esa-sceva/satcom-chunk-collection huggingface dataset:
# Production and Maintenance Planning for a Deteriorating System with Operation-Dependent Defectives
## 4 Optimization Method Description  
With the aim to determine the optimal joint control policy, i.e., the optimal value of the production rate, and the optimal repair/major maintenance scheduling, we use a numerical technique called Kushner's approach to solve the HJB Equation (16). Such technique was proposed by [PERSON] and [PERSON] [19] and [PERSON] et al. [20], and the idea behind this procedure is to approximate the value function \(v(\alpha,x,a)\) by a discrete function \(v^{h}(\alpha,x,a)\), and the first-order partial derivatives of the value function \(\partial v(\cdot)/\partial x\) and \(\partial v(\cdot)/\partial a\) are approximated by:  
\[\frac{\partial v}{\partial x}(\alpha,x,a)=\left\{\begin{array}{ll}\frac{1}{h _{x}}(v^{h}(\alpha,x+h_{x},a)-v^{h}(\alpha,x,a))&\mbox{if }\dot{x}\geq 0\\ \frac{1}{h_{x}}(v^{h}(\alpha,x,a)-v^{h}(\alpha,x-h_{x},a)&\mbox{otherwise} \end{array}\right. \tag{18}\]  
and  
\[\frac{\partial v}{\partial a}(\alpha,x,a)=\frac{1}{h_{a}}(v^{h}(\alpha,x,a\ +h_{a})-v^{h}(\alpha,x,a)) \tag{19}\]  
where \(h_{x}\) and \(h_{a}\) define the length of the finite difference interval of the state variables \((x,a)\). The [PERSON]'s technique is useful because it converts the continuous minimization problem to a discrete-time, discrete-state decision process, for further details about this technique [PERSON] et al. [21] can be consulted. Without loss of generality, we study the case of a manufacturing system with three defective rates depending on its productivity. Such rates are defined as follows:  
\[\beta(u(t))=\left\{\begin{array}{ll}b_{1}&\mbox{if }u(t)<u_{1}\\ b_{2}&\mbox{if }u(t)\in(u_{1},u_{2}]\\ b_{3}&\mbox{if }u(t)\in(u_{2},u_{max}]\end{array}\right. \tag{20}\]  
where \(b_{3}\geq b_{2}\geq b_{1}\), and \(0\leq u_{1}\leq u_{2}\leq u_{max}\). Thus, due to the consideration of production-dependent-defectives, we obtain the following approximated valued functions for the operational mode:  
Figure 4: Increasing failure rate.  
of severe deterioration. We note that condition (24) is satisfied by the parameters presented in Table 1, used in the numerical example.  
Further, we define \(k=1\). The cost parameters for the numerical example are presented in Table 2.  
The structure of the obtained joint control policy is discussed as follows:
## 4 Analysis and Discussion
### Finite Element Simulation Results
#### 4.2.3 Disturbance Analysis of Pre-Built Diaphragm Walls  
For a foundation pit with a zonal excavation, the diaphragm wall construction for each sub-pit was carried out first. The displacement disturbance of the pre-built diaphragm walls by the first foundation pit excavation may directly affect the subsequent pit excavations.  
Figure 8 illustrates the horizontal displacement nephograms for sub-pits 3A5-2, 3A6, and 3A7 during the excavation to the bottom of the foundation pit for conditions 2 and 3. In condition 2, the pre-built diaphragm wall at the junction of sub-pit 3A6 and 3A7 displaced towards the east side of the foundation pit due to the excavation of sub-pit 3A10-1. As a result, the diaphragm wall on the east side of the sub-pit produced a maximum displacement of 2.4 mm at the junction of sub-pits 3A6 and 3A7. As the post-excavation pit is not adjacent to the excavation area, the horizontal displacement at the junction of sub-pits 3A5-2 and 3A6 was significantly smaller than that at the junction of sub-pits 3A6 and 3A7. The displacement trend of the diaphragm wall on the west side of the pit was identical to that on the east side, with only a difference in the depth of the diaphragm wall. In condition 3, the excavation action of sub-pits 3A10-2 and 3A8 together resulted in a maximum displacement of 15 mm at the junction of sub-pits 3A6 and 3A7. Under the  
Figure 7: Changes in bending moments of diaphragm walls during the excavation. (**a**) Diaphragm walls of 3A10-1 sub-pit. (**b**) Diaphragm walls of 3A10-2 sub-pit. (**c**) Diaphragm walls of 3A5 sub-pit. (**d**) Diaphragm walls of 3A8 sub-pit.  
influence of the excavation of sub-pit 3A5, the center of the pre-built diaphragm wall of sub-pit 3A5-2 showed a large bulging belly-type deformation, with a maximum deformation of 6 mm.  
