esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Radar Techniques for Space Observation and Products
### Radar Imaging of Space Objects  
Different focusing techniques can be used to image space objects. The choice of the method depends on the bandwidth of the transmitted radar waveform, on the attitude motion of the object (i.e., slow or fast rotating), and on the spanned k-space. Conditioned by these parameters, either a fully coherent, a partially coherent, or an incoherent processing is possible. The basic prerequisite of all these reconstruction techniques is similar, as they exploit the variation in the aspect angle between the space object and the radar during the data acquisition to image the object. This variation leads to a change in the Doppler frequency over time. For all these methods, the spatial resolutions achieved in both image dimensions are independent of the object range. This is a main advantage compared to optical systems.  
The major challenge of radar imaging for space observation is the accurate estimation of the translational and rotational motion of the object. Indeed, the knowledge of the object kinematic is a prerequisite to form sharp images of the rotating space object. This dynamic information has to be estimated directly from the radar data as space objects are uncooperative targets, meaning that accurate enough information about their attitude motion and positions (requiring cm/mm-precise ephemerides depending on the center frequency used) is not available. During the processing time required to compute a radar  
Figure 6: NILESAT cluster (16 March 2023).  
image, the rotation vector should be constant. Otherwise, the imaging plane varies, and the radar images are distorted.
# Using Long-Range Wireless Sensor Network to Track the Illegal Cutting Log
## 2 Research Methodology and Proposed System
### Long-Range (LoRa) Configuration  
LoRa is set at a frequency of 923 MHz, spreading factor (SF) = 12, payload (PL)= 8 bytes, coding rate (CR) = 4/5, and bandwidth (BW) = 125 kHz. Header (H) = 0 and data rate optimization is disabled (DE) = 0. Time sequence of the network protocol can be seen in Figure 8. The communication between sensor nodes (Tx1 and Tx2) and the gateway delivered the real data environment. After conducting the data communication, Tx1 and Tx2 will be in the sleep duration until the next cycle. The gateway then aggregates the data from Tx1 and Tx2, and then transfers the data into the network server. The network server transfers data to the application server and forwards it to the user devices.
## 2 Methodology
### Productivity of Tracking Systems  
The function of solar tracking systems is to position the energy transformation technology (photovoltaic, concentration technology, and thermoelectric, among others) in a normal position towards the Sun throughout the day, maximizing the energy capture. The productivity of these systems can be defined based on the energy balance defined in general form by [29]:  
\[E_{tot}(\tau)\ =\ E_{g}(\tau)-E_{c}(\tau) \tag{1}\]  
where \(E_{g}(\tau)\) is the energy produced by the photovoltaic system and \(E_{c}(\tau)\) is the required energy for tracking action and monitoring parameters, both in the period time \(\tau\). The increase in energy produced depends on the alignment accuracy throughout the day, defined as the tracking error. In this work, two-axis tracking systems in azimuth-elevation configuration are considered because they are the most efficient. According to [10], a mixed tracking method is recommended to increase the performance of the systems, which uses an algorithm to calculate the solar position and a solar pointing sensor to reduce the error. Tracking strategies define how the sun should be tracked to reduce the pointing error, which can be continuous or step-by-step tracking. The continuous strategy increases the energy required for the tracking action since it remains in constant motion. Therefore, the step-by-step tracking strategy is the most used. Additionally, the accuracy adjustment is performed by a control strategy. [PERSON] et al. presented in [PERSON] et al. (2018) an extensive study of the different tracking control strategies, where the most common controls for two-axis solar tracking systems are On-Off, Proportional-Integral-Derivative (PID), and Fuzzy PID. Hence, the tracking accuracy can be expressed as tracking error, which is defined by the difference between the desired position and the actual position of the axis. In a two-axis system, the mixed tracking error is expressed by [29]:  
\[\epsilon_{m}^{2}\ =\ \epsilon_{\beta}^{2}+\epsilon_{\gamma}^{2} \tag{2}\]  
where \(\epsilon_{\beta}\) and \(\epsilon_{\gamma}\) are the azimuth and elevation errors, respectively. However, the tracking accuracy increases the conversion efficiency of the photovoltaic technology but increases energy expenditure and component wear due to excessive motion. The system energy consumption due to the tracking action depends mainly on the electronic and electromechanical components that compose the tracker and can be reduced by implementing energy-saving strategies, as presented in [29]. Based on [31], the profitability of the tracking system in terms of energy is assured when the consumed energy by the tracker is between 2% and 3% of the energy increasing due to solar tracking.
