esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Support Vector Machines for Regression (SVR) Models  
In contrast to ANN models, support vector machines (SVM) is a kind of machine learning technique focused on the structural risk minimization instead of the empirical risk minimization principle. The main objective of this method is maximizing the margin distance [21]. The model can be formulated as the following equation:  
\[y(x)=w^{T}\phi(x)+b, \tag{4}\]  
where \(b\) denotes the bias term and \(w\) is the vector of weights. \(\phi(x)\) represents the kernel function used to deal with the nonlinear problem, mapping the input data into a higher dimensional (feature) space where data can be linear. First introduced for classification problems, the \(\varepsilon\)-insensitive loss function, presented in Equation (5), has enabled its use in regression problems:  
\[L_{\varepsilon}(y)\left\{\begin{array}{ll}0,&\quad\text{if }\left|f(x)-y \right|\leq\varepsilon\\ \left|f(x)-y\right|-\varepsilon,&\quad\text{otherwise}\end{array}\right., \tag{5}\]  
In Equation (5), \(\varepsilon\) denotes the area of \(\varepsilon\)-insensitive. The process is the following: first, the input data are mapped into a new space of higher dimensional features, called feature space, by a non-linear mapping a priori using a kernel transformation. The aim of this feature space is to detect a linear regression function that can be fit the output data with the input data. This linear regression corresponds to the nonlinear regression model in the original space. Equation (6) represents the problem that should be optimized:  
\[\begin{array}{ll}\min\limits_{w,b,\xi}&\frac{1}{2}\|w\|^{2}+\ C\sum\limits_{i= 1}^{N}\left(\xi_{i}^{+}+\xi_{i}^{-}\right)\\ \text{subject to : }\\ w\cdot x_{i}+b-y_{i}\leq\varepsilon+\xi_{i}^{+}\\ y_{i}-w\cdot x_{i}-b_{i}\leq\varepsilon+\xi_{i}^{-}\\ \xi_{i}^{+},\xi_{i}^{-}\geq 0\end{array} \tag{6}\]  
with \(i=1,\dots,l\). \(\xi_{i}^{-}\) and \(\xi_{i}^{+}\) are the slack variables that deal with the training error on the top and the bottom, respectively. These variables take zero values within the \(\{-\varepsilon,\,\varepsilon\}\) area, and non-zero values outside it. \(\frac{1}{2}\|w\|^{2}\) is the structure risk concerning the flatness of the model and the parameter \(C\) is a correction factor that deals with the trade-off between the flatness and the error. In this work, the Gaussian kernel was chosen as kernel function. The dual optimization problem can be solved with the Lagrangian multiplier method [21]. Finally, the method obtains the support vectors as the final decision, which are the observations with non-zero coefficients of the Lagrangian multipliers.
# Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
## 6 Discussion
### SAR Sensors for Damage Estimation  
A common alternative for mapping the impact of natural hazards are SAR images [11]. SAR data have the advantage that there are no data gaps due to clouds, which are a main challenge for optical remote sensing, particularly in the tropics. Indeed, for our study we rely on four months of data to gather enough cloud-free Sentinel-2 images. It remains unclear if a SAR sensor (and with which bands or polarisations) could identify changes of the tree foliage and serve as a basis for density estimation. This is an open question for future investigation. On the other hand, it is an advantage of our model that it does not mainly observe tree stems but the actual foliage, and therefore can directly retrieve canopy changes, not only the removal (or planting) of entire trees. This capability is crucial for our application, since the large majority of affected coconut trees suffer damage only to the canopy, which is likely to affect their health and yield, while their stems remain in place.
