esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Ambulance Allocation in Dynamic Urban Environments: A Historic Data-Driven Approach
## 2 Literature Review
They defined and applied the concept of \"preparedness\", which represents the rate of readiness for emergency response. In addition, some studies presented that COVID-19 led to a surge in demand for emergency services and suggested strategies for efficient ambulance operations [15; 16]. In addition, there exists a study that optimizes the addition of resources to a fire department to minimize the response time to an emergency call [17]. Their research issimilar to ours in that it focused on the addition of resources within a fire department, but differs in that it was simulation-based. [PERSON] et al. [PERSON] et al. (2018) discussed the optimization of the utilization of emergency department resources to reduce waiting times for emergencies in the department. They studied the optimization of emergency department resource operations based on simulation and integer programming. Their study is similar to ours in that it focused on the efficient utilization of emergency resources, while ours focused on multiple ambulances in a specific region. During natural disasters such as earthquakes, there exists research on optimizing supply chains and routes to maximize the demand for relief supplies [PERSON] et al. (2018). In addition to optimizing emergency resources, [PERSON] et al. [PERSON] et al. (2018) addressed the location and routing optimization of centers to ensure timely delivery of goods to customers. These studies are similar to ours in that they optimized resource management strategies to best satisfy demand. However, our work differs in that it involved the dynamic reallocation of ambulances in multiple periods. In this paper, we present a methodology for generating a network based on historical ambulance dispatch data to address the ambulance relocation optimization problem. Furthermore, since the demand for ambulance calls is influenced by diseases and some of these diseases exhibit seasonality, we introduced a methodology for seasonal ambulance relocation and assessed its effectiveness. Additionally, we outline a methodology to integrate real-time traffic information into the ambulance operational strategy. Finally, we present the results of a case study experiment to robustly calculate the number of zero zones according to the season in order to efficiently manage zero zones, which are areas where it is difficult for ambulances to dispatch in the golden time.
## 3 Results  
### Baseline Characteristics  
The study included 2085 patients. Table 1 illustrates the baseline demographic and clinical characteristics, ranked by in-hospital survival status. Continuous variables are presented as the mean \(\pm\) SD or median (interquartile range) according to the data distribution, whereas categorical variables are expressed as proportions. The mortality rate was 13.4% (280/2085). The mean age was 70.27. The percentage of males was 66.24%, significantly greater than the percentage of women. The survivors were more likely to take aspirin and statins, and had a lower AF count. For categorical variables, we applied the chi-square test to analyze differences between groups. For continuous variables, we utilized the Mann-Whitney U test. There were significant differences between survivor and non-survivor groups with regard to several variables, including age, LOS, APSIII, BMI, heart rate, AF count, ACEI, history of congestive heart failure, and history of renal disease (\(p<0.001\) for all).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Baseline Characteristics** & **All** & **Survival** & **Non-Survival** & \(p\)**-Value** \\ \hline N & 2085 & 1805 & 280 & \\ Age & 71 (62.00, 79.00) & 70 (62.00, 78.00) & 76 (67.00, 83.25) & \textless{}0.001 \\ LOS (day) & 2.58 (1.48, 4.58) & 2.36 (1.43, 4.21) & 3.86 (2.23, 8.13) & \textless{}0.001 \\ APSIII & 42 (31.00, 56.00) & 39 (30.00, 53.00) & 62 (47.75, 77.00) & \textless{}0.001 \\ BMI & 27.89 (25.72, 30.37) & 27.89 (25.81, 30.44) & 27.89 (24.74, 29.65) & 0.04 \\ Heart Rate (bpm) & 83 (75.00, 95.00) & 81 (74.00, 92.00) & 93 (79.75, 107.00) & \textless{}0.001 \\ SBP (mmHg) & 113 (109.00, 118.00) & 113 (108.01, 118.00) & 113 (113.00, 115.25) & 0.708 \\ DBP (mmHg) & 57 (54.00, 60.00) & 57 (54.00, 60.00) & 57 (54.00, 57.00) & 0.133 \\ MAP (mmHg) & 76 (73.00, 79.00) & 76 (73.00, 79.00) & 76 (74.0, 76.0) & 0.209 \\ Temperature (F) & 98.1 (97.70, 98.50) & 98.1 (97.70, 98.50) & 98 (97.68, 98.52) & 0.91 \\ AF count & 0 (0.00, 2.00) & 0 (0.00, 0.00) & 0 (0.00, 29.25) & \textless{}0.001 \\ Gender & & & & \\ Male & 138 (66.24\%) & 120 (66.59\%) & 179 (63.93\%) & 0.418 \\ Female & 704 (33.76\%) & 603 (33.41\%) & 101 (36.07\%) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Baseline demographic and clinical characteristics of patients diagnosed with NSTEMI included in the study.
