esa-sceva/satcom-chunk-collection huggingface dataset:
# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 2 Evolution and Enablers of Industrial Internet of Things
### Industry 4.0-Key Enabling Technologies
With the evolution of Industry 4.0, 5G is rapidly evolving in order to meet the requirement of IIoT applications mainly real time functioning, energy efficiency, less power consumption, shared spectrum regulation, reliable communication, and handing massive amount of data. Almost 90% of needs met using fixed line 3G and 4G cellular technologies, but need for deployment of industrial revolution can be fulfilled using 5G. 5G as an enabler of industry 4.0 gives multi-channel, capability, multi-network management, operating both local and global networks, supporting heterogeneous networks [27]. Mobile computing and cloud computing brings accurate data for IIoT application and provide efficiency to industry 4.0 infrastructure. Details of cloud computing in comparison with fog and edge computing is explained in the next section.
# A Review of Lithium-Sulfur Batteries Based on Metal-Organic Frameworks: Progress and Prospects
### Overview of Lithium-Sulphur Batteries  
The operational principle of lithium-sulfur batteries (LSBs) is rooted in the electrochemical reaction between sulfur and lithium. During the discharge process, sulfur reacts with lithium to produce lithium sulfide (Li\({}_{2}\)S), a reaction that is reversed during charging. Despite the apparent simplicity of this chemical transformation, the practical deployment of LSBs encounters numerous challenges [18]. These include inherent electronic and ionic insulation, the shuttle effect, the growth of lithium dendrites, and the volume expansion of polysulfides, which collectively impair the performance of LSBs. These issues become more pronounced at high sulfur loadings, resulting in rapid capacity degradation [19; 20; 21]. Consequently, the development of materials that can facilitate charge transfer, mitigate the shuttle effect, and enhance conversion kinetics is crucial for realizing the practical application of LSBs with high energy densities [22; 23; 24].
# MILP-Based Approach for High-Altitude Region Pavement Maintenance Decision Optimization
## 4 Model Transformation
### Transformation of Nonlinear Terms
\begin{array}{l}s_{i,u}\leq S_{i,0}\times e^{u_{i,1} \times u+\beta_{i,1}}+\sum\limits_{m\in\mathbf{M}}\sum\limits_{u\in\{u\}\in\{u\}} \sum\limits_{j\in\mathbf{I}}z_{i,j,uu,m}\times E_{j}\times e^{u_{i,1}\times(u-u)+ \beta_{i,1}},\forall i\in\mathbf{I},u\in\mathbf{U}\\ s_{i,u}\geq S_{i,0}\times e^{u_{i,1}\times u+\beta_{i,1}}+\sum\limits_{m\in\mathbf{M }}\sum\limits_{u\in\{u\}\in\{u\}}\sum\limits_{j\in\mathbf{I}}z_{i,j,uu,m}\times E _{j}\times e^{u_{i,1}\times(u-u)+\beta_{i,1}}-M\times v_{i,u},\forall i\in\mathbf{ I},u\in\mathbf{U}\\ s_{i,u}\geq S_{\max}-M\times(1-v_{i,u}),\forall i\in\mathbf{I},u\in\mathbf{U}\\ r_{i,u}^{\prime}=\ln(\alpha_{i,2})+\beta_{i,2}\times s_{i,u},\forall i\in\mathbf{I },u\in\mathbf{U}\\ \sum\limits_{j\in\mathbf{I}}\sum\limits_{i\in\mathbf{I}}n_{i,j,u,m}\times