esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Disaster Response in Vehicular Tunnels: Technologies for Search and Rescue Applications
## 2 Related Works  
### IoT and Monitoring Technology  
Recently, various sensors and situation cognitive technologies have been developed with advances in IoT technology, and accordingly, various solutions for fire and accident detection have been proposed for roads and tunnels. In Ref. [1], temperature sensors for detecting fires in tunnels were introduced. A sensor network using them was installed, and the sensing distance performance was measured. Although the transmission distance varied depending on the surface conditions and structure of the tunnel, it was shown that it is possible to sufficiently monitor an actual tunnel environment. In Ref. [2], it was demonstrated that IoT devices in these tunnels play a very important role in generating and delivering accurate information in the event of a fire.  
Among the various IoT sensors, the most commonly used is the camera [3; 4; 5]. There are many vehicle-tracking approaches for roadside intersection monitoring, and they assume predictable vehicle motions [6]. Feature-based approaches can be used to handle partial occlusions in intersections [7]. There are also in-vehicle road monitoring solutions to estimate the pose and motion parameters of an oncoming vehicle by means of various signal processing filters [8; 9]. However, such camera-based vehicle detection techniques have the disadvantage that the throughput is poor when the number of vehicles increases sharply or when there are many environmental obstacles to be processed on the road. Recently, many alternative techniques for real time tracking of vehicle speed and direction, and algebra-using radar systems, have been widely studied. [PERSON] et al. [10] proposed a microwave radar detector that detects individual vehicles on the road by reducing the noise signal and side lobes within each traffic lane. [PERSON] et al. [11] proposed another radar-based vehicle detection method that mitigates the interference of urban obstacles, such as trees and buildings. This radar system has been fully demonstrated in a relatively poor environment, the tunnel [12; 13].  
In addition to vehicle accidents, methods for detecting fires have also been utilized in various ways. Typically, fire detection methods use cameras [14; 15; 16]. [PERSON] et al. [14] proposed a novel flame-detection scheme based on multiple features, such as chromatic, dynamic, texture, and contour features, to improve the sensitivity and reliability. [PERSON] et al. [15] reliably detected fire and rejected non-fire motion on a large dataset of real videos. In Ref. [16], probability density functions were generated to explore irregular flame patterns, especially under poor external conditions such as shadows, reflective surrounding area, and changing neon signs.
# Adaptive 3D Imaging for Moving Targets Based on a SIMO InISAR Imaging System in 0.2 THz Band
## 5 Experiments
### Target Tracking and Imaging
Due to the variation of observing view, the images in the side windows are slightly different with that in the middle, but the body of the model can be identified clearly. Figure 10: ISAR images of the plane model obtained by the (**a**) virtual sum signal, and receiving channels (**b**) Rx1, (**c**) Rx2, and (**d**) Rx4. Figure 11: InISAR imaging results of the plane model. **(a)** The 3D view, **(b)** the front view, **(c)** the side view, and **(d)** the bird view. Figure 12: ISAR and InISAR imaging results of the moving target in the left window. **(a)** 2D ISAR image, **(b)** 3D InISAR image. The experimental results verify the feasibility and effectiveness of the proposed system. There is no doubt that the high-quality imaging results will support the target identification greatly and may find potential applications in many fields.
# Developing Productivity-Safety Effectiveness Index Using Data Envelopment Analysis (DEA)
## 2 Background
### Methodological Background  
#### 2.2.1 Productivity and Safety Performance Indexes  
There are many production economics indexes that explain productivity and safety performance; however this study chooses the representative indexes national statistics [18; 19] and safety performance [21; 22], as described in Table 1.  
On the one hand, productivity is identified with three indexes: labor productivity (LP), production capacity (PC), and operation ratio (OR). The LP is the amount of output produced per unit of labor input and a measure of the efficiency of workers in producing goods or services. The PC is the concept of production capacity that refers to the maximum amount that a business can produce under normal operating conditions. It refers to the maximum amount of goods or services a business can produce with its resources, measuring the capability of a company or facility to generate output within a specified period. The OR, also known as the capacity or facility utilization rate, measures how effectively a company or a facility utilizes its production capacity; it is calculated as the actual production divided by the capacity.  
