esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of Data Detection Techniques for 5G Massive MIMO Systems
## 2 Background
### Successive over Relaxation  
The symmetric positive definite is the key property of (2) and it is diagonally dominant which is the basis of the SOR detector to achieve the MMSE performance and low computational complexity [26; 27]. The SOR detector is an iterative manner to avert a high dimensional matrix inversion with the support of a relaxation parameter (\(\omega\)) and the matrix splitting pattern: \(\mathbf{A}=\mathbf{D}+\mathbf{U}+\mathbf{L}\). The transmitted signal vector is estimated as:  
\[\mathbf{\hat{x}}_{(n+1)}=(\mathbf{D}-\frac{1}{\omega}\mathbf{L})^{-1}(\mathbf{ y}_{MF}+((1-\frac{1}{\omega})\mathbf{D}+\frac{1}{\omega}\mathbf{U})\mathbf{ \hat{x}}_{(n)}), \tag{3}\]  
where \(\mathbf{y}_{MF}=\mathbf{H}^{H}\mathbf{y}\), \(\mathbf{D}\), \(\mathbf{U}\), and \(\mathbf{L}\) are the diagonal, the upper triangular, and the lower triangular matrices, respectively. The bit-error-rate (BER) performance and the convergence rate of the SOR detector are substantially liable on the values of \(\omega\). In general, the performance of the SOR detector is usually close to the optimum, and the complexity is relatively low. Unfortunately, it suffers from a high performance loss in high channel correlation scenarios [28]. In addition, it always difficult to obtain the optimal value of \(\omega\)[29].
# Application of Absolute Nodal Coordinate Formulation in Calculation of Space Elevator System
## 5 Discussion  
### Axial Loading  
The calculation results of FEM and LM model are very close: both methods use true strain as a measure of rope deformation, while the ANCF method uses Green-Lagrangian strain as a measure of rope deformation. The difference in deformation measures will result in a numerical difference in the stress and strain obtained under the same deformation. In the calculation of the space elevator system rope, the displacement of the zenith anchor is the sum of the deformation of each point of the rope; thus, these numerical differences will amount to the difference in the calculation of stress and displacement. In actual application, this should be compared with the actual situation and a more accurate strain measurement should be used.
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 3 Material and Methods
### Segmentation with 2D Architectures
#### 3.3.2 LinkNet  
LinkNet [10] is an efficient neural network architecture designed for semantic segmentation tasks. It incorporates skip connections, residual blocks, and an encoder-decoder structure. Similar to UNet, LinkNet adopts a U-shaped architecture, but with some key differences. In LinkNet, each level of the encoder and decoder is replaced with a residual module (res-block) instead of a conventional convolution structure. Additionally, the fusion of deep and shallow features in LinkNet follows an \"adding\" approach rather than \"stacking\", as used in UNet [23].  
One of the key features of LinkNet is its rich shortcut connections, which facilitate the transmission of shallow information to deeper layers in the network. By employing residual skip connections, LinkNet effectively combines features from the encoder and decoder using element-wise summation. This approach avoids increasing the number of input channels and network parameters, unlike concatenation used in UNet skip connections.  
The design principles of LinkNet focus on enhancing network efficiency while maintaining competitive segmentation performance. By leveraging residual blocks and efficient skip connections, LinkNet achieves effective feature fusion and preserves spatial information during the segmentation process. Figure 2, adapted from [10], shows the general architecture of LinkNet.  
In our implementation of the LinkNet architecture, the model comprises a total of 11.5 million parameters. There are 120 layers in all, including both parameterized and  
Figure 1: UNet architecture.  
Figure 2: LinkNet architecture.  
parameterless layers. There are precisely 71 layers with parameters. For a more thorough explanation of LinkNet's architecture, see [10].
