esa-sceva/satcom-chunk-collection huggingface dataset:
# Robo-HUD: Interaction Concept for Contactless Operation of Industrial Robotic Systems
## 3 Modules
### Collision Module  
The skeleton, provided by the sensor module, is processed by the collision module to allow the user to interact with virtual objects such as safety planes or augmented UIs. All object interaction is calculated in three-dimensional Euclidean space and is based on a \(point(x,y,z)\), to which we refer as _SpacePoint_. For example, the skeleton as estimated by the Kinect SDK, consists of 25 _SpacePoints_.  
_ColBase_, a base class for collision, contains the name of the virtual object and a boolean attribute of the momentary collision. _ColPoint_ is an inheritance of _ColBase_ and contains a _SpacePoint_ as its center and a radius in which a collision can occur. The main collision detection uses the algorithm for a sphere-sphere collision detection:  
\[|c_{1}-c_{2}|<r_{1}+r_{2}\]  
which compares the distance between the two sphere centers \((c_{1},c_{2})\), with the combined radii \((r_{1},r_{2})\). An overlap or collision is possible only if the distance between the sphere  
Figure 3: Final schematic representation for the interaction of the components (system concept).  
centers is smaller than the radius combined. Further objects with increased complexity can be created by grouping _ColPoints_ together: _ColLine_ consists of different _ColPoints_, _ColQuad_ of different _ColLines_, etc. The full structure of collision objects can be seen in Figure 4.  
To fill in the spaces between two _ColPoints_, a linear interpolation can be used. This allows the creation of mesh-like objects that require only the outer points and a subdivision count for definition. The radius \(r\), when considering the distance \(l\) between two _SpacePoints_, should be at least \(\frac{1}{2}\sqrt{2}\,l\) in 2D and \(\frac{1}{2}\sqrt{3}\,l\) in 3D to ensure a gap-free collision object.  
A _ColQuad_ can be defined by its outer corners and span a mesh of _ColPoints_ to be impenetrable for collision detection. The advantage of this approach versus the use of a rectangle or a plane is that it can adapt to the real-world geometry, which is usually inexact. These imperfections are then leveled by the two-dimensional interpolation forming a curved mesh, which suits the real world better than a mathematically defined rectangle (Figure 5). A good use for a _ColQuad_ would be as a virtual barrier for a safety fence or an interaction zone. In Figure 6, an interaction between a skeleton and a _ColQuad_ can be seen.  
Figure 4: _ColPoint_ Class diagram in UML. _ColPoint_ contains a _SpacePoint_ as a center. _ColBase_ inherits its attributes (name and collision) to _ColPoint_, _ColLine_, _ColQuad_ and _ColOcto_.  
Figure 5: Representation of a _ColQuad_ with a subdivision factor of five from three different perspectives. Note: in this example point A is not on the plane defined by BCD. The _ColQuad_ is not a quadrilateral restricted to 2D but a 3D construct.
## 6 Conclusions
One promising approach is the incorporation of additional data sources and constraints to inform and refine the inversion process in Optirrig. For instance, combining soil surface moisture estimates obtained from radar data with leaf area index (LAI) observations derived from optical data can offer significant benefits to the inversion process. Especially notable is the upcoming launch of the new SAR mission NISAR in the L-band, which offers the potential for more precise and deeper SSM (surface soil moisture) estimations. This capability will be particularly advantageous in regions with dense canopy cover, as the L-band SAR signal can penetrate further into the soil and crop canopy. L-band SAR data bring a distinct advantage due to their superior ability to penetrate dense vegetation cover, resulting in more accurate estimations of soil moisture content within such areas. To tackle the challenge of observation frequency and to further refine the accuracy of soil moisture estimations and irrigation detection, a potential strategy involves incorporating L-band NISAR data as an additional data source complementing the existing C-band data. The integration of these two data types can lead to an increased observation frequency, thereby furnishing valuable insights into both water balance and crop growth dynamics within the Optirrig model. By leveraging such complementary information and further validation using a larger dataset encompassing diverse crops and irrigation methods, it becomes possible to enhance the accuracy and reliability of the method's performance and potentially develop a near real-time tool for irrigation detection. Conceptualization, M.H., B.C. and N.B. ; data curation, M.H., B.C. and D.C.; formal analysis, M.H., B.C. and N.B. ; methodology, M.H., B.C. and N.B. ; software, M.H., B.C. and N.B. ; supervision, N.B. and B.C. ; validation, M.H., B.C., N.B. and M.Z. ; writing--original draft, M.H. ; writing--review and editing, B.C., N.B., D.C. and M.Z. All authors have read and agreed to the published version of the manuscript. This research received funding the National Research Institute for Agriculture, Food and the Environment (INRAE); and the RSEAU project financed by the French Environment and Energy Management Agency (ADEME). The data presented in this study are available on request from the corresponding author. The authors wish to thank the French Space Study Center (CNES, TOSCA 2021) and the National Research Institute for Agriculture, Food and Environment (INRAE). The authors wish to acknowledge the support and funding received from Campus France and CNRS-L for [PERSON]'s PhD thesis. Furthermore, they extend their appreciation to the European Space Agency (ESA) for granting access to the Sentinel-1 data and to the French Land Data Center (Theia) for providing the Sentinel-2 data with atmospheric corrections. The authors declare no conflict of interest.
# TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device
## 5 Results and Evaluation
### Analysis of Experimental Results
#### 5.2.1 Influence on Different Users  
To verify the influence of different users on the experimental effect. We selected a total of 5 volunteers to participate in the experiment, including 2 females and 3 males. As can be seen from Figure 10a, the recognition accuracy of the three male users of user 1, user 2, and user 5 was usually higher than that of the two female personnel of user 3 and user 4, but the overall average accuracy was small. Figure 10b shows the results of the recognition accuracy of different users using the walls of different materials. It also demonstrated that the recognition accuracy of male personnel was indeed higher than that of female personnel in general. However, the influence of different testers on the final detection accuracy of the experiment was relatively small. We also used four kinds of walls for experimental verification. It can be seen that the average recognition accuracy rate was the highest when passing through the glass wall, followed by a slightly lower recognition accuracy rate when passing through the wooden door. Due to the gypsum wall and concrete wall structure being more complex, the recognition accuracy through the gypsum wall is lower than that through the door, and the recognition accuracy through the concrete wall is the lowest. The final experimental results showed that under the condition of different wall materials, different experimenters had different final detection accuracy of the experiment. The experimental accuracy of glass walls was the highest, and the experimental accuracy of the concrete wall was the lowest. In the case of the same wall material, the experimental accuracy of the user was not much different, thus verifying the stability of the system performance.
## 3 Motivation  
This section introduces a use case and challenges that motivate the experimentation part of this article. The use case involves a large-scale Healthcare Provider with multiple branches (such as hospitals and clinics) spread across an association of countries (i.e., the European Union). Each branch of the Healthcare Provider is the bearer of its patients' data, with data governance obeying regional legislation. The goal is to develop medical AI applications trained on the available data. Researchers focus on various areas like cancer tumor detection in MRIs and abnormalities detection in patient electrocardiograms (ECGs). Although each branch can initiate ML model training using its own data, training complex medical models requires abundant data and computational resources. While computational resources can be shared among branches, data are unshareable due to regulatory restrictions.  
Therefore, federated learning (FL) is the preferred training paradigm, where models are trained collaboratively on distributed data without sharing it. To facilitate FL implementation, the healthcare provider's IT team adopts a Kubernetes cluster management service. This service manages the diverse computational resources available at each branch, linking them through a common control plane (Figure 2). Kubernetes enables provisioning of the FL workflow by containerizing Flower server and client instances for distributed training. By establishing a single administrative domain, the healthcare provider can effectively offer an FL system to multiple researchers, providing them with sufficient computational resources to train complex models on their localized data. However, as more researchers adopt FL and resources are divided among them, resource allocation and workload distribution become critical challenges. Hence, from the Healthcare Provider's perspective, the interest is in reducing the FL model training time when utilizing a fixed set of (computational) resources. Subsequently, the throughput for multiple FL workloads will increase by reducing the training completion time.  
Next, we highlight two main challenges faced in this deployment:resource fitting and parallel FL workflow execution.
# Research of Modal Analysis for Impeller of Reactor Coolant Pump
## 2 Modal Analysis of the Impeller
### Result Analysis  
Table 1 shows detailed simulation data of natural frequencies. Simulated first-order natural frequencies values of mode shape with one nodal diameter under Condition A, B and C are 2520.3 Hz, 1974.2 Hz, and 2045.5 Hz, respectively. Making a comparison between simulation results with test results, it can be concluded that errors of simulation results are 5.0%, 6.5%, and 7.7%, respectively. These errors may result from impeller machining internal defects, bearing damping and instability of the motor speed. Since these errors are small, it is highly reliable to analyze modal characteristics of a full-scale RCP impeller on the simulation platform.  
