esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theoretical Modeling of Sandstone Failure with Hill-Tsai Criterion  
Sandstone, as one type of natural composite material, often consists of sand-size grains of multiple single materials of varying characteristics with certain combinations. Physical properties and mechanical behavior of sandstone materials vary significantly depending on the random distribution of material constituents. From this perspective, sandstone materials cannot be viewed and analyzed simply through some particular models and theories aiming at single non-composite materials. Models and theories for investigating mechanical performance of composite materials are demanded.  
Based on [26; 27], in this study, the classical stress-based Hill-Tsai yield criterion (Equation (2)) put forward by [PERSON] in 1948 for characterizing the properties and behavior of anisotropy composite materials [27] was adopted for theoretical analysis of sandstone failure process.  
\[\frac{\cos^{4}\theta}{X^{2}}+\left(\frac{1}{S^{2}}-\frac{1}{X^{2}}\right)\cos ^{2}\theta\sin^{2}\theta+\frac{\sin^{2}\theta}{Y^{2}}=\frac{1}{\sigma^{2}} \tag{2}\]  
where \(X\), \(Y\), \(S\) represent axial strength, lateral strength, and shear strength, respectively. For example, the axial strength, lateral strength, and shear strength of sandstone are around 40, 20, and 10 MPa, respectively. The letter \(\theta\) symbolizes off-axis angle, while \(\sigma\) means failure stress.  
Figure 3 shows the relation between stress strength and off-axis angle for sandstone. It can be seen from Figure 3 that the stress strength of sandstone is 40 MPa when \(\theta\) = 0. As \(\theta\) increases, stress strength drops at first and then goes up. When \(\theta\) = 60\({}^{\circ}\), the smallest stress strength reaches 18.3 MPa. When \(\theta\) is 90\({}^{\circ}\), the stress strength arrives at 20 MPa. That is to say, the minimal stress strength occurs at the position where a 60\({}^{\circ}\) angle is formed. This identifies the weakest point where failure easily occurs. The Young's elasticity modulus of this class of sandstone is about \(E=5\times 10^{3}\) MPa. Combining the failure stress of 18.3 MPa, as obtained by the Hill-Tsai criterion, through [PERSON]'s modulus formula (Equation (3)), failure strain \(\varepsilon\) of the sample sandstone can be calculated as \(3.7\times 10^{-3}\).  
\[\varepsilon=\frac{\sigma}{E} \tag{3}\]  
where \(\varepsilon\) represents the dimensionless strain and \(\sigma\) means the stress. \(E\) is [PERSON]'s modulus.  
Figure 2: Implementation of digital image correlation (DIC) for strain measurement.  
These theoretical results may be utilized as numerical benchmarks against which the testing results obtained from the following physical experiments are to be compared. These mutual comparisons and cross-checks are expected to help to validate the feasibility of the DIC technique for reliable characterization and forecasts on the failure patterns and modes of sandstone materials.
# A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery
## 3 Methods
### BCI Thresholds
After experimenting with different water identification methods, we chose to mask areas with Normalized Difference Water Index (NDWI) values greater than -0.05. This approach effectively removes water and gray roofs, resulting in a more precise detection of bare coal areas.
# Autonomous Load Regulation Based Energy Balanced Routing in Reshargeable Wireless Sensor Networks
## 3 System Model  
To recharge the battery and gather data from nodes, an MCV is employed. Ideally, we aim to solve a problem where the MCV can stop anywhere within the two-dimensional plane and charge sensor nodes wirelessly. However, an infinite number of possible locations can lead to an infinite size of search space. We simplify this problem by introducing a logical cellular structure, as shown in Figure 1. The two-dimensional plane is partitioned with hexagonal cells with a side length of \(R\). The cells that the MCV need to charge for are determined by locations and energy states of nodes. The MCV uses its center as an anchor point and only works when it sojourns at these points. The MCV travels from the base station to each anchor point at a speed of \(S\) along the direction of the arrows in order to charge the nodes. Due to the low power reception efficiency caused by the long distance, we assume that the node can be charged only if the MCV sojourns at its cell. It returns to the base station to supplement its own energy when all charging cells have been charged.  
