esa-sceva/satcom-chunk-collection huggingface dataset:
# Phase Compensation Technique for Effective Heat Focusing in Microwave Hyperthermia Systems
## 2 The Antenna Array System for Microwave Hyperthermia Treatment
### The Specific Absorption Rate and Temperature Increase  
The EM field produced by the TSA antenna array elements propagates toward the inside of the breast phantom and concentrates in the target area. The localized EM energy is used to calculated the SAR parameter, which is defined as a measure of energy absorbed per unit mass of the human body in the unit time t. The SAR is defined as the time derivative of the incremental energy (dW) absorbed by the incremental mass (dm) [29].  
\[\text{SAR }=\left(\frac{\text{d}}{\text{dt}}\right)\left(\frac{\text{d}W}{ \text{dm}}\right)=\left(\frac{\text{d}}{\text{dt}}\right)\left[\frac{\text{d}W }{\rho(\text{d}V)}\right] \tag{1}\]  
Figure 4: The 3D model of the cylindrical TSA antenna array with vertical polarization: (**a**) xz-plane and (**b**) xy-plane.  
Figure 3: Simulated E-field distribution at 2.45 GHz in the breast phantom derived from a single TSA antenna.  
The incremental mass dm can be replaced with a volume element dV for a given density of the tissue \(\rho\). The SAR factor can also be defined for time-harmonic E-fields using the Poynting vector theorem [30]:  
\[\text{SAR}\ =\ \left(\frac{\sigma}{2\rho}\right)\left|\overline{E_{i}}\right|^{2} \tag{2}\]  
where \(\sigma\) is the electrical conductivity of the tissue material and \(\overline{E_{i}}\) is the intensity of the internal E-field in volts per meter. The average SAR parameter is defined as the ratio of the total power absorption per unit mass of the exposed human body. The induced E-field interacts with the body and changes the temperature depending on the square of the magnitude of the \(E\)-field strength.  
The analysis of temperature distribution as a function of the applied external E-field was conducted to broaden our study to incorporate multiphysics methods. To show the rate of temperature increase in human tissue exposed to external microwave energy, the Pennes bioheat equation is given below [31]:  
\[\rho C_{\text{P}}\frac{\partial T}{\partial t}\ =\ \frac{1}{2}\sigma\left| \overline{E}\right|^{2}+\
abla\cdot(k\
abla T)+W_{\text{b}}C_{\text{b}}(T_{ \text{b}}-T)v \tag{3}\]  
where \(\rho\), \(C_{\text{P}}\), \(\mathbf{k}\), and \(\sigma\) are the density, heat capacity, thermal conductivity, and electric conductivity of tissue, respectively, and \(W_{\text{b}}\) and \(C_{\text{b}}\) are the perfusion rate and heat capacity of blood, respectively. The external E-field is an essential factor for determining the temperature increase in the exposed tissue over a certain period of time following the EM exposure of the TSA antennas' array.
## 5 Case Study
### Mapping Campaign Quality: Vibrant Visualization of Parcel Grading  
The ability to locate plots on a map facilitates detailed tracking of specific agricultural practices in each area. Geospatial representation of plots allows for maintaining a detailed history of past harvests. This information is crucial for assessing trends over time and anticipating potential variations in quality.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Ticket** & \begin{tabular}{c} **Amount** \\ **(Kg)** \\ \end{tabular} & **Score** & **Kilo-Points** & **Value (EUR)** & \
\begin{tabular}{c} **Varietal** \\ **Coefficient** \\ \end{tabular} &
\begin{tabular}{c} **Final Value** \\ **(EUR)** \\ \end{tabular} \\ \hline R1 & 2,000,000 & 6.550 & 13,100,000 & 3,383,701.4077 & 0.85 & 1,1135,000 & 3,070,790.1942 \\ R2 & 500,000 & 9.625 & 4,812,500 & 1,243,058.2462 & 1.20 & 5,775,000 & 1,592,619.0724 \\ R3 & 1,000,000 & 8.275 & 8,275,000 & 2,137,414.4388 & 1.00 & 8,275,000 & 2,282,064.5583 \\ R4 & 1,200,000 & 1.800 & 2,160,000 & 557,923.2855 & 0.90 & 1,944,000 & 536,112.8098 \\ R5 & 130,000 & 0.000 & 0 & 0 & 1.00 & 0 & 0 \\ W1 & 1,000,000 & 8.550 & 8,550,000 & 2,208,446.3386 & 0.87 & 7,438,500 & 2,051,376.0987 \\ W2 & 20,000 & 9.775 & 195,500 & 50,497.2233 & 1.15 & 224,825 & 62,001.8325 \\ W3 & 140,000 & 7.300 & 1,022,000 & 263,980.3694 & 0.85 & 868,700 & 239,568.5174 \\ W4 & 200,000 & 3.000 & 600,000 & 154,978.6904 & 1.00 & 600,000 & 165,466.9166 \\ W5 & 110,000 & 0.000 & 0 & 0 & 1.00 & 0 & 0.0000 \\ \hline Total: & 6,300,000 & & 38,715,000 & 10,000,000 & & 36,261,025 & 10,000,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Score and final values using the Kilo-point criteria for the test data. The r index is related to different varieties of red wine, and the W index is related to different varieties of white wine. According to varietal coefficients, the percentage of money allocation could be different.  
