esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Case 1: Results Using Rural SAR Data Only
Figure 8: (**a**) TerraSAR-X image showing flooding in the Tewkesbury area on 25 July 2007 (52\({}^{\circ}\) N, 2.2\({}^{\circ}\) W, pixel intensities are DN backscatter values, and dark areas are water, 2.6 \(\times\) 2 km, \(\copyright\) DLR, after [39]), and (**b**) flood return-period map (black areas masked out). Figure 7: (**a**) Correspondence between the SAR image of 13 February 2014 and aerial photograph flood extents in the urban area of Blackett Close, superimposed on the LiDAR image (lighter grey – higher) and (**b**) extract from the SAR image of 13 February 2014; (**c**,**d**) as (**a**,**b**) for the SAR image of 14 February 2014. Averaged over all six SAR subimages, the flood detection rate was 94% and the precision was 92%. As Case 1 used only SAR and not model data, it allowed a comparison of the present method with that described in [42]. The latter used the SAR data to detect flooding in the urban, as well as the rural, area and employed a SAR simulator in conjunction with LiDAR data of the urban area to predict areas of radar shadow and layover in the image caused by buildings and taller vegetation. Three of the SAR subimages used in the present study were also employed in the previous one, namely Wraysbury 12 February 2014, Blackett Close 13 February 2007 and Tewkesbury 25 July 2007. Considering the percentage of the urban flood extent visible in the aerial photo that was detected by the SAR, in the previous study, the average flood detection rate was 79%, with a precision of 89%. [PERSON] et al. (2018) [42] concluded that \"flooding could be detected in the urban area by this method to good, but perhaps not very good, accuracy, partly because of the SAR's poor visibility of the ground surface due to shadow and layover.\" For the same three subimages using the present Case 1 method, the average flood detection rate was 93%, and the precision was 86%, giving a lower overall error. As a result, the present method seems both more accurate than the previous method and simpler to implement, as no SAR simulation of shadow and layover in the urban area is required.
# Feasibility of LoRa for Smart Home Indoor Localization
## 2 Literature Review
### Objectives and Scope  
To overcome the limitation, this study investigates the feasibility of applying LoRa for smart home localization. Especially, this study focuses on indoor localization of typical residential apartments. Thus, the research team needs to identify whether the 2D location of a LoRa tag can be tracked under LOS and NLOS conditions, which are commonly encountered in a furnished apartment, with a limited number of static anchors. To this end, the team carried out an experiment for LoRa that evaluated the accuracy of ranging and trilateration without continuous retraining or system configurations under both LOS and NLOS conditions. This study also focuses on determining the locations of static tags instead of dynamically moving tags the accuracy of which could potentially be impacted by movements. The impact of dynamic motion, speed, and placement of LoRa tags on the localization performance would be investigated in a separate study in the future.
## 4 Experiment Analysis
### Analysis of Simulation Results
#### 4.3.2 Impacts on Delivery Ratio  
Figures 5 and 6 shows the delivery performance of the PEBN protocol when the number of nodes and simulation time are different, respectively. From Figure 5 we can see that the delivery ratio of messages by the PEBN protocol is higher than other schemes due to better choices while sending the messages. This is because our solution sets a different degree of selfishness to each  
performance than other models. Since we apply the node cooperation to our proposed model, we can also perform well in scenarios where the node has selfish features. PNEC can predict transmitting neighbors better than other models; the message will be forwarded to a node with a high probability of satisfying the destination in the transmission route, which effectively reduces the cost loss of sending the message to the non-cooperative node. In addition, we can also observe that as the number of nodes increases, the amount of routing overhead also increases. An increase in the number of nodes means the number of candidate nodes for message selection increases. Messages need to be passed to more nodes, thereby increasing the overhead of information being propagated over the network. Moreover, this same observation can be seen in Figure 10, which shows that routing overhead increases as the simulation time is increased. Because the nodes do not need to continuously calculate and decide through the proposed model during the information transmission process, the transmission task of nodes becomes simpler. Furthermore, we have more complex probability models to choose neighbors, so our overhead increase ratio is relatively smaller than other models with the increase of simulation time.  
The conclusion can be drawn by comparing the three indicators of several schemes with the number of nodes and the simulation time. The PEBN algorithm adds social relationships and cooperation relationships when predicting probabilities so that a node can deliver a message to a cooperating node that has a high probability of encountering a destination node. Experiments show that the PEBN algorithm outperforms other algorithms in terms of the transmission success rate, routing overhead, and hop count. Specifically, the algorithm not only improves the data transmission efficiency in the network, but also adopts the updated information to adapt to the current network environment when the network topology changes dynamically. In addition, handling complex  
Figure 10: The relationship between time and overhead.  
