esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring-System Development for a Bottom-Set Gillnet through Time-Domain Dynamic Simulations
## 3 Environmental Conditions  
Table 2 summarizes the environmental conditions used in this study. The measured current and wave data, acquired in the southwest coast of Korea for three years, were utilized in this study. The corresponding probabilities of current velocity, significant wave height (\(H_{\text{s}}\)), and peak period (\(T_{p}\)) had been analyzed by the authors of [13] in previous research. The operating conditions were selected from the analyzed data. Moreover, the fishery-prohibition condition in Korea, which is a significant wave height of 5 m, was additionally simulated to observe the corresponding global performance of the system [30]. A JONSWAP wave spectrum was utilized to generate irregular-wave time histories. In the operating conditions, the enhancement parameter was fixed as 1.0, which is the same as in the Pierson-Moskowitz wave spectrum. Furthermore, an enhancement parameter of 2.14 for the fishery-prohibition condition was based on the average value of the Korean site [31]. A current profile was produced by the power-law method. The current velocity at the bottom was assumed to be 10% of the free-surface-current velocity. Waves and currents were linearly superposed where their nonlinear interaction effects were not considered. The directions of waves and currents were collinear and were fixed to a positive \(x\)-axis (0 degrees). Detailed descriptions for wave and current generations can be found in [PERSON] et al. [13].  
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{5}{c}{**Wave and Current Conditions**} \\ \hline Significant wave height, \(H_{\text{s}}\) (m) & 1.0 & 2.0 & 3.0 & 5.0 \\ Peak period, \(T_{p}\) (s) & 6.0 & 7.0 & 8.0 & 10.0 \\ Enhancement parameter, \(\gamma\) & 1.0 & 1.0 & 1.0 & 2.14 \\ Surface current velocity (m/s) & 0.2\(\dots\) & 0.4\(\dots\) & 0.6\(\dots\) & 0.8\(\dots\) & 1.0 \\ \hline \end{tabular}
\end{table}
Table 2: Wave and current conditions for the parametric study.  
Figure 2: Side (**a**), front (**b**), and top (**c**) views of the designed numerical model of the bottom-set gillnet.
# A Comprehensive Review of Open Caisson Modeling Technology: Current Practices and Future Prospects
## 2 Methodological Route for Study Selection  
The study selection criteria were established to ensure transparency and reproducibility in the selection of evaluated papers. The methodological route for study selection is provided in Figure 2. Considering both physical and computational modeling techniques, the goal to concentrate on open caisson construction in soft urban underground space was established. Relevant literature was retrieved from Web of Science, Google Scholar, Scopus, and CNKI, targeting research articles from 1940 to 2025. Specific keywords like open caisson and physical and numerical modeling were utilized as a search strategy. Based on relevancy, methodologically sound and well documented studies were included, while irrelevant and methodologically lacking details were excluded from the study. The screening and selection process was adopted based on abstract and full text reviews, with cross verifications from multiples authors. Finally, selected datasets including physical modeling, numerical modeling, theoretical advancements, and case studies were compared, and challenges and future directions were provided.  
Open caisson shaft construction is an important engineering technology utilized in deep foundation systems, especially in soft urban underground spaces (UUSs) with high groundwater levels. Recent developments in modeling techniques have greatly improved the accuracy, efficiency, and dependability of open caisson design and construction. These innovations are powered by the combination of physical model testing, numerical tools, and data-driven approaches. In the upcoming section, a detailed review of some of these  
Figure 2: Methodological route for study selection.  
developments, with an emphasis on their practical implications in open caisson modeling, is presented. The framework for this study is shown in Figure 3. The influence of these modeling techniques and their integration, as well as their limitations, will be explored as along with the advances in the whole design and construction process.
