esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Parameter-Free Slope Unit Division Method That Integrates Terrain Factors
## 3 Experimental Results  
### Slope Units Extracted via the TFPF-SU Method  
In this study, on the basis of a DEM with a spatial resolution of 8.5 m, three key terrain parameters were precisely extracted: curvature, slope, and aspect using ArcGIS software. The data were provided by the China Aero Geophysical Survey and Remote Sensing Center for Natural Resources and downloaded from Bigemap. To ensure the comparability of results, all relevant data were unified to use the 2000 National Geodetic Coordinate System, with a projected system based on the Gauss-Kruger projection in 3\({}^{\circ}\) zones, and the elevation reference adopted the 1985 National Elevation Datum. Using these parameters, we conducted an in-depth integration and classification analysis aimed at achieving a refined and scientific division of slope units. During the classification process, we strictly  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Slope Division** & **Code** \\ \hline
0–5\({}^{\circ}\) & I \\ \hline
5–15\({}^{\circ}\) & II \\ \hline
15–25\({}^{\circ}\) & III \\ \hline
25–35\({}^{\circ}\) & IV \\ \hline
35–45\({}^{\circ}\) & V \\ \hline
45–90\({}^{\circ}\) & VI \\ \hline \hline \end{tabular}
\end{table}
Table 1: Framework for slope division.  
adhered to the principle of terrain similarity, ensuring that each divided slope unit exhibited high consistency in terms of the continuity of the slope gradient (a slope should change smoothly between adjacent units, without sudden changes), the regularity of the aspect distribution (the direction of the slopes within a unit should be relatively concentrated, avoiding mixed and inconsistent directions), and the uniformity of curvature characteristics (the curvature features within a unit should remain consistent, reflecting a homogeneous terrain form). Through this comprehensive consideration and rigorous classification of slope, aspect, and curvature parameters, we ensured the precision and rationality of slope unit division, effectively revealing the subtle features and spatial distribution patterns of the terrain structure in the study area. The TFFF-SU method extracted a total of 4407 slope units within the study area.
## 6 Risk Classification and Experimental Context  
To address the influence of bias in objectively evaluating the security posture of the PHGL-COVID platform, we used a hybrid risk classification model that considers multiple dimensions. The risk levels would be defined by the combination of the CVSS v3.1 Base Score (for known CVEs), exploitability factors (e.g., privileges required, user interaction, and attack vector scope), and impact explicitly stated to the triad of confidentiality, integrity, and availability (CIA). Also, severity levels were aligned to OWASP standards for mobile and containerized/generic environments, to maintain balance with industry standards. For BLE-related vulnerabilities, the attack feasibility was assessed in practice using aspects of sniffing range, replay success, and exposure quantity while transmitting data.  
All assessments included in this risk assessment were completed in a standard testing environment. A test environment was specified, relative with sufficient ethical and technical control measures in place, to ensure a standard mHealth deployment context. The backend and server side of the system was run locally in an Ubuntu 22.04 LTS virtual machine using Docker Engine with version 24.0. The hosted containers limited external access to the server firewall, and there were no public management interfaces. The mobile component was tested using a Samsung Galaxy A55 (SM-A556E) running Android 14 with One UI 6.1, and the wearable component was Samsung Galaxy Watch 7 (SM-L305F) with Wear OS 4. The BLE traffic data were obtained with a USB Bluetooth adapter using btmon and wireshark (version 4.2.1), allowing packet-level inspection of the GATT characteristics and the advertising frames.  
All test devices remained on their factory firmware, and no rooting or unlocked bootloaders were implemented, which reflect the types of conditions in which end-users may use the devices. No real personal health data were recorded, all biometric values were simulated using test harnesses and test/dummy APIs. Also, since all participants were made aware of the testing nature and progression of this study by their informed consent, they were encouraged not to actually use their wearable and mobile device capabilities while engaged in this study. No telemetry was sent outside of the local test environment. Security testing on the mobile application followed the development process with Android, and all sessions from the test devices and applications were retained on local storage for analysis in post-testing. The ethical integrity of the security testing process was maintained throughout each audit step, especially to ensure misrepresentation or misuse of participants' biometric identifiers did not take place.
