esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Program
### Recovery Stress Transfer Test  
To measure the compressive stress on a concrete member transferred from the Fe-SMA of which the SME activated, a cement-based mortar bar was prepared and tested as shown in Figure 5a. The dimensions of the cement-based mortar bar were with the width 55 mm, thickness 30 mm, and length 500 mm, respectively and the compressive strength of it was 87.67 MPa. The embedded Fe-SMA had dimensions of width, thickness, and length 20 mm, 1.5 mm, and 600 mm, respectively and was pre-strained to 4% at ambient temperature. To prevent crack development on the surface of the cement-based mortar bar due to the thermal expansion of the Fe-SMA, steel fibers with dimensions of 0.5 mm and 30 mm in diameter and length, respectively, were mixed with 2% by volume of the specimen bar. The SME of the Fe-SMA was activated by the resistive electronic heating with the relatively high density of 2 A/mm\({}^{2}\) from the power supply system with a capacity of 510 A. The K-type thermocouples were attached on the Fe-SMA to measure the recovery temperature at the mid-length. In addition, two strain gauges were attached on the top and lateral side surfaces at the mid-length of the cement-based mortar bar to measure the elongation strain of the specimen due to either thermal expansion effect or thermal contraction effect from the heated Fe-SMA. The recovery temperatures ranging from 100 \({}^{\circ}\)C to 190 \({}^{\circ}\)C with increments of 30 \({}^{\circ}\)C were considered as experimentally variable and are given in Table 3.  
Figure 5: Recovery stress transfer test: (**a**) Cement-based mortar bar containing Fe-SMA; (**b**) Test setup of recovery stress transfer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimen** & **Pre-Strain (\%)** & **Recovery Temperature (\({}^{\circ}\)C)** \\ \hline Com-PS4-T100 & 4 & 100 \\ Com-PS4-T130 & 4 & 130 \\ Com-PS4-T160 & 4 & 160 \\ Com-PS4-T190 & 4 & 190 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of variable for cement-based mortar bar specimen.
## 2 State-of-the-Art Overview
In [67], face verification was performed using a hybrid method based on SURF and a neural network classifier. The entire system can be applied in real time to confirm individuals' IDs in congested areas such as airports. To boost overall performance, [PERSON] presented the Embedding Unmasking Model, which works on top of current face recognition algorithms [68]. The authors of [69] presented a dual-branch training technique to direct the model's attention to the top half of the face in order to extract strong features for masked face recognition. During training, the characteristics gained at the global branch's intermediate layers are supplied into the suggested attention module, which functions as a local branch and aids in resilience. The Masked Face Detection Dataset (MFDD), the Real-World Masked Face Recognition Dataset (RMFRD), and the Synthetic Masked Face Recognition Dataset (SMFRD) are the three types of masked face dataset proposed by [PERSON] et al. [70], allowing for a more realistic evaluation of face classification algorithms. [PERSON] et al. [71] proposed a new dataset called Diverse Masked Faces and advised that the YOLOX model be modified with a new composite loss that combines CIoU and alpha-IoU losses and retains both benefits. [PERSON]'s mask creation module [72], on the other hand, used facial landmarks to generate more realistic and reliable masked faces for training in addition to using existing datasets. The loss function search module aimed to find the best loss function for face recognition. [PERSON] et al. [68] presented an Embedding Unmasking Model (EUM) that would work over current face recognition methods. They also provided an innovative loss function, the Self-Restrained Triplet (SRT), which allowed the EUM to generate embeddings that resembled those of unmasked faces of the same individuals. Table 2 presents the comparison of recognition accuracy of different methods, tested on a masked face dataset. In summary, biometric face recognition systems have gained widespread use in various applications such as security, access control, and identification [83]. However, there are several challenges and limitations that affect their performance and accuracy. One major challenge is the variability of lighting conditions, which can cause shadows, reflections, and other distortions that can affect the quality of captured images. This can lead to poor recognition performance, especially in outdoor environments [84]. Another challenge is the change in facial appearance over time, such as aging, hairstyles, glasses, and makeup. These variations can cause problems for systems that are trained on a single image of a person, leading to poor recognition accuracy [85]. Additionally, facial recognition systems can be affected by the presence of occlusions such as masks, hats, and scarfs, which can make it difficult to accurately identify a person [86].