# A Power Processing Circuit for Indoor Wi-Fi Energy Harvesting for Ultra-Low Power Wireless Sensors
## 7 Actual Experimentation of the RFEHS
For the actual experimentation, the targeted voltage across \(C_{STORE}\) is 1 V. The time taken for the voltage to reach that level is approximately 20 h. The harvesting time approximation can be calculated by:  
\[T=\frac{C_{Store}}{3600\dot{i}_{c}} \tag{14}\]  
where \(T\) is the time in an hour, and \(\dot{i}_{c}\) is approximately close to 3 \(\upmu\)A which also has been discussed in [14]. From the recorded results, the current through the capacitor, the power absorbed and the energy stored in the capacitor are calculated and reported in Figure 19. Comparison with others finding as listed in Table 4 is labeled in the figure. The proposed system needs less than five hours to gain the minimum power requirements for all the listed sensors. The RF to DC conversion efficiency of the system is calculated by dividing the output power, \(P_{store}\), of the storage capacitor by the available input power of the matching circuit, \(P_{RF}\). However, since the value of \(P_{store}\) increases over time as calculated, the efficiency of the system is calculated in term of ranges. If the highest signal strength recorded (\(\approx-\)50 dBm) is assumed to be the dominant incident power to the proposed system over time, that gives the available power to the circuit being close to \(P_{INC}=(-55+30)\) dBm = \(-\)20 dBm or 10 \(\upmu\)W. The efficiency of the system is calculated and illustrated in Figure 19b. As shown in the figure, the efficiency of the overall system increased over time and the highest efficiency achieved is close to 30%. The efficiency dropped close to 6% as the voltage across the storage capacitor reached a constant level. As illustrated in Figure 14, the efficiency of the proposed system before the PMU is connected at the end of boost circuit is less than 1% for \(-\)20 dBm input power. However, with the addition of the PMU, the efficiency is improved with respect to time. Finally, from all the measurement values, the key features of the proposed RFEHS are listed Table 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sensor Spec.** & [6] & [15] & [16] & [17] & [18] \\ \hline Max.Input Supply (mV) & 1000 & 30–50 & 1000 & 600 & 1200 \\ Power Req. (nW) & 220 & 1.1–6.25 & 190 & 10.8 & 71 \\ Circuit Eqn. Impedance (Ohm) & 6.2 M & 400 k & \textless{}50 & - & 2.5 M \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Recent low power wireless sensor characteristics. Figure 19: The actual measurement results, (**a**) Output voltage, current, power, and energy and (**b**) The proposed harvesting system efficiency versus time.
# Speech Enhancement Algorithms: A Systematic Literature Review
## 2 Background
The process of speech degradation caused by background noise and the subsequent application of noise suppression techniques is depicted in Figure 2. As speech signals travel from source to destination, they often encounter additive background noise in uncontrolled environments [4]. Therefore, SEAs are an important optimization method for enhancing distorted signals and removing noise. The critical challenge in developing highly effective SEAs lies in selecting suitable processing techniques for noise reduction and clarity improvement, along with accurately modeling the statistical properties of noise and speech. Techniques such as spectral subtraction and Wiener filtering are commonly employed for noise suppression. Additionally, the choice of an appropriate Probability Density Function (PDF) to model noise and speech signals significantly impacts the performance of such algorithms. There are different statistical PDFs for modeling these random signals, which can be stationary or nonstationary. For instance, different works have used Gaussian PDFs [9; 30; 41]. These are widely used due to their simplicity, but may not accurately represent real-world noise in nonstationary environments. Suppressing nonstationary noise is a more complex task than suppressing stationary noise. To address this, more advanced PDFs use Laplacian [20; 42] or Gamma [43] distributions for more accurate noise modeling. Many studies have emphasized the importance of matching the PDF to the noise type in order to increase the algorithm's accuracy. Techniques such as Deep Neural Networks (DNNs), Kalman filtering, Non-negative Matrix Factorization (NMF), and MMSE estimation further leverage statistical models to refine speech signals, demonstrating the critical role of PDF selection in achieving optimal results. Significant advancements have been made in recent decades thanks to the availability of open-source resources, techniques, and measurements for building and evaluating SEAs. This study seeks to review and analyze recent existing research on SEAs while providing a comprehensive overview of their concepts, development, and challenges.