# Optimized Detection Algorithm for Vertical Irregularities in Vertical Curve Segments
## 3 Investigation of the Relationship Between Vertical Curves and Track Irregularities
### Impact of Vertical Curve Gradient and Radius on Detection Accuracy  
In this section, the vertical curve length remains constant while the radius and gradient are varied to investigate their impact on detection accuracy. Based on Equation (1) and the selected \(a_{\text{sh}}\) values, calculations were performed, and the vertical curve radius was rounded to obtain a range of 6000-14,000 m. The specific test conditions are detailed in Table 3.  
After obtaining the virtual dynamic responses under varying vertical curve gradient and radius conditions, virtual irregularities were computed. Similar to the previous section, a 500 m segment was extracted from the vertical curve centre for a time-frequency domain analysis in both directions. The results are shown in Figure 6. As demonstrated in the previous section, the effect of vertical curve gradient on the error standard deviation and irregularity wavelength components is negligible.  
Figure 6a shows that the vertical curve radius is a critical factor influencing the detection accuracy of vertical irregularities in curve segments; smaller radii result in larger error standard deviations. The location of peak detection errors is determined solely by the vertical curve length. Figure 6b indicates that changes in vertical curve gradient and radius do not affect the overall trend of the PSD curve of the virtual irregularities, with only limited impact on PSD amplitude.  
Figure 5: Time–frequency domain comparison of virtual and excitation irregularity metrics under varying vertical curve gradients and lengths (**a**) Comparison of error standard deviation (**b**) Comparison of power spectral density.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Operating** & **Train Speed** & **Radius R** & **Length** & **Gradient** & **Single-Side** \\
**Conditions** & \(V\) (km\(\cdot\)h\({}^{-1}\)) & **(m)** & **L (m)** & **Difference Ai (\%o)** & **Gradient \(i\) (\%o)** \\ \hline
1 & & 6000 & & 66.66 & \(\pm\)33.33 \\
2 & 120 & 10,000 & 400 & 40 & \(\pm\)20 \\
3 & & 14,000 & & 28.58 & \(\pm\)14.29 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental conditions for variations in vertical curve gradient and radius.
# Differential Analysis of a Cryptographic Hashing Algorithm HBC-256
###### Abstract  
The article observes the new hashing algorithm HBC-256. The HBC-256 algorithm is based on the block cipher of the compression function CF (Compression Function) and produces a 256-bits hash value. Like any new cryptographic structure, the HBC-256 algorithm requires careful research process in order to confirm its cryptographic properties, namely: pre-image resistance and resistance to collisions of the first and second order. As a result of the research, for the HBC-256 hashing algorithm differential properties of nonlinear elements (S-boxes) and various options for constructing round characteristics are considered. A hypothesis has been advanced about the existence of paired differences, which will make it possible to construct round characteristics for hashing and for the function of round keys generating. It is shown that even for the most optimal way of constructing chains of differences, the probability of finding correct pairs of texts is less than the probability of a complete enumeration of one 128-bit block of input data, which makes the method of differential cryptanalysis unsuitable for finding collisions.  
hash function; cryptography; cryptanalysis; collision; algorithm; differential cryptanalysis +
Footnote †: journal: Applied Sciences
