esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
extracted indicators of greenness, heat, dryness, and wetness, and constructed a Remote Sensing Ecological Index (RSEI) based on Principal Component Analysis (PCA) [29]. This model has been successfully applied to different ecological scenarios [30; 31; 32; 33]. However, EE faces challenges due to the large data size and difficulty in processing. The Google Earth Engine (GEE) can process large amounts of geospatial data and is a useful tool for large-scale and long-term ecological evaluation [34; 35]. Currently, research on the EE evaluation of CLP remains insufficient. For example, [PERSON] et al. evaluated the EE of the coal mining area in Datong, Shanxi Province, China by integrating RS, GIS, and field survey methods [36]. [PERSON] et al. analyzed land-use and land-cover changes caused by the GGP in China's CLP [37; 38]. [PERSON] et al. constructed an urbanization index using nighttime lighting data and examined the spatial and temporal evolution of urbanization in CLP [39]. Previous studies on EE in the CLP have primarily focused on analyzing the impact of individual factors on a specific region or the entire region. However, there has been limited research examining the impact of multiple factors on EE. Given the diverse landforms of the Loess Plateau, which includes numerous mining and urban regions, it is important to select representative areas for detailed analysis, especially in light of the GGP. Due to the variability of CLP's geomorphology, the ecological impact of GGP varies significantly in different geomorphological units. For example, hilly and gully regions are generally where GGP is implemented, while river valley plain regions are mostly urbanization expansion regions, and coal mining affects the variation of EE in nearby regions. In this study, we comprehensively analyzed the temporal and spatial characteristics of ecological environment (EE) changes in typical regions of the China Loess Plateau (CLP) from 2002 to 2022. We used MODIS time-series images and the Remote Sensing Ecological Index(RSEI) constructed through principal component analysis (PCA) to analyze the temporal characteristics of EE changes. Additionally, we evaluated the spatial characteristics of EE variations in the entire study area and investigated the impacts of coal mining and urbanization on EE in sample regions such as the Dafosi mine and the Guanzhong Plain urban agglomeration in CLP. Based on the long RSEI time series, we predicted the EE evolution trend. Finally, to verify the conclusions made in this paper, we introduced Geodetector to perform an attribution analysis of the factors resulting in EE evolution in CLP. In summary, the authors studied the temporal-spatial variation of EE in the study area since 2002 under the influence of GGP, coal mining, and urbanization. The results support the achievement of green and sustainable development in the relatively underdeveloped region in northwestern China.
# Encoder-Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation
## 2 Materials and Methods
### Semantic Segmentation Model
#### 2.1.3 Feature Pyramid Attention Mechanism  
Although the pyramid structure [33] can extract multiscale feature information and effectively increase the size of the receptive field, it lacks global context prior attention and cannot select features in the dimension direction. On the other hand, the channel-based attention mechanism is not sufficient to extract multiscale features effectively, lacking pixel-level information [23]. To increase the size of the receptive field and improve the segmentation accuracy of small objects, we fused the attention mechanism and spatial pyramid structure to construct the FPA mechanism and extract accurate and dense multiscale features. We also used the convolution kernels of different scales (3 \(\times\) 3, 5 \(\times\) 5 and 7 \(\times\) 7) to extract the contextual information of the input feature map, and the FPA structure to fuse the features of different scales layer-by-layer. After the 1 \(\times\) 1 convolution layer, the original features were multiplied by the multiscale features pixel-by-pixel to efficiently capture the context information of the multiscale image. The specific structure of the FPA module is shown in Figure 5.  
After the 3 \(\times\) 3 convolution operation, the size of the output feature map was 1/8 of the input feature. In the figure, \"up\" is the deconvolution operation, the purpose of which is to increase the scale of the feature map for channel-by-channel fusions with high-resolution features. The 1 \(\times\) 1 convolution on the main path was used to reduce the number of channels. High-level features were not conducive to pixel-level classification, so we performed average pooling of the high-level features, which then underwent a 1 \(\times\) 1 convolutional layer to obtain a 1 \(\times\) 1 \(\times\) C vector with global information to weight the channels of the low-level features. The context features of different scales can be fused with the FPA module to expand the feature representation range of advanced feature maps. The context information was multiplied with the original feature map pixel-by-pixel without introducing excessive computation.
