esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Frontal Collision Safety of Trains Based on Collision Dynamics
## 3 The Nonlinear Factors and Their Mathematical Models in a Multibody System
### Mathematical Model for Car-End Contact  
When a high-speed collision occurs, insufficient energy absorption by the coupler and anti-climbing device can cause car-end contact. Establishing a simple and fast algorithm for car-end contact is crucial for solving high-speed collision problems. [PERSON] et al. (2018) proposed a geometry-based compliant contact model for solving contact problems between two polyhedral bodies, which was applied to the dynamic simulation of a space station manipulator. However, it mainly focuses on calculating solid polyhedra under small deformation contact conditions. For train collision problems, the carbody can be considered a hollow, thin-walled polyhedron predominated by longitudinal length. The original formula has been modified for applicability based on the relationship between deformation and load during longitudinal impact. The original method, originally used for contact calculation between two solid polyhedra, is adapted for calculating the hollow carbody by introducing a stiffness weakening coefficient. Meanwhile, the original formula only applies to situations involving small plastic deformation. A steady-state coefficient is introduced to characterize the features of the steady-state crush stage. With the introduction of the stiffness weakening coefficient and steady-state coefficient, the car-end contact force can be expressed as:  
\[P=\left\{\begin{array}{l}\lambda\gamma\pi\frac{E}{2(1-v^{2})}\frac{A}{ \tau}x,0\leq x<x_{\text{t1}}\\ f_{\text{Interp}}(x),x_{\text{t1}}\leq x\leq x_{\text{t2}}\\ \eta P_{\text{max}},x>x_{\text{t2}}\end{array}\right. \tag{10}\]  
where \(\lambda\) is the stiffness weakening coefficient. \(\eta\) is the steady-state coefficient. \(\gamma\) is a case coefficient. a and b represent the elastic modulus and Poisson's ratio of the materials for the colliding carbody, respectively. \(A\) represents the geometry contact area. \(\tau\) represents the shape coefficient. \(x\) denotes the car-end compression deformation. \(P_{\text{max}}\) represents the peak force. \(x_{\text{t1}}\) and \(x_{\text{t2}}\) are transition points of contact force, and \(f_{\text{Interp}}(x)\) is the transition interpolation function for compression.  
For ease of engineering applications, further research was conducted on the determination of shape coefficient, stiffness weakening coefficient, and steady-state coefficient based on the structural analysis of a representative high-speed train through geometric derivation and numerical simulations.
# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
###### Abstract  
Efficient and reliable systems are required to detect and monitor disasters such as wildfires as well as to notify the people in the disaster-affected areas. Internet of Things (IoT) is the key paradigm that can address the multitude problems related to disaster management. In addition, an unmanned aerial vehicles (UAVs)-enabled IoT platform connected via cellular network can further enhance the robustness of the disaster management system. The UAV-enabled IoT platform is based on three main research areas: (i) ground IoT network; (ii) communication technologies for ground and aerial connectivity; and (iii) data analytics. In this paper, we provide a holistic view of a UAVs-enabled IoT platform which can provide ubiquitous connectivity to both aerial and ground users in challenging environments such as wildfire management. We then highlight key challenges for the design of an efficient and reliable IoT platform. We detail a case study targeting the design of an efficient ground IoT network that can detect and monitor fire and send notifications to people using named data networking (NDN) architecture. The use of NDN architecture in a sensor network for IoT integrates pull-based communication to enable reliable and efficient message dissemination in the network and to notify the users as soon as possible in case of disastrous situations. The results of the case study show the enormous impact on the performance of IoT platform for wildfire management. Lastly, we draw the conclusion and outline future research directions in this field.  