The network is defined by the directed graph G (C,V,E). The \(c_{m}\in C\) represents a cell and \(m\) is the serial number of a cell. Each vertex \(v_{i}\in V\) represents a sensor node and each edge \(l_{ij}\in E\) represents a directed communication link between node \(v_{i}\) and its forward node \(v_{j}\), where \(i\) and \(j\) are serial numbers of nodes. The communication link can only exist if \(v_{j}\) is within the transmission range of \(v_{i}\). There are \(N\) randomly distributed fixed sensor nodes in a two-dimensional plane. Every sensor has a battery  
Figure 1: Example of a sensor network with a mobile charger vehicle (MCV).  
capacity of \(E_{o}\) and is fully charged initially. Each of them, with a maximum communication distance of \(D\), has the corresponding energy consumption when the node collects, sends, and receives data. The highest energy consumption of the node is derived from the process described above, so the remaining energy consumption is ignored in this paper [23]. All packets are of the same constant size.
# Energy Efficiency Improvement of Diesel-Electric Trains Using Solar Energy: A Feasibility Study
###### Abstract  
Nowadays, productivity challenges in modern manufacturing systems have been the driving force in generating energy-efficient technologies in every industry, including diesel-electric locomotives. The diesel-electric locomotive is one of the most widely used methods in rail transportation, especially in North America. More precisely, the evolution of the electric transmission has allowed the locomotive's effective tractive effort to increase its diesel engine horsepower. In this paper, we study a new way to improve the energy efficiency of diesel-electric trains using photovoltaic solar panels. This solution is suitable for reducing greenhouse gas emissions of the diesel-electric locomotive system, particularly in cold climates. We explore the amount of energy produced by the PV solar panels and compare it with that produced by the auxiliary diesel-generator during a train's journey. This comparison clarifies the actual percentage of energy that solar panels can cover. Thus, this paper presents a validation of feasibility and profitability as a function of the train's specific operating conditions and the meteorological data associated with their routes. Based on the results, the minimum annual fuel reduction of auxiliary generators allowed using PV solar panels is above 50% in all cases and wagon classes, proving this solution's feasibility. Regarding the comparison, case 3 (Sept-Iles to Schefferville) and case 4 (Luxor to Aswan) are the best, with over 100% of the energy provided by PV solar panels in all the wagons' classes. The payback period ranges from 2.5 years to 9.1 years, while the CO\({}_{2}\) emission reduction's revenues range from $460 to $998 per year/wagon.  
energy efficiency; diesel-electric train; photovoltaic solar panel; emission reduction
# Effects of Isochoric Freezing on the Quality Characteristics of Raw Bovine Milk
## 2 Materials and Methods
### Experimental Protocol  
Raw bovine milk was purchased from a local market located in Albany, California. Four different conditions were compared: refrigerated raw milk (RF), refrigerated milk initially treated with high-temperature short-time (HTST) pasteurization, raw milk held under isochoric freezing (IF) at \(-\)5 \({}^{\circ}\)C/77 MPa, and raw milk held under IF at \(-\)10 \({}^{\circ}\)C/96 MPa. These conditions were chosen based on preliminary results in which IF \(-\)15 \({}^{\circ}\)C/150 MPa led to phase separation in the milk within just one day of storage, rendering them unsuitable for long-term storage.  
Initial tests and treatments began seven days before the labeled expiration date of the milk. Refrigerated (RF) raw bovine milk was used as the control. For the HTST treatment, raw bovine milk was heated to 72 \({}^{\circ}\)C and pasteurized for 15 s using a multipurpose sterilization unit (UHT/HTST Lab Microthermics model 25 EHVH, Raleigh, NC, USA). The pasteurized milk was collected and divided aseptically into 3 sterilized polypropylene bottles through the drain-out line. The initial quality of both RF and HTST milk was immediately tested to measure microbiological activity, physiochemical properties, and retention of certain compounds. The samples were then stored in a refrigerator (4 \({}^{\circ}\)C) for up to 5 weeks.  
For the IF treatments, samples of the milk were prepared on the same day as the RF and HTST samples. Three bags of about 150 mL of raw bovine milk each were processed for each treatment. The sterilized bags were heat sealed with negligible air bubbles within them and transferred to four separate isochoric chambers (total of twelve bags) filled with cold water. Cold water helped prevent accelerated bacteria growth as the chambers cooled to their preset temperatures in the chest freezers. After the treatment, the chamber was placed in a freezer set at 5 \({}^{\circ}\)C for 14 h to melt all the ice within the chamber. Milk samples were treated under isochoric conditions for 2 and 5 weeks.
# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 2 Railway Infrastructure Monitoring Use Cases and Satellite Data Potential
### Monitoring of Rail Track Deformations (TD)  
Railway infrastructure is composed of a complex collection of substructures, such as rail tracks, sleepers, embankments, tunnels, and bridges. All these substructures depreciate over time and cumulatively affect the structural health of the railway line. Traffic frequency increases, in combination with aging and higher axle loads and speeds, affect the geometry of the rail track. It is important to detect, identify, and analyze rail track deformations at an early stage to ensure the safety of passengers on board. The structural health of the railway can be monitored by conventional methods, which entail the use of survey trains or in situ measurement devices [23, 24], such as displacement sensors and tilt meters [25]. In general, these methods are expensive, labor-intensive, and time-consuming. Therefore, they are usually applied at locations where the structural health is expected to be compromised. Monitoring of wide areas, including railway lines that are in near-continuous use, would contribute to large-scale railway structural health monitoring. This can be achieved using satellite data, primarily using data obtained from synthetic aperture radar (SAR) and interferometric SAR (InSAR) systems that allow measurement of displacements on ground structures down to the millimeter level with high spatial resolution (e.g., [22, 26, 27]).  
SAR is a form of radar used to create detailed, high-resolution images of the Earth's surface. It works by transmitting microwave signals toward the ground and analyzing the reflected signals to generate images. SAR operates in all weather conditions (it penetrates clouds, rain, and even snow), provides day and night imaging (it uses its own microwave illumination, making it independent of sunlight), captures fine detail (by simulating a large antenna aperture using the motion of the satellite), achieves high spatial resolution, measures surface deformation as SAR can detect millimeter-scale changes in surface structure, such as land subsidence or uplift, through techniques like interferometric SAR (InSAR). InSAR involves combining two or more SAR images of the same area taken at different times. By analyzing the phase differences between these images, it can detect surface deformations with high precision.  
Some notable satellites equipped with SAR sensors are Sentinel-1A, 1B, and recently launched 1C (ESA), Radarsat-2 and Radarsat Constellation Mission (RCM) (Canada), TerraSAR-X and TanDEM-X (Germany), ALOS-2 (Daichi-2) (Japan), RISAT-1A/1B and 2 (India), COSMO-SkyMed (Italy), SAOCOM 1A and 1B (Argentina), as well as commercial SAR satellites: ICEYE Constellation (Finland), Capella Space (USA). There have also been several important historical SAR missions that have paved the way for current technologies: ERS-1 and ERS-2 (ESA), ENVISAT (ASAR) (ESA), and SEASAT (NASA).
# Analysis and Optimization of the Stray Capacitance of Rogowski Coils
## 4 Optimization of the Skeleton Cross-Section
### Optimal Design of the Skeleton Cross-Section  
In this subsection, we consider an improved cross-section of the skeleton of Rogowski coils with smooth and convex boundary, as presented in Figure 13, where \(R_{1}\) and \(R_{3}\) are the inner and outer radii of the skeleton, respectively. Note that \(R_{1}\) is usually fixed to a pre-given value, there are four geometrical parameters remaining to be determined, namely \(z_{0}\), \(z_{\text{max}}\), \(R_{2}\), and \(R_{3}\). The goal is to optimize these parameters to obtain a skeleton with the minimal perimeter under constant magnetic flux. To achieve this goal, a constrained variational problem is defined and solved.  
Figure 12: Skeleton with oval cross-section, where \(d\) and \(b\) denote the radius of the half-circles and the length of the rectangular domain, respectively.
# A Simple and Robust Equalization Algorithm for Variable Modulation Systems
## 5 Conclusions  
The VM-DDLMS adaptive equalization algorithm for the VM system is proposed in this paper, which utilizes the characteristics of the QPSK-like modulation in the modulated frame header and modulated pilot from the PL frame to perform the equalization decision. This algorithm updates the filter coefficients and improves the adaptability and stability of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Equalization Algorithm** & **CMA** & **MCMA** & **VM-DDLMS** \\ \hline DSP & 184 (9.58\%) & 184 (9.58\%) & 176 (9.17\%) \\ \hline LUT & 2364 (0.98\%) & 2286 (0.94\%) & 2300 (0.95\%) \\ \hline Register & 4542 (0.94\%) & 4539 (0.94\%) & 4440 (0.92\%) \\ \hline Workable Clock (MHz) & 263.15 & 256.41 & 333.33 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parallel 11 th-order equalization algorithm resource consumption.  