Figure 9: The relationship between the number of nodes and overhead.  
transmission decisions through base stations or edge devices reduces the node overhead. By combining the social and cooperative relationships between nodes with a probabilistic prediction model, a better transmission path can be selected to reduce the number of hops to the destination node. Therefore, our scheme can improve performance in the transmission environment.
# Multi-Layer Fusion 3D Object Detection via Lidar Point Cloud and Camera Image
## 2 Related Works
### Point Cloud-Based 3D Object Detection  
Point cloud-based object detection networks can be divided into three types. (1) A method based on the original point cloud. This method can retain the position information of objects in three-dimensional space to the maximum extent, such as 3 DSSD [11]. (2) A method based on the projection of a point cloud. This method projects the point cloud into two-dimensional views from different angles and then uses a mature two-dimensional object detection network to achieve 3D object detection, such as RangeDet [12]. (3) A method based on point cloud voxelization. Disordered point cloud data are organized into ordered voxel expressive forms, and a 3D convolutional network is applied to extract the voxel features to achieve 3D object detection, such as SE-SSD [13]. Although this kind of network can accurately obtain the location information of the object, the information contained in the network is relatively sparse, resulting in heavy computation and loss of the object in the distance.
## Appendix E Respondent Burden and Presentation  
15. Is the time to complete reported and reasonable? OR, if it is NOT reported, is the number of questions appropriate for the intended application?
16. Is there a description of the literacy level required by the instrument?
17. Is the entire instrument available for public viewing (e.g., published with the citation or information provided about how to access a copy)?  
\begin{table}
\begin{tabular}{l} \hline \hline
**A. Conceptual Model** \\ \hline
1. Has the construct of usability/cognitive workload been specifically defined?
2. Has the intended respondent population been described?
3. Does the instrument’s conceptual model address whether a single construct/scale or multiple subscales are expected? \\ \hline
**B. Content Validity** \\ \hline
4. Is there evidence that members of the targeted respondent population were involved in the instrument’s development?
5. Is there evidence that content experts were involved in the instrument’s development?
6. Is there a description of the methodology by which items/questions were determined (e.g., focus groups and interviews)? \\ \hline
**C. Reliability** \\ \hline
7. Is there evidence that the instrument’s reliability was tested (e.g., test–retest and internal consistency)?
8. Are the reported indices of reliability adequate (e.g., ideal: \(r\geq 0.80\); adequate: \(r\geq 0.70\)) or otherwise justified? \\ \hline
**D. Construct Validity** \\ \hline
9. Is there reported quantitative justification that single or multiple subscales exist in the instrument (e.g., factor analysis or item response theory)?
10. Is the instrument intended to measure change over time? If YES, is there evidence of both test–retest reliability AND responsiveness to change? Otherwise, award 1 point if there is an explicit statement that the instrument is NOT intended to measure change over time.
11. Are there findings supporting expected associations with existing instruments or with other relevant data? \\ \hline
**E. Scoring and Interpretation** \\ \hline
12. Is there documentation regarding how to score the instrument (e.g., a scoring method such as summing or an algorithm)?
13. Has a plan for managing and/or interpreting missing responses been described (i.e., how to score incomplete surveys)?
14. Is information provided about how to interpret the instrument’s scores (e.g., scaling/anchors and what high and low scores represent) and/or normative data? \\ \hline
**E. Respondent Burden and Presentation** \\ \hline
15. Is the time to complete reported and reasonable? OR, if it is NOT reported, is the number of questions appropriate for the intended application?
16. Is there a description of the literacy level required by the instrument?
17. Is the entire instrument available for public viewing (e.g., published with the citation or information provided about how to access a copy)? \\ \hline \hline \end{tabular}
\end{table}
Table 1: Checklist for the critical appraisal of retrieved assessment methods 1.
# MPC for Optimized Energy Exchange between Two Renewable-Energy Prosumers
## 3 Simulation Results  
### Load and Supply Forecasting  
To evaluate this MPC approach requires realistic data for residential and commercial loads, RER supply profiles for the locations of these loads, and methods for forecasting these four quantities into the future. The load profiles used are derived from residential apartment meter readings and a model for a commercial-kitchen electrical load, resulting in hourly demand \(P_{LR}(k)\) and \(P_{L}C(k)\) for one year [6]. At each hour \(k\), the demand forecasts \(P_{LR}(k+i|k)\) and \(P_{LC}(k+i|k)\) for \(i=1,\ldots,N_{P}\) are selected randomly and uniformly from 60% to 140% of the actual future demand, where \(N_{P}\) is the predicted horizon of future load demand and equal to 24 h.  
