esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Bilateral Ankle-Dorsiflexion Force Control Tasks  
We used an isometric foot-force measurement system (SEED TECH Co., Ltd., Bucheon, Republic of Korea; Figure 2a) incorporating two platforms with force transducers (Micro Load Cell-CZL635-3135, range = 330 lbs, Phidgets Inc., Calgary, AB, Canada). Moreover, we modified each platform and metal strap based on participant's foot length and metatarsal height for proper foot stabilization. Participants were seated comfortably at a distance of 80 cm away from a 21.5 inch LCD (1920 \(\times\) 1080 pixels; 60 Hz refresh rate), and generated isometric force against the straps. We administered them to maintain a standardized posture (Figure 2b): (a) 90-100\({}^{\circ}\) of knee flexion, (b) 90-95\({}^{\circ}\) of hip flexion and (c) 90\({}^{\circ}\) of ankle dorsiflexion [34; 35].  
All participants conducted the first MVC trial for 5 s, and after 110 s of rest time they completed the second MVC trial for 5 s. For each participant, we determined MVC level by averaging two peak forces obtained from each trial. To perform bilateral force control tasks, a submaximal targeted force level was chosen as 10% of each participant's MVC because this level reflects the fine motor demands of diverse lower limb activities in daily life (e.g., stair climbing or driving a car) [36]. The goal of a bilateral force control task was to generate and sustain a combined ankle-dorsiflexion force from both feet near the targeted force level for a duration of 20 s. During the task, participants relied on real-time visual feedback displayed on a monitor (Figure 2c): (a) white line indicating a targeted force level and (b) red line indicating the total force of ankle-dorsiflexion generated by both feet. A  
Figure 2: Bilateral ankle-dorsiflexion force control task and tDCS protocol. **(a)** Isometric foot-force measurement system; **(b)** ankle-dorsiflexion position; **(c)** visual feedback condition; **(d)** bilateral tDCS protocol. tDCS = Transcranial direct current stimulation.  
total of 10 trials for each tDCS condition (i.e., active-tDCS and sham stimulation) were conducted per participant, with a 110 s rest time between each trial to minimize fatigue [37].  
All experimental tasks were conducted using custom Microsoft Visual C++ (Microsoft Corp., Redmond, WA, USA). Force signals were recorded at a sampling frequency of 200 Hz using a 16 bit analog-to-digital converter (A/D; ADS1148 16-Bit 2 Ksps and a minimum detectable force of 0.0192 N), and subsequently amplified using INA122 with an excitation voltage of 5 V (Texas Instruments Inc., Dallas, TX, USA).
## 2 Load Test Data  
A dataset of 74 piles compiled from the FHWA _Deep Foundation Load Test Database_ (DFLTD) v.2 [26] and Olson's Database [27; 28; 29; 30] was employed in this study. Dimensions of piles employed for analysis along with associated soil conditions are summarized in Appendices A and B, for DFLTD and Olson databases, respectively. These load tests were ported to a relational database for easy access [31]. The scope of this paper was limited to statically loaded steel pipe piles and hollow concrete piles with ample soil profile data for capacity calculations (\(Q_{c}\)), and interpretable compression load tests for capacity measurement (\(Q_{m}\)). The piles ranged between 0.25-2.54 m (10-100 in) in diameter and 7.5-113 m (24.6-370 ft) in length. The distribution of the diameter and length of the studied piles is shown in Figure 1. A summary of the bearing layer of the piles in the dataset is also shown in Figure 1, showing that nearly 65% of the pile toes employed in this study bear in sand, while approximately 35% bear in clay. All load tests were carried in compression according to ASTM D1143 [32], but information regarding the time duration between installation and load testing was not always available.  
Missing or misinterpreted data is one of the dominant issues when dealing with geotechnical databases. Therefore, all the soil data associated with the chosen piles were reviewed by the research team to check its integrity and usability. Available geotechnical design parameters were employed to compute \(Q_{c}\) where possible, but many pile cases lacked sufficient measurements, so the team used empirical relationships from established guidelines such as those provided by The Naval Facilities Engineering Command Design Manual 7.01 [33] and [PERSON] et al. [34] to supplement laboratory data. More details about data handling are available in [PERSON] et al. [35].  
Figure 1: Distribution of pile diameter and length of load tests employed in this study. The distribution of the bearing layer is also depicted in the top right.
