esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Implementation of Attitude Stabilization System for Marine Satellite Tracking Antenna
###### Abstract  
Marine Satellite Tracking Antenna (MSTA) is an important shipboard device for ships to bidirectionally communicate with the outside. The attitude stabilization system is an important part of MSTA for keeping the antenna dish tracking the geostationary satellite in the presence of severe ship dynamics. In this paper, the designed high-performance attitude stabilization system is introduced, including hardware system, model identification and controller design. The detailed procedure for identifying the stepper motor model is stated, which is different from conventional procedure. The design procedure of the robust controller based on \(\mathcal{H}_{\infty}\) loop shaping is given. The designed attitude stabilization system was tested on a Stewart platform that was used as the ship simulator. From the testing results, it can be seen that the performance of designed attitude stabilization system is very high and the tracking angle error can be limited to within \(\pm\)0.15 deg, which satisfies the performance requirements of Ka-band MSTA product.  
Keywords: attitude stabilization; marine satellite tracking antenna; robust control; \(\mathcal{H}_{\infty}\) loop shaping +
Footnote †: journal: Electronics
## 2 Design of Low-Pass Filter
### Calibration and Bandwidth Tuning
#### 2.2.3 Calibration  
The concept of calibration of the resistor tuning bank used for the LPF structure is shown in Figure 6. The calibration is done by comparing the value of an external temperature insensitive reference resistor \(\text{R}_{\text{ext}}\) to the value of an internal resistor bank \(\text{R}_{\text{int}}\). The internal resistor bank \(\text{R}_{\text{int}}\) is designed to have nominal value equal to \(\text{R}_{\text{ext}}\) and same tuning specification as the resistor tuning bank in the LPF. Two internal stabilized current sources are used to create a voltage drop across both resistors, while a comparator is used to compare and tune the value of \(\text{R}_{\text{int}}\).  
Usually, direct transceiver architecture employs loopback-based direct channel calibration methods using a reference signal sweep to calibrate baseband induced imbalances, for example, amplitude and phase mismatches of the quadrature path signal [27]. Values of capacitors are much more stable over temperature compared to resistors, hence a single frequency sweep based on direct calibration can be used to determine the PVT-caused offset in the capacitor banks. Alternatively, dummy circuits can be added in the design to calibrate the value of the capacitor bank.
## 2 System Model and Problem Formulation  
### System Model  
The INAC system utilizing LEO satellites is depicted in Figure 1, where the satellites simultaneously provide broadband communication and navigation services to ground users. Communication users need only to establish a bidirectional data transmission link with a single satellite, whereas navigation users must receive broadcast signals from four or more satellites for passive positioning. INAC users are equipped with the capabilities for both communication and navigation. This differs from a multiple-access communication system [39]. As a waveform that is compatible with OFDM, OTFS provides a reliable communication solution for LEO satellites [40].  
OTFS transmits symbols in the DD domain, and the modulated data matrix is denoted as \(\mathbf{X_{dd}}\in\mathbb{C}^{M\times N}\), where \(M\) is the delay bins and \(N\) is the Dop  
Figure 1: The INAC system based on LEO satellites. Communication users transmit data to a satellite via uplink and downlink signals, while navigation users receive downlink broadcast signals from a minimum of four satellites for positioning. INAC users possess both functionalities.  
shows an OTFS signal with a bandwidth \(B=M\Delta f\) and a frame duration of \(NT_{i}\); the delay resolution and Doppler resolution of the DD grid are \(1/M\Delta f\) and \(1/NT\), respectively, where \(\Delta f\) and \(T\) are the subcarrier spacing and the symbol period, respectively, and \(T\Delta f=1\).  