The representation on a map of quality calculations using the Kilo-points criterion would involve assigning graduated colors or categories to parcels based on their points obtained. The color scale employed for representing the quality calculation using the Kilo-points criterion resembles a heatmap, with a gradient ranging from red for lower scores to green for the highest quality. In this visual scheme, parcels with poorer quality are depicted in warmer tones, transitioning through yellows and oranges, while those with superior quality are highlighted in cooler, greener hues. This color-coded approach serves as an intuitive and efficient means of conveying the spatial variations in grape quality across the mapped area. Figure 14 demonstrates how quality is represented on the map.  
Specifically, the scale is represented using a gradient from red to green:  
* Red (0-3): Indicates low-quality assessments, highlighting areas with significant issues affecting grape quality.
* Yellow (4-6): Represents medium-quality assessments, signifying moderate levels of grape quality.
* Green (7-10): Denotes high-quality assessments, showcasing regions with optimal grape quality conditions.
## 6 Conclusions  
In recent years, edge computing and the Internet of Things (IOT) have provided a technological basis for the development of intelligent manufacturing. In order to support the intelligent interconnection and application of all kinds of equipment in the industrial field, edge computing should be close to or embedded in all kinds of equipment nodes in the industrial wireless network. In order to better access or embed edge computing on various device nodes of industrial wireless networks, the channel characteristics of the Industrial Internet of Things (IIoT) should be studied first. Therefore, it is very meaningful to study the wireless network design of the IIoT.  
In this paper, the measurement results of electromagnetic noise at three different test positions were given in an automobile factory. The spectrum occupancy of a factory wireless environment in the 300 MHz-3 GHz band was obtained by frequency domain measurement. By calculating the average power of path loss and electromagnetic noise in different frequency bands, the optimal frequency band for the IIoT is 916 MHz. In time domain measurements, we analyzed the distribution of electromagnetic noise in different plant areas and in different frequency bands by measuring four statistical parameters of 315 MHz, 433 MHz, and 916 MHz. According to the measurement results, we concluded that the time-varying characteristics of electromagnetic noise can be characterized by CHMM.  
According to the research results, we suggest that under an IIOT scenario, 916 MHz is the best frequency band for the equipment. The 779 MHz band with the most serious noise interference should be avoided as much as possible. The focus of this paper is the noise characteristics in an IIOT scene and its modeling analysis. Therefore, the research in this paper has some limitations. In the following research, the authors will further explore the channel and noise characteristics in the IIOT scene, understand the IIOT scene more deeply, and try to find effective methods to reduce electromagnetic noise interference in the IIOT scene.  
Conceptualization, H.L. and L.L.; Data curation, Y.L., Z.Y., and K.Z.; Formal analysis, Z.Y. and K.Z.; Project administration, L.L.; Writing--original draft, H.L.; Writing--review and editing, H.L.  
The research was supported in part by the Key Laboratory of Universal Wireless Communications (BUPT, KFKT-2018105), Ministry of Education, China, Beijing Natural Science Foundation (No. L172030), the Beijing Municipal Natural Science Foundation under Grant 4174102.  
The authors declare no conflict of interest.  
Figure 10: Comparison of measurement and simulation noise. (**a**) Time domain waveform; (**b**) Amplitude probability distribution (APD) value comparison of simulation noise and measurement noise; (**c**) Noise amplitude distribution (NAD) value comparison of simulation noise and measurement noise.