## 4 Applications of Intelligent Acoustic Monitoring
### Smart Cities and Urban Monitoring
#### 4.2.2 Urban Sound Classification  
In [109], [PERSON] et al. investigated how to effectively classify urban sounds by comparing two primary methods: the traditional signal processing approach (with manually selected acoustic features) and the deep learning approach, where sounds are transformed into images (such as mel-spectrograms or scalograms) and then classified by CNN models. They conducted experiments on two popular datasets, ESC-10 and UrbanSound8k,without any prior audio preprocessing. They applied classic SVM and k-NN algorithms to acoustic features, as well as deep learning models, including ResNet-18, ShuffleNet, and their five-layer CNN model. Their hybrid approach--combining acoustic features with visual representations of sound--proved the most effective. The best results were achieved by combining scalograms and acoustic features using the AVCNN model, reaching 98.33% accuracy on ESC-10 and 97.70% on UrbanSound8k using mel-spectrograms with acoustic features classified by an SVM. The conclusions of the work indicated that combining classical sound parameters with images generated from acoustic signals yielded the best results, and techniques known from speech recognition could be effectively transferred to sound classification in the context of smart cities.
# Evaluation of RGB-D Multi-Camera Pose Estimation for 3D Reconstruction
## 2 Materials and Methods
### Pre-Processing RGB-D Data
#### 2.4.3 Temporal and Spatial Filters  
To diminish the noise artefacts of the data, two post-processing techniques were applied to the captured frames, a temporal filter and a spatial filter. Both filters were implemented in RealSense SDK and these have been well explained by [PERSON] and [PERSON] [39].  
The temporal filter performs a time average of the frames to improve the depth calculation. The filter implements three parameters, those being the alpha, delta, and persistence filter. The alpha parameters represent the temporal history of the frames. The delta is a threshold to preserve edges, and the persistence filter tries to minimise holes by keeping the last known value for a pixel.  
The spatial filter implemented in the SDK is based on the work of [PERSON] and [PERSON] [40]. It preserves edges while smoothing the data. It takes four parameters, alpha and delta, which have the same function as in the temporal filter. This method also involves the filter magnitude, which defines the number of iterations, and hole filling parameters, which improve artefacts.
# A Systematic Review of Insole Sensor Technology: Recent Studies and Future Directions
## 4 Insole Sensor Technology
### Applications  
These devices aim to assist researchers and medical professionals by generating reports that facilitate the study of patient progress and aid in identifying pathologies. Additionally, they are valuable for designing and testing orthotics, enhancing athletic techniques, and providing information about treatments, thereby promoting further research. Numerous reported developments have been conducted with future applications in mind, including monitoring, pattern extraction, rehabilitation, and disorder detection, among others [16].  
Intelligent insoles have effectively monitored gait cycles and/or plantar pressure [31]. Gait is a rhythmic and periodic movement necessitating coordination, balance, and synchronization, facilitated by the proper functioning of the central and peripheral nervous systems. Movement is driven by ground reaction forces (GRF) applied to the body through its contact with the ground. Just as individuals have unique fingerprints, they also possess distinctive gait characteristics and movement patterns. Aging impacts the dynamics of foot pressure distribution during normal walking. In elderly individuals, the forces and  
Figure 8: Example of a plantar anatomic region divided into three zones and the position of each sensor for Pedar® software (novel gmbh, Munich, Germany) [4].  
pressures exerted on the medial foot regions are diminished, leading to reduced propulsion during the transition from the heel strike phase to the toe-off phase [26].  
The analysis of human movement encompasses the concepts of kinetics and kinematics. Kinetics describes the causes of movement, focusing on the forces (mass and acceleration), torques, and power generated. In contrast, kinematics focuses on the linear and angular descriptions of movement, including changes in velocity, position, displacement, and acceleration over time. Consequently, gait analysis requires a quantitative study of force parameters and temporal and spatial parameters by calculating key characteristics. Recognizing the importance of gait analysis is essential for detecting and managing various neurological and musculoskeletal disorders. Identifying gait events is valuable for enhancing gait analysis, developing precise monitoring systems, and evaluating treatments for pathological gait [10].  