## 3 Data Preparation
### Data Pre-Processing  
Through field investigations, three types of data were collected: (1) in-vehicle video footage of LKAS disengagement (Figure 1a), (2) disengagement reasons recorded via a mobile app, and (3) forward-facing video from an external camera (Figure 1c). These heterogeneous sources were synchronized by date and time and merged into a unified dataset using Python (version 3.8.19) \"pandas (version 2.0.3)\" library.  
The main goal of this study is to develop an algorithm that identifies lane markings in image data and determines LKAS engagement status. To this end, images at LKAS disengagement moments were extracted from the integrated dataset and used for training. This involved manual timestamp synchronization between the LKAS database and video  
Figure 1: Devices installed for data collection: (**a**) an in-vehicle camera capturing the LKAS activation status; (**b**) a mobile application recording GPS coordinates and LKAS disengagement reasons; (**c**) external cameras capturing front and rear vehicle views.  
footage, followed by selecting relevant frames. During the field survey, 1595 LKAS disenggements were recorded, 244 of which were due to lane detection issues. Among these, segments with disenggements over one second were selected, and frames were extracted at 10 fps, resulting in 330 images. The process is shown in Figure 3.
### Civil Airports Protection Requirements and Current Solutions  
The European Commission (EC) has created a roadmap of requirements for civil airport protection that will increase the security of critical assets. First, there is the ability to detect threats before they enter the protected zone (outside the airport fence), detection and identification of threats around the protected area, and tracking of objects (unidentified) over the entire surface of the designated area.  
The current solution to EC requirements is a microwave radar system that simultaneously detects, tracks, and pinpoints the location of potential intruders. Radar systems also allow continuous monitoring of a wide area, regardless of time and weather conditions. Advanced software solutions analyze the entire perimeter and can track objects beyond the airport fence. The software integrates the radar with cameras, allowing the guard to follow the object on monitors. As mentioned before, this solution is efficient and up to date, but _it leads to routine security behavior_.  
The airport has an important characteristic: there are many internal zones with different authorization levels, but literally no protection (physical barrier) can be applied due to the specificity of an airport's operation: the \"flat area\" requirement. Sensing techniques can only detect (not protect against) unauthorized access. The best results are achieved by using information from many \"complementary\" systems [11; 12]. A survey of related techniques can be found in [13; 14; 15]. Table 1 summarizes the advantages and disadvantages of different security solutions for airports.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & & **Key-Points** \\ \hline \multirow{2}{*}{Passive perimeter protection (fence)} & \(\bullet\) & Limited intrusion prevention \\  & \(\bullet\) & No intrusion detection \\ \hline \multirow{4}{*}{Active perimeter protection (with motion detection systems, e.g., fiber)} & \(\bullet\) & Limited intrusion prevention \\  & \(\bullet\) & Intrusion detection \\  & \(\bullet\) & False alarms possible \\  & \(\bullet\) & No intruder tracking after entry \\ \hline \multirow{4}{*}{Video surveillance systems, CCTV (visible or infrared)} & \(\bullet\) & Intruders/staff location and limited identification \\  & \(\bullet\) & Require constant operator attention \\ \cline{1-1}  & \(\bullet\) & Single-object tracking (per device) \\ \cline{1-1}  & \(\bullet\) & Sensitive to obstacles \\ \cline{1-1}  & \(\bullet\) & Visible-light systems are sensitive to environmental conditions (rain or fog) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of surveillance methods for airport prone.