## 1 Introduction  
Orthodontic treatment involves the movement of teeth by application of forces via removal and fixed appliances [1]. Orthodontic tooth movement (OTM) is the result of bone resorption on the pressure side and bone apposition on the tension side [2]. To put it simply, OTM is made possible by a combination of bone formation and resorption [3]. A number of factors can affect the rate of OTM. These include systemic disease such as diabetes [4], osteoporosis [5] and other metabolic disorders. Drugs such as non-steroidal anti-inflammatory drugs (NSAIDs) and bisophosphonates [6] have also been observed to affect the rate of OTM. Furthermore, oral diseases such as periodontitis also adversely affect the outcome of orthodontic treatment [7]. To improve the rate of OTM and reduce the probability of orthodontic relapse, a number of methods have been proposed. These include surgery, transeptal fiberectomy and low-level laser therapy [8, 9]. However, surgery is traumatic and procedures such as laser therapy have limited efficacy. Moreover, rapid OTM may cause root resorption. Additionally, periodontitis can exacerbate the root and bone resorption induced by OTM [10].  
More recently, local and systemic application of drugs and hormones has been suggested to improve the rate of OTM [11]. Parathyroid hormone (PTH) is a hormone that is produced and secreted by the parathyroid gland. Its primary function is to regulate the serum calcium concentration by exerting its effect on bone, kidney and the intestine [12]. Its synthetic (in the form of teripartide) and recombinant forms are used in the treatment of parathyroid deficiency and osteoporosis [13]. Unsurprisingly, the effect of PTH on periodontal tissues during OTM has also been studied [14]. It has been observed that low doses of intermittent PTH (in local or systemic forms) may accelerate OTM, reduce the probability of relapse [15] and may reduce alveolar bone resorption caused by periodontitis during OTM [16]. However, to date, no extensive systematic review has been published that comprehensively assesses the overall quality of the studies and summarizes their general outcomes. Hence, the aim of this systematic review is to assess the quality of literature and summarize the overall effects of local and systemic administration of PTH on OTM.
## 2 Materials and Methods
### Bioactive Phytochemicals
#### 2.4.3 Qualitative and Quantitative Profiles of Polyphenols and Carotenoids  
HPLC Analysis  
Qualitative and quantitative profiles of polyphenols were determined according to the method proposed by [23] with slight modifications. Analysis was conducted on a Waters Liquid Chromatograph (Waters Series 600, Mildford, MA, USA). A C18 guard column (Symmetry\({}^{\text{\textregistered}}\) C18) and an Alltech adsorbosil C18 reversed-phase packing column (250 \(\times\) 4.6 mm i.d. 5 \(\upmu\)m particle size and 125 A pore size) were used for separation throughout this study. The PDA acquisition wavelength was set in the range of 216-600 nm. Mobile Phase: Solvent A with acetonitrile/pure water (5:95 \(\
u\)/\(\
u\)), and 0.2% TFA; Solvent B (acetonitrile (100%), pure grade, with 0.2% TFA; flow rate = 1 mL/min. The gradient was 0-2 min (100% B), 2-28 min (60% B), and 28-30 min (100% B).  