The RER profiles \(P_{RER_{k}}\) and \(P_{RER_{C}}\) are each created as a mix of wind and solar power, using wind turbines 3 m\({}^{2}\) swept area per turbine), fixed position solar panels 2 m\({}^{2}\) per panel), and a model that converts TMY data for Ohio into hourly turbine and solar PV output [6]. The residential RER system has 1 turbine, 150 panels, and 220 kWh battery capacity; and the commercial RER system has 2 turbines, 120 panels, and 180 kWh battery capacity. The RER output forecasts at each hour \(k\), \(P_{RER_{k}}(k+i|k)\) and \(P_{RER_{C}}(k+i|k)\) for \(i=1,\ldots,N_{P}\) are selected randomly and uniformly from 80% to 120% of the actual RER powers, where \(N_{P}\) is the predict horizon of future supply and equal to 24 h.  
Figures 3 and 4 show examples of demand and supply profiles for the residential and commercial systems. The vertical line in each subplot indicates current time \(k\), corresponding to midnight, with one day of past (historical) values to the left and one day of future forecasted values to the right. Actual future load and supply (which would not be known in a real application) are also shown to illustrate forecasting error.
## 3 Constraint-Handling Techniques in Metaheuristic Algorithms
### Metaheuristic Algorithms
#### 3.1.2 Particle Swarm Optimization  
The PSO algorithm was proposed by [PERSON] and [PERSON] [47]. This algorithm is inspired by the collaborative behavior of species in search of food and is shown in Algorithm 2.  
```
1: Initialize the swarm position \(u_{i}\in U_{0}\) with \(NP\) particles.
2: Evaluate the swarm \(U_{0}\).
3: Initialize the best known position \(X_{0}=U_{0}\).
4: Initialize the best position of the swarm \(x_{u^{min}}^{best}\).
5: Initialize the velocity of each particle \(v_{i}\in V_{0}\).
6:\(G\gets 0\)
7:while\(G\leq G_{max}\)do
8: Update the inertial weight \(w\) based on (13).
9:for all\(u_{i}\in U_{G}\)do
10: Update the velocity \(v_{i}\) based on (14).
11: Update the position \(u_{i}\) based on (15).
12: Evaluate \(u_{i}\).
13:endfor
14: Update the best known position \(X_{G+1}\) and the best particle in the swarm \(x_{u^{min}}^{best}\) with the use of CHT between \(X_{G}\) and \(U_{G}\).
15:\([X_{G+1},x_{u^{min}}^{best}]\gets fIncCHT(X_{G},U_{G},x_{u^{min}}^{best})\) (This function is associated to the CHT.)
16:\(G\gets G+1\)
17:endwhile
```  
**Algorithm 2** Particle swarm optimization pseudocode.  
The PSO algorithm initializes with a swarm \(U_{G=0}\) with \(NP\) particles. Each particle is located in a position \(u_{i}\) with a velocity \(v_{i}\)\(\forall\)\(i=\{1,2,...,NP\}\). The best known positions of the particles are saved in the swarm \(X_{G=0}\). The best particle in the swarm \(X_{0}\) is stored in \(x_{u^{min}}^{best}\). In the time \(G\) (iteration), the \(i-th\) position and velocity of each particle \(u_{i}\) are updated by Equations (13)-(15), where \(w\) is the inertial weight, \(v_{max}\) and \(v_{min}\) are the maximum and minimum velocity values, \(x^{best}\) is the best neighborhood particle of \(x_{i}\) in a ring topology, and \(C_{1}\), \(C_{2}\) are weighting factors.  
\[w=v_{max}-\frac{G}{G_{max}}(v_{max}-v_{min}) \tag{13}\]  
\[v_{i}=wv_{i}+rand(0,1)C_{1}(x^{best}_{i}-x_{i})+rand(0,1)C_{2}(x^{best}_{i}-x_{i}) \tag{14}\]  
\[u_{i}=u_{i}+v_{i} \tag{15}\]  
Finally, the best particle \(x^{best}_{n^{warm}}\) and the best particles positions \(X_{G}\) are updated considering the CHT as is defined in Section 3.2. Then, the best solution is found in \(x^{best}_{n^{warm}}\).
