esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Dynamic Simulations
#### 3.2.3 Annual Energy Demand  
The annual energy demand generated in the houses composed of walls with different lightening levels was calculated for the analyzed IPCC scenarios. Figure 5 shows the respective heating and cooling demand. It is observed that the cases where the TMY with historical information is used result in lower energy requirements, meaning that out studies carried out on the use of energy in buildings could result in an underestimation of energy requirements necessary to keep a house in thermal comfort. When analyzing the energy requirements in scenarios A1B (2030 and 2050) and A2 (2050), it is observed that, in general, there would be a greater energy demand. The energy demand associated with air conditioning would increase; however, the heating requirement would be reduced over the years.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Case** & **TMY** & **A1B-2030** & **A1B-2050** & **A2-2050** \\ \hline \multirow{4}{*}{Average} & Case 0 & 0.39 & 0.80 & 0.98 & 0.94 \\  & Case 1 & 0.43 & 0.84 & **1.02** & 0.98 \\  & Case 2 & 0.44 & 0.85 & **1.03** & 0.99 \\  & Case 3 & 0.48 & 0.89 & **1.07** & **1.02** \\ \hline \multirow{4}{*}{Maximum} & Case 0 & 3.43 & 3.99 & **4.21** & 4.00 \\  & Case 1 & 3.36 & 3.92 & 4.14 & 3.91 \\  & Case 2 & 3.32 & 3.88 & 4.09 & 3.88 \\  & Case 3 & 3.25 & 3.79 & 4.00 & 3.85 \\  & Case 0 & 3.43 & 3.99 & 4.21 & 4.00 \\ \hline \multirow{4}{*}{Maximum} & Case 0 & \(-\)3.42 & \(-\)3.03 & \(-\)2.78 & \(-\)2.90 \\  & Case 1 & \(-\)3.24 & \(-\)2.85 & \(-\)2.60 & \(-\)2.73 \\ \cline{1-1}  & Case 2 & \(-\)3.18 & \(-\)2.78 & \(-\)2.53 & \(-\)2.66 \\ \cline{1-1}  & Case 3 & \(-\)3.01 & \(-\)2.61 & \(-\)2.38 & \(-\)2.48 \\ \cline{1-1}  & Case 0 & \(-\)3.42 & \(-\)3.03 & \(-\)2.78 & \(-\)2.90 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Averaged promoted mean vote (PMV) without air conditioning systems.  
Figure 5: Annual energy requirements per studied case.  
This figure clearly shows that applying lightweight cellular concrete would reduce the overall energy requirement of the house. For example, in scenario A1B-2030, when comparing the energy required for the heating and cooling systems in the house for Case 0 (8025 kWh) and Case 3 (5850 kWh), a reduction of 37% is observed. Likewise, the energy demand for cooling the house reduces by 25% for A1B-2030 and A1B-2050. As for the heating, the required energy would be reduced over the years. However, when applying the lightened concrete, it decreases by 43% for both A1B-2030 and A1B-2050 scenarios.  
When comparing the energy consumption to cool the house, it was found that these will increase by 35% (A1B-2030), 52% (A1B-2050), and 49% (A2-2050) for the historical TMY and the traditional construction of the house (Case 0). In the same way, energy consumption to heat the house will be reduced, on average, 96% (A1B-2030), 61% (A1B-2050), and 58% (A2-2050).
# OffshoringDSS: An Automated Tool of Application Maintenance Offshoring
## 5 Limitations of the Proposed Tool  
To our knowledge, there is no such automated and novel tool to make the application maintenance offshoring decisions. The OffshoringDSS is a novel tool that helps decision makers to make effective decisions of application maintenance offshoring. However, the automated tool has several limitations that may hamper its wide application in the outsourcing industry.  
To make sourcing decisions, IT professionals need to evaluate factors and alternatives. Based on the type of project and client's requirements, the influencing factors are quantified. Therefore, decision makers need to be experts in offshore outsourcing and GSD to make effective sourcing decisions.  