In clinical practice, the CoP is frequently utilized to assess an individual's postural control stability, which is closely linked to various neurological diseases and movement disorders, such as Alzheimer's disease, Parkinson's disease, and chronic ankle instability. These conditions typically involve extended development or rehabilitation processes, necessitating long-term CoP monitoring [21].
# Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations
## 3 Mobile Channel Simulations by IMT-2020 Channel Model
### Simulation Results  
A single-cell MIMO system was assumed at 3.5 GHz with 200 MHz bandwidth in a UMa NLoS scenario. At the Tx side, a UPA with 128 cross-polarized antennas was used; at the Rx side, a single-antenna ODA was used. Channel realizations in simulations were based on 12,000 drops. It is noted that the number of antennas is 1 on the Rx side since only the angles at the departure side could be observed. Moreover, the multiantenna array used at the Rx side in our measurement can be seen as an equivalent single antenna with an omnidirectional antenna pattern.  
The MS moving trajectory is shown in Figure 7a. The Rx moves along in the direction of the arrow. Each direction takes 3000 drops. Based on Tx and Rx locations and the setting scenario, the path loss model is plotted in Figure 7b. The slope of the path loss model changes with the turning of directions. Blue bubbles show corresponding shadowing fading \(\sigma_{SF}=6\) dB. Figure 7c shows the AoD and EoD evolution profiles of the first three clusters (with the three largest powers). Cluster angles change direction when the moving route changes, and the evolution process turns smoothly and continuously. We also calculated the standard deviations (STD) of the delay, and the AoD and EoD from the measurements and simulations with and without the spatial-consistency feature (see Figure 7d). The simulation group without the spatial-consistency feature always has the largest values. Compared to the STD from the simulation group without this feature, the simulation group with it gets closer to the measurement group, which implies that spatial consistency improves simulation performance.
## 2 Materials and Methods
### Acquisition Method of CR Images  
The CR block contains holes of 57.2 mm in depth and 25.4, 19.1, 12.7, 9.5, 6.4, and 3.2 mm in diameter, at intervals of 5 mm, arranged in pairs separated by twice the width of the respective holes (Figure 1b). CR-block images were acquired with four different contrast medium concentrations and in eight different positions (12, 1.5, 3, 4.5, 6, 7.5, 9, and 10.5 o'clock) with each machine. In particular, the methodology of acquiring images with different directions aimed to examine the validity of the related result values by considering all directions that can be acquired at the time of actual QC. For CR evaluation, the required difference in radiodensity between the holes and the ROIs (the acrylic medium between the holes) was 10 HU (Hounsfield units). However, there are no specific criteria for contrast medium concentration for CR evaluation. Therefore, we utilized four different concentrations to measure the radiodensity, in order to prevent users from exploiting the maximum concentration to achieve the required CR. The contrast medium used was 0.612 g/mL isopamidol, of which 0.3 g/mL was iodine (Pamiray 300; DongKook Pharmaceutical Co. Ltd., Seoul, Korea) and the concentrations used were 0.01%, (Figure 2a), 0.015% (Figure 2b), 0.02% (Figure 2c), and 0.025% (Figure 2d) of the contrast medium diluted in sterile distilled water.
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 3 Comparative Analysis of IEEE 802.11 ah with Previous IEEE 802.11 Amendments
### Comparison of IEEE 802.11 Amendments Based on MAC Features
#### 3.1.13 Dynamic Bandwidth Management  
IEEE 802.11 ac has also introduced dynamic bandwidth management to optimize the use of available bandwidth. This scheme allows the transmitter and receiver to select an interference free channel before initiating transmission.  
3.1.14. Subchannel Selective Transmission (SST)  
This feature has been introduced by IEEE 802.11 ah. It allows stations to rapidly select and switch to different channels between transmissions to counter fading over narrow subchannels.  