Figure 8: \(Cont\).  
Similarly, the same condition was observed in the diaphragm walls of sub-pits 3A1-1 to 3A5-1 (Figure 9). During the zonal excavation, the pre-built diaphragm walls may be affected by constructed sub-pits. These deformations might be transmitted through the diaphragm walls orthogonal to the foundation pit enclosure structures under construction and acted on the foundation pit at a greater distance. This horizontal displacement effect was mainly on two non-adjacent pits, and the effect of the displacement was usually small. The difference was that the excavation area adjacent to the pre-built diaphragm wall produced a more significant deformation effect on the pre-built diaphragm wall. Pre-built diaphragm walls may exhibit deformation patterns similar to those of excavated foundation pits. Whether the larger deformation of pre-built wall affects the effectiveness of the support for the excavated sub-pit deserves to be investigated from an engineering perspective during the design and construction stages.  
Figure 8: Horizontal displacements nephograms of pre-built diaphragm wall at the western edge of the sub-pit.  
Figure 9: _Cont._  
Figure 9: Horizontal displacement of the pre-built diaphragm wall at the southern edge of the pit.
# Formal Modeling of IoT-Based Distribution Management System for Smart Grids
## 3 Problem Statement and System Model
### Cables and Transmission Lines  
Power cables, including transmission and distribution-level lines, establish crucial links between the generation and load. These lines often transport low-voltage power that is stepped down from the transmission grid or generated by distributed generating systems. Transmission lines carry voltages from transmission to distribution points, whereas the distribution lines carry voltage from distribution to domestic use, such as to homes, offices, and buildings. The faults in the transmission lines network are classified into four categories [38], which are illustrated in Figure 3, with a brief explanation as follows.  
* **Single-line-to-ground fault** The most frequent transmission line fault is the single-line-to-ground (SLG) fault that might be caused by a vehicle accident, by tree branches, or by flashovers over dusty insulators during rain showers, which will cause one of the phase conductors to collapse and come in touch with the ground.
* **Line-to-line fault** When two phases of a three-phase line are unexpectedly coupled, a line-to-line fault occurs. The fault current will flow during both phases in this instance.
* **Double-line-to-ground fault** The two lines, as well as the ground, come in touch with each other in a double-line-to-ground fault. Such faults have a nearly 10% chance of occurring.
* **Triple-line-to-ground fault**  
Figure 2: Drive-by/Walk-by automated meter reading.  
A triple line-to-ground fault occurs when three lines come in touch with the neutral wire or lie on the ground.  
In our proposed monitoring network of transmission lines, several wireless sensors are mounted on the chosen towers. The working principle of these sensors is to collect information about the operating conditions of the transmission lines as well as their surroundings. These sensors, after collecting data, send the data to the nearest IED through a communication gateway. Figure 4 shows the communication infrastructure between the sensors and IEDs. These IEDs will send the collected data to the control center. The important point to be considered here is that sensors do not need to be installed on all of the towers.  
Figure 4: Transmission line monitoring.  
Figure 3: Transmission line faults.
# Automated Methodology for Dependability Evaluation of Wireless Visual Sensor Networks
## 4 Proposed Methodology
### Data input  
The automated framework developed to implement the proposed methodology requires some supplementary data from the user in order to characterize the network and the application requirements. This information is related to network configuration, visual coverage attributes, nodes and the evaluation process itself, which are:  
1. Nodes parameters: * Number of visual nodes, \(nVS\); * Number of scalar nodes, \(nSS\); * (x,y) position of nodes, \((A_{x_{i}},A_{y_{i}})\); * Radio communication range, \(R_{c}\); * Hardware failure and repair rates, \(\lambda_{h\
u}\), \(u_{h\
u}\); * Battery discharge and repair rates, \(\lambda_{bl_{i}}\) and \(\mu_{bt}\), and number of stages, \(nStages\); * Link failure and repair rates, \(\lambda_{lt}\), \(\mu_{lt}\);