###### Abstract  
A previous report indicated that collagen hydrolysate fraction (F7) from Spanish mackerel (_Somberomonas niphonius_) skins showed high reducing power and radical scavenging activities on 2,2-Diphenyl-1-picrylhydrazyl (DPPH) (EC\({}_{50}\) value of 1.57 mg/mL) and hydroxyl (EC\({}_{50}\) value of 1.20 mg/mL). In this work, eight peptides were isolated from F7 and identified as Gly-Pro-Tyr (GPY, 335.31 Da), Gly-Pro-Thr-Gly-Glu (GPTGE, 459.47 Da), Pro-Phe-Gly-Pro-Asp (PFGPD, 531.52 Da), Gly-Pro-Thr-Gly-Ala-Lys (GPTGAKG, 586.65 Da), Pro-Tyr-Gly-Ala-Lys-Gly (PYGAKG, 591.69 Da), Gly-Ala-Thr-Gly-Pro-Gln-Gly (GATGPQG, 586.61 Da), Gly-Pro-Phe-Gly-Pro-Met (GPTGPM, 604.73 Da), and Tyr-Gly-Pro-Met (YGPM, 466.50 Da), respectively. Among them, PFGPD, PYGAKG, and YGPM exhibited strong radical scavenging activities on DPPH (EC\({}_{50}\) values of 0.80, 3.02, and 0.72 mg/mL for PFGPD, PYGAKG, and YGPM, respectively), hydroxyl (EC\({}_{50}\) values of 0.81, 0.66, and 0.88 mg/mL for PFGPD, PYGAKG, and YGPM, respectively), superoxide anion (EC\({}_{50}\) values of 0.91, 0.80, and 0.73 mg/mL for PFGPD, PYGAKG, and YGPM, respectively), and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) cation (EC\({}_{50}\) values of 0.86, 1.07, and 0.82 mg/mL for PFGPD, PYGAKG, and YGPM, respectively) in a positive concentration-activity relationship. Furthermore, PFGPD, PYGAKG, and YGPM could effectively reduce Fe\({}^{3+}\) to Fe\({}^{2+}\) and inhibit lipid peroxidation. Hence, eight collagen peptides from hydrolysate of Spanish mackerel skins might be served as antioxidant candidates for various industrial applications.  
spanish mackerel (_Somberomonas niphonius_); skin; antioxidant activity; collagen hydrolysate; peptide +
Footnote †: journal:
## 3 Results and Discussion  
### Fiber Preparation by Electrospinning  
The first challenge of fiber preparation is the solubilization of poorly soluble active pharmaceutical ingredients. In this study, the BCS class II drug ITZ, with a water solubility of less than 5 ng/mL at an acidic pH, was dissolved in a dichloromethane and ethanol mixture (1:1 volume ratio) under magnetic stirring at 500 rpm, at room temperature. The next step was the addition of a polymer, also under continuous stirring. Scouting experiments were performed to adequately adjust the concentration of PVP. The viscoelasticity of the polymer's dispersion, the charge density carried by the jet, and the surface tension were the main parameters influencing the formation of beaded fibers [32; 33; 34; 35]. Increasing the PVP concentration resulted in smooth-surfaced bead-free fibers.  
Microfiber-based nonwoven fabrics were obtained using drug-polymer dispersions in a dichloromethane and ethanol mixture. The electrospinning resulted in randomly oriented smooth fibers at all the investigated parameters. The average fiber size ranged from 887 nm to 1175 nm, depending on the electrospinning parameters (Figure 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Code** & **Flow Rate (mL/hour)** & **Level** & **Needle–Collector Distance (cm)** \\ \hline N1 & 1 & \(-1\) & 10 \\ N2 & 2 & \(+1\) & 10 \\ N3 & 1 & \(-1\) & 15 \\ N4 & 2 & \(+1\) & 15 \\ N5 & 1.5 & 0 & 12.5 \\ N6 & 1.5 & 0 & 12.5 \\ N7 & 1.5 & 0 & 12.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electrospinning parameters and independent factor level of ITZ sheets.  
Empty fibers were prepared at a 1.5 mL flow rate and a 12.5 cm needle-collector distance, resulting in smooth and bead-free fibers with a diameter of 796 \(\pm\) 157 nm.
