esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments  
The primary focus of this study lies in writer-dependent offline handwritten signature verification, employing signature datasets from seven different languages. Specifically, Chinese, Uyghur, Kazakh, and Kirgiz scripts are included, each consisting of 200 individuals. For each individual, there are 24 genuine samples and 24 forged samples. Additionally, to assess the effectiveness of the experiments, two publicly available datasets, namely, CEDAR and BHSig260, were incorporated. Based on the foundations of other relevant works, we allocated 80% of the samples for training, reserving the remaining 20% for testing. Conse  
Figure 4: Structure of CA.  
Figure 5: Structure of SA.  
quently, our self-constructed dataset encompasses a total of 38,400 handwritten signature samples, far exceeding the combined scale of the three publicly available datasets, which comprise a mere 16,640 signature samples. This demonstrates the substantial applicability of our established dataset in offline handwritten signature verification research, spanning multiple languages.  
In the experiment, we employed a single NVIDIA GeForce RTX 3090 graphics card with 24 GB of memory, an Intel(R) Xeon(R) Platinum 8338C CPU operating at 2.60 GHz, and 80 GB of RAM. The system configuration consisted of Ubuntu 20.04, Python 3.8, PyTorch 1.11.0, Cuda 11.3 for code compilation, and PyCharm 2021.1 Professional as the integrated development environment. Prior to training, the images were resized to 224 \(\times\) 224 pixels, and a batch size of 32 was set. We utilized the Adam optimizer with a learning rate of \(5\times 10^{-4}\) and the weight decay set to \(5\times 10^{-2}\).
## 9 Results and Discussion
### Container Utilization  
We calculate utilization as the percentage of time containers are actively processing requests, excluding periods of inactivity after the last request until the container times out. Higher utilization indicates more efficient resource use, as it means fewer resources are sitting idle. Figure 12 presents a detailed evaluation of container utilization across the four test applications. The Hybrid model consistently achieves over 80% utilization regardless of workload type. This improvement is attributed to the Hybrid Prediction Model, which enables accurate proactive scaling and the aggressive reuse of containers by leveraging available buffer capacity. The most notable gains are observed in long-lived applications. This enhanced utilization also translates into a smaller container fleet size, as the Hybrid model requires fewer containers to handle the same workload, as previously discussed.
# Preliminary Design of the Electrical Power Systems for DTT Nuclear Fusion Plant
## 2 DTT Power Plant  
### Main Features  
Figure 1 summarizes the power demand estimated for the DTT facility. As a tokamak, DTT has an intrinsically pulsed behavior, strictly related to the physics of plasma, which implies that the grid will supply the power in a discontinuous way [3,10,13]. In particular, as shown in Figure 1, the DTT nominal operations are characterized by a baseline with a power absorption lower than 100 MVA and by pulses reaching about 300 MVA for a duration in the range 100-200 s. These periodic pulses are repeated every 3600 s (duty cycle \(\approx\) 200 s/3600 s) and are due to the actual experimental phases, in which a high power is required to control the fusion plasma.  
The power profile reported in Figure 1 derives from the superimposition of some high-power pulsed loads on a constant baseline produced by loads characterized by small variations. This difference will be exploited for a first load classification and for the selection of the distribution structure. As DTT is an experiment with successive upgrades and possible improvements, the power demand estimation also takes into account some theoretical considerations and experiences in the design of other tokamaks (such as ITER, JT-60 SA and FTU [10]). The estimated power demand is compared in Figure 1 with the load profile requested for the connection to the Italian Transmission System Operator (TSO).  
Since the electrical grid present in the zone of the DTT facility is not adequate to support such high and variable power, the grid infrastructures will be significantly developed. Two new 150-kV lines will be installed (for about 15 km) to connect a new TSO substation to the closest 380-kV line. The new TSO substation will be placed in proximity to the ENEA Research Center. Then, the DTT facility will be supplied by the National High Voltage Grid (NHVG) through a single (not redundant) underground 150-kV cable (with a length of about 1 km) connecting the new TSO substation with a new substation, called SSO, located inside the ENEA Research Center.
