esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Methodology for Planning Urban Road Safety Interventions
## 3 Novel Methodology
### Cost-Benefit Analysis for Road Safety Interventions
#### 3.4.2 Reduction in Social Costs  
This approach provides a monetary value for preserving human lives and reducing serious injuries, offering a useful metric for comparing different solutions and justifying investments. Thus, it is recommended to adopt an approach that centers on the reduction in the social costs arising from road accidents as a key criterion in evaluating design alternatives. Social cost is a complex indicator that translates the financial burdens society faces due to accidents, including healthcare, insurance, legal costs, and lost productivity. Specifically, the loss of productivity refers to an individual's inability to contribute to the country's GDP following an accident [28].  
In Italy, the methodology for calculating the social cost of road accidents was defined by the Ministry of Infrastructure and Transport in 2022 [28] and formalized by the Director's Decree No. 37 in 2023. To apply these social cost values in the analysis, the most recent data from 2018 [29] were used, indicating the following reference values.  
* Social cost per person died in a road accident \(\upxi\)1,812,989;
* Social cost per person seriously injured \(\upxi\)467,159;
* Social cost per person lightly injured: \(\upxi\)8519.  
According to Istat [4], the total social cost of road accidents in Italy in 2023 was nearly \(\upxi\)18 billion, equivalent to 1% of the national GDP. This underscores the importance of implementing effective road safety policies to alleviate the economic burden on society.  
It is important to note that the iRAP model used in the proactive analysis does not account for minor injuries, which were excluded from this study. However, although the social cost of minor injuries is much lower than that of serious injuries, future analyses should consider this category as well, especially since over 200,000 injuries are recorded annually in Italy [4].  
The social cost associated with fatalities is calculated for each black spot using Equation (4).  
\[SC_{F}=F\times uSC_{F} \tag{4}\]  
where \(F\) is estimated annual fatalities (persons); \(uSC_{F}\) represents unitary social cost for each fatality (\(\upxi\)/person); and \(SC_{F}\) is total social cost due to fatalities (\(\upxi\)). Similar procedures can be followed to calculate the social cost for serious injuries and the total social cost for each road segment is then calculated by summing the costs for fatalities and serious injuries.  
This procedure can be applied to both the current conditions of the black spot and the proposed design solutions. The economic benefit of the design alternative is determined by calculating the reduction in annual social costs through Equation (5).  
\[SC_{saved}=SC_{before}\times SC_{after} \tag{5}\]  
where \(SC_{saved}\) is annual reduction in social cost; \(SC_{before}\) represents annual social cost under current conditions; and \(SC_{after}\) is annual social cost after road improvement intervention.
# Water Pipeline Leakage Detection Based on Coherent \(\varphi\)-OTDR and Deep Learning Technology
## 3 Data Collection and Preprocessing
### Sensing Characteristic  
In our study, the accuracy of vibration signal restoration was validated using a Piezo-electric Transducer (PZT) as the vibration source. Multiple sinusoidal signals with peak voltages of 600 mV and frequencies of 100 Hz, 150 Hz, 200 Hz, and 250 Hz were applied to the PZT. By utilizing a \(\varphi\)-OTDR system, the phase evolution chart of the demodulated signal within a time window of 0.1 s was obtained. This approach successfully reconstructed the original vibration signal. The corresponding power spectral density (PSD) is displayed beneath the respective time domain chart in this paper. From Figure 3, it is evident that the peak frequencies in the four sets of PSD charts align precisely with the vibration signal frequencies, thereby confirming the accuracy of vibration signal restoration. The signal-to-noise ratios for the datasets were 40.0 dB, 42.0 dB, 45.0 dB, and 51.9 dB.  
A sinusoidal signal with a frequency of 100 Hz and a peak voltage from 100 mV to 600 mV in 100 mV increments was applied to the PZT, and the corresponding differential phase response peak was recorded. The obtained voltage-phase response curve was linearly fitted to obtain its fitting curve. To demonstrate the reproducibility of the experiment, the above operations were repeated at 150 Hz, 200 Hz, and 250 Hz, and three new fitting curves were obtained; these are summarized and displayed in Figure 4. The slopes of the four curves were almost identical, being 4.56 rad/V, 4.56 rad/V, 4.52 rad/V, and 4.64 rad/V,  
Figure 2: Schematic diagram of the optical signal at the disturbance location.  
and the correlation coefficients were 0.99913, 0.99896, 0.99927, and 0.99924, respectively. This confirms the stability and reproducibility of the system's demodulation.
