esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Low Earth orbit (LEO) satellite communication (SATCOM) networks have gradually been recognized as an efficient solution to enhance ground-based wireless networks. As one of the main characteristics of LEO SATCOM, the beam-edge area could be covered by multiple satellite nodes. In this case, user terminals (UTs) located at the beam-edge have the chance to connect one or more LEO satellites. To develop the diversity gain of multiple nodes in the overlapping area, we propose two high spectral efficiency cooperative transmission strategies, i.e., directly combining (DC) and selection combining (SC). In the DC scheme, signals arrived at the UT simultaneously could be combined into one enhanced signal. For downlink time division multiplexing, the SC scheme enables the UT to select the strongest signal path. Further, as there exists a significant channel gain difference of the beam-center and beam-edge areas, UTs in these two areas can be allocated in one resource block. In this case, we derive co-carriers based on DC and SC, respectively. To deeply analyze the novel methods, we study the ergodic sum-rate and outage probability while the outage diversity gain is further provided. Simulation results show that the co-carrier-based DC method has the ability to provide a higher ergodic sum-rate while the SC method performs better in terms of the outage probability.  
LEO communication networks; cooperative transmission; co-carrier transmission; multiple coverage 24244533 2024244533 202424533 June 202427 June 202411 July 202411 July 202411 July 202413 July 202413 July 2024244533
## 2 Related Work
### ID-Based ECC  
Existing schemes in the fourth group aim to minimize the system costs of the ID-based BP approach by utilizing an identity (ID) approach based on elliptic curve cryptography (ECC) [31; 32; 33; 34; 35; 36; 37], which are more efficient.  
The ID-based ECC and ID-based BP schemes need participating RSUs for the authentication process. Some schemes in this group store the system's master key in the TPD of RSU. Nevertheless, the limitations of RSU utilization are as follows: (i) a single compromised RSU is enough to render the whole system insecure; (ii) RSU are expensive; and (iii) some existing schemes add a TPD to both OBU and the RSU, making the system even more costly.
# Swin-YOLO for Concealed Object Detection in Millimeter Wave Images
## 3 Theoretical Model
### The Proposed Object Detection Network
#### 3.2.3 Local Perception Swin Transformer (LPST) Layer
The specific structure of LPM is shown in Figure 4b, which consists of depth-wise convolution and GELU activation function, and the local feature is extracted  
Figure 5: Schematic diagram of SW-MSA. (**a**) Window partitioning scheme of the regular W-MSA; (**b**) Shifted window partitioning of the SW-MSA; (**c**) Action of the shifted windows; (**d**) Effective batch calculation process used for self-attention in the shift window partitioning. through residual structure. Depth-wise convolution can increase the receptive field of spatial images, so as to better encode a wide range of contextual information. LPM can be defined as the Equation (8):  
\[\text{LPM}(X)=\text{GELU}(\text{DWConv}(X))+X, \tag{8}\]  
where \(X\in R^{H\times W\times d},H\times W\) is the resolution input, \(d\) represents the dimensionality of the feature, and DWConv (\(\cdot\)) represents depth-wise convolution. The Swin Transformer possesses a powerful context modeling capability to capture long-range dependencies, but it still faces challenges. Recently, in the field of optical image object detection, researchers have incorporated the Transformer into the basic YOLOv5 module [48], using the Transformer Block combined with the C3 module to replace the CSP Bottleneck in the original version of YOLOv5, which is called C3 TR, and its specific structure is shown in Figure 6.  
However, according to the experimental results in Section 4.3, the Transformer brings a higher computational cost and the hybrid structure of C3 TR performs poorly. Therefore, we use the LPST Layer, which alleviates the impact of the Transformer's quadratic computational complexity. Moreover, LPST restricts self-attention calculation within local windows through a shifting window mechanism, which brings higher efficiency in self-attention computation. LPST also combines the advantages of CNNs to optimize local information acquisition. Since MMW human concealment security inspection requires real-time performance, we add LPST Layer in the neck network part, which can not only enable the YOLOv5 network to obtain global contextual information for efficient detection of MMW small objects but also effectively save computational costs.