## 2 Materials and Methods
### System Setup  
The samples were excited with a mode-locked, Ti:Sapphire femtosecond laser (Tsunami, Newport/Spectra-Physics, Santa Clara, CA, USA), with 80 MHz repetition rate, pumped by a 5 W, 532 nm laser (Millennia vs., Newport/Spectra-Physics). The laser was tuned to 750 nm for the cy7 excitation and 800 nm for the cy7.5 excitation. The excitation beam was split to a fast photodiode (PHD-400, Becker & Hickl GmbH, Berlin, Germany) for signal synchronization, and to the center fiber bundle of a fluorescence inspection probe (Oriel 77558, Newport, Santa Clara, CA, USA). The Oriel inspection fiber has a central core with a fiber bundle that carries the excitation radiation to the samples. The core is surrounded by a ring of fibers that collect and deliver the fluorescence from the sample to the output channel. The source detector separation, between the central core (input) and the fiber ring (output), was less than 1 mm. The collected fluorescence is delivered through the distal end of the output channel into a filter wheel switching between a band-pass filter FF01-780/12 (Semrock, Rochester, NY, USA) for the Cy7 and long-pass filters RG830 and RG850 (Newport) for the Cy7.5 collection. The emission was measured using a fast photomultiplier tube (PMT) head (H7422P-50, Hamamatsu, Japan). The PMT was controlled using the DCC-100 detector controller (Becker & Hickl GmbH). Data acquisition was accomplished using the TCSPC module (SPC-730, Becker & Hickl GmbH) synchronizing between the SYNC signal arriving from the photodiode and the constant fraction discriminator (CFD) signal from the PMT.
## 3 Method
### Paper Selection Process  
The basis for the selection of papers for the present literature analysis were all research papers that were published in the respective venues between 2010 and up to 2018 (inclusive). We developed a decision tree to decide in a standardized and step-wise manner, whether or not to include each paper. This decision tree is depicted in Figure 2. It features four steps represented through binary decisions, where each has to be answered with \"yes\" for a paper to be included into our analysis. To pass the first step of the decision tree, the paper had to contain at least one of a set of keywords related to driving automation in the full text (see below). These keywords were selected to initially reduce the amount of papers in a reasonable way, while at the same time ensuring that no potentially relevant paper would be excluded. The first step of the decision tree was carried out by querying the respective online data bases using the following search terms:  
_\"automated driving\" OR \"autonomous driving\" OR \"self-driving\" OR \"self driving\" OR \"autonomous vehicle\" OR \"automated vehicle\"_  
Papers that did not feature AD represented by at least one of the keywords, as well as short papers, posters, and adjunct proceedings [62], were excluded in this step. For the remaining papers, the next step in the selection process consisted of examining whether the papers' objective was an empirical study to discard other literature reviews, as well as juridical, theoretical, or ethical papers [11; 63; 64]. The subsequent step of the decision tree aimed at the actual primary focus of the empirical papers. If this was not research and development of AD, the respective paper was excluded from further analysis. This step was incorporated to rule out work on concepts that, in principle, could be used for the development of AD, but was not originally investigated with that purpose [65]. In the last step of the selection process, we took a closer look at the levels of automation [1] that were investigated. The level of mere driver assistance (Level 1) as well as concepts which do not count asdriving automation according to SAE definition [66; 67] were out of scope for this literature analysis. Thus, only papers examining L2 and/or higher levels of automation (i.e., simultaneous lateral and longitudinal vehicle control) were included.  
Overall, n = 161 research papers passed all steps of the decision tree and were included in the present work. To ensure inter-rater reliability, a random selection of 10 papers were compared by means of intra-class correlations (ICC). ICC estimates were calculated based on a single-rating regarding the inclusion criteria (outcome of the decision tree to find out whether a paper should be included for further analysis), using a two-way random-effects model. Results revealed a high inter-rater reliability with a correlation of \(r\) = 0.809 (\(F\)(9,36) = 22.170, \(p<0.001\)).  
Figure 2: Decision tree for paper selection.
# Implementation of Fault-Tolerant Control for
## 6 Experimental Results
### Experimental Setup  
The experimental setup is shown in Figure 9 with a 3-DOF FARA-AT2 robot manipulator. This robot manipulator has 6-DOF, but for these experiments joints 4, 5, and 6 were blocked. The 3-DOF FARA-AT2 robot had a CSMP series motor at each joint, and aCSMP-02 BB driver was used for joints 1 and 2 while the CSMP-01 BB driver was used for Joint 3. The gear box at each joint is 120:1, 120:1, 100:1 at Joints 1, 2, and 3, respectively. The encoder at each joint was a 2048 line count incremental encoder. The controller ran on Labview-FPGA NI-PXI-8110 and NI-PXI-7842R PXI cards with the frequency control set at 500 Hz. NI-PXI-8110 was run on a Windows operating system.  
