esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Ad Hoc Solving Method
* ChargingStation: represents the charging station involved in a request. * ElectricVehicle: represents the electric vehicle involved in a request. * OptimizationResult: the output of the algorithm, including control signals and expected costs for each charging request. Table 1 details the input data required by the algorithm, along with their class, type, and database storage location. Table 2 shows the algorithm output data description. The generated output data must be stored in the database for future optimization. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Name** & **Description** & **Type** & **Source** \\ \hline \multirow{4}{*}{Battery} & \(X_{0}\) & Initial level of charge & double & Battery table \\ \cline{2-5}  & \(X_{min}\) & Minimum level of charge (battery-specific) & double & Battery table \\ \cline{2-5}  & \(X_{max}\) & Maximum level of charge (battery-specific) & double & Battery table \\ \cline{2-5}  & \(\xi\) & Battery performance coefficient & double & Battery table \\ \hline \multirow{2}{*}{EVModel} & \(\Delta P^{EV}_{max}\) & Maximum charging/discharging power allowed by EV & double & EV models table \\ \cline{2-5}  & \(\Delta P^{EV}_{min}\) & Minimum charging/discharging power allowed by EV & double & EV models table \\ \hline \multirow{3}{*}{ChargingStation} & \(\Delta P^{CS}_{max}\) & Maximum charging/discharging power allowed by the CS & double & Charging station table \\ \cline{2-5}  & \(\Delta P^{CS}_{min}\) & Minimum charging/discharging power allowed by the CS & double & Charging station table \\ \hline \multirow{2}{*}{PowerInfo} & \(P^{ref}\) & Desired mean power & double & PowerInfo table \\ \cline{2-5}  & \(P\) & Maximum power available for EV fleet & double & PowerInfo table \\ \hline \multirow{3}{*}{ChargingRequest} & \(C\) & Electricity tariff & double & EnergyInfo table \\ \cline{2-5}  & \(X^{ref}\) & Desired level of charge & double & Charging request table \\ \cline{2-5}  & \(E\) & Departure time slot (chosen by the driver) & integer & Charging request table \\ \cline{2-5}  & \(S\) & Arrival time slot & integer & Charging request table \\ \cline{2-5}  & \(\varepsilon\) & Tolerance factor for estimated cost & double & Charging request table \\ \cline{2-5}  & \(f\) & Flexibility & Boolean & Charging request table \\ \hline \multirow{4}{*}{CSCCPlanner} & \(I\) & Time slot of the last optimization & integer & CSCC planner table \\ \cline{2-5}  & \(\mu\) & Trade-off parameter & double & CSCC planner table \\ \cline{2-5}  & \(\Lambda\) & Diagonal matrix & double & CSCC planner table \\ \cline{2-5}  & \(T\) & Sampling time & double & CSCC planner table \\ \cline{2-5}  & \(M\) & IDs of connected EV & integer & (calculated at runtime) \\ \cline{2-5}  & \(M_{k}\) & IDs of connected EV at \(k\)-th time slot & integer & (calculated at runtime) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Algorithm input data description.
# Firewall Best Practices for Securing Smart Healthcare Environment: A Review
## 3 Firewalls Types for Smart Healthcare Environment
### Application Proxy Firewalls  
This type of firewall is the most efficient in current times. The application proxy provides security at the OSI application layer (level 7). It follows a stateful mechanism, but unlike the stateful inspection, is also able to proxy the network acting as a gateway, with a man-in-the-middle behavior from server to client [51]. In other words, this firewall does not allow any packet directly sent from an application to the user and vice versa. Therefore, the application proxy can translate addresses and perform any additional control before allowing a connection to the server. The application proxy provides a deep packet inspection, preventing spoofing attacks and other hacking techniques, such as the stateful inspection firewall, but by keeping acceptable network performances. In addition to this, application proxy can block dangerous SQL commands and other web application malicious attacks, so providing an effective layer of security for different network topologies (such as cloud environments), optimal security isolation, and anonymity levels [26]. However, even though they have fewer maintenance requirements than stateful inspection firewalls, application proxies may entail bottleneck risks and other bad network performances if not periodically checked and updated. This category of firewalls typically applies to some specific categories of applications, such as e-commerce websites and other vendor platforms relying on web applications.