## 1 Introduction  
Rocks in-situ have properties that can vary significantly over short distances. To identify and map out these variations in properties, a series of site investigation activities are a necessity on every rock engineering project. This includes both a series of laboratory tests to assess the intact rock properties, as well as a range of in situ tests, conducted directly on the undisturbed material to measure the in-situ properties of the rock mass. In situ testing has the advantage of not disturbing the material and can therefore be a useful way of determining the actual characteristics and behavior of rock mass. The static deformation modulus of the rock mass is a key parameter in the design of geotechnical structures like dams, tunnels, and caverns. This parameter represents the deformational behavior of rock mass in response to any loading and unloading. Empirical relationships such as those established by [PERSON] [1], [PERSON] [2], and [PERSON] [3] and summarized by [PERSON] and [PERSON] [4] that relate the rock mass deformation modulus to different rock mass classifications are estimations that can be used in the preliminary stages of the design. Furthermore, these relations do not indicate modulus anisotropy in different directions. Because of the effect of discontinuities in the rock mass, the modulus obtained from laboratory tests for intact rock does not represent the in situ deformational behavior of the rock mass. Therefore, in situ tests are typically more reliable procedures and employed in major rock engineering projects.  
The plate load test is one of the most commonly used in-situ tests since it involves a large volume of the rock mass to be tested. The rock mass is loaded by a set of hydraulic jacks by two circular steel plates and the associated rock mass deformations are measured by extensometers installed within the boreholes drilled within the rock mass beneath the loading plates. Then elasticity theory (Boussinesq relation) for a semi-infinite medium is used to calculate the deformation modulus. This is the suggested method by ISRM to calculate the deformation modulus of the rock mass [5].  
The assumptions used in the ISRM method are not in harmony with the actual mechanism involved in rock mass deformation. In this method, it is assumed that the rock mass is continuous, isotropic, and has a linear elastic behavior. In the real world, the behavior of rock is more complex, in particular when the structure goes through loading/unloading stages, some plastic deformation occurs within the rock mass. In this paper, a parametric study of key parameters in the plate load test was investigated. The plate load tests conducted at the Bazoff concrete dam site (Iran) were simulated numerically to demonstrate the governing test mechanisms and to interpret data for the determination of deformation modulus. The effect of plate diameter, location of anchors, and the amount of plastic strain in each loading-unloading cycle on the values of moduli were investigated employing sophisticated 3D numerical modeling considering the governing boundary conditions of the plate load test.
# Fungi of the _Trichoderma_ Genus: Future Perspectives of Benefits in Sustainable Agriculture
## 4 Application of _Trichoderma_ Fungi in the Control of Fungal Plant Pathogens
### Antibiosis  
Antibiosis belongs to a particular type of interaction between microorganisms, in which the antagonist produces various metabolites that interfere with the proper function \begin{table}
\begin{tabular}{c c c} \hline