On the other hand, safety performance is monitored with three indexes: injury rate (IR), frequency rate (FR), and severity rate (SR). The IR is defined as the rate of the number of workers injured by accidents in unit workers, specifically 1000 workers in South Korea. The FR is the measurement of the rate of the number of accidents in 200,000 work hours by the OSHA 1996 standard [26], but in South Korea, it is 1,000,000 work hours. The SR is the rate of losses per day caused by an accident, measured in unit working hours, specifically 1000 work hours in South Korea.
## 4 Conclusions  
In this paper, the beam-spring model and the shield tunnel-foundation three-dimensional finite element model is established based on the water-intake project of an immersed tunnel in a nuclear power sea area. Additionally, we implemented secondary development in ANSYS to create equivalent linear dynamic constitutive and viscoelastic boundary element subroutines. Our research focused on the seismic performance of the immersed tunnel under ultimate seismic ground motion (SL-2), with particular attention to the seismic analysis of the tunnel joints. The primary findings are as follows:  
(1) Using the response displacement method, we found that increases in joint stiffness and peak ground acceleration do not affect the variation trend of joint openings along the longitudinal position of the immersed tunnel. However, significant changes in joint openings are observed at locations where the overburden thickness changes abruptly or where there is an uneven distribution of soft and hard soil. Additionally, higher stiffness of the joint GINA water stop results in a smaller maximum opening value and a reduced maximum relative angle of the joint.  
(2) Variations in soil thickness on either side of the foundation and tunnels significantly impact the maximum compression-tension value of the immersed tunnel joint water stop. As the stiffness of the GINA water stop increases, the compression-tension value gradually decreases, with diminishing reductions at higher stiffness levels. When the stiffness exceeds a certain threshold, its influence on the compression-tension value weakens.  
(3) The deformation of the immersed tunnel is influenced by the surrounding soil layer's deformation. To mitigate forced displacement of the relatively rigid immersed tunnel due to uneven geological conditions, especially the adverse effects of ground motion, flexible joint structural measures should be adopted based on the actual foundation conditions. Additionally, it is essential to fully mobilize the supporting forces of the surrounding rocks to accommodate uneven soil layer deformation during earthquakes.  
(4) The analysis results indicate that time-history analysis provides relatively quantitative and accurate results for seismic response analysis of structures such as immersed tunnels. Qualitative analyses can also be performed using the response displacement method in practical applications. Future research should focus on developing calculation programs for the generalized response displacement method.  
Received: 2021/  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Gansu Comprehensive Railway Engineering Contracting Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A Security Framework for the Internet of Things in the Future Internet Architecture
## 6 Delegation-Based Key Provisioning Protocol
### Proof of Concept
#### 6.8.1 Modularized Prototype Framework  
Figure 10 shows the modularized prototype framework design. Each party's protocol package includes three major components: _Instance Manager_, _Protocol Manager_ and _Utility Functions_. Each party should be able to work independently to perform tasks, such as receive messages, parse messages, perform designated computations, construct messages and send messages. In the protocol system model, there are two client/server based communication channels. For the channel between the Guardian and the Child, the Guardian serves as the \"server\", while the Child is the \"client\". On the other channel between the Parent and the Guardian, the Parent serves as the \"sever\" while the Guardian is the \"client\".  
Modularized design allows easy adjustment if the protocol design changes or the parameters change. The instance manager creates an instance of the corresponding party at the device, and then performs configurations, e.g., setting up a port number.  
The protocol manager manages all of the sessions of the protocol instance on the device. The protocol manager consists of four modules: _Session Manager_, _Finite State Machine_, _Networking_ and _Message_. As one party may open more than one protocol session related to different peer parties, the _Session Manager_ is introduced to coordinate different sessions and handle related operations, including initiating a new protocol session, terminating an active protocol session, update and query local session database. For example, the Guardian as a local group authority may receive several registration requests from different edge IoT devices so that it needs to open multiple protocol sessions (one for each requesting IoT device), which requires a session manager to coordinate. This module is also necessary even for edge IoT devices as one edge device as a Child might join two or more different IoT networks at the same time. The _Networking_ module is responsible for receiving and transmitting encoded messages through the network interface. The _Message_ module parses and constructs messages as well as performs related cryptographic computations. The _Finite State Machine_ (FSM) is the core module that handles protocol state transitions. We incorporate an event-driven FSM to take actions upon input events, including a message being constructed and sent successfully, successfully receiving a message, time out, result of parsing and verifying a message, etc.  