# A Study on the Impact of Low-Frequency Random Loading and Unloading on Silo Structures
## 2 Reinforced Concrete Silo Force Analysis
### Analysis of Silo Wall Pressure Monitoring
#### 2.2.3 Arrangement of Monitoring Points and Collection Methods  
The monitoring process adopted a vibrating wire acquisition sensor system with a collection frequency of 1.0 Hz that can display sensor values in real time, upload data to the internet, and enable remote viewing.  
Seven unloading ports were monitored in the silo for this study, which were divided into seven unloading areas numbered from 1 to 7, as shown in Figure 3.  
The unloading method was diagonal unloading. At any one time, only two unloading ports were used for unloading, and the unloading process lasted for 15 min before switching to the next set of unloading ports, following the sequence shown in Figure 4.  
The sensors for this project were installed in unloading areas 1 and 3. Therefore, there were four possible states during the production process: both discharge areas 1 and 3 were discharging simultaneously; discharge area 1 was discharging, while discharge area 3 was not; discharge area 3 was discharging, while discharge area 1 was not; or neither of them were discharging.  
A total of 40 manometers were deployed for this monitoring project. Within discharge area 1, 13 sensors were installed on the main line and 7 on the auxiliary line of the inner wall. Similarly, in discharge area 3, 13 sensors were installed on the main line and 7 on the auxiliary line. The sensors on the main line were spaced approximately 2 m apart, while those on the auxiliary line were spaced approximately 4 m apart. The sensors near the top of the pressure-reducing cone were more densely distributed, while the others were slightly further apart, and the specific layout is shown in Figure 5.  
Figure 4: Unloading flow chart. (The numbers 1–7 in the figure represent the serial numbers of different unloading areas respectively; the green part represents the unloading area, and the white part represents the non-unloading area.).  
Figure 3: Unloading port location and sensor layout plan.
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 4 QoS-Aware Security Framework: Proposed Scheme  
The proposed framework follows a cross-layered structure. Cross-layer design in the context of MANETs refers to the sharing of information among the different layers for the efficient use of available network resources and promoting adaptability. The main reason for cross-layer design is to sustain the functionalities found in the original layers and additionally allowing interaction, joint optimization, and coordination of cross-layer protocols. Each layer in the design is characterized by having unique parameters to help them determine the best adaptation rules and control knobs for the current network status. The optimization aspect of the design comes with the customization of variables and constraints from other multiple layers. The design speaks not only towards an isolated problem but also other parts of the system that may encounter unintended effects while the problem is being resolved. This is the reason why security and QoS come as a balanced scale because the complexity of the design might affect other elements of the system and then resulting in poor transmission of packets or a weakened system. The highlight of the model is the intrusion detection system and is embedded in the different layers of the model. The uniqueness of the proposed framework is that it incorporates QoS and security implementation in more than one functional layer.
## 2 Background and Proposed System Model
### Glrt
#### 2.3.1 GLRT with DF  
For the GLRT with DF, denoted as GLRT-DF, SUs locally compute the SCM of received signals, its eigenvalues, and the test statistic for making an individual decision on the occupation state of the sensed PU band. SUs send these decisions to the fusion center via the control channels, which combines them for achieving a cooperative decision. It is possible to build the matrix of sensing samples \(\mathbf{Y}_{u}\in\mathbb{C}^{L\times N}\) collected by the \(L\) antennas of \(u\)-th SU to compute the covariance matrix as:  
\[\mathbf{Y}_{u}^{\mathrm{T}}=[\mathbf{y}_{1,u}^{\mathrm{T}},\mathbf{y}_{2,u}^{ \mathrm{T}},\ldots,\mathbf{y}_{l,u}^{\mathrm{T}}],\ldots\mathbf{y}_{L,u}^{ \mathrm{T}}], \tag{10}\]  
where \(\mathbf{y}_{l,u}=[y_{l,u}(1),y_{l,u}(2),\ldots,y_{l,u}(N)]\), with \(\mathbf{y}_{l,u}\in\mathbb{C}^{1\times N}\), represents the \(N\) samples collected by the \(l\)-th antenna of \(u\)-th SU, and the \(n\)-th sample comes from \(y_{l,u}(n)\) in (1). Then, the SCM \(\mathbf{R}_{u}\in\mathbb{C}^{L\times L}\) at the \(u\)-th SU is:  
