esa-sceva/satcom-chunk-collection huggingface dataset:
## 2. Stray Current Corrosion Mechanism and Interference Characteristics
### Corrosion Mechanism and Interference Characteristics of AC Stray Current
#### 2.2.1 Rectification Model  
The rectification model states that the occurrence of AC corrosion is due to the current released by the metal during anodic polarization being greater than the current during cathodic reduction. During the positive half-cycle of the alternating current, metal ions are released from the metal surface. In the negative half-cycle, some of these metal ions should be reduced and deposited back onto the metal surface, but this is not fully realized. The accumulated loss of metal ions ultimately leads to the occurrence of corrosion [22]. The overall current produced by polarization is different from the total current produced by cathodic polarization, resulting in a net current. However, the rectification model assumes that only oxygen reduction occurs in the cathode process and is based on the absence of cathodic protection. This makes it have significant limitations.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 4 Signal Processing from RSL to Rainfall Map
### Rainfall Map Reconstruction  
Interpolation algorithm and tomographic reconstruction technology are mainly used to generate rainfall map from path-average rain rate ([PERSON] et al., 2020; [PERSON] et al., 2021). Inverse distance weighting (IDW) and ordinary kriging (OK) are two of the most common spatial interpolation algorithms for a geography information system (GIS) ([PERSON] et al., 2020; [PERSON] et al., 2021). IDW is a fairly simple and robust spatial interpolation point rainfall measurement method. In IDW, if a rainfall intensity value for a given location needs to be predicted, the points closer to the predicted location have a greater impact on it, and so the weight of the surrounding points is set based on the distance to the predicted location, i.e., a short distance means higher weight for a surrounding. The OK algorithm requires enough statistical information of the sample field. Kriging is very suitable for interpolating highly irregular data points. However, OK has its own limitations and needs to make some assumptions, such as isotropy and statistical stationarity. [PERSON] et al. (2020) compared the performance of the IDW and OK algorithms, and the results show that the performance improves with the increase in decorrelation distance (i.e., less intermittent field). The OK interpolation technique uses more prior/auxiliary information and correlates slightly better with ground truth than IDW, and the performance of OK and IDW-based algorithms with multiple points representing a CML is slightly better than that with only one point representing a CML. [PERSON] et al. (2020) applied the tomography technology to reconstruct the two-dimensional rainfall accumulation field, and it was found that the accuracy of the reconstruction algorithm can be improved by increasing the network density. For the reconstruction of 2-D rainfall field, [PERSON] et al. (2020) described the statistical characteristics of the measurements and then used the compressed sensing (CS) theory, i.e., phase transition diagram, to solve the rain-field sparsity problem.
## 3 Methods
### Loss Function  
During the development of deep learning, various loss functions have been proposed. \(L_{1}\) loss and perceptual loss [39] are the commonly used loss functions. \(L_{1}\) loss can significantly improve the peak signal-to-noise ratio (PSNR) value of super-resolution results and is simple and effective to calculate. However, \(L_{1}\) loss also has the problem of excessive smoothing, which is not conducive to the recovery of image details. For the progressive structure, different loss functions were used at different levels, and the losses at different levels were allowed to interact with each other.  
Perceptual loss was used at the first and second BSRC, and a combination of perceptual loss and \(L_{1}\) loss was used at the third and fourth BSRC. The loss function of the first and second BSRC can be formulated as  
\[Loss_{1}=L_{p}, \tag{11}\]  
\[Loss_{2}=L_{p}+\frac{\alpha}{m_{2}}Loss_{1}, \tag{12}\]  
where \(L_{p}\) is the perceptual loss and \(\alpha\) is a constant adjusted for training. This is because the recovery of image details relies heavily on the first two levels, and once a detail is lost somewhere, it is difficult to make up for it subsequently. Perceptual loss enables better control of detail loss by comparing the multi-scale feature map differences between the output and the ground truth. In the third and fourth BSRC, while controlling the loss of details, the output needs to match the human eye's perception. Thus, the \(L_{1}\) loss was added to the perceptual loss, and the sum can be formulated as:  
\[L_{p,1}=\phi_{1}L_{p}+\phi_{2}L_{1}. \tag{13}\]where \(\phi_{1}\) and \(\phi_{2}\) are adjustable weights. The \(L_{p,1}\) is used as part of the 3 rd and 4 th BSRC's loss.  