## 6 Trusted Routing Operation in AODV
### Trusted Information Exchange  
Existing trust models rarely consider exchange of trust information. However, trust information exchange is important in trust model applications and succeeded to reduce extra routing overhead. In our trust model, we derive an efficient trust information exchange mechanism by using three kind of messages that to be exchanged between neighbor nodes i.e., Trust Request Message (TREQ), TWARN (warning message) and Trust Reply Message (TREP) as shown in Figure 5. When a vehicle A wants to know neighbor vehicle B's updated trust score, it will broadcast a TREQ message to its neighbors. This TREQ message follows the format given in Figure 5, with the Type field set to 0 and the trustee field filled with the IP address of vehicle B. In the same manner,if destination node let say vehicle C receive this TREQ message, node C will reply with an TREP message. The Type field of this TREP is set to 1 and the opinion field is filled with the opinion values from C to B. Note that, in this recommendation protocol, a node can request or reply several opinion values of different vehicles simultaneously in one TREQ or TREP packet. In this way, we can efficiently exchange trust information without introducing much packets overhead.  
In trusted routing discovery procedures, every routing request and reply carries trust information, including opinions towards originator vehicle A and destination vehicle B, which will be employed to calculate the credibility of A and B. When a vehicle is required to provide its certificate information, it will fill the fields of trust information with its own signature, as proposed by some traditional security solutions for mobile ad hoc networks.  
A TWARN message, which is sent by a node to report invalid activity in original AODV procedure by type field set to 2, alarms other nodes to the worst trust warning. That is, If a vehicle A cannot verify certification then its opinion from the neighbor point of view set to be \((0,1,0,0)\), which means total disbelief, and neighbor will broadcast an TWARN message and type field set to 2. Every vehicle before making any path to vehicle B first verify B's trustworthiness then perform the corresponding update.  
\begin{table}
\begin{tabular}{c c c c} \hline \(b_{X}^{A}\) & \(d_{X}^{A}\) & \(u(n_{X}^{A},e_{X}^{A})\) & **Action** \\ \hline  & & \(\geqslant\)0.5 & Request to verify digital signature. \\  & \(>\)0.5 & & Distrust node till next request. \\ \(>\)0.5 & & & Trust a node and share resources. \\ \(\leq\)0.5 & \(\leq\)0.5 & \(\leq\)0.5 & Request and verify authentication. \\ \hline \end{tabular}
\end{table}
Table 1: Trust judgment rules to authenticate a node at a certain security level.
## 3 Method
### Methods for Recapping Shale Free Gas Content
#### 3.2.1 Differential Enrichment Model of Free Gas  
A mathematical model was developed to calculate the differential enrichment of free gas in the Longmaxi shale located in the Weiyuan area. The model allows for the computation of the evolution process of free gas during different geological periods, providing a representation of the migration and enrichment process of free gas under various structural styles (Figure 3).  
The diffusion and migration of free gas in shale follow the principle of energy conservation. Free gas tends to move from areas of high fluid potential energy to those of low fluid potential energy, resulting in its accumulation in regions with low fluid potential energy [16; 42]; therefore, shale gas enrichment is usually favored by positive structures over negative ones. Not all high points in positive structures, however, will result in shale gas enrichment, and not all negative structures are incapable of enriching shale gas. Positive structures aim to rise despite the vertical and lateral loss caused by free gas diffusion and supply from negative structures, respectively. On the other hand, the lower part of the negative structure is the prominent area for gas enrichment, which also incurs both lateral free gas loss and vertical free gas diffusion loss. The vertical diffusion loss in the positive structure is always more significant than that in the negative structure due to the denudation of the high points of the positive structure before the low points of the negative structure.  
Figure 3: Free gas transport model under adjacent structure of Longmaxi shale in Weiyuan area.
## 2 Data and Methods
### Data  
The COSMIC-2 temperature profiles (the so-called dry temperature retrievals, atmPrf, near real-time version 0001.0001) in the latitudes of 5\({}^{\circ}\)S-35\({}^{\circ}\)N from December 2019 to November 2021, which are obtained from the COSMIC Data Analysis and Archive Center (CDACC), are used to extract the GW Ep profiles over the Indian region (60\({}^{\circ}\)E-100\({}^{\circ}\)E, 0\({}^{\circ}\)-30\({}^{\circ}\)N). Note that considering the requirements for the retrieval of the GW Ep profiles and for the construction of the three-dimensional (3D) LS GW Ep grid field, the geographic area covered by the selected RO data is wider than the studied region.  