## 4 Discussion  
The influence of the various locations of a wearable ARCM near the body on the parameters of EMR emissions (antenna parameters) and the related SAR in tissues adjacent to the ARCM was analyzed. All the parameters were analyzed as relative values (according to the relationship X/X\({}_{\text{APD-chest[A]}}\), where X--the value of a particular parameter, X\({}_{\text{APD-chest[A]}}\)--the reference value of a particular parameter obtained in the APD exposure scenario with an anatomical [A] model of chest, taking into account their values expressed on a linear scale in order to uniformly analyze the variability of various quantities). The applied reference values from the exposure scenario with an ARCM on the chest were due to the following: (1) the chest model is one of the most commonly used models in such research studies; (2) low gain and S11 values in such cases; and (3) relatively high frequency mismatch and 10g-SAR values in such cases [7; 8; 10; 20; 21; 29].  
Figure 4: The 10g-SAR distributions in the simplified [S] and anatomical [A] models of a user of a wearable WIT device in the APD exposure scenarios, vertical cross-sections in the plane perpendicular to the ARCM, logarithmic scale. (Note: (1) the highest 10g-SAR were found in the skin and subcutaneous fat tissues; (2) green box on the left—a side view of the ARCM); (3) the protruding part of the body on the left side of the Head [A] case belongs to the ear located near WIT.  
Additionally, for each exposure scenario, the 10g-SAR values regarding a fixed eirp emitted from the ARCM were compared with the corresponding values obtained for a fixed input power to the ARCM.
## 3 Results and Discussion
### Optimization for the Removal of the FD&C Red 40 Dye in an Aqueous Solution
#### 3.2.2 Effect of pH  
The pH is one of the most critical parameters affecting the adsorption processes since it influences the degree of ionization of the dye and the surface charge of the adsorbent [29]. The experimental design results show that decreasing the initial pH of the aqueous solution (from 7.0 to 3.0) increases the removal efficiency of FD&C Red 40 dye. This effect can be explained by taking into account the surface charge of the CS-TiO\({}_{2}\)-GLA beads. The primary amino groups of CS are protonated in acidic environments, increasing the interaction capacity between beads and dye molecules with negative net charges [12]. It is possible to state that FD&C Red 40 dye molecules are negatively charged in the pH range used in this study (1.64 to 8.36) [30].  
The decrease in pH results in a greater electrostatic attraction force that occurs mainly between the positively charged protonated amino groups (\(-\)NH\({}^{3+}\)) and the negatively charged sulfonate groups (\(-\)SO\({}^{3-}\)) of FD&C Red 40 dye [28], without neglecting the contribution of the oxonium ions coming from TiO\({}_{2}\) in the interaction with the dye [14].  
On the other hand, increasing the pH favors the formation of dye molecules with negative net charges. However, simultaneously the deprotonation of the positively charged amino groups of the CS occurs, decreasing the number of adsorption active groups in the beads, so the final effect is a decrease in the removal percentage of the dye.
## 2 Literature Review
### Josephus Permutation for Image Cryptography  
The Josephus permutation is a classical method of rearranging sequences named after [PERSON], a Jewish-Roman historian [14]. Originating from his historical account \"The Jewish War\", the method recounts an event where [PERSON] and 40 soldiers faced capture in a cave, opting for a structured mass suicide in a circle. Each soldier killed the one (i.e., \(k=1\)) to their left until one remained. Unexpectedly, [PERSON] strategically positioned himself to be the last survivor. This Josephus permutation is depicted in Figure 1.  
Table 1 shows where someone must stand to survive among \(n\) people. In [PERSON]'s case with 40 soldiers, he had to stand in the 19 th position. The survivor's position can also be efficiently calculated using the binary representation, where the number of soldiers \(n\) undergoes the \"Rotate Left\" operation, i.e., the leftmost bit position (Most Significant Bit or MSB) becomes the rightmost bit position (Least Significant Bit or LSB) in its binary order. This new binary arrangement is the position where someone can survive. It is important to know that the standard Josephus permutation uses a constant key of \(k=1\).  
However, the Josephus permutation-based cryptography methods do not focus on who ends up at the last turn but rather on the execution sequence. In other words, for a sequence of numbers totaling \(n\), the order from the first to \"dead\" and to the \"survivor\" will be calculated. [PERSON] et al. [15], [PERSON] et al. [16], and [PERSON] et al. [17] used this type of implementation to encrypt secret images and achieve confusion in their research, making  
\begin{table}
\begin{tabular}{c c} \hline \(n\) & \(\mathcal{S}(n)\) \\ \hline
1 & 1 \\ \hline
41 & 19 \\ \hline
100 & 73 \\ \hline \end{tabular}
\end{table}
Table 1: Number of soldiers \(n\) and the survivor’s position \(S(n)\).  