The transmitter first converts the DD matrix \(\mathbf{X}_{\text{dd}}\) to the time-frequency (TF) matrix \(\mathbf{X}_{\text{tf}}\in\mathbb{C}^{M\times N}\) by the inverse symplectic fast Fourier transform (ISFFT):  
\[\mathbf{X}_{\text{tf}}[k,l]=\frac{1}{\sqrt{NM}}\sum_{n=0}^{N-1}\sum_{m=0}^{M-1 }\mathbf{X}_{\text{dd}}[m,n]e^{j2\pi\frac{nl}{N}-\frac{mk}{M}} \tag{1}\]  
where \(m\) and \(n\) are the delay index and the Doppler index, \(k=0,\ldots,M-1\) is the subcarrier index, and \(l=0,\ldots,N-1\) is the symbol index.  
Next, the OFDM modulator uses the Heisenberg transform to convert the TF matrix \(\mathbf{X}_{\text{tf}}\) to a continuous-time waveform:  
\[s(t)=\frac{1}{\sqrt{M}}\sum_{l=0}^{N-1}\sum_{k=0}^{M-1}\mathbf{X}_{\text{tf}}[ k,l]\Pi(t-lT)e^{j2\pi f_{k}(t-lT)} \tag{2}\]  
where \(f_{k}=k\Delta f\) is the frequency of the \(k\)-th subcarrier. \(\Pi(t)\) is a rectangular pulse defined as follows:  
\[\Pi(t)=\left\{\begin{array}{ll}1&0\leq t\leq T\\ 0&\text{otherwise}\end{array}\right. \tag{3}\]  
The original OTFS system does not incorporate a dedicated navigation component, which limits the ranging performance and the number of navigation users. Therefore, it is necessary to insert continuous broadcast signals to meet the requirements of the PNT applications. Additionally, the navigation and communication components in the new scheme should be orthogonal to maintain low MI.  
Figure 2: The ISFFT of the original OTFS modulation; each symbol in the TF grid is derived from all the navigation PRN codes and communication symbols in the DD grid.
## 2 Materials and Methods
### Data
#### 2.2.2 Vegetation Index  
MOD13C2 and MOD11C3 are vegetation index datasets ([[https://earthdata.nasa.gov](https://earthdata.nasa.gov)]([https://earthdata.nasa.gov](https://earthdata.nasa.gov)), accessed on 5 March 2024), acquired by moderate resolution imaging spectroradiometer (MODIS) sensors, with a spatial resolution of 0.05\({}^{\circ}\) and a temporal resolution of one month. Normalized difference vegetation index (NDVI) and land surface temperature (LST) data covering the period from 2001 to 2021 were employed to calculate VCI and TCI, respectively.
## 2 Obtaining Maximum Inlet Spacing (Methods)
### Determining Inlet Spacing
#### 2.3.2 Inlets and Their Individual Spacing  
[PERSON] et al. [36] mentions that Spanish guidelines call for dividing the roadway into small catchments to allow for working with flow from individual inlets; using this approach, spacings may be rearranged. In a roadway, if the on-grade slope is zero, \(S_{L}=0\), then equally spaced inlets will receive the same amount of rainfall because their drainage areas are equal (Equation (3)). That is unlike \(S_{L}\
eq 0\) for which only the first and last inlet spacings needs modification, while the inlets placed between the two continue to receive equal flow (under the assumption that the slopes are uniform throughout and no cross-slope interruptions or irregularities exist). Lack of respacing the first and the last inlets leads to excess of resources (first inlet) and to potential dangers to the road users and the structure via deterioration (last inlet). Respacing is then inevitable in order to keep the inlet design the same while avoiding the disadvantages of fixed spacing. If an area is defined with a right triangle of legs \(b+W\) and \(x_{1}\), as shown in Figures 1 and 2, then that area is subtracted from the first inlet and added to the last (in sum). How large the area becomes is determined by the angle that the roadway centerline forms with the resultant vector of cross slope and longitudinal slope \(S_{x}\) and \(S_{L}\), respectively (Figure 2):  
\[\alpha=\sin^{-1}\Bigl{(}S_{x}/\sqrt{{S_{x}}^{2}+{S_{L}}^{2}}\Bigr{)} \tag{7}\]  
where \(\sqrt{{S_{x}}^{2}+{S_{L}}^{2}}\) is the resultant slope. To equate the two inlets in flow means equaling them in area, so the first and the last inlets must have different spacing. It is straightforward from the geometry that:  
\[x_{1}=(b+W)/\tan\alpha \tag{8}\]  
where \(b\) and \(W\) are defined as shown in Figure 1. For the first inlet:  
\[x_{\text{first}}=x+0.5x_{1} \tag{9}\]  
The spacing between the last inlet at the sump and the one upstream:  
\[x_{\text{last}}\leq x-0.5x_{1} \tag{10}\]  
It is always safer to design inlets larger for the one placed at the sump or placed in close proximity to each other, so spacing could be shorter than provided above.
# Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms
## 4 Experiment
### Experimental Result
#### 4.3.2 Comparison with Methods in Other Papers
For the MSVD dataset, the CIDEr score of our model is significantly higher than TDDF and STAT, but lower than them on MSR-VTT dataset. This result can be analyzed from the dataset. The MSVD dataset has fewer videos than the MSR-VTT dataset, but each video has 40 description statements, whereas the MSR-VTT dataset has only 20 description statements. Our model mainly obtains the effective features of the video from the temporal, spatial, and channel attention, and the single video (which has more descriptions) helps the feature acquisition ability of the attention mechanism. Therefore, our model has better performance for more diverse descriptions. At the same time, the base feature of our model only use image features, whereas TDDF uses both image features and motion features and STAT uses image features, motion features and local features, which is beneficial for datasets with more action categories like MSR-VTT. Some results of video descriptions are shown in Figure 4. We can see that for the first two scenarios, the model gives a very appropriate statement to describe the content of the video objectively. A bad example is also shown in this figure. The statement description is \"a man is singing a a\", which shows that the statement has an obvious error. The model cannot give the correct word after the article \"a,\" which may be a word similar like \"song.\" This may be due to the lack of attention mechanism for the ability to model abstract nouns such as \"'song\" that cannot express specifically.
# Deep Learning in Financial Modeling: Predicting European Put Option Prices with Neural Networks
## 5 Discussion  
### Key Implications  
The results of this study highlight several important points regarding the accuracy and practical utility of the proposed dense neural network (DNN) for option pricing. First, the model demonstrates remarkable accuracy in capturing complex relationships between financial parameters and option prices, even in highly nonlinear contexts. The low Mean Absolute Error (MAE) of 0.5123 indicates that the average prediction error is minimal, while the high coefficient of determination (R\({}^{2}\)) confirms the model's ability to faithfully replicate theoretical option prices derived from the Black-Scholes analytical solution.  
Beyond accuracy, the neural network exhibits notable advantages when compared to traditional analytical models. While the dataset used in this study was generated under the idealized assumptions of the Black-Scholes framework, the DNN has the potential to adapt to environments where these assumptions break down. For example, it is well-suited for scenarios involving stochastic volatility or abrupt parameter shifts, which are not easily handled by closed-form solutions. Its flexibility allows it to capture complex dynamics that extend beyond the capabilities of traditional models, making it a promising tool for real-world financial applications.  
The strong performance of the DNN suggests several potential applications in practical settings. One key area is algorithmic trading, where the model's ability to generate fast and accurate predictions could be integrated into automated strategies. Additionally, the model holds promise for risk analysis, as it can simulate complex market scenarios and assess their impact on portfolio performance. These capabilities underline the adaptability and scalability of the DNN for tasks that require both speed and precision.