In addition to the RO data, the monthly winds of the same period, distributed at 37 pressure levels between 1000 hPa and 1 hPa with the horizontal resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) (longitude \(\times\) latitude), provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset (CF-1.6 Conventions), are used to calculate the monthly gridded zonal/meridional winds and wind shears over the Indian region. The monthly gridded outgoing long-wave radiation (OLR) data, with a horizontal resolution of 2.5\({}^{\circ}\)\(\times\) 2.5\({}^{\circ}\) (CF-1.2 Conventions), is obtained from the National Oceanic and Atmospheric Administration (NOAA) Climate Diagnostics Center (CDC) and is used as the proxy for convection.
## 2 Materials and Methods
### Data Processing
#### 2.3.3 Color Indices  
In this study, UAV RGB images of the five growth stages were used to study the relationship between LAI and image features. Based on previously published results and the relationship between image features and LAI, 25 commonly used color indices involving different color spaces such as RGB, HSV, and CIE L*a*b* were extracted from RGB images [13; 35]. Detailed information for the color indices is listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Name** & **Definition** & **Reference** \\ \hline C1 & Color index of vegetation extraction (CIVE) & 0.441R \(-\) 0.881G + 0.385B + 18.78745 & [31] \\ C2 & Combination of green 1 (COM1) & ExG + CIVE + ExGR + VEG & [29] \\ C3 & Combination of green 2 (COM2) & 0.36 ExG + 0.47 CVLTE + 0.17 VEG & [29] \\ C4 & Excess green (EXG) & 2G \(-\) B \(-\) R & [29] \\ C5 & Excess green minus excess red (EXGR) & 3G \(-\) 2.4R \(-\) B & [29] \\ C6 & Excess red (EXR) & 1.4R-G & [29] \\ C7 & Green leaf index (GLI) & (2G \(-\) R \(-\) B)/(2G \(+\) R \(+\) B) & [39] \\ C8 & Green minus red index (GMR) & G \(-\) R & [34] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definition and description of the RGB-CIs used in this study.
# Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring
## 5 Validation of the Models and Power Consumption Analysis
### Active Mode Power Consumption  
For the active mode power consumption analysis, we selected 12 possible settings: all combinations of 4 TX power settings and 3 ECG sampling rates measured in samples per second (sps). All measured model parameters for power consumption estimation of the 12 settings are summarized in Table 4. Table 5 presents both measured and model-based power and current values for the active mode. The results show that the model-based values and the measured values are very close for all settings with the lowest sampling rate (128 sps). In the case of higher sampling rates, the model overestimates the current consumption.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Sampling Rate = 128 sps** & **Sampling Rate = 256 sps** & **Sampling Rate = 512 sps** \\ \hline \(tp_{conn}\) & 80.0 ms & 36.0 ms & 14.0 ms \\ \hline \(tp_{sample}\) & 7.8 ms & 3.9 ms & 2 ms \\ \hline \(tp_{msg}\) & 109.2 ms & 54.6 ms & 28 ms \\ \hline \(tp_{tick}\) & 10 ms & 10 ms & 10 ms \\ \hline \(t_{conn}\) & 2.987 ms & 2.987 ms & 2.987 ms \\ \hline \(t_{msg}\) & 3.543 ms & 3.543 ms & 3.543 ms \\ \hline \(t_{sample}\) & 0.373 ms & 0.373 ms & 0.373 ms \\ \hline \(t_{tick}\) & 0.133 ms & 0.133 ms & 0.133 ms \\ \hline \(P_{sleep,active}\) & 2.980 mW & 2.980 mW & 2.980 mW \\ \hline \(e_{conn,\text{0 dBm}}\) & 73.13 \(\upmu\)J & 73.13 \(\upmu\)J \\ \hline \(e_{conn,-6 dBm}\) & 69.06 \(\upmu\)J & 69.06 \(\upmu\)J \\ \hline \(e_{conn,-12 dBm}\) & 66.63 \(\upmu\)J & 66.63 \(\upmu\)J \\ \hline \(e_{conn,-18 dBm}\) & 65.60 \(\upmu\)J & 65.60 \(\upmu\)J \\ \hline \(e_{msg}\) & 79.35 \(\upmu\)J & 79.35 \(\upmu\)J \\ \hline \(e_{sample}\) & 2.78 \(\upmu\)J & 2.78 \(\upmu\)J & 2.78 \(\upmu\)J \\ \hline \(e_{tick}\) & 1.85 \(\upmu\)J & 1.85 \(\upmu\)J & 1.85 \(\upmu\)J \\ \hline \hline \end{tabular}
\end{table}
Table 4: BLE active mode evaluation parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mode** & \(P_{model}\) **[mW]** & \(I_{model}\) **[mA]** & \(P_{measured}\) **[mW]** & \(I_{measured}\) **[mA]** & **Error [\%]** \\ \hline Slow & & & & & \\ Advertising & 1.213 & 0.294 & 1.210 & 0.293 & +0.34 \\ \hline Fast & & & & & \\ Advertising & 2.259 & 0.547 & 2.292 & 0.555 & \(-1.44\) \\ \hline Advertising & 1.308 & 0.317 & 1.308 & 0.317 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of measured and model-based power and current consumption for idle mode. Model error is presented as percentage of the measured value.