**Types of Environments** & **Species of _Trichoderma_ & **Reference** \\ \hline soil from onion crops & _T. afroharziantum, T. asperelloides, T. asperellum, T. azevedoi,_ & [46] \\ \cline{2-3}  & _T. erinaccum, T. lentiforme, T. longbrachiatum, T. peberdyi_ & [46] \\ \hline soil from rice crops & _T. asperellum, T. atroviride, T. brevicompactum, T. capillare,_ & \\  & _T. erinaccum, T. fertile, T. hamatum, T. harzianum, T. koningiopsis,_ & [45] \\  & _T. longbrachiatum, T. polysporum, T. saturnisporum,_ & [45] \\  & _T. spirale, T. volleitnum, T. virens_ & \\ \hline soil from wheat crops & _T. asperellum, T. atroviride, T. brevicompactum, T. erinaccum,_ & [45] \\  & _T. hamatum, T. harziantum, T. koningiopsis, T. virens_ & [42] \\ \hline root of plant & _T. afroharziantum, T. asperelloues, T. asperellum, T. guizhouense,_ & [42] \\  & _T. harzianum, T. resei, T. strigosum, T. virens_ & [42] \\ \hline bark of tree & _T. atroviride, T. erinaccum, T. harzianum, T. hebeiensis,_ & [52] \\  & _T. longbrachiatum, T. pararesei, T. resei_ & [52] \\ \hline decaying woods & _T. atroviride, T. citrinoviride, T. cremeum, T. gamsii, T. harzianum,_ & [40] \\  & _T. koningiopsis, T. longbrachiatum, T. longipile,_ & [40] \\  & _T. paraviridescens, T. trixiae, T. viriide, T. viriidescens_ & \\ \hline lignocellulosic compost & _T. asperellum, T. citrinoviridae, T. harzianum, T. lixii_ & [49] \\ \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)  
Figure 2: Direct mechanisms of _Trichoderma_ fungi in the control of fungal plant pathogens (author’s own scheme). ing of pathogens [21; 31]. Secondary Metabolites (SM) are antibiotics and toxic substances that limit the growth, development, and colonization of pathogens [56; 57] by producing reactive oxygen forms (ROS), deactivating the proteins and enzymes responsible for basic metabolic processes and biosynthesis of the structural elements of cells, and also the formation of channels in the cell membrane, leading to cytoplasm leakage and death of the microorganism [58; 59; 60; 61; 62; 63]. _Trichoderma_ sp. produces a wide range of antifungal compounds (Table 2). It is estimated that these fungi produce about 1000 different metabolites [59]. SM are the natural low-molecular-weight biosynthesis products of primary metabolites produced in specialized pathways [60; 62]. These compounds are not necessary for the proper growth and functioning of the organism but support its defense and signaling [63]. [PERSON] et al. [61] distinguished 11 groups of secondary metabolites produced by fungi of the genus _Trichoderma_ during contact with the pathogen, which include anthraquinones, azaphilones, butenolides, epipolythiodioxopiperazines, koninginins, lactones, peptaibols, pyridones, pyrones, steroids, and trichothecenes.
## 3 Results
### Mechanical Strength Characteristic Tests
#### 3.2.3 Triaxial Test  
According to the results of total stress and effective stress of diatomite soil under different dry densities, the relationship curve between effective average stress \(p^{\prime}\) and deviatoric stress \(q^{\prime}\) is shown in Figure 3.  
It should be noted that in this triaxial test, the normal consolidated saturated clay sample is used for the consolidated undrained (CU) test, and the theoretical cohesive force  
\begin{table}
\begin{tabular}{c c c c c} \hline \(a_{\text{\tiny{p}}}\) (MPa\({}^{-1}\)) & \(E_{\text{\tiny{s}}}\) (MPa) & \(C_{\text{\tiny{c}}}\) & \(C_{\text{\tiny{s}}}\) & \(C_{\text{\tiny{p}}}\) (cm\({}^{2}\)/s) \\ \hline
1.03 \(\sim\) 1.01 & 2.272\(-\)2.277 & 0.342\(-\)0.336 & 0.043\(-\)0.060 & ( 2.58 \(\sim\) 2.87) \(\times\) 10\({}^{-5}\) \\ \hline \end{tabular} \(a_{\text{\tiny{p}}}\) = compression coefficient; \(E_{\text{\tiny{s}}}\) = compressive modulus; \(C_{\text{\tiny{c}}}\) = compression index; \(C_{\text{\tiny{s}}}\) = rebound index; \(C_{\text{\tiny{c}}}\) = consolidation coefficient.  
\end{table}
Table 2: Results of consolidation test.  
