esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Pattern Recognition Methods and Discussions
### Grayscale and Texture Features of the Image  
Grayscale histogram is a type of histogram that indicates the number of pixels for each grayscale value in the whole image. Some statistical variables can be calculated accordingly such as average, variance, smoothness, skewness, entropy, etc. [6]. It is proven to be an efficient and simple way in identifying coal and gangue [39]. Figure 10 shows the grayscale histogram of coal and gangue. The co-occurrence matrix is used to quantify the numerical features based on the spatial arrangement of the selected region.  
Figure 9: Spectrum widths \(\Delta(h_{q})\) of: (**a**) the original coal and gangue curve series; and (**b**) the shuffled series. A clear threshold value at 0.4 is marked with dot dash line in (**b**) while no such confident line can be drawn in (**a**).  
Meanwhile, it should be noted that there are nine variables or arguments in grayscale histogram and co-occurrence matrix in the feature extraction process [6], which will take too much computer memory leading to the dimension disaster. To solve the emerging problem, excluding insignificant arguments and picking identical ones can speed up the recognition process. Therefore, the average of the grayscale histogram is chosen as one of the notable grayscale features to be extracted based on the chemical properties of coal and gangue. Besides, the second-order moment of the co-occurrence matrix is used to represent the texture features of the image to help coal-gangue identification.  
In our study, more than 500 sampled figures are used for the training of BP, RBF, and \(k\)NN neural networks, and 50 sets of new figures are imported as the test data. The corresponding recognition rates are summarized in Table 1. The proposed method with grayscale, texture and geometric features can achieve a recognition rate of 97.5%.
# A Closer Look at the Sleep/Wake Habits and Dark Triad Traits
## 1 Introduction
On one hand, sleep deprivation can make people more vulnerable to stressful situations; on the other hand, emotional dysregulations and an inability to cope with stress can put us at a greater risk for mental problems. Therefore, understanding the risk factors of disturbed sleep can help us to cope more effectively with sleep complaints. There are only a few inconclusive studies that specifically explored the relationship between sleep/wake variables and the DT. An early study [24] demonstrated a positive relationship between the tendency to have later bed/wake times (i.e., eveningness) and the composite of the DT. Aptly, [11] found an association between the DT and low bedtime cortisol and reduced sleep. Another study [13] reported a positive link between the DT and higher sleep disturbances. In the following year, [25] yielded a positive correlation between the DT and poor sleep quality. Accordingly, the symptoms of insomnia and lower sleep quality were positively associated with psychopathy and Machiavellianism but not with narcissism [25; 26; 27]. Contrary to the above-mentioned research, a recent study [28] on a male-only forensic psychiatric inpatient population did not observe any relationship between the DT and sleep quality. To the best of our knowledge, all of the above-mentioned studies dealing with the relationship between sleep and the DT focused on connecting the DT to an overall sleep outcome (e.g., sleep disturbance, objective and subjective sleep quality, or sleep deprivation). However, before calculating the overall sleep quality, it seems necessary to check which sleep/wake routines result in poor or satisfying sleep quality. Routines such as sleep and wake times on free days and weekdays will form one's subjective image of how well one has slept and if he/she finds it satisfactory. None of the studies above tried to further investigate the sleep/wake habits, daytime sleepiness, or sleep/wake timing stability of those who graded high on the DT. The reasons for this can only be speculated upon, but it might be because of an outcome-oriented approach neglecting the process itself. Consequently, in this study, we aim to examine the bed/wake time, alertness, and sleep timing stability of individuals with variable levels of DT traits in order to uncover the association between the DT and sleep measures. Since the previous works have focused on the relationship between DT traits and overall sleep outcomes, it is of high interest for us to explore the potential links between DT traits and sleep behavior manifested in sleep/wake timing and its stability, along with daytime sleepiness as a new overall sleep outcome, besides replicating the findings for previously used variables such as subjective sleep quality. Thus, our approach in this piece of work is quite exploratory. In the first step, we will try to disclose the potential relationships, and then we will seek to predict sleep behavior variables using DT traits.