# Monitoring the Dynamics of Ephemeral Rivers from Space: An Example of the Kuiseb River in Namibia
## 3 Data Used for This Study  
### Sentinel Missions  
The Sentinel satellites constellation is composed of six satellites that has been developed by the European Space Agency (ESA) to support the Earth observations needs of the Global Monitoring for Environmental Security (GMES), which is a joint initiative between the European Commission and the ESA [23; 24]. These satellites were designed to ensure data continuity from former missions, such as SPOT/Landsat for multispectral imagery and ERS-1-2 for radar, by providing daily observations of the Earth. A variety of remote sensing sensors on board the first three satellites, Sentinel-1, Sentinel-2, and Sentinel-3, provide long-term time-series of measurements with high spatial and temporal coverage [24]. Hence, data and products derived from these Sentinel missions offer a significant scientific opportunity [24] as sources of information to address global <<grand challenges>>. For this study we used Sentinel-1 imaging radar and Sentinel-2 multispectral imaging sensor.
# Color Visual Secret Sharing for QR Code with Perfect Module Reconstruction
## 1 Introduction
proposed an improved algorithm that needs only \(k\) (\(k<n\)) shares in order to reconstruct the secret QR code [27]. In summary, to facilitate fast processing and to preserve the error correction code (ECC) capability, a module-based VSS for QR code with perfect reconstruction should be designed. However, for black and white (B/W) QR code, achieving these two goals simultaneously is a challenging problem. To design QR code VSS algorithm with module-based processing and preserving ECC, this paper proposes VSS-QR ( VSS for QR code ) system by exploring the extra freedom provided by color QR code. The secret QR code can be any standard B/W QR code. VSS-QR can hide this secret QR code into three meaningless color shares. At the decoding side, a standard QR code decoder can perfectly reconstruct the original secret QR code, using only the standard pre-processing in QR code decoder. VSS-QR is different from the ordinary color VC algorithm in [28], which is designed for natural color image and the secret image cannot be perfected reconstructed. The contributions of the proposed algorithm can be summarized as follows. 1. Compared with [PERSON]'s algorithm [24], the proposed algorithm is module-based. The basic processing unit of VSS-QR is a module in QR code, not pixel. So, the computational complexity can be significantly reduced. Compared with [PERSON]'s algorithm [25] and [PERSON]'s algorithm [26], the proposed VSS-QR does not sacrifice ECC capability of the QR code, i.e., the secret QR code can be completely reconstructed at decoder.
## 3 Numerical Proof of the Theory  
This proof intends to show that Equation (8) can be fitted with high accuracy on measured data, emphasizing its strengths and weaknesses. We chose to check it on a series of hourly data. On the one side, hourly sampling is usual in solar irradiation modeling, and, on the other side, \(\Delta t=1\) h can be regarded as an arbitrary chosen value of \(\Delta t\) within a day. The boundary cases \(\Delta t=0\) (instantaneous diffuse fraction) and \(\Delta t=\) daylight_length are discussed too. The models' performance is evaluated using two statistical indicators, normalized root mean square error (\(nRMSE\)) and normalized mean bias error (\(nMBE\)). The two indicators are defined in Appendix A.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 3 Fundamentals of Channel Estimation
### Classification of Channel Estimation Schemes
#### 3.1.1 Overview of Pilot Based (Non-Blind) CE
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Aim of Research** & **CE Schemes Applied** & **Applicable Technique or Waveform** & **Approach** & **Obtained Results** \\ \hline \multirow{6}{*}{[94]} & Design of a novel CE technique for MU-MIMO OFDMA systems & Virtual pilot based CE using iterative detection and decoding (IDD) & Pilot-assisted DDCE for MIMO -OFDM & Estimation of channel information of interfering signals using demodulation reference signal (DM-RS) & Proposed estimator achieves substantial decoder error (MIE) performance gain over conventional single user CE schemes such as MMSE with SUD and MUD interfering signals & Proposed estimator achieves substantial block error rate (MIE) performance gain over conventional single user CE schemes such as MMSE with SUD and MUD such as MMSE with SUD and MUD interfering signals & Proposed estimator achieves significant block error rate (MIE) performance gain over conventional single user CE schemes such as MMSE with SUD and MUD \\ \hline \multirow{6}{*}{[95]} & Development of an inverse channel based blind CE (QBC) scheme for CP-OFDM OFDMA & Blind CE for SISO-CP-OFDM & \multirow{6}{*}{[105]} & Development of a predetermined null subspace that corresponds to the transmitted CP-OFDM signal vector subspace & ICRC scheme outperforms the existing re-modulation based blind CE (RMPC) and repetition based blind CE (RMPC) techniques in terms of MSE at low numbers of received blocks and SNR values & \multirow{6}{*}{[105]} & \multirow{6}{*}{[105]} \\  & CP-OFDM to improve the CP performance with limited number of received blocks & & Exploitation of the inverse CIR vector orthogonality property to predetermined null subspace & & \\ \hline \multirow{6}{*}{[96]} & Design of a novel semi-blind iterative space alternating (SAGE) based CE & Semi-blind iterative SAGE & Pilot/data-assisted SAGE based CE for Massive MIMO & Iterative update of pilot based MMSE using SAGE algorithm & \multirow{6}{*}{[105]} & \multirow{6}{*}{[105]} \\  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
# Protection Strategy for Edge-Weighted Graphs in Disease Spread
## 2 Basic Definitions  
### Graphs  
The following definitions come from [31; 32].  