All the prepared solutions were filtered through 0.45 \(\upmu\)m membranes (Fisher Scientific) and the mobile phase degassed before injection onto HPLC. The volume of injection was 20 \(\upmu\)L and samples were analyzed in triplicate. Calibration curves were obtained at a detection wavelength 280 nm (gallic acid and 4-hydroxybenzoic acid), 320 nm (\(p\)-coumaric), 360 nm (rutin). Standard solutions over the concentration range from 0.10 to 100.00 mg/L were prepared for the identification and quantification of the following compounds: 4-hydroxybenzoic acid (y = 10.0 \(\times\) 10\({}^{4}\)\(\times\) \(-\) 31507; R\({}^{2}\) 0.998), \(p\)-coumaric acid (y = 12.6 \(\times\) 10\({}^{4}\)\(\times\) \(-\) 4232; R\({}^{2}\) = 1); rutin (y = 29.5 \(\times\) 10\({}^{4}\)\(\times\) + 8065; R\({}^{2}\) 0.999), and gallic acid (y = 48.7 \(\times\) 10\({}^{3}\)\(\times\) \(-\) 4520; R\({}^{2}\) = 0.999) expressed as milligrams per mL of DW. All calibration curves were linear over the concentration ranges tested, with correlation coefficients of 0.999.  
Individual carotenoids content was analyzed by HPLC-DAD, in a Vydac 201 TP54 C-18 column (250 mm-4.6 mm), equipped with a C-18 reversed-phase packing column (250 \(\times\) 4.6 mm i.d. 5 \(\upmu\)m particle size and 125 A pore size). The carotenoid profile was performed by chromatographic separation as described by [24]. Solvent A with ethyl acetate (Merck pure grade) and solvent B 90:10 acetonitrile:water (Merck pure grade and pure water, 1.0 mL/min flow rate, at room temperature.  
The UV-vis detector was set between 270 and 550 nm. Individual carotenoids were quantified based on a calibration curve built with pure standards: \(\beta\)-carotene, lycopene (y = 2 \(\times\) 10\({}^{8}\) x \(-\) 44717, R\({}^{2}\) = 0.999), zeaxanthin (y = 3 x 10\({}^{8}\)\(\times\) \(-\) 3 x 10\({}^{6}\) x; R\({}^{2}\) = 0.999) and lutein (y = 2 x 10\({}^{8}\)\(\times\) + 2 x 10\({}^{6}\), R\({}^{2}\) = 0.998) and expressed as milligrams per kilogram of DW (mg/Kg DW).
# A Joining Procedure and Synchronization for TSCH-RPL Wireless Sensor Networks
## 6 Results  
The following figures explain the results that were obtained by the previously described testbed and experiments. The different graphs show the probability of synchronization and connection success for each node in the network. In this way, it is possible to verify which configuration and which mechanism improve the process for joining the network.  
In addition, the number of messages that are transmitted, which is directly associated with the mechanism of connection to the network, such as EBs or the control messages of RPL, has been also analysed. This study allows us to obtain an estimate of the energy consumption for each node. Other interesting results include the maximum time to connect all of the nodes in the topology, synchronized and connected by RPL.  
Figure 7 illustrates the direction in which the synchronization should be propagated. For instance, until nodes 2 and 5 are connected, the synchronization cannot be propagated to the rest of the network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{_Description_} & **Time TX Mode (ms)** & **Current Consumption (TX (mA))** & **Time RX Mode (ms)** & **Current Consumption (RX (mA))** & **Power Consumption (ms)** \\ \hline \begin{tabular}{c} _Broadcast TX slot_ \\ _power consumption_ \\ \end{tabular} & 4.256 & & - & 0.0740544 \\ \hline \begin{tabular}{c} _Unicast TX slot_ \\ _power consumption_ \\ \end{tabular} & 4.256 & & 2.4 (ACK) & & 0.1213344 \\ \hline \begin{tabular}{c} _Broadcast RX slot_ \\ _power consumption_ \\ \end{tabular} & - & 17.4 & 5.452 & & 0.1074044 \\ \hline \begin{tabular}{c} _Unicast RX slot_ \\ _power consumption_ \\ \end{tabular} & 2.4 (ACK) & & 5.452 & & 0.1491644 \\ \hline \
\begin{tabular}{c} _RX slot without_ \\ _packet reception_ \\ \end{tabular} & - & 2.2 & & 0.04334 \\ \hline
\begin{tabular}{c} _Scan phase_ \\ \end{tabular} & - & 10 & 0.197 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The power consumption for the different slot types.  
