esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Grouting technology is a pivotal methodology for enhancing the mechanical properties of defective surrounding rock masses in tunnel engineering. Through uniaxial compression tests on intact, hole-containing, and grouted marble specimens, the influence of cement grout filling on the mechanical behavior of marble containing holes was investigated. Based on the experimental results, discrete element method (DEM) models were established for the three types of specimens, revealing the mesoscopic crack propagation mechanisms and stress distribution in potential stress concentration zones during failure. The experimental results demonstrated that the implementation of cement grouting enhanced the strength properties of the specimens by 22.38%. In terms of failure modes, the failure mode of the grouted specimens was similar to that of the intact specimens, and the filling material transformed the failure mode from tensile to shear failure. Numerical simulations revealed differences in microcrack evolution: cracks in the hole-containing specimens initiated near the upper and lower ends of the holes, while cracks in the grouted specimens originated around the filling material, with both types propagating axially. Microcracks in the grouted specimens initiated earlier, but the majority of microcracks in both types developed after peak stress and were predominantly tensile. The stress concentration coefficients for the intact, grouted, and hole-containing specimens were approximately 0.84, 2.25, and 2.96, respectively. The grouted specimens shortened the duration and alleviated the degree of stress concentration in the defect zones. This study elucidates the grouting reinforcement mechanisms in defective tunnel surrounding rock through a multiscale approach, providing theoretical underpinnings for optimizing tunnel support systems and preventing engineering hazards including collapse and rockburst.  
grouting; hole filling; crack extension; DEM; stress concentration Article
## 2 Data and Methods  
### TEC Data  
GNSS systems utilize two or three frequencies to assess the characteristics of the ionosphere. These frequencies aid in the assessment of the ionosphere itself rather than its influence on the signals. Having access to dual frequencies allows for a more accurate examination of relative phase delays between signals, such as L1 and L2. A greater electron density in the ionosphere results in a more pronounced difference between the signals, as the impact of density on signals varies depending on their frequency [19]. Signals are affected by the ionosphere in terms of both amplitude and phase [20]. The extent of the dispersive effect is directly proportional to the total electron content [21].  
In the conducted study, data from six GNSS stations affiliated with the Institute of Geophysics at the National Autonomous University of Mexico were utilized (see Table 1). The RINEX files were processed to exclusively retain the necessary parameters for the calculation of Total Electron Content (TEC). For slant TEC, particularly in the case of TECcalculated from pseudorange, Equation (1) [22] was employed, characterized by noise yet maintaining ambiguity-free characteristics [23]. To derive phase-based TEC, Equation (2) was applied, yielding results with less noise albeit being ambiguous.  
\[TEC_{R}=9.52(R_{1}-R_{2}) \tag{1}\]  
\[TEC_{\Phi}=9.52(\lambda_{1}\cdot\Phi_{1}-\lambda_{2}\cdot\Phi_{2}) \tag{2}\]  
The values of slant TEC depend on the satellite's elevation angle, making it necessary to compute the vertical TEC. The vertical TEC is obtained using the mapping function (Equation (3)), which assumes an ionospheric point of interest at a height of 350 km [24].  
\[VTEC=STEC\cdot cos\bigg{[}arcsin\frac{R_{e}cos(\theta)}{R_{e}+h}\bigg{]} \tag{3}\]  
Here, \(R_{e}\) represents the radius of the Earth (6371.2 km), \(h\) represents the height of the ionosphere relative to the Earth's surface (350 km), and \(\theta\) denotes the elevation angle between the satellite and the receiver, measured in degrees.