## 3 Results
### Year 2021 Optimization of the Irrigation Water Volume for Each Clone
#### 3.2.4 Berry and Must Quality Parameters and Oenological Potential  
In 2021, there were no significant differences in pH, total acidity, IM, and juice % among clones (Table 10). Moreover, vines from clone 360 (irrigated with more water in 2021, Table 2) showed a significantly lower berry weight and water percentage and a higher concentration of tartaric and malic acids and YAN than the rest of clones. High-vigor clones (4, 94, and 188), despite being irrigated with less water, showed a similar berry weight, a higher % water, and a lower TSS (188, 94) and tartaric/malic ratio (clone 4) than low-vigor clones (Table 10). WS also decreased berry weight, juice, and water percentages in a significant way compared to the control, but some of these effects depended on the clone. For example, WS significantly decreased the berry water percentage only in clones 276 and 372, and the juice percentage only in clone 276. Moreover, under stress conditions, clone 360 had significantly more malic acid than clones 372, 373, and 94 (Table 10). The analysis of mineral composition of the must in 2021 revealed that clone 360 had the highest concentration of K, Ca, Mg, P, and Cu, and clone 276 had the highest concentration of Zn, significantly higher than the rest of clones (Table S4). In contrast, clone 373 had the lowest concentration of Ca and Mg. The significant interactions also indicated that WS enhanced the concentration of Ca (360), Mg (360, 372), P and Zn (276), and B (372, 276, and
# Research on Key Technology of Wind Turbine Drive Train Fault Diagnosis System Based on Digital Twin
## Case Study
### Experimental Verification of Fault Diagnosis Model
First, the signal is decomposed by IVMD, let the search range and step size of \(K\) and \(\alpha\) be \(K\in[2,7]\) and \(\alpha\in[1000,10,000]\), respectively, and the search step size \(\Delta\alpha=500\); the tolerance error (tol) is set to \(1\times 10^{-7}\), and the maximum number of iterations is set to 50. The signal is then decomposed using IVMD, and according to the minimum envelope entropy criterion, the optimal IMF components are selected. After that, feature extraction is performed on the IMF components, setting the NCMDE parameters, where the class \(c\), embedding dimension \(m\), and delay \(d\) are set to 6, 3, and 1, respectively. The NCMDE features are calculated in the scale range of 4-9 and combined with 10 time-domain features, including mean, variance, root mean square, absolute mean amplitude, skewness, kurtosis, crest factor, waveform factor, pulse factor, and margin factor, as well as 3 frequency-domain features,  
Figure 17: Envelope analysis results: (**a**) IVMD envelope analysis results; (**b**) VMD envelope analysis results. including center frequency, mean square frequency, and frequency variance, resulting in a total of 19-dimensional data to construct the feature vector set, which is then input into PSO-LSSVM for fault mode identification. For this experiment, the PSO algorithm is set with a population size of 200, a maximum of 50 iterations, learning factors \(C_{1}\) and \(C_{2}\) both set to 2, the width \(\sigma^{2}\) of the Gaussian kernel function set in the range of \([0.01\sim 200]\), and the penalty coefficient \(C\) set in the range of \([0.01\sim 300]\). The experimental results are shown in detail in Figure 18 and Table 4.  
The above experimental results show that compared with other diagnostic models, the IVMD-PSO-LSSVM algorithm has excellent performance, the diagnostic results are extremely accurate, and it can accurately detect and identify various types of faults. At the same time, under the premise of ensuring accuracy, its computational efficiency is outstanding, and large-scale data processing and analysis can be completed in a very short period of time, providing a reliable guarantee for fast and efficient fault diagnosis. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Signal Decomposition** & **Classification** & **Accuracy** & **Time** \\
**Algorithm** & **Algorithm** & & \\ \hline VMD & PSO-LSSVM & 95.7\% & 14.4 s \\ IVMD & LSSVM & 96.6\% & 16.8 s \\ IVMD & PSO-SVM & 98.3\% & 26.4 s \\ IVMD & PSO-LSSVM & 99.1\% & 21.2 s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Diagnostic accuracy and computation time for four different combinations of algorithmic models. Figure 18: Diagnostic accuracy of four different combinations of algorithmic models.
## 4 Discussion
### Hyperspectral Characterization and Mapping of Plant Functional Types
For example, NASA's Airborne Visible/Infrared Imaging Spectrometer collected aerial hyperspectral imagery over the APEX field site in 2017 and 2018 as part of the Arctic and Boreal Vulnerability Experiment (ABoVE) [100]. With the launch of NASA's Hyperspectral InfraRed Imager (HySPIRI) planned for 2021, global satellite hyperspectral imagery will become available to the remote sensing research community within the next several years [101]. The National Ecological Observatory Network also recently launched its hyperspectral Aerial Observation Platform with the mission of bridging spatial scales from organisms to landscapes [102]. A 2017 survey published by the National Academies of Science, Engineering and Medicine regarding the future of earth observation placed a high priority on hyperspectral data acquisition for the purposes of tracking diversity and changes in PFT cover (National Academies Press 2017). Our results establish statistical relationships between remotely sensed hyperspectral measurements, species diversity, and plant functional cover that could be used in future studies to identify and track community- and ecosystem-level changes in arctic and boreal regions.