The current version of the tool is based on 10 influencing factors. These factors were identified by conducting two systematic literature reviews [12; 13]. Furthermore, these factors were validated by an empirical study [14]. Therefore, the developed tool considers no more than 10 factors to make sourcing decisions.  
It is an automated tool capable of ranking the onshore model, nearshore model and offshore model. The ranking is based on the supplied ratings of variables and alternatives. The created tool, however, can only rank three different sourcing options. Although other multi-criteria decision-making strategies, such as the analytic network process, are available, the current version of the tool relies solely on the AHP technique to make offshore decisions.
# Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN)
## 6 Conclusions  
In this study, we propose a BNGCNN model to predict earthquakes with satisfying accuracy adopting multistation and 3 channel waveform data consisting of 1477 events with magnitude 3 or more than 3 from 187 stations. After preprocessing the data, CNN of the proposed model with batch normalization before activation is applied to the input data. We found that it not only helps to extract valuable features but also improves training by reducing the number of epochs and reducing nonlinearity which helps in fast training and improves prediction accuracy. We also found that incorporating station location information in feature vectors and applying GNN on the feature vector also improves the prediction accuracy of our model. We have analyzed the performance of our proposed model by analyzing its different hyperparameters which help us to find the best values of the model parameters to reduce training error. Comparison with the baseline model proves the superiority of our model on the same dataset by reducing the error rate to 3 km, 0.7 km, and 0.04 for longitude and latitude, depth, and magnitude, respectively.  
Figure 9: Residuals of the epicentral locations (in first column) and overlay of the locations of seismic stations (in second column) on the interpolated prediction error (in km) on the California dataset. (**a**) Residuals of the epicentral locations and overlay of the locations of seismic stations by GNN; (**b**) Epicentral residuals and an overlay of seismic station locations by BNGCNN.  
For future work, we aim to explore other variants of deep learning models such as GNN with attention layer mechanisms [34] and transformers [2; 31]. We also aim to test deeply deep learning models on multiple datasets with sparse and dense stations with and without station location information. The proposed model can also be applied to assess the damage done by an earthquake [9].  
Conceptualization, Y.J. and Y.W.; Formal analysis, M.A.B., M.P.A. and M.Y.; Funding acquisition, Y.J. and Y.W.; Investigation, M.Y.; Methodology, M.P.A.; Software, M.A.B.; Supervision, Y.W.; Visualization, M.A.B.; Writing--original draft, M.A.B.; Writing--review & editing, M.P.A. All authors have read and agreed to the published version of the manuscript.  
National Key R & D Plan: 2021 YFC2901801.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Classification of Routing Protocols
### Low Energy Adaptive Clustering Hierarchy (LEACH) Protocol  
This is a hierarchical clustering-based routing protocol used for WSN design. In this protocol, cluster heads are changed randomly and one by one all the nodes become cluster heads as shown in Figure 2. For scalability and robustness, LEACH uses localized coordination for the dynamic network and does data aggregation at the cluster head level. Thus, the data transmitted to the sink are reduced and bandwidth is saved.  
The LEACH protocol's basic flow diagram is shown in Figure 3. The clustering system procedure begins with a setup step in which all nodes use an indicator function to pick CHs. This process is given in Figure 3. The chosen cluster heads then broadcast an Advertisement (ADV) message using the non-persistent protocol which includes CH's ID (also called CHID) [8; 9].  
Figure 1: Classification of clustering based energy efficient routing protocols.
## 3 Optimization Method
### Cable Crossing Detection  
To reduce the OPEX and failure rate, the crossed cable connection layout is not desirable in the OWFCCLP. Hence, a specific algorithm [16] is introduced in this work to prevent the emergence of crossed cables.  
It is assumed that there are crossed cables in a certain cable connection layout which is shown in Figure 6.  