**Definition 1**.: _A graph \(G\) is a finite nonempty set \(V\) of objects called vertices together with a possibly empty set \(E\) of 2-element subsets of \(V\) called edges._  
To indicate that a graph \(G\) has vertex set \(V\) and edge set \(E\), we write \(G=(V,E)\). If the set of vertices is \(V=\{v_{1},v_{2},\ldots,v_{n}\}\), then the edge between vertex \(v_{i}\) and vertex \(v_{j}\) is denoted by \(e_{ij}\).  
If \(e_{ij}\) is an edge of \(G\), then \(v_{i}\) and \(v_{j}\) are adjacent vertices. Two adjacent vertices are referred to as neighbors of each other. The set of neighbors of a vertex \(v\) is called the open neighborhood of \(v\) (or simply the neighborhood of \(v\)) and is denoted by \(N(v)\). If \(e_{ij}\) and \(e_{jk}\) are distinct edges in \(G\), then \(e_{ij}\) and \(e_{jk}\) are adjacent edges.  
Definition 2: The number of vertices in a graph \(G\) is the order of \(G\) and the number of edges is the size of \(G\).  
Definition 3: The degree of a vertex \(v\) in a graph \(G\), denoted by \(deg(v)\), is the number of vertices in \(G\) that are adjacent to \(v\). Thus, the degree of \(v\) is the number of vertices in its neighborhood \(N(v)\).  
On the other hand, an important generalization of the simple graph consists in the definition of weighted graph, more specifically edge-weighted graph. Informally, an edge-weighted graph is a graph whose edges have been assigned a weight.  
Definition 4: An edge-weighted graph is a pair \((G,w)\) where \(G=(V,E)\) is a graph and \(w:E\rightarrow\mathbb{R}\) is a weight function. If \(e_{ij}\in E\) then \(w(e_{ij})=w_{ij}\).  
The adjacency matrix of \(G\) is the \(n\times n\) matrix \(A(G)=[a_{ij}]\), or simply \(A=[a_{ij}]\), where  
\[a_{ij}=\begin{cases}w_{ij}&\text{if }\ e_{ij}\in E(G)\\ 0&\text{if }\ e_{ij}\
otin E(G).\end{cases}\]  
Definition 5: The strength of a vertex \(v_{i}\), denoted by \(S(v_{i})\), is defined as the sum of the weights of all edges incident to it, this is to say  
\[S(v_{i})=\sum_{v_{j}\in N(v_{i})}w_{ij}.\]
## 4 Classification Models
### Support Vector Machine  
SVM uses a hyperplane to classify data samples into two categories. The hyperplane is built in such a way that it allocates the majority of points of the same category on the same side of the hyperplane while trying to maximize the distance of data samples from both categories to this hyperplane. The subset of data samples closest to the separating hyperplane is denoted as the support vectors [23].  
Assume that data samples in the training set \(x_{i}\), where \(x_{i}\in R^{n}\), with class labels \(y_{i}\), where \(y\)\(\in\{1,-1\}\) for \(i=1,\ldots,N\). The optimal hyperplane is defined as [24]  
\[wx+b=0 \tag{1}\]  
where \(w\) represents a weight vector and \(b\) represents bias.  
The goal is to maximize the margin, which can be achieved through a constrained optimization problem  
\[min_{w\mapsto b}\frac{1}{\gamma(w,b)}\quad subj.to\quad y_{i}(wx_{i}+b)\geq 1. \tag{2}\]  
By introducing slack parameters, the objective function is updated to:  
\[min_{w\mapsto\epsilon}\frac{1}{\gamma(w,b)}+C\sum_{n}\epsilon_{i}\quad subj.to \quad y_{i}(wx_{i}+b)\geq 1-\epsilon_{i},\epsilon_{i}\geq 0. \tag{3}\]  
The introduction of kernelization into the SVM allows it to solve a difficult nonlinear task in data mining. For the SVM classifier, we searched through the following parameters:  
* Kernel: linear, RBF, poly
* C: 0.1, 0.2, 0.5, 1, 3, 5, 7, 10, 20, 30, 35, 40, 45, 50, 70, 100 (only for poly and RBF kernel), 1000 (only for poly and RBF kernel)
* Gamma (only RBF): 0.0001, 0.001, 0.01, 0.025, 0.5, 0.1
* Polynomial degree (only poly): 1, 2, 3, 4, 5.