2. Cameras parameters: * Viewing angle, \(\theta\); * Orientation, \(\alpha\); * Sensing radius, \(R_{s}\);
3. Evaluation parameters: * Evaluation period, \(T\); * Time step, \(t_{s}\).
4. Minimum visual coverage percentage, \(A_{min}\);
5. Routing protocol strategy, DIRECT or FLOODING.
## 5 Performance Evaluation
### Simulation Setup
#### 5.1.3 Weather Model  
To truly simulate the global communication scenario under different weather conditions, we use hourly weather data from 8 December 2020 to 9 December 2020. We selected 150 cities with the highest gross domestic product (GDP) globally and assumed that users communicate with satellites at these locations. These cities are spread across six continents, and each latitude has a wide range of representation.  
As Starlink has an inclination angle of \(53^{\circ}\), it cannot communicate with some high-latitude cities (such as Moscow). Finally, 147 cities can communicate with satellites. Due to the fast speed of the satellite, the longest time for each link is about 173 s. Hence, we consider the weather for a single connection to be the weather at the beginning of the connection.
# A Novel Cooperative Opportunistic Routing Scheme for Underwater Sensor Networks
## 2 Related Work
In another group of studies, depth information is employed to route the packets towards their destinations (one of the sonobuoys on the water surface). Depth-Based Routing (DBR) [26] is the first depth-based routing protocol proposed for UWSNs. Nevertheless, forwarding set selection is not performed in an optimal way (having the duplicated packets problem), and neither proposes any recovery method to solve the void problem. Depth-Based Multi-hop Routing (DBMR) and Energy-Efficient Depth-Based Routing (EEDBR) [32; 33] are also proposed in this category, which consider the residual energy of the nodes in their forwarding set selection; however, they still provide no solution for the local maxima nodes. On the other hand, HydroCast [14] and Void-Aware Pressure Routing (VAPR) [8] represent the pressure-based routings, which are enhanced by opportunistic data forwarding and void handling. These protocols are sender-based approaches, which means that each forwarding node selects the candidate nodes and puts their IDs in the packet header. The receiving node collaborates in packet forwarding if its ID is included in the packet header [29]. These protocols try to select a subset of forwarding candidates with maximum advancement towards the destination, while also addressing the hidden terminal problem. However, HydroCast relies on the use of a 2D surface flooding method to discover a recovery path for local maxima nodes in the near-surface layer. More importantly, void areas can appear in deeper regions of the water, which is not considered in this protocol. VAPR tries to bypass void areas by holding information of up to two-hop neighbouring nodes, which imposes high overhead to the system. Moreover, the beaconing procedure in VAPR (for a multi-sink architecture) is not properly utilised in a way that beacons carry additional useful information in addition to the hop count. For this reason, in VAPR, each node is forced to periodically measure the distance to its neighbouring nodes and to broadcast the measured information for them. As another problem, the packet can only be forwarded up or down depending on the selected direction, which cannot utilise subsets of nodes in the horizontal direction (including nodes with a lower depth and a higher depth together in the forwarding set). Subsequently, in facing a convex void, packets will traverse a longer distance because they cannot be forwarded in a horizontal direction to bypass the void. Furthermore, this leads to less available candidates and, hence, increases the packet failure probability.
# TMVDPatch: A Trusted Multi-View Decision System for Security Patch Identification
## 5 Experiments and Results
### Ensemble Methods  
In our work, the TMVDPatch combines the evidence of the commit message view and the code diff view. It should be noted that the credible and interpretable decisions discussed in this paper are essentially an ensemble strategy. In order to fully evaluate the effectiveness of the ensemble method proposed in this paper, this subsection conducts comparative experiments on different ensemble methods. Based on the single-view classification models SV-CMS and SV-CDP, we use three different feature fusion methods (vector summation, vector concatenation and vector averaging) to obtain \(MV_{sum}\), \(MV_{cat}\) and \(MV_{arg}\), respectively. Since the parameter settings of the LSTM hidden layer between different views are inconsistent, we set them to 64 and 128, respectively, to obtain the corresponding \(MV_{sum\_64}\), \(MV_{sum\_128}\), \(MV_{avg\_64}\) and \(MV_{avg\_128}\). In order to further evaluate the TMVDPatch, we use TMVD-NG to represent a trusted multi-view decision model without Grey Relational Analysis feedback to conduct related experiments.  