# GNSS Applications to Assess Performance in Olympic Sailors: Laser Class
## 3 Results  
Table 1 shows the analysis for each of the groups of sailors according to their level of performance in the regatta. It was observed that, in upwind course, the T1 sailors obtained greater VMG compared to the T2 and T3 sailors and also between the T2 and T3 groups. Similarly, the VMG was higher in group T1 compared to group T3 and between T2 and T3 in downwind. The high-level male sailors showed greater VMG compared to the other two groups of sailors in upwind and in the downwind course. Similarly, the females had greater VMG in upwind, and this same situation was observed when it was analyzed in the downwind course and in broad reach. With respect to distance, the sailors of group T1 traveled a shorter distance compared to the sailors of group T3 in upwind, although the analysis of the mean distance values did not show differences in downwind and broad reach. When the males group was assessed, it was observed that the high-level group showed a lower distance compared to the other sailors in upwind, and no differences were found in either downwind or broad reach courses. Regarding maneuvers in upwind, no differences were detected on the basis of the level of performance by the total sample, females or males, in any of the analyzed courses. In time spent upwind, the sailors in group T1 and T2 obtained lower values than group T3. The males of the high-level group showed shorter time in upwind, whereas the females of the same level obtained shorter time in broad reach.
## 3 Brain Stimulation Techniques
### tDCS in Depression  
tDCS is considered a new therapeutic approach for MDD with no significant adverse effects [49]. In MDD, the anodic electrode is usually placed over the area of the left dorsolateral prefrontal cortex, the activity of which is affected by depression. Although rTMS has the advantage of high temporal and spatial resolution, it is much more expensive and complicated to use than tDCS, which is portable and battery-powered. In addition, these two techniques have different mechanisms of action, and it is still unclear which of them would be more advantageous in terms of efficacy in the treatment of MDD [49].  
In recent years, several researchers have studied the effects of tDCS on patients suffering from depressive disorders (Table 1). Specifically, the use of tDCS stimulation showed a significant improvement in symptoms in depressive patients compared to the control groups, as measured by the Hamilton Depression Rating Scale (HDRS) [53; 54] or the Montgomery-Asberg Depression Rating Scale (MADRS) [55]. On the other hand, a systematic review and meta-analysis on the use of tDCS in bipolar depression showed a reduction in depressive symptoms but also noted the risk of manic conversion, and the authors recommended future randomized control trials to investigate the effectiveness of tDCS versus placebo or mood stabilizers in bipolar depression [56].  
tDCS is a potential treatment for people with major depression who cannot or do not want to take current first-line treatments. With high levels of acceptability, portability, and cost-effectiveness, tDCS is a potential first-line treatment for major depression [60].  
Studies such as [PERSON] et al. [59] have demonstrated that tDCS can be administered at home, with supervised treatment, which in this way supports high adherence to the protocol, is a safe method without long-term adverse effects, and significantly reduces symptoms of depression. Compared to a control group, they demonstrated a higher response rate of 58.3% and remission of 44.9%, thus demonstrating the importance of the method, especially for cases resistant to standard treatment [59].  
Used from the beginning of treatment, tDCS can help with quick intervention on the symptoms of depression and remission rate and help reduce the risk of the long-term impact of depression. Antidepressant medication treatment can change the course of brain deficits associated with MDD, so effective early intervention is significant for the prevention of brain deficiencies that may occur in patients with depression [61]. If psychiatrists traditionally start by prescribing a low dose for up to 8 weeks and then assess whether the treatment was effective or not and then increase the dose or change the treatment, recent evidence suggests that this long-term effect may have a significant long-term impact [62].
# Internet of Drones: Routing Algorithms, Techniques and Challenges
## 5 Future Research Directions
### Throughput and Delay  
One paradigm that affects a WSN is throughput, which means the amount of data successfully transferred through the network. The efficiency of the whole network is depicted by throughput in accordance with a reduction in latency. Different clustering techniques are used to improve the efficiency in terms of throughput of WSNs. Generally, there is a trade-off between energy consumption and delay [72]. This means that the greater the delay, the greater the energy consumption. The features of 5G, such as high speed and low latency, have increased the demand for wireless technology. These features require continuous connectivity and sustainability in low power networks with efficient routing. The authors in [73] have proposed a comprehensive survey on UAV communication in 5G wireless networks.  
It is time-consuming for engineers to configure the WSNs manually. Some of the main difficulties include a large number of nodes, distance from the remote management, replacing and updating of failure nodes, and the relocation of existing nodes inside the coverage area. These problems make up the backbone of the UAV based WSN. The time that UAV takes to reach each node accounts in turn for its delay.