## 2 Materials and Methods
### Methods
#### 2.2.6 Farmland Abandonment Feature Analysis  
Farmal abandonment is affected by various factors, which can be divided into natural and human factors. Natural factors include the topography, climate, precipitation, and distance from forests, while human factors include the distance from residential areas, labor loss, and transportation accessibility [21; 46; 47]. In this study, two natural factors, elevation and slope, and two human factors, distance from settlement and road network density, were selected to analyze the influence of farmland abandonment. Elevation and slope were calculated based on the DEM. The elevation was divided into four grades according to intervals of 200 m, and the slope was divided into six grades according to intervals of 5\({}^{\circ}\). For the distance from the residential area, multi-ring buffers at 200 m intervals were constructed according to the boundary of the residential area vector. For the density of the road network, the density was calculated according to the road network vector line elements and was graded according to natural breaks.  
After obtaining the grades for the different influence factors, the results of the extraction of influence factors and abandoned farmland were overlain and analyzed to obtain the number of abandoned farmland grid points in each grade for each influence factor.  
The ratio of abandoned farmland grid points to the total number of points in each grade was calculated, which was then used to measure the magnitude of the effect of the factor on farmland abandonment. Then, the abandonment rate was calculated based on the proportion of abandoned grid points to the total number of grid points in the region. The calculation methods for the _magnitude_ and _abandonment rate_ are as follows:  
\[Magnitude=\frac{A_{i}}{A_{total}} \tag{12}\]  
\[Abandonment\ rate=\frac{A_{i}}{F_{total}} \tag{13}\]  
where \(A_{i}\) is the number of abandoned grid points in the region or different grades of influence factors, \(A_{total}\) is the number of all abandoned grid points, and \(F_{total}\) is the number of all farmland grid points.
## 3 Experimental Technique
### Test Results and Data Analysis  
#### 3.4.1 Analysis of the Change in Temperature across Different Test Points  
In order to facilitate the analysis, the temperature data of all the test points were collected and plotted for 254 h. The time points were 0 h, 39 h, 44 h, 48 h, 63 h, 71, 93, 110 h, 118, 135, 160 h, 182 h, 190 h, 212 h, 233 h, and 254 h. The test points A, B, C, D and E are analyzed in detail below.  
As can be seen from Figure 5, it takes about 44-72 h for all the test points to attain the peak temperature. The maximum temperature of approximately 74 \({}^{\circ}\)C was attained at test point C2. After attaining the peak temperature, the rate of drop in temperature was the greatest for the corner test point E1, located at the top 50 mm of the foundation. On the other hand, the temperature of test points A3-E3, which located at the bottom 50 mm of the foundation, was relatively stable. Test points A2-E2, which were located in the mid depth of the concrete slab, had pronounced effect of the heat of hydration as illustrated by their high temperatures. The cooling rate was no more than 2.0 \({}^{\circ}\)C/d, which met the requirements of the standard GB50496-2009 [20].  
Figure 4: Monitoring the temperature of the foundation slab. (**a**) Layout of sensors of the plan; (**b**) Temperature reading instrument; (**c**) Layout of sensors along the depth. The unit is mm.
# Faster Algorithms for Mining Shortest-Path Distances from Massive Time-Evolving Graphs
## 1 Introduction
### Our Contribution  
For 2-hop-cover labelings, two dynamic algorithms that do not compromise on query performance are known, both employing the common strategy of identifying and updating only the part of the labeling that is compromised by a graph change. In details, the algorithm proposed in [8], named resume-2 hc, is able to handle incremental updates only while the algorithm proposed in [26; 27], named bidir-2 hc, can manage decremental updates only. Both have been shown, experimentally, to be rather effective and faster on average than the recomputation from scratch and to preserve the quality of the labeling in terms of query time and compactness (while there exists a dynamic approach that tolerates degradation on query time to achieve fast update [31]). Moreover, both have been combined and tested as a single fully dynamic algorithm in [26]. However, while the former exhibits extremely low update times that are compatible with real-time applications, the latter is slower, and sometimes only slightly better, on average, than the recomputation from scratch in some categories of input instances that are highly relevant to application domains, e.g., sparse weighted graphs.  
In this paper, we focus on this latter issue and try to overcome the above limiting factor of bidir-2 hc by introducing a new dynamic algorithm, called queue-2 hc, to update 2-hop-cover labelings when decremental update operations affect the input graph. We prove its correctness and formally analyze its worst-case performance. Since its worst-case time complexity cannot be directly compared with that of bidir-2 hc, which depends on different parameters, we assess its effectiveness through an extensive experimental evaluation employing both real-world and synthetic inputs. Our results show that queue-2 hc improves, in some case up to orders of magnitude, upon the update times of bidir-2 hc, especially in some input categories where such algorithm was shown to be not so effective. Our results also show queue-2 hc is always much faster than the recomputation from scratch, despite being worse in terms of worst-case complexity. This is a feature in common with bidir-2 hc and resume-2 hc, have been observed to be very effective in practice, despite their worst-case bound on the running time. The same holds for the preprocessing method, which is much faster in practice than what the worst-case bound suggests. This is a quite common behavior in the field of research dedicate to dynamic algorithms, typically due to two main reasons: either the worst-case essentially never or very rarely occurs in practical cases, or that the analysis is not tight. Nonetheless, having solutions like the one proposed in this paper is necessary to allow the use of the 2-hop-cover labeling method in the real-world. Thus, to summarize, queue-2 hc can be considered a step forward towards real-time distance mining for general, massive time-evolving graphs.