###### Abstract
[24] provided an overview of experimentally verified propagation models for wireless sensor networks (WSNs) and quantitative comparisons of propagation models used in WSN research under different scenarios and frequency bands. Furthermore, the large differences between V2V propagation channels and fixed-to-mobile (F2M) propagation channels, transmitter (Tx) and receiver (Rx) heights, propagation environments, and frequency bands mean that the propagation models developed for the deployment of F2M systems cannot be applied in the performance evaluation and development of future ITS applications over VANETs, thus forcing characterization and modeling of the radio channel at 700 MHz and in the DSRC band at 5.9 GHz for future V2X systems. This paper presents the vehicular channel characterization by using the dual-slope path loss propagation model in urban with high traffic density (U-HD), urban with low traffic density (U-LD), and suburban (SU) scenarios in the city of Valencia, based on real propagation measurements carried out in 2012 at 700 MHz and 5.9 GHz. The paper is organized as follows: Section 2 describes the measurement system and the main characteristics of the propagation scenarios where the measurements were performed. Section 3 presents the results derived from the analysis of the measurements, where the path loss exponent (PLE) behavior of the received signal is analyzed and classified according to the propagation scenarios. Finally, the conclusions derived from the study are summarized in Section 4.
## 4 System Components
### Computing System
#### 4.2.2 Swarm Intelligence
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Optimization** & **Performed Function** & **Technique Deployed** \\ \hline
[81] & PSO & Implemented path planning for swarms & Deploys jump-out and revisit methods to avert \\  & & & both null search attempts and local optimum \\ \hline
[82] & PSO & Implemented for finding moving targets & Employs the Bayesian theory to convert a search \\  & & using UAVs & problem to optimized cost function which will \\  & & & represent the probability of discovering targets \\ \hline
[83] & PSO & Implements cooperate path planning for & Deploys time stamp model to manage UAV \\  & & Multi-UAV operations & coordination expenses \\ \hline
[84] & PSO & Used to create exploratory trajectories for UAV & Delay tolerant networking approach is used for \\  & & networks & the team of UAV to follow \\ \hline
[85] & BCO & Implements UAV formation, obstacle avoidance & Deploys metaheuristic optimization approach \\  & & control and target tracking & exploited from the intelligent behavior of \\  & & & honeybee swarm \\ \hline
[86] & BCO & Implements flight planning solution for & Executed based on RNA coding procedure and \\  & & Multi-UAVs networks & so creates a coding technique pool to improve \\  & & & global search capability \\ \hline
[87] & BCO & Implements to achieve efficient Node & Deploys UAV anchors to reduce \\  & & localization process in UAV networks & localization oversights \\ \hline
[88] & ACO & Optimizes energy consumption and efficient & Uses pheromone enhancement technique to \\  & & path planning for UAV collision avoidance & implement a gain function for efficient \\  & & & path planning \\ \hline
[89] & ACO & Implements cooperative mission planning for & Deploys time-sensitive target probability map \\  & & UAV swarm target attack & for determining targets \\ \hline
[90] & ACO & Resolves cooperative search attacks mission & Uses distributed control architecture which \\  & & planning in Multi-UAV networks & separates the global optimization problem into \\  & & & separate sets to implement \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimization-based Algorithms. bilities to achieve a resolution. Paxos is also deployed as trade-off to fault tolerance in distributed systems. As an alternative to provide harmonization in distributed systems, the Raft algorithm [79] aims to decompose the consensus problem through sub-groupings. The number of sub-groups to considered are clarified through determinism thus limiting inconsistent incidences between logs. In these sub-groups, elected leaders take responsibility to manage replicated logs, accept log entries from clients and replicate to other servers together with a notification of when to apply the log entries to their machines. In Table 4, we detail sample consensus algorithms implemented based on Paxos and Ralf models to provide harmonization in distributed systems.
## 5 The Problems of Crowd Sourced Audio-Visual Evidence
### The Problem of Near Duplicate Audio-Visual Evidence  
The propensity of SMSs means that there are likely to be numerous versions of individual audio-visual evidence submitted. Users may have found videos of evidence in their SMS space and may feel compelled to submit it in response to a call for evidence. This results in duplicate or near duplicate versions of an audio-visual. Whilst hash checking will highlight duplicate data, it does not cater for near duplicate audio-visuals.  