T_{u,m}, \forall u\in\mathbf{U},m\in\mathbf{M}\\ \sum\limits_{m\in\mathbf{M}}\sum\limits_{u\in\mathbf{U}}\sum\limits_{j\in\mathbf{I}}\sum \limits_{i\in\mathbf{I}}C_{j}\times L_{i}\times B_{i}\times z_{i,j,u,m}\leq C_{ \max}\\ \sum\limits_{m\in\mathbf{M}}\sum\limits_{j\in\mathbf{I}}\sum\limits_{i\in\mathbf{C}_{j} \times L_{i}\times B_{i}\times z_{i,j,u,m}\leq C_{u,\max},\forall u\in\mathbf{U}\\ h_{ij}\geq\frac{G_{j}\times x_{ij}\times L_{i}\times B_{i}}{Q\times O},\forall i \in\mathbf{I},j\in\mathbf{J}\\ h_{ij}\leq\frac{G_{j}\times x_{ij}\times L_{i}\times B_{i}}{Q\times O}+0.99999, \forall i\in\mathbf{I},j\in\mathbf{J}\\ y_{i,u,m}\geq 0,\forall i\in\mathbf{I},u\in\mathbf{U},m\in\mathbf{M}_{s}\\ y_{i,u,m}=0,\forall i\in\mathbf{I},u\in\mathbf{U},m\in\mathbf{M}_{u}\\ z_{i,j,u,m}\geq x_{ij}+y_{i,u,m}-1,\forall i\in\mathbf{I},j\in\mathbf{I},u\in\mathbf{U},m \in\mathbf{M}\\ z_{i,j,u,m}\leq x_{ij},\forall i\in\mathbf{I},j\in\mathbf{J},u\in\mathbf{U},m\in\mathbf{M}\\ z_{i,j,u,m}\leq y_{i,u,m},\forall i\in\mathbf{I},j\in\mathbf{J},u\in\mathbf{U},m\in\mathbf{M}\\ n_{i,j,u,m}\leq h_{ij,j}-h_{\max}\times(1-y_{i,u,m}),\forall i\in\mathbf{I},j\in\mathbf{ J},u\in\mathbf{U},m\in\mathbf{M}\\ n_{i,j,u,m}\leq h_{i,j,u,m}\times y_{i,u,m}\forall i\in\mathbf{I},j\in\mathbf{J},u\in\mathbf{U},m \in\mathbf{M}\\ n_{i,j,u,m}\geq 0,\forall i\in\mathbf{I},j\in\mathbf{J},u\in\mathbf{U},m\in\mathbf{M}\\ \text{Constraints}(2),(3),(5),(6),(10)-(12)\\ x_{ij}\in\{0,1\},\forall i\in\mathbf{I},j\in\mathbf{J}\\ v_{i,u}\in\{0,1\},\forall i\in\mathbf{I},u\in\mathbf{U}\\ y_{i,u,m}\in\{0,1\},\forall i\in\mathbf{I},u\in\mathbf{U},m\in\mathbf{M}\\ z_{i,j,u,m}\in\{0,1\},\forall i\in\mathbf{I},j\in\mathbf{J},u\in\mathbf{U},m\in\mathbf{M}\\ \end{array} \]
# Congestion Control in CoAP Observe Group Communication
## 4 Evaluation Setup
### Traffic Scenarios  
Two traffic scenarios were set to investigate the effects of the two congestion control mechanisms on the performance of the Obs communications.  
* Continuous traffic: The CoAP server delivered the CON request notifications to the CoAP client. As the server obtained a reply from the client, it immediately transferred another CON request. Sending messages back-to-back from a large number of servers at the same time can create congestion. In this study, we used nine servers in order to achieve different levels of congestion. The test was performed continuously for 300 s and was repeated 20 times for each specific configuration.
* Burst traffic: This scenario simulated the burst traffic by increasing the congestion level in every topology. It started with a low congestion level at the server, where four nodes generated the continuous traffic (Obs) of back-to-back CON requests. Afterwards, a burst of traffic was generated by another group of servers consisting of the rest of the servers in the topology. For instance, network topology I had a group of three servers for burst traffic, i.e., three nodes, in order to achieve different levels of congestion. The test in this scenario was done in a similar manner to the continuous traffic scenario.