In Figure 6, the coordinates of 4 WTs are shown as \(E_{1}=(ex_{1},ey_{1})\), \(E_{2}=(ex_{2},ey_{2})\), \(E_{3}=(ex_{3},ey_{3})\), and \(E_{4}=(ex_{4},ey_{4})\). If segment \(E_{1}E_{2}\) crosses \(E_{3}E_{4}\), then there \(a\in[0,1]\) and \(b\in[0,1]\) should exist, which satisfy Equation (15) as follows:  
\[\left\{\begin{array}{l}a\cdot ex_{1}+(1-a)\cdot ex_{2}=b\cdot ex_{3}+(1-b) \cdot ex_{4}\\ a\cdot ey_{1}+(1-a)\cdot ey_{2}=b\cdot ey_{3}+(1-b)\cdot ey_{4}\end{array}\right. \tag{15}\]  
Figure 5: The final cable connection layout with selected cable types.  
Figure 6: An example of cable crossing.  
Furthermore, the matrix reformulation of Equation (15) can be obtained as follows:  
\[\left[\begin{array}{cc}ex_{1}-ex_{2}&ex_{4}-ex_{3}\\ ey_{1}-ey_{2}&ey_{4}-ey_{3}\end{array}\right]\left[\begin{array}{c}a\\ b\end{array}\right]=\left[\begin{array}{c}ex_{4}-ex_{2}\\ ey_{4}-ey_{2}\end{array}\right] \tag{16}\]  
By substituting the following equations into Equation (16),  
\[EM=\left[\begin{array}{cc}ex_{1}-ex_{2}&ex_{4}-ex_{3}\\ ey_{1}-ey_{2}&ey_{4}-ey_{3}\end{array}\right]=\left[\begin{array}{cc}em_{11 }&em_{12}\\ em_{21}&em_{22}\end{array}\right]\text{, }EQ=\left[\begin{array}{c}ex_{4}-ex_{2}\\ ey_{4}-ey_{2}\end{array}\right]=\left[\begin{array}{c}eq_{1}\\ eq_{2}\end{array}\right]\text{, }EZ=\left[\begin{array}{c}a\\ b\end{array}\right]\text{,}\]  
Equation (15) can then be rewritten as  
\[EZ=\left(EM\right)^{-1}EQ=\frac{1}{\det(EM)}\left[\begin{array}{cc}em_{22} \cdot eq_{1}-em_{12}\cdot eq_{2}\\ -em_{21}\cdot eq_{1}+em_{11}\cdot eq_{2}\end{array}\right] \tag{17}\]  
From (17), the values of both \(a\) and \(b\) can be easily calculated; then, the judgment of crossed cables can be fulfilled by detecting the value of \(a\) and \(b\). By searching all the connection layouts ergodically, the total number of crossed cables, \(CI\), can then be obtained.
# Study on Lightweight Model of Maize Seeding Object Detection Based on YOLOv7
## 3 Results and Analysis
### Ablation Test Comparison  
In order to provide the impact of different modules on the detection accuracy and speed of the model, the ablation experience results are shown in Table 6.  
In Table 6, we used YOLOv7's metrics as a baseline, which is shown in the first row of Table 4. mAP, parameters, and FPS of YOLOv7 are 92.7%, 74.8 M, and 121 f/s, respectively.  
Introducing the GhostNet module, the mAP decreased by 1.2%, but the total parameters also decrease by 21.39%. The value of FPS is 94, which meets the real-time requirements.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Name** & **Precision** & **Recall** & **mAP** & **Parameters** & **FPS** \\  & **(\%)** & **(\%)** & **(\%)** & **(M)** & **(f/s)** \\ \hline Faster-RCNN & 62.6 & 58.1 & 55.6 & 108 & 14.3 \\ YOLOv3 & 88.4 & 82.2 & 91.3 & 123.4 & 82 \\ YOLOv4 & 89.9 & 83 & 92.2 & 105.4 & 107 \\ YOLOv5 & 89.2 & 79.8 & 89.3 & 91.4 & 114 \\ YOLOv8 & 91.2 & 83.3 & 93.2 & 87.6 & 125 \\ LW-YOLOv7 & 89.3 & 85.5 & 93.2 & 59.4 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison results of different detection algorithm models.  