The experimental results are shown in Table 4. The trusted multi-view decision system proposed in this paper outperforms other ensemble models in all four metrics, achieving an accuracy of 0.8529 and a F1 score of 0.9001. In addition, by comparing the experimental results of TMVD-NG, it is confirmed that adding Grey Relational Analysis feedback can effectively improve the identification ability of the model.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline SV-CMS & 0.7391 & 0.7617 & 0.9099 & 0.8293 \\ SV-CDP & 0.7013 & 0.7020 & **0.9922** & 0.8222 \\ SQ-CDP & 0.6954 & 0.7012 & 0.9803 & 0.8176 \\ SQ-A & 0.7798 & 0.7909 & 0.9296 & 0.8546 \\ PatchRNN & 0.8357 & 0.7572 & 0.7366 & 0.7468 \\
**TMVDPatch** & **0.8529** & **0.8542** & **0.9511** & **0.9001** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of model comparison.
## 3 Additional Features  
In the Quick Search and Global Search results page, a button \"chemicalize\" links to a page that displays calculated values for the compound; see Figure 4. Chemicalize (www.chemicalize.org) is developed by ChemAxon. Chemicalize uses ChemAxon's name to structure parsing to identify chemical structures from web pages and other text sources. It provides a large variety of predicted data related to each structure [7].  
Figure 3: Global Search Result Page of the combined CAS registry number synonym, and structure search for maslinic acid.  
What are the Differences between CWM Global Search and Other Systems such as PubChem or ChemSpider?  
Unlike other systems such as PubChem or ChemSpider, CWM Global Search is NOT a database. We always search the most current snapshots of the supported data sources, thus also making sure that recently added records in the various data sources can be located.  
This eliminates the problem that links to newly added PubChem records in ChemSpider and _vice versa_ may not be found because of pending updates in the underlying database.  
A major strength of CWM Global Search is the chemical structure search via the integrated structure editor (JDraw from Accelrys, Inc.). This structure editor supports copy/paste operations with major chemical drawing packages allowing the user to keep using his favorite structure drawing tool. Another unique feature of CWM Global Search allows you to draw a reaction; with one click you can find information for all reactants, reagents and products such as suppliers or safety information.  
Comprehensive search--with a single click you can search for a structure, one or many associated CAS Registry Number(s), plus an arbitrary list of associated free text such as synonyms, brand names, and identifiers.
# A Novel Deep Learning Stack for APT Detection
## 4 Deep Learning Detection Stack
### Anomaly Mapping  
The final phase of the detection process is mapping anomalies, where their interconnections are analysed with a graph algorithm. The purpose of this layer is to map outliers and reveal their hidden connections, which may remain invisible otherwise. There are two possible solutions for this phase, either using Graph Database (GDB) or Graph-based Neural Network (GNN) implementation that finds spatial, temporal and semantic relationships between data points.  
[PERSON] [22] proposed GDB for knowledge management in his research. GDB is used to reveal connections among user created documents and how they are used. Data from relational database are easily converted to GDB and connections made visible. The author stated three benefits for using GDB: (i) they are ideal for modelling scenarios in a network; (ii) many-to-many relationships are easy to implement; and (iii) quick access to data with simple queries. [PERSON] [14] stated that, as GDBs deals with properties and connections, they represent a lot of information, which stays hidden without research. It is also pointed out that GDB supports describing all kinds of complex modern networks. Moreover, [PERSON] [21] et al. stated that data integrated via GDB can store network topology directly, thus it supports highly complex network presentations.  