# Study on Stability of Elastic Compression Bending Bar in Viscoelastic Medium
## 2 Methods
### Deflection Function for an Elastic Compression-Bending Bar in a Viscoelastic Medium
#### 2.2.3 Deflection Solution of an Elastic Slender Bar in a Kelvin Medium  
The constitutive equation for a Kelvin body is given by \(\sigma=q_{0}\varepsilon+q_{1}\dot{\varepsilon}\), where \(q_{0}\) represents the Young's modulus (\(E\)) and \(q_{1}\) represents the viscosity coefficient (\(\eta\)). By applying a Laplace transform to this constitutive equation and subsequently rearranging it, the reference value for the ground reaction coefficient \(\overline{k}_{0}(s)\) can be determined:  
\[\overline{k}_{0}(s)=\frac{\overline{\sigma}}{\overline{\dot{\varepsilon}}}=E+ \eta s \tag{24}\]where: \(\overline{k}_{0}\) is reference value for the ground reaction coefficient  
\(\overline{\sigma}\) is viscoelastic stress  
\(\overline{z}\) is viscoelastic strain  
Consequently, the ground reaction coefficient \(\overline{k}(s,\ x)\) within the phase space formulation, as derived from the Kelvin model, is given by:  
\[\overline{k}(s,x)=A(E+\eta s)(L-x) \tag{25}\]  
Continuing with the example where \(i=2\), Equation (23) can be solved to determine the expression for the coefficient \(\overline{w}_{n}(s)\), where \(E_{k}\) denotes the elastic modulus of the spring element within the Kelvin model. \[\overline{w}_{1}(s)=\frac{288L^{3}\pi^{2}\bigg{(}81\pi^{4}\big{(}9 EI\pi^{2}-4 L^{2}P\big{)}Q_{1}+8 Ab_{1}L^{5}\bigg{(} \begin{array}{c}(27\pi^{2}-28)Q_{1}\\ +2\big{(}62-9\pi^{2}\big{)}Q_{2}\end{array} \big{)}(E_{k}+\eta s)\bigg{)}}{s \bigg{(} \begin{array}{c}+144 Ab_{1}L^{5}\pi^{4}\big{(}4592L^{2}P-20\big{(}511 EI+27L^{2}P\big{)}\pi^{2}+1107 EI\pi^{4}\big{)}(E_{k}+\eta s)\\ +64A^{2}b_{1}L^{10}\big{(}405\pi^{4}-3096\pi^{2}-8320\big{)}(E_{k}+\eta s)^{2} \end{array} \bigg{)}} \tag{26}\]  
\[\overline{w}_{2}(s)=\frac{288L^{3}\pi^{2}\bigg{(}36L^{2}P\pi^{4}Q_{2}-9 EI\pi^{ 6}Q_{2}+8 Ab_{1}L^{5}\bigg{(} \begin{array}{c}2(9\pi^{2}-62)Q_{1}\\ +9\big{(}28-3\pi^{2}\big{)}Q_{2}\end{array} \big{)}(E_{k}+\eta s)\bigg{)}}{s \bigg{(} \begin{array}{c}256A^{2}b_{1}L^{10}\big{(}62-9\pi^{2}\big{)}^{2}(E_{ k}+\eta s)^{2}\\ -9\bigg{(}\begin{array}{c}EI\pi^{6}-4L^{2}P\pi^{4}+\\ 8 Ab_{1}L^{5}\big{(}3\pi^{2}-28\big{)}(E_{k}+\eta s)\end{array}\big{)}\bigg{)} \end{array} \bigg{(} \begin{array}{c}81\big{(}9 EI\pi^{6}-4L^{2}P\pi^{4}\big{)} \\ +8 Ab_{1}L^{5}\big{(}27\pi^{2}-28\big{)}(E_{k}+\eta s)\end{array} \bigg{)}\end{array} \tag{27}\]  
By applying an inverse Laplace transform to Equations (26) and (27), the two coefficients \(w_{1}(t)\) and \(w_{2}(t)\) for the deflection function \(w(t,x)\) can be determined as:  
\[w_{1}(t)=\frac{144\mathrm{e}^{-N_{\mathcal{H}}t}L^{3}\pi^{2}}{(N_{3}+E_{k}(N_{ 4}+E_{k}N_{5}))N_{6}}\Bigg{(} \begin{array}{c}\big{(}\mathrm{e}^{N_{8}t}-1 \big{)}\bigg{(}\begin{array}{c}N_{2}(2N_{3}+E_{k}N_{4})\\ -N_{1}(N_{4}+2E_{k}N_{5})\\ +(N_{1}N_{6}+E_{k}N_{2}N_{6})\big{(}2\mathrm{e}^{N_{8}t}-\mathrm{e}^{N_{8}t}-1 \end{array}\bigg{)}\end{array}
# Mining Deformation Life Cycle in the Light of InSAR and Deformation Models
## Materials and Methods
### Deformation Modelling
#### 2.2.2 Model Application and Parameter Estimation  
The main objective of this deformation modelling was to assess the extent and magnitude of mining influences on the ground surface caused by extractions in 2017 in the northern part of USCB (Figure 8), and in particular extractions from two longwalls, No. 5 and 6, in the 503 coal seam of the Bytom-III mining area (Figure 9a). Vertical and horizontal displacements of the ground surface (north-south and east-west direction) were calculated for points in a regular mesh with the side length of the mesh element equal to 20 m. The dimensions of the mesh cover the area of interest in a range according to an angle of draw. The calculations were carried out for six-day intervals according to the dates of the acquired satellite images from 02.01.2017 to 30.12.2017 (60 ascending passes and 54 descending passes; see Section 2.1).  
The parameters of the introduced modelling theory, also known as the Knothe-Budryk theory, were determined by inverse analysis. Five lines were drawn following the measured levelling benchmarks in the area (Figure 7) to capture the range of mining influences and maximum value of vertical displacements over the considered longwalls.  
