esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In addition to remote sensing data, climate data, such as precipitation and air temperature, might be important elements in predicting the plant density, since it could affect seedling establishment and plant growth such as leaf area expansion [5; 30; 31]. The aim of this study was to develop a satellite-based model for estimating soybean plant density through machine learning techniques. Specifically, the high-throughput measurement of actual plant density quantified by UAV imagery and YOLOv3 models was used as the response variable for plant density estimation models, while PlanetScope imagery and climate data were used as predictors for the models. The prediction accuracies of partial least squares (PLS) regression and random forest (RF) regression were compared in developing estimation models, as these methods were reported to be robust for dealing with remote sensing imagery data [32; 33; 34; 35; 36; 37]. Finally, the spatial pattern of plant density was further assessed using the developed model to examine the potential implications for better crop management practices and the limitations of the satellite-based models.
# Waveguide Manufacturing Technologies for Next-Generation Millimeter-Wave Antennas
## 6 Conclusions  
This article has presented an overview of different manufacturing techniques for state-of-the-art waveguide-based devices for the front-end of communication systems at the millimeter-wave band. Many types of manufacturing techniques have been developed during recent years, especially with the evolution of additive manufacturing, which has spread to all fields of engineering. Nevertheless, in the context of modern communication systems, there is not an optimum technique that can be used to build all devices. On the contrary, it is important to know which technique is most suitable for each device, and also to tailor the design of the device to the manufacturing technique that will be used. In fact, it is typical that different manufacturing techniques must be combined to build a single device.  
The application of high-precision subtractive manufacturing techniques was illustrated with a monopulse antenna. Direct metal laser sintering was shown with a reconfigurable linear phased array antenna, which presented good performance despite the inherent roughness achieved by DMLS. Finally, devices combining stereolithography with metallization were shown. This approach can overcome certain roughness problems that appear in DMLS devices, while also producing lighter and less expensive devices.  
Conceptualization, L.P.-L., P.S.-O., E.G.-M., J.A.R.-C., J.C., J.L.M.-C., J.R.M.-G. and J.M.R.; methodology, L.P.-L., P.S.-O. and E.G.-M.; software, J.C., J.A.R.-C., L.P.-L., P.S.-O. and E.G.-M.; validation, L.P.-L., P.S.-O., E.G.-M. and J.L.M.-C.; writing--original draft preparation, L.P.-L. and P.S.-O.; writing--review and editing, all authors; supervision, J.A.R.-C., J.R.M.-G., J.L.M.-C., J.C. and J.M.R.; project administration, J.A.R.[PERSON] and [PERSON]; funding acquisition, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**High-Precision**} & \multicolumn{2}{c}{**Direct Metal Laser Sintering**} & \multicolumn{2}{c}{**Sterolithography +**} \\  & **Subtractive Machining** & **Direct Metal Laser Sintering** & **Post-Metallization** \\ \hline Surface finish & Perfect & Poor & Good \\ Internal channel & No & Yes & Yes \\ manufacturing & & & \\ Effective & & & \\ conductivity & Very high & Medium & High \\ Cost & Medium & Medium & Low \\ Weight & Heavy & Heavy & Light \\ Maturity & High & Low & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between manufacturing technologies for millimeter-wave antennas.  
**Funding:** This work was supported by the Spanish Government under Grant TEC2016-76070-C3-1/2-R (ADDMATE); in part under Grant PID2020-116968 RB-C32/33 (DEWICOM), Agencia Estatal de Investigacion MCIN/AEI/10.13039/501100011033, Fondo Europeo de Desarrollo Regional: AEI/FEDER, UE. This work was also partially supported under Grant S2013/ICE3000 (SPADER-ADARCM), Madrid Regional Government.
# The Response of NDVI to Climate Change in the Lowest and Hottest Basin in China
## 2 Materials and Methods
### Research Methods
#### 2.3.2 Theil-Sen Trend Analysis and Mann-Kendall Significance Test  
The vegetation dynamics trends were assessed by combining the Theil-Sen median trend analysis with the Mann-Kendall (M-K) significance test [25]. This combined approach offers the following advantages: (1) no requirement for normality in data distribution; (2) high resistance to outliers; and (3) unaffected by missing values and anomalies. The specific combined approach is as follows:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} [[https://doi.org/10.5281/zenodo.3185722](https://doi.org/10.5281/zenodo.3185722)]([https://doi.org/10.5281/zenodo.3185722](https://doi.org/10.5281/zenodo.3185722)) \\ accessed on 2 November 2024 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data sources and basic parameters.  