Based on GhostNet, introducing the CBAM attention mechanism module, the mAP and total parameters were 92.3% and 59.4, and they decreased by 0.4% and 20.58%, respectively.  
Introducing the BiFPN module, the mAP was 92.9%, which increased by 0.2%. The total parameters were 59.4 M, which decreased by 20.58%.  
Introducing the SIoU function, the mAP was 93.2%, which increased by 0.5%. The total parameters were 59.4 M, which decreased by 20.58%.  
The study results indicate, by introducing the GhostNet model, the total parameters can be reduced and the slightly reduced detection accuracy. Adding the CBAM module, the accuracy of the model will increase. Moreover, through introducing the module of BiFPN and CIoU function, the accuracy also increases, and the total parameters keep unchanged.
# Portable L-Band Radiometer (PoLRa): Design and Characterization
## 1 Introduction  
The modern age of space-borne L-band (1-2 GHz) microwave radiometers began with the European Space Agency (ESA) Soil Moisture and Ocean Salinity Satellite (SMOS) [1] in 2010. This was followed by the National Aeronautics and Space Administration (NASA) satellites Aquarius [2] and Soil Moisture Active Passive (SMAP) [3]. L-band radiometry typically occurs in the protected frequency band from 1400-1427 MHz. Retrievals of environmental state parameters, such as soil moisture [4, 5], sea surface salinity [6], vegetation optical depth [7, 8], snow liquid water [9], snow density [10, 11, 12], soil freeze/thaw [13, 14], and sea ice thickness [15], have all been demonstrated, based on dual-polarization microwave brightness temperatures in this band.  
Near-surface L-band radiometry, such as with the Portable L-band Radiometer (PoLRa), allows L-band radiometry at high spatial resolution from a number of platforms. The compact and low-mass design allows use on unmanned aerial vehicles (UAVs) or drones, wheeled vehicles, or fixed on towers, poles, or buildings. The drone-mounted PoLRa is capable of providing ground resolution of a few meters (<10 m).  
UAV-based L-band radiometers have been demonstrated previously in [16, 17]. Neither of these systems provide dual-polarization off-nadir antenna temperatures which are optimal for use with established retrieval algorithms such as the Tau-Omega (TO) [18, 19] or Two-Stream (2S) emissionmodels (EMs) [5]. The PoLRa is a direct-detection radiometer providing calibrated dual-polarization L-band antenna temperatures with resolution of \(\sim\)0.14 K at 1 s integration, and total uncertainty ranging between 0.6-1.5 K, depending on integration time and input antenna temperature. PoLRa uses a unique dual \(2\times 2\) patch array antenna with an air-gap substrate for high gain and low Ohmic losses. A unique antenna temperature correction scheme allows correction for the relatively wide antenna power full beamwidth of 37\({}^{\circ}\) at \(-\)3 dB sensitivity. The correction convolves the antenna pattern with modeled angular dependent brightness temperatures of facets, while also considering polarization mixing introduced by nature of the geometry at large angles off of boresight (see Appendix in [20]). PoLRa is a research-ready radiometer system, and its characterization is demonstrated herein.  
The following sections present the radiometer hardware, its characterization, initial results, and then conclusions. Hardware includes the radiometer electronics and antenna. Characterization includes the radiometer resolution and stability, calibration, and uncertainty. Initial results include drone-based antenna temperature measurements and soil moisture retrievals.
# Step 12: Compile the grid search model: compile_model(grid_search_model)
## 3 Results and Discussion
### Location Prediction and Formation of Distribution Routes  
The location prediction model in this study utilizes a deep learning approach with an artificial neural network (ANN) algorithm. The model is trained using data generated from an emergency facility location model that includes three open distribution centers and 38 demand points. Its purpose is to predict the location of the assigned distribution center based on the geographic coordinates of newly opened demand points. By inputting the coordinates of these new demand points into the model, it can accurately predict the corresponding location of the assigned distribution center. This predictive capability aids in efficient and effective decision making for logistics planning in humanitarian operations.  