The matching of the subsidence trough profile along the drawn lines was carried out by determining the value of the subsidence factor in further iterations for a defined range of tangent \(\beta\) values and the exploitation rim. Based on the residuals between the measured and calculated subsidence values, the standard deviation (Std) was determined to evaluate the accuracy of the approximation. Table 1 presents values of the determined parameters of the Knothe-Budryk theory for exploitation of the 503 seam with the analysed longwalls and standard deviation values of matching.  
The mentioned subsidence indices were calculated using DAMAGE software (v.5.0.3) implemented by [PERSON] [47] in the Central Mining Institute, which is based on the classic equations of the Knothe-Budryk theory. The calculations do not include a seam dip because this  
Figure 7: Levelling network in the Miechowice district (**left**) and the chosen reference dates for each levelling cycle (**right**).  
parameter did not have a significant influence on the distribution of ground deformation for the area of interest. The average value of the seam dip is equal to 8\({}^{\circ}\).  
The determined values of the parameters and coefficients are as follows:  
* subsidence factor: \(a=1.13\);
* dispersion parameter of primary mining influences (rock mass parameter): \(\tan\beta=3.6\) (the angle of draw is about 75\({}^{\circ}\));
* exploitation rim: \(p=90\;m\);
* time factors describing immediate mining influences: \(A_{1}=0.8\) and \(c_{1}=300\) per year;
* time factors describing residual mining influences: \(A_{2}=0.2\) and \(c_{2}=6\) per year.
## 2 Materials and Methods
### Sample Preparation  
Fifty human mandibular molars, extracted due to periodontal problems, were collected and stored in purified filtered water at room temperature. The teeth were subjected to radiographic examination and teeth with previous endodontic treatment, external and internal root resorption, root caries, anomalies, cracks, fractures, unmatured apices, calcified root canals, and mesial roots with more than two root canals were excluded from the study.  
The access cavity was prepared on the teeth and the canal patency was obtained by #10 k file (Mani Inc, Utsunomiya, Tochigi, Japan). In order to measure the working length, a #10 k file was inserted into the root canal until its tip was visible from the apex. The length of the file was assessed and the real working length determined to be 1 mm shorter than the calculated file length. Mesial canals were chosen for simulating endodontic errors due to the higher incidence of errors in these canals as compared to the distal canals. The mesial root canals were prepared by the crown down technique using the gold ProTaper rotary system ([PERSON], Ballaigues, Switzerland) by endo motor (Eighteeth, Changzhou Sidary Technology Co, Changzhou, China) and 1 mL of 2.5% sodium hypochlorite as irrigant between instruments. The teeth were then randomly divided into two groups based on endodontic errors, that is, file separation and strip perforation. In each group, 25 of 50 canals were randomly chosen for simulating the errors, while the other 25 canals were considered as the control group without simulation of the endodontic errors.  
In group one (file separation group), in order to break the file into the canal, a fracture point was created 2 mm from the tip of the gold ProTaper F2 rotary file. The file was inserted in the prepared canal and twisted till it was broken at the end of canal. The rest of the canal was obtrated by the lateral condensation technique using an AH 26 sealer (Dentsply Maillefer, Ballaigues, Switzerland) and gutta percha points (Meta Biomed, Cheongju-si, Korea).  
In group two (strip perforation group), number 2 and 3 Gates Glidden drills (Mani Inc., Tochigi, Japan) were used in the coronal third of the canal to create strip perforation in the danger zone. The perforation area was confirmed by the insertion of a #20 k file and the canal was obtrated by lateral condensation technique. The control canals were prepared using the ProTaper rotary system and were obtrated by gutta percha points and the AH 26 sealer.  
The teeth were mounted in the previously prepared sockets of molar teeth in a dry mandible. The mandible was covered by 0.5 cm red wax (Polywax, Izmir, Turkey) as soft tissue equivalent.
###### Abstract  
Servers in a data center are underutilized due to over-provisioning, which contributes heavily toward the high-power consumption of the data centers. Recent research in optimizing the energy consumption of High Performance Computing (HPC) data centers mostly focuses on consolidation of Virtual Machines (VMs) and using dynamic voltage and frequency scaling (DVFS). These approaches are inherently hardware-based, are frequently unique to individual systems, and often use simulation due to lack of access to HPC data centers. Other approaches require profiling information on the jobs in the HPC system to be available before run-time. In this paper, we propose a reinforcement learning based approach, which jointly optimizes profit and energy in the allocation of jobs to available resources, without the need for such prior information. The approach is implemented in a software scheduler used to allocate real applications from the Princeton Application Repository for Shared-Memory Computers (PARSEC) benchmark suite to a number of hardware nodes realized with Odroid-XU3 boards. Experiments show that the proposed approach increases the profit earned by 40% while simultaneously reducing energy consumption by 20% when compared to a heuristic-based approach. We also present a network-aware server consolidation algorithm called Bandwidth-Constrained Consolidation (BCC), for HPC data centers which can address the under-utilization problem of the servers. Our experiments show that the BCC consolidation technique can reduce the power consumption of a data center by up-to 37%.  