Figure 7: Comparison of the BER curves of three equalization algorithms.  
the equalization algorithm for the VM system. In addition, the VM-DDLMS is suitable for various modulation methods, and it is not necessary to adjust the correction parameters and switch the symbol decision according to the modulation mode. The simulation result shows that VM-DDLMS is better than CMA and MCMA among the four modulation methods QPSK, 8 PSK, 16 APSK, and 32 APSK. When the BER is \(10^{-5}\), the performance loss of VM-DDLMS is less than 1 dB in the four modulation methods. The implementation result showed that the resource consumption of VM-DDLMS was almost the same as that of CMA and MCMA and that the operating frequency could be as high as 333.33 MHz in the parallel 11 th-order, which has a comparatively higher practical value in the VM system.  
Conceptualization, W.H. and Z.W.; methodology, W.H.; software, W.H.; validation, W.H. and R.M.; formal analysis, W.H.; investigation, W.H. and R.M.; resources, W.H.; data curation, W.H.; writing--original draft preparation, W.H.; writing--review and editing, M.L. and R.M.; visualization, R.M.; supervision, W.H. and Z.W.; project administration, Z.W. and W.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (Class A) (Subject Number: XDA153501, Sub-Subject Number: XDA15350103).  
The authors declare no conflict of interest.
# Cancer Treatment: An Overview of Pulsed Electric Field Utilization and Generation
## 7 Electrode Shapes  
There are many shapes of electrodes, as shown in Figure 6, that can be used to deliver the generated electric field to infected tissues or organs; the four most widely used electrodes are the catheter, clamp, needle, and plate types [7].  
* Plate electrodes, shown in Figure 6a, utilize two parallel plates separated by a distance to deliver a homogenous electric field to tissues; unfortunately, they cannot be fitted easily to treat affected organs internally without the need for surgery. Therefore, they can be used only with accessible types of tumours.
* Needle electrodes, shown in Figure 6b, can be built using a different number of electrodes (starting at two), and the needles can be placed in different relevant formations with respect to each other. The number and placing of electrodes affects the shape of the electric field, leading to different effects on the tumour for the same pulse parameters.
* Clamp electrodes, shown in Figure 6c, have almost the same shape as plate electrodes. They are made up of two electrodes fixed onto the inner faces of the clamp, which is used to hold the organ or tissues in direct contact with the electrodes. As with plate electrodes, these can only be used for accessible tumours.
* Catheter electrodes are inserted in catheter tubes to reach the affected organ, which requires skilled clinicians able to navigate to the target and fix the tubes without harming any surrounding areas.  
To optimize the delivery of IRE to the tissue under study, the number of electrodes, their shape, and the gap between them all need extensive research.
## 3 Results
### In the One-Dimensional Case  
In this section, a one-dimensional shock tube problem is used to demonstrate the performance of the optimized TDG method with third-order Taylor time expansion. It should be noted that the Sod shock tube problem represents a common test case for nonlinear multi-physics problems. The main equations defining the Sod shock problem are the Euler equations, which are expressed as follows:  
\[\frac{\partial\mathbf{U}}{\partial t}+\frac{\partial\mathbf{f}}{\partial x}=\mathbf{0}. \tag{35}\]  
where \(\mathbf{U}\) is the solution vector, and \(\mathbf{f}  
the optimized TDG method (outside form and inside form) maintain similar numerical computational accuracy.  