The model training results, as shown in Figure 8, indicate that the model achieves an impressive accuracy rate of 96.15% after 50 epochs. This high accuracy demonstrates that the model has effectively learned from the training data and can make accurate predictions. The accuracy rate gradually increases with each epoch, indicating that the model continuously improves its internal parameters to enhance its performance. With such a high accuracy rate of 96.15%, the model holds significant potential for reliable predictions, and can be effectively applied in various contexts, including location prediction and data classification tasks.
## 2 Materials and Methods
### Motion Configurations Tested and Design of the Simulations
#### 2.3.3 Second Set, S2: Scattering of the Markers within Each Cluster  
In the S2 set, the scattering of the markers in each cluster was changed by tuning of the gains \(k_{\text{S}}\) and \(k_{\text{F}}\), as detailed in Section 2.1.1. Three values of each gain were selected, equal to 0.75, 1, and 1.25. The 9 combinations of the gains were tested for configurations A and D, with the default range of motion of flexion/extension equal to (+20, \(-30\)) deg. A default cycle duration equal to 1.2 s was considered.
## 5 Case Study
### PM from Different Perspectives
#### 5.4.4 Business Process Comparison Considering Complaint Problems  
In terms of case duration, the median case duration of service problem complaint is 27.4 hours and the mean case duration is 49.3 hours; the median case duration of sale problem complaint is 46.5 hours and the mean case duration is 3.4 days. That is, the case duration of sale problem complaint is significantly longer than overall level compared with service problem complaint. However, the case lengths of service problem complaint and sale problem complaint are similar to the overall level.  
Figure 11: Process models of not in time (**a**) and other cause (**b**) CHS cases.  
In terms of case variants, of the 2934 service problem CHS cases, 1154 types of case variants are involved; of the 1348 sale problem CHS cases, 834 types of case variants are involved. For service problem CHS cases, RC-1 \(\rightarrow\) FCHRCH \(\rightarrow\) FCHRCEB \(\rightarrow\) FCHRCEB \(\rightarrow\) TBC (294 cases) is the most frequent case variant; for sale problem CHS cases, RC-1 \(\rightarrow\) FCHRCH \(\rightarrow\) FCHRCEB \(\rightarrow\) FCHRCEB \(\rightarrow\) TBC (89 cases) is the most frequent case variant as well.  
Figure 13 presents the mining results of CHS cases with different complaint problems by Disco 2.2.1 [36]. For both two types of CHS cases, the most frequent activity is FCHRCEB. When it relates to the difference between the two kinds of CHS cases, the positions of AP-1 and AP-2 in the mined models are highlighted. Figure 14 depicts the process comparison of CHS Cases with different complaint problems [39]. The control-flow difference of the two types of CHS cases is 22.22%. The execution frequency in two groups of RC-1, FCHRCH, FCHRCEB and TBC share little difference. However, in terms of task handover frequency, FCHRCEB \(\rightarrow\) FCHRCH, FCHRCH \(\rightarrow\) TBC, TBC \(\rightarrow\) FCHRCH and FCHRCEB \(\rightarrow\) RC-1 are more frequently in sale problem CHS cases compared service problem CHS cases. In terms of task handover efficiency for TBC \(\rightarrow\) FCHRCH, service problem CHS cases perform better than sale problem CHS cases.  
Figure 12: Process comparison of CHS cases with different complaint causes.  
Figure 13: Process models of service problem (**a**) and sale problem (**b**) CHS cases.
## 4 Experimental Results and Analysis
### Impacts of the Orbital Parameters on LEO-LEO Occultation Events
#### 4.1.3 Impact of the Inclination
Figure 4 shows the occultation number trend beside the global coverage fraction trend with time for the constellations shown in Figure 3. One can see that the occultation number of those constellations with the same Tx and Rx inclination increases in proportion to the time, while, except for in the Rxi170-Txi10 case, the constellations with supplementary Tx and Rx inclinations increase nonlinearly, and the occultation numbers grow very slowly during certain periods. The nonlinear characteristics of the occultation number and the GCF curves are caused by the RAAN precession, which will be analyzed in Section 4.2.