## 4 Analysis of Crack Width
### Correction Factor for [PERSON]'s Crack Width Estimation Equation
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Thickness**} & \multirow{2}{*}{**Rebar Stress**} & \multicolumn{2}{c}{**Bottom**} & \multirow{2}{*}{**Rebar**} & \multicolumn{2}{c}{**Neutral**} & \multicolumn{2}{c}{**Calculated Crack**} & \multicolumn{2}{c}{**Calculated-To**} & \multirow{2}{*}{**Ratio**} \\  & & & **Concete** & & & **Axis** & & & **Width Using** & & & **Measured Crack** & \\ \cline{2-13}
**Specimen** & \multirow{2}{*}{\(h\)} & & \multirow{2}{*}{\(f_{s}\)} & \multirow{2}{*}{\(d_{v}\)} & \multirow{2}{*}{\(s\)} & \multirow{2}{*}{**k**} & \multirow{2}{*}{\(w_{v}\)} & \multirow{2}{*}{\(w_{v}\)/\(w_{w_{m}}\)} & & & & & \\ \cline{3-13}
**Name** & & & & & & & **1 st** & **2 nd** & **3 rd** & **1 st** & **2 nd** & **3 rd** & **2 nd** & **3 rd** \\ \cline{2-13}
**Unit** & **(mm)** & & & **(MPa)** & & **(mm)** & & **-** & & **(mm)** & & & - & - \\ \hline SA-H16 (PR)-2 & 304.0 & 220.8 & 246.9 & 250.9 & 60.2 & 200 & 1.32 & 0.33 & 0.33 & 0.33 & 1.08 & 1.18 & 1.19 & 1.09 & 1.11 \\ SA-H19 (PR)-1 & 305.7 & 250.9 & 270.4 & 283.0 & 59.2 & 200 & 1.32 & 0.33 & 0.34 & 0.34 & 1.20 & 1.27 & 1.32 & 1.06 & 1.10 \\ SA-H19 (PR)-2 & 304.5 & 249.1 & 251.9 & 72.7 & 59.0 & 200 & 1.32 & 0.33 & 0.33 & 0.34 & 1.19 & 1.20 & 1.20 & 1.01 & 1.08 \\ SA-H22 (PL)-1 & 300.3 & 201.0 & 221.3 & - & 59.5 & 200 & 1.35 & 0.33 & 0.33 & - & 1.01 & 1.09 & - & 1.08 & - \\ SH-H19 (PL)-1 & 217.5 & 217.5 & 242.4 & 246.7 & 58.0 & 150 & 1.23 & 0.33 & 0.33 & 0.33 & 0.94 & 1.02 & 1.04 & 1.09 & 1.12 \\ SH-H19 (PR)-1 & 214.3 & 214.3 & 239.6 & 26.7 & 58.3 & 150 & 1.22 & 0.33 & 0.33 & 0.33 & 0.33 & 0.93 & 1.02 & 1.04 & 1.09 & 1.12 \\ SH-H19 (PL)-2 & 225.3 & 225.3 & 237.6 & 259.2 & 57.7 & 150 & 1.23 & 0.33 & 0.33 & 0.33 & 0.96 & 1.00 & 1.08 & 1.04 & 1.12 \\ SH-H19 (PR)-2 & 238.6 & 236.6 & 256.3 & 261.3 & 58.8 & 150 & 1.23 & 0.33 & 0.34 & 0.34 & 1.02 & 1.08 & 1.10 & 1.06 & 1.08 \\ SK-H22 (PL)-1 & 199.3 & 199.3 & 213.7 & 283.3 & 58.3 & 150 & 1.17 & 0.32 & 0.31 & 0.33 & 0.93 & 0.98 & 1.23 & 1.06 & 1.32 \\ SK-H22 (PL)-1 & 206.2 & 206.2 & 217.1 & 224.9 & 59.0 & 150 & 1.18 & 0.32 & 0.32 & 0.32 & 0.96 & 1.00 & 1.03 & 1.04 & 1.07 \\ SK-H22 (PL)-2 & 211.2 & 211.2 & 240.1 & 253.6 & 57.8 & 150 & 1.17 & 0.31 & 0.32 & 0.32 & 0.32 & 0.97 & 1.07 & 1.12 & 1.11 & 1.16 \\ SK-H22 (PR)-2 & 195.8 & 195.8 & 227.3 & - & 58.5 & 150 & 1.17 & 0.32 & 0.32 & - & 0.92 & 1.03 & - & 1.13 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Calculated crack widths against measured crack width of 0.3 mm (3 major cracks in selected specimens, Equation (3)). Figure 12: CDFs for the calculated-to-measured crack width ratios produced by the (**a**) original equation for 0.3 mm crack width, (**b**) modified equation for 0.3 mm crack widths, by the (**c**) original equation for 0.5 mm crack width, and (**d**) modified equation for 0.5 mm crack widths.
# A Regional Brightness Control Method for a Beam Projector to Avoid Human Glare
## 3 Proposed System
### Transformation Profile Generation Module  
In the proposed system, the transformation profile refers to a geometric transformation model and a color relationship transformation model to compare the similarity of the images with considerations of such two-type transformation relationships caused by the screen's position and the characteristics of a camera observing the screen and the beam projector.  