Figure 7: The propagation direction for each level.
# A Review on Deep-Learning-Based Cyberbullying Detection
## 5 Deep-Learning-Based Models
### Recurrent Neural Network (RNN)  
RNN [44] stands for Recurrent Neural Network, which is used for the sequential text data as input, for example, if there is a sentence and there needs to be a prediction of whether this sentence contains positive context or negative context. In such a situation, we can use RNN. Spam classifiers, time-series data, sales forecasting, stock forecasting, and many more problems can be addressed with a better accuracy by using RNN. For other models, when the input is given as a sequence of text data by using text preprocessing techniques (such as Word2 Vec, TF-IDF, BagOfWord, etc.), we need to preprocess the raw data and convert them into vectors. For applying ML algorithms over the sequential data, we need to convert them into vectors. When a sentence is converted into a vector, the sequence information is discarded. Once the sequence information is discarded, the accuracy will decrease. We will also discuss text representation. Since we may analyze cyberbullying from textual content, RNN is used for controlling this sequence information. RNN has an internal memory that helps it to control the sequence information. In other neural networks, all the input is basically the vector, which is totally independent, but in RNN, every input is dependent on its previous output and current input. In this way, RNN restores the context of the whole sentence.  
In Figure 17, the current state input is \(h_{t}=f(h_{t-1},X_{t})\). Then, we have to apply activation functions such as sigmoid, ReLU, or tanh, and then, the output will be, \(y_{t}=W_{ht}h_{t}\) where \(W_{ht}\) is the weight of the output. RNN has some problems:  
1. The training of an RNN is very difficult.
2. It cannot process with a very long sequence of sentences.
3. RNN does not support long-term memory storage.  
For solving these problems, LSTM was introduced.
# Neural Network Based Quality Control of CYGNSS Wind Retrieval
###### Abstract  
Global Navigation Satellite System - Reflectometry (GNSS-R) is a relatively new field in remote sensing that uses reflected GPS signals from the Earth's surface to study the state of the surface geophysical parameters under observation. The CYGNSS is a first of its kind GNSS-R constellation mission launched in December 2016. It aims at providing high quality global scale GNSS-R measurements that can reliably be used for ocean science applications such as the study of ocean wind speed dynamics, tropical cyclone genesis, coupled ocean wave modelling, and assimilation into Numerical Weather Prediction models. To achieve this goal, strong quality control filters are needed to detect and remove outlier measurements. Currently, quality control of CYGNSS data products are based on fixed thresholds on various engineering, instrument, and measurement conditions. In this work we develop a Neural Network based quality control filter for automated outlier detection of CYGNSS retrieved winds. The primary merit of the proposed ML filter is its ability to better account for interactions between the individual engineering, instrument and measurement conditions than can separate thresholded flags for each one. Use of Machine Learning capabilities to capture inherent patterns in the data can create an efficient and effective mechanism to detect and remove outlier measurements. The resulting filter has a probability of outlier detection (PD) >75% and False Alarm Rate (FAR) < 20% for a wind speed range of 5 to 18 m/s. At least 75% of the outliers with wind speed errors of at least 5 m/s are removed while ~100% of the outliers with wind speed errors of at least 10 m/s are removed. This filter significantly improves data quality. The standard deviation of wind speed retrieval error is reduced from 2.6 m/s without the filter to 1.7 m/s with it over a wind speed range of 0 to 25 m/s. The design space for this filter is also analyzed in this work to characterize trade-offs between PD and FAR. Currently the filter performance is applicable only up to moderate wind speeds, as sufficient data is available only in this range to train the filter, as a way forward, more data over time can help expand the usability of this filter to higher wind speed ranges as well.  