Internet of Things; 5G and beyond networks; named data networks; wildfire management 17 June 2019; Accepted: 11 July 2019; Published: 15 July 2019
# Therapeutic Potential and Challenges of Pioglitazone in Cancer Treatment
## 3 Therapeutic Use of Pioglitazone
### Therapeutic Effect on Different Types of Cancer
#### 3.6.5 Colorectal Cancer  
Similarly to hepatocellular carcinoma, it has been indicated that TZDs reduce the risk of colorectal cancer occurrence in patients with T2 DM after metanalytical assessment of about 2.5 million T2 DM patients [85]. Direct effects were reported during in vitro experiments with pioglitazone and its analog, \(\Delta\)2-pioglitazone treated HCT116 and HT29 cells. There were similarities in the endpoints, such as cell growth arrest and differentiation, in addition to key differences in the ways those were achieved, namely arrest in the S phase when treated with the former and in the G0/G1 phase when treated with the latter [86]. Additionally, xenograft studies on rodents using HT29 and SW480 cell lines indicated antiproliferative and antimetastatic activity towards the liver activity [85]. As an adjuvant, pioglitazone can be used in immunotherapy due to the autophagic degradation of PD-L1 mediated by PPAR-\(\gamma\) whose binding with the immunomodulatory ligand is similarly enhanced by said xenobiotic agent in the case of lung cancer [62]. Investigations in the field of pharmaceuticals addressing pioglitazone's poor solubility have shown that its combination with capecitabine, an established chemotherapeutic molecule against colorectal cancer, in the form of nanoparticles, enhances its bioavailability and overall proapoptotic effect on HT29 and HCT119 cells [87].
# Multimodal Natural Disaster Scene Recognition with Integrated Large Model and Mamba
## 4 Experiments
### Experimental Analysis
#### 4.4.4 Confusion Matrix  
To further analyze the recall, we introduced a confusion matrix, as shown in Figure 4. Upon examining the matrix, we observed that the likelihood of misclassification increased when two natural disaster scenarios exhibited highly similar characteristics. In the selected results, snow was frequently misclassified as haze, with nine instances recorded. Additionally, floods and rain were confused with each other twice, haze and sandstorms were mistaken for frost twice, and floods were misidentified as earthquakes on two occasions.  
These misclassifications can be attributed to shared features among these natural phenomena. For example, both snow and haze reduce visibility due to the presence of  
Figure 4: Comparison of confusion matrices for natural disaster classification.  
small particles in the air--whether snowflakes or pollutants--resulting in a blurred effect. Snowflakes in the air during snowfall can obscure vision, while suspended particles in haze create a similar hazy appearance. Moreover, both scenes tend to exhibit a generally dim environment. Likewise, floods and rain both result in water covering surfaces such as roads. If the model fails to detect the critical feature of raindrops, it may confuse these two scenarios.
## 2 Materials and Methods
### Server Design
#### 2.1.2 Separated Server Design  
The separated server design builds the DB and AP servers on 2 physical servers. Figure 2 presents a design adopted by 1 tertiary hospital and 2 secondary hospitals in Korea, showing a case of establishing an OHDSI OMOP CDM environment by installing a DB server in 1 physical server and an AP server in the other cloud server. Because these hospitals could support the computing resources of 1 physical server and 1 cloud server, they adopted a separate server design that was built separately from the DB and AP servers. There were firewalls between the physical server, cloud server, and analytics workstation, and they have a safelist, such that they only allow specific IPs.  
The advantage of the separated server design is that DB and AP servers are functionally separated; therefore, they do not share or compete with computing resources, thereby maximizing each server's resources and improving the performance and speed of each server. Unlike the combined server design, this design requires less configuration tuning of the DB server when additional data need to be loaded. Similarly, when applications in the AP server need to be updated, it is convenient for users to directly access the DB and conduct analysis because the AP server is separated from the DB server. In addition, data in the DB server are safe because the DB server is not affected, even if the AP server to which many researchers access is down or attacked.  
It is difficult to adopt a separated server design when computing resources are insufficient, such as in primary and secondary hospitals. In addition, depending on the infrastructure environment of each server, such as the bandwidth between the AP and DB servers, there may be a decrease in the communication speed between the servers. When communicating between servers, it is not called by localhost; additional server-to-server connection tasks such as IP and port registration between servers are required. Furthermore, user management is complicated because the whitelist is managed separately for each server.