Figure 1: Josephus permutation of the number sequence 1, 2, 3, 4, 5 with a fixed key of one produces a new sequence 2, 4, 1, 5, 3.  
the encrypted images unrecognizable compared to the original ones. Research conducted by [PERSON] et al. [18] demonstrated the capability of the Josephus permutation in modifying bit levels for cryptography. This modification, which is easy to perform, offers a greater possibility of generating different bit combinations from the original bits. Consequently, the changes in pixels become more significant.  
It is evident that the Josephus permutation is more saturated in data security in cryptography and less utilized in data security in steganography. Therefore, this study opts for combining the Josephus permutation and LSB embedding techniques, specifically, LSB 3-3-2, for image steganography.
## 4 Results
### Influence of the Change of Satellite Elevation
Therefore, we can conclude that there is a weak negative correlation between the satellite elevation and the STD value; this conclusion is only satisfied in this paper when the satellite elevation is between \(30^{\circ}\) and \(90^{\circ}\). The period with high satellite elevation will produce better ranging accuracy, but the accuracy changes slightly with the satellite elevation. The higher the satellite elevation, the greater the signal power and the lesser the multi-path effect. So, the high satellite altitude angle has a smaller code signal path difference. Since the satellite altitude angle only affects the code signal path differences of a single satellite within a period and has nothing to do with the signal structure differences between different frequency points. We can draw the above conclusions by processing only one satellite for each frequency point.
# Renyi Entropy-Based Adaptive Integration Method for 5G-Based Passive Radar Drone Detection
## 2 5G Passive Radar Principles
After the thresholding operation, a set of detections is obtained. Usually, the echo from a single target is spread over several adjacent range and velocity cells, where the midpoint typically marks the correct position estimation of the target. For this reason, it is necessary to perform an extraction operation, which consists of grouping the CAF cells corresponding to a single detection. Methods known from image processing techniques, i.e., clustering, as well as morphological operations of opening or closing, can be used here [28]. When individual CAF cells are grouped into sets of detections, the parameters of each can be estimated. In the simplest case, the distance and bistatic velocity of the CAF cell with the largest amplitude value can be taken. However, in this case, the accuracy of measurement of these parameters is identical to the resolution of the bistatic distance and velocity. To improve the estimation, it is possible to use a fairly simple estimator with curve fitting, such as a parabola [29]. With a sufficiently high detection SNR (about 20 dB), it is possible to improve the accuracy of bistatic distance measurement by up to 10 times over the assumed measurement resolution [23]. The final stage of processing is the creation of routes of detected targets and their localization [30; 31]. The tracking stage is very often implemented using the Kalman filter algorithm. Tracking can be carried out in bistatic coordinates, as well as Cartesian coordinates (after localizing the targets). This article presents results that were obtained for a single receiving station, hence the implementation of tracking and localization algorithms was not undertaken.
# Numerical Study on the Phase Sensitivity Variation in Low Frequency Primary Microphone Calibrations
## 3 Independent Microphone Simulation
### Results
#### 3.2.2 Results in Time Domain  
Transient simulations are performed at 0.1 Hz and 1 Hz. The sound pressure waveforms in two cycles are obtained as shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c} \hline
**Item** & **Symbol** & **Value** \\ \hline Diaphrag radius & \(r_{\text{m}}\) & 4.5 mm \\ Diaphrag tension & \(T_{\text{m}0}\) & 3160 N/m \\ Diaphrag thickness & \(t_{\text{m}}\) & 5 \(\upmu\)m \\ Diaphrag density & \(\rho_{\text{m}}\) & 8900 kg/m\({}^{3}\) \\ Gap & \(h\) & 19 \(\upmu\)m \\ Young’s modulus of diaphragm & \(E_{\text{m}}\) & \(2.21\times 10^{11}\) Pa \\ Poisson’s ratio of diaphragm & \(v_{m}\) & 0.4 \\ Back-plate radius & \(r_{\text{BP}}\) & 3.7 mm \\ Holes radius & \(r_{\text{H}}\) & 0.28 mm \\ Location of inner holes & \(r_{1}\) & 1.6 mm \\ Location of outer holes & \(r_{2}\) & 2.8 mm \\ \hline \end{tabular}  
* Properties of the diaphragm are used in Section 4.  