# Estimation of Chlorophyll Fluorescence at Different Scales: A Review
## 4 SIF Retrieval Methods in Space Scale
### The Principle of Satellite SIF Retrieval Methods  
The satellite SIF retrieval methods are more complicated than the above-mentioned two platforms due to atmospheric effects. The SIF signal is very weak, and it can be easily affected by the atmosphere. It has been proved that SIF can be retrieved from the oxygen absorption bands or Fraunhofer lines,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Meaning** \\ \hline \(\rho^{*,RTM}\) & The modeled apparent reflectance \\ \(r_{50}\) & Bi-directional reflectance of target \\ \(L_{\text{sats}}^{\text{meas}}\) & Solar irradiance \\ \(L_{\text{sky}}^{\text{abs}}\) & Sky irradiance \\ \(r_{dos}\) & Hemispherical-directional reflectance factor of target \\ \(F_{\text{off}}^{RTM}\) & Modeled fluorescence in the observation direction \\ \(\rho^{*,\text{meas}}\) & The measured apparent reflectance \\ \(\rho_{\text{th}}^{*,\text{RTM}}\) & The modeled baseline reflectance inside the absorption band \\ \(\rho_{\text{th}}^{*,\text{meas}}\) & The measured baseline reflectance inside the absorption band \\ \(\lambda_{\text{malls}}\) & The band between 400–900 nm \\ \(\lambda_{\text{abs}}\) & Spectral ranges within the 640–850 nm \\ \(p\) & The posterior value of the model parameters \\ \(p_{0}\) & The priori values of the model parameters \\ \(\sigma_{p0}\) & The expected standard deviation \\ \(f\) & The cost function \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in Equations (7) and (8).  
in which the proportion of SIF increases compared with other spectral regions. Therefore, these two spectral ranges are often used for the retrieval window.  
The radiation received at the sensor consists of four components: (1) atmospheric path radiance, (2) the sun and sky irradiation reflected by the target, (3) the sun and sky irradiation reflected by the background, and (4) SIF radiance on the canopy.  
Assuming fluorescence and reflectance emission are isotropic, the radiance in the top of atmosphere (TOA) will be expressed as the addition of atmospheric contribution, surface-reflected radiance and the contribution of fluorescence signal. In Equation (9) [33; 38; 39; 41; 51; 98], the meaning of the parameters are introduced in Table 3.  
\[L_{TOA}=\frac{E_{0}\cos\theta}{\pi}\rho_{so}+\frac{E_{0}\cos\theta}{\pi}\frac{ \tau_{\uparrow}\rho\tau_{\downarrow}}{1-S\rho}+\frac{SIF\tau_{\uparrow}}{1-S\rho} \tag{9}\]  
The solar irradiation at the surface is affected by absorption and scattering effects related to atmospheric gases and aerosols. In our wavelengths of interest (600-800 nm), the main absorbers are oxygen (O\({}_{2}\)) and water vapor (H\({}_{2}\)O), and some narrow bands without the effect of oxygen and water vapor are used as retrieval window [47].
## 3 Pseudo-Dynamic Tests and Numerical Analysis of Model Structure
### Experimental and Numerical Results  
#### 3.3.1 Cracking Process and Failure Modes  
In general, cracks mainly occurred on the tubular columns during the PDTs, however, the steel components including the truss, diagonal braces and A-shaped frames kept elastic. Figure 8 shows the measured strains of the steel truss members and diagonal brace during the final PDT case with PGA of 0.8 g. It can be found that the steel truss and diagonal braces did not yield. Moreover, it was observed that all of the four tubular columns exhibited similar failure modes. Table 3 summarizes the cracking details of column C1 as designated in Figure 3a under PDTs. Initially, no noticeable cracks occurred at peak acceleration levels of 0.05 and 0.1 g, indicating that the tubular columns were nearly under elastic stage. When PGA came up to 0.20 g, the initial cracks were observed on the bottom of column. Figure 9 shows the observed cracking patterns of columns after PDTs. Figure 10a,b respectively show the numerical tensile damage distribution patterns of columns at the end of loading case with PGA of 0.20 and 0.60 g, which agreed well with the test results. Both the experimental and numerical results indicated that the cracks formed and developed on the bottom of column, lower part of corbel and upper part of corbel successively with the increase of PGA. Analysis showed that the observed damage coincided with the expected design results. Because the steel truss and A-shaped frames bear the important plants directly due to the technological requirements in TPPs, are expected to  
Figure 7: Schematic of finite element (FE) model for the test specimen: (**a**) Whole model; (**b**) Photo of actual connection between the diagonal brace and column; (**c**) Simplification of the brace-to-column connection; (**d**) Photo of actual truss-to-column connection; (**e**) Simplification of the truss-to-column connection.