## 3 MPP Samples
### Samples Manufacturing  
The first step for the realization of an MPP layer was the material choice and, consequently, the standard thickness of the samples. Taking into consideration the sub-millimeter perforation diameter and the high quality standard in terms of durability and endurance, the choice was stainless steel. The aim was to obtain six samples with circular cross-sections and a diameter of \(\sim\)40 mm in order to allow the measurements in the impedance tube according to the technical standard ISO 10534-2 [26]. These three types of samples allowed to obtain different combinations between different samples, including double-layer structures with two equal samples. Considering the required perforation size (\(<\)1 mm) and geometry (circular cross-section), the stainless steel samples were manufactured using a 3D additive printing technique. A 316L grade stainless steel powder was used, specific for laser powder bed fusion (LPBF). The 3D printing process parameters are reported in Table 1.  
This choice allowed us to print the three different types of samples in a single cylinder of stainless steel with a diameter of \(\sim\)40 mm and a height of \(\sim\)30 mm as shown in Figure 5, respecting the working restrictions of the printer. Once the stainless steel was printed, the problem was to find a specific cutting technique to make samples of 1 mm thickness. The only possible choice was to use a wire electrical discharge machining (WEDM). The WEDM technique works without increasing the heat during the cutting.  
Thanks to this technique, two samples of each type were obtained from the cylinder. The thickness of each sample was respected with a tolerance of \(\approx\pm\)0.3-0.6 mm, as shown in Figure 6.  
Considering the scale of the perforation size, the real shapes of the perforations were not as expected and the real dimensions were bigger than expected: this was due to the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Stainless steel powder grade & 316L (LPBF) \\ Powder grains diameter & 30–40 μm \\ Powder layer height & 60 μm \\ Laser rated power & 360 W \\ Laser rated diameter & 50 μm (max 80 μm ) \\ Energy density & 130 J/m3 \\ Material specific weight & 7.98 g/cm3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: 3D printing process parameters. LPBF, laser powder bed fusion.  
limited accuracy of the processing and manufacturing machinery. Thus, all of the resulting perforations had irregular cross-sections: [PERSON] et al. [29] demonstrated that increasing the specific surface area of a perforation could increase the sound energy dissipation and expand the sound absorption bandwidth, when the inscribed and circumscribed circle are assumed to be unchanged. In fact, keeping constant the outer diameter of the hole, an irregular cross-section increases the length of the hole perimeter and can add sharp edges; in this situation, viscous dissipation increases the sound absorption and the bandwidth.
## 6 Results
### Latency  
#### 6.2.1 DTLS Handshake  
Here, we want to show the impact of repetitions on the latency of the handshake. Figure 10 shows the average handshake duration for the implemented scenarios with respect to radio conditions 1 and 2. The figure shows that the degradation of the radio conditions (from 1 to 2), and thus the increase of repetitions (from 1 to 32), leads to a higher latency of handshake for all the scenarios (IP, IP+SCHC, NIDD and NIDD+SCHC). It is obvious from the figure that the case of IP without SCHC leads to the highest latency, while SCHC over NIDD achieves the lowest in both radio conditions. Due to SCHC and optimization of the DTLS, SCHC reduces the duration of the handshake by about 35-40% for IP and 24% for NIDD.
## 4 Rupture Mechanism of Strong Mining Tremors
### Identification of Source Rupture Type  
The moment tensor is decomposed into an isotropic (ISO) component, a compensated linear vector dipole (CLVD) component, and a double-couple (DC) component. The rupture type of rock mass can be explained according to the relative proportions of the three components of the moment tensor (see Figure 12).  
According to the acoustic emission test, [PERSON] [34] proposed to identify the type of fracture in rock mass by using the proportion of double-couple \(M_{\text{DC}}\), but did not consider the compression state of the sample. Based on the identification criterion of dislocation angle \(\alpha\), its accuracy depends greatly on the physical and mechanical properties of coal rock mass at the source location. [PERSON], [PERSON] and [PERSON] et al. [35; 36; 37] propose a modified criterion for rock mass fracture that considers the proportion of ISO, CLVD, and DC components based on [PERSON]'s research (seen in Figure 12).  
Figure 12: Criterion of focal mechanisms of mining tremor (normal vector \(\mathbf{n}\) and slip vector \(\mathbf{v}\) of focal plane).  