## Nomenclature  
PDS Power Distribution System  
LVDS Low-Voltage Distribution Systems  
STPC Single-to-Three-Phase Converter  
KEPCO Korea Electric Power Corporation  
PTS Power Transmission System  
DOL Direct-On-Line  
CP Concrete Pole  
PDE Power Distribution Equipment  
PQ Power Quality  
DER Distributed Energy Resource  
THD Total Harmonic Distortion  
ESS Energy Storage System  
PCS Power Conditioning System  
UPS Uninterruptible Power Supply  
STATCOM Static Synchronous Compensator  
P.TR Pole-mounted Transformer  
PI Proportional Integral  
PR Proportional Resonant  
RMS Root Mean Square  
MCCB Molded Case Circuit Breaker  
COS Cut-Out Switch  
\begin{table}
\begin{tabular}{c c c} \hline
**Comparison Criteria** & **Conventional Method** & **Proposed STPC** \\ \hline Initial Installation Cost & High (due to line and & Lower \\  & transformer construction) & Moderate (semiconductor aging, \\  & & fan/capacitor life) \\  & & \\  &
## 4 Experimental Results and Analysis  
### Vision System Construction  
The experimental system consists of a data acquisition and data processing device. The data are collected using a binocular camera, transmitted using a GigE network port connection, and then analyzed and processed using a computer. Based on the information in the data, the parts are identified and located.  
The data acquisition section consists of two Hikvision monocular industrial CMOS (complementary metal-oxide-semiconductor) cameras. The cameras' model is MV-CA013-20 GC from Hikvision in Hangzhou, China. The resolution of cameras is 1280 \(\times\) 1024 pixels. Both camera lenses have a focal length of 12 mm, and the field of view is approximately 410 mm \(\times\) 280 mm at a working distance of 800 mm. The cameras are attached by a bracket plate and fixed on a tripod, as shown in Figure 11. Multiple sets of images of 10 mm \(\times\) 10 mm glass calibration plates were taken for camera calibration to obtain the internal and external reference matrices of the camera and the reprojection error of the calibration. The calibration results are shown in Figure 12, and the average reprojection error is 0.15 pixels. The input image size of the calibration experiment is 1280 \(\times\) 1024 pixels, so the average reprojection error is 0.0092% of the diagonal of the image, which shows that the calibration results are reliable.  
Figure 10: Schematic diagram of feature point matching.  
The calibration experiment can obtain the rotation matrix \(R\) and translation matrix \(T\) centered on the left camera, as follows:  
\[\mathbf{R}=\begin{bmatrix}0.9982&0.0083&-0.0584\\ -0.0080&1.0000&0.0047\\ 0.0584&-0.0043&0.9983\end{bmatrix} \tag{16}\]  
\[\mathbf{T}=\begin{bmatrix}-58.9648&0.4425&2.3870\end{bmatrix} \tag{17}\]  
Figure 11: Binocular vision system.  
Figure 12: Multigroup reprojection error of the camera.
# Generalization of ALMM Based Learning Method for Planning and Scheduling
## 7 Reinforcement Learning vs. ALMM Based Learning
### Environment  
Both methods utilize the state space and both build the best possible trajectory (or its parts). In both cases the ultimate goal of an algorithm is to find a most profitable (overall) sequence of actions/decisions. The environment for the ALMM Based Learning method, however, is defined by the Algebraic Logical Metamodel of Multistage Decision Processes, while for the RL method it is defined as the Markov Decision Process. Thus, the ALMM Based Learning environment is deterministic, while the Reinforcement Learning one is stochastic. The next difference stems from the fact that ALMM Based Learning may utilize the so-called generalized states space \(S=X\times T\), with \(X\) corresponding to a space of proper states and \(T\) representing the time instants space. As a consequence, ALMM Based Learning suits non-stationary environments well. All components defining the environment may be non-stationary, including the transition function \(f(u,s)=f(u,x,t)\) as well as possible decision sets \(U_{p}(s)\), (matching subsets of actions available in particular states) and constraints defining goal state set \(S_{G}\) and not-admissible state set \(S_{N}\).