# A Multi-Objective Path-Planning Approach for Multi-Scenario Urban Mobility Needs
## 1 Introduction
developed a multi-objective path-planning method for unmanned aerial vehicle inspection in oilfields, enhancing the efficiency and reliability of route planning [14]. [PERSON] et al. designed a multi-objective optimization approach to balance connectivity quality and energy consumption in UAV path planning [15]. [PERSON] et al., [PERSON] et al., and [PERSON] et al. independently introduced novel multi-objective evolutionary algorithms with dual constraint-handling mechanisms [16], dimension exploration, differential evolution [17], and an adaptive evolutionary multi-objective estimation of distribution algorithm [18], respectively. These approaches yield both short and safe routes in UAV path-planning applications. Single-objective optimization algorithms are limited in their ability to balance multiple conflicting objectives, making them less practical for path planning that requires comprehensive consideration of multiple factors. Multi-objective optimization provides a more comprehensive and flexible path-planning solution suited to diverse application scenarios and user requirements. However, optimizing multiple objectives simultaneously in path planning significantly increases the problem's complexity. Current research lacks in-depth consideration of user-specific requirements for different driving scenarios within urban traffic systems [19; 20; 21]. This study employs a genetic algorithm to optimize the A* path-planning method and introduces a dynamic weight-adjustment mechanism. This mechanism enables multi-objective path planning to dynamically prioritize objectives based on specific scenarios and user requirements, thereby providing route options that better align with user preferences. This approach holds significant potential for enhancing the efficiency of transportation systems, reducing environmental pollution, and ensuring driving safety.
# Hybrid Deep Learning-Based Intrusion Detection System for RPL IoT Networks
## 5 Experiment Setup and Evaluation
### Experiment Scenarios  
Different experiments are designed for various possible scenarios to evaluate the performance of the IoT IDS-DL model using the IoTR-DS dataset. The first experiment was designed to evaluate the model's performance in predicting a single unlabeled attack based on training with the normal traffic. The model was first trained using the normal traffic dataset, and then it was tested with a single attack type using the dataset containing the normal and attack traffic. The second experiment used combined datasets to evaluate detecting two unseen and untrained attacks, among other trained attacks. The third experiment was set to evaluate the supervised binary classification based on having only one known (labeled) attack. Each time, an individual attack dataset was used to train the model and to identify normal and malicious (attack) traffic. The fourth experiment was conducted to evaluate the system in detecting multi-trained (labeled) attacks using multi-class classification. The combined dataset was used to train the model to distinguish between four classes: normal, DIS, rank, and wormhole attacks. Finally, the system was evaluated against four different ML-DL learning models, which are J48 [30], KNN [31], SVM [32], and LSTM [33]. In all of the supervised experiment scenarios, the datasets were split into 70% train, 30% evaluate, and 30% test from the 70% train. For predicting unseen attacks using mainly the semi-supervised model, the normal dataset is the only one used for the training and evaluation (70% train, 30% evaluate), whereas the full attack datasets are used for testing purposes. This was to ensure that the attack behavior was not seen (trained) by the model.  
Since the IoT-DL models' performance depends on the optimal hyperparameters configuration, such as the number of hidden layers, the number of neurons per layer, the learning rate, the activation function, and others. Tables 5 and 6 show the near-optimal hyperparameters configuration for DANN and DAE, respectively, based on the best results of running different experiment trials.
## 4 Results and Discussion
### Assessment of Thickness Homogeneity  
In terms of the dielectric constants calculated by the Common Mid-Point Method, the thickness distribution of the asphalt concrete base in the tested area is shown in Figure 6. In total, there were 350 tested points, where 7 intervals with 1.5 m along the width (line 1, line 2, line 3, line 4, line 5, line 6 and line 7) and 50 intervals with 1 m along the 50 m long pavement were chosen equally, respectively. It was found that the thickness of 88 tested points of thickness was less than 9 cm. Its proportion of total test area was 25%. The thickness correction factor and the mean  
In Table 5, the maximum mean value of thickness was Line 6 and the minimum one was Line 3. The mean value, the standard deviation, and the coefficient of variation of thickness in all the test areas were 9.45, 1.17, and 0.12 cm, respectively. The results showed that the thinner areas were located in Line 1 and Line 3, which was close to the edge of the pavement and the middle of the asphalt paver.  