# Seareed Exhibits Therapeutic Properties against Chronic Diseases: An Overview
## 3 Results  
A systematic review of the published studies yielded 213 articles. Then, through identification, screening, eligibility, and inclusion, 107 articles that fulfilled the review criteria were selected. Previous studies have observed that the use of seaweed modulates several common chronic diseases, including diabetes, neurodegenerative diseases, obesity, cancer, liver disease, cardiovascular disease, osteoporosis, and arthritis (Figure 1). Most of the studies were conducted using in vitro (39.4%) and in vivo (36.4%) models, and only 2.3% of the studies that we found were clinical studies.  
Figure 1: Number of publications on functional seaweed and chronic disease based on (**a**) the type of chronic disease and (**b**) the experimental model.  
We summarized seaweed research on chronic disease based on three groups of seaweed: brown (Phaeophyceae), red (Rhodophyceae), and green (Chlorophyceae) (Figure 2). Studies of chronic disease were conducted on brown seaweed (68%), red seaweed (18%), and green seaweed (14%). Among the brown seaweeds, the most studied species were _Ecklonia_ (21.3%), _Sargassum_ (20.2%), and _Fucus_ (9%). In red seaweed, _Gracilaria_ (20.8%) and _Gelidium_ (16.7%) were the top two most studied species for potential use in the treatment of chronic diseases. The most studied green seaweed species were _Uliva_ (47.4%), _Codium_ (26.3%), and _Caulerpa_ (15.8%). The phylum of Phaeophyta was the most studied species on cancer (40%), diabetes (85%), arthritis (67%), neurodegenerative diseases (71%), obesity (59%), osteoporosis (46%), liver disease (80%) and cardiovascular disease (84%). The studies of seaweed in treating chronic diseases were conducted _in vivo_, in vitro, in silico and as clinical studies. There was a lack of clinical studies conducted; however, a clinical study was conducted to test a Maritech(r) seaweed extract formulation containing extract of _Fucus vesiculosis_, _Macrocystis pyrifera_, and _Laminaria japonica_ on osteoarthritis patients [36]. The study showed that seaweed extract can reduce the symptoms of osteoarthritis in a dose-dependent manner.  
Figure 2: Percentage of published papers of seaweed research on chronic disease based on (**a**) the phyla Chlorophyta (green color), Rhodophyta (red color), and Phaeophyta (brown color); (**b**) chronic disease for each phylum; and (**c**) experimental model for each phylum.
## 2 Materials and Methods
### Experimental Design
#### 2.3.3 Sentinel-2 LAI Retrieval Approaches  
For the retrieval of LAI from Sentinel-2 observations, two GPR model calibration approaches were investigated: single-stage and two-stage calibrations (Figure 3). In order to perform an independent validation of both GPR model approaches, an equal (i.e., 40/40) split was randomly applied to the 80 ground measurements in order to generate training and validation datasets. The single-stage calibration involved using the training measurements to fit the retrieval model directly to the overlapping Sentinel-2 20-m reflectance data. The two-stage calibration approach involved, first, calibrating the fine-scale UAV data with the training dataset (see Section 2.3.2). The UAV fine-scale LAI estimates are then spatially averaged to the overlapping Sentinel-2 20-m pixel grid cells. Second, the resultant spatially aggregated LAI was then used to calibrate the LAI retrieval algorithm from the Sentinel-2 spectral bands (i.e., 705 and 865 nm) used in this analysis, sampled at 20 m. The two calibration approaches were then applied to the Sentinel-2 observations covering measurement points included in the validation dataset and compared to the spatially aggregated (20 m) UAV LAI data that was also estimated using the validation dataset.  
Figure 3: Overview of approaches for calibrating GPR models used for retrieving LAI from Sentinel-2 data. Including: single-stage calibration based on ground measurements and indirect calibration involving, first, upscaling ground measurement using high-resolution UAV data and, second, calibrating the Sentinel-2 retrieval model based on UAV LAI data. Validation of both approaches performed using UAV data averages per Sentinel-2 pixel.