Figure 3: Relationship curve of \(p^{\prime}\) and \(q^{\prime}\).  
of clay is 0. In this test, the samples with dry density higher than that of natural diatomite soil are used for the test. During the pre-consolidation process, the set consolidation stress is directly increased to the required value of the test, making the sample in the over consolidation state, Moreover, the soil sample in this test had a large cohesion.  
The triaxial test results of diatomite soil with different dry densities show that the effective internal friction angle of diatomite soil is 5.6\({}^{\circ}\) and the effective cohesion is 30.9 kPa when the dry density is 1.30 g/cm\({}^{3}\). Meanwhile, the effective internal friction angle of diatomite soil is 6.4\({}^{\circ}\) and the effective cohesion is 44.3 kPa when the dry density is 1.40 g/cm\({}^{3}\). The effective internal friction angle of diatomite soil is 14.5\({}^{\circ}\) and the effective cohesion reaches 49.6 kPa when the dry density is 1.50 g/cm\({}^{3}\).
# Method to Control the Deformation of Anti-Slide Piles in Zhenzilin Landslide
## 3 Methodology and Validation
### Calculation Method for an Anti-Slide Pile above the Slip Surface  
To ensure engineering safety, the section of an anti-slide pile above the slip surface is generally considered as a cantilever beam. If the thrust distribution of the landslide behind the pile is triangular, according to structural mechanics, the shear force (Q\({}_{\mathrm{y}}\)) and bending moment (M\({}_{\mathrm{y}}\)) for the pile above the sliding surface can be expressed as follows:  
\[\left\{\begin{array}{l}Q_{\mathrm{y}}\ =\ \frac{q_{1}\left(h_{1}-\left|y \right|\right)^{2}}{2h_{1}}\ \left(y<0,\left|y\right|\leq h_{1}\right)\\ M_{\mathrm{y}}\ =\ \frac{q_{1}\left(h_{1}-\left|y\right|\right)^{3}}{6h_{1}}\ \left(y<0,\left|y\right|\leq h_{1}\right)\end{array}\right. \tag{6}\]  
where \(y\) is the distance from the origin (see Figure 5a); \(h_{1}\) is the length of the anti-slide pile above the slip surface; and \(q_{1}\) is the intensity of the distributed driving force acting on the pile at the sliding surface (refer to Figure 5a).  
The horizontal displacement (\(\Gamma_{\mathrm{y}}\)) is related to the pile deformation (\(\Delta\)E), rotation (\(\Delta\)R), and displacement (x\({}_{0}\)) caused by the triangular distributed driving force (see Figure 5b); hence, the displacement of the pile is established as follows:  
\[\Gamma_{y}\ =\ x_{0}+\Delta R(y)+\Delta E(y)\ \left(y<0,\left|y\right|\leq h_{1}\right) \tag{7}\]  
The rotation(\(\Delta\)R) can be expressed as follows:  
\[\Delta R(y)\ =\ \theta_{0}\times\left|y\right|\ \left(y<0,\left|y\right|\leq h _{1}\right) \tag{8}\]  
Here, \(x_{0}\) and \(\theta_{0}\) denote the horizontal displacement and rotation angle of the pile at the slip surface, respectively. As stated above, the displacement (\(\Delta\)E) of a pile that was induced by triangular thrust load of the landslide can be calculated as follows:  
\[\Delta E(y)\ =\ \frac{q_{1}}{120 Eln_{1}}\Big{[}4h_{1}{}^{5}-5h_{1}{}^{4} \Big{(}h_{1}-\left|y\right|\Big{)}+\Big{(}h_{1}-\left|y\right|\Big{)}{}^{5} \Big{]}\ \left(y<0,\left|y\right|\leq h_{1}\right) \tag{9}\]  
Then, Equations (8) and (9) are introduced into Equation (7). The complete expression of the horizontal displacement of an anti-slide pile above the slip surface can be obtained as follows:  
\[\Gamma_{y}\ =\ x_{0}+\theta_{0}\times\left|y\right|+\frac{q_{1}}{120 Eln_{1}} \Big{[}4h_{1}{}^{5}-5h_{1}{}^{4}\Big{(}h_{1}-\left|y\right|\Big{)}+\Big{(}h_{ 1}-\left|y\right|\Big{)}{}^{5}\Big{]}\ \left(y<0,\left|y\right|\leq h_{1}\right) \tag{10}\]
## 6 Conclusions  
* When the debris impacted the satellite tank at the velocity of 7000 m/s, it passed through the tank at \(t=1196\) us, and the residual velocity was 263 m/s, indicating that the debris velocity was significantly reduced by the hydrodynamic ram effect.
