adversarial networks (GANs) represent two major categories of multimodal fusion techniques, each offering distinct advantages and challenges. Transformer-based multimodal fusion models, such as Vision-Language Transformers (ViLTs) and Multimodal BERT variants, employ self-attention mechanisms to model cross-modal dependencies. These models have demonstrated state-of-the-art performance in tasks like image-text understanding, video captioning, and multimodal reasoning. However, the quadratic complexity \(O(n^{2})\) of standard self-attention mechanisms poses significant computational challenges, making them inefficient for large-scale multimodal datasets in cloud computing environments. Cross-modal GANs, including CycleGAN and CLIP-guided GANs, leverage adversarial training to enhance feature alignment across different modalities. These models have been widely applied in domain adaptation, data augmentation, and cross-modal synthesis. Despite their effectiveness, GAN-based methods are often plagued by mode collapse and unstable training dynamics, making them less reliable for structured multimodal tasks that require robust feature representations. In contrast, the proposed sliding-window attention mechanism optimizes local-global feature fusion while reducing computational costs, making it more suitable for privacy-preserving multimodal learning in cloud-based environments. The sliding loss function further enhances the adaptability of the model by prioritizing local feature variations, addressing limitations observed in both Transformer-based and GAN-based multimodal approaches.
# Study of 5G-NR-MIMO Links in the Presence of an Interferer
###### Abstract  
Many communication systems are based on the Multiple Input, Multiple Output (MIMO) scheme, and Orthogonal Space-time Block Transmit diversity Coding (OSTBC), combined with Maximal Ratio Receive Combining (MRRC), to create an optimal diversity system. A system with optimal diversity fixes and optimizes the channel's effects under multi-path and Rayleigh fading with maximum energy efficiency; however, the challenge does not end with dealing with the channel destruction of the multi-path impacts. Susceptibility to interference is a significant vulnerability in future wireless mobile networks. The 5 th Generation New Radio (5G-NR) technologies bring hundreds of small cells and pieces of User Equipment (UE) per indoor or outdoor local area scenario under a specific Long Term Evolution (LTE)-based station (e-NodeB), or under 5G-NR base-station (g-NodeB). It is necessary to study issues that deal with many interference signals, and smart jammers from advanced communication equipment cause deterioration in the links between the UE, the small cells, and the NodeB. In this paper, we study and present the significant impact and performances of \(2\times 2\) Alamouti Phase-Shift Keying (PSK) modulation techniques in the presence of an interferer and a smart jammer. The destructive effects affecting the MIMO array and the advanced diversity technique without closed-loop MIMO are analyzed. The performance is evaluated in terms of Bit Error Rate (BER) vs. Signal to Interference Ratio (SIR). In addition, we proved the impairment of the orthogonal spectrum assumption mathematically.  
interferer; 5G-NR-MIMO; Alamouti
###### Abstract
This study highlights the importance of a centralized node, where the collected data from multiple APs can be reported and utilized for localization purposes. This centralized approach enables more robust and accurate localizations of the spoofer. The main contributions of this work are as follows:  
* Detecting spoofed UAVs and estimating their ToA based on the power received from a single AP in an IoT environment. * Locating the position of UAVs using the estimated distances between the UAVs and different points, thereby leveraging the predictions. * Conducting a performance comparison between the MLP and LSTM models, thus resulting in the determination that the MLP model is capable of detecting the presence of a spoofer and estimating its ToA with the received power. The remainder of this paper is organized as follows. In Section 2, the relevant literature on spoofing attacks in IoT networks is discussed. Section 3 describes the system model. Section 4 provides details on the methodology and materials used, including the two-way protocol and the dataset derived, as well as the feature extraction process. Section 5 explains the comparison between the MLP and LSTM neural network models. Finally, Sections 6 and 7 present the simulation results and the conclusions of the paper, respectively.
## 5 Conclusions  
By analyzing forest fire and meteorological data from 1991 to 2021 in Yunnan Province and employing stepwise regression and ARIMA models, the influence of meteorological factors on fire occurrence was revealed, and predictions were made. The results indicate a stable trend of fires from 1991 to 1997, followed by frequent occurrences from 1998 to 2010. From 2014 to 2021, effective government strategies significantly reduced the number and scale of fires. Rainfall showed a negative correlation with fires, while wind speed exhibited a positive correlation.  
The ARIMA model predicts an initial increase, then a decrease, and again an increase in fires from 2022 to 2033, with significant fluctuations in the number of fires, especially in 2027 and 2033. The predicted area affected by fires is expected to grow more notably from 2028 onwards. This study achieved its goal of enhancing understanding and prediction of forest fire trends, contributing significantly to forest fire prediction methodologies and providing a scientific basis for future fire prevention and response strategies, as well as informed decision-making for resource allocation and risk management. However, the study's limitation lies in its reliance on historical data analysis, not accounting for potential impacts of future climate change on fire patterns.  
Investigation, Y.S. and C.F.; data curation, C.F. and S.Y.; writing--original draft preparation, Y.S. and C.F.; writing--review and editing, Y.S. and C.F.; All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. Not applicable. Not applicable. Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to reasons related to sensitivity and confidentiality.  
This research was partially supported by the Training Plan for Young Backbone. Teachers in Colleges and Universities in Henan Province (2019 GGJS098, and 2020 GGJS098).  
The authors declare no conflicts of interest.