In order to assess the thickness homogeneity of the asphalt concrete base more qualitatively, the test area (Line 1 to Line 7) was divided into 70 units, and each unit was 1.5 m in width and 5 m in length, shown in Figure 5b. The mean thickness values of every area were calculated and are shown in Figure 7. The red line was the design thickness of the asphalt concrete base. The results of all lines showed thickness variation, and the thickness of Line 3, Line 1, and Line 7 were lower than that of the designed thickness value. The Line 5 thickness had the highest variation. The thickness of the asphalt concrete base showed that the thinner area was close to the edge of the pavement and the middle section of the asphalt paver.  
Figure 8 is the thickness coefficient of variation of the test area. The mean coefficient of variation in Line 4 was 0.073 and showed the best thickness homogeneity.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Test Contents** & **Sample 1** & **Sample 2** & **Sample 3** \\ \hline Coring Thickness & 11.2 cm & 9.2 cm & 10.6 cm \\ Calculated Thickness & 12.0 cm & 10.0 cm & 12.0 cm \\ Coring/Calculated & 0.933 & 0.920 & 0.883 \\ Correction Factor & & Mean = 0.912 & \\ \hline \end{tabular}
\end{table}
Table 4: Thickness correction factor.  
Figure 7: Mean values of thickness in 70 evaluated areas.
# A Two-Stage Routing Protocol for Partitioned Underwater Wireless Sensor Networks
## 3 Methodology  
The proposed scheme consists of two phases: the neighborhood information acquisition phase, and the relay selection and transmission phases. During the neighborhood information acquisition phase, nodes transmit beacons to share their residual energy and hop count information. Those nodes which do not receive a beacon until a predefined threshold time, request beacon by transmitting beacon requests. In the relay selection and transmission phase, upon reception of data, nodes select the best next hop node to forward the received data towards the sink node. The next hop node is selected based on link condition, residual energy and hop count.
# Breast Density Transformations Using CycleGANs for Revealing Undetected Findings in Mammograms
## 2 The Proposed System Overview
### The VinDR-Mammo dataset (digital)  
The VinDr-Mammo [17] dataset is a large-scale dataset of full-field digital mammograms consisting of 5000 four-view examinations accompanied by breast-level assessments and findings annotations. To the best of our knowledge, VinDr-Mammo is currently the largest public dataset (containing approximately 20,000 scans) of full-field digital mammograms that also provides breast-level BI-RADS assessment categorization together with suspicious or possible benign findings that require follow-up examination as well as ACR breast tissue/finding level annotations.
# Dynamic Characteristic Analysis of Permanent Magnet Brushless DC Motor System with Rolling Rotor
## 2 Dynamic Model of the Rotor-Bearing-Stator System for the BLDCM
### Oil-Film Force of Sliding Bearing  
Considering the change of oil-film thickness, the Reynolds equation can be described as  
\[\frac{1}{R_{r}^{2}}\frac{\partial}{\partial\theta}\left(\frac{h^{3}}{12\eta} \frac{\partial p}{\partial\theta}\right)=\frac{1}{2}(\omega-2\dot{\psi}_{r}) \frac{\partial h}{\partial\theta}+\dot{e}_{r}\cos\theta \tag{1}\]  
\[h=C(1+\varepsilon cos\theta) \tag{2}\]  
where \(R_{r}\) is the radius of the shaft neck, \(h\) is the oil-film thickness, \(\eta\) is lubricating oil viscosity, \(P\) is the oil-film pressure, \(\omega\) is rotational speed, \(e_{r}\) is the eccentric distance of the shaft neck, \(\psi\) is the angle between the center of the shaft neck and the horizontal direction, \(\varepsilon\) is the eccentricity ratio of the shaft neck, and \(C\) is the bearing radius clearance.  