# Composing as a Service: A Real-World IoT Implementation
## 4 Field Evaluation
### IoT Gateway  
The IoT gateway is deployed at the edge-abstraction layer of our architecture. The gateway utilizes the Liota open-source IoT gateway agent created by VMWare [20]. The Liota agent aggregates all sensor data and sends them to the cloud composting service through an MQTT (Message Queuing Telemetry Transport) communication channel over Secure Sockets Layer (SSL) protocol. Additionally, it performs scheduled tasks sent by the composting service, such as stirring the material and ventilating the frame, by instructing the PLC module. Finally, it exposes a middleware/API to allow remote management and manipulation of the actuators by the composting cloud service. A lightweight dashboard is also available for on-site management and data logging.
# Pairing Red Wine and Closure: New Achievements from Short-to-Medium Storage Time Assays
## 1 Introduction
Regarding phenolic compounds, during in-bottle storage, in addition to migration phenomena from cork stoppers, oxidation-reduction reactions may also occur depending on the type of closure used [11; 21]. Oxidative polymerization reactions, which can cause a rapid decline in free anthocyanins in wines in the presence of dissolved oxygen, have been assigned specifically to natural cork stoppers [11]. While these studies have provided valuable insights into specific aspects of closures and their impact on wines' chemical and sensorial characteristics, none of them have conducted an extensive and holistic evaluation that encompasses multiple critical parameters of wines, such as their general physicochemical characterization, sensory profile, and volatile and phenolic composition. Furthermore, research on the impact of closures on red wines remains limited, as most studies have focused on white and sparkling wines [2; 12; 13; 22]. Therefore, this study aims to address this gap and was designed to holistically evaluate the impact of different types of closures on two blended red wines from different Appellations, with short and medium in-bottle storage periods: one from the Douro Appellation(Portugal), and the other from Burgenland (Austria). The study employed natural and micro-agglomerated cork stoppers and screw cap closures. To achieve the objective, a comprehensive set of analyses was implemented, including the determination of volatile compounds using advanced gas chromatography techniques, such as comprehensive gas chromatography-mass spectrometry with a time-of-flight analyzer (GC \(\times\) GC-ToFMS), and the determination of phenolic profiles through ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-DAD-MS\({}^{\mathrm{n}}\)). Additional analyses covered OTR, chromatic parameters, acidity (total and volatile), sulfur dioxide levels (free and total), pH, and sensorial evaluations conducted by experienced panels. Data from the various analytical domains were processed and integrated using univariate and multivariate statistical tools to draw meaningful conclusions. The wine profiles explored in this study highlight how the interplay between closure and in-bottle storage duration influences the chemical and sensory pathways of red wines, providing a more integrated understanding of their behavior during short-to-medium-length storage periods.
# Multi-Body Dynamics Modeling and Simulation of Maglev Satellites
###### Abstract  
The Lorentz force magnetic levitation gim2 bal stabilized platform (LFMP), as a new generation of high-precision turntable for maglev satellites, can meet the requirements of future spacecraft for ultra-high attitude pointing accuracy and stability. To solve the problem of three-module multi-body attitude control under maneuvering conditions, the platform subsystem is first dynamically modeled based on the second type of Lagrangian equation, and the payload subsystem is dynamically modeled based on the Newton-Euler method. Secondly, a multi-loop control system is designed, consisting of high-precision and fast attitude pointing control for the payload, position tracking control for the platform subsystem, and tracking control for the maglev module. The final simulation results verified the feasibility and effectiveness of the payload-centered control method. An evaluation of the stability with a specific model has been performed, and the attitude accuracy of the payload is within 0.00002\({}^{\circ}\) and the attitude stability is within 0.00005\({}^{\circ}\)/s.  