Due to our progressive structure and multi-level supervision, it is also necessary to consider the relationship between losses of multiple BSRCs. The loss of the 3 rd and 4 th BSRCs can be formulated as  
\[Loss_{n}=L_{p,1}+\sum_{l=1}^{n-1}\frac{\alpha_{l}Loss_{l}}{m_{l+1}m_{l+2} \cdots m_{n}}. \tag{14}\]  
As seen in the above equation, the loss of each BSRC includes the loss of the BSRCs before it. This is because the loss of detail in the output of the earlier BSRC will have an impact on the super-resolution performance of the subsequent BSRC. The penalty for the loss of detail in the earlier BSRC should be increased. It should also be noted that the weights of the losses in the previous BSRC are also different. The further the distance between two BSRCs, the larger the product of \(m_{l+1}m_{l+2}\cdots m_{n}\). A smaller weight should be given to the BSRC that is farther away as a balance.
# Research on Non-Contact and Non-Fixed Cable Force Measurement Based on Smartphone
## 2 The Proposed Method for Cable Force Determination
### Image Data Processing and Analysis
#### 2.2.5 Vibration Displacement Correction  
Distinct from previous approaches using fixed cameras to collect vibration video of cables, in this study, inevitable shaking of the handheld terminal exists. As a result, the shooting angles and distances will constantly change in the video data, which adversely impacts the displacement measurement. In order remove such noise, the distance between two fixed points on the structure is used for correction. Specifically, the actual distance between P3 and P4 remains unchanged, yet the pixel distance \(\left|\text{P}_{3}\text{P}_{4}\right|\) varies in different frames. Thus, the pixel distance in the i-th frame \(\left|\text{P}_{3}\text{P}_{4}\right|_{i}\) reflects the noises caused by the handheld shooting method. By multiplying a correction coefficient, \(\frac{\left|\text{P}_{3}\text{P}_{4}\right|_{i}}{\left|\text{P}_{3}\text{P} _{4}\right|_{i}}\), the estimated distance between targets on the cable in the \(i\)-th frame will be corrected. The displacement-time history is presented in Figure 7.
# Blockchain-Enabled Privacy-Preserving Ecosystem for DNA Sequence Sharing
## 3 System Design and Implementation
### System Architecture
#### 3.1.1 Decentralized Identity Chain  
The decentralized identity chain manages the storage and verification of digital identities and personal materials. Patients and research institutes must register using their ID numbers and wallet addresses, validated by the Ministry of the Interior. Upon successful certification, the Ministry generates a personal contract for each entity, representing their digital identity on the blockchain. These contracts, fully owned and accessible only by their registrans, facilitate the storage of personal data needed for system services.  
The decentralized identity chain is based on the blockchain-based decentralized identity framework by [PERSON] et al. [6], with enhancements tailored for the system. Ethereum serves as the underlying platform due to its robust wallet support and immutability.  
* Identity Manager Contract: This contract manages the creation and binding of decentralized identities. Trusted institutions, such as the Ministry of the Interior, can invoke functions like createUser() to generate new identities. This streamlined process eliminates unbound contracts and ensures data integrity.
* Identity Contract: Each user is assigned a unique identity contract, granting them exclusive ownership. Users can securely store and manage personal materials using functions such as addData() and getData(). To enhance privacy, all materials are encrypted with the user's wallet public key.
## 3 Results  
### Water Bio-Optical Conditions  
The bio-optical parameters in our investigated water regions showed very large dynamic ranges, which were collected from the four cruises (Figure 7). TSM varied from 0.6 to 223.9 mg\(\cdot\)L\({}^{-1}\) and had a mean of 11.2 \(\pm\) 22. 7 mg\(\cdot\)L\({}^{-1}\). The large variation coefficient (CV)value (202.51%) indicates its large variation. The particulate backscattering coefficient at 488 nm, \(b_{\text{bp}}\) (488), ranged between \(\sim\)0 and 1.21 m\({}^{-1}\), had a mean of 0.19 \(\pm\) 0.17 m\({}^{-1}\), and had a large CV (141.7%).  
Correspondingly, the particulate scattering and beam attenuation coefficients, \(b_{\text{p}}\)(488), \(c_{\text{p}}\)(532), and \(c_{\text{p}}\)(555), also showed wide variation ranges, as well as large CV values. The particulate backscattering ratio \(B_{\text{p}}\)(488), i.e., \(b_{\text{bp}}\)/\(b_{\text{p}}\), changed from \(\sim\)0 to 0.10 when the PSD slope, \(j\), was observed to be in the range of 3.2-5.0. Note that a large span also appeared for the bulk refractive index of particles \(n_{\text{p}}\). Meanwhile, the collected in situ \(R_{\text{rs}}\)(\(\lambda\)) showed large variations in terms of both magnitude and spectral shapes (Figure 8). These \(R_{\text{rs}}\)(\(\lambda\)) spectra had similar spectral properties to those seen in previous reports on coastal waters, for example, the studies of [PERSON] et al. (2013) [32] and [PERSON] et al. (2014) [36], which indicate typical optically complex turbid water conditions in the study area.  