## 5 Testbed Setup and Analysis
### Analysis Methodology  
According to our simulation results, the goodput performance of each protocol stack is observed from approximately normal populations. With this observation, the statistical \(t\)-statistic [60] was adopted for pair-wise comparisons among the protocol stacks.  
As mentioned, we are interested in a performance comparison of four DTN protocol configurations--LTP LTP PRIM (L_L_P), UDP LTP PRIM (U_L_P), LTP UDP SEC (L_U_S) and LTP TCP SEC (L_T_S)--for each combination of BER and link delay.  
Let \(W_{L\_P\_1},W_{L\_P\_2},\ldots,W_{L\_L\_P\_16}\) be sixteen (16) samples of goodput performance from LTP LTP PRIM normal populations with mean \(\mu_{L\_L\_P}\) for each combination of three BERs and nine link delays.  
Let \(X_{U\_L\_P\_1},X_{U\_L\_P\_2},\ldots,X_{U\_L\_P\_16}\) be sixteen (16) samples of goodput performance from UDP LTP PRIM normal populations with mean \(\mu_{U\_L\_P}\) for each combination of three BERs and nine link delays.  
Let \(Y_{L\_U\_S\_1},Y_{L\_U\_S\_2},\ldots,Y_{L\_U\_S\_16}\) be sixteen (16) samples of goodput performance from LTP UDP SEC normal populations with mean \(\mu_{L\_U\_S}\) for each combination of three BERs and nine link delays.  
Let \(Z_{L\_T\_S\_1},Z_{L\_T\_S\_2},\ldots,Z_{L\_T\_S\_16}\) be sixteen (16) samples of goodput performance from LTP TCP SEC normal populations with mean \(\mu_{L\_T\_S}\) for each combination of three BERs and nine  
\(S\ldots\) denotes the standard deviation of the goodput for each protocol configuration; \(n\ldots\) denotes the sample size for each protocol configuration. This size is chosen as sixteen (16) in this work according to [PERSON]'s comparison procedure [60] based on \(|\overline{y_{i}}-\overline{y_{j}}|\geq t_{\pi/2,\pi(n-1)}\sqrt{\frac{2 MS_{\pi}} {n}}\).  
The \(p\)-value for each comparison pair is computed and found using the Student's \(t\) table [60]. The \(p\)-value serves as a measure of the evidence against the null hypothesis \(H_{0}\)[61], as clarified below:  
* The test result of (\(p<0.01\)) provides very strong evidence to reject \(H_{0}\), implying significant goodput difference between the two configurations.
* The test result of (\(0.01\leq p<0.05\)) provides moderate evidence to reject \(H_{0}\), implying reasonable goodput difference between the two configurations.
* The test result of (\(0.05\leq p<0.1\)) provides suggestive evidence to reject \(H_{0}\), implying implicative goodput difference between the two protocols.
* The test result of (\(p>0.1\)) provides no real evidence to reject \(H_{0}\), implying no goodput difference between the two protocols.
# Shadow Fleets: A Growing Challenge in Global
## 4 Approaches to Detecting Dark and Gray Fleet Characteristics
### Inconsistent Information  
Inconsistent information regarding a ship's name, flag, or registration details is a significant red flag. Covert fleets frequently manipulate or change their identification information to sow confusion and evade tracking. Discrepancies between the information from official sources and the vessel's reported details, or frequent changes in identification information, can indicate the vessel's potential involvement in illicit activities [40].  
This practice of altering or providing inconsistent information is a deliberate attempt to obfuscate the vessel's true identity and ownership, making it difficult for authorities and other interested parties to track its history or investigate its activities. Such maneuvers may involve changing the vessel's name, flag (the country of registration), or even manipulating its physical appearance through repainting and modifications [41; 42].