CYGNSS; quality control; Neural Network; outlier detection; wind retrieval; GNSS-R Article
# Early Gapping and Platoon Merging Strategies for Autonomous Vehicles using Local Controllers
## 3 Results
### Merging Success as a Function of Platoon Sizes  
Following the results shown in Figures 5 and 6, we continue the investigation for a linear profile, a preparation section of 75% and gapping onset speeds of 75 km/h. Here, we show the effect of platoon size at the main and at the merging lanes. Figure 7 presents the effect of platoon sizes of merging and main lanes on obtained traffic flow [vel./h] (Figure 7a), on merging success (%) (Figure 7b) and on maximal time duration [min] along the section (Figure 7c).  
Higher flow rates are obtained for larger main lane platoons (Figure 7a), and higher merging successes are obtained for larger merging lane platoons (Figure 7b). The duration along the road section (Figure 7c) decreases with the ratio between main-to-merging lane platoon size.  
The merging rate is sensitive to the actual dynamics at the merging site. Therefore, it is critical that the vehicles reach the merging position at a specific \(g_{merge}\) and at a specific velocity that provides a better opportunity for a large number of vehicles to smoothly merge in and continue down the road without interference. Therefore, larger gaps are aimed for (and thus a large stabilization section is essential).  
There are two main advantages in forming large gaps for multiple vehicles to merge. The first is that both the vehicles from the main lane and the merging vehicles are already organized in a platoon form, with fitted gaps and velocities ready to blend in. In this way, the traffic flow is not affected by the merging, and no traffic congestion is formed. The second advantage is that each vehicle experiences fewer accelerations/decelerations, which may disturb passengers' comfort and affect the total energy consumption and durability of the mechanical systems.  
One can suggest that it is preferable, then, to accumulate even larger gaps, e.g., condense the 6 gaps of section B (Figure 3a) to a single larger gap that allows the merging of a large platoon [35]. However, this requires a central control unit and cannot be implemented with the method we suggest here (which is based only on proximity sensors to the neighboring vehicles).  
Figure 6: Obtained merging percentage for different onset gapping speeds and section sizes.
# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
## 4 Evolution of Pavement Image Processing Techniques
### Deep Learning Approaches
#### 4.3.1 Image Classification  
Image classification aims to categorize input images into predefined classes. In 2016, [PERSON] et al. [39] first applied the CNN to pavement distress detection, demonstrating superior crack identification performance compared to SVM and Boosting methods, with enhanced accuracy in distinguishing cracks from background features. Subsequently, [PERSON] et al. [40] developed a multi-receptive-field CNN architecture that classifies pavement images into five categories: intact surfaces, transverse cracks, longitudinal cracks, block  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Objective** & **Technical Contribution** \\ \hline MST [30] & Crack curve detection in & Developed CrackTree algorithm for \\ (Minimum Spanning Tree) & pavement images & pavement crack pattern recognition \\ \hline AdaBoost [31] & Classification of defective vs. intact & Reduced workload by pre-filtering \\  & pavement surfaces & images requiring manual inspection \\ \hline Random Forest (RF) [32] & Robust crack detection under & Proposed CrackForest framework with \\  & complex/noisy conditions & enhanced processing speed \\ \hline RF/SVM/AdaBoost [33] & Concrete bridge deck & Introduced STRUM classifier with \\  & crack identification & spatial-tuned multi-feature computation \\ \hline RF/SVM/KNN/ANN [8] & Pavement distress & Comparative evaluation of algorithms \\  & (cracks/potholes) classification & with parameter optimization \\ \hline SVM [34] & Crack type classification & Demonstrated superior performance \\  & (transverse/longitudinal/alligator) & over BPNN and RBFNN \\ \hline RF/SVM/ANN [35] & Multi-class crack identification & Achieved highest accuracy using \\  & & SVM classifier \\ \hline \hline \end{tabular}
\end{table}
Table 1: Application of machine learning algorithms in pavement distress detection.  
cracks, and alligator cracks. In 2019, [PERSON] et al. [41] further confirmed CNN's advantages for pothole recognition. The advancement of deep learning has significantly improved image classification accuracy.