As shown in Table 2, for the computation of the one-dimensional Euler equations, the computational efficiency of optimized TDG is significantly superior to that of conventional TDG when using the third-order Taylor time expansion form. On average, using optimized TDG to calculate the one-dimensional Euler equations can save 90% of the time consumed. The results of density for one-dimensional Euler equations in the Sod shock tube problem are presented in Figure 4. In this work, the numerical experiments employ the same computer system, which is configured with an IntelR i7-9750H CPU, with 32 GB of memory, and Windows 11, with a 64-bit operating system.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Conventional TDG**} & \multicolumn{5}{c}{**Optimized TDG**} \\ \cline{3-7}  & & \multicolumn{2}{c}{**Inside Form**} & \multicolumn{2}{c}{**Outside Form**} \\ \hline \(K\) & **L1** & **L-Infinite** & **L1** & **L-Infinite** & **L1** & **L-Infinite** \\ \hline \(150\) & \(1.53\times 10^{-2}\) & \(1.35\times 10^{-1}\) & \(1.57\times 10^{-2}\) & \(1.39\times 10^{-1}\) & \(1.59\times 10^{-2}\) & \(1.38\times 10^{-1}\) \\ \(200\) & \(1.19\times 10^{-2}\) & \(1.31\times 10^{-1}\) & \(1.20\times 10^{-2}\) & \(1.42\times 10^{-1}\) & \(1.21\times 10^{-2}\) & \(1.41\times 10^{-1}\) \\ \(250\) & \(1.09\times 10^{-2}\) & \(1.41\times 10^{-1}\) & \(1.17\times 10^{-2}\) & \(1.42\times 10^{-1}\) & \(1.14\times 10^{-2}\) & \(1.41\times 10^{-1}\) \\ \(300\) & \(1.06\times 10^{-2}\) & \(1.40\times 10^{-1}\) & \(1.07\times 10^{-2}\) & \(1.40\times 10^{-1}\) & \(1.06\times 10^{-2}\) & \(1.40\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The results of the L1/L-infinite errors for one-dimensional Euler equations for the order of polynomials \(N=3\) and different values of the number of elements K, by conventional TDG method, optimized TDG inside method, and optimized TDG outside method.  
Figure 4: The density of Sod shock tube problem for Euler equations by the conventional TDG and the optimized TDG inside forms.
## 2 Theory
### The Magnifications \(M_{L}\) and \(M_{T}\) of the Images in the In-Line Interferometer
#### 2.1.2 Image Reconstruction  
The hologram \(u_{i}(x_{2},y_{2})\) is propagated a distance \(\Delta z_{r}\) to reconstruct the focused image \(u_{r}(x_{r},y_{r})\), as is shown in Figure 3. The Fresnel diffraction integral is used to depict the propagation of \(u_{i}(x_{2},y_{2})\) as follows:  
\[u_{r}(x_{r},y_{r}) = \frac{\exp(i\text{k}\Delta z_{r})}{i\lambda\Delta z_{r}}\iint \limits_{-\infty}^{\infty}u_{i}(x_{2},y_{2})\exp\bigg{\{}i\frac{k}{2\Delta z_{ r}}\Big{[}(x_{2}-x_{r})^{2}+(y_{2}-y_{r})^{2}\Big{]}\bigg{\}}dx_{2}dy_{2}. \tag{17}\]  
Afterwards, the magnified diffracted image given by Equation (16) is substituted into Equation (17), leading to the wavefront of the reconstructed image as follows:  
\[u_{r}(x_{r},y_{r}) = \exp[ik(\Delta z_{o}+\Delta z_{r})]\frac{1}{2}u_{o}\bigg{(}\frac{ x_{r}}{\text{M}_{\text{T}}},\frac{y_{r}}{\text{M}_{\text{T}}}\bigg{)}. \tag{18}\]  
The complete derivation of the wave front propagation from the object plane to the focused image plane is shown in the Supplementary Materials.  
The numerically reconstructed focused image is a version of the object, scaled and inverted. The coordinates of the object and image are given by Equations (7)-(9).  
Based on the above obtained relations, the following main conclusions can be stated:  
1. Both transverse magnification and longitudinal magnification can be determined from the focal length \(f\) of the lens and the distance \(s_{i}\) between the lens and the CCD camera.
2. The reconstruction distance \(\Delta z_{r}\) depends on the longitudinal magnification \(\text{M}_{\text{L}}\) and the object displacement \(\Delta z_{o}\). Meanwhile, the transverse magnification \(\text{M}_{\text{T}}\) of the reconstructed image remains unchanged with the axial displacement of the object.  
Figure 3: Scheme of the magnified focused image reconstruction.