LFMP; LFMB; multibody dynamics; multiple closed-loop control; attitude control; payload-centered +
Footnote †: journal: Applied  
1 Graduate School, Space Engineering University, Beijing 101416, China; [EMAIL_ADDRESS]  
2
# AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
## 3 Results
### Linear Regression for Pilot Overhead Reduction  
To further reduce pilot overhead while maintaining or improving spectral efficiency (SE) and bit error rate (BER), this study explored the application of supervised machine learning techniques. Initially, we investigated linear regression coupled with singular value decomposition (SVD) to optimize pilot placement. The training data for the model consisted of features such as the number of transmitter and receiver antennas, streams, pilot bits, data symbols, and channel capacity. The effectiveness of this method in reducing pilot overhead is presented in Figure 8.  
Building upon the pilot overhead reduction achieved with k-clustering, the linear regression SVDHB system demonstrates comparable performance. Figure 9 illustrates the  
Figure 8: Linear Regression Pilot Overhead vs. SNR.  
Figure 7: SE vs. SNR after pilot placement optimization using K-Clustering.  
superior BER performance of the linear regression SVDHB system. The same improvement as in the case of k-clustering is observed, with a system accuracy of 88%.  
Figure 10 shows the SE performance. Again, the performance is comparable to the k-clustering method using 16 clusters. The high accuracy of the linear regression model, as evidenced by the near-perfect match between actual and predicted SE, further validates its effectiveness.
# Blockchain-Based Authentication in Internet of Vehicles: A Survey
## 6 Future Research Directions and Open Challenges
### Cloud Scalability, Security, and Privacy  
Since IoV paradigm is based on big data and high-performance computing, cloud computing infrastructures constitute the most important building blocks for providing data storage. Even in blockchain-assisted IoV platforms, the cloud servers (CS) cluster constitutes the blockchain network.  
Thus, developing the cloud technologies is a critical aspect to guarantee the security and the privacy of the blockchain-based authentication system in vehicular networks. Cloud-related security and privacy issues might be encountered during the process of transmitting vehicles' data to/from the cloud or while being stored in the different cloud servers. This imposes different security challenges in both processes which should be addressed in the future to guarantee a high level of efficiency for the blockchain-based cloud-assisted IoV authentication systems.  
Data transmission challenges: Transmitting data back and forth between the cloud servers, the vehicles, and the other IoV entities via an open wireless network may impose high risks on the security of the blockchain-based authentication system and the privacy of the users. Some schemes attempt to mitigate the security risk by adopting wired communication wherever possible, i.e., between the cloud servers and the intermediate nodes. However, this arrangement is not practical nor feasible for all IoV-based scenarios. Moreover, this can only mitigate the risk at the stationary end of the system while the mobile part, i.e., the V2I communication is still exposed to different security threats due to its wireless nature. Thus, the existing wireless communication protocols should be well investigated and developed to insure private and secure exchange of users' authentication data between the different parts of the IoV-based system.  
Data storage challenges: Having users' sensitive data such as the authentication data including users' real identities, public keys and certificates stored in a common-access platform such as the cloud is another threat to consider. The privacy of users is threatened to be violated if no proper encoding schemes exist within the cloud. Thus, the need for developing efficient encryption algorithms that can be adopted in both data storage and transmission to protect the privacy and security of the whole IoV cloud-based paradigm while ensuring full compatibility with the rapidly evolving and heterogenous cloud technologies is another important challenge which we hope to be tackled in the future.
## 7 Long-Range Underground Monitoring
### Technology Description
#### 7.2.6 Mitory  
MIOTY is a LPWAN technology developed by the Fraunhofer Institute IIS, and the technology is standardized by ETSI [114]. It offers data rates of 407 bps and operates in 868 and 915 Sub GHz ISM bands. In rural areas, MIOY offers ranges of up to 15 km and a range of up to 5 km in urban environments [10; 115].  
The network consists of gateways, nodes and network management software [116] and has a capacity for 1M+ devices and an uplink capacity of around 1.5 million messages per day per base station [117]. The technology uses Telegram Splitting Multiple Access (TSMA) by transmitting sub-packets over different frequencies and times. A message is split in sub-packets and sent on different frequencies on pseudo-random times and reassembled by the receiver, making the technology less prone to interference [118].  
