esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Performance of MC Algorithms within the Context of eSSPM  
Although most PA zoning essays reported by literature are based on implementations of particular clustering approaches (e.g., fuzzy c-means, FANNY, McQuitty) [10; 11], results presented here show that more than one MC algorithms tend to be necessary to delineate MZ with pest control purposes. This is because the spatial nature of data sets to be clustered during field partitioning essays could (and many times do) vary considerably depending on the pest to be managed and on the portion of the crop field being partitioned. Thus, clustering methods of proven efficiency to develop binary field partitions do not necessarily correspond to those that offer the best performance during complementary ones (whether presence-only or absence-only), even when the same pest is being considered. Similarly, any set of MC algorithms used to partition a crop field assuming a pest \"A\" will rarely perform accurately during partitioning essays developed for a pest \"B.\"  
Despite this result, a general pattern was observed where partitioning essays intended to distinguish between pest presence and pest absence sites (i.e., binary, absence-only) were better resolved by hierarchical MC algorithms (i.e., pests 1, 3, 4, 5, and 6), whereas more sophisticated approaches (i.e., SOM, DIA, MCL) were necessary to find differentiated levels of pest presence within general presence clusters (i.e., pests 1, 4, 5 and 6). This trend is consistent with published research that highlights the efficiency of hierarchical MC algorithms (i.e., SL, CL, WL) to partition sets of well-separated binary data (whether biologically meaningful or not) [10; 65; 66], as well as that of partitioning and model-based algorithms (i.e., MCL, DIA, SOM) to perform this same task with data sets that include observations more closely positioned in statistical space [50; 67; 68]. It must be noted, however, that plenty of other research works successfully explore the implementation of hierarchical clustering methods to partition spatially close data sets, as well as partitioning and model-based approaches to partition well separated binary data sets. Taking these observations into account, we recommend developing eSSPM field partitioning essays (i.e., binary, complementary) based on a combination of MC algorithms that best suit the particularities of the specific pests and fields to be managed.
# Hybrid Graph Models for Traffic Prediction
## 3 Preliminaries  
### Traffic Prediction on Road Graphs  
In this paper, we define the traffic network as a directed graph \(G=(V,E,A)\), where \(V\) is a set with \(|V|=N\) nodes, \(E\) is a set of edges representing the connectivity between the nodes, and \(A\in R^{N\times N}\) denotes the adjacent matrix of the graph representing the proximity between nodes. The intelligent transportation system records the road conditions \(X=\{X_{0},X_{1},\ldots,X_{t},\ldots\}\) of all nodes in the traffic network, where \(X_{t}=\{x_{1,t},x_{2,t},\ldots,x_{N,t}\}\in R^{N\times 1}\) represents the road information at time \(t\). The goal of traffic prediction is to predict the future state based on the observed historical state. In this paper, we formulate the traffic prediction problem as finding a function \(F\) to predict the next \(Q\) time steps based on historical \(P\) steps of historical data:  
\[\hat{Y}=\{X_{t+1},X_{t+2,\ldots,X_{t+Q}}\}=F_{\theta}(X_{t},X_{t-1},\ldots,X_{ t-P+1}) \tag{1}\]  
where \(\theta\) denotes learnable parameters.
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 7 Proposed System Architecture  
The survey concluded that research involved in detecting and classifying plant diseases used convolutional neural network models and object detection techniques to detect and classify plant diseases. Although these models give us high accuracy results, there is a tendency for further improvement. Furthermore, these models are lacking in the early detection of diseases which requires more advancement and hybridization to improve this detection time, accuracy, performance, detection time, and range of conditions. Hence, we proposed a hybrid model (CNN-SVM) for the early detection of plant leaf diseases where many drones captured images will be used as the data set. Then this proposed hybrid model will process the data and generate early decisions. Our model consists of three main parts a CNN model with an attention mechanism applied and an SVM classifier, as shown in Figure 9.
# Data-Aided and Non-Data-Aided SNR Estimators for CPM Signals in Ka-Band Satellite Communications
###### Abstract  
Adaptive coded and modulation (ACM) is an effective measure to resist rain attenuation in Ka-band satellite communications. The accuracy of the estimator for signal-to-noise ratio (SNR) is one of the main factors that affect ACM performance. This paper establishes the channel model of satellite communication in Ka-band and derives and analyzes data-aided (DA) maximum likelihood (ML) and method-of-moments estimators for SNR of continuous phase modulation (CPM) in Ka-band fading channel. Simulations and analysis indicate that the normalized mean square error (NMSE) of the DA ML estimator is closer to the Cramer-Rao bounds (CRB) at low SNR, but the performance worsens with the SNR increases from medium to high values. The M\({}_{2}\)M\({}_{4}\) estimator performs poorly at low SNR and best at medium SNR. When the SNR is low, the performance of the DA ML estimator is better than the M\({}_{2}\)M\({}_{4}\) estimator; however, the two estimators have similar performance when SNR is high. The performances of the two estimators will become increasingly better the greater the lengths of the observation signals become. However, the influence of the signal length will become increasingly smaller as the SNR becomes larger.  