## 3 Methodology
### Finite Element Simulation  
The simulation model is set up using the software SIMULIA Abaqus CAE/2023 as a two-dimensional implicit model. All tool parts are defined as rigid bodies based on the described principle (Figures 4 and 5). A plane strain state is assumed for the sheet metal since the width of the sheet is significantly larger than the thickness of the sheet [13; 23]. Due to the shorter calculation time, an x-symmetry is also applied to the center of the die. The time sequence of the simulation process is divided into three steps. In the first step, all individual parts are fixed and the sheet metal is placed in the center of the die. In the second step, all springs are preloaded. In the third step, forming takes place by moving the punch downwards. The displacement of the punch required to achieve the target angle \(a_{\text{des}}^{\text{(unl)}}\) is calculated using the trigonometric relationship:  
\[s_{\text{P}}(a_{\text{des}}^{\text{(loa)}})=\left(\cos(\frac{a_{\text{des}}^{ \text{(loa)}}}{2})+\cos(\frac{\alpha_{\text{max}}}{2})\right)\cdot l_{d}+s_{0} \tag{11}\]  
It consists of the cosine of the target angle in the process and the angle of the die at the uppermost point (\(x_{\text{d}}=0\)), the length of a die half and the sheet thickness. A constant friction coefficient of \(\mu=0.1\) is applied to model the contact between the tool and the sheet metal, which is a widely accepted approximation for metallic contacts in metal forming simulations without detailed surface characterization (see [24]). After the third step, the deformed component is loaded into a new model in its final state of the previous simulation and the applied loads are removed. This causes the springback to form and the unloaded bending angle (\(a_{\text{act}}^{\text{(unl)}}\)) can be measured. Figure 8 shows the described process and the simulation model schematically. In order to map the influence of uncertainty occurring in the semi-finished product, multiple data sets are created in which uncertainty is applied to two material parameters (\(v,E\)). An overview of the magnitude of the maximum uncertainty applied and the sample size of the different data sets are shown in Table 3. The applied uncertainty is chosen stochastically (uniformly distributed) at the start of every simulation. The data sets are labeled as follows: no uncertainty (nu), low uncertainty level (lu), medium uncertainty level (mu) and high uncertainty level (hu).  
Figure 8: Schematic sequence of the simulative forming process with springback for a target angle of 80\({}^{\circ}\)and DC01 with a sheet thickness of 1 mm: (**a**) Initial state (**b**) Preloading springs (**c**) Begin of forming operation (**d**) Completed forming operation (**e**) Simulation of springback (**f**) Final geometry.  
Figure 7: Structure of the AI-supported product controller when applied to the die bending process.
# An Efficient Dynamic Engineering Implementation Architecture for MIMO Radar System
## 3 Efficient Dynamic Engineering Implementation Architecture for MIMO Radar Systems
### Engineering Implementation Process
#### 3.2.1 FPGA-Based Engineering Implementation Flow  
To validate the performance of the proposed system, this paper presents an engineering implementation architecture based on the FPGA platform. The process is depicted in Figure 4 and is outlined as follows:  
**Step 1:** First, initialize the antenna array parameter configuration according to the actual arrangement of the antenna array and configure the system mode to state I.  