The ISO, CLVD, and DC components are obtained after the moment tensor decomposition, as shown in Equation (12).  
\[\left\{\begin{array}{l}M_{\text{ISO}}=\frac{1}{3}(M_{1}+M_{2}+M_{3})\\ M_{\text{CLVD}}=\frac{2}{3}(M_{1}+M_{3}-M_{2})\\ M_{\text{DC}}=\frac{1}{2}(M_{1}-M_{3}-|M_{1}+M_{3}-2M_{2}|)\end{array}\right. \tag{12}\]  
where \(M_{\text{ISO}}\), \(M_{\text{CLVD}}\), and \(M_{\text{DC}}\) are the three components of the moment tensor \(M\); \(M_{1}\), \(M_{2}\) and \(M_{3}\) are the characteristic values of \(M\), respectively, \(M_{1}\geq M_{2}\geq M_{3}\); \(\mathbf{e_{1}},\mathbf{e_{3}}\) are the eigenvectors \(M_{1}\) and \(M_{3}\), respectively.  
Furthermore, the proportion of ISO, CLVD, and DC of moment tensor \(M\) are obtained as follows:  
\[\left\{\begin{array}{l}P_{\text{ISO}}=\frac{M_{\text{ISO}}}{|\mathbf{M}|}\times 1 00\,\%\\ P_{\text{CLVD}}=\frac{M_{\text{CLVD}}}{|\mathbf{M}|}\times 100\,\%\\ P_{\text{DC}}=\frac{M_{\text{DC}}}{|\mathbf{M}|}\times 100\,\%\end{array}\right. \tag{13}\]  
In summary, the modified criteria can be expressed as:  
\[\left\{\begin{array}{l}P_{\text{DC}}\geq 60\%,\text{ Pure shear}\\ P_{\text{DC}}\leq 40\%\text{ and }P_{\text{ISO}}>0,\text{ Pure tensile}\\ P_{\text{DC}}\leq 40\%\text{ and }P_{\text{ISO}}<0,\text{ Pure compression}\\ 40\%<P_{\text{DC}}<60\%\text{ and }P_{\text{ISO}}>0,\text{ Shear- tensile}\\ 40\%<P_{\text{DC}}<60\%\text{ and }P_{\text{ISO}}<0,\text{ Shear-compression}\end{array}\right. \tag{14}\]
## 3 Robust Co-Optimization Considering N-1 Constraints and Active Set Method  
In this section, we will develop a robust energy and reserve joint dispatch co-optimization model considering N-1 contingency constraints for both transmission lines (or transformers) and generators, as well as the zonal reserve optimal placement and dispatch strategy. It is worthwhile to note that it is a \"preventive\" model, which is the supplemental of the initial model.  
It is noted that the transmission lines and generators under the N-1 contingency have different impacts on the power system re-dispatch ability. Specifically, the N-1 contingencies on generators will result in the imbalance of active power while the shift distribution factor matrix is maintained. Consequently, the original dispatch scheme must be renewed and the reserve should be dispatched upwards to compensate the active power mismatch to deal with the contingency of the generator N-1. Thus, the optimal variables of the spinning reserve are to be reset and the model needs to be resolved so that the power balance is achieved (once the generator outage occurs). In contrast, the N-1 contingencies on transmission lines have no influence on the power balance constraint, but only the shift distribution factor matrix is changed. Since the power is still balanced (and thus the reserve and optimal variables remain), it only requires examining the limits for transmission line capacity under each contingency. Hence, the contingency constraints of transmission lines and generators are treated separately as follows:
## 4 Results
### Triggered and Background Seismicity Separation
seismicity is described by state 1 (red) with occurrence rate \(\lambda_{1}=0.58\) events/day, which we set as rate threshold for the primary classification of the clusters. Finally, dataset D3 also contains some major sequences, the 2013 \(M_{w}5.8\)[60], the 2014 \(M_{w}6.9\) Samothraki [61] and the 2017 \(M_{w}6.4\) Lesvos earthquake sequences [62], whose aftershock temporal distribution is approximated by states 6 (dark cyan), 4 (dark blue), 3 (orange) and 2 (yellow) of the model (Figure 3c). The rate threshold value is set equal to \(\lambda_{thr}=\lambda_{1}\). In general, we observe significant variations in the temporal evolution of the seismic excitations between the CG and the CII and NAS areas. Figure 3 illustrates that the daily frequency of events during seismic sequences in CII and NAS is decreasing in time, typical of mainshock-aftershock sequences, whereas in CG we observe large fluctuations in the daily frequency, common for earthquake swarms, as in 2014 when multiple seismic excitations occurred in the western subarea of CG. Consecutive events above the rate threshold \(\lambda_{thr}\) are classified into groups which we call potential clusters, and then, we test four different sets of temporal constraints, \((T,dt)\), to the three datasets. Potential clusters within a temporal interval \(T\) are merged into one and events that occurred in \(\pm dt\) time from the potential cluster are also included. Next, the DBSCAN algorithm is implemented to the merged clusters in order to separate them based on their spatial density. The minimum number of neighbors for the determination of a cluster is set equal to 4 (\(N_{pts}=4\)) for avoiding insignificant cases with fewer events. This is an appropriate choice for two-dimensional data according to [PERSON] et al. [35]. For the determination of the distance threshold, \(e\), we computed the \(k\)-distances which is a procedure proposed by [PERSON] et al. [35], which is commonly used to constrain the distance threshold [4]. In Appendix C we provide more details on the choice of the parameters and how they affect the spatio-temporal evolution of background seismicity.