\[\mathbf{R}_{u}=\mathbf{Y}_{u}\mathbf{Y}_{u}^{\mathrm{T}}/N. \tag{11}\]  
The \(u\)-th SU may compute the GLRT-DF statistic as [6]:  
\[T_{\mathrm{GLRT}_{u}}=\frac{L\lambda_{1,u}}{\sum_{i=1}^{L}\lambda_{i,u}}, \tag{12}\]  
such that \(\lambda_{1,u}\geq\lambda_{2,u}\geq\cdots\geq\lambda_{L,u}\) are the eigenvalues of \(\mathbf{R}_{u}\) in (11). If \(T_{\mathrm{GLRT}_{u}}>\gamma_{u}\), the \(u\)-th SU makes a decision in favor of hypothesis \(\mathcal{H}_{1}\), or \(\mathcal{H}_{0}\) if \(T_{\mathrm{GLRT}_{u}}>\gamma_{u}\), where the local decision threshold \(\gamma_{u}\) is predetermined at \(u\)-th SU. The fusion center makes the final decision by applying the \(K\)-out-of-\(U\) rule, in which the fusion center reaches a final occupation decision favoring \(\mathcal{H}_{1}\) when at least \(K\) out of the \(U\) sent decisions by the SUs are also in favor of the hypothesis \(\mathcal{H}_{1}\). It decides in favor of \(\mathcal{H}_{0}\) otherwise. When \(K=U,K=1\), or \(K=\lfloor U/2+1\rfloor\), the \(K\)-out-of-\(U\) rule becomes AND, OR, MAJ voting rules, respectively, where \(\lfloor\cdot\rfloor\) represents the floor function. It is worth highlighting that it is here considered a perfect (error-free) CC, thus occupation decisions sent by SUs and received by the FC are identical.
# Water Pipeline Leakage Detection Based on Coherent \(\varphi\)-OTDR and Deep Learning Technology
## 3 Data Collection and Preprocessing  
### Experimental Setup  
The schematic of the \(\varphi\)-OTDR system in this paper is illustrated in Figure 1. A narrow-linewidth laser (UNFSRL-1550-20-PMF-FC/APC-M4, Tanguang Technology Co., Ltd., Wuhan, China) with a line width of 1 kHz emits continuous light, which is split into two beams after passing through a coupler. Among these beams, the probing light is modulated by an acousto-optic modulator (G-1550-80-L-D-T-AA-G3-T-L, Tanguang Technology Co., Ltd., Wuhan, China) to generate pulsed signals with a pulse width of 100 ns and a repetition frequency of 10 kHz. Additionally, this modulation introduces an 80 MHz frequency shift. The modulated probing light then enters the tested optical fiber (G657A1, Guangzhou Pingtong Optical Cable Co., Ltd., Guangzhou, China) via an erbium-doped fiber amplifier (PMEDFA-PA35-09-1-1-M1, Tanguang Technology Co., Ltd., Wuhan, China) following amplification. The scattered signal from the probing light, further amplified by the erbium-doped fiber amplifier, combines with the local oscillator light emitted by the narrow-linewidth laser and enters the coupler. Subsequently, the signal is directed to a balanced detector (MBD-350M-A, Tanguang Technology Co., Ltd., Wuhan, China) with a bandwidth of 350 MHz. Finally, the received signal is sampled by a data acquisition card (PCIE-6920-DAQ-250 MSPs, Guangyi Intelligent Technology Co., Ltd, Guilin, China) operating at a sampling rate of 250 MHz.
# Performance Analysis of Mesh Based Enterprise Network Using RIP, EIGRP and OSPF Routing Protocols +
## 3 Problem Description and Main Contribution  
The primary objective of this research is to evaluate and compare the proposed routing protocols using various performance metrics. This evaluation is carried out both theoretically and through simulation. Routing is the process of transferring data from one source to a destination. Typically, this data is routed through a series of intermediary devices. The objective of the routing protocols is to give the information necessary for sending the packet appropriately to these intermediate devices. Therefore, the routing protocols are essential in ensuring prevent network devices from connecting with one another. Every routing protocol has an algorithm, and this algorithm must define techniques for routing protocols to work correctly. Simulates networks using the CISCO Packet Tracer 8.0 simulator.  
The conventional procedures are:  
These steps are used to receive and send network information.  
Second, finding the best route to a location and adding it to the routing database.  
Finally, it is a procedure to identifying, responding and notifying network changes.  
As a result, different algorithms can affect total network performance. Thus, these significant research achievements are:  
To create two network topologies with RIP, EIGRP and OSPF to analyze their performance.  
To simulate various network topologies using packets, transfer and observe the performance differences between the OSPF, EIGRP and RIP networks.  
Summarize and analyze the simulated findings.