Figure 3 shows the transformation relationships between the images. In Figure 3, image \(A\) is sampled by the camera, and image \(B\) is the original image. Image A is transformed into image \(A^{\prime}\) by tearing off a part of image \(A\) and adjusting its rotation angle. To perform this, the geometric transformation relationship between image \(A\) and image \(B\) is referred to. This rule for geometric transformation from image \(A\) to image \(A^{\prime}\) forms the Geometric Transformation Profile (GTP).  
Figure 3: Relationships between images for conversion.  
Figure 2: Overall functional architecture of the proposed system.  
Image \(B\) can be transformed to image \(B^{\prime}\) by referring to the color transformation relationship between image \(B\) and image \(A^{\prime}\). Accordingly, this rule for the color transformation relationship from image \(B\) to image \(B^{\prime}\) forms the Color Transformation Profile (CTP).
# Synthesize and Segment: Towards Improved Catheter Segmentation via Adversarial Augmentation
## 5 Discussion  
Based on our experiments, it is evident that the synthetic catheter images generated by our proposed method maintain visual quality and enable improved segmentation of real catheters compared to models trained on limited annotated data. We observed a significant performance gain of 7.61% when synthetically generated images were used alongside labeled data as compared to applying standard data augmentation techniques. Furthermore, using only synthetic catheter alongside camera catheter samples shows a performance increase of 2.33% and 2% in comparison to the U-Net model(baseline) and U-Net model trained with standard augmented data, respectively.  
The baseline U-Net model [26] trained with 140 labeled images failed to accurately identify the catheter due to the limited variability of training samples, as shown in the Figure 8. An initial attempt was made to enable the model to generalize to unseen samples via standard data augmentation. However, this form of augmentation did not yield any potential improvements in segmenting the catheters on the test set. By augmenting the training data with synthetic images, we could improved the baseline results, since generation provides more diversity in the training set. Despite being successful in most cases, the models trained with generated images still failed to segment certain parts of the catheter especially for outliers, such as surgical sutures. To solve this, we further increased the initial training set with 30,000 generated images, leading to improved overall segmentation performance with less failure cases on unseen surgical sutures.  
Despite the improved performance of the proposed method, we wish to highlight a few limitations that require careful attention. First, the performance of the proposed synthesis method is difficult to measure, due to the absence of labeled catheter data. Furthermore, the current synthesis method does not consider the segmentation step in the generation of synthetic images; however, we assume that the synthetic image generation can be improved by integrating catheter segmentation into the proposed synthesis method. Although we trained the segmentation models for both catheters and guidewires, a more accurate segmentation could be accomplished by using separate models per input type and later combine the outputs. In future, we plan to address these drawbacks and investigate the use of synthetic data as well as develop an architecture that incorporates both generation and segmentation in an unsupervised manner. Post-processing methods may be used to further boost segmentation and guarantee the robustness of the system when applied to low contrast X-ray fluoroscopic images. Moreover, we would like to compare our proposed approach to more sophisticated GAN based methods.
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
## 3 LoRa, Channel and Noise
### LoRa: Modulator and Demodulator
#### 3.1.2 Demodulator  
The transmitted signal propagates in a transmission channel represented by \(f(s(t))\), where \(s(t)\) represents the transmitted signal. During this propagation, the signal is affected by noise and interference, represented by \(n(t)\) and \(i(t)\), respectively. The received signal \(y(t)\) can then be expressed as follows:  
\[y(t)=f(s(t))+n(t)+i(t), \tag{3}\]  
The received signal is first dechirped by multiplying it by a sequence of inverted chirps \(c(t)\) without frequency shifting, as shown in the following equation:  
\[x(t)=y(t)*c(t), \tag{4}\]  
where \(c(t)\) is defined as:  
\[c(t)=\sum_{n=1}^{N}e^{(j\pi Bt-j\pi\frac{B}{T_{S}}t^{2})}*\delta(t-nT_{S}), \tag{5}\]  
If we consider an ideal channel, the received signal is not altered by the function associated with this channel. Consequently, we can conclude that the channel function, represented by \(t\), satisfies \(f(s(t))=s(t)\), so the dechirped signal is defined by the Equation (6):  
\[z(nT_{S})=\sum_{n=1}^{N}e^{-j\pi(\;Bn^{2}T_{S}+2*n\pi m_{n})}, \tag{6}\]  
It is very important to identify the spreading factor and bandwidth of the received LoRa signal prior to the dechirping process. To achieve accurate dechirping, the chirp slope of the received LoRa signal must be identical to that of the dechirped signal, in addition to precise symbol synchronization to ensure accurate dechirping. Once the signal has been dechirped, a fast Fourier transform is used to demodulate it. For non-coherent demodulation, the power spectral density is analyzed to extract the dominant frequency  
Figure 1: Example of two symbols modulation.  