## 2 Experiments
### Experimental Apparatus and Main Procedures
#### 2.2.1 Contact Angle Measurement  
A commonly used method for determining the wettability characteristics of a solid surface by dripping a liquid droplet is contact angle measurement. It offers important insights regarding the interaction between liquid and solid at the interface, and is especially pertinent when researching phenomena associated with rock wetting. Rock wettability also can be measured by using the contact angle value. Angle (0) created at the contact line of the liquid-fluid interface and the solid substrate is typically used to calculate the contact angle [24]. In applications involving oil recovery, the contact angle of a droplet of water or oil is measured for a rock surface that has already been treated with the substance anticipated to have an impact on the wettability [25; 26; 27]. The experimental steps were as follows and shown in Figure 1:  
1. We polished the sandstone rock slices and washed and dried them with deionized water, immersed the slices in light crude oil, and then placed them inside a drying oven at 60 \({}^{\circ}\)C for 10 days. After taking out pieces from the oil, we rinsed them quickly with N-Heptane (C\(\gamma\)H\({}_{16}\)). We then left them to air dry at room temperature for 1 day. We used contact angle measurement equipment to measure the wetting angle;
2. At room temperature, we prepared KCl LSW by adding 500 mg/6500 mg KCl to 1 L of deionized water. The solution is stirred for 1 h by using a magnetic stirrer to make sure that all KCl was fully dissolved in the deionized water. For the SiO\({}_{2}\) solution, we added 4 g of SiO\({}_{2}\) nanoparticles to 1 L of deionized water. The solution was stirred for 1 h using a magnetic stirrer, and after that, we used an ultrasonic cleanser to fully break up and dissolve all the formed SiO\({}_{2}\) clumps. Then, the rock slices were immersed in the solutions, and we then took pictures of the wetting angle at 6 h, 12 h, 24 h, 36 h, 48 h, 60 h, and 72 h timepoints;
3. Using contact angle measuring equipment, we measured the wetting angle for each solution at different times;
4. After obtaining the best result from the LSW and NF solutions, we mixed them to form a mixed hybrid solution. We placed the oil-wet rock slices in the solutions, and then took pictures of the wetting angle at 6 h, 12 h, 24 h, 36 h, 48 h, 60 h, 72 h timepoints;
5. To summarize, oil-wet rock slices were soaked in different solutions. By measuring the wetting angle and recording the difference in the change in the angles, we analyzed and compared them. Using, Excel v.2021 and Origin v.2022 we obtained post-processing results.
# Locating the Epidemic Source in Complex Networks with Sparse Observers
## 3 Experiments and Analysis
### Algorithm Performance under Unknown Parameters  
In the previous experiment, in order to use the PESL algorithm to carry out the epidemic source localization, we must suppose that the infection probability \(\beta\) and recovery probability \(\gamma\) in the SIR model are all known. Only in this way can we calculate the parameters of the PESL algorithm \(\alpha=\beta+\gamma-\beta\gamma\) and carry out the subsequent optimization process. However, it is difficult to know in advance the exact value of the infection probability \(\beta\) and the recovery probability \(\gamma\) for many real-life epidemic source localization problems. Therefore, it is necessary to study the effects of parameters \(\beta\) and \(\gamma\) on PESL algorithm. The specific results are shown in Figure 6, the vertical dotted line represents the true infection probability in the SIR model \(\tilde{\beta}\) and the horizontal coordinate denotes the parameter \(\beta\) used in PESL. When these two parameters are equal \(\beta=\tilde{\beta}\), the PESL algorithm has the best source localization performance. When the parameters \(\beta\) and \(\tilde{\beta}\) differ greatly, the source localization accuracy of the algorithm will decrease significantly, indicating that the PESL algorithm is very sensitive to  
Figure 5: MSE of initial time in tree network. (**a,c**) represent the relationship between MSE of \(t_{0}\) and \(\beta\) under various \(\delta=0.3,0.6,0.9\) in ERT and BAT respectively. (**b,d**) represent the relationship between MSE of \(t_{0}\) and \(\delta\) under various \(\beta=0.2,0.5,0.8\) in ERT and BAT respectively. The ordinate in the figure represents the mean square error of the initial time \(t_{0}\). The lower the mean square error is, the more accurate the estimation of PESL becomes. In all experiments, the number of observers was \(K=10\) and the recovery probability in the SIR model was \(\gamma=0.1\).  
parameters. In practical problems, it is necessary to estimate the spread parameters as accurately as possible to better use the PESL algorithm to solve the source localization problem.