SNR estimation; Ka-band fading channel; CPM signals; DA ML estimation; M\({}_{2}\)M\({}_{4}\) estimation +
Footnote †: journal: Information Information
# A Coal Mine Excavation Tunnels Modeling Method Based on Point Clouds
## 3 Methods
### Tunnel Mesh Modeling  
This paper establishes the mesh model of the tunnel based on the Poisson reconstruction algorithm [15]. Poisson reconstruction is a commonly used implicit surface reconstruction method, where the input is a set of oriented point clouds of the object surface, and the output is a three-dimensional mesh of the object surface. The Poisson surface reconstruction algorithm transforms surface reconstruction into a problem of solving the Poisson equation. It stitches the extracted iso-surfaces to obtain the reconstructed model. This method considers all points simultaneously from a global perspective to obtain the optimal solution under specific criteria. It can handle noisy data and generate smoother surfaces.  
Figure 11: Point cloud normal optimization: (**a**) Before optimization. (**b**) After optimization.
## 4 The Proposed DSB Workflow Scheduling Algorithm  
### Assumptions  
The current study assumes that the workflow application executes in a single cloud data center so that one possible source of execution delay and data transfer cost between data centers is eliminated. The average bandwidth between the VM instances is assumed to be fixed during the execution of \(W\). The estimated runtime of task on a VM instance, denoted as \(RT^{r}_{i}\) is known to the scheduler in advance. It is obtained by Equation (1). The runtime of virtual entry and exit nodes is zero. Moreover, the tasks do not have the ability to checkpoint their work or get preemption. In other words, a task has to be restarted in case of its failure. Furthermore, we assume that for every VM type, the processing capacity is furnished either from the IaaS provider or can be estimated based on the work of [27]. We assume a VM boot time of 97 s based on the measurements presented by [PERSON] and [PERSON] [9] for EC2 cloud. We adopted a performance degradation model based on results achieved in Amazon EC2 clouds by  
Figure 1: A workflow system architecture. DB: Database.  
[PERSON] et al. (2018). Therefore, we consider the loss in VM performance based on a normal distribution with mean of 15% and standard deviation of 10% in our proposed approach as well.  
Similarly, the amount of data needed to be communicated between the tasks of a workflow are considered to be known in advance. The communication time for the tasks allocated to the same VM is considered to be zero. Moreover, data communication cost between resources are assumed to be zero, since in IaaS clouds, data communication inside a data center is free. Furthermore, the cloud datacenter is considered to have unlimited resources, so resource contention is almost negligible. Additionally, we assume that all VMs have enough memory to run any task of the workflow.
# Self-Adaptive Differential Evolution with Gauss Distribution for Optimal Mechanism Design
## 5 Numerical Simulation
### Modified Differential Evolution for Real-World Problem
#### 5.4.3 Design Problem of Compression Spring  
A compression spring will be considered in this part [25]. The target is to minimize weight of spring, while constraint functions are limitation of deflection, shear stress, surge frequency, moreover, outside diameter and design variables are limited in certain range. The optimized parameters were wire diameter \(x_{1}(d)\), outer diameter \(x_{2}(D)\), and number of turns \(x_{3}(N)\), as seen in Figure 25. This optimization problem is described by Equations (33)-(37).  
Minimize:  
\[f(x)=(x_{3}+2)x_{2}x_{1}^{2} \tag{33}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Best** & **Mean** & **Worst** & **Standard Deviation** \\ \hline CDE & 6059.7340 & 6085.2303 & 6371.0455 & 43.013 \\ CPSO & 6061.0777 & 6147.1332 & 6363.8041 & 86.454 \\ CGA & 6288.7445 & 6293.8432 & 6308.1497 & **7.413** \\ EOBLDE & 6059.7143 & 6093.8431 & 6370.7797 & 83.671 \\ G-ISADE & 6059.60 & 6067.26 & 6117.06 & 19.8682 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical results for air-storage tank problem.  
