esa-sceva/satcom-chunk-collection huggingface dataset:
# Label-Setting Algorithm for Multi-Destination \(K\) Simple Shortest Paths Problem and Application
## 3 Methodology
### Heuristic Approach  
As shown in the complexity analysis, the number of labels is not limited to \(k\) but, in the worst case, could grow exponentially due to the presence of cycles in between. This study circumvents this issue by proposing a heuristic method that limits the number of labels created for each node to \(k\). This heuristic improves the time complexity of the algorithm and makes it strongly polynomial with a worst-case complexity of \(O\big{(}mnk^{2}\big{)}\), but it does so by sacrificing accuracy, as it violates Proposition 1. The idea of limiting the labels in the heuristic draws its inspiration from the graph reduction technique [47]. The graph reduction approach is a related concept applied in the single-destination case where the network size is reduced to limit the search space for efficiency gain. While we cannot reduce the network size in the multi-destination case since we need to find paths to each destination, the proposed heuristic achieves efficiency gain by limiting the number of labels without modifying the network. The tradeoff between the gain in computational efficiency and the loss in accuracy is analyzed later in the paper. The pseudocode for the heuristic approach is given below as Algorithm 3 (the maximum number of labels created for any node is limited to \(k\) by replacing lines 20-28 of Algorithm 1 with lines 20-37 of Algorithm 3). Limiting the number of labels also enables other practical improvement strategies. Since not more than \(k\) labels are stored for any node, a new label for a node is not added to \(T\) unless it is better than the worst of \(k\) labels found until then (line 30 of Algorithm 3). Additionally, the cycle check is only performed when the new label is better than the existing labels, as it is expensive. Although these improvements do not change the worst-case complexity, they improve the performance in practice. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Problem Variant** & **Termination Condition** & **Remark** \\ \hline KSSP-OA & \(T\) is \(\varnothing\) or \(p_{i}\geq k\,\forall i\in N\setminus\{s\}\) & No more temporary labels to process or at least \(k\) permanent labels to every destination are found \\ KSSP-OM & \(T\) is \(\varnothing\) or \(p_{i}\geq k\,\forall i\in D\) & No more temporary labels to process or at least \(k\) permanent labels to each destination \(i\) in \(D\) are found \\ CCSSP-OA & \(T\) is \(\varnothing\) or \(d_{\alpha,3}^{\alpha_{n}}>\alpha\) & No more temporary labels to process or the cost of the permanent label in the latest iteration to some node \(u\) exceeds \(\alpha\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Termination conditions of the label-setting algorithm for the multi-destination KSSP problems.
## 3 Performance Evaluation  
In this section, we evaluate the navigation performance of the proposed signal and compare it with the original OTFS, traditional GNSS, and Locata signals. The selected traditional navigation signal is BPSK(5), the selected Locata signal is Pulse-BPSK(5), and the duty cycle of the pulsed signals is 1/10.
## 3 Proposed Scheme
### Network Model
For the resource reuse method, we assume that the M-D2D communication is allowed to reuse only the inner region cellular resource and the M-D2D group has to be located within the outer region of each cell. In order to calculate the number of resource blocks assigned to different sub-bands, it is necessary to analyze the density of cellular users in the inner region of each cell. Hence, the probability density function (PDF) of the cellular user, \(c^{in}\), in polar coordinates (\(l_{c^{in},B},\theta\)) is expressed as [30]:  
\[f\left(l_{c^{in},B}\right)=\frac{2\left(l_{c^{in},B}-l_{c^{in},B}^{min}\right) }{\left(r\ -\ l_{c^{in},B}^{min}\right)^{2}},\forall\ l_{c^{in},B}^{min}\leq l_{c^{in},B}\leq r \tag{1}\]  
and:  
\[f(\theta)=\frac{1}{\left(\frac{2\pi}{3}\right)},\forall 0\leq\theta\leq\frac{2 \pi}{3} \tag{2}\]  
where \(\theta\) is the angle of the cellular user location and \(l_{c^{in},B}\) is the distance between the cellular user, \(c^{in}\), in the inner region of a cell and the eNB. In the proposed scheme, we considered that the uplink interference of a cellular network is known and is defined by the matrix, \(\mathbf{I}\), as:  
\[\mathbf{I}=\left[ \begin{array}{cccc}I_{1,1},&I_{1,2},\ldots,&I_{N_{C},1}\\ I_{1,2},&I_{1,2},\ldots,&I_{N_{C},2}\\ \vdots&\vdots&\vdots\\ I_{1,N_{C}},&I_{1,N_{C}},\ldots,&I_{N_{C},N_{C}}\end{array} \right] \tag{3}\]  