# An Improved Fuzzy Vector Signature with Reusability
## 1 Introduction
### Related Work
** [PERSON] et al. first proposed a fuzzy vector signature [20] following the subset-based sampling method of Reference [17]. The fuzzy vector signature requires the signing parameter like helper data in fuzzy extractor, to be reusable, but the size of the signing parameter is much smaller than that of helper data in Reference [17]. In addition, a fuzzy vector signature can tolerate sub-linear errors, while a reusable fuzzy signature [19] can only tolerate logarithmic errors. However, the size of verification key in Reference [20] is still huge, which results in high computational costs for verification. In addition, security models for reusability are incomplete as in Reference [19], and, as a result, no formal proof of reusability is provided.
# Integrating Prior Knowledge into Attention for Ship Detection in SAR Images
## 5 Experiments and Results
### Ablation Study  
#### 5.8.1 Insertion Strategy  
The insertion of BDAM can be divided into two ways, one is inserted between the backbone's stages, and the other is inserted into the backbone's blocks. BDAM does not fit into the second scheme. There is a big difference between BDAM and CBAM: the input of spatial attention in BDAM is the prior knowledge map while the input of CBAM is the output of the upper convolutional layer. If we want to insert BDAM into the block, it is not as easy as CBAM, and we have to solve the input problem for each block. For one input image, only one prior knowledge map can be generated. It means that we need to generate multiple weight maps of different sizes from a single prior knowledge map, which sounds like feeding a single image into a convolutional neural network to obtain a multi-scale feature map. However, the prior knowledge map is different from images, which are obtained by clustering based on our prior knowledge, and it contains certain interpretability itself. However, if it is fed into a very deep network to extract weighted graphs at multiple scales, we cannot guarantee whether the knowledge recorded on the prior knowledge map can be retained. Moreover, the insertion of BDAM into the block causes the effect of pre-trained parameters to be affected. According to [PERSON] et al.'s study [94] on pre-trained parameters, the amount of data in HRSID and SSDD does not support us to train from scratch. The data in Table 9 confirm our concerns, where the BDAM with * represents the insertion into the block.  
Figure 11: Ground truth and recognition results of some images. (**a**) Ground truth. (**b**) SSD. (**c**) SSD with BDAM. (**d**) RetinaNet. (**e**) RetinaNet with BDAM.  
If we take the first way, since the size of the feature map output from each stage is different, we can change the problem to which size of the feature map we should adjust with BDAM. We conduct experiments on SSDD using Faster R-CNN. The backbone used in the experiments is ResNet50, and the results of the experiments are shown in Table 10.  
According to the experimental results, we find that using BDAM to adjust the feature maps that are reduced by a factor of 8, each relative to the initial input length and width, works best. That is, it is better to insert BDAM between the second stage and the third stage.
## 3 Input Data Interpretations
of Floor, and (h) Total Floor Area show Gaussian distribution, whereas (a) Captive Columns appear to have a bimodal distribution. The absence of data are visible for (c) Concrete Wall Area (EW) and (d) Concrete Wall Area (NS). Figure 5a-h represents data from Haiti. It shows the exponential distribution for the (b) Column Area, (e) Masonry Wall Area (EW), (f) Masonry Wall Area (NS), (g) No. of Floor, and (h) Total Floor Area, respectively. It can be concluded that there is no linear distribution of parameters. (a) Captive Columns have a bimodal distribution. For Nepal in Figure 6a-h, it gives Gaussian distribution for the (b) Column Area, (g) No. Floors, (h) Total Floor Area, and (f) Masonry Wall Area (NS), whereas the distribution pattern for (e) Masonry Wall Area (EW) is exponential. (a) Captive Columns is bimodal, and there is no data to show for (c) Concrete Wall Area (EW), (d) Concrete Wall Area (NS). The data distribution for Pohang in Figure 7a-h; it is visible that Pohang has very little sample data in the dataset. The (b) Column Area represents a Gaussian distribution over the data, whereas the (c) Concrete Wall Area (EW), (d) Concrete Wall Area (NS) and (h) Total Floor Area are distributed exponentially. (e) Masonry Wall Area (EW) and (f) Masonry Wall Area (NS) clearly shows the absence of any data. Figure 5: Distribution of data of (**a**) Captive Columns, (**b**) Column Area, (**e**) Masonry Wall Area (EW), (**f**) Masonry Wall Area (NS), (**g**) No. of Floor, and (**h**) Total Floor Area for Haiti. Figure 6: The histogram plot shows the distribution of data of (**a**) Captive Columns, (**b**) Column Area, (**e**) Masonry Wall Area (EW), (**f**) Masonry Wall Area (NS), (**g**) No. of Floor, and h) Total Floor Area for Nepal.