The oil-film forces of the right sliding bearing along the radial and tangential directions can be derived as  
\[\begin{array}{l}F_{rr}=6\eta R_{r}L_{r}\big{(}\frac{\xi}{2}\big{)}^{2} \Big{[}(\omega-2\dot{\psi}_{r})E_{1}(e_{r})+2\dot{\varepsilon}E_{2}(e_{r}) \Big{]}\\ F_{tr}=6\eta R_{r}L_{r}\big{(}\frac{\xi}{2}\big{)}^{2}\Big{[}(\omega-2\dot{ \psi}_{r})E_{3}(e_{r})+2\dot{\varepsilon}E_{4}(e_{r})\Big{]}\end{array} \tag{3}\]  
where  
\[\begin{array}{l}E_{1}(e_{r})=\frac{2\varepsilon^{2}}{(1-\varepsilon^{2})(2+ \varepsilon^{2})}\\ E_{2}(e_{r})=\frac{1}{(1-\varepsilon^{2})^{3/2}}\Big{[}\frac{\pi}{2}-\frac{8 }{\pi(2+\varepsilon^{2})}\Big{]}\\ E_{3}(e_{r})=\frac{\pi\varepsilon}{(1-\varepsilon^{2})^{1/2}(2+ \varepsilon^{2})}\\ E_{4}(e_{r})=\frac{2\varepsilon}{(1-\varepsilon^{2})(2+\varepsilon^{2})} \end{array} \tag{4}\]  
Figure 1: Model of the BLDCM: (**a**) 3D structure schematic diagram of the BLDCM; (**b**) dynamic model of the BLDCM.  
Thus, the oil-film forces between the right sliding bearing and shaft along the horizontal and vertical directions can be expressed by  
\[\begin{array}{l}F_{xr}=F_{rr}\sin\psi_{r}-F_{tr}\cos\psi_{r}\\ F_{yr}=-F_{rr}cos\psi_{r}-F_{tr}\sin\psi_{r}\end{array} \tag{5}\]  
Similarly, the left oil-film forces can be described as  
\[\begin{array}{l}F_{xl}=F_{tl}\sin\psi_{l}-F_{tl}\cos\psi_{l}\\ F_{yl}=-F_{rl}cos\psi_{l}-F_{tl}\sin\psi_{l}\end{array} \tag{6}\]
## 1 Introduction
Reference [27] introduced a nonsingular fast terminal SMC approach for a closed kinematic chain manipulator (CKCM) with uncertain dynamics. The method utilizes time-delay estimation (TDE) for synchronized goal attainment, as validated through simulations on a two-degrees-of-freedom planar CKCM manipulator. In [28], various enhanced ADRC methods were introduced for K/(Ts + 1) N-type high-order processes, focusing on their efficacy in attenuating the input interference and measurement noise. The experimental findings indicate that Smith-based LADRC and other refined ADRC methods have similar noise suppression capabilities under consistent parameter settings. Based on this and considering interference and delay, we propose a LADRC-SMC-SP scheme to improve the tracking performance. This study contributes in the following ways: (i) It introduces a novel multi-loop control scheme (LADRC-SMC-SP) that addresses challenges in foot-trajectory tracking and leg-compliance control for legged robots, distinguishing it from existing theoretical and applied research. (ii) Reducing the order of the leg-dynamics model and conducting a comparative design of multiple controllers: unlike traditional methods and control laws, this scheme employs a reduction algorithm for the leg-constrained dynamics model and successfully simplifies it. The LADRC, SMC, and SP were integrated into multiple coupled control loops for the first time, effectively addressing the control challenges of legged robot systems. (iii) Enhancing control performance and overcoming limitations in reference control: compared with traditional single-intelligence control methods, the LADRC-SMC-SP scheme overcomes the limitations of LADRC-SMC in contact-force tracking, reducing errors by ten times. It demonstrates outstanding foot force and position-tracking performance. These contributions offer practical solutions for improving system performance and addressing control issues. The subsequent structure is as follows: Sections 2 and 3 simplify the single-leg robot dynamics model to aid the controller design. Section 4 presents the LADRC-SMC-SP scheme based on a dynamic model and various issues. In Section 5, three sets of comparative simulations are presented, and the advantages of the multi-loop control strategy are demonstrated through performance index analysis. Finally, the sixth part summarizes the research results and puts forward a future research direction according to the limitations of this paper.