# Nonlinear Extended-state ARX-Laguerre PI Observer Fault Diagnosis of Bearings
###### Abstract  
This paper proposes an extended-state ARX-Laguerre proportional integral observer (PIO) for fault detection and diagnosis (FDD) in bearings. The proposed FDD technique improves fault estimation using a nonlinear function while generating a robust residual signal using the sliding mode technique, which can indirectly improve the performance of FDD. Experimental results indicate that the system modeling error in a healthy condition is less than \(2.5\times 10^{-10}\) N.m. In the next step, the ARX-Laguerre PIO is designed to define the state and output of the system observer. The high gain extended-state observer is designed in the third step to estimate the mechanical (bearing) faults based on the nonlinear function. In the last step, robust residual signals are generated based on the sliding mode algorithm for accurate fault identification. This approach improves the performance of an ARX-Laguerre linear PIO method. Employing the proposed method, we demonstrate that in the presence of uncertainties and disturbances, the ball, inner, outer, inner-ball, outer-ball, inner-outer, and inner-outer-ball failures with various motor torque speeds (300 RPM, 400 RPM, 450 RPM, and 500 RPM) and crack sizes (3 mm and 6 mm) are detected, identified, and estimated efficiently. The effectiveness of the proposed technique is compared with an ARX-Laguerre proportional integral observation (ALPIO). Experimental results indicate that the proposed technique outperforms the ALPIO technique, yielding 17.82% and 16.625% performance improvements for crack sizes of 3 mm and 6 mm, respectively.  
fault diagnosis; extended-state observation technique; proportional integral observer; ARX-Laguerre algorithm; bearings; sliding mode algorithm +
Footnote †: journal: Applied
## 2 Materials and Methods
### Notions and Base Data for Scheduling  
Sticking to the industrial scheduling also known as the Job Shop problem, let us define the main notions that we will use in the formulation:  
* The enterprise functioning process utilizes resources of different types (for instance machines, personnel, riggings, etc.). The set of the resources is indicated by a variable \(R=\{R_{r}\}\), \(r=1,\ldots,|R|\).
* The manufacturing procedure of the enterprise is formalized as a set of operations \(J\) tied with each other via precedence relations. Precedence relations are brought to a matrix \(G=<g_{ij}>\), \(i=1,\ldots,|J|\), \(j=1,\ldots,|J|\). Each element of the matrix \(g_{ij}=1\) iff the operation \(j\) follows the operation \(i\) and zero otherwise \(g_{ij}=0\).
* Each operation \(i\) is described by duration \(\tau_{i}\). Elements \(\tau_{i}\), \(i=1,\ldots,|J|\), form a vector of operations' durations \(\longrightarrow\tau\).
* Each operation \(i\) has a list of resources it uses while running. Necessity for resources for each operation is represented by the matrix \(Op=<op_{ir}>\), \(i=1,\ldots,|J|\), \(r=1,\ldots,|R|\). Each element of the matrix \(op_{ir}=1\) iff the operation \(i\) of the manufacturing process allocates the resource \(r\). All other cases bring the element to zero value \(op_{ir}=0\).
* The input orders of the enterprise are considered as manufacturing tasks for the certain amount of end product and are organized into a set \(F\). Each order is characterized by the end product amount \(v_{f}\) and the deadline \(d_{f}\), \(f=1,\ldots,|F|\). Elements inside \(F\) are sorted in the deadline ascending order.  
Using the definitions introduced above, we can now formalize the scheduling process as residing all \(|J|\) operations of all \(|F|\) orders on the set of resources \(R\). Mathematically this means defining the start time of each operation \(i=1,\ldots,|J|\) of each order \(f=1,\ldots,|F|\).