# Hydrological Analysis of Extreme Rain Events in a Medium-Sized Basin
###### Abstract  
The hydrological response of a medium-sized watershed with both rural and urban characteristics was investigated through event-based modeling. Different meteorological event conditions were examined, such as events of high precipitation intensity, double hydrological peak, and mainly normal to wet antecedent moisture conditions. Analysis of the hydrometric features of the precipitation events was conducted by comparing the different rainfall time intervals, the total volume of water, and the precedent soil moisture. Parameter model calibration and validation were performed for rainfall events under similar conditions, examined in pairs, in order to verify two hydrological models, the lumped HEC-HMS (Hydroologic Engineering Center's Hydrologic Modeling System model) and the semi-distributed HBV-light (a recent version of Hydrologiska Byrans Vattenbalansavdelning model), at the exit of six individual gauged sub-basins. Model verification was achieved by using the Nash-Sutcliffe efficiency and volume error index. Different time of concentration (\(T_{c}\)) formulas are better applied to the sub-watersheds with respect to the dominant land uses, classifying the \(T_{c}\) among the most sensitive parameters that influence the time of appearance and the magnitude of the peak modeled flow through the HEC-HMS model. The maximum water content of the soil box (\(F\)C) affects most the peak flow via the HBV-light model, whereas the _MAXBAS_ parameter has the greatest effect on the displayed time of peak discharge. The modeling results show that the HBV-light performed better in the events that had less precipitation volume compared to their pairs. The event with the higher total precipitated water produced better results with the HEC-HMS model, whereas the rest of the two high precipitation events performed satisfactorily with both models. April to July is a flood hazard period that will be worsened with the effect of climate change. The suggested calibrated parameters for severe precipitation events can be used for the prediction of future events with similar features. The above results can be used in the water resources management of the basin.  