MIOTY technology is used for applications for smart cities, buildings, logistics and agriculture [118]. In underground mining, MIOY has also been tested, resulting in a 3 km underground range in a tunnel system [37]. The technology is also used for mine climatization control [119] and aimed at further applications of asset tracking, environment monitoring, waste management and predictive maintenance in mining environments [120].  
Further research into MIOY underground use cases is not known, to the best knowledge of the authors, and should be considered for evaluating the technology performance in underground environments.
# Gait Biomechanics for Fall Prevention among Older Adults
## 4 Biomechanics of Dynamic Balance  
Even after tripping or slipping, falls can still be prevented as long as balance is restored. 'Balance' has been commonly used in many everyday contexts, but it was not until recently that human balance was properly quantified based on biomechanical concepts. In the simplest definition, balance is secured when COM is within BOS in the transverse plane [26]. This fundamental definition holds true as long as the body remains static, ensuring absolute safety. This concept is, however, not practical in predicting falls risks. First, BOS in bipedal human locomotion is defined as the foot contact area, plus the area between the two feet during the double support phase [54]. Following this balance definition, human gait can be viewed as a continuum of 'balance loss and balance recovery' by linking the single- and double-limb supporting phases alternately [55]. It is, however, obvious that balance loss during the single-support phase (when COM is outside BOS) is not necessarily related to falls, but it can be rather functional for the purpose of walking (i.e., forward progression). Hazardous balance loss needs to be clearly distinguished from functional requirements. This issue can be overcome by considering virtual (projected) BOS that has been introduced by [PERSON] et al. [26], in that both foot positions in the transverse plane define the area regardless of whether the foot is in contact with the walking surface (Figure 5). A balance assessment can be relative to this virtual BOS to characterise a hazardous balance loss that leads to falls. Second, in dynamic situations such as human walking, velocity needs to be considered rather than looking only at positional relationships between COM and BOS. Even in the same positional relationships, COM velocity can be a large determinant for balance conditions in dynamic situations.  
Figure 4: (**Left**) Characteristics of ground reaction forces (GRF) in the sagittal plane, with GRFver/hor indicating vertical/horizontal components, respectively; (**right**) required coefficient of friction (RCOF) characteristics throughout the stance phase.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 5 Multiple Input Multiple Output Technology
### MIMO Beam Forming
The beamformer can work with IMU sensors whose function is to feed the system with position of the airborne platform. Several techniques are available to decode the instantaneous channel coefficients, such as pilot signal, channel sounding, semi-blind estimation [149], blind estimates [150], and feedback channels [151; 152].
# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
## 3 System Modeling
### Time-Delay Model
For the flight delay \(d_{\text{fly m,n}}(t)\) between UAV \(m\) and user device \(n\), which is affected by the maritime environment, it can be expressed as Equation (20):  
\[d_{\text{fly m,n}}(t)=\frac{L_{m,n}(t)}{\omega\cdot d_{m}}, \tag{20}\]  
Therefore, the total delay between UAV \(m\) and the associated user \(n\) can be expressed as Equation (21):  
\[d_{m,n}(t)=d_{\text{trans m,n}}(t)+d_{\text{fly m,n}}(t)+d_{\text{comp m,n}}(t), \quad n\in\mu, \tag{21}\]  
where the mission transfer time \(d_{\text{trans m,n}}(t)\) and computational delay \(d_{\text{comp m,n}}(t)\) can be expressed as Equations (22) and (23), respectively:  
\[d_{\text{trans m,n}}(t)=\frac{q_{m,n}}{\omega\cdot r_{m,n}(t)}, \tag{22}\]  
\[d_{\text{comp m,n}}(t)=\frac{k\cdot q_{m,n}}{f_{m}}, \tag{23}\]Finally, we define the total system delay \(d_{\text{total}}(t)\) as Equation (24). Assuming that the quality of service (QoS) delay metric for user \(n\) is \(\alpha\), it needs to satisfy the threshold \(\alpha\) to meet the demand of user \(n\). Otherwise, the current device cannot satisfy the demand of user \(n\):  
\[d_{\text{total}}(t)= \begin{cases}d_{\text{Leo,n}}(t),&\text{if connected to satellite}\\ d_{\text{tbs,n}}(t),&\text{if connected to base station}\\ d_{m,n}(t),&\text{if connected to UAV}\end{cases}  \tag{24}\]