Figure 8: Tracking error at each joint in the simulation with multiple joint faults. (**a**) Joint 1; (**b**) Joint 2; (**c**) Joint 3.  
Figure 7: Fault estimation results in the simulation with multiple joint faults. (**a**) Joint 1; (**b**) Joint 2; (**c**) Joint 3.  
The desired trajectory at each joint is given as  
\[q_{i}(t)=\frac{\pi}{6}\sin(\frac{\pi t}{1600})\ (i=1,2,3) \tag{53}\]
# Single-Carrier Rotation-Interleaved Space-Time Code for Frequency-Selective Fading Channels +
## 4 Simulation Results and Discussion
### Number of multipaths is three (\(L=3\))
In terms of the number of states in the decoder, Proposed-G\({}_{32}\) requires 32 states in the BCJR decoder whereas \(G_{4^{*}}\)-STTC requires 64 ML states. Hence, it again indicates that the proposed scheme with iterative receiver is capable of outperforming the existing STTC scheme of comparable receiver complexity. The proposed scheme thus enables the use of any powerful STC among a large body of existing STC schemes designed for flat fading channels and transform it into effective STC for frequency-selective fading channels. Finally, as shown in Figure 10, the performance of using random rotation in the proposed scheme is as good as using deterministic rotation since both are able to extract the diversity gain inherent in the MIMO frequency-selective fading channel. Figure 10: Performance of the proposed scheme based on QPSK\(-\)modulated STTC, \(M_{T}=2\), \(M_{R}=2\), \(L=M=3\). Figure 9: Performance of the proposed scheme based on BPSK\(-\)modulated STTC, \(M_{T}=2\), \(M_{R}=2\), \(L=M=3\).
# An Experimental Study on the Migration of Pb in the Groundwater Table Fluctuation Zone
## 1 Introduction
analyzed chemical quality predictions in the Baoding Plain for when the groundwater table rises using hydrogeochemical simulations [21]. Heavy metals (Pb) pose potential risks to the soil ecosystem and human health [22; 23; 24]. China and other nations have focused on soil pollution prevention and control plans and the study of Pb migration characteristics [25; 26]. Domestic and foreign scholars have studied the adsorption, desorption, and migration of heavy metal pollutants such as Cu, Cr, and Zn in soil [27; 28; 29; 30]. Nonetheless, there are few reports on the migration laws of heavy metals under groundwater table fluctuations. Therefore, in this article, technical methods, including adsorption and desorption tests, water table fluctuation experiments, etc., were used to analyze the distribution characteristics of Pb in the vadose and saturated zones under different typical media. Our methods help research the migration of Pb in the groundwater table fluctuation zone and provide theoretical support for heavy metal pollution treatments for soil and groundwater, remediation, and protection.
# Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST
## 3 Results
### Detailed Explanation of Result Calculations
* Sustainability Score Calculation  
The Overall Sustainability Score was calculated by averaging the scores for economic, environmental, and social impacts. Each of these three dimensions was assigned equal weight, and the scores were normalized to a range between \(0\) and \(100\). The formula for calculating the Overall Sustainability Score is  
\[\textit{Sustainability Score}=\frac{\textit{Economic Impact Score}+\textit{Environmental Impact Score}+\textit{Social Impact Score}}{3}\]  
For MES, the calculated scores were as follows:  
* Economic Impact Score: Based on cost reduction and increased operational efficiency; this was rated at \(85/100\). * Environmental Impact Score: Based on the waste reduction and energy savings, calculated earlier at \(21.5\%\). * Social Impact Score: Evaluated based on improved decision making and other social benefits, rated at \(85/100\). Thus, the Overall Sustainability Score for MES was calculated as  
\[\textit{Sustainability Score for MES}=\frac{85+21.5+85}{3}=63.83\%\]  
* Explanation of Technologies and Results  
The results of the study indicate that the Industrial Internet of Things (IIoT) achieved the highest overall sustainability score (\(90/100\)), primarily due to its substantial impact on resource management, predictive maintenance, and reduced emissions. Additive Manufacturing (AM) also performed exceptionally well, achieving a sustainability score of \(88/100\), primarily due to its minimal waste production and localized manufacturing capabilities, which significantly reduce carbon emissions. This radar chart (Figure 8) illustrates the collective impact of Manufacturing Execution Systems (MES), Industrial Internet of Things (IIoT), Additive Manufacturing (AM), and Information and Communication Technology (ICT) on sustainability. The axes represent resource efficiency, waste reduction, energy savings, and social