The Theil-Sen calculation formula is as follows:  
\[\beta=\text{Median}\bigg{(}\frac{\text{x}_{\text{j}}-\text{x}_{\text{i}}}{\text{ j}-\text{i}}\bigg{)},\ \forall\text{i}<\text{j} \tag{3}\]  
In Formula (3), \(\beta\) represents the trend of vegetation change, where i and j are time series indices, and x\({}_{\text{i}}\) and x\({}_{\text{j}}\) represent the NDVI values at the i-th and j-th time points in the time series, respectively. When \(\beta>0\), it indicates an increasing trend in NDVI within the time series; when \(\beta<0\), it indicates an increasing trend in NDVI.  
The Mann-Kendall test is a non-parametric statistical method used to judge the significance of trends in data variation [26]. The calculation formula is as follows:  
\[Z=\left\{\begin{array}{l}\frac{\text{S}-1}{\sqrt{\text{var}(\text{S})}},\ \text{S}>0\\ 0,\ \text{S}=0\\ \frac{\text{S}+1}{\sqrt{\text{var}(\text{S})}},\ \text{S}<0\end{array}\right. \tag{4}\]  
where  
\[\text{S}=\sum_{\text{i}=1}^{n-1}\sum_{\text{j}=i+1}^{n}\text{sgn}(\text{X}_{ \text{j}}-\text{X}_{\text{i}}) \tag{5}\]  
\[\text{sgn}(\text{X}_{\text{j}}-\text{X}_{\text{i}})=\left\{\begin{array}{l }+1,\ \text{X}_{\text{j}}-\text{X}_{\text{i}}>0\\ 0,\ \text{X}_{\text{j}}-\text{X}_{\text{i}}=0\\ -1,\ \text{X}_{\text{j}}-\text{X}_{\text{i}}<0\end{array}\right. \tag{6}\]  
\[\text{Var}(\text{S})=\frac{\text{n}(n-1)(2n+5)}{18} \tag{7}\]  
In Formulas (4)-(7), Z is the statistic for trend testing, and n is the length of the time series. This study conducts a significance test on the interannual change trends of the NDVI in the Turpan-Hamri at a confidence level of \(\alpha=0.05\), categorizing the NDVI trend levels into 6 grades [27], as detailed in Table 2 below:
Sensors_**2014**, _14_, 23017-23040; doi:10.3390/s141223017  
**Sensors**  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Local Energy Consumption Prediction-Based Clustering Protocol for Wireless Sensor Networks**  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 1, [PERSON] 1 and [PERSON] 3**  
\({}^{1}\) School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, Shandong, China; E-Mails: [EMAIL_ADDRESS] (L.J.); [EMAIL_ADDRESS] (X.G.)  
\({}^{2}\) Faculty of Information Technology, Macau University of Science and Technology, Macau, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS] or [EMAIL_ADDRESS]; Tel./Fax: +86-633-398-0462.  
External Editors: [PERSON] and [PERSON]  
_Received: 24 October 2014; in revised form: 24 November 2014 / Accepted: 26 November 2014 / Published: 3 December 2014_  
**Abstract:** Clustering is a fundamental and effective technique for utilizing sensor nodes' energy and extending the network lifetime for wireless sensor networks. In this paper, we propose a novel clustering protocol, LECP-CP (local energy consumption prediction-based clustering protocol), the core of which includes a novel cluster head election algorithm and an inter-cluster communication routing tree construction algorithm, both based on the predicted local energy consumption ratio of nodes. We also provide a more accurate and realistic cluster radius to minimize the energy consumption of the entire network. The global energy consumption can be optimized by the optimization of the local energy consumption, and the energy consumption among nodes can be balanced well. Simulation results validate our theoretical analysis and show that LECP-CP has high efficiency of energy utilization, good scalability and significant improvement in the network lifetime.  