Figure 7: Frequency distributions of **(A)** TSM, (**B**) \(b_{\rm p}\)(488), (**C**) \(b_{\rm p}\)(488), (**D**) \(B_{\rm p}\)(488), (**E**) \(c_{\rm p}\)(532), (**F**) \(c_{\rm p}\)(555), (**G**) particle size distribution (PSD) slope, \(j\), and (**H**) \(n_{\rm p}\). The black lines represent log-normal distribution fitting curves.
## 4 Discussion
### Radar Line of Sight (RLOS)  
Both spaceborne radar and GB radar observation methods are limited by the feature of the RLOS observation, and displacement perpendicular to the line of sight cannot be measured. The movement direction of glaciers is actually three-dimensional. In addition to movement in the line of sight direction, glaciers also move in the vertical direction. In the following research, combining multi-source data analysis of glacier vertical movement is considered to achieve a three-dimensional measurement of glacier movement speed through vector synthesis.
## 1 Introduction
### Park and Ang Damage Index  
#### 1.2.1 Definition  
The damage index (DI) introduced by Park and Ang [11; 12] is expressed by Equation (1). This DI value is based on the structural deformation and the hysteretic energy response due to seismic excitations.  
\[DI=\frac{u_{m}}{u_{u}}+\beta\frac{E_{h}}{F_{y}u_{u}} \tag{1}\]  
where:  
\(u_{m}\): Maximum displacement response of structure element due to earthquake,  
\(u_{u}\): Ultimate displacement capacity under a monotonic loading,  
\(E_{h}\): Hysteretic energy dissipated by the structural element,  
\(F_{y}\): Yield force,  
\(\beta\): Non-negative parameter based on repeated loading effect.  
The relationship between the DI and the physical damage appearance for RC elements suggested by Park and Ang [12] is shown in Table 1. The parameters in Equation (1) are explained in the following passages.
## 2 Materials and Methods
### Electrode Fabrication and Electrochemical Studies  
The electrode slurries for the electrode's functional layers were prepared by mixing the NCNCO00-NCNCO04 electrode powders with ethanol and a 5 wt.% alcohol solution of the polyvinyl butyl binder in an agate mortar for 1 h. The electrode layers were deposited on the polished electrolyte substrates by brushing with a thickness of 20-30 \(\upmu\)m and a relative deposition density of 7-10 mg cm\({}^{-2}\). The layers were sintered at different temperatures in the range of 900-1000 \({}^{\circ}\)C for 2 h in order to establish the optimal sintering conditions. In the case of the two-layered structure, the current collectors with the composition of LNF + 3 wt.% CuO or LSM + 2 wt.% CuO were deposited with a thickness of 20-30 \(\upmu\)m over the sintered functional layer and sintered at 900 \({}^{\circ}\)C for 2 h and at 950 \({}^{\circ}\)C for 2 h, respectively. The CuO-modified LNF and LSM collectors were developed earlier and successfully applied to the different electrodes to ensure stable current collection [31; 33; 51; 52]. The microstructure of the electrodes was explored using an electron microscope, Tescan Vega 4 (Tescan, Kohoutovice, Czech Republic). The error of the particle size calculation was equal to 3%.  
The electrode performance was studied by an impedance spectroscopy method using an electrochemical station comprising a 1260 Solartron impedance analyzer and a 1287 Solartronpotentiostat/galvanostat (Ametek, West Sussex, UK). The measurements were carried out in a frequency range of 0.01 Hz-500 KHz with an applied AC signal amplitude of 30 mV, 30 points per decade. The samples were placed into the measurement cell between the platinum current collecting grids (mesh size of 0.1 \(\times\) 0.1 cm) connected to the measuring station via the two-electrode four-cable scheme. The EIS spectra were collected in humidified air (a flow rate of 25 SCCM) in the temperature range of 600-850 \({}^{\circ}\)C with a step of 50\({}^{\circ}\) and an equilibration time of 30 min before measurements. Each measurement was finalized by recording the total _dc_ cell resistance (\(R_{dc}\)). The spectra obtained were fitted using Z-View2 software and pyDRTtools. The measurement error was estimated as \(\sim\)3%.