By comparing natural frequencies of the impeller under Condition B with those under Condition A, it can be found that added mass leads to a decrease in natural frequencies, with about 20.0% for 0 nodal diameter mode, about 21.7% for one nodal diameter mode and about 14.0% for two nodal diameters mode. Compared with those of the static impeller in the air (Condition A), natural frequencies of the underwater rotating impeller (Condition C) reduce by about 14.7% for 0 nodal diameter mode, about 18.8% for one nodal diameter mode and about 3.7% for two nodal diameters mode. According to the simulation results of the impeller under Condition A and D, the changes of natural frequencies are so subtle that they can be ignored.
## 2 Materials and Methods
### fNIRS  
fNIRS can be used to measure neuronal activity-dependent changes in hemoglobin by measuring variations in the light transmitted through the cerebral cortex [56]. We used fNIRS to monitor the changes in O\({}_{2}\)Hb, deoxyhemoglobin (HHb), and total hemoglobin (THb) in PFC, and these signals recorded through the experiment from pre-exercise rest to post-exercise rest.  
This study used 24 channels of fNIRS (OMM-3000; Shimadzu Corporation, Kyoto. Japan), and the distance between the emission probe and detector probe was set to 3.0 cm. Fpz (On the line from the root of nose to protuberance occipitalis externa, starting from the root of nose, a point at 10% of the total length is defined as Fpz) was positioned on channel 2 in accordance with the International 10-20 system (Figure 2). Although the fNIRS data included three measures of cerebral hemoglobin concentration (i.e., O\({}_{2}\)Hb, HHb, and THb), we used changes in the concentration of O\({}_{2}\)Hb for the main statistical analysis because it is considered to be the most reliable indicator of the changes in regional cerebral blood flow [57; 58]. O\({}_{2}\)Hb data were averaged at each channel, and a 0.1 Hz low pass filter was used to decrease noise from the heartbeat [59; 60]. The device used in this study and in previous studies [61; 62] can measure the cerebral hemoglobin concentration using three differential continuous waves (780 nm, 804 nm, and 830 nm), and is based on the modified Beer-Lambert law [63]. That is because, for each wavelength, absorbance at the start of measurement was defined as the initial absorbance. As it was not possible to measure the differential path-length factor using the continuous-wave NIRS system, it was assumed that it was constant, and hemoglobin signal changes were denoted in arbitrary units of millimolar-centimeter (mM \(\cdot\) cm) [64; 65].  
Figure 1: Methodology of the 2-back test, in which a sequence of 12 questions was presented. Participants were required to remember the last number of the previous result for each trial. Subsequently, the last number of the result from the two previous trials in the sequence was reported, and the reaction time and accuracy were recorded.
## 5 Experimental Results  
### Benchmark Functions  
In order to verify the effectiveness and robustness of the proposed PTBO algorithm, PTBO is applied in experimental simulation studies for finding the global minimum of the entire 28 test functions of the CEC 2013 special session [44]. The CEC 2013's test suite, which has been improved on the basis of CEC 2005 [38], covers various types of function optimization, and is summarized in Table 4.  
The search range of all functions is between \(-100\) and \(100\) in every dimension. These problems are shifted or rotated to increase their complexity, and are treated as black-box problems. The explicit equations of the problems are not allowed to be used. The test suite of Table 4 consists of five uni-modal functions (F01 to F05), 15 multimodal functions (F06 to F20) and eight composition functions (F21 to F28).
## 3 Results and Discussion  
### The Used Data Sets  
In order to test the robustness of the proposed neural architecture, in this section, we present the results of its application to a selection of 13 benchmark data sets widely used in the literature, and we compare these results to those produced by other methods. The selected data sets differ in nature and size and are related to different application domains. Each data set was split into two subsets used, respectively, for the training process and the test of generalization on unseen examples. More information about these data, including their source and the number of elements in each subset, are provided in Table 2.
