esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Antenna Performance Enhancement Techniques  
Antennas are one of the pivotal parts of 5G devices and a main part of a communication system. Due to the natural phenomena of using microstrip technology, especially in 5G, plenty of radiation losses as well as issues in the key performance parameters are presented, such as narrow bandwidth, lower efficiency, and less gain. To overcome all these burning issues, we need to design such antennas to fulfill the very basic requirements in terms of wide or broad bandwidth, high gain, and high efficiency, which would provide an excellent provision for low latency, ultrareliable flawless communication with better spectral efficiency. The one and only solution is to exercise such excellent techniques which might be able to overcome these issues and make the design as the best solution. The main focus is on the latest key techniques responsible for improvement in bandwidth, gain, and efficiency, and in case of MIMO isolation enhancement is the main target discussed in the literature. First, we elaborate the layout of all key performance parameters to summarize our study and make a provision for better understanding other researchers' working in the same field. Researchers have also worked to find out the human body effects on the performance of antenna functionality. It is a challenging task for research industry to practically implement the techniques to minimize these effects and make functionality and efficiency of antennas better. Some other challenges, such as the effects of high-frequency signals on the human tissues are also key challenges to be addressed.
# Recycling of Low-Quality Carbon Black Produced by Tire Pyrolysis
## 3 Results and Discussion
### Hydrophilic Pigment for Use in Mortars and Construction Materials  
In the initial stage, rCB underwent a dry process similar to that of hydrophobic pigment/filler. However, storage induced the reagglomeration of ground material, leading to rCB parameter deterioration and rendering it unsuitable for use as a pigment due to the uneven particle mixing. Furthermore, rCB contained a notable amount of steel wire fragments (up to 1 wt%) with lengths of 3-20 mm, necessitating sieving through a 10-15 mesh vibrosieve or, alternatively, magnetic separation.  
To address reagglomeration issues, a system was devised based on cavitation wet processing, as described in Section 2.2.2. The resulting pulp swiftly separated into a solid paste and water, with the concentrated paste containing approximately 50% water.  
Concrete test specimens, incorporating varying amounts of rCB into the cement mortar, were produced (depicted in Figure 4). Carbon black proves suitable for application as a pigment in cement and lime-based mortars, construction mixtures, paints, and elements with a concrete binder (paving stones, blocks, tiles, etc.). The product can withstand alkaline environments well but is unsuitable for acidic settings. The addition of up to 10% pigment to the mixture weight has no significant impact on the final product's mechanical properties.
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 2 Basic Principles
### Attention Mechanism  
The attention mechanism was first proposed in the field of machine vision. It imitates human vision and divides different regions with different weights to show the difference. The attention mechanism has been widely used in the field of deep learning in recent years due to its ability to strengthen key information and weaken invalid information. The calculation process for the attention mechanism can be divided into three stages, as shown in Figure 4.  
In the first stage, the attention score corresponding to each key is obtained by calculating the similarity between the query and the key. In the second stage, the attention scores are normalized using the Softmax function to obtain the weighting coefficients. In the  
Figure 4: Structure of the attention mechanism.  
Figure 3: Structure of bidirectional long short-term memory network.  
third stage, different weight coefficients are weighted and summed to highlight important information and ignore unimportant information. The formulas are shown below.  
\[\begin{array}{c}F(Q,K_{i})=Q^{T}\cdot K\\ a_{i}=softmax(F(Q,K_{i}))=\frac{\exp(F(Q,K_{i}))}{\sum\
olimits_{i=1}\exp(F(Q,K _{i}))}\\ A(Q,K,V)=\sum\limits_{i=1}^{I}a_{i}\cdot V_{i}\end{array} \tag{5}\]  
where \(Q\) is the query matrix, \(K\) is the key matrix, \(V\) is the numerical matrix, and \(a_{i}\) is the weight coefficient. When \(Q=K=V\), the attention mechanism becomes a self-attention mechanism.