# An Automatic Geometric Registration Method for Multi Temporal 3D Models
## 3 Methodology
### Spatial-Feature-Point Matching  
When the feature points of all 3D-model texture images are extracted and their spatial positions are calculated, it is necessary to establish the corresponding relationship between the spatial-feature points of the base 3D model and the spatial-feature points of the 3D model to be registered. Based on the 128-dimension normalized feature vector of SIFT points, the similarity value of the feature vector is obtained after calculating the Euclidean distance, and then it is determined whether the two feature points are corresponding points [28]. In conventional image-matching, the feature points and feature vectors of image A and B are obtained, respectively, and then the similarity between the feature points set of image A and image B are determined successively to complete the two-view image matching. After the feature points and feature vectors of a batch 3D model are completely acquired, the exhaustive search and matching method cannot be realized due to the large amount of data. Therefore, this paper adopts the axis-aligned bounding-box (AABB) search method to match the corresponding points between the feature points of the base 3D model and the feature points of the 3D model to be registered.  
After extracting the feature points of the base 3D models, the set of their spatial-feature points is generated, as shown by the blue dots in Figure 5a. In the same way, the spatial-feature-point set of the model to be registered is generated, as shown in Figure 5b. The set of spatial-feature points in Figure 5a is evenly divided into AABB bounding boxes, so that the number of feature points contained in each bounding box is less than a threshold (value 3000), as shown in the bounding box set \(\{B_{i}|i=1,2,\cdots 6\}\) in Figure 5a. Then, according to the prior knowledge r of the positioning error between the base model and the model to be registered, the bounding box is expanded by r to form the search bounding box \(\{S_{i}|i=1,2,\cdots 6\}\) for the set of feature points to be registered. After the similarity matching between the bounding box and the searching bounding box is completed, a complete list of corresponding points between the base feature points and the feature points to be registered can be obtained.
## 2 System and Channel Models
### Pointing Error Impairments  
Pointing error impairments occur due to the effects of wind, the thermal expansion of buildings, the undesirable fixing of the transmitter and the receiver, and so on, can lead to non-alignment of the transmitter beam and the receiver aperture. Mathematically, pointing error impairments can be expressed as [23]  
\[f_{I_{pe}|\theta_{a}}\big{(}I_{pe}\big{)}=B^{-\xi^{2}}\xi^{2}I_{pe}\xi^{2}-1 \cos\theta_{a},0\leq I_{pe}\leq B, \tag{6}\]  
where \(B=2\left(r_{r}/r_{L}\right)^{2}\), \(\xi=r_{L}/2\sigma_{c}\), \(r_{r}\) denotes the receiver aperture radius, and \(r_{L}\) stands for the beam radius at a distance \(L\). The variance \(\sigma_{c}^{2}\) denotes the sum of the random displacements of the beam centroid and the receiver aperture, which are caused by the jitter of drone and the thermal expansion of buildings, respectively. The angle between the incident light and the normal to the receiver aperture \(\theta_{a}\) can be expressed in terms of an approximate Rayleigh distribution as [23]  
\[f_{\theta_{a}}(\theta_{a})=\frac{\theta_{a}}{\sigma_{a}^{2}}\exp\left(-\frac{ \theta_{a}^{2}}{2\sigma_{a}^{2}}\right)\,, \tag{7}\]  
where \(\sigma_{a}^{2}\) represents variance.