The main goal of this paper is to minimize the interference into the least value so as to improve the system performance. Considering the interference matrix, we assumed a communication scenario where an M-D2D group reuses the resource of a cellular user. Then, the resource assignment matrix, \(\mathbf{Z}\), is defined as:  
\[\mathbf{Z}=\left[ \begin{array}{cccc}Z_{1,1},&Z_{1,2},\ldots,&Z_{N_{C},1}\\ Z_{1,2},&Z_{1,2},\ldots,&Z_{N_{C},2}\\ \vdots&\vdots&\vdots\\ Z_{1,N_{C}},&Z_{1,N_{C}},\ldots,&Z_{N_{C},N_{C}}\end{array} \right] \tag{4}\]  
To formulate the problem of an optimal resource allocation scheme, we assume one M-D2D group reuses at most one cellular resource, and each resource can be reused by at most one M-D2D group. That is:  
\[\sum_{c=1}^{N_{C}}Z_{c,g}\ \leq 1,\ \forall\ g\ e\ G\\ \sum_{g=1}^{N_{C}}Z_{c,g}\ \leq 1,\ \forall\ c\ e\ C\end{array} \tag{5}\]  
It is important to note that the number of receivers in a group should be more than one; if the number of receivers in a group is equal to one then the system is in a unicast communication scenario. In the system model, each cellular communication has a serving eNB and we considered that the D2D transmitters are reusing cellular links. Each D2D transmitter has the same signal for all the determined D2D receivers and the serving eNB has the knowledge of the multicast message of each D2D transmitter. In the proposed method, to guarantee the QoS requirements of the cellular users and D2D users, D2D users are allowed to reuse the resources only when the distance between cellular users and D2D users is far enough. If cellular users and D2D users are close to each other, considerable interference will generate.
# Experimental Study on Strength of Polypropylene Fiber Reinforced Cemented Silt Soil
## 2 Sample Preparation and Test Scheme of Unconfined Compressive Strength  
### Preparation of Test Samples  
The silty clay used in the test was taken from Nansha Terminal in Guangzhou, with a depth of 5 m below the surface. The moisture content of the soil sample was 60%, the void ratio was 1.64, and the liquid limit and plastic limit were 46.2% and 26.4%, respectively. The physical and mechanical parameters of silt were measured by the standard of the geotechnical test method (GB/T 50123-2019). The physical and mechanical parameters are shown in Table 1. The cement used in the test was 42.5 ordinary Portland cement of the Shijin brand in Guangzhou. The main components were calcium oxide, silicon dioxide, and aluminum oxide. The fibers used in the experiment were polypropylene fibers with bundled monofilaments, the specific parameters were given by the merchants. As shown in Figure 1 and Table 2. It was a polymer compound formed by polymers and has high tensile strength, light weight, high acid-base resistance, and good dispersion.  
The preparation methods of test samples were as follows: (1) The undisturbed soil was dried between 105 \({}^{\circ}\)C and 110 \({}^{\circ}\)C for 24 h, and then placed in a crusher for crushing, with the soil sample requiring a 2 mm sieve; (2) The screened soil sample was fully mixed with monofilament polypropylene fiber by using the dry mixing method, and then an appropriate amount of water was added to ensure that the moisture content was 60%, and it was kept for 1 d after sealing; (3) Cement with different ratios was added to the infiltrated fiber soil and placed in the mortar mixer for uniform mixing; (4) The cylindrical mold with a diameter of 39.1 mm and a height of 80 mm was adopted, and Vaseline was uniformly smeared on the inner wall of the cylindrical mold. The mixed uniform fiber cement soil was placed in the mold in three layers, and the bubbles were eliminated by vibrating the vibration table. After one day of curing, the sample was removed from the mold and placed in the curing box. The curing temperature was controlled at (25 \(\pm\) 2) \({}^{\circ}\)C, and the relative humidity was not less than 95%. The sample of fiber cement soil is shown in Figure 2. The test samples were suitable for the UCS test, triaxial test, and scanning electron microscopy (SEM) test.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Water** & **Density/** & **Dry Density/** & **Relative Density of** & **Void** & **Liquid** & **Plastic** & **Liquidity** & **Plasticity Limit** \\
**Content/\%** & **(g/cm\({}^{3}\))** & **(g/cm\({}^{3}\))** & **Particle/-** & **Ratio/-** & **Limit/\%** & **Limit/\%** & **Index/-** & **Index/\%** \\ \hline
60 & 1.65 & 1.03 & 2.71 & 1.64 & 46.2 & 26.4 & 1.7 & 19.8 \\ \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical properties of silty soil in Nansha District, Guangzhou.  