## 4 Discussion
### Limitations and Uncertainties  
The optimized VHI in the study improved the accuracy of agricultural drought monitoring in the YRB, but there were still some limitations and uncertainties.  
Firstly, the datasets utilized in this study are mainly sourced from remote sensing observations and meteorological models. These sources are subject to limitations, including sensor resolution, observation frequency, atmospheric conditions, and systematic errors during data integration [87; 88; 89]. Such limitations can significantly impact the accuracy of drought monitoring [90].  
Secondly, the VHI calculations span from 2001 to 2021, constrained by the availability of remote sensing data. Therefore, SPEI, calculated over a longer time frame, serves as a standard for climate-scale data. This methodology facilitates the calibration of VHI calculations for shorter time scales, enhancing the comparability of VHIopt with climate-scale data and mitigating the issue of insufficient representativeness in shorter time series [15; 91; 92; 93].  
Thirdly, SPEI is a standardized drought index, and its standardization process can mitigate the differences in climatic characteristics between regions, thereby enhancing spatial comparability. However, the significance of SPEI at different time scales is not entirely consistent in different climatic zones. This study uses multi-time-scale SPEI to optimize VHI, meaning that the performance of VHIopt will be influenced by the accuracy of SPEI [94; 95; 96].  
Fourthly, there may be a lag effect between temperature and vegetation conditions [97]. However, the lag effect typically occurs within a month, and since our research is conducted on a monthly scale, the impact of the lag effect is effectively minimized to a certain extent [98; 99].  
Finally, although human activities, such as alterations in farmland moisture, can impact the accuracy of VHI [9], this study found that VHIopt maintains accuracy comparable to VHIori even in farmland areas significantly influenced by human activities, which suggests that VHIopt is robust against such variations and retains reliable monitoring performance.
# Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs
## 5 Evaluation
### Experimental Results
#### 5.2.2 Object Detection with the LBP Classifier  
The runLBPClassifierStumpSimple kernel accounts for most of the LBP algorithm's execution time. Its occupancy is 30%, and the VGPR usage is the main occupancy limiting factor. Unlike the Haar algorithm, the LBP algorithm produces 384 wavefronts on the APU environment, which means that the global work size is large enough to supply sufficient work-items, as well as wavefronts. Accordingly, _wave_ did not need to be applied because the algorithm activates more than 320 wavefronts. Therefore, we applied only _occup_ to the object detection with the LBP algorithm.  
Figure 11 shows the throughput of the LBP object detection algorithm. With _occup_, mapping data from VGPR to LDS memory improved the occupancy by 30% to 40%. Therefore, the performance of the algorithm was improved by up to 31% and 21% in the APU and d-GPU environments as compared to the baseline.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Baseline** & **Occup** & **Wave** & **Occup + Wave** \\ \hline GlobalWorkSize & 768/8/1 & 768/8/1 & 2560/8/1 & 2560/8/1 \\ WorkGroupSize & 8/8/1 & 8/8/1 & 8/8/1 & 8/8/1 \\ Time & 154.42 & 109.40 & 102.88 & 89.15 \\ LocalMemSize & 3840 & 3840 & 3840 & 3840 \\ VGPRs & 76 & 47 & 76 & 47 \\ Wavefronts & 96 & 96 & 320 & 320 \\ FetchSize & 414,025 & 423,297 & 415,139 & 432,954 \\ VALUInsts & 3,303,635 & 2,774,580 & 991,668 & 832,949 \\ SALUInsts & 626,184 & 626,184 & 188,395 & 188,395 \\ LDSInsts & 389,382 & 390,784 & 116,815 & 117,238 \\ VALUBusy & 37.80 & 38.28 & 54.38 & 49.40 \\ SALUBusy & 6.35 & 8.11 & 9.15 & 10.58 \\ MemUnitBusy & 48.65 & 68.51 & 66.29 & 74.04 \\ FlatVMemInsts & 524,978 & 526,380 & 157,493 & 157,917 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Hardware performance counter results of runHaarClassifier.  