\end{table}
Table 2: Properties of a microphone.  
Figure 9: Phase difference between the associated sound pressure and the diaphragm deformation of the microphone.  
Apparently, the pressure leakage and heat conduction effects will not only decrease the amplitude of the generated sound pressure but also result in the phase lead of the diaphragm deformation. A greater pressure attenuation will induce a greater phase lead. The results of the transient and frequency domain analyses can be verified by each other.
# Optimizing Herbicide Use in Fodder Crops with Low-Cost Remote Sensing and Variable Rate Technology
## 3 Results
### Variations of % of Crop and Weed Plants and Productivity  
The total database has 42 sample points, 21 of which are from moment M1 and 21 from moment M2. Therefore, the productivity (t ha\({}^{-1}\)) was calculated for moment M2, when the crop was harvested. Outliers were removed before conducting the statistical analysis, and the processed data are presented in Table 3.  
The data for the dependent variables do not follow a normal distribution, so the Kruskal-Wallis test was used as a non-parametric alternative to assess the influence of the plot, the moment, and the dose applied. These results are presented in Table 4.  
The results of the Kruskal-Wallis test indicate that there is a very significant difference between the plots and the doses applied when it comes to productivity. These two  
\begin{table} \begin{tabular}{c c c c c} \hline \hline
**Dependent** & **Shapiro–** & **Normality** & **Plot** & **Moment** & **Dose** \\
**Variable** & **Wilk Test** & **Test** & & **Applied** \\ \hline \% crop & 0.002 & Kruskal-Wallis & 0.070 * & 0.040 * & 0.040 * \\ \% weed & 0.000 & Kruskal-Wallis & 0.005 ** & 0.020 * & 0.003 ** \\ Productivity & 0.02 & Kruskal-Wallis & 0.000 *** & - & 0.000 *** \\ \hline \hline \end{tabular}   
Significance codes: 0 ***\({}^{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text  
groups also showed high significance for % weed. For % crop, both groups showed lower significance, as did the moment for % weed. Despite these differences, all groups showed significance for all variables. For the plot and dose applied groups, which presented some significance for all of the variables' parameters, post hoc analysis was carried out to identify which groups differed from one another. [PERSON]'s test was used for multiple comparisons. The results are presented in Table 5.  
The % crop parameter only presents a difference between the group without herbicide and with 0.50 l ha\({}^{-1}\). For this dependent variable, all of the other approaches showed no significance. For % weed, there was a difference between the 0 dose and the other two doses, but there was no difference between the 0.50 and 0.75 l ha\({}^{-1}\) doses. There were also differences between the No-Herbicide plot and the others, with no difference between VRA and fixed-rate application. The productivity parameter follows the same trend as % weed. The boxplots in Figure 6 show the differences explained by [PERSON]'s test. The median % weed drops from around 30 % in the No-Herbicide plot to around 5 % in the VRA and fixed-rate plots. The differences found in relation to the doses applied are very similar. As for yields, the median of the No-Herbicide plot (<0.2 t ha\({}^{-1}\)) is clearly lower than the yields of the VRA and fixed-rate plots. As with the doses applied, where the herbicide application was zero, yields were very low compared to the sites where either of the two doses studied were applied.