**Keywords:** wireless sensor networks; clustering; local energy consumption prediction; energy efficiency; network lifetime
# Waveform Design for 5G and beyond Systems
###### Abstract  
Currently, 5G communication systems are being commercially deployed in many countries. It is also true that several new applications are expected to be realized within the pre-defined 5G capabilities. To accommodate these various new applications, 5G traffic has very diverse requirements for data rate, delay, and reliability. Such challenging requirements are met when the deployment of 5G is based on proper radio access technology. Consequently, multiple OFDM numerologies were adopted in the 5G NR (New Radio) standard, i.e., legacy OFDM of the 4G system was scaled up to cope with the requirements for enhanced mobile broadband, enhanced machine-type communications, and ultra-reliable low latency communications.  
The concept of using multiple OFDM numerologies likely meets the multiple requirements for a 5G system to some extent. However, this cannot be an ultimate solution, because the traffic is radically accruing more varied characteristics and requirements when compared to the initial stage of 5G, which focused mainly on the high-speed multimedia data application. For instance, applications such as vehicular communications and robotic control require high reliability and ultra-low delay. In addition, various emerging machine-to-machine (M2M) applications have sparse traffic, with a small amount of data to be delivered. The state-of-the-art OFDM technique has some limitations when addressing the aforementioned requirements at the same time. Meanwhile, numerous waveform alternatives, such as FBMC, GFDM, and UFMC, have been explored to overcome this situation. However, they also have their pros and cons due to their intrinsic waveform properties. Hence, it is very timely to come up with modifications/variations/combinations to the waveform alternatives to OFDM, together with the state-of-the-art OFDM technique, or a new innovative waveform design for 5G systems and beyond. This Special Issue aims to provide the latest research and advances in the field of waveform design, along with the related issues of 5G systems and beyond.  
26 August 2021  
Subject: 1 September 2021  
Subject: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 3 Methodology
### Experimental Setup and Application Description  
MathWorks Simulink(r) 2021B was selected as the model-based software platform due to its comprehensive catalogue of software tools, which provide all of the functionality required to enable digital validation. One key feature of Simulink that was critical to its selection as the model-based platform was the Simulink PLC Coder tool. No other model-based platform has integrated PLC code generation capabilities, to the best of the authors' knowledge, making Simulink uniquely suited to industries where equipment control is predominantly programmed with PLCs. Its wide range of compatible PLC software targets PLC software vendors, such as Rockwell, Siemens, B&R, PLC Open and others, enabling the equipment designer to take a vendor agnostic approach to equipment design.  
Table 1 presents the Simulink modules and their associated function used throughout this work. Solidworks(r) 2021 from Dassault Systems was used to create the initial model of the mechanical system and Studio 5000(r) Logix Designer from Rockwell Automation was used to configure the control environment of the physical system.  
The chosen application for the proof of concept of the approach was the creation of a state machine to interface with the Rockwell Automation Motion Axis Move (MAM) function block and control the position of a servomotor. Servomotors are a commonly used actuating component in manufacturing equipment, thus the demonstrator is applicable to multiple manufacturing domains. A state machine is a computational model which may only be active in one of a finite number of states at a given time and is defined by its initial state, a list of its possible states, and the inputs required to trigger a state transition. State machines are commonly used to perform a predetermined set of functions based on a specific combination of inputs [41]. The physical demonstrator used in this work was a Rockwell Automation Training Workstation (Figure 5), controlled by an Allen-Bradley CompactLogix(r) 5380 PLC.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Module** & **Function** \\ \hline Simscape Multibody & Simulation Environment for 3D Mechanical Systems \\ \hline StateFlow & Control Logic Modelling Environment \\ \hline Simulink Requirements & Requirements Management \\ \hline Simulink Test & Test Management \\ \hline Simulink Design Verifier & Design Error Detection \\ \hline Simulink PLC Coder & Automated PLC Code Generation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulink modules and respective functions required for digital validation.  
Figure 5: Rockwell Automation Training Workstation, incorporating two servo motors (only one is used in the application in this paper).