# The Synthesis and Biological Evaluation of 2-(1H-Indol-3-yl)quinazolin-4(3H)-One Derivatives
## 3 Materials and Methods
### Synthesis
#### 3.3.7 Synthesis of 2-(1-benzoyl-1\(H\)-indol-3-yl)quinazolin-4(3\(H\))-one (**3i**)  
To a solution of 2-(1\(H\)-indol-3-yl)quinazolin-4(3\(H\))-one (**3a**) (1.0 mmol, 0.261 g) and triethylamine (2.0 mmol, 279 \(\mu\)L) in dimethylacetamide (2.0 mL) benzoyl chloride (2.0 mmol, 232 \(\mu\)L) was added. The reaction mixture was heated at 150 \({}^{\circ}\)C for 4 h (TLC control). Then, the reaction mixture was poured into H\({}_{2}\)O (50 mL). Then, NaHCO\({}_{3}\) (4.2 mmol, 353 mg) was added portion wise. The reaction mixture was stirred at room temperature for 2 h. The formed precipitate was filtered. The product **3i** was purified using column chromatography on silica gel using the mixture of petroleum ether/EtOAc (2:1) as an eluent and recrystallized from an acetone. A yield of 0.288 g (79%) was obtained and it was a white solid. Mp. = 317-318 \({}^{\circ}\)C, decomposition (acetone). \({}^{1}\)H NMR (400 MHz, DMSO-\(d_{6}\)) \(\delta\) = 12.45 (br.s, 1H), 8.89-8.87 (m, 1H), 8.76 (s, 1H), 8.38-8.36 (m, 1H), 8.14-8.12 (m, 1H), 7.88-7.82 (m, 4H), 7.79-7.75 (m, 1H), 7.70-7.66 (m, 2H), and 7.52-7.48 (m, 3H) ppm; \({}^{13}\)C{\({}^{1}\)H} NMR (100 MHz, DMSO-\(d_{6}\)) \(\delta\) = 168.4, 161.8, 148.8, 148.5, 136.1, 134.4, 133.1, 132.4, 130.9, 129.5 (2C), 128.7 (2C), 127.8, 127.3, 126.3, 125.7, 125.5, 124.6, 123.1, 121.0, 115.6, and 113.9 ppm. HRMS (ESI) Calcd for C\({}_{23}\)H\({}_{16}\)N\({}_{3}\)O\({}_{2}\) [M + H]\({}^{+}\) 366.1237; Found 366.1246.
# TRSDL: Tag-Aware Recommender System Based on Deep Learning-Intelligent Computing Systems
###### Abstract  
In recommender systems (RS), many models are designed to predict ratings of items for the target user. To improve the performance for rating prediction, some studies have introduced tags into recommender systems. Tags benefit RS considerably, however, they are also redundant and ambiguous. In this paper, we propose a hybrid deep learning model TRSDL (tag-aware recommender system based on deep learning) to improve the performance of tag-aware recommender systems (TRS). First, TRSDL uses pre-trained word embeddings to represent user-defined tags, and constructs item and user profiles based on the items' tags set and users' tagging behaviors. Then, it utilizes deep neural networks (DNNs) and recurrent neural networks (RNNs) to extract the latent features of items and users, respectively. Finally, it predicts ratings from these latent features. The model not only addresses tag limitations and takes advantage of semantic tag information but also learns more advanced implicit features via deep structures. We evaluated our proposed approach and several baselines on MovieLens-20 m, and the experimental results demonstrate that TRSDL significantly outperforms all the baselines (including the state-of-the-art models BiasedMF and I-AutoRec). In addition, we also explore the impacts of network depth and type on model performance.  
deep learning; machine learning; neural networks; rating prediction; recommender systems; tag-aware recommendations +
Footnote †: journal: Applied Sciences
# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion
### RS Data Used in Wetland Studies
#### 3.4.1 Data Type
Regarding the data type usage, as shown in Figure 11, 247 studies employed multispectral and hyperspectral optical images, followed by SAR images (\(n=116\)), LiDAR/DEM data (\(n=65\)), and UAV/Aerial imagery (\(n=39\)). Moreover, the integration of optical and SAR imagery includes 32 studies, followed by the integration of optical and LiDAR/DEM with 22 studies.
# Cyclostationary Crosstalk Cancelation in High-Speed Transmission Lines
## 4 Experimental Results  
### Measurement Setup  
The measurements of electromagnetic emissions caused by the data sequence transferred along transmission line in the presence of crosstalk interference were implemented by the test setup shown in Figure 9. The binary PRBS with the length equal to (2\({}^{10}\)-1) binary symbols was generated by the Anritsu pulse pattern generator MP1604B.  
The wiring diagram of the measurement equipment is presented in Figure 9a. Two identical microstrip lines of zigzag configuration were placed in parallel with 1 mm between them. One line was assumed as a victim and the other one as the aggressor. Microstrip lines were fed by the same Amristu pulse pattern generator, but the aggressor's sequence was delayed by some random time duration for eliminating the correlation between PRBSs. Microstrip lines were fabricated using a 1.6 mm thick FR4 substrate with a characteristic impedance of 50 \(\Omega\) having the length of 50 mm and the width of 1.85 mm. The photo of the microstrip line is shown in Figure 9b.  