## 3 System Descriptions and Assumptions
### Communication Model  
MEC servers are homogeneous devices with the same computing, communication, and storage capabilities [24; 25]. The available bandwidth of server \(d\) is \(b\)Hz, with \(F\) orthogonal wireless subchannels. The bandwidth of subchannels \(f\in F\) is \(b=B/F\), where \(\forall f\in\{1,2,...,F\}\). Each user can only occupy one channel to communicate with the server. The set \(\forall^{d,t}=\{v_{1}^{d,t},v_{2}^{d,t},...,v_{N}^{d,t}\}\) represents a server's wireless channels' allocation strategy while providing RCO service, and elements subjects to \(\forall v_{n}^{d,t}\in\{0,1,2,...,F\}\), where \(0\) signifies that the user processes computational data on their local device. However, users will generate transmission energy consumption, which is determined by two variables: the volume of transmitted data and the uplink transmission rate. The uplink transmission rate is influenced by the number of users occupying the channel \(Q\in[0,N]\), user's channel gain \(\zeta_{n}\), and the transmission power \(\psi_{n}\). According to the Shannon-Hartley theorem, the minimum effective uplink transmission rate for mobile user \(n\) managed by server \(d\) in time slot \(t\) is [26]  
\[l_{n}^{d,t}=\text{blog}_{2}(1+\frac{\zeta_{n}\psi_{n}}{\sum\limits_{m\in\{Q-n \},v_{m}^{d,t}=v_{n}^{d,t}}\zeta_{m}\psi_{m}+\sigma^{2}}), \tag{3}\]  
where \(\sigma^{2}\) represents the second central moment of Gaussian white noise. It is observed that the effective transmission power of user \(n\) decreases as the number of users \(Q\) occupying the same channel increases. Too many users occupying the same channel will increase noise interference \(\sum_{m\in\{Q-n\},v_{m}^{d,t}=v_{n}^{d,t}}\zeta_{m}\psi_{m}+\sigma^{2}\), resulting in a decrease in energy consumption benefits for task offloading, reducing the energy efficiency of task offloading and increasing task latency. Therefore, the impact of channel allocation must be considered in task offloading strategies to optimize performance.
# Analysis of AMI Communication Methods in Various Field Environments
## 3 Wireless Communications for AMI  
In this section, we use two types of modems, Wi-SUN and ZigBee modems, in order to test wireless communication methods for AMI. We then introduce measurement methods and a linear regression analysis to examine various field environments.
# A Privacy Measurement Framework for Multiple Online Social Networks against Social Identity Linkage
## 3 Problem Descriptions and Notation  
In actual scientific research, there is not an effective way to warn users how much their privacy will be exposed when PII is divulged or certain changes are made to their privacy settings. Without a practical and effective approach to quantify, measure, and evaluate privacy, it is hard for users to determine how much information they are willing to share and understand the risk involved. It is impossible for OSN service providers to make appropriate policies to protect user privacy. Meanwhile, privacy measurement, as a virtual concept, is a challenging issue because the definition of privacy is subjective. Users have different opinions and expectations about privacy. Social networking sites protect privacy by profile setting, which includes attributes consisting of structured data; privacy can be identified by this structured information, such as name, hometown, birthdate, etc. Therefore, using the attributes of a profile is a good way to measure the privacy of individuals.  
However, the usage of attributes also introduces problems. Platforms vary regarding the attributes they require users to provide [1; 2; 40], and each attribute has a different impact on individual privacy; for example, birthdate, phone number, and address cause unequal levels of privacy leakage. Therefore, we first must determine a method of quantifying the degree of leakage for attributes.  
Here, we introduce the mathematical notation we will use in the rest of our paper. We use a set of n users \(\mu\)\(=\{\)u\({}_{1}\),u\({}_{2}\).......u\({}_{n}\)\(\}\) corresponding to the individuals participating in OSNs. Each user has a set of m attributes or profile items \(\alpha_{n}\)\(=\{\)a\({}_{1}\),a\({}_{2}\).......a\({}_{m}\)\(\}\); for instance, the PII includes items such as username, gender, birthdate, and hometown. In addition, for convenience, we consider that \(\beta=\{\)b\({}_{1}\),b\({}_{2}\).......b\({}_{s}\)\(\}\) corresponds to the same attribute on s OSNs. More specifically, \(\beta_{m}^{\varepsilon}=\{\)b\({}_{1}\),b\({}_{2}\).......b\({}_{s}\)\(\}\) corresponds to the extraction difficulty of attribute m on s OSNs.  
Other general notations that are used in our framework are presented in Table 1.