## 3 Result
### Sensitivity of MOCAST+ to Hydrology and Glaciers in the Andes
#### 3.1.2 Sensitivity to Subsurface Hydrology in South America
This is because the Amazon seasonal signal has most of the energy at low degrees with a spectrum rapidly decaying at higher frequencies. For the other two basins, the spectra decay more slowly, resulting in a remarkable leap in resolution. The Orinoco could be seen up to d/o 53 with MOCAST+, while GRACE stopped at 35. A similar increase in resolution was obtained for the Parana basin. Apart from the increase in resolution, MOCAST+ was able to detect smaller amplitude seasonal signals with respect to GRACE. For instance, we can lower the Orinoco signal curve by two orders of magnitude in order to reach the noise level of MOCAST+. Since the seasonal mass variation of the Orinoco is about 80 Gt, MOCAST+ would be able to monitor a basin with a similar areal extension but with a lower seasonal mass variation, down to about 10 Gt. For GRACE, the threshold is higher, so that a basin with seasonal mass variations of about 25 Gt is observable.
# Visible Light Communication: An Investigation of LED Non-Linearity Effects on VLC Utilising C-OFDM
## 2 Test Setup
### C-Transform  
We retrieved the OFDM system that applies DCT and WHT separately from [26]. Moreover, in terms of reducing complexity, both transforms were compacted into one transform: the C-transform was proposed in [27] as follows:  
\[\text{C}_{8\times 8}=\left[\left(\text{DCT}_{8\times 8}\right)\right]\left[ \left\{\left(\text{WHT}_{8\times 8}\right)\right\}^{\text{T}}\right]\]  
where \(\left(\cdot\right)^{\text{T}}\) denotes the bit-reversed order and \(\left(\cdot\right)^{\text{T}}\) signifies the transpose. For clarity and without a loss of generality, matrix \(8\times 8\) of the C-transform is shown as follows:  
\[\text{C}_{8\times 8}=\left[\begin{array}{cccccccc}1&0&0&0&0&0&0&0\\ 0&1&0&0&0&0&0&0\\ 0&0&0.92&0.38&0&0&0&0\\ 0&0&-0.38&0.92&0&0&0&0\\ 0&0&0&0&0.91&-0.07&0.37&0.18\\ 0&0&0&0&0.21&0.77&-0.51&0.32\\ 0&0&0&0&-0.32&0.51&0.77&0.21\\ 0&0&0&0&-0.18&-0.37&-0.07&0.9-01\end{array}\right]\]  
Given that CT is a real orthonormal matrix, its inverse was the same as its transpose [27].  
\[\text{ICT}=\text{CT}^{\text{T}}\]  
It was evident that CT had a diagonal block structure (DBS). Similarly, more than 65% of its elements were zero, which led to reduced numbers of real additions and multiplications. Therefore, that made it advantageous in terms of reducing the PAPR, as well as the N-class matrix.  
On the receiver side, prior to serial-to-parallel conversion and zero padding elimination, AWGN was added to the received data \(y(t)\) at the receiver. These data were subsequently injected into the forward C transform (C-OFDM), DCT (F-OFDM) or FFT (conventional OFDM), respectively. The elimination of the Hermitian symmetry was an extra step in relation to FFT-OFDM. Finally, the modulation alphabet was de-mapped from the results.  
In this work, we selected several bias points along with the LED electro-optic transfer function and varied the AC level chosen. By running the LED in a quasi-linear segment for its characteristic around the chosen bias point and AC level, the BERs of the F-OFDM and C-OFDM signal were monitored to examine the distortion levels. Consequently, this resulted in a better understanding of the optimal bias conditions.
# Flutter of a Plate at High Supersonic Speeds
## 7 Results and Discussion  
Collocation solutions were found for \(N=10,12\), and \(14\) and sufficient convergence was observed for \(N=10\). Table 1 shows the convergence of the results for the flutter velocity \(u_{f}\) while increasing the number of collocation points. The collocation points were chosen to be equally spaced for \(x=-1/2,...,+1/2\) in all cases.  
The solution of (48) (analytical solution) was found by an iterative shooting method [27; 28; 29]. Table 2 shows the comparisons between the collocation and analytical solutions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Collocation Points** & \(u_{f}\) **(Non-Dimensional Flutter Velocity)** \\ \hline
8 & 90.9697280425894 \\
10 & 91.1667370035824 \\
12 & 91.1563146276537 \\
14 & 91.1564970192325 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The convergence of the results while increasing the number of collocation points.  