# A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study
## 3 Material and Methods
### Datasets  
The datasets utilized in this study encompass a range of speech recordings from various sources, each designed to capture distinct voice characteristics and pathologies, providing a comprehensive foundation for analysing and detecting voice disorders. The SLI dataset was developed by the Czech Technical University and contains recordings from 15 boys and 35 girls. Their ages range from 4 to 12 years. All recordings are in WAV format, featuring a 44.1 kHz sampling rate and a 16-bit depth. Entirely, the dataset includes 3258 recordings, with 2173 from patients and 1083 from healthy individuals [9]. In addition, the SVD dataset was downloaded from [10], which comprises recordings from over 2000 individuals, including voice utterances from 1354 individuals with more than 71 different pathologies, alongside 687 healthy individuals. The duration of these voice recordings ranges from 1 to 3 s, and four specific voice disorders were selected from the database: Hyperfunctional Dysphonia, Hypofunctional Dysphonia, Dysphonia, and Laryngitis. These disorders did not cause significant degradation in voice utterances, so they are selected as the initial scope of this study. Ultimately, the SVD dataset contains 1079 voice samples, of which 446 are pathological, consisting of vowel recordings of /a/, /i/, and /u/, pronounced in three different intonations: high, low, and neutral. Finally, the VOICED dataset consists of recordings of subjects holding the vowel 'a' for five seconds continuously. The VOICED dataset comprises 208 recordings sampled at 8000 Hz with a 32-bit resolution. It is divided between 58 healthy samples and 150 with vocal fold disorders [11].  
To analyze these datasets, key features from raw speech signals were extracted using the Librosa library [29]. Several spectral and rhythm features are extracted, including Mel-Frequency Cepstral Coefficients (MFCC), Mel spectrogram, frequency and autocorrelation Tempogram, Chromagram or the power spectrum, spectral contrast, roll-off frequency, and flatness. As well as, tonal centroid features, spectrogram poly-feature, zero-crossing rate, spectral centroid and bandwidth, and the root mean square error of each frame. All features are computed per frame, and the mean and standard deviation are used to create the dataset. The final created dataset contains 849 features, and one binary class of two labels (healthy or pathological). The strength of extracted features is that the MFCC encodes spectral envelopes through mathematical transformations, while spectrograms visualize frequency changes over time, aligning better with human auditory perception. Furthermore, the Tempogram analyzes rhythmic patterns, and chroma features represent tonal content by grouping frequencies into twelve pitch classes. Other spectral features, including spectral centroid, bandwidth, flux, flatness, and zero-crossing rate, provide further insights into the signal's frequency content and waveform characteristics.
# Review of Power Electronics Technologies in the Integration of Renewable Energy Systems
## 3 Literature Survey
### Grid-Integrated Solar PV Inverters  
Power systems connected with PV modules and converters have become increasingly popular as a sustainable and renewable energy solution. These systems harness the power of sunlight by converting it into electrical energy through the use of PV modules. Power systems connected with PV modules and string inverters [52; 53; 61] have revolutionized the way we harness solar energy, as have power systems connected with microinverters.  
Figure 9: Microturbine configuration.  
The connections between solar panels and microinverters are crucial for optimal power generation. Each solar panel is furnished with a microinverter, which transforms the DC power into AC power. This ensures that each panel operates at its maximum potential, even if other panels in the system are shaded or experiencing different levels of sunlight.  
With traditional string inverters, if one panel in the string is shaded or not performing optimally, it can significantly impact the output of the entire string. However, with micro inverters, each panel operates independently, allowing for maximum energy production even in less-than-ideal conditions. Another benefit of power systems connected with microinverters [45; 76] is their scalability. Extrasolar PV panels can be easily added into the system, as each panel operates independently with its own microinverter. This makes it easier to expand the system and meet increasing energy demands. Furthermore, these power systems are reliable and require minimal maintenance. Since each solar panel has its own microinverter, any issues or malfunctions can be easily identified and addressed without affecting the performance of the entire system. This also simplifies troubleshooting and maintenance tasks. These systems are scalable, easy to maintain, and environmentally friendly, making them an excellent choice for generating clean electricity. The various inverter configurations are depicted in Figure 10. A comparison between central inverters, string inverters, and microinverters is presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Central Inverter** & **String Inverter** & **Microinverter** \\ \hline Size of the system & Large & Medium & Small \\ \hline Mode of installation & Single inverter for the entire power system & One inverter for group of panels & One inverter for each solar panel \\ \hline Effect during faults & Low & Moderate & Very high \\ \hline Initial cost & Low & Moderate & High \\ \hline Impact of disturbance & Complete shutdown & One string may be affected & One panel may be affected \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between central inverters, string inverters, and microinverters.  
Figure 10: _Cont._  
Figure 10: Grid-tied solar PV inverter configurations: (**a**) central inverter; (**b**) microinverter; (**c**) string inverter.