To understand the problem of near-duplicate evidence, consider this. Digital investigators rely on hashes--also referred to as message authentication codes (MAC) to identify identical data. A single bit change between two identical items of data results in a completely different hash, in such cases where two items of audio-visual are essentially duplicates but have had a small change made to them, we refer to these are _near duplicate_. Examples of this can include a colour photograph \(i_{1}\) that has been converted to black and white/sepia or has had its format converted from BMP to TIFF to make \(i_{2}\), or a video \(v_{1}\) that has had the audio quality reduced or its version changed from AVI to MKV to produce \(v_{2}\). In this case, \(i_{1}\) and \(i_{2}\) are near duplicate as are \(v_{1}\) and \(v_{2}\).  
In other cases, data may deliberately have been altered to avoid detection, for instance law enforcement agencies use hash databases to speed up the process of photograph grading in child exploitation cases. It is not uncommon for suspects to have deliberately altered single bits in images to avoid detection. This is also common in more simple efforts to avoid spam/malware detection filters.  
A failure on the part of a digital investigator to recognise that two items of data are duplicates or near duplicates can have a considerable impact on an investigation; hence a lot of research has focused on the detection of near duplicate images [26, 27], videos [28] and audio [29, 30, 31].  
Near duplicate detection is most difficult for video, this is because of the complexity of video as compared to still images and audio. Videos footage can transgress multiple dimensions--for instance it comprises of thousands of photo frames (in some cases tens of thousands), it can contain multiple audio tracks, subtitles and other rich information. A change in any single bit within one of these elements makes the video a near duplicate.  
A number of efforts have been deployed to address this and these include correlation based detection systems [32], the Hadoop MapReduce programming model [33] and technologies that look at the constituent elements of an image (from which the video for instance is constructed), PhotoDNA is an example of the latter [34]. PhotoDNA identifies the most prominent contrast edges--called intensity gradients in each image and uses these to compare images with each other.
# Investigation of Wind Power Potential in Mthatha, Eastern Cape Province, South Africa
### Wind Direction for the Mthatha Area  
The wind directions were analysed for the whole period and its seasons. The variation in the annual wind direction for the period from January 2013 to August 2023 is shown in Figure 7. The most prevailing direction is east-southeast (ESE) with a frequency of 12.0877%. For 36.5201% of the time, the wind speed was between 2 and 4 ms\({}^{-1}\) in varying directions, while for 13.3845% of the time, null wind speed was observed. Only 0.0035% of the period had a wind speed of above 16 m/s, with 0.0011% in the south-southwest (SSW) direction and 0.0023% in the westerly (W) direction. The wind direction statistics are shown in Table 6 in terms of the period, most prevailing wind direction (MPWD), MPWD total frequency (MPWD\({}_{\text{tf}}\)), most frequent wind speed range (MFWSR), MFWSR frequency (MFWSR\({}_{\text{f}}\)) null wind frequency (NWF), lowest speed range (LSR), LSR frequency (LSR\({}_{\text{f}}\)), highest speed range (HSR), and HSR frequency (HSR\({}_{\text{f}}\)).  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Period** & **MPWD** & **MPWD\({}_{\text{tf}}\)** & **MFWSR** & **MFWSR\({}_{\text{f}}\)** & **NWF** & **LSR** & **LSR\({}_{\text{f}}\)** & **HSR** & **HSR\({}_{\text{f}}\)** \\ \hline Whole & ESE & 12.0877 & \(2\leq\
abla<4\) & 36.5201 & 13.3845 & \(0\leq\
abla<2\) & 24.3440 & \(\
abla\geq 16\) & 0.0035 \\ \hline Summer & ESE & 16.4171 & \(1.25\leq\
abla<2.5\) & 29.2531 & 5.2035 & \(0\leq\
abla<1.25\) & 3.0290 & \(\
abla\geq 10\) & 0.1392 \\ \hline Autum & S & 9.3503 & \(2\leq\
abla<4\) & 33.8274 & 24.9341 & \(0\leq\
abla<2\) & 25.8152 & \(\
abla\geq 16\) & 0.0082 \\ \hline Winter & W & 8.6034 & \(1.25\leq\
abla<2.5\) & 30.3175 & 28.0764 & \(0\leq\
abla<1.25\) & 6.6694 & \(\
abla\geq 10\) & 1.1164 \\ \hline Spring & ESE & 15.5468 & \(2\leq\
abla<4\) & 38.8477 & 7.7734 & \(0\leq\
abla<2\) & 19.0605 & \(\
abla\geq 16\) & 0.0048 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Wind direction statistics of Mthatha for the whole period and its seasons.  