_Utility Functions_ component provides two categories of utility functions: _Message Encoding/Decoding_, which provide API for the _Message_ module to parse and construct messages, and _Crypto Library Wrapper_, which wraps the underlying cryptography library and provides cryptographic computation APIs for the _Message_ module.
## 4 Proposed System for Patient-Robot Co-Navigation
### Leg Detection and Gait Velocity Calculation  
The first step of our system is to detect the legs of the patient using the data from the rear laser scanner and then to calculate the gait velocity of the patient. Our prior work [15] presented a convolutional neural network (CNN)-based method to detect the legs. This method uses a U-Net architecture [48], which is a fully convolutional network developed  
Figure 1: The hardware setup of the multi-purpose robotic system.  
Figure 2: Overview of the patient-robot co-navigation framework.  
predominantly for image segmentation tasks and works with fewer training data. The data from the laser scanner are transformed into a binary grayscale image, and it is called the occupancy grid image (shown in Figure 3a). The occupancy grid includes critical information about the environment, such as the patient's legs, walls, and other obstacles. The U-Net is trained on leg segmentation from the dataset provided by [PERSON] et al. [49], which consists of laser scanner data and segmented leg positions. The U-Net segments all the legs in the scene, but only the one closer to the robot's rear is considered the patient. Let the position of the segmented legs be the vector \({}^{L}\mathbf{p}\) with respect to the coordinate system of the laser scanner \(\{L\}\) (shown in Figure 1). It is important to compute the position of the segmented legs with respect to the coordinate system of the robot's base \(\{R\}\) (shown in Figure 1). The transformation matrix \({}^{R}_{L}\mathbf{T}\) of the coordinate system \(\{L\}\) with respect to the coordinate system \(\{R\}\) is known. The position of the segmented legs \({}^{R}\mathbf{p}\) with respect to the coordinate system \(\{R\}\) is calculated by Equation (2).  
\[{}^{R}\mathbf{p}={}^{R}_{L}\mathbf{T}\cdot{}^{L}\mathbf{p} \tag{2}\]  
The final step is to use the position of the legs calculated in the robot's coordinate frame as input to an extended Kalman filter [50], which outputs the patient's gait velocity. The patient's gait velocity is then used by the reinforcement learning method, which is discussed in the next subsection.
# Compensation for Group Velocity of Polychromatic Wave Measurement in Dispersive Medium
## 4 Experimental Results
To evaluate the accuracy of proposed method, we solved the true value of group velocity using the time of arrival of group velocity of the signals in the time domain. However, the reflected signal at 120 m is too small to find out due to the attenuation. Because of this, it is very difficult to obtain the group velocity through the time delay between the highest peak points of the signal in the time domain, and the group velocity in the farther than 120 m can not be calculated. The TFCC graph is shown in the Figure 4b. As the signal propagates, attenuation of the signal occurs, which slows the group velocity and increases the time delay. Figure 4b depicts a TFCC graph based on the constant group velocity measured on 40 m. Therefore, the distance errors of 80 m and 120 m is getting larger. Based on unwrapping algorithm and Hilbert transform, the instantaneous phase of the transmitted signal can be derived and shown in Figure 5a. The signal having the positive slope of the instantaneous phase was extracted, and the frequency band of the extracted signal was obtained by FFT algorithm. In Figure 5a, the time duration of the signal is obtained by extracting the signal where the slope of the instantaneous phase is positive. The Figure 5b shows the frequency band of second signal of total signal. The changed values, time duration and frequency region, were obtained and substituted into Equation (7) to compensate the group velocity. As seen in Table 1, the group velocity in TFCC method seems to be equal regardless of the propagation distance. On the contrary, in the proposed method, the shifted terms, \(a(f),b(f),c(f)\), can be obtained from the center frequency, \(f_{rc}\), of the received signal from Figures 1 and 5. The compensation time delay term is calculated according to Equation (8). The travelling distance, D, is the known value of the cable length and is the integral of the velocity determined by the center frequency of the transmitted signal. Based on the derived compensation time delay, \(t_{com}\), and the distance value, \(D\), the average velocity during propagation of the transmitted signal can be obtained and are shown in Table 1. The measurement values in Table 1 were calculated by the time of arrival of group velocity of signal in Figure 5a. As the distance between each signal is set as 40 m, the group velocity, measurement value, can be obtained by dividing the distance by the measured group delay. The accuracy values in Table 1 were calculated by dividing the group velocity of proposed method by measurement value. As seen in Table 1, when the signal propagates to a short distance, the existing method is highly accurate, but the existing method does not reflect the change in group velocity when the signal propagates over a long distance. On the other hand, the proposed method compensates the group velocity change due to the dispersion, so that the accuracy of group velocity is good regardless of the distance.