Figure 11: Object detection throughput using runLBPClassifierStumpSimple, values normalized to the baseline.  
Table 8 summarizes the measurements of the hardware performance counters while running the runLBPClassifierStumpSimple kernel. The baseline showed 30% of occupancy due to 67 VGPR usage, but _occup_ achieved 40% occupancy by reducing its VGPR usage to 64. Significant differences in the activation ratio of the ALU and memory units did not appear between _baseline_ and _occup_ because the original activation ratio in the _baseline_ is already high. However, both the VALUBusy and VALUInsts values demonstrate that the performance was improved because of the increased active work-items by achieving higher occupancy.
## 2 Concepts and Applications of TMPR
### Three-Dimensional Centerline Reconstruction of a Vascular Map Utilizing TMPR
Likewise, if the index of \(\mathbf{r}_{corr}(t+2)\) is less than or equal to \(t\) or \(t+1\), \(\mathbf{p}_{opt}\) corresponding to \(r_{ref}(k+2)\) cannot be determined. Similarly, if no \(\mathbf{r}_{corr}\) points satisfy the distance constraint, as shown in Figure 4 ij, \(\mathbf{p}_{opt}\) cannot be assigned. After assigning \(\mathbf{p}_{opt}\) to all \(\mathbf{r}_{ref}\) points, the points \(\mathbf{r}_{ref}\) which have \(\mathbf{p}_{opt}\) are classified as Group 1 and the points \(\mathbf{r}_{ref}\) which do not have \(\mathbf{p}_{opt}\) are classified as Group 2. The points \(\mathbf{p}_{opt}\) for Group 2 are assigned using the interpolation method. Figure 4i,j illustrates the process for determining \(\mathbf{p}_{opt}\) for \(\mathbf{r}_{ref}(k+6)\) of Group 2 due to the absence of \(\mathbf{r}_{corr}\) satisfying the distance constraint. \(\mathbf{r}_{ref}(k+5)\) and \(\mathbf{r}_{ref}(k+7)\) belong to Group 1, and if their \(\mathbf{p}_{opt}\) are \(\mathbf{r}_{corr}(t+6)\) and \(\mathbf{r}_{corr}(t+7)\), respectively, then \(\mathbf{p}_{opt}\) for \(\mathbf{r}_{ref}(k+6)\) is selected from the point lying on the line connecting \(\mathbf{r}_{corr}(t+6)\) and \(\mathbf{r}_{corr}(t+7)\) in such a way that the ratio of the number of pixels between \(\mathbf{r}_{ref}(k+5)\) and \(\mathbf{r}_{ref}(k+6)\) (\(a_{1}\)) to the number of pixels between \(\mathbf{r}_{ref}(k+6)\) and \(\mathbf{r}_{ref}(k+7)\) (\(a_{2}\)) is equal to the ratio of the number of pixels between \(\mathbf{r}_{corr}(t+6)\) and \(\mathbf{p}_{opt}\) (\(a_{3}\)) to the number of pixels between \(\mathbf{r}_{corr}(t+7)\) and \(\mathbf{p}_{opt}\) (\(a_{4}\)). After determining the \(\mathbf{p}_{opt}\) for every \(\mathbf{r}_{ref}\), initial \(Z\) coordinates can be calculated using (9) and (10). Finally, through the \(\alpha\)-expansion method that searches the optimal \(Z\) with the proposed initial \(Z\) coordinates, an energy function can be minimized and the 3D coordinates of a blood vessel centerline can be calculated. In all iterations, TMPR was used to transform between the 3D coordinate system of the C-arm and the 2D coordinate system of the X-ray image.