Figure 6: WPDFs superimposed on to the measured data histogram for the four seasons of the year.  
Seasonal wind directions are shown in Figure 8. Summer and spring have winds blowing from ESE, while Winter and Autumn have southerly and westerly winds, respectively. The frequency of the most prevailing wind direction ranges from 8.6034% in Winter to 15.5468% in Spring, and in all cases, the most prevailing wind speeds are less than 4 m/s.  
Figure 8: Seasonal wind directions for the Mthatha area for 2013–2023.  
Figure 7: Yearly wind directions for the Mthatha area for 2013–2023.
# A PID Parameter Tuning Method Based on the Improved QUATRE Algorithm
## 5 Experiment Analysis
### PID Parameter Tuning Experiment Results Analysis  
MATLAB software was used to realize the simulation experiment. Two intelligent algorithms, the PSO algorithm, and the improved QUATRE algorithm are combined with PID parameters. The purpose of the intelligent algorithm is to find the combination of Ki, Kp, and Kd with the best control effect for the control system through its optimization ability. In the entire parameter tuning process, the value of the fitness is expressed by the error, and generally, the ITAE of the error integration criterion of the control system is adopted. The control objects are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mathematical Model** & **Properties of the Model** \\ \hline \(\frac{e^{-12}}{20s^{+1}}\) & First-order delay mode \\ \(\frac{e^{-1}}{(s+1)^{2}}\) & Second-order delay model \\ \(\frac{e^{-s}}{(s+1)^{3}}\) & Third-order delay model \\ \hline \hline \end{tabular}
\end{table}
Table 1: Control object model.  
Figure 5: Schaffer’s function.  
The algorithm parameter settings included: the number of the initial population is 100; the maximum number of iterations is 500; the value range of \(K_{p}\) and \(K_{i}\) is [0,30]; and \(K_{d}\) is [\(-\)20,20].  
From the step response curve of the control object, it can be concluded that the PID based on the improved QUATRE algorithm has less overshoot than that of the standard QUATRE algorithm and particle swarm algorithm. The system has a small shock. On the other hand, the adjustment time is relatively small. Therefore, on the one hand, the improved PID parameter adjustment improves the stability of the control system; on the other hand, it reduces the risk of large vibration. Therefore, the PID parameters of the improved algorithm should be better for the control system. The experiment shows that the improved QUATRE algorithm is feasible in the PID parameter setting.
# Autonomous Load Regulation Based Energy Balanced Routing in Reshargeable Wireless Sensor Networks
## 6 Performance Evaluation
### Analysis of Power Allocation Algorithm Performance  
In order to verify the performance of the power allocation algorithm to extend the network life cycle, we compared ALRMR with the ALRMR-PROPORTION for different values of \(P\) in the range of 0 to 0.5 with a step of 0.05 and \(\gamma=0.4\). Compared with ALRMR, the MCV in the ALRMR-PROPORTION only allocates power for nodes in the same cell according to their residual energy. Other than this difference, these two algorithms are the same. Taking node \(v_{i}\) as an example, its charging power and charging time are calculated as follows:  
\[\sum_{c_{m}\in CV}t_{c_{m}}=T-t_{sp} \tag{16}\]  
\[t_{c_{m}}/ce_{c_{m}}=t_{c_{\chi}}/ce_{c_{\chi}}\forall m,k\in CV \tag{17}\]  
\[P_{i}=P\cdot\frac{(E_{o}-E_{i})}{\sum\limits_{j\in c_{m}}(E_{o}-E_{j})}\;i\in c _{m}. \tag{18}\]  
In the ALRMR-PROPORTION, the cells with a high charging efficiency can also obtain more charging time and the nodes with low energy can gain more power according to their residual energies. The corresponding contrast results are shown in Figure 4.  
The network lifetime increases with the increase of \(P\). However, compared with the ALRMR-PROPORTION, the network lifetime is extended more quickly in the ALRMR. This is because the ALRMR considers the charging efficiency when allocating power and the MCV will increase the power for the low energy nodes with a longer charging distance in order to balance their energies. Figure 5 demonstrates the above analysis. With the increase of \(P\), the MREKN is obviously larger in the ALRMR-PROPORTION, while it is stable in the ALRMR. This means that the MCV is  
Figure 4: Network lifetime with different values of \(P\).  
more effective at charging low-energy nodes with the power allocation algorithm in the ALRMR, which balances the residual energy of nodes and extends the network lifetime effectively.