## 5 Titanium Dioxide as a Photocatalyst for Water Remediation Applications
Titanium(IV) Oxide Modified by Doping or in Heterojunctions with Other Metal Oxides as a Photocatalyst for Water Remediation Applications  
One of the drawbacks in applying a pure TiO\({}_{2}\) as a photocatalyst is the requirement to use near-UV irradiation to effectively induce the process of photocatalysis in the reaction environment. This limitation stems from titania's band gap: 3.2 eV for anatase and 3.0 eV for rutile. The required irradiation wavelength constitutes only 4% of visible light and falls well into the UV spectrum [71], rendering irradiation with visible light highly inefficient. However, this disadvantage can be overcome by a number of measures, especially by creating hybrid or composite materials with compounds that increase TiO\({}_{2}\) photosensitizing potential towards visible light. Titania's photocatalytic properties can be further enhanced by doping with other compounds. In particular, modifications of TiO\({}_{2}\) with noble metal ions can lead to satisfactory results in terms of photoconversion. Such doped materials with a broad band gap could be excited with visible light and thus could be considered as catalysts for photoremediation in visible light (Figure 6). The reviewed materials were divided into two categories: single-doped--when only one dopant was used, and multi-doped--when more than one dopant was used. In the graph below (Figure 7) are presented admixtures for TiO\({}_{2}\) and representative catalytic decomposition processes in which they can be applied.
# Topology Optimisation Using MPBILs and Multi-Grid Ground Element
## 3 Performance Enhancements of Multi-Objective, Population-Based Incremental Learning
### Adaptive Learning Rate  
The last method for MPBIL performance enhancement is using an adaptive learning rate, which proposes to modify the learning rate during the entire process [28]. A small value of learning rate is usually recommended for conventional PBIL to keep the algorithm reliable, but it usually causes low convergence rate. To balance the reliability and speed of convergence in all iterations, the learning rate needs to adapt. A model of adaptive learning rate has been proposed by [PERSON] et al. [15] that satisfies the previous conditions. That model is shown as follows  
\[L_{R}=L_{R0}+(L_{RM}-L_{R0})e^{-(\frac{SI}{NT})} \tag{3}\]  
in which \(SI\) is the successive iterations with improvements in the objective function in the most recent \(NT\) iterations. \(L_{R0}\) and \(L_{RM}\) are the minimum and maximum learning rates that the designer defines before an optimization run. The learning rate depends on the ratio of \(SI/NT\). Additionally, the high value of this ratio means that it is possible to locate better solutions using its current probability matrix, and consequently the learning rate should be small. In contrast, a low value of this ratio means the current probability matrix which is insufficiency, so the learning rate should be increased. Moreover, the outline of multi-objective, population-based incremental learning with adaptive learning rate(MPBILADLR) is slightly different from the traditional MPBIL, which uses Equation (3) to replace the original equation for finding \(L_{R}\). This algorithm is shown as follow.  
```
Initialization probability matrix \(\mathbf{P}=[\mathbf{0}.\mathbf{5}]_{N\times n\text{b}}\), external Pareto archive \(\mathbf{P}\mathbf{areto}=[]\).
1: Generate a binary population \(\mathbf{B}\) from \(\mathbf{P}\).
2: Decode the binary population to be \(\mathbf{x}_{n\times Np}\) and find the objective values \(\mathbf{f}_{m\times Np}\).
3: Update \(\mathbf{P}\mathbf{areto}\) by replacing it with non-dominated solutions of union set \(\mathbf{P}\mathbf{areto}\cup\mathbf{x}\).
4: If the number of members in \(\mathbf{P}\mathbf{areto}\) exceeds the predefined archive size \(N_{A}\), remove some of them by using an archiving technique.
5: If the termination criterion is fulfilled, stop the procedure. Otherwise, go to step 6:
6: Update \(\mathbf{P}\).
```  
**Algorithm 3** MPBIL with adaptive learning rate.
## 4 Experiments Datasets  
### Datasets  
This study used two real-world datasets for traffic forecasting, namely SZ-taxi and Los-loop.  
1. **SZ-taxi:** The dataset is the Shenzhen taxi trajectory from 1 January to 31 January 2015. The 156 major roads in Luohu District were selected as the study area, and the experimental data consisted of two main parts: one was a 156 \(\times\) 156 adjacency matrix describing the spatial relationships between roads, with each row representing a road and the values in the matrix, indicating the connectivity between roads; the other was a feature matrix describing the change in speed over time on each road, with each row representing a road and each column representing the speed of traffic on the road at different times of the day. The speed of traffic on each road is calculated every 15 min.
2. **Los-loop:** This dataset is collected in real time using loop detectors on motorways in Los Angeles County. It consists of 207 sensors with traffic speeds collected from 1 March 2012 to 7 March 2012. These traffic speed data are aggregated every 5 min.