## 3 Results
### Experimental Results
Again, the denoiser brought a greater DNSMOS improvement between the NLMS and NSLMS compared to the improvement between the respective systems in the RES settings. For PESQ, the same trend could be observed: during DT + NE, the PESQ gap was 0.36 in the residual echo suppression setting and 0.40 in the denoiser setting, and during DT, the gap in the residual echo suppression setting was 0.58 and 0.81 in the denoiser setting. Overall, it could be seen that in all scenarios, the gap between the NLMS and NSLMS performances in the denoiser setting was greater than the respective gap in the residual echo suppression settings. In other words, the denoiser benefited more from choosing NSLMS over NLMS than the proposed RES, which further asserted that the outputs produced by the NSLMS were more akin to noise than the outputs produced by the NLMS. Therefore, although NSLMS was preferable over NLMS in all settings when employing a pre-trained speech denoiser to the task of residual echo suppression, using NSLMS as a linear AEC resulted in significantly superior performance compared to using NLMS, showing that the proper choice of a linear AEC was even more critical in this setting. Finally, the performances of the NSLMS and NLMS as linear AECs, as well as combined with the proposed RES model, were compared for different SERs and ENRs. Figure 6 shows the PESQ scores for different values of SER in the double-talk scenario. NSLMSachieved superior PESQ over NLMS in all SERs, both as a standalone linear AEC and combined with the RES model. Furthermore, in all SERs, the RES model did not improve PESQ when employing the NLMS linear AEC. On the other hand, in the more challenging scenarios of lower SERs, the RES model improved in PESQ when employing the NSLMS linear AEC. Figure 7 shows the ERLE for different values of ENR during far-end single-talk periods. Again, NSLMS achieved superior performance over NLMS in all ENRs, both as a standalone linear AEC and combined with the RES model. In the challenging low ENR scenarios, the performance gap between the NLMS + RES and NSLMS + RES systems was greater than the respective gap in higher ENRs, further showing the advantage of using NSLMS over using NLMS in challenging scenarios. Overall, the graphs show the superiority of NSLMS over NLMS, both as standalone linear AECs and combined with the proposed RES model, in various conditions and settings. Furthermore, the graphs show that the advantage of using NSLMS over NLMS was even more significant in challenging scenarios and conditions.
# Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach
## 3 Results  
### Differences across Classification Algorithms and Measures  
Table 4 shows the values of accuracy, specificity, and sensitivity with different classification algorithms categorized according to the type of measures and their respective features. The outcome of our analysis showed that the classification algorithms RF and XFB led to higher accuracy (A), recall (R), and specificity (S) than the SVM and LR algorithms for all three types of measures. Highest values were seen for the measures of muscle activity with RF (A: 94.5%, R: 93.6%, S: 95.5%) and XGB (A: 94.6%, R: 94.9%, S: 94.5%) algorithms with 48 features obtained from all four EMG sensors. This was followed by whole-body stability and the least predictability was seen for trunk motion measure. Values reduced with reduction in the number of sensors (as well as features), yet the good predictability was obtained using RF and XGB algorithms. For instance, XGB (A: 86.1%, R: 86%, S: 86.3%) was effective in classifying fatigue with data obtained from a single EMG sensor located on the lower back (ereactor spinae) muscle. On the other hand, using the data obtained from the force plate with RF (A: 92.9%, R: 91.9%, S: 94.1%) and XGH (93.5, 94.1%, 93.1%) led to high predictability. Overall outcomes show that floor-embedded force plates, and EMG sensors can be more effective in detecting fatigue compared to trunk movement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance Measure** & **Acronym** & **Relation to Study Aims** & **Formulation** \\ \hline Accuracy & \(A\) & Ability of the model to detect fatigued & \(A=\left(\frac{TP+TN}{TP+FP+TN+FN}\right)\times 100\%\) \\ \hline Sensitivity/Recall & \(R\) & Ability of the model to correctly identify all instances of the fatigued state. & \(R=\left(\frac{TP}{TP+FN}\right)\times 100\%\) \\ \hline Specificity & \(S\) & Ability of the model to correctly identify non-fatigued states. & \(S=\left(\frac{TN}{TN+FP}\right)\times 100\%\) \\ \hline Precision & \(P\) & Reliability of the model’s predictions of the fatigued state. & \(P=\left(\frac{TP}{TP+TP}\right)\times 100\%\) \\ \hline F1-score & \(F1\) & A metric to evaluate the overall effectiveness of the model in detecting fatigued states. & \(F1=2\times\left(\frac{R\times P}{R+S}\right)\times 100\%\) \\ \hline G-index & \(GI\) & Provides an overall performance of the model in detecting fatigued states. & \(GI=\sqrt{(1-R)^{2}+(1-P)^{2}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measures for performance evaluation of classification algorithms. _TP_: trunk bending activity labeled as fatigued correctly, _TN_: trunk bending activity labeled as not fatigued correctly, _FP_: trunk bending activity labeled as fatigued incorrectly, and _FN_: trunk bending activity labeled as not fatigued incorrectly.