Figure 1: Polypropylene fiber.
# Enhanced High-Definition Video Transmission for Unmanned Driving in Mining Environments
## 2 Video Transmission Requirements Analysis  
High-definition cameras are utilized in unmanned mining operations to capture 360-degree environmental images surrounding unmanned vehicles and transmit them in real-time to the remote-control platform. In this context, the primary requirements for video transmission include the following aspects:  
* Single-channel high-definition video: the system must support video resolutions exceeding 2K, equivalent to a single-channel uplink rate of over 6 Mbps [20].
* Multi-channel simultaneous transmission: the system should enable the transmission of seven video streaming channels, facilitating panoramic monitoring of the vehicle's surroundings [21].
* Low-latency real-time transmission: Achieving an average end-to-end latency of approximately 20 ms is crucial for ensuring prompt remote monitoring [22].  
To fulfill these requirements, the video transmission system for unmanned mining operations should guarantee a single vehicle's uplink transmission rate of no less than 42 Mbps. Considering the concurrent operation of multiple vehicles (e.g., five vehicles) and other 5G service transmission demands, the uplink transmission rate should be about 400 Mbps to maintain a steady and dependable video transmission for unmanned mining vehicles.  
Presently, the predominant frequency duplex mode for 5G NR in mining operations [23; 24] is Time Division Duplex (TDD), wherein the uplink and downlink are transmitted within the same frequency band but are separated by a time interval. For instance, utilizing the 3.5 GHz band and typical time slot ratio, it can support a downstream transmission capacity exceeding 1 Gbps, but only 280 Mbps for the uplink. Hence, this paper advocates for the adoption of mine 5G super uplink technology [25; 26; 27] to enhance the uplink transmission rate and facilitate efficient and stable transmission of unmanned mining videos.
## 4 Conclusions  
A high-speed light signal transmitter based on ultraviolet radiation is realized on GaN-on-silicon platform. The modulation bandwidth and transmission speed of the light signal transmitter is significantly influenced by the absolute value of negative junction capacitance in positive bias voltage range, which depends on the size of the active area. We fabricated the light signal transmitter with ultra-small active area (850 \(\mu\)m\({}^{2}\)) by a double-etching process. The dominant EL peak of transmitter is about 380 nm in the ultraviolet range. With the increase of current, the dominant EL peak remains stable and the light output power is lineally modulated. Under 4 V bias voltage, the absolute value of negative junction capacitance of the transmitter with 10 KHz, 100 KHz and 1 MHz AC signals are 105.3 pF, 58.3 pF and 22.5 pF, respectively. A free-space data transmission test in the ultraviolet range with 250 Mbps was conducted to present that the high-speed optical communication in the ultraviolet range is feasible for a light signal transmitter in our study.  
Conceptualization, X.L. and Y.W. ([PERSON]); methodology, X.L. and Y.W. ([PERSON]); data curation, J.Y., S.N. and Y.J.; investigation, X.L., Y.W. ([PERSON]) and C.Q.; writing--original draft preparation, X.L.; writing--review and editing, J.L.; visualization, X.L.; supervision, X.L.; project administration, Y.W. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by China Postdoctoral Science Foundation funded project (2018M640508); Natural Science Foundation of the Jiangsu Higher Education Institutions (18 KJB510025); National Natural Science Foundation of China (61322112, 61531166004 and 11847022); 1311 Talent Program of Nanjing University of Posts and Telecommunications, National Self-Funding Project of Nanjing University of Posts and Telecommunications (NY218013); Special Project for Intergovernment Collaboration of State Key Research and Development Program (2016 YFE0118400); Natural Science Foundation of Jiangsu Province (BE2016186 and BK20170909).  