# Reducing Q-Value Estimation Bias via Mutual Estimation and Softmax Operation in MADRL
## 4 Multi-Agent Softmax Twin Delayed Deep Deterministic Policy Gradient (MASTD3)  
### Theory Related to Softmax Bellman Operation  
The standard Bellman operator is defined as:  
\[\mathcal{T}Q(s,a)=R(s,a)+\gamma\sum_{s^{\prime}}P\big{(}s^{\prime}\mid s,a \big{)}\max_{a^{\prime}}Q\big{(}s^{\prime},a^{\prime}\big{)} \tag{15}\]  
We define the softmax Bellman operation symbol as:  
\[\mathcal{T}_{\text{soft}}\,Q(s,a)=R(s,a)+\gamma\sum_{s^{\prime}}P\big{(}s^{ \prime}\mid s,a\big{)}\frac{\exp(\tau Q(s^{\prime},a^{\prime}))}{\sum_{s}\exp( \tau Q(s^{\prime},a))}Q\big{(}s^{\prime},a^{\prime}\big{)} \tag{16}\]  
The optimal action-value function \(Q^{*}(\ s,a)\) outputs the maximum cumulative return that can be obtained after choosing action \(a\) at state \(s\). \(Q^{*}\) is the fixed point of the standard Bellman operator \(\mathcal{T}Q\); that is, starting from any starting \(Q_{0}\), we have:  
\[\lim_{k\rightarrow\infty}\mathcal{T}^{k}Q_{0}=Q^{*} \tag{17}\]However, due to the noise of the function approximator, the maximization operation of the standard Bellman operator always selects the maximum Q value for each state. This makes the algorithm exceptionally sensitive to the overestimated Q value of the action, and the overestimated Q value will be explicitly updated, which leads the Q value to the overestimation problem.  
Softmax operation is generally considered to result in suboptimal action-value function and interfere with the convergence of the Bellman operator. [PERSON] et al. (2014) proved that the softmax Bellman operator can quickly converge to the standard Bellman operator, and for any\((s,a)\), when \(k\) approaches positive infinity, the difference between \(\mathcal{T}_{\text{soft}}^{k}\,\mathcal{Q}_{0}(\,\text{s},\text{a})\) and \(Q^{*}(\,\text{s},\text{a})\) is bounded:  
\[\begin{split}\limsup_{k\to\infty}\mathcal{T}_{\text{soft}}^{k} \,\mathcal{Q}_{0}(\,\text{s},\text{a})\leq Q^{*}(\,\text{s},\text{a})\\ \liminf_{k\to\infty}\mathcal{T}_{\text{soft}}^{k}\,\mathcal{Q}_{0 }(\,\text{s},\text{a})\geq Q^{*}(\,\text{s},\text{a})-\frac{\gamma(m-1)}{(1- \gamma)}\max\biggl{\{}\frac{1}{\tau+2},\frac{2Q_{\max}}{1+\exp(\tau)}\biggr{\}} \end{split} \tag{18}\]  
Through experiments, [PERSON] et al. discovered that the combination of softmax Bellman operator and Deep Q Network yields better results than the Double Q-learning algorithm. They proved that the softmax Bellman operator can reduce the high estimation bias of Q value and believe this is the key reason for the superior performance of this method.
# Research Progress and Engineering Applications of Viscous Fluid Mechanics
## 2 Advances in the Study of the State of Motion of Viscous Fluids
### Experimentation and Research
#### 2.1.2 Experiments and Studies on Droplet Impacts in Viscous Fluids
In addition to some droplet collision deposition and flight problems, researchers used viscous liquids of different materials and sprinkler heads of different specifications to simulate the spraying and collision process of viscoelastic fluid through experiments and observed the influence of viscoelasticity caused by the presence of polymers in the solution on the droplet deposition process and the influence of sprinkler heads on the atomization rupture of the solution with high accuracy [56; 57]. Figure 6: Experimental system installation diagram. In the specific experiment, the lifting table surface is first controlled to fall, and the distance between the needle and the substrate is 3 mm (determined by the scale). The distilled water is controlled by a microinjection system and gathers into droplets at the needle point, breaking the quasi-static equilibrium and falling freely onto the substrate. Then, the table rises by 3 mm, and the second drop and the first drop collide and coalesce, making a large drop twice the volume of a single drop. After the ascending table reaches the preset falling height h, small droplets are released from the tip of the needle in the same vertical position to ensure that the collision droplet is centered on the fixed droplet. The experiment is repeated four times for each group. The whole process is monitored in real time by high-speed cameras and computers, taking images of the impact [53]. 1—microfluidic injection system; 2—2 mL syringe; 3—impingement droplet; 4—solidified droplet; 5—substrate; 6—scale; 7—high-precision three-direction elevator; 8—optical platform; 9—large-stroke elevator; 10—high-speed camera; 11—computer; 12—light source. In the above experiments, the construction of the experimental setup and the collection and prediction of the results are mostly based on the new century high-speed camera, which can instantly turn the droplet collision into thousands of frames for the researcher to study the mechanism of droplet collision in a more detailed and accurate way. Therefore, high-speed cameras play an important role in the study of viscous liquids and the motion of some other fluids.