improvements. These values are obtained using the FI-RST model and the fuzzy evaluation method (Table 3). A review of this type has been conducted to perform a comprehensive comparative analysis of the different emerging Industry 4.0 technologies, including MES, IIoT, AM, AR, MR, and ICT, from the standpoints of economy, environment, and social compatibility. The radar chart depicts the lines from each axis of this polygon provided with economic advantage, social advantage, and environmental impact. In mapping technologies, the \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Technology**} & \multicolumn{2}{c}{**Resource Efficiency**} & \multirow{2}{*}{**Waste Reduction (\%)**} & \multirow{2}{*}{**Energy Savings (\%)**} \\  & **(\%)** & & \\ \hline MES & 20 & 25 & 18 \\ IIoT & 22 & 30 & 20 \\ AM & 18 & 35 & 15 \\ ICT & 18 & 20 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Values derived using the FI-RST model and fuzzy evaluation methods. performance of each is stated, and the lines that connect the points to form polygons can be used to visually compare the strengths of a technology.
## 4 Advanced Persistent Threat Life Cycle Analysis
### Eleven Stage Attack Model  
ATT and CK analyses of tactics are shown to be the distinct stages of an attack that a threat actor works through to accomplish the strategic goal [1]. ATT and CK matrix describes the next tactics:  
1. Initial access: Consists of the initial contact with the target to search for patient zero.
2. Persistence: The attacker seeks to gain access for a long time in the target.
3. Privilege escalation: To obtain privileges in the network is necessary to install malware or gain access to confidential data.
4. Discovery: Consists of obtaining relevant information from the target, such as system location or usernames.
5. Lateral movement: Refers to how the attacker moves within the network to search for important vulnerable information or services.
6. Collection: Collecting relevant information.
7. Exfiltration: Extracting the collected data. The following stages achieve the objective of the attack, and can be executed in parallel with the previous seven stages.
8. Execution: The execution of malware through remote connections that are carried out between the initial access stage and lateral movement.
9. Defence evasion: Consists of not being detected by the defence and detection mechanisms, for example, firewall or logs.  
Credential access: Refer to accessing the compromised system with valid credentials.
* Command and control: Consists of creating a C&C channel to communicate the attacker servers with the compromised systems of the target.  
The proposed life cycles have similarities in the methods and techniques used by the attackers in each stage. Consequently, one stage of a life cycle can be divided into several stages in other approaches to explain in more detail how the APT attack works. For this reason, researchers can select the life cycle that suits their work or take a proposed life cycle as the basis for creating a new life cycle. Each APT attack has unique characteristics, and several may use a very similar life cycle.
# Fast Deployment of a UWB-Based IPS for Emergency Response Operations
## 3 System Design
### Architecture  
The architecture of the proposed UWB-based IPS is split into three main subsystems, as can be observed in Figure 2:  
1. The **GNSS-RTK self-location system** enables the fast deployment of UWB anchors outside the target building. The highly accurate self-location of these devices is conducted thanks to the information received from both GNSS satellites and GNSS-RTK ground-based reference stations.
2. The **UWB positioning system** allows us to track an FR inside a building in real time during an ER operation. As in most RF-based IPSs, it consists of a group of anchors and a tag, which in our case will be attached to an FR.
3. The **command and control center** includes all the necessary systems to manage the IPS configuration, store the generated positioning data, and display it remotely.  
Under this architecture the anchors play a triple role: First they are used to establish an absolute and very accurate reference system for the IPS by means of the GNSS-RTK self-location system. Then, they send the obtained GNSS-RTK positions to the command and control center. Lastly, their responses to the UWB requests of the tag will be used by the latter to estimate its own position.  
As for the tag, it first communicates with the data management server to obtain the well-known positions of the network anchors. Then, it feeds a multilateration algorithm with the dictionary of anchors' positions and the UWB computed rangings to generate new position estimations as the outcome. The obtained positions will periodically be sent to the data management server.