GNN, as presented by [PERSON] et al., is a general class neural network for graphs, which is proposed to seek underlying relationships among data [18]. [PERSON] et al. stated that GNN _\"is a dynamic model where the hidden representation of all nodes evolve over time\"_[15]. [PERSON] et al. used GNN to study structural image presentation and classification. They stated that GNN is capable of processing both positional and non-positional graphs. Their experimental tests show that GNN has great potential for classifying data [17]. In GNNs, edges represent relationships, while nodes represent objects or concepts [15; 17; 18]. Thus, we can state that, for our purposes, nodes represent devices and their properties in a network and edges connections between the nodes.
# Channel Correlation-Based Adaptive Power Transmission for Free-Space Optical Communications
## 2 Theoretical Analysis  
The theoretical analysis is divided into two sections. The mathematical model of the turbulence channel is described in detail in Section 2.1, and the operation principle of the proposed adaptive power transmission technique is elaborated in Section 2.2.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 6 UAV Applications Areas
### Disaster Management  
In the case of a man-made or natural disaster such as terrorist attacks, tsunami and floods, UAVs can gain access to disaster truck locations that are hazardous for manned activity. These disasters can severely damage telecommunication infrastructure, transportation, power and water utilities. UAVs can help to collect information, necessitate rapid solutions and navigate debris. Radars, sensors and high quality cameras incorporated in UAVs can assist rescue teams to identify damage and immediately start recovery operations and send resources such as first aid medical kits and manned helicopters. UAVs can assist in finding timely disaster estimation, providing disaster alerts and help in finding efficient countermeasures. In case of wildfire, a swarm of drones containing fire extinguishers can monitor, examine and trace any area without endangering human lives. Thus, UAVs can assist in real-time coverage of large areas without risking the safety and the security of the personnel involved. Early warning through UAVs can help to rescue human beings and wildlife at risk.
# Mechanical Properties of Cement-Treated Soil Mixed with Cellulose Nanofibre
## 3 Effect of CNF on Strength Increase
### Flexural Strength  
One of the problems of cement-treated soil is its low tensile and flexural strengths. As it was anticipated that the CNF mixture would improve these properties, some tests were conducted to investigate the flexural strength. Since the flexural failure of treated  
Figure 7: Appearance of sheared surface (Method 1).  
soil depends on its tensile strength, its flexural strength can indirectly indicate the tensile strength of the treated soil. The method was implemented in accordance with JIS R 5201 [20]. In this test, a prismatic specimen (40 mm high \(\times\) 40 mm deep \(\times\) 160 mm long) was placed on support rolls placed at a distance of 100 mm, and the roll was loaded in one direction at the centre of the specimen at a loading rate of 50 N/s. Then, the relationship between bending stress and deflection was investigated, and the peak value was taken as the flexural strength. Figures 8 and 9 show the relationship between the bending stress and deflection for each test case and the micrographs of the tensile fracture surfaces, respectively.  
The flexural strength of the specimens without CNF was approximately 400-470 kN/m\({}^{2}\), which was 25-50% of the unconfined compressive strength, regardless of the age of the specimens. Previous studies [23; 24; 25] showed that the flexural strength was approximately 20-50% of the unconfined compressive strength (on average around 30%), and our results were consistent with these findings. The flexural strength of the specimens with TEMPO-CNF  
Figure 8: Relation between bending stress and deflection in bending strength tests (Method 1).  
Figure 9: Appearance of tensile fracture surface (Method 1).  
was 590 \(\rm{kN/m^{2}}\) (at 28 days of age) and 810 \(\rm{kN/m^{2}}\) (at 112 days of age), indicating that the flexural strength increased with the addition of CNF. The ratio of flexural strength to unconfined compressive strength was as large as 50-65%, which was not the case for normal cement-treated soil. The increase in flexural strength can be attributed to the increase in the tensile strength. The flexural strength of the specimens with ACC-CNF was 560 \(\rm{kN/m^{2}}\) (at 28 days of age) and 660 \(\rm{kN/m^{2}}\) (at 12 days of age), and the ratio to the unconfined compressive strength was 33-56%. The addition of ACC-CNF also increased the flexural strength, although not as much as that of TEMPO-CNF. The low flexural and tensile strength of the treated soil is frequently a problem in the field, and the addition of CNF was found to be a possible solution to this problem. The micrographs showed fibrous material in the ACC-CNF specimen, as well as in the sheared surfaces from the unconfined compression test, while the TEMPO-CNF specimen showed voids and material that could be CNF in the void.