## 2 Methodology
### Thematic Analysis of the Top-Cited Articles  
In addition to the bibliometric mapping, a qualitative thematic analysis was performed on the 20 most-cited articles identified in the corpus. Each article was reviewed in full and thematically coded using an inductive approach, guided by recurring categories such as research objective, AI/ML technique, application domain, and observed or simulated outcomes. This thematic classification was conducted following a qualitative interpretive method inspired by the principles of grounded theory [34], allowing themes to emerge directly from the content of the articles without applying a predefined taxonomy. This process enabled the identification of major research streams, which were subsequently cross-referenced with the bibliometric clusters to ensure consistency, coherence, and conceptual saturation.
# Non-Iterative Cluster Routing: Analysis and Implementation Strategies
## 3 Methods  
### Notation and Terminology  
Several terms and definitions are used in the relevant literature, defined as follows:  
* **v**, _Vote_: A vote is the prediction of a capsule, resulting from the multiplication of a weight matrix with a capsule.
* **D**, _Depth or Length of a Capsule_: The number of convolutional units in a capsule.
* **u**, _Capsule_: A capsule with length or depth of **D**.
* **K**, _Number of Votes per Cluster_: Each cluster has **K** votes.
* **N**, _Number of Clusters_: The number of vote clusters in each channel of a capsule layer.
* **W**, _Weight Matrix_: A weight matrix is multiplied with a capsule to create a vote **v**. There are **K** matrices to produce **K** votes in a cluster.
* **C**, _Number of Channels_: Each capsule layer has **C** channels.
* **G**, _Number of Groups_: Each group has a number of clusters.  
In cluster routing, a capsule layer has **C** channels, each channel has **N** clusters, and each cluster has **K** votes.
## 4 Applying ADMM to the Ordered Ridge Regression
### Experimental Results of Real Data
The reason for this is that strongly correlated variables are present in the leukemia dataset. In general, the ordered elastic net performs better than the ordered \(\ell_{1}\) and lasso. Figure 4 shows the ordered elastic net solution paths and the variable selection results. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**q** & **Method** & **Test Error** & **\#Genes** & **Time** & **\#Iter** \\ \hline \multirow{4}{*}{0.1} & Lasso & 2.352941 & 6 & 5.490234 s & 10,000 \\  & The ordered \(\ell_{1}\) & 2.235294 & 56 & 16.208356 s & 10,000 \\  & The ordered \(\ell_{2}\) & 2.117647 & All & 0.176351 s & 216 \\  & The ordered \(\ell_{1,2}\) & 2.352941 & 109 & 1.391347 s & 2104 \\ \hline \multirow{4}{*}{0.2} & Lasso & 2.352941 & 6 & 5.419032 s & 10,000 \\  & The ordered \(\ell_{1}\) & 2.352941 & 65 & 14.9903179 s & 10,000 \\  & The ordered \(\ell_{2}\) & 2.117647 & All & 0.167623 s & 197 \\  & The ordered \(\ell_{1,2}\) & 2.352941 & 107 & 1.046763 s & 1597 \\ \hline \multirow{4}{*}{0.3} & Lasso & 2.352941 & 6 & 5.394828 s & 10,000 \\  & The ordered \(\ell_{1}\) & 2.352941 & 85 & 15.477436 s & 10,000 \\  & The ordered \(\ell_{2}\) & 2.117647 & All & 0.140347 s & 185 \\  & The ordered \(\ell_{1,2}\) & 2.117647 & 108 & 0.820148 s & 1276 \\ \hline \multirow{4}{*}{0.4} & Lasso & 2.235294 & 7 & 5.470428 s & 10,000 \\  & The ordered \(\ell_{1}\) & 2.352941 & 90 & 12.206446 s & 10,000 \\  & The ordered \(\ell_{2}\) & 2.117647 & All & 0.135451 s & 178 \\  & The ordered \(\ell_{1,2}\) & 2.0 & 107 & 0.685255 s & 1055 \\ \hline \multirow{4}{*}{0.5} & Lasso & 2.0 & 8 & 5.387423 s & 10,000 \\  & The ordered \(\ell_{1}\) & 2.352941 & 126 & 13.226632 s & 10,000 \\  & The ordered \(\ell_{2}\) & 2.117647 & All & 0.126034 s & 172 \\  & The ordered \(\ell_{1,2}\) & 2.0 & 102 & 0.603964 s & 871 \\ \hline \multirow{4}{*}{Average} & Lasso & 2.2588234 & 6.6 & 5.426189 s & 10,000 \\  & The ordered \(\ell_{1}\) & 2.3294116 & 84.4 & 14.4218098 s & 10,000 \\ \cline{1-1}  & The ordered \(\ell_{2}\) & 2.117647 & All & 0.1491612 s & 189.6 \\ \cline{1-1}  & The ordered \(\ell_{1,2}\) & 2.1647058 & 106.6 & 0.9094954 s & 1380.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of variable selection in leukaemia dataset.