Figure 25: Tension/compression-spring design problem.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Design Variables** & **CDE** & **CPSO** & **CGA** & **EOBLDE** & **G-ISADE** \\ \hline \(x_{1}\) & 0.8125 & 0.8125 & 0.8125 & 0.8125 & 0.78603 \\ \(x_{2}\) & 0.4375 & 0.4375 & 0.4375 & 0.4375 & 0.46406 \\ \(x_{3}\) & 42.0984 & 42.0912 & 40.3239 & 42.0984 & 42.0000 \\ \(x_{4}\) & 176.6376 & 176.7465 & 200.0000 & 176.6365 & 177.8603 \\ \(g_{1}\) & \(-6.677\times 10^{-7}\) & \(-0.0001\) & \(-0.0343\) & \(-1.1102\times 10^{-16}\) & \(-0.0019\) \\ \(g_{2}\) & \(-0.0358\) & \(-0.0359\) & \(-0.0528\) & \(-0.0358\) & \(-0.0368\) \\ \(g_{3}\) & \(-3.6830\) & \(-116.3827\) & \(-27.1058\) & \(-2.3283\times 10^{-10}\) & \(-2.3283\times 10^{-10}\) \\ \(g_{4}\) & \(-63.3623\) & \(-63.2535\) & \(-40.0000\) & \(-63.3634\) & \(-62.1396\) \\ \hline \(f(x)\) & 6059.7340 & 6061.0777 & 6288.7445 & 6059.7143 & **6059.60** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Best solution for air-storage tank problem.  
Subject to  
\[g_{1}\ =\ 1.0-\frac{x_{2}^{3}x_{3}}{7,1785x_{1}^{4}}\leq 0; \tag{34}\]  
\[g_{2}\ =\ \frac{4.0x_{2}^{2}-x_{1}x_{2}}{12,566(x_{2}x_{1}^{3}-x_{1}^{4})}+\frac{1. 0}{5,108x_{1}^{2}}-1.0\leq 0; \tag{35}\]  
\[g_{3}\ =\ 1.0-\frac{140.45x_{1}}{x_{2}^{2}x_{3}}\leq 0; \tag{36}\]  
\[g_{4}\ =\ \frac{x_{1}+x_{2}}{1.5}-1.0\leq 0; \tag{37}\]  
where \(0.05\leq x_{1}\leq 2.0\), \(0.25\leq x_{2}\leq 1.3\), and \(2.0\leq x_{3}\leq 15.0\).  
Our optimal solution for this problem was evaluated with the four methods, namely CDE, CPSO, CGA, and EOBLDE. The best solution for all approaches is shown in Table 7, while their statistics are expressed in Table 8. The optimal solution obtained by G-ISADE is better than those of the other approaches.
# Overview and Perspective of Integrated Photovoltaics with a Focus on the European Union
## 3 Integrated Photovoltaics Options
### Infrastructure-Integrated Photovoltaics
#### 3.4.3 Parking Lots and Individual Carports  
A huge untapped potential for the installation of PV systems and accelerating the transition towards electromobility are PV systems on the numerous parking lots and individual parking canopies. The idea is not new, but so far only limited applications have been realized [43, 44]. The reason is that this kind of installation still has a number of hurdles to overcome. These structures are classified, as buildings, thus the applicable standards as well as the planning time can be considerably longer than conventional roof-mounted PV systems. As the PV system is not just an add-on, the necessary roofing structures have to be included in the cost calculation.  
In a number of EU countries, an increasing number of car parks are being equipped with PV systems. It is assumed that this trend is driven by the increase in electric vehicles.  
In France and in six German federal states, it has become a legal requirement to equip car parks over a certain size with solar PV systems [45; 46; 47; 48; 49; 50; 51]. The main difference is that the French Renewable Energy Acceleration Act (loi ApER), adopted in 2023, applies to all car parks with more than 80 parking lots or an area of 1500 m\({}^{2}\), whereas the obligations in the six German federal states applies only to new car parks with over 35 parking lots.  
In France, the car parking owners now have five years to comply with this obligation and the French government expects a new PV capacity of 11 GWp on these parking lots.  
As the German obligation applies only to new car parks, it is difficult to estimate how much new capacity will be installed. However, the Fraunhofer ISE has conducted a study calculating that 59 GWp could be installed on the largest 300,000 parking lots alone in Germany [52].
# Traffic-Aware Optimization of Task Offloading and Content
## 6 Conclusions  
To address the resource wastage issue arising from redundant content requests in the IoV, we propose an innovative solution that integrates task offloading and content caching optimization. Utilizing the FOST deep learning model, we extract spatial and temporal correlations to forecast traffic patterns. Building upon this foundation, we decompose the multi-objective problem of latency and energy consumption optimization into separate single-objective objectives, employing the Tchebycheff weight aggregation method within the MOEA/D algorithm. Our approach, named TOCC, is rigorously validated through comprehensive experiments. The results unequivocally demonstrate that TOCC outperforms LOCAL by 29% in terms of the average execution time, surpasses TFO by 21%, and exhibits a slight performance gap compared to F_NSGA-III. In the context of energy consumption, TOCC excels by 43% over LOCAL, outperforms TFO by 83%, and surpasses F_NSGA-III by 79%.  