# Analytic Model for U-Nb Liquidus and U-6 Nb Melting Curve
## 3 Application to the Uranium-Niobium System
### The \(P=0\) Value of \(dT_{m}(P)/dP\) for U from the Clausius-Clapeyron Formula  
To validate the value of the initial slope of the U melting curve, 51 K/GPa, given by Equation (13), we calculate this value independently, using the Clausius-Clapeyron (CC) formula, which is valid at the first-order phase transition point:  
\[\left.\frac{dT_{\rm tr}(P)}{dP}\right|_{P_{\rm tr}}\;=\;\frac{\Delta V_{\rm tr }}{\Delta S_{\rm tr}}\;=\;T_{\rm tr}\;\frac{\Delta V_{\rm tr}}{\Delta H_{\rm tr }},\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Lattice Constant (Å)** & **Density (g/cm\({}^{3}\))** & \(P_{m}\) **(GPa)** & \(T_{m}\) **(K)** & \(\Delta T_{m}\) **(K)** \\ \hline
3.70 & 15.607 & \(-9.5\) & 810 & 75 \\
3.50 & 18.438 & 6.1 & 1690 & 125 \\
3.35 & 21.027 & 29.7 & 2540 & 125 \\
3.25 & 23.028 & 55.5 & 3260 & 125 \\
3.20 & 24.125 & 72.8 & 3670 & 125 \\
3.15 & 25.292 & 93.9 & 4120 & 125 \\ \hline \end{tabular}
\end{table}
Table 1: The six ab initio melting points of \(\gamma\)-U, of the form \((P_{m},\,T_{m}\pm\Delta T_{m})\), obtained from the \(Z\) method implemented with VASP, along with the values of lattice constants and densities at the corresponding \(T_{m}\).  
where \(\Delta V_{\rm tr}\), \(\Delta S_{\rm tr}\), and \(\Delta H_{\rm tr}=T_{\rm tr}\Delta S_{\rm tr}\) are, respectively, the volume, entropy, and enthalpy changes across the transition point \((P_{\rm tr},T_{\rm tr})\). In our case of the melting of \(\gamma\)-U, the corresponding values of \(\Delta V_{\rm m}\) and \(\Delta H_{\rm m}\) are, respectively, 0.3624 cm\({}^{3}\)/mol [34] or 0.2874 cm\({}^{3}\)/mol [35], and 8.47 \(\pm\) 1.0 kJ/mol [21]. Note that the estimate of Ref. [21] does not take into account the value \(\Delta H_{\rm m}=9.142\) kJ/mol of Ref. [36], which was later on corrected by [PERSON] [11]: 9.190 kJ/mol. With this value, the estimate of Ref. [21] would have been 8.6 \(\pm\) 0.8 kJ/mol, which we adopt as the value of \(\Delta H_{\rm m}\) for the melting of U. Thus,  
\[\Delta V_{\rm m}\,=\,0.325\pm 0.04\ \ {\rm g/cm^{3}},\ \ \ \Delta H_{\rm m}\,=\,8.6 \pm 0.8\ \ {\rm kJ/mol}. \tag{14}\]  
Therefore, the CC formula gives \(dT_{\rm m}(P)/dP|_{P=0}=53.2\pm 8.2\) K/GPa, which our value of 51.0 K/GPa in Equation (13) is in full agreement with.