## 2 Hot Topics
### Non-PGM Catalyst Development  
Theoretically, due to a high pH environment, AEMWE can permit the substitution of PGM electrocatalysts with Earth-abundant first-row transition metals (e.g., Mn, Fe, Ni, Cu, and Mo) to catalyze OER and HER reactions. Currently, IrO\({}_{2}\), Ni, Ni-Fe alloys, graphene, Pb\({}_{2}\)Ru\({}_{2}\)O\({}_{6.5}\), and Cu\({}_{0.7}\)CO\({}_{2.3}\)O\({}_{4}\) are used as OER catalysts, while Pt black, CuCoOx, Ni/CoO\({}_{2}\)-La\({}_{2}\)O\({}_{3}\)/C, Ni, and graphene could be used as HER catalysts [2]. Ir and Pt could be replaced by Fe, Ni, Mn, Cu, and Mo, considerably decreasing the prices of the global system. PGM-free electrocatalysts are already used to catalyze OER, with Ni-based electrocatalysts, which are currently state-of-the-art. However, for the HER, the state-of-the-art electrocatalysts are still based on precious metals [4].  
[PERSON] et al. [13] wrote a complete review paper with an overview of the latest electrocatalysts for HER and OER. The authors reported that transition metal catalysts with very good electrocatalytic properties can play a role in improving AEMWE performance. For instance, the NiMo-NH\({}_{3}\)/NH\({}_{2}\) for HER coupled with a Fe-NiMo-NH\({}_{3}\)/NH\({}_{2}\) for OERreached 1 Acm\({}^{-2}\) at 1.57 V. [PERSON] et al. [3] illustrated that OER kinetics can be influenced by a higher catalyst loading at the anode, and a lower utilization rate, particularly when the concentration of hydroxyl ions in the electrolyte has diminished, and active sites on the catalyst have been blocked by the inevitable high pressure of gas evolved in the MEA. The authors also stated that Ni-Mo alloyed materials, Co\({}_{3}\)O\({}_{4}\) nanocrystals, Fe, and Ni-Fe alloys are good candidates to improve OER performance; however, stability through a period of one thousand hours needs to be confirmed. [PERSON] et al. [13] reported that bifunctional (the same for HER and OER reactions) catalysts should be investigated, making a simpler electrode system. For OER, many of the reported studies demonstrated high activity and efficiency of transition metal-based catalysts, predominantly NiFe-based, however, the majority still present high overpotentials beyond 200 mV, at 10 mAcm\({}^{-2}\)[13]. The high overpotentials are associated with the slow and difficult OER mechanism [3]. Considering HER electrocatalysts, the use of transition metals is still challenging because most of these catalysts are not as active as Pt/C. HER is also slower in alkaline media than in acid media [18]. Consequently, to decrease the overpotential and improve the activity of HER transition metal catalysts, there is a need to understand their reaction mechanism related to the adsorption of water molecules and bond breaking. One possible approach, reported by [PERSON] et al. [3], is the addition of heteroatoms, such as N-doping, possibly lowering the adsorption-free energy, facilitating HER. In addition, their stability and efficiency must be confirmed for real-world applications.
## 2 The IDIVS Concept and Application Domains  
### The IDIVS Concept  
The IDIVS is a logical entity/instance (software) producing a sensor-like output in real time that can be a numerical or categorical value, e.g., the number of people or activity in an environment. IDIVSs may be used in a hierarchical architecture, where an IDIVS may use the output from other IDIVSs as input, some sensors may be hidden from some IDIVSs, and services may access all or a sub-set of IDIVSs' output, as illustrated in Figure 1.  
We assume there are mechanisms for both securing data access, i.e., access control for entities, as well as for supporting the discovery of sensors, IDIVSs, and services. The IDIVSs are primarily, but not exclusively, intended for use in environments where people are or may be present, where they may use services relying on information from the IDIVS, and where they may provide information to the IDIVS, e.g., in interactive learning. We see the following roles a human user may have in relation to the IDIVS.  