component, which represents the original offset and contains the symbol value information. This is how the LoRa symbol is recovered:  
\[S=T_{S}*argmax[Y(f)], \tag{7}\]  
where \(Y(f)=\text{FFT}\{y(t)\}\) and \(S\in\{0,\,1,\,\ldots,\,M-1\}\) is the recovered symbol value.  
For coherent demodulation, the frequency shift waveform that maximizes the convolution magnitude represents the estimated symbol as follows:  
\[S=argmax\left[\int_{0}^{\infty}y(t)z_{k}(t-\tau)d\tau\right], \tag{8}\]  
where \(Z_{k}\left(t\right)\) is the signal modulated with a frequency offset \(k\)/\(T_{s}\), represented as \(z_{k}(t)=e^{i2\pi\frac{k}{T_{s}}t}\) and \(k\) is an integer representing all possible symbols \(k\in\{0,1,\ldots,M-1\}\). Figure 2 shows an example of the demodulation of two symbols, providing a visual understanding of the CSS demodulation used by LoRa.
## 3 Future Work
### Prosumer Communities
#### 3.1.4 Prosumer Market Design  
This section presents several possible future works in the prosumer market design area. Future research areas and directions include prosumer behavior, design issues, deployment and enforcement issues, distributive impact, regulatory and policy issues, economic factors, business opportunities, environmental effects, and public view and opinions [36].  
A closer examination of these socio-techno-economic elements could help in improving the design and development of prosumer markets.  
The proposed prosumer market models have their primary roles and functions as well as challenges. A poorly designed prosumer market may lead to failure of these markets which may have a serious impact on consumer empowerment, sustainability, and continuous improvement initiatives. Thus, future work could cover additional studies for a more informed technological perspective and sufficient awareness of the strengths and challenges of the various approaches [16].  
Further, different market designs also cater to different market objectives. Thus, future research could cover the multiple interdependencies between social, economic, and political market objectives [53]. A deeper understanding of the correlation of these objectives is essential for the suitable consideration and evaluation of the energy market.  
Concerning the federated power plant (FPP) market structure, further research could also cover several areas of interest [49]. First is to identify new methods that determine complementary prosumer capabilities that will provide efficient grid services. Second research area is about trust, knowledge and skill development towards participating in FPPs. Another research area is about the P2P energy trading and how it could be affected by prosumer locations and DER characteristics such as flexibility, variability, and capacity. Finally, the necessary regulatory changes brought about by new business models also need to be examined [49].  
Appropriate ICT for the local energy market should also be determined, which should be able to link the prosumer market participants for them to work together effectively [53].  
Furthermore, large-scale advanced prosumer markets would involve stronger participation and more meaningful contributions from researchers, vendors and energy providers, policy makers, regulatory bodies, and other overall industry-wide participants [16].  
Additionally, as the prosumer network grows, it will become more challenging to manage groups as different prosumers may be part of more than one group. For example, a prosumer may be selling energy to two different suppliers. In this case, there will be an emerging need to identify/detect communities as well as methods to allocate energy supply to different suppliers. This is a new and emerging area for smart grid research. However, existing investigations of this area were from an online social networks perspective [54].
# A Study of OWA Operators Learned in Convolutional Neural Networks
## 5 Experimental Framework
### Experiments  
We designed three experiments to understand the kind of OWA operators learned by the network when different configurations are used. We focus on the initialization parameter \(\alpha\), the number of learned features \(C_{f}\), and the sorting metric.  
1. Initialization and convergence. The first experiment was to determine the optimal initialization variance \(\alpha\) of the weighing matrix and gave an initial view on the convergence of the parameters. We chose a fixed ordering metric (Total Variation, as described in Section 4.2) and a fixed number of learned OWAs (\(C_{f}=64\), equal to number of input layers) and tested the effect of a range of \(\alpha\) on the final learned weights. We then plotted different metrics for each OWA weighting vector (namely, _orness_ and _dispersion_ as described in Section 2) to check if the network was learning a variety of operators or if they were limited in their expressivity.