**Acknowledgments:** Thanks to the Lattice Power Corporation for providing GaN-on-silicon platform.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 7: (**a**) Free-space data transmission test setup; (**b**) transmit signal loaded on light signal transmitter with 250 Mbps random binary sequence and received signal captured by avalanche photodiode module; (**c**) eye diagrams of the light signal transmitter measured at 250 Mbps; (**d**) 3 dB modulation bandwidth of transmitter at 40 mA current.
# Stimulation Montage Achieves Balanced Focality and Intensity
## 2 Materials and Methods  
### Framework and Computational Model  
To formulate this optimization problem, we consider the head as a volume conduction model, which consists of multiple tissues, each with different electrical conductivity. Furthermore, the cortex is discretized into \(n\) elements, and the electric field in the cortex is denoted by \(\mathbf{e}_{3n\times 1}\). The realistic head model in this study is derived from a FieldTrip template [26], which provides anatomical information of the scalp, skull, CSF, and cortex. The conductivity values are adopted from the literature [20], where \(\sigma_{Scalp}=0.465\) S/m, \(\sigma_{Skill}=0.01\) S/m, \(\sigma_{CSF}=1.65\) S/m, and \(\sigma_{Cortex}=0.2\) S/m.  
For a stimulation system with \(m\) electrodes, we use \(\mathbf{s}_{m\times 1}\) to denote the injected current in the system. In this study, we choose \(m=342\); thus, \(\mathbf{s}\) has a dimension of \(342\times 1\). This high-density electrode system, with more degrees of freedom, is able to provide a better stimulation pattern than a low-density system [18]. The electrode location is based on the international electroencephalography (EEG) system. The electrode model was constructed using SolidWorks (Dassault Systemes SOLIDWORKS Corp., Waltham, MA, USA). To simulate real clinical conditions, the electrode has both a metal layer and gel layer.  
Considering the fact that head tissues are mainly resistive when tDCS is applied, the electric field distribution can be regarded as quasi-static. Under this condition, the applied current \(\mathbf{s}_{m\times 1}\) and electric field \(\mathbf{e}_{3n\times 1}\) are linearly related as \(\mathbf{e}_{3n\times 1}=\mathbf{K}_{3n\times m}\mathbf{s}_{m\times 1}\). The coefficient matrix \(\mathbf{K}_{3n\times m}\), known as the lead field matrix, provides the mapping information between the injected currents of the electrode system, and the electric field value at each voxel of the brain. \(K_{3n\times m}\) is obtained by FEM and solving the Laplace equation in COMSOL Multiphysics (COMSOL Inc., Burlington, MA, USA).
## 2 Problem Description  
The PCMCA-WT can be described according to the following definitions. A directed graph \(G=(V,\ A,\ R)\) is given, with \(V=\{1,\ldots,n\}\) being a set of vertices and \(A\subseteq V\times V\) a set of arcs, with a non-negative cost \(c_{ij}\) associated with every arc \((i,j)\in A\). It represents the traversing time for that arc. The set \(R\subset V\times V\) contains precedence relationships. Let \(d_{j}\) be the time step at which the flow enters vertex \(j\in V\), with \(d_{r}=0\). For any \((s,t)\in R\), we impose \(d_{t}\geq d_{s}\). This implies that the flow cannot enter vertex \(t\) before entering vertex \(s\)but it can wait at any vertex before servicing it. We define \(w_{j}\) as the waiting time at vertex \(j\in V\), with \(w_{r}=0\). The waiting time at a vertex \(j\) that is visited after another vertex \(i\) in the current solution is defined as \(w_{j}=d_{j}-(d_{i}+c_{ij})\). Given a vertex \(r\in V\) being the root of the arborescence, the target of the optimization is to retrieve an arborescence \(T\) (with root \(r\)) that provides the lowest possible sum of the total cost plus the total waiting time.  
An example of PCMCA-WT is provided in Figure 1. In the instance (top part of the figure), the precedence relationship \((1,3)\in R\) is represented as a dashed arrow, while in the bottom part of the figure, an optimal solution for the given instance is shown. The corresponding values of \(d_{i}\) and \(w_{i}\) are exposed near each vertex. The cost of the solution is \(8\) (obtained by adding the cost of the traversed arcs and the waiting times payed at vertices). In the example, observe the waiting time of \(1\) unit at vertex \(3\) (\(d_{1}=4\), \(d_{3}=3\) and \((1,3)\in R\)).