Figure 1: The hierarchical structure of IDIVS and service support.  
User roles:
* Service user, i.e., a user of a service relying on the IDIVS output;
* Interactive service user, i.e., a user that also interacts with the system, providing input to the system that can help the system learn/adapt. IDIVS configuration roles:
* System configurator, i.e., defining what set of sensor/IDIVS to use as input, what states to detect, where to distribute the output, etc.;
* IDIVS teacher, e.g., orchestrating scenarios, which is used for training IDIVSs in a systematic manner.  
The idea is that each IDIVS is connected to an environment, e.g., a room in a building or a square in a city, etc., where sensors of different types may be present at different times. It may also be connected to an object or even a person, e.g., being a personal IDIVS.
## 3 SWIFTNET: Protocol for Fast-Reactive Monitoring
### Compressed Sensing
#### 3.1.2 Sensing Matrix  
Given a sensing matrix \(M=\Phi\Psi\), where \(\Phi\) is an \(m\times n\) measurement matrix drawn randomly from suitable distribution and \(\Psi\) is an arbitrary orthobasis, it has been shown by [41] that, to have a robust signal recovery, the sensing matrices must obey the Restricted Isometric Property (RIP) condition, _i.e._, matrices with the property that column vectors taken from arbitrary subsets are nearly orthogonal. Thus, the larger these subsets, the better the recovery process. According to [41], it has been proven that any random matrix generation that obeys the RIP condition [42] is suitable for a signal recovery process, provided that  
\[m\geq C\cdot K\text{log}(n/K) \tag{11}\]  
where \(C\) is a constant chosen depending on the instance. Consider the following matrix generation processes:  
1. Generate matrix \(M\) by sampling \(n\) column vectors uniformly at random on the unit sphere of \(\mathbb{R}^{m}\).
2. Generate matrix \(M\) by sampling independent identically distributed (i.i.d) entries from a normal distribution with variance \(=1/m\) and mean \(=0\).
3. Generate matrix \(M\) by a random projection \(P\) as in \"Incoherent Sampling\" and normalize: \(M\) = \(\sqrt{n/m}P\).
4. Generate matrix \(M\) by sampling independent identically distributed entries from a symmetric Bernoulli distribution (P(\(M_{i,j}=\pm 1/\sqrt{m})=1/2\)).  
For all these example matrices, the probability of sampling a matrix not obeying RIP when Equation (11) holds is exponentially small in \(m\)[41]. The authors further asserted that using a randomized matrix, together with \(l_{1}\) minimization, can offer a near-optimal sensing strategy. Alternatively, if we fix the orthobasis \(\Psi\) and generate a measurement matrix \(\Phi\) according to any of the rules in Conditions (1)-(4) above, then with an overwhelming probability the matrix \(M\) obeys the RIP constraint, provided condition (11) is met [41]. This means, we can accurately reconstruct nearly sparse signals from under-sampled data in an incoherent domain. We therefore anchor the design of SWIFTNET on these assertions, by generating matrix \(M\) and sampling \(n\) column vectors uniformly at random on the unit sphere of \(\mathbb{R}^{m}\) with the hope that we can solve the convex optimization problem using the \(l_{1}\) minimization method (see Equation (9)). One key contribution of our approach is that we have been able to successfully apply practically these theories to achieve an energy-efficient data acquisition method that is more robust than the traditional sense-transmit or prediction approaches.  
Next, we present the prediction algorithm we have used in our design.