## 4 Multi-Objective Coverage Optimization Designs
### Local Search Strategy  
The local search strategy of memetic algorithms is a process of screening the excellent individuals in the local area. The combination of global search algorithms and local search algorithms often shows good convergence and strong global search capability in solving the multi-objective optimization problem, but there are no uniform standards and guides in the choice of local search strategies, as well as the position, mild and frequency, so we need to discover the local search strategy which is suitable for the unresolved problem. Synthesizing the advantages of a variety of local search algorithms can make the process of the local search converge faster and have higher solution quality by designing specific local search access policies.  
In order to achieve dynamic local search, we propose some self-adaption scheduling rules of the local algorithm. In every iteration of MOCOAMA, some algorithms from the pool of local search algorithms are randomly and alternately selected to make local search and get diverse solutions. Then the sink searches \(m\) searched nearest points from the initial point of the local search and the situation of solution fitness improvement corresponding to the local search algorithms used in the \(m\) points is evaluated. The one of the local search algorithms having the most obvious improvement on fitness is eventually used in the multi-objective optimization. The proposed rules learn from the search history information to regulate the local search strategy with adaptive, self-learning features.  
The pool of local search algorithms is a collection of many local search algorithms, including the tabu search algorithm [22], hill climbing search algorithm [23] and adaptive directional local search strategy [24]. Tabu search is an optimization algorithm for simulating human intelligence, which mimics the human memory function, saves the searched optimal solution to the tabu table in the process of solving, and marks the solutions to avoid repeating the same search in order to gain a broad search range [22]. The hill-climbing algorithm is a heuristic search algorithm based on greedy search strategy, which selects a random solution as the current solution in the solution space and compares with the solutions in the neighborhood scope one by one until you find a local optimal solution [23]. The adaptive directional local search strategy dynamically adjusts the neighborhood radius and/or local search probability, depending on the relative local and global effectiveness of evolutionary operators and the local search operator [24].
# ExtractEO, a Pipeline for Disaster Extent Mapping in the Context of Emergency Management
###### Abstract  
Rapid mapping of disasters using any kind of satellite imagery is a challenge. The faster the response, the better the service is for the end users who are managing the emergency activities. Indeed, production rapidity is crucial whatever the satellite data in input. However, the speed of delivery must not be at the expense of crisis information quality. The automated flood and fire extraction pipelines, presented in this technical note, make it possible to take full advantage of advanced algorithms in short timeframes, and leave enough time for an expert operator to validate the results and correct any unmanaged thematic errors. Although automated algorithms aren't flawless, they greatly facilitate and accelerate the detection and mapping of crisis information, especially for floods and fires. ExtractEO is a pipeline developed by SERTIT and dedicated to disaster mapping. It brings together automatic data download and pre-processing, along with highly accurate flood and fire detection chains. Indeed, the thematic quality assessment revealed F1-score values of 0.91 and 0.88 for burnt area and flooded area detection, respectively, from various kinds of high- and very-high-resolution data (optical and SAR).  
rapid mapping; flood; fire; water detection; burnt area detection; automatic classification; optical imaging; radar imaging; high resolution; image processing; satellite imagery; Sentinel-1/2 2022  
28 June 2022  
2022
## 3 Results and Discussion
### Regeneration of ZGO Analysis  
The regeneration and stability of zeolite with graphene oxide (ZGO) were evaluated through successive cycles of desorption and readsorption. Desorption was performed using 10 mL of 0.1 N HCl under agitation at 200 rpm for 4 h. Subsequently, the material was separated, washed, and dried before being subjected to readsorption in 10 mL of a methylene blue (MB) solution at 10 mg L\({}^{-1}\), with agitation at 250 rpm for 3 h. This procedure was repeated for five cycles.  