Synchronous measurement of signals passing through the transmission lines were implemented by a serial data analyzer LeCroy SDA 813 Zi-A with a sampling frequency of 40 GSa/s. The total number of samples registered in each realization for implementing the ensemble averaging procedure was chosen around five million, which is less than the duration of the PRBS. This allows to confirm the randomness of the registered data.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 3 Methodology
### Why ERFO Improves Final Solutions  
While our proof shows that ERFO attains the same worst-case rate as FedAvg up to second-order terms, it does not capture why ERFO's entropy regularizer leads to higher final accuracy on non-IID data. We hypothesize that by maximizing update entropy, ERFO prevents over-specialization on local idiosyncrasies and thus yields global models that generalize better across heterogeneous clients. A deeper theoretical analysis of this phenomenon--e.g., via stability or bias-variance trade-off lenses--would be valuable future work.
# Construction Progress and Aviation Flight Test of BDSBAS
## 2 Overview of BDSBAS  
BDSBAS shares the GEOs and ground segment facilities with BDS-3. At present, three GEO satellites have been launched and are working in orbit; the ground section facilities have been almost completed, and BDSBAS has formed the initial operational capability.
# Full-Scale Measurements of Wind Characteristics on a High- Rise Building during Typhoon Sarika
## 3 Wind Characteristics of Typhoon Sarika
### The Measured Probability Density Characteristics of Typhoon Sarika  
The distribution of fluctuating wind is often assumed to follow the Gauss distribution in the current methods to predict the dynamic response of structures under the influences of seasonal trade winds:  
\[P(u)=\frac{1}{\sqrt{2\pi\sigma_{u}}}e^{-\frac{u^{2}}{2\sigma_{u}^{2}}} \tag{15}\]  
To study whether the probability density function of a typhoon follows the Gaussian distribution, the measured wind field data is analyzed systematically, and the resulting function is compared with the Gaussian distribution. The skewness coefficient (\(S\)) and kurtosis coefficient (\(K\)) are vital parameters to judge whether the measured time-history data accord with the Gaussian distribution. Provided that \(S=0\) and \(K=3\) accord with the Gaussian distribution. The formulas for the calculating skewness coefficient and kurtosis coefficient are as follows:  
\[S=\frac{1}{n}{\sum\limits_{j=1}^{n}}{\left[\frac{C_{ai}(t_{j})-C_{ai,mean}}{C _{ai,std}}\right]^{3}} \tag{16}\]  
\[K=\frac{1}{n}{\sum\limits_{j=1}^{n}}{\left[\frac{C_{ai}(t_{j})-C_{ai,mean}}{C _{ai,std}}\right]^{4}} \tag{17}\]  
In Equations (16) and (17), \(n\) is the sample size, \(C_{ai,mean}\) is the sample mean, and \(C_{ai,std}\) is the sample standard deviation. This paper discusses whether the probability density of wind speed in the whole process of typhoon landing conforms to Gaussian distribution. Figure 14 shows the probability density of total samples in the entire landing process of Typhoon Sarika. The skewness coefficient is 0.1787 and the kurtosis coefficient is 2.7572 during the landing process of Sarika. Criteria of \(|S|<0.5\) and \(2.0<|K|<4.0\) are used for judging whether the probability density of the sample follow a Gaussian distribution. Therefore, the wind speed samples follow the Gaussian distribution during the landing process of Typhoon Sarika.
# A Compact Triple Band Antenna Based on Multiple Split-Ring Resonators for Wireless Applications
## 6 Conclusions  
In this paper, a miniaturized multiband antenna (0.19 \(\lambda\times\) 0.2 \(\lambda\times\) 0.01\(\lambda\)) based on multiple split-ring resonators was proposed for modern wireless applications. The integration of SRRs enabled a significant miniaturization of 81.6% compared to a conventional antenna, while achieving satisfactory gains of 1.5 dBi, 2 dBi and 3.2 dBi across the operating bands 2.4-2.8 GHz, 3.25-3.75 GHz and 4.5-7.84 GHz, respectively. The antenna also exhibited excellent bandwidths of 14.55%, 14.28% and 54.13%, respectively. An analytical study was conducted and validated by an equivalent circuit model and numerical simulations, showing strong agreement between the results. A detailed analysis has revealed that the multiband capability and broadband performance of the antenna is primarily due to the ability of the metamaterial to exhibit negative permeability at certain resonant frequencies. This confirms the critical role of the integrated SRR structure in enhancing the overall performance and functionality of the proposed antenna. The proposed multi-SRR-based antenna offers a miniaturized size and efficient solution for multiband wireless communication systems, making it well-suited for potential applications in 5G sub-6 GHz mid-band (2.5/3.5/5 GHz), RFID (2.45/5.8 GHz), WiMAX, WiFi 5/6/6E and WLAN technologies.  