2. Number of operators. The second experiment analyzed the impact of the number of learned operators \(C_{f}\). We performed the experiment with a single ordering metric and fixed initialization and varied \(C_{f}\) from 1 to 64 (the number of input channels). We gathered both the top 5 accuracy results for the network and the _orness_ and _dispersion_ profiles of the different configurations.
3. Impact of ordering metric. Finally, for the third experiment, we froze all the parameters except for the ordering metrics presented in Section 4.2. This allowed us to observe the behavior of the network with different global information introduced into the OWA layer through these metrics. We analyzed both the _orness_ and _dispersion_ profiles of the operators learned for each different metric.  
Additionally, we manually selected some of the learned OWA weighting vectors from the third experiment to compare them with the Binomial OWA, Stancu OWA, and exponential RIM OWA operators defined in Section 2.2.
# Experimental Study on Dynamic Exhaust Law for Ventilation System of Gas Tunnel
## 3 Similarity Experiment
### Experimental Scheme  
Methane sensors are equidistantly arranged in the gas tunnel model, with 189 measuring points in total, as shown in Figure 4. There are 21 rows in the length direction, and the distance between adjacent methane sensors is 2 m; there are 3 rows in the width direction, and the distance between adjacent sensors is 0.3 m; there are 3 rows in the height direction, and the distance between adjacent sensors is 0.18 m. An SJH-type methane sensor is used in the experiment, with a measuring range of 0-100%VOL, a resolution ratio of 0.01%VOL, and a sampling frequency of 10 Hz.  
Under the initial condition that the gas tunnel is full of gas, the control variable method is used to simulate the process of gas discharge from the gas tunnel. The variables are different exhaust velocities, ventilation sizes and initial gas concentrations. The most important index to evaluate a ventilation system in engineering is ventilation efficiency, which is generally reflected by gas concentration variation and ventilation duration. Considering the convenience of engineering application, the average gas concentration in the tunnel at a certain time during the ventilation process is selected for subsequent analysis. The calculation formula for average gas concentration is  
\[\varphi=\frac{1}{n}\sum_{i=1}^{n}\varphi_{i} \tag{6}\]  
The time from the beginning of exhaust to the time when the gas concentrations at all positions in the tunnel decrease below 1%VOL (the alarm line--refer to GB 50838-2015 \"Technical code for urban utility tunnel engineering\" [35]) is defined as the ventilation duration.  
The experimental steps are as follows: firstly, open the methane cylinder, control the volume of methane entering the gas tunnel model through the pressure regulating valve and flowmeter, and make the methane gas fully diffuse in the gas tunnel model; secondly, after the methane gas sensors at each position in the model meet the preset concentration, set the exhaust velocity, open the fan at the ventilation outlet, and simulate the ventilation process of the gas tunnel; finally, collect the dynamic changes in methane concentration at each monitoring point until the methane concentrations at all monitoring points are less than 1%VOL. Thirteen ventilation scenarios are selected in this experiment, as shown in Table 1.  
Figure 4: Layout of methane sensors.  
Figure 3: Similarity experimental platform of the gas tunnel.
# Cooperative Friendly Jamming Techniques for Drone-Based Mobile Secure Zone
###### Abstract  
Threats of eavesdropping and information leakages have increased sharply owing to advancements in wireless communication technology. In particular, the Internet of Things (IoT) has become vulnerable to sniffing or jamming attacks because broadcast communication is usually conducted in open-network environments. Although improved security protocols have been proposed to overcome the limitations of wireless-communication technology and to secure safe communication channels, they are difficult to apply to mobile communication networks and IoT because complex hardware is required. Hence, a novel security model with a lighter weight and greater mobility is needed. In this paper, we propose a security model applying cooperative friendly jamming using artificial noise and drone mobility, which are autonomous moving objects, and we demonstrate the prevention of eavesdropping and improved security through simulations and field tests. The Cooperative Friendly Jamming Techniques for Drone-based Mobile Secure Zone (CFJ-DMZ) can set a secure zone in a target area to support a safe wireless mobile communication network through friendly jamming, which can effectively reduce eavesdropping threats. According to the experimental results, the average information leakage rate of the eavesdroppers in CFJ-DMZ-applied scenarios was less than or equal to 3%, an average improvement of 92% over conventional methods.  
IoT; RF radio communication; Wi-Fi direct; D2D; drone-based mobile secure zone; friendly jamming; mobility +
Footnote †: journal: Sensors  
doi:10.3390/s22030865