**Step 2:** Acquire the ADC data, perform the Range FFT, and store the data in the DDR mass memory.  
**Step 3:** Determine the system mode and perform Doppler FFT. If it is mode state I, read the whole frame and perform a full range dimension Doppler FFT, that is, perform Range FFT on all data.  
**Step 4:** Perform target detection processing on the data after Doppler FFT, obtain the distance and speed of the target, and switch the system mode to state II.  
**Step 5:** Based on the distance information of the target, selectively read the Doppler dimension data at the target distance from the DDR memory for Doppler FFT, and obtain the Doppler data vectors of all the virtual antenna channels with the target distance index.  
**Step 6:** Select the data at the target of all virtual antenna channels from the Doppler data vector based on the velocity information of the target detection result. Obtain the target point data set for all virtual antennas.  
**Step 7:** Using the antenna array information, reorder the data for all virtual antenna channels and filter out redundant channel data to ensure that the data arrangement adheres to the predefined array configuration rules.  
**Step 8:** Conduct angle estimation processing in horizontal and vertical dimensions.  
**Step 9:** Complete the processing for the current data frame and proceed to the next frame.  
Figure 4: Efficient dynamic MIMO radar system engineering implementation process.
# Categorization of the Condition of Railway Embarments Using a Multi-Attribute Utility Theory
## Methodology
### Selection of Attributes
#### 2.2.2 Attribute 2: Visual Assessment of External Irregularities  
Visual inspections of railway embankments are performed by experienced engineers, with the overall aim of recording visible irregularities along the line. The engineers' remarks about the observed irregularities, as well as detailed photo-documentation, are used to define Attribute 2. It has to be noted that it is very challenging to quantify the irregularities due to their different nature and impact on the overall embankment condition as well as their extent. For example, a logical question could be, would a section with two small landslides observed have a higher QV value than a section with a single larger landslide? To overcome these issues, a percentage of length of irregularity extent in comparison to total section length is defined as the relevant quantifiable attribute. The following irregularities should be considered:  
* Observable embankment slope slips (material failure in the side slope);
* Observable embankment bulging (evidence of the slide slopes expanding laterally);
* Observable crest settlement (suggesting settlement of the material beneath the embankment);
* Broken sleepers.  
From the whole data set, the maximum number of irregularities (Vmax,RI,i) per section is assigned a quantification starting value (QSV) of 1, while 'no visible irregularities' is assigned a value 0. The rest are calculated as follows:  
\[\text{QV}_{\text{C2,i}}=\frac{\text{V}_{\text{IR,i}}}{\text{V}_{\text{max,IR,i} }}, \tag{2}\]  
where C2 represents Attribute 2, QV\({}_{\text{C2}}\) represents the quantification value of Attribute 2, and VIR,i represents the number of visually determined irregularities for an observed section of a railway line, while Vmax,RI,i represents the number of visually determined irregularities for the section with the most irregularities along the investigated railway line.
# An Architecture for Reliable Transportation of Delicate Goods
## 3 Technical and Domain Contextualization  
### The Main Scenario  
To understand the issues at stake, it is important to contextualize what is the normal transportation process, what is supposed to be done to supervise the transport conditions and what are the main problems to solve.  
Our solution, designed to ensure that the service is provided under the contractual conditions, involves monitoring the transport of the merchandise and providing reliable information about it, namely of any non-conformities that may occur. Above all, there must be a guarantee that the information collected is true and with that information is possible to identify the defaulters and assign responsibilities. From the perspective of the authors, the goal is not to pursue those who do not comply, but to encourage the compliance. If everyone involved has the perception that there is an effective control system, with identification of the responsible for the non-compliances and with imputation of responsibilities, only the ones that truly intend to comply with the transportation conditions will accept to perform it (because the costs of non-compliance are usually much higher than the values charged for the transport service).  
It should be noted that, in most transports, several operators are involved, both in the transport itself and in storage. On longer routes, the service can even be provided using several operators, that is, the service is contracted to one operator, who in turn hires others, each responsible for part of the route. It is even normal for a subcontractor to have other subcontractors. Thus, there will certainly be a transfer of merchandise, from operator to operator, and it may even happen that the transfer requires a change of container or means of transport. It is also common for transport to be made through several countries, having to go through several customs, and sometimes being temporarily held in storage places. Nevertheless, the important thing is that the contractual conditions are ensured throughout the entire transport process and that this is proved before all involved.