r 2021  
Applied  
22 March 2021  
Published: 26 May 2021  
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# Variable Neighborhood Search for the Two-Echelon Electric Vehicle Routing Problem with Time Windows
## 2 Related Literature
[26] proposed a heuristic based on large neighborhood search (LNS) for the 2E-VRP with time window and satellite synchronization constraints. [PERSON] et al. [27] developed a matheuristic based on VNS and integer programming. Moreover, [PERSON] et al. [28] formulated the 2E-VRP with simultaneous pickup and delivery constraints as a two-index mixed-integer programming model and developed a hybrid metaheuristic combining variable neighborhood descent and local search. The related literature on two-echelon electric vehicle routing problems is still rather scarce. The study by [PERSON] et al. [29] was one of the first works proposing a 2E-EVRP considering the option of battery-swapping stations (BSS). More specifically, instead of charging empty batteries, the authors consider the possibility of swapping the battery of an electric vehicle with a full one when needed. A hybrid algorithm combines column generation and LNS to solve the proposed problem. At first, the battery-swapping scenario may seem helpful to overcome deficiencies due to long battery charging times. Nevertheless, the necessity of each BSS to maintain a certain number of spare batteries poses numerous problems related to the environment, safety, logistics, and storage. Therefore, the EVRP literature mainly focuses on the scenario of charging batteries instead of swapping them. Another work in this line is the one by [PERSON] et al. [30] in which the authors extended their previous work (see [31]) with the idea of using electric vehicles in the second echelon of the distribution network. They proposed a metaheuristic approach based on LNS and an exact mathematical programming algorithm that utilizes decomposition and pricing techniques. Furthermore, [PERSON] et al. [32] studied the design of a two-echelon reverse logistics network for the collection of recyclable waste considering a mixed fleet of electric vehicles and conventional vehicles. Instead of an integrated mathematical model the authors present two separate models, one for each echelon. The hybrid genetic algorithm is tested on a single-instance set. [PERSON] and [PERSON] [33] developed a branch and price algorithm to solve a 2E-EVRP. They tested the proposed solution approach on small and medium-sized instances containing up to 20 customers and two charging stations. The performance comparison shows that the proposed algorithm can provide optimal results faster than CPLEX. Recently, [PERSON] and [PERSON] [34] introduced a 2E-EVRP with time windows and battery-swapping stations. They developed a MILP model that minimizes transportation, handling, and fixed costs for the vehicles used in the first and second echelon, in addition to battery-swapping costs. However, the time spent on battery swapping is not considered. A VNS algorithm was proposed to solve large sized problem instances.
# TSINet: A Semantic and Instance Segmentation Network for 3D Tomato Plant Point Clouds
## 4 Discussion
### Cross-Species Evaluation for Model Generalization  
We utilized the Pheno4D dataset to perform cross-species validation in order to assess TSINet's generalization capability. Specifically, the TSINet model pretrained on tomato plants was tested on maize plants. The maize point clouds in Pheno4D underwent preprocessing procedures similar to those applied to the tomato dataset. These included removing ground noise, limiting the number of points per plant to 4096, and converting the data into HDF5 format for testing. We focused on the instance segmentation task of maize plants, as it poses a greater challenge and better reflects the underlying performance of semantic segmentation.  
The cross-species experimental results are presented in Table 6. Although the model was trained solely on tomato plants, TSINet still exhibited strong generalization ability when applied to maize plants in the Pheno4D dataset. In the instance segmentation task, TSINet achieved an mPrecision of 58.82%, mRecall of 60.04%, mCov of 62.34%, and mWCov of 64.48%. In summary, the experimental results demonstrate that TSINet not only achieves excellent segmentation performance for tomato plants, but also maintains strong generalization in cross-species scenarios. This highlights its potential for broader application to phenotypic analysis of various crop species.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **mPrec (\%)** & **mRec (\%)** & **mCov (\%)** & **mWCov (\%)** \\ \hline TSINet & & & & \\ (Testing on Maize) & 58.82 & 60.04 & 62.34 & 64.48 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cross-species instance segmentation performance: training on tomato, testing on maize.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Component**} & \multirow{2}{*}{**Mean**} \\  & & **GAFEB** & & & \\ \hline \multirow{3}{*}{mPrec (\%)} & A & \(\surd\) & \(\times\) & 80.14 \\  & B & \(\times\) & \(\surd\) & 79.86 \\  & C & \(\surd\) & \(\surd\) & 81.54 \\ \hline \multirow{3}{*}{mRec (\%)} & A & \(\surd\) & \(\times\) & 78.43 \\  & B & \(\times\) & \(\surd\) & 78.10 \\  & C & \(\surd\) & \(\surd\) & 81.69 \\ \hline \multirow{3}{*}{mCov (\%)} & A & \(\surd\) & \(\times\) & 81.82 \\  & B & \(\times\) & \(\surd\) & 79.82 \\ \cline{1-1}  & C & \(\surd\) & \(\surd\) & 81.60 \\ \hline \multirow{3}{*}{mWCov (\%)} & A & \(\surd\) & \(\times\) & 83.97 \\ \cline{1-1}  & B & \(\times\) & \(\surd\) & 82.14 \\ \cline{1-1}  & C & \(\surd\) & \(\surd\) & 86.40 \\ \hline \hline \end{tabular} Note: “\(\surd\)” indicates the component is included; “\(\times\)” indicates the component is excluded.  
\end{table}
Table 5: Ablation study of instance segmentation.