## 3 Calibration and Verification of Model Mesoscopic Parameters
### Verification of Mesoscopic Parameters  
Table 5 compares the failure modes observed during indoor uniaxial compression tests and numerical simulations, providing additional validation of the selected microscopic parameters. In the table, numerical letters (e.g., 1a) in both test and simulation results refer to macroracks initiating specimen failure. It is clear from the table that the prominent macroscopic cracks on the surface of specimens observed during indoor testing corresponded one-to-one with the simulation results, and the damage patterns were similar. At \(\alpha=15^{\circ}\), the primary cracks (1a and 1b) extending from fissure tips grew perpendicularly, eventually causing tension-shear failure at the specimen's top left and bottom right corners. Similarly, at \(\alpha=45^{\circ}\), primary cracks (2a and 2b) originating from fissure tips penetrated both the fissure and the specimen edge, while a secondary crack (2c) extended from the fissure tip to the specimen's top surface. Due to the anisotropic nature of the sandstone samples, certain forms of crack propagation differed between the indoor test and numerical simulation; however, the outcome was still mixed tensile-shear failure. At \(\alpha=75^{\circ}\), the fissure tip experienced primary cracks (3a and 3b) and a secondary crack (3c), which  
Figure 5: Comparison of mechanical properties between indoor test and numerical simulation. (**a**) Comparison of stress-strain curves; (**b**) comparison of peak strength and elastic modulus.  
extended from the bottom left to the top right corners of the specimen, causing pure shear failure. The direction of the failure zone is approximately parallel to the fracture direction.  
Through a comparison between indoor experiments and numerical simulations, it can be concluded that the mechanical characteristic parameters of the specimens are highly consistent, as well as the crack propagation pattern and failure mode. Therefore, the group of microscopic parameters can be employed to simulate the mechanical characteristics of symmetrical fissure-hole sandstone accurately. This group of parameters can also be utilized to analyze the effects of different fissure morphologies on the mechanical properties of hole-containing sandstone under conventional triaxial compression.
# An Improved Empirical Wavelet Transform and Its Applications in Rolling Bearing Fault Diagnosis
## 3 Proposed Method of Fast Empirical Wavelet Transform  
In this paper, the above two defects of EWT are discussed and expounded: spectrum segmentation and boundary optimization. A new improved empirical wavelet transform method is proposed. This method obtains the trend component of the spectrum by obtaining the Fourier transform function (key function) of the spectrum. Minimum points of the trend component are used as boundaries to reconstruct the signal. Selecting different points in key functions will reconstruct different trend components. Suitable trend components can obtain reasonable boundaries for EWT. Figure 2 shows the process of the proposed method. The specific steps are as follows:  
Step 1: Collect periodic impact signals \(y(t)\). The fast Fourier transform (FFT) algorithm is used to acquire spectrum \(Y(f)\). The Fourier transform function of \(Y(f)\) is obtained through the FFT algorithm again, namely, the key function \(K(f)\).  
Step 2: A certain number of points in the key function are extracted to inverse the Fourier transform and reconstruct the trend component (\(T_{c}(f)\)) of the spectrum. When more points are used to reconstruct the complex trend component, the number of extreme points of the trend component will be larger, so that the spectrum will be divided into many parts. The reconstructed function is close to the trend of spectrum changes, and the spectrum will be partitioned into fewer parts. Section 3.1 describes the details of this method.  
Step 3: Calculate the boundaries of the spectrum and optimize.  
Taking the trend component as the basis function, the minimum point sequence is used as the boundary array. When the trend component is complex, the boundary array is optimized by the wavelet threshold. The method of optimizing spectrum boundaries based on threshold denoising reduces the invalid components caused by noise to a certain extent. This method will be introduced in Section 3.2.  
Step 4: Perform signal processing using empirical wavelet transform.  