## 3 Principle of the Proposed Control Algorithm  
The schematic diagram of the control algorithm proposed in this paper is shown in Figure 3. The input is the inertial reference speed \(\omega_{\mathrm{r}}\), and the output is the actual inertia speed \(\omega\) of OES. The module SAKF represents the state-augmented Kalman filter, and the torque disturbance \(d\) is observed and compensated based on the voltage \(u\) and the rotational speed \(v\) differentiated by the encoder. Meanwhile, the SAKF also filters the speed \(v\) to obtain a speed estimation result \(\vartheta\) with a higher SNR for feedback control. The \(C_{\mathrm{v}}(s)\) module is a feedback controller. The above control modules SAKF and \(C_{\mathrm{v}}(s)\) constitute a speed tracking unit, as shown in the red dashed box in Figure 3. The gyro is then mounted on a carrier with its sensitive axis parallel to the rotational axis for sensing the inertial rotation of the carrier. If the actual speed of the carrier in the inertial space is \(\omega_{\mathrm{d}}\) and the output of the gyroscope is \(\omega_{\mathrm{g}}\), then \(\omega_{\mathrm{d}}=\omega_{\mathrm{g}}\). In this case, the difference between the reference speed \(\omega_{\mathrm{r}}\) and \(\omega_{\mathrm{g}}\) is used as the reference speed \(v_{\mathrm{r}}\) of the speed tracking unit, and then the stable pointing of the OES in the inertial space can be realized. So in this controller, the voltage \(u\) is the manipulation variation, rotational speed \(v\) is the controlled variation, and the reference speed \(v_{\mathrm{r}}\) is the setpoint variation.  
Figure 2: Transfer function block diagram of (**a**) the complete speed open-loop system, and (**b**) the simplified speed open-loop system.
## 3 Methodology
### Preprocessing of Input Layers
#### 3.1.2 Canopy Height Maps from DSM and DTM  
Finally, DTM and DSM data were collected for the forest height measurement. The DTM used in this study is a 5 m DEM produced by the National Geographic Information Institute (NGII) (Figure 5a). For DSM, 12 m resolution WorldDEM data were obtained which is created by using TerraSAR-XX-band radar interferometry (Figure 5b) from German Aerospace Center (Deutsches Zentrum fur Luft- und Raumfahrt e.V., DLR). Both data were resampled into 10 m based on the GSD (Ground Sample Distance) of the Sentinel-2 image, and the forest height map was generated by subtracting DTM which means the terrain height of ground and DSM data which includes the height of ground objects such as canopies and buildings. The canopy height map was created. In addition, DSM data were used to correct the terrain of Sentinel-2 images.  
In addition, the canopy height maps were created in consideration of the fact that the forest vertical structure is closely related to the canopy height. The canopy height map is effective for measuring tree height over large areas which could be calculated from the difference between DSM and DTM [23]. In this study, 5 m resolution DTM (NGII DEM) generated based on 1:5000 digital topographic maps (NGII, Suwon, South Korea) and the 12 m resolution DSM (WorldDEM) generated using the TerraSAR-X SAR interferometry (InSAR) were resampled to 10 m as Sentinel-2 image and used to extract the canopy height from German Aerospace Center (Deutsches Zentrum fur Luft-und Raumfahrt e.V., DLR). Average canopy height and standard deviation canopy height maps were generated by calculating the mean and standard deviation in a window with the kernel of \(5\times 5\).  
DTM data could be less accurate in slope areas, and DSM could be underestimated as radar signals penetrate through forest areas. Thus, the average canopy height map estimated from InSAR-based DSMs may appear somewhat lower than the actual canopy. The standard deviation canopy height map shows higher values in areas with large differences in canopy heights and smaller values in smooth areas. As such, the height of the forest that shows the distribution characteristics of the forest existing in the image could be calculated by subtracting the DTM in the DSM.