Looking forward, our work will include coping with dynamic changes in resource allocation, task prioritization under emergency situations [31], and other issues. Furthermore, we hope to extend our approach to more realistic application scenarios, ensuring its adaptability to diverse and evolving vehicle environments.  
Conceptualization, P.W.; methodology, P.W. and Y.W.; software, P.W., Y.W. and J.Q.; validation, P.W. and J.Q.; formal analysis, P.W. and Y.W.; investigation, P.W. and Y.W.; resources, P.W.; data curation, Y.W. and J.Q.; writing--original draft preparation, P.W. and Y.W.; writing--review and editing, P.W., Y.W. and Z.H.; visualization, P.W. and Y.W.; supervision, P.W.; project administration, P.W.; funding acquisition, P.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) under Grant 61602109, the DHU Distinguished Young Professor Program under Grant LZB2019003, Shanghai Science and Technology Innovation Action Plan under Grant 22511100700, and Fundamental Research Funds for the Central Universities.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: BikeNYC: [[https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) mhlPrRE#list/path=%2F]([https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) mhlPrRE#list/path=%2F) (accessed on 25 September 2023); FOST: [[https://github.com/microsoft/FOST](https://github.com/microsoft/FOST)]([https://github.com/microsoft/FOST](https://github.com/microsoft/FOST)) (accessed on 25 September 2023).  
The authors declare no conflict of interest.
## 3 Numerical Verification
### Multiple Damage Identification  
This section will further discuss its performance in multiple damage scenarios. One multiple damage scenario is discussed. In Case 7, Elements 7, 13, and 18 experience damage levels of 30%, respectively. Figure 6 examines the variational modal decomposition-continuous wavelet transform derived from the contact point acceleration. As shown in Figure 6, three distinct peaks are evident at the damaged locations. The finding suggest that the variational modal decomposition-continuous wavelet transform extracted from contact point acceleration is effective for identifying damage across multiple damage scenarios.  
Figure 5: Continuous wavelet transform of contact point response in Cases 1–4.  
Figure 6: Multiple damage scenarios (# indicates damage level).
## 1 Introduction
* Microwave ranging relies on a carefully designed transceiver antenna with relatively wider main lobe angle of a few degrees, which greatly reduces the burden of the high-precision pointing mechanism used for optical metrology. At the same time, microwave ranging can operate in pseudo-code mode or carrier mode for coarse/precise ranging if necessary, as required by mission control, providing flexible solutions to the GNC system. * We have inherited a great deal of microwave ranging technology from existing space experience. MWR has been developed and tested previously, and successfully verified in one LEO formation mission [15]. * As an essential supplement to the MWR system, BeiDou III B1C/B2a dual-frequency new-generation navigation signal receive and process technology can be fully tested and verified in HEO formation missions. The B1C/B2a dual-frequency receiver on-board can provide highly precise time synchronization solutions with nanosecond precision, which serves as a time-scale benchmark between formation spacecrafts. Moreover, the receiver can provide precise stand-alone navigation solutions while using a precise orbit determination algorithm. * Microwave can also be used for real-time data transmission between spacecraft for original scientific data exchange, differential GPS measurement data transmission for relative navigation, etc. This paper focuses on the principle, design, development, and testing of MWR equipment that will be deployed in the HEFFD mission. The following content is organized as follows: Section 2 introduces the development of the submillimeter-level accuracy microwave ranging technique and MWR equipment. Section 3 describes the relative orbit models and perturbations considered during real formation flight in space. Section 4 provides the relative navigation filter algorithm that deals with process noise uncertainty, and the simulation results using MWR and proposed methods are finally given in Section 5.
## 5 Experimental Results  
In this experimental results section, the optimization framework is verified by using two test cases in Sections 5.1 and 5.2. The test cases are carefully chosen to prove the framework's ability to search quickly for optimal performances, as well as its capability for fine tuning the parameter space in wireless sensor networks. For this work, the optimization process is based on the tuning of parameter configurations of existing protocols to achieve the best optimal behavior. In both cases, the parameter space under investigation consists of the four parameters of the unslotted CSMA/CA algorithm [17], which are _macMinBE_, _macMaxBE_, _macMaxCSMABackoffs_, and _macMaxFrameRetries_. With the range from 3 to 8 for _macMaxBE_, 0 to _macMaxBE_ for _macMinBE_, 0 to 5 for _macMaxCSMABackoffs_, and 0 to 8 for _macMaxFrameRetries_, the total number of solutions has a wide possibility of 1,872 combinations in the parameter space. Figure 6 shows the flowchart of the unslotted CSMA/CA algorithm.  