## 3 Results and Analysis
### Patterns of Change in Ground Settlement  
Figure 6 displays the layout and results of the ground settlement monitoring. The arch and ground settlement exhibit a similar trend, with an increase in the arch settlement resulting in a corresponding increase in the ground. The rate of settlement growth is faster during the removal of temporary supports, excavation of side drift, and excavation of the lower body. The largest incremental ground central settlement, accounting for 16.7% of the total settlement, resulted from the removal of the partition wall. During the model test, the ground settlement was greater than the arch, with the maximum value at Point 6. Specifically, the peak settlement at the central near the outer excavation face is 1.07 mm, which converts to 37.45 mm when using the similarity ratio. The settlement trend of the general area is consistent with the arch. The tests showed that construction excavation had a quicker effect on arch settlement, but a longer effect on ground. Arch settlement can quickly reach a steady state under certain conditions, while ground settlement takes longer to stabilize.  
It is prudent to exercise caution when excavating lower rock layers above a sandy stratum, as this may disturb the upper soil, resulting in vibration liquefaction and uneven ground settlement. In extreme cases, the ground settlement value may exceed the amount of settlement of the arch. Therefore, it is essential to utilize small machinery for digging and to monitor any uneven ground settlement throughout the construction process. In the event of construction on significant ground, it is imperative to reinforce ground settlement monitoring and implement strategies to regulate substantial deformation settlement.  
\begin{table}
\begin{tabular}{c c} \hline
**Excavation Condition Number** & **Classification of Excavation Conditions** \\ \hline
1–9 & Excavation of the central drift \\
10–12 & Removal of temporary support for partition wall \\
13–18 & Excavation of the side drift \\
19–20 & Removal of initial support for side partition wall \\
21–22 & Excavation of the soil below \\ \hline \end{tabular}
\end{table}
Table 6: Classification of critical excavation conditions.
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 3 Sound Source Localization Methods for Two-Dimensional (2D) Structures
### Triangulation Method
#### 3.1.1 Triangular Positioning Method with Few Sensors  
In order to save costs and improve efficiency, many scholars have proposed methods using fewer sensors for positioning.  
Although at least three sensors and two sets of time differences can determine the two-dimensional coordinates of the acoustic emission source, in order to obtain a single accurate solution, four sensors and three sets of time differences are often used to locate the  
Figure 3: Triangular time difference localization method.  
Figure 2: Method for 2D sound source localization.  
acoustic emission source, such as in the four-probe rhombic localization method proposed by [PERSON] et al. [3], and the planar square-triangle localization method, shown in Figure 4a,c. The normalized square localization method proposed by [PERSON] et al. [4] is shown in Figure 4b. Although these acoustic emission source localization methods based on time difference require a small number of sensors, they are only applicable to materials with known constant velocity and isotropy.
# Effect of Temperature on Channel Compensation in Optical Camera Communication
###### Abstract  
General-purpose Complementary Metal Oxide Semiconductor (CMOS) sensors perform the image desegregation in three channels (red, green, and blue) as a result of a band-pass wavelength filtering carried out using Foveon or Bayer filters. This characteristic can be used in Optical Camera Communication (OCC) systems for increasing the links' data rate by introducing Wavelength Division Multiplexing (WDM) or Color Shift Keying (CSK) modulation schemes. However, these techniques need a compensation stage to mitigate the cross-talk between channels introduced by the filters. This compensation is performed by a Channel State Information (CSI) estimation and a zero-forcing compensation scheme. The impact of the temperature effects of light-emitting diode (LED) emissions on the zero-forcing compensation scheme's performance has not been analyzed in depth. This work presents a comprehensive methodology and experimental characterization of this impact for Foveon and Bayer-based image sensors, assuming that the CSI is estimated under temperature conditions different from the LED's stationary temperature regime. Besides, Signal-to-Interference-plus-Noise Ratio (SINR) and Bit Error Rate (BER) performance metrics are presented in order to estimate the repercussion in an OCC link. The results reveal that the Foveon sensor obtains more unsatisfactory performance than the Bayer-based sensor. On the other hand, the blue band is the most penalized by the thermal effect.  