3.1.15. Traffic Indication Map (TIM)  
In legacy IEEE 802.11, the Beacon frame contains this element through which the sleeping power saving stations are informed of the presence of buffered traffic intended for them at the AP. This element is sent in the form of a bitmap, where each bit represents the Association ID (AID) of stations. A bit is set in TIM when corresponding station has buffered data at the AP. The Delivery Traffic Indication Message (DTIM) serves a similar purpose, indicating the presence of buffered multicast frames.  
3.1.16. Target Wake Time (TWT)  
TWT is a function that permits an AP to define a specific time or set of times for individual stations to access the medium.  
3.1.17. Hierarchical AID  
IEEE 802.11 ah proposed hierarchical network organization where stations are grouped together based on their similarities. Each station is assigned a four level AID structure encompassing page, block, sub-blocks and station fields. As an important outcome, this mechanism helps in supporting increased number of stations.  
3.1.18. Dynamic AID Reassignment  
This mechanism allows the AP to change the page/group of a station due to a change in its traffic characteristics or for load distribution among the channels.  
3.1.19. Restricted Access Window (RAW)  
It is a new contention-free channel access mechanism that is designed to reduce collisions by improving the channel efficiency. The AP coordinates the uplink channel access of the stations by defining RAW time intervals in which specific class of devices are given exclusive access of the shared medium.  
3.1.20. Group Sectorization  
This scheme is developed by IEEE 802.11 ah that allows stations to transmit in different sectors (positions) around the AP in a time division multiplexing manner (i.e., after each Beacon, a different sector is given access to the shared medium). The Beacons transmitted by a sectorized BSS carry sector option element and each station is allocated a group ID based on sectorization operation.  
3.1.21. Relay Operations  
IEEE 802.11 ah has defined a mode of operation to utilize relays within the network to facilitate the exchange of frames between stations and APs. Relays allow stations to utilize higher data rates and TXOP sharing.  
3.1.22. Power Saving at AP  
IEEE 802.11 ah proposes to include AP power saving features in IEEE 802.11 ah.
## 4 Conclusions
To our knowledge, our system performed, for the first time, range-resolved measurements of CO\({}_{2}\) concentration and flux in a volcanic plume and the findings reported laid down the basis for a new and smarter generation of active optical devices, specifically conceived for long-term volcanic gas observation. Even though several improvements have been made (high distances reached, fully automated operational routines, reduction of system weight, and independent measurements of wind velocity), further developments are still necessary before DIAL-Lidar systems can become operative tools for real-time volcano monitoring. Efforts will have to be made, in particular, to improve portability (the current system weight is about 1100 kg), to reduce power requirements (currently, 6.5 KW) and to integrate existing system control and data processing routines to create a unique, user-friendly, and fully automated software framework for the end-users. In the near future, it will be desirable to deploy both ground-based (using a newly designed laser-based system such as the one described in this paper together with conventional sensors) and airborne (or space borne) platforms, forming a robust, integrated network for eruptions forecasting for prolonged periods. This will allow us to provide useful information to volcanologists, concerning the time evolution of volcanic gases in hazardous regions while working remotely and safely. [PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON], and [PERSON] wrote the paper with contributions from all co-authors. All authors have read and agreed to the published version of the manuscript. The research leading to these results received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007/2013)/ERC, grant agreement n 305377 (PI, Aiumpa). The authors are grateful to ENEA, in general, and [PERSON], [PERSON], and [PERSON], in particular, for their constant encouragement. We also thank the staff of INGV-OE, and especially the Director [PERSON] and [PERSON] for the logistical support and granting access to the INGV observatory \"Pizzi Deneri\" at Mt. Etna. Societa Enel Produzione spa is kindly acknowledged for logistical support and for providing access to \"Centrale ENEL di Stromboli\" during the field campaign. The authors wish to thank the commune di Lipari, Circoscrizione di Stromboli, for authorizing the use of the lidar on Stromboli. The support from the ERC project BRIDGE, no. 305377, is gratefully acknowledged. The authors declare no conflict of interest.