Conceptualization, M.A.; methodology, M.A.; software, M.A.; validation, M.A. and M.B.; formal analysis, M.A.; investigation, M.A.; resources, M.A.; data curation, M.A.; writing--original draft preparation, M.A.; writing--review and editing, M.A.; visualization, M.A. and M.B.; supervision, A.G.; project administration, M.A.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data that support the findings of this study are included within the article.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Year of Pub**} & \multirow{2}{*}{**Antenna Size mm\({}^{3}\)**} & \multirow{2}{*}{**Substrate Material**} & \multirow{2}{*}{**Frequency (GHz)**} & \multirow{2}{*}{**Gain (dBi)**} & \multirow{2}{*}{**Efficiency (\%)**} & \multirow{2}{*}{**BW (\%)**} & \multirow{2}{*}{**Techniques**} \\ \cline{6-9} \cline{8-9} \cline{8-9} \cline{8-9} \cline{8-9} \cline{8-  
**Acknowledgments:** The authors have reviewed and edited the output and take full responsibility for the content of this publication.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Review of the Vibration Arthography Technique Applied to the Knee Diagnostics
## 3 Signal Analysis
### Signal Processing
#### 3.2.2 Frequency Domain Analysis  
The parameters defined previously reflect the signal's temporal shape and its variation over time. However, it is sometimes more interesting to explore the frequency content of a signal. The frequency analysis of VAG signals can be performed using Fourier Transform [32], which allows the conversion of signals from the time domain to the frequency domain. For example, significant differences in the mean power of the knee acceleration signal were detected in the 100-500 Hz range for two knee disorders: spondyloarthritis and rheumatoid arthritis [19]. Patellofemoral joint diseases were extensively investigated by [PERSON] et al. [17]. The spectrograms for different diseases are plotted in Figure 5.  
Figure 4: Example of a waveform obtained by an accelerometer on a healthy knee in (**a**) and from a knee with pathology in (**b**) (from [20]). The short-time burst in (**a**) is often referred to as a “patellar click”.  
Furthermore, peak frequency, as well as the power content and decibel level of their VAG signals, have been computed by [PERSON] et al. [7]. They have enabled the identification and categorization of different signal types in healthy knees. As discussed earlier, VAG signals may be of different forms, as shown in Figure 5. A typical example of these signal types is the Physiological Patellofemoral Crepitus (PPC). This is found in 99% of people when the knee angular velocity is lower than 5 degrees per second. PPC is a manifestation of the slip-stick friction phenomenon generated by the patella on femoral comdes. It is believed to reflect the integrity of the articular cartilage [7]. Nevertheless, the exact origin of most of the VAG signal remains unknown.
###### Abstract
For instance, in these articles, the wave glider is considered as a repeater of the signal from the underwater glider and back, this helps to instantly control its operation [29; 30]. In this article, we present a system for the autonomous identification and tracking of ocean fronts by coordinating the sampling efforts of a heterogeneous team of autonomous surface vehicles and autonomous underwater vehicles [31]. Interaction of robots with scientists off-site and work in a hierarchical structure for the autonomous collection of visual frames of interesting underwater areas at various scales and environments is described in the work [32]. As can be seen, in many articles published to the date, the wave gliders plays the role of a repeater that allows us to receive and transmit data from underwater vehicles (including underwater gliders). However, these works did not consider the problems of group interaction of devices. Moreover, approaches to the interaction of heterogeneous groups consisting of wave and underwater gliders have not been considered before. The authors propose the development of fundamentally new constructive and functional approaches for the interaction of groups of underwater and wave gliders based on: (1) the presented algorithms of the planning and maintaining system; (2) taking into account the design features of the device. The proposed solutions make it possible to expand the range of MRC tasks to be solved, such as, for instance, patrolling and monitoring underwater potentially dangerous objects, searching for hydrocarbon deposits, etc. At the same time, the use of groups of gliders is increasing the duration of the work performed, which will significantly reduce economic costs. Everything in the complex leads both to the need to work out the latest structural and functional approaches to the use of marine robotic systems (gliders) and their groups with a high level of functionality, mobility and autonomy, and to the necessity to verify the results of the study in full-scale experimental conditions and pilot operations. Below, in Section 2, we provide general information about the objects, goals and objectives of the study, as well as existing developments and solutions in this area. A detailed description of the development of a group interaction system for a heterogeneous group of marine vehicles is described in Section 3.