LED; Optical Camera Communication; temperature effects; spectrum; channel compensation
# Design and Performance Evaluation of eLoran Monitoring System
## 3 Near-Field Monitoring Station Performance Analysis
### Transmission Control Precision Calculation Method  
The transmission control accuracy is expressed by the standard deviation of the measured 1 PPS time difference, once data are measured per second, for \(i\) sets of data. It is represented by Formula (1).  
\[\sigma=\sqrt{\frac{\sum_{1}^{i}(T_{Xi}-T_{X})^{2}}{i}} \tag{1}\]  
In the formula,  
\(\sigma\) is the transmission control accuracy in ns;  
\(T_{Xi}\) is the time difference data obtained from the \(i\)th measurement of the time interval counter, in ns;  
\(T_{X}\) is the mean value of the time difference data in ns.  
Figure 3: Component structure of far-field monitoring station.
# A Two-Step High-Order Compact Corrected WENO Scheme
## 2 Numerical Methods
### Introduction to the 1D Benchmark Cases  
Without loss of generality, this study first employs a series of widely recognized benchmark tests based on the one-dimensional Euler equations to validate and evaluate the proposed method. These tests aim to assess the performance of numerical schemes in resolving fine-scale structures (such as turbulence) and accurately capturing significant discontinuities (such as shock waves). The one-dimensional Euler equation, regarded as a simplified version of the more complex three-dimensional Navier-Stokes equations, is expressed in vector and conservation form as follows:  
\[\frac{\partial}{\partial t}\begin{bmatrix}\rho\\ \rho u\\ E\end{bmatrix}+\frac{\partial}{\partial x}\begin{bmatrix}\rho u\\ \rho u^{2}+p\\ u(E+p)\end{bmatrix}=0, \tag{14}\]  
where \(\rho\), \(u\), and \(E\) represent density, velocity, and total energy, respectively. \(p\) represents pressure and can be calculated through \(p=(\gamma-1)\Big{(}E-\frac{1}{2}\rho u^{2}\Big{)}\).
# Specific Resonant Properties of Non-Symmetrical Microwave Antennas
## 2 Materials and Method
### Material for Measuring the Properties of Microwave Antennas  
If the antenna model is to radiate around the model aircraft in the same way as the corresponding antenna on an actual aircraft, both antennas must have approximately the same shape, proportional dimensions, similar electrical properties, etc. However, implementing such an antenna model would be complicated and time-consuming. For a single use, it would be also too costly. To make this problem more efficient and easier to solve, the research team instead created several sets of antenna models, with different shapes, different lengths, and different transverse dimensions [10]. An illustration of three-part sets of two antenna models (cylindrical and flat types) is shown in Figure 1a. Twelve six-piece sets of the produced antenna models are shown in Figure 1b. For the purposes of this article, three additional sets of flat microwave antenna models were produced from 0.35 mm thick brass sheet. For good handling during the measurements, thin antennas were prepared for their quick and easy clamping, and a conductive artificial ground measuring 10 \(\times\) 10 cm was used [11].  
During the measurements, the VSWR value of the individual antennas was evaluated in the frequency range from 1 GHz to 4 GHz. The minimum value of VSWR on the frequency axis determined the required resonant frequency of the antenna model. The VSWR record of the antenna for determining the value of the sought resonant frequency is shown in Figure 2, where the first (quarter-wave) resonant frequency is 2063.29 MHz (point M1) and the second (half-wave-parallel) resonant frequency is 3645.57 MHz (point M4) [12; 13; 14; 15].  
After each evaluation of the resonant frequencies of one set of six antennas, the results were plotted as shown in Figure 3. The sequence number of Record 1 corresponds to the longest antenna (low frequency) and the sequence number of Record 6 corresponds to the smallest antenna (high frequency) [16].  
Figure 1: Measured antennas: (**a**) Illustration of cylindrical and flat antennas; (**b**) A dozen models produced of sets of antennas.  
Figure 3: Example of one set of resonance frequencies of the antennas.  
Figure 2: VSWR antenna recording for determining the value of the resonance frequency.