* The shock wave pressure on the front wall was the highest, which exceeded the yield strength of titanium, so perforations and bulges occurred. Except for the front wall, the pressure on the tank walls was smaller than the yield strength of titanium. Under the action of debris penetration and  
Figure 19: Attenuation curves of the translation velocity when the debris impacted the satellite tank with angular velocities of \(w_{y}=1\) rad/us, \(w_{y}=2\) rad/us, \(w_{y}=3\) rad/us, \(w_{y}=4\) rad/us, and \(w_{y}=5\) rad/us.  
Figure 18: Front and back perforations caused by the impact of debris with angular velocities of \(w_{y}=1\) rad/us, \(w_{y}=2\) rad/us, \(w_{y}=3\) rad/us, \(w_{y}=4\) rad/us, and \(w_{y}=5\) rad/us.  
high pressure hydrazine, perforations and bulges also occurred on the back wall. In the structural design of the satellite tanks, the material strength of the walls should be increased as much as possible, and the protection should be strengthened in vulnerable areas.
* With the reduction in the liquid-filling ratios, the damage caused by the hydrodynamic ram effect weakened and the satellite tank fully-filled with liquid hydrazine was the most severely damaged.
* When the debris impacted the satellite tank with the angular velocities in the \(x\) direction, the debris trajectory did not deflect. When the debris impacted the satellite tank with the angular velocities in the \(y\) and \(z\) direction, the debris trajectory deflected to the negative direction of the \(z\) axis and \(y\) axis under the action of the Magnus force.
* There was a steady angular velocity in the \(x\) direction. Under the steady angular velocity, the damage to the tank was the most serious. With the increase in the angular velocity in the \(y\) and \(z\) direction, the damage to the tank became more serious.  
Conceptualization, C.C.; Methodology, J.Z.; Software, B.Z.; Validation, B.Z.; Formal analysis, B.Z.; Investigation, B.Z.; Resources, J.Z.; Data curation, C.C.; Writing--original draft preparation, B.Z.; Writing--review and editing, J.Z.; Visualization, Y.D.  
This research received no external funding.  
This research was done with the financial support of the State Key Laboratory of Laser Propulsion and Application. We wish to express our sincere gratitude to Dr. [PERSON] for his valuable help.  
The authors declare no conflicts of interest.
# Energy Efficient and Reliable Routing Algorithm for Wireless Sensors Networks
## 4 Ds-Eera
### BPA Function Based on Triangular Membership Function  
In this paper, the frame of discernment \(\Theta\) includes \"next hop\", \"non-next hop\" and \"fuzzy\", which is described as \(A=\{A_{1},A_{2},A_{3}\}\) under the three indexes. The three attribute indexes represent three kinds of evidence respectively, i.e., \(m_{1}\) (transmission energy efficiency ratio), \(m_{2}\) (idleness degree) and \(m_{3}\) (energy density factor).  