## Materials and Methods
### Dynamic Detection of Rhizosphere Pathogens by Real-Time Quantitative Polymerase Chain Reaction  
Real-time quantitative PCR (RT-qPCR) was used to detect rhizosphere changes in _P. nicotianae_-infected soil after 10 days. The specific experimental methods are according to previous studies [28], with a few modifications. Briefly, RT-qPCR using DNA obtained rhizosphere soil, which was purified using the DNEasy(r) PowerSoil(r) Kit (Qiagen, Hilden, Germany) according to the manufacturer's protocol. Real-time quantitative PCR amplification was performed using primers (SP: 5'-TGAAGAACGCTGCGAACTGC-3', and AP: 5'-CTGACATCTCTCCACCGACTA-3') designed on the 18 SrDNA gene sequence of _P. nicotianae_ published in the NCBI database. The amplified fragment was 172 bp. Real-time quantitative PCR reactions were conducted in 20 uL reaction volumes containing 2.0 uL cDNA, 10.0 uL SYBR premix (TAKARA), 0.4 uL SP (10 uM/uL), 0.4 uL AP (10 uM/uL), and 7.2 uL ddH2O. The cycling protocol consisted of the following: 94 \({}^{\circ}\)C for a 5 min holding stage; 40 cycles of 94 \({}^{\circ}\)C for 20 s, 65 \({}^{\circ}\)C for 40 s, and 72 \({}^{\circ}\)C for 40 s.  
The genomic DNA of _P. nicotianae_ was diluted into six concentration gradients with three replicates per gradient with 10 dilutions, and the negative control was treated with sterile ultra-pure water. Quantitative PCR was used to collect fluorescence signals, confirm the amplification and melting curves, analyze the data by software, make standard curves, and generate standard curve equations.
# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 1 Introduction  
Fifth-generation (5G) and beyond communications are mainly characterized by (i) massive connectivity, (ii) ultra-reliability and low latency, and (iii) increased throughput. Satisfying these objectives in conjunction with the rapid growth of the Internet of Things (IoT) applications represents a challenging task, especially in highly dynamic and heterogeneous environments. A promising approach is to adopt unmanned aerial vehicles (UAVs) as aerial user equipments (UEs) or flying base stations (BSs). In particular, UAV-based communications can improve the network performance in emergency situations by providing rapid service recovery and by offloading in extremely crowded scenarios. These characteristics have attracted the interest of the standardization bodies [1] and academia. Furthermore, the integration of artificial intelligence (AI) and machine-learning (ML) techniques in wireless networks can leverage intelligence for addressing various issues. Thus, the combination of AI/ML and UAVs appears to be strongly correlated in different disciplines and applications and throughout the network layers, promising unprecedented performance gains and complexity reduction. In the following subsections, a short introduction on the areas of UAVs and ML is presented while relevant surveys are discussed, identifying the current gap in the literature that has motivated the current work.
## 1 Introduction
* Additionally, we propose the survivability analysis method based on Markov process (SAM-MP) to evaluate the survivability performance of the UAV cluster network and analyze the ability of the proposed UAV cluster network based on DWCA-DCH to complete the communication service when the system suffers from energy depletion, failure, or accidents. * Simulation results demonstrate the effectiveness of the proposed algorithm in improving system availability and stability. The results also showcase the feasibility of the proposed SAM-MP for analyzing the survivability of the UAV network.
## 4 Discussion
### Limitations and Perspectives of This Study  
While this study quantitatively assessed the impact of irrigation on cropland CWUE, it did not comprehensively explore the synergistic mechanisms among irrigation policies, management practices, and natural environmental factors. For instance, traditional irrigation methods commonly used in Henan Province often result in substantial water resource inefficiencies. However, this study failed to quantify the dynamic coupling between anthropogenic management practices and environmental variables, such as soil organic matter and fast-acting nutrients. Future research should integrate high-resolution remote sensing data, ecological process models, and socio-economic data through multidisciplinary approaches. This integration would support the development of a dual-driven CWUE response model that captures the interactions between natural and human factors. Such a model would enable multi-source data fusion and the coordinated optimization of carbon-water processes, offering an evidence-based pathway to support Henan Province's \"storing grain in the ground\" strategy--one that simultaneously promotes water conservation, carbon sequestration, and stable yields.