## 3 Proposed Methodology
### Estimation of Link Delay
#### 3.3.6 Computation of the Link Delay Matrix  
Thus, under low contention scenarios, using the DNN to predict exact one-hop wireless delay (\(\mathcal{D}_{iWL}\)) and wired delay (\(\mathcal{D}_{iWL}\)) at each node, we can compute the link delay matrix \((D[N,N,2])\) containing link delays of each and every link of the network in both communication channels required for the routing algorithm, as shown in Equation (35):  
\[D[i,j,k]\ =\ \left\{\begin{array}{l}\mathcal{D}_{iWL}-\overline{\mathcal{R} _{i}}\times(4\overline{t_{prop,i}}+4t_{prop,ijk})\ ;\ if\ link\ i-j\ is\ wireless\ (k=0)\\ \mathcal{D}_{iWI}-\overline{\mathcal{R}_{i}}\times(\overline{t_{prop,i}}+t_{ prop,ijk})\ ;\ if\ link\ i-j\ is\ wired\ (k=1)\\ 0\ ;\ if\ i=j\\ \large{\large{\it large}\ ;\ if\ link\ i-j\ in\ k^{th}\ communication\ channel\ does\ not\ exist\end{array}\right. \tag{35}\]  
Note that, as evident from Equation (35), the delay for its own link \(D[i,i,k]\) is zero. In Equation (35), \(t_{prop,ijk}\) is the propagation delay from the \(i\)th node to the \(j\)th node using the \(k\)th communication channel type. Note that, within the same communication channel, even for links existing between a given source node and different other nodes, a link delay difference occurs due to the propagation delay, which is very low. However, between different communication channels (wired and wireless), even from the same source node (\(i\)) to another given destination node (\(j\)), the link delay can be significantly different, as computed from the delay prediction machine learning model given in Figure 10.
# Using Satellite-Derived Vegetation Products to Evaluate LDAS-Monde over the Euro-Mediterranean Area
## 3 Results
### Impact of the Assimilation on SSM and LAI

Table 1: Statistics between the simulations (open-loop and analysis) and the observations for CGLS SSM, CGLS LAI, GLEAM Evapotranspiration (E), FLUXNET-MTE GPP and between observed GOME-2 SIF and simulated GPP over the Euro-Mediterranean area from 2007–2015: bias, correlation (R), Root-Mean-Square Difference (RMSD), Standard Deviation of Differences (SDD) and the number of observations.
## 1 Introduction  
A WSN is made up of several sensor nodes that are connected in an ad hoc and transitory fashion to detect data and transmit it to a centralized location known as a base station (BS) or sink node [1]. Sensor nodes face several limitations in terms of processing power, energy resources, memory, and storage, which are not present in standard wireless networks. The main purpose of WSNs is to arbitrarily disperse sensor nodes in unsupervised places and enable wireless communication. Various routing algorithms have been designed in conventional networks to improve network performance and growth in terms of delay and average throughput. Conventional algorithms are not ideal for IoT applications because the structure of the IoT is complicated and more dynamic in an unstable wireless environment. Numerous information and network vulnerabilities happen as a consequence of the rapid expansion of IoT systems, obstructing their growth. In general [2], data aggregation and forwarding techniques can be divided into two groups. First, there is a framework technique which gathers sensor information without the need for a fixed framework and conducts data aggregation utilizing incomplete data. Second, there is a framework technique that splits the network field into various clusters. A single local data-aggregator node, which gathers data from all its connected members and executes aggregation operations, exists within each region. It then sends the consolidated data to the sink node over a pre-established communications network. Sensor nodes operate independently in most WSN applications and are vulnerable to a variety of security risks.  
A WSN is a network of several nodes which senses the environment, manipulates the acquired data and thereafter transmits and receives the sensed information in a particular network [3]. The fact that these sensor nodes have low maintenance costs, are self-configurable and have an abundance of applications in which they play a significant role have made them a favorite for every sector of human life. If the above conditions are met, A WSN facilitates us in applications in different sectors such as agriculture, military operations, health monitoring and environmental sensing [4]. The basic architecture of IoT-WSN is demonstrated in Figure 1. The sensor nodes sense the data and transmit them to the base station or sink. From there, the data are forwarded to the user via the Internet to perform further necessary operations. Each of the sensor nodes used in a WSN is resource-constrained due to its small-sized configuration [5]. The main components of any sensor node include a processing unit for different computations, a transceiver for transmission and reception of data, a sensing unit comprising sensor and ADC and a power unit for providing a power source to the sensor node.