The results indicated that, during the first three cycles, the adsorption capacity of ZGO remained nearly unchanged, with MB removal slightly decreasing from 98% to 96%. However, from the fourth cycle onward, a slow decline in efficiency was observed, reaching 84% in the fifth cycle (Figure S4). This trend suggested a gradual loss of adsorption capacity, possibly due to incomplete desorption, leading to residual MB accumulation within the zeolite pores, thereby reducing the accessibility of active sites and partially blocking the adsorption channels. Additionally, repeated exposure to the acidic medium could induce structural modifications in zeolite, such as cation leaching, and alter the functionalization of GO, ultimately impairing its long-term performance.  
The obtained results demonstrate the remarkable stability and reusability of ZGO in multiple cycles of MB adsorption. The high removal efficiency observed, even after several cycles, suggests that ZGO exhibits excellent adsorption properties for MB and strong potential for application in wastewater treatment systems.
# Using Feature Selection with Machine Learning for Generation of Insurance Insights
## 4 Methods and Materials
### Experimental Results Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Per. of.**} & \multicolumn{2}{c}{**No. Sel.**} & \multirow{2}{*}{**GFSA**} & \multirow{2}{*}{**LS**} & \multirow{2}{*}{**Specu**} & \multirow{2}{*}{**USFSM**} & \multicolumn{2}{c}{**All-Features**} \\ \cline{3-3} \cline{6-9}  & 0.2 & 4 & 0.0301 & 0.0528 & 0.0528 & 0.0077 & \\ \cline{2-9}  & 0.5 & 8 & 0.0301 & 0.0045 & **0.0541** & 0.0056 & 0.0090 \\ \cline{2-9}  & 0.8 & 14 & 0.0082 & 0.0090 & 0.0418 & 0.0261 & \\ \hline \multirow{3}{*}{D2} & 0.2 & 18 & 0.0059 & 0.0033 & **0.0030** & 0.0064 & \\ \cline{2-9}  & 0.5 & 44 & 0.0070 & 0.0065 & 0.0066 & 0.0065 & 0.0038 \\ \cline{2-9}  & 0.8 & 70 & 0.0073 & 0.0038 & 0.0066 & 0.0065 & \\ \hline \multirow{3}{*}{D3} & 0.2 & 3 & 0.0865 & 0.0848 & **0.1001** & 0.0848 & \\ \cline{2-9}  & 0.5 & 7 & 0.0871 & 0.0669 & 0.0903 & 0.0467 & 0.0871 \\ \cline{2-9}  & 0.8 & 11 & 0.0871 & 0.0871 & 0.0927 & 0.0871 & \\ \hline \multirow{3}{*}{D4} & 0.2 & 3 & 0.0000 & Mem Ex & Mem Ex & Mem Ex & \\ \cline{2-9}  & 0.5 & 7 & 0.0000 & Mem Ex & Mem Ex & Mem Ex & \\ \cline{2-9}  & 0.8 & 11 & **0.0001** & Mem Ex & Mem Ex & Mem Ex & \\ \hline \multirow{3}{*}{D5} & 0.2 & 2 & 0.0001 & 0.0000 & 0.0001 & 0.0000 & \\ \cline{2-9}  & 0.5 & 5 & 0.0001 & 0.0038 & 0.0000 & 0.0000 & 0.0000 \\ \cline{2-9}  & 0.8 & 8 & 0.0000 & **0.0294** & 0.0000 & 0.0000 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: NMI accuracy for insurance datasets. Table 5 depicts the SVM classification accuracy. Like clustering accuracy and SVM classification accuracy, for all datasets except D3, there is at least one subset of features which outperforms using the whole dataset. This emphasizes the existence of noisy features that negatively affect the KNN accuracy of the whole dataset. The only exception case in our experiments is the SVM classification accuracy for D3. We found that the best SVM accuracy occurred for the whole dataset. There is no subset that outperforms SVM classification accuracy using all features in D3.