high performance computing; data centers; resource allocation; profit; energy consumption; machine learning; reinforcement learning; server consolidation +
Footnote †: journal: Low Power Electronics and Applications  
1
# FPN-SE-ResNet Model for Accurate Diagnosis of Kidney Tumors Using CT Images
## 3 Results and Discussion
### Discussion
#### 3.2.2 Model Accuracy and Model Loss  
The SE-ResNet model's performance is evaluated using metrics like loss and IoU Score to minimize discrepancies between predicted and actual values. It measures the overlap between predicted bounding boxes and the ground truth, assessing accuracy in segmenting kidney tumors and kidneys. IoU Score measures the overlap between predicted and accurate segmentation, while the loss function quantifies the model's performance in minimizing the error between predicted outputs and ground truth, optimizing segmentation results. In Figures 6 and 7, Model accuracy and Model loss Seresnet50. Our strategies remained immune to overfitting issues due to the appropriate dataset.  
The discussion on the performance evaluation of different versions of the SE-ResNet model in terms of loss and IoU score. Loss is a metric that measures how well the model minimizes the difference between predicted and actual values. IoU score, on the other hand, measures the overlap between the predicted bounding boxes and the ground truth bounding boxes. According to the results, the models exhibit consistent performance in terms of loss, with Seresnet18 and Seresnet34 having a loss value of 0.753. The other models, namely Seresnet50, Seresnet101, and Seresnet152, show slightly lower loss values. Lower loss values indicate that these models are better at minimizing the difference between their predictions and the actual values. However, when it comes to the IoU score, there is some variation among the models. Seresnet50 achieves the highest IoU score of 0.987, indicating that it is the most accurate model for object detection.  
On the other hand, Seresnet18 and Seresnet34 have the lowest IoU score of 0.979, suggesting that they may struggle to detect objects accurately in some cases. Overall,  
Figure 5: SE-ResNet Accuracy IoU and F1-score Using Validation Images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SE-ResNet** & **Loss** & **IoU Score** & **F1-Score** \\ \hline Seresnet18 & 0.753 & 0.979 & 0.989 \\ Seresnet34 & 0.753 & 0.979 & 0.989 \\ Seresnet50 & 0.752 & 0.987 & 0.993 \\ Seresnet101 & 0.752 & 0.985 & 0.992 \\ Seresnet152 & 0.752 & 0.986 & 0.993 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results for the SE-ResNet Accuracy IoU and F1-score Using Validation Images.  
the SE-ResNet models perform well in object detection tasks, with Seresnet50 being the most accurate model based on the IoU score. However, it is significant to consider other factors, such as model size and computational complexity, when selecting the most appropriate model for a specific task.
# A Hybrid Soft Actuator Inspired by Grass-Spike: Design Approach, Dynamic Model, and Applications
## 4 Experiments
### System Identification to Estimate the Friction Coefficient \(\mu\)  
In Table 1, the friction coefficient \(\mu\) is marked as \"needs to be identified\". The idea of estimating it by the system identification process is that: (i) we measure the traveled distance of the HSA, and (ii) we find the \(\mu\) that best replicates the measured distance under the simulation. By following [21], we can assume \(\mu_{+}=\mu_{-}\) by only considering \(\eta\) as an input. Therefore, we only needed to estimate one \(\mu\) value. The system identification process is as follows:  
1. Apply various motor revolution speeds \(\Omega\) to a real HSA and record the traveled distance \(x\) after 5 s. \(\Omega\) was set to 80, 120, 160, and 200 rev/s. For each \(\Omega\) value, ten traveled distances were recorded. Then, the recorded distances were averaged after excluding the minimum and the maximum value.
2. Initialize \(\mu\) as an arbitrary value. Let \(\Delta t=0.0001\) s, and run the simulations using Equations (20)-(23). Store the outputs \(\phi_{s},\omega_{x},x_{s},v_{s}\) (here, the subscript \"s\" is employed to distinguish the real measurement value from the simulation outputs).
3. Define the error function to be \(e:=\|x-x_{s}\|^{2}\), and find the \(\mu^{*}\) that minimizes \(e\).