As shown in Figure 6, the transmitter node first performs a random backoff duration and checks the channel status. If the channel is detected to be busy, and the number of CCA attempts is larger than the protocol parameter _macMaxCSMABackoffs_, then a Channel Access Failure (CAF) is reported. If, on the other hand, the number of CCA attempts is smaller than _macMaxCSMABackoffs_, the transmitter node will go back for a new round of random backoff process. When the channel is indicated as free by CCA, the transmitter node will send a packet to its transceiver and trigger over-the-air transmission. After the transmission of an ACK required data packet, the protocol will make the transmitter node wait within a fixed time period (0.864 ms or 54 symbols) for the ACK frame confirmation. If ACK is received in time, this transmission is regarded as successful. Otherwise, the packet retransmission process will start. A Collision Failure (CF) occurs only after failure to receive ACK frame _macMaxFrameRetries_ times, which might be caused by collision of data packets or collision between data packets and the ACK frames. In addition, as long as there are pending data packets in the node, they will be uploaded to the transceiver immediately, once the transmission process is over (no matter whether a success or a failure), which is to guarantee the timeliness and reliability of data transmission. In this work, the unslotted CSMA/CA algorithm is implemented in our SystemC-based simulation engine [32, 33], and iMASK is used to fine tune simulation's parameter space (_macMinBE, macMaxBE, macMaxCSMABackoffs, macMaxFrameRetries_) for the optimum performance metrics based on the requirements of the given application.  
In the following two sub-sections, the first case applies a weighted sum fitness function as the trade-off for evaluating multiple performance metrics. The second case is for a simultaneous multi-objective optimization under multi-scenario conditions. All the experiments performed in this section were executed on an Intel Xeon server operating with Linux CentOS (4 CPUs, 8 GB RAM, 2.4 GHz).
# An Improved Model-Free Adaptive Nonlinear Control and Its Automatic Application
## 3 Model-Free Adaptive Control Design with Output Error Rate (MFAC-OER)
### Stability Analysis
Since the error and expectation are bounded, it is easy to know that the output sequence is bounded--it must simply be proven that the input sequence is bounded. According to Equations (10) and (27), and Assumption 3, the following formula can be obtained:  
\[\left|\Delta u(t)\right|\leq\left|\frac{\hat{\phi}_{2}(t)\theta}{\lambda+l_{ 1}|\phi_{2}(t)|^{2}}\right|\left|e(t)\right| \tag{31}\]  
Using inequalities \(\sqrt{ab}\leq a+b\) and \(\lambda_{\text{min}}<\lambda\), we can obtain  
\[0<M_{1}\leq\left|\frac{\hat{\phi}_{2}(t)\theta}{\lambda+l_{1}|\phi_{2}(t)|^{2 }}\right|\leq\left|\frac{|\phi_{2}(t)|\theta}{|\phi_{2}(t)|\sqrt{\lambda_{ \text{min}}l_{1}}}\right|=\frac{\theta}{2\sqrt{\lambda_{\text{min}}l_{1}}}= \frac{1}{b} \tag{32}\]  
Furthermore, from Equations (31) and (32), we can obtain  
\[ \begin{array}{l}\left|u(t)\right|\leq\left|u(t)-u(t-1)\right|+\left|u(t-1)\right| \\ \leq\left|\Delta u(t)\right|+\left|u(t-1)-u(t-2)\right|+\left|u(t-2)\right|\\ \leq\left|\Delta u(t)\right|+\left|\Delta u(t-1)\right|+\cdots+\left|\Delta u (2)\right|+\left|u(1)\right|\\ \leq\left|\frac{1}{b}\right|\left(\left|e(t)\right|+\left|e(t-1)\right|+\cdots +\left|e(2)\right|\right)+\left|u(1)\right|\\ <\left|\frac{1}{b}\right|\left(M^{t}|e(1)\right|+M^{t-1}|e(1)|+\cdots+M|e(1) \right)+\left|u(1)\right|\end{array}  \tag{33}\]  
Since \(\left|\frac{1}{b}\right|\) and \(M\) are bounded, it is not difficult to establish that the input sequence is bounded. To sum up, the conclusion (1)-(3) of Theorem 2 are proven.