Because all three attribute indexes are benefit type, the BPA function can be established based on the same triangular membership function (shown in Figure 2). In Figure 2, \(G_{1}\) is the membership of the forward neighbor node belonging to \"next hop\", \(G_{2}\) is the membership belonging to \"non-next hop\", and \(G_{3}\) is the membership belonging to \"fuzzy\". Once obtain the index value of node \(a\) under any three indexes \(P(a),\ C_{idlle}(a),\ I(a)\), the membership degree of the next hop, non-next hop and fuzzy can be obtained according to \(G_{1}\), \(G_{2}\), \(G_{3}\) respectively. The three membership functions are established as follows:  
\[G_{1}(g(a))=\left\{\begin{array}{cc}0&0<g(a)\leq g_{\min}\\ \frac{g(a)-g_{\min}}{g_{\max}-g_{\min}}&g_{\min}<g(a)\leq g_{\max}\\ 1&g_{\max}<g(a)\leq 1\\ \end{array}\right. \tag{13}\]  
\[G_{2}(g(a))=\left\{\begin{array}{cc}1&0<g(a)\leq g_{\min}\\ \frac{g(a)-g_{\max}}{g_{\min}-g_{\max}}&g_{\min}<g(a)\leq g_{\max}\\ 0&g_{\max}<g(a)\leq 1\\ \end{array}\right. \tag{14}\]  
\[G_{3}(g(a))=\left\{\begin{array}{cc}1-\frac{|g(a)-\frac{g_{\min}+g_{\max}} {2}|}{\frac{g_{\max}-g_{\min}}{2}|},\ |g(a)-\frac{(g_{\min}+g_{\max})}{2}|\leq\frac{g_{\max}-g_{\min}}{2}\\ 0\ \,\ \ |g(a)-\frac{(g_{\min}+g_{\max})}{2}|\geq\frac{g_{\max}-g_{\min}}{2} \\ \end{array}\right. \tag{15}\]  
where \(g(a)\) can represent any three attribute indexes \(P(a)\), \(C_{i\mu l}(a)\), \(J(a).g_{\min}\) and \(g_{\max}\) are the minimum and maximum values of each index.  
As can be seen from Figure 2, when \(g(a)<g_{\min}\), the membership degree of the node \(a\) selected as the next hop is 0, i.e., under this index, the probability that the node \(a\) is selected as the next hop is 0. When \(g(a)>g_{\max}\), the probability that node \(a\) is selected as the next hop under this index is 1.  
After the membership function of each index is obtained in the above, the value of the membership function determines the possibility that the node belongs to the corresponding category. Then the membership function is normalized as the BPA function, and BPA values of next hop, non-next hop and fuzzy can be obtained. Furthermore, the weight of the index in Equation (12) is combined with the corresponding BPA value. The larger the weight of an index, the more important the index is when selecting the next hop. Thus, the BPA function of the forward neighbor node \(a\) of any node \(i\) under attribute \(b\) shown as follows:  
\[m_{b}(A_{t}^{a})=\frac{w_{b}\cdot G_{t}(g(a))}{\sum\limits_{t=1}^{3}w_{b} \cdot G_{t}(g(a))}\ \ \ \ b=1,2,3;t=1,2,3 \tag{16}\]  
Figure 2: Membership function curve under each index.
## 2 Vehicle Detection Framework Using Selective Multi-Stage Features
### Methodology Overview  
Figure 1 shows that the proposed vehicle detection framework consists of the training and the detection stages. The training stage includes the following three steps:  
1. CNN model design. We designed a CNN model for the proposed vehicle detection framework. The design is important because the features to be extracted depend on the CNN model structure, which should ensure that the characteristic vehicle image information can be learned. We trained the CNN model with a prepared vehicle dataset using the stochastic gradient with momentum and back-propagation algorithms.
2. Feature extraction and fusion. We extracted and fused the features containing the characteristic vehicle image information in the trained CNN model using a visualization technique that considered certain aspects of the feature map from each model layer. This step is critical, because the visualization technique implied that we do not need to use all the available feature maps, but can selectively extract features that include only the characteristic vehicle image information.
3. Detector training. We trained the detector with the extracted features using the AdaBoost algorithm.  
The detection stage included vehicle hypothesis and verification processes. The vehicle hypothesis defined an ROI that was probably a vehicle using a sliding window method. Once the ROI was defined, the trained vehicle detector verified it as either a vehicle or a non-vehicle.