The frequency domain of the signal is normalized to \([0,\pi]\); choose Meyer wavelet as the basis function; define the empirical scaling function \(\mathcal{O}_{n}(\omega)\) and the empirical wavelets \(\Psi_{n}(\omega)\); build adaptive filters; decompose the signal into different components.
## 4 Proposed Methodology of Hybrid Channel Modeling
### Electromagnetic Field Analysis in the Mine Gallery
Hence, the parameter \(F_{1}(m,n)\) can be stated as  
\[F_{1}(m,n)\cong E_{o}\frac{\pi}{\sqrt{1-(\frac{m\pi}{2 ak})^{2}-(\frac{n\pi}{2 b})^{2}}}e^{-j(\frac{m\pi}{2 ak}x_{o}+\frac{n\pi}{2b}y_{o})} \tag{20}\]  
Similarly, the sum of complex modes in Equation (18) can be obtained by converting the first part of the ray sum in Equation (15). Consequently, the Poisson summation expression can be applied on Equation (15) and \(F_{2}(m,n)\), \(F_{3}(m,n)\), \(F_{4}(m,n)\) coefficients can be simplified using a Saddle-point approach. Then, the field on the excitation plane can be achieved using  
\[ \begin{split} E^{Rx}(x,y,0)=\frac{1}{4a}\frac{1}{4b}\sum_{m=- \infty}^{\infty}\sum_{n=-\infty}^{\infty}[F_{1}(m,n)+F_{2}(m,n)+F_{3}(m,n)+\\ E^{Rx}(x,y,0)=\sum_{m=-\infty}^{\infty}\sum_{n=-\infty}^{\infty} \frac{E_{o}\pi}{16 ab\sqrt{1-(\frac{m\pi}{2 ak})^{2}-(\frac{n\pi}{2b})^{2}}}e^ {j\frac{m\pi}{2 ak}x}e^{j\frac{m\pi}{2 ak}y}\\ (e^{-j\frac{m\pi}{2 ak}x_{o}}e^{-j\frac{m\pi}{2 ak}y_{o}}+e^{j\frac{m \pi}{2 ak}x_{o}-m\pi}e^{j\frac{m\pi}{2 ak}y_{o}-n\pi}-e^{j\frac{m\pi}{2 ak}x_{o}} e^{j\frac{m\pi}{2 ak}y_{o}-n\pi}-\\ e^{j\frac{m\pi}{2 ak}x_{o}-m\pi}e^{j\frac{m\pi}{2 ak}y_{o}})\end{split}  \tag{21}\]  
\[ \begin{split} E^{Rx}(x,y,0)=\sum_{m=-\infty}^{\infty}\sum_{n=1}^ {\infty}\frac{E_{o}\pi}{ab\sqrt{1-(\frac{m\pi}{2 ak})^{2}-(\frac{n\pi}{2 bk})^{2 }}}\\ \sin\Bigl{(}\frac{m\pi}{2a}x_{o}+\varphi_{x}\Bigr{)}\cos\Bigl{(} \frac{n\pi}{2b}y_{o}+\varphi_{y}\Bigr{)}\sin\Bigl{(}\frac{m\pi}{2a}x+\varphi_ {x}\Bigr{)}\cos\Bigl{(}\frac{n\pi}{2b}y+\varphi_{y}\Bigr{)}\end{split}  \tag{22}\]  
Equation (21) is the actual summation form of Eigen-functions of all propagation modes given in Equation (4) and the Eigen-function of each mode represents the mode intensity \(C_{mn}\) as given in Equation (22)  
\[C_{mn}=\frac{E_{o}\pi}{ab\sqrt{1-(\frac{m\pi}{2 ak})^{2}-(\frac{n\pi}{2 bk})^{2 }}}\sin\Bigl{(}\frac{m\pi}{2a}x_{o}+\varphi_{x}\Bigr{)}\cos\Bigl{(}\frac{n\pi} {2b}y_{o}+\varphi_{y}\Bigr{)} \tag{23}\]  
The field can be computed at a point within the mine gallery by substituting Equations (4), (7), (8) and (23) into Equation (6). Let \(P_{t}\) be the power of the transmitted signal, \(G_{t}\) be the gain of transmitter and \(G_{r}\) be the antenna gain of the receiver; the computed received power at the coordinate \((x,y,z)\) can be calculated using  
\[P_{r}(x,y,z)=P_{t}G_{t}G_{r}\Biggl{(}\frac{1}{E_{o}}\sum_{m,n}C_{mn}E_{m,n}^{ sign}(x,y)e^{-(\alpha_{mn}+j\beta_{mn})z)}\Biggr{)}^{2} \tag{24}\]