## 4 CKC-Based Taxonomy on Security Threats to SF and PA
### The Taxonomy
#### 4.2.6 Threats Related to the C2 Stage  
This stage is about how hackers control the victim system after completing the installation stage. This commonly happens via registering a C2 server [118]. Our studies highlighttwo C2 mechanisms in attacks on SF and PA, the first of which depends on network protocols [2, 88] and the second uses removable media [37, 101]. Thus, we categorise threats related to the C2 stage as explained below.
* **C2s using Network Protocols:** Many common attacks on SF and PA use HTTP/HTTPS, ICMP, DNS, FTP, SMTP and other standard network protocols for their communications in the C2 stage [119, 120]. For example, in many scenarios, when direct connection to an external mail server or an agriculture database server is not possible, hackers rely on backdoors that use protocols such as FTP or SMTP to penetrate into the server via sending files or emails [37, 101]. Moreover, to bypass common network security mechanisms, hackers may perturb DNS packets, which makes the attack more difficult to trace [121].
* **C2s using Removable Media:** Given the features of removable media (such as USB storage), they are commonly used to bypass networks for the exfiltration of data. For example, when a disk is formatted for decreasing the size of a partition, hundreds of megabytes of data (including malicious files) can be stored at the last addresses of the disk without being lost [37].
## 3 In Situ Measurement and Analysis on Deformation Pattern
### Qualitative Analysis of the Deformation Mechanism
#### 3.3.1 Plastic Deformation along the Interlayer Faces of Basalt  
For the saturated rupture surface, the increasing pore-pressure reduces the effective normal compressive stress and its shear strength [35]. The reduction in shear strength can induce landslide, e.g., the Beauregard case [1]. For the unsaturated rupture surface, which becomes submerged after the reservoir impoundment, complex physical and chemical reactions may occur between the infiltrated water and the rock mass and they could weaken the rock mass [36; 37]. The results in both cases are irreversible plastic deformation of the slope. For the Xiluodu case, the class of unweathered basalt is II and the class of interlayer faces of basalt is III according to the RMR system. The range of the drill core RQD (%) of unweathered basalt is 80-90. The range of drill core RQD (%) of interlayer faces is 50-80. The orientation of the rock strata is 3\({}^{\circ}\)-7\({}^{\circ}\). The rock strata at the dam site are intact and nearly horizontal according to pre-project exploration. The monitored deformation pattern during the initial impoundment shows no obvious jump across interlayer weak planes, which should be rupture surfaces (Figure 9). Thus, the possibility of plastic deformation along the rupture surface in the dam abutment is low.
## 2 Inverse Analysis Containing to LFWD Test
### Three-Dimensional Numerical Model  
The ABAQUS standard software is adopted to develop a three-dimensional FE model to realize the LFWD testing on a railway track (Figure 3). Considering the symmetry of the cross-section, a half-track model is developed. The load is assumed to act at the same position as the test point of M\({}_{0}\), and the load pulse is idealized using a half sine function with a duration of 40 ms and an amplitude of 35 kN. It should be noted that the duration and amplitude of the load pulse used in the FE model are consistent with LFWD loading obtained in the field tests. Moreover, it has been demonstrated that the load pulse using a half sine function form is reasonable, following the study of [PERSON] et al. [14]. A 20-node quadratic brick reduced integration element (C3D20R) is used to represent the ballast, subgrade, and ground elements. In any dynamic analysis, the finite element size has to be selected carefully to ensure the accuracy of results. In general, the element size of the FE model was estimated based on the smallest wavelength that allows the high-frequency motion to be simulated correctly [30]. Accordingly, the sizes of used 3D finite elements were taken as 0.05 \(\times\) 0.15 \(\times\) 0.1, 0.075 \(\times\) 0.08 \(\times\) 0.1 0.13 \(\times\) 0.11 \(\times\) 0.1, 0.13 \(\times\) 0.24 \(\times\) 0.1, and 0.4 \(\times\) 0.56 \(\times\) 0.1 for the ballast, top layer, bottom layer, embankment, and foundation, respectively. Hence, the FE mesh contains 235,812 elements and 253,518 nodes.