# Energy-Efficient Partial LDPC Decoding for NAND Flash-Based Storage Systems
###### Abstract  
A new decoding method for low-density parity-check (LDPC) codes is presented to lower the energy consumption of LDPC decoders for NAND flash-based storage systems. Since the channel condition of NAND flash memory is reliable for most of its lifetime, it is inefficient to apply the maximum-effort decoding with the full parity-check matrix (H-matrix) from the beginning of the lifespan. As the energy consumption and the decoding latency are proportional to the size of the H-matrix used in decoding, the proposed algorithm starts the decoding with a partial H-matrix selected by considering the channel condition. In addition, the proposed partial decoding provides various error-correcting capabilities by adjusting the partial H-matrix. Based on the proposed partial decoding algorithm, a prototype decoder is implemented in a 65 nm CMOS process to decode a 4 KB LDPC code. The proposed decoder reduces energy consumption by 93% compared to the conventional LDPC decoding architecture at maximum.  
low-density parity-check (LDPC) decoding; low-power technique; NAND flash memory; storage system +
Footnote †: journal: Electronics
## 2 Materials and Methods
### OFDM Communication and Radar Sensing
#### 2.3.2 OFDM Radar Sensing  
The received signal of an OFDM signal, which is reflected by an object at a range \(R_{d}\) with a Doppler shift \(f_{D}\) due to relative movement, is expressed as  
\[\begin{split} y(t)&=\sum_{\mu=0}^{N_{\text{sym}}-1}e ^{j2\pi f_{D}t}\\ &\cdot\sum_{n=0}^{N_{c}-1}A(\mu,n)\bigg{\{}d_{Tx}(\mu N+n)e^{-j2 \pi f_{n}\frac{2R_{d}}{c}}\bigg{\}}e^{j2\pi f_{n}t}\text{rect}\bigg{(}\frac{ t-\mu T_{OFDM}-\frac{2R_{d}}{c}}{T_{OFDM}}\bigg{)},\end{split} \tag{13}\]  
where \(d_{Tx}\) is the user data symbol; \(c\) is the light velocity \(c\approx 3\times 10^{8}\) m/s.  
At the receiver, the individual modulation symbols are recovered to one OFDM symbol by observing the received signal for only the elementary OFDM symbol duration \(T<T_{OFDM}\)[7].
## 5 Conclusions
**Notation**  
ANN Artificial neural network  
AdaBoost Adaptive boosting  
KNN k-nearest neighbor  
NSE Nash-Sutcliffe efficiency coefficient  
R\({}^{2}\) Coefficient of determination  
RF Random forest  
RFM Rockfill material  
RMSE Root mean square error  
RSR Ratio of RMSE to the standard deviation of the  
measured data  
ISRM International Society of Rock Mechanics  
SVM Support vector machine  
\(D_{10}\) Steve size at 10 percent passing  
\(D_{30}\) Steve size at 30 percent passing  
\(D_{60}\) Steve size at 60 percent passing  
\(D_{90}\) Steve size at 90 percent passing  
\(C_{c}\) Coefficient of curvature  
\(C_{u}\) Coefficient of uniformity  
\(GM\) Gradation modulus  
\(FM\) Fineness modulus  
\(R\) ISRM hardness rating  
\(UCS_{\text{min}}\) Minimum uniaxial compression strength  
\(\gamma\) Dry unit weight  
\(UCS_{\text{max}}\) Maximum uniaxial compression strength  
\(\sigma_{n}\) Normal stress  
\(\tau\) Shear strength  
\(\varphi\) Angle of internal friction  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline
**Case** & **Location** & \(D_{18}\) & \(D_{29}\) & \(D_{36}\) & \(D_{40}\) & \(D_{50}\) & \(C_{6}\) & \(C_{7}\) & \(C_{8}\) & **GM** & **FM** & **R** & \( \begin{array}{c}\textit{UCS}_{\text{min}}\\ \textbf{(MPa)}\end{array} \) & \(\textbf{UCS}_{\text{max}}\) & \(\boldsymbol{\gamma}\) & \(\sigma_{8}\) & \(\boldsymbol{\tau}\) \\
**No. ** & & & & & & & & & & & & & & & \\ \hline
97 & Iran & 0.4 & 2.3 & 12.2 & 44.4 & 1.08 & 30.5 & 3.3 & 5.69 & 4 & 50 & 100 & 18.7 & 0.794 & 0.577 \\
98 & Iran & 0.4 & 2.3 & 12.2 & 44.4 & 1.08 & 30.5 & 3.3 & 5.69 & 5 & 100 & 250 & 24.5 & 0.994 & 0.864 \\
99 & India & 0.5 & 1.5 & 4.6 & 15.6 & 0.98 & 9.2 & 4.22 & 4.8 & 4 & 50 & 100 & 24.5 & 1.384 & 0.881 \\
100 & India & 0.95 & 2.8 & 12.5 & 34.9 & 0.66 & 13.16 & 3.02 & 5.97 & 4 & 50 & 100 & 24.5 & 1.369 & 0.836 \\
101 & India & 1.3 & 4.6 & 18.9 & 55.9 & 0.86 & 14.54 & 2.4 & 6.54 & 4 & 50 & 100 & 24.5 & 1.358 & 0.803 \\
102 & Iran & 0.5 & 3 & 10.4 & 31.2 & 1.73 & 20.8 & 3.36 & 5.63 & 4 & 50 & 100 & 26.2 & 1.056 & 0.625 \\ \begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline
**Case** & **Location** & \(D_{\text{log}}\) & \(D_{\text{log}}\) & \(D_{\text{log}}\) & \(D_{\text{log}}\) & \(D_{\text{log}}\) & \(C_{\text{c}}\) & \(C_{\text{a}}\) & **GM** & **FM** & \(R\) & \(UCS_{\text{min}}\) & **UCS_max & \(\gamma\)** & \(\sigma\)**s** & \(\tau\) \\
**No.** & & & & & & & & & & & & & & (MPa) & (MPa) & (K/km/\(\gamma\)) & (MPa) & (MPa) \\ \hline  
\hline \end{tabular}
## 5 Issues Relating to the Fundamental Design Aspects
### UAV and Sensor Node Scheduling  
When a UAV visits nodes in an area, it requires a schedule to determine which nodes to receive data from and in what order. In other cases, UAVs might need to schedule their arrival at stopping points so that sensor nodes within that range can upload their data to the UAV. Similarly, flight, hovering, transmission times, and sensor node uplink transmissions to the UAV should be properly scheduled. In networks with CPs, sensor nodes may also need to schedule their transmissions to CPs, while CPs schedule theirs to UAVs. These scheduling processes could affect the age of information of the data sensed and sampled by sensors or IoT nodes. Many UAV and sensor node scheduling methods have been proposed in the literature and scheduling could be performed concerning association with CPs or even UAVs. ML has recently gained popularity for scheduling in UAV-assisted data collection for WSN/IoT applications. Schedule policies could vary significantly, for instance, in random, greedy, and distance-based policies or exploring heuristics to ensure IoT nodes have a higher AoI chosen to transmit status updates [9].  
Packet allocation is another form of scheduling considered in UAV-aided data-gathering architectures. Optimal packet allocation and scheduling should be prioritized in such applications. In many cases, nodes should have a relatively uniform packet size allocatedto them, while in other cases application requirements might necessitate that some nodes have special functions and thus larger packet size allocation. This is especially important if the number of visits of the UAV for a particular node depends on the packet allocation, as nodes with large packet sizes may be rarely visited, which can significantly impact the AoI. An example is investigated in [86].  
Scheduling IoT Devices  
Scheduling in this case has to be performed in good time, bearing in mind that if an IoT node is not scheduled in a particular time round, it has to wait longer to transmit its data, leading to higher accumulated AoI. Modeling network architectures involving sensor node-CP association can be constructed in various ways, for instance, using a binary indicator to specify whether a sensor node is uploading to a UAV or not [84]. Each SN could be scheduled to connect to a single CP. The sensor node-CP association influences the AoI. Similarly, uploading the sequence of sensor nodes to UAVs could significantly impact the AoI. Thus, it is highly important to strike an optimal balance between sensor node uploading time and UAV flight time. To achieve this, the optimal set of CPs has to be found as well as the best-choice trajectory through the CPs.  
The scheduling of UAVs and sensor nodes involves many considerations, making it quite challenging due to other associated factors: architecture, application, number of UAVs, UAV height, optimization algorithm, and many more. The UAV altitude could either decrease or increase the AoI because it impacts the propagation characteristics between IoT devices and UAVs, as well as UAVs and the base station [8].
# Regional Geomagnetic Field Modeling Based on Associated Legendre Polynomials
## 1 Introduction
The model enhances the computational efficiency of parameter estimation through incorporation of the QR decomposition technique [16]. In addition, this study develops a comprehensive evaluation index that combines the effects of outliers with the accuracy of model predictions to determine the optimal truncation level of the model. To address the issue of boundary effects in the model, the box plot approach is first used to detect and remove outliers. Subsequently, the exponential moving average method is applied to re-predict these outliers, resulting in a high-precision regional geomagnetic anomaly field model. The combined application of these methods offers an accurate and efficient framework for regional geomagnetic anomaly modeling.