The collocation solution agreed with the analytical solution. To derive the solutions analytically, we used another method, the shooting method [27,29], which agreed with the collocation method solutions, as shown in Table 3. As mentioned, the collocation method gives results quickly; the analytical solution here is meant to check the correctness of the collocation solution. Another validation of the results is presented here by way of comparison with [PERSON] 1966, which shows similar results, bearing in mind that in the mentioned work, full potential theory was used, and the solution was carried out for an infinite plate in the direction of flow. The results obtained in this study for small \(d\) agreed with [PERSON] [7,25]. Although this is a theoretical study of flutter, a simple comparison with experiment was carried out. The type of experiment for the solution attempted here seems to be rare. The NACA Technical Note 3914 (1957) ([30]) lists some comparable measurements on thin panels. In that experimental study, some of the boundary conditions were also different. Furthermore, it was extremely difficult to experimentally imitate the plate boundary conditions. But, the magnitude of the numbers should give some idea about the validity of the theoretical solution. The notation and the system of units used were also different. But, after an adjustment, the following values for the experiment and the present computations were obtained.  
Considering the differences in the setup and the difficulty in experimentally realizing the boundary conditions, it can be safely stated that the computations were in agreement with the experiment. For the cases of brass and aluminum, the computed flutter velocities were beyond the capabilities of the experiment. Therefore, it is natural that no flutter was observed.
# LogRESP-Agent: A Recursive AI Framework for Context-Aware Log Anomaly Detection and TTP Analysis
## 3 Proposed Method  
This section presents a three-stage framework for log anomaly detection and autonomous threat analysis using a self-directed AI agent. The architecture addresses the limitations of static models by supporting goal-driven reasoning, tool coordination, and iterative analysis.  
As shown in Figure 1, the agent receives a user-defined objective (e.g., identify the attack type of a process) and operates through a plan-act-observe loop, selecting tools  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Log Format** & **Recursive** & **Recursive** & **Autonomous** & **Multi-Log** & **Integration** & **Inference** & \multirow{2}{*}{**Explainability**} \\  & **Flexibility** & **Planning** & **Reasoning** & **Flow** & **Integration** & **+ Automation** & & & **Type** & \\ \hline HitAnomaly [36] & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & Static & \(\Delta\) \\ \hline NeuralLog [37] & \(\Delta\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & Static & \(\Delta\) \\ \hline LogBERT [17] & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & Static & \(\Delta\) \\ \hline BERT-Log [38] & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & Static & \(\Delta\) \\ \hline LogGPT [18] & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & Static & \(\Delta\) \\ \hline LogFiT [19] & \(\circ\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & Static & \(\Delta\) \\ \hline IDS-Agent [26] & \(\Delta\) & \(\Delta\) & \(\times\) & \(\Delta\) & \(\Delta\) & \(\Delta\) & Partially & \(\circ\) \\ \hline SERC [27] & \(\Delta\) & \(\Delta\) & \(\times\) & \(\Delta\) & \(\Delta\) & \(\circ\) & Partially & \(\circ\) \\ \hline Audit-LLM [39] & \(\Delta\) & \(\Delta\) & \(\times\) & \(\Delta\) & \(\Delta\) & \(\Delta\) & Partially & \(\Delta\) \\ \hline LogRESP-Agent (Proposed) & \(\circ\) & \(\circ\) & \(\circ\) & \(\circ\) & \(\circ\) & \(\circ\) & Dynamic & \(\circ\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of model characteristics and limitations across Transformer-based and LLM-based anomaly detection methods. (\(\circ\): fully supported, \(\Delta\): partially supported, \(\times\): not supported).  
and refining its analysis as new evidence is gathered. To achieve this, the agent first interprets the given objective and selects relevant tools--such as a rule matcher, anomaly scorer, or threat mapper--based on the initial context. It then invokes these tools to extract meaningful signals from the log data, including anomaly scores, rule matches, and contextual history. The results are integrated to update the agent's internal hypothesis, and a natural language explanation is generated to summarize the findings. This loop continues adaptively until a confident conclusion is reached, enabling a flexible and interpretable analysis of semi-structured logs. Each component is detailed in the following subsections.