esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Photocatalytic Tests and Analytical Determination  
Photocatalytic tests are performed as in [42] (see Figure 1A,B for the schematic illustration of the experimental setup). A UV cutoff solution (NaNO\({}_{2}\) 0.5 M) is pumped through the quartz sleeve to allow the investigation of the photocatalytic activity under visible light conditions (\(\lambda>400\) nm), as reported by others [43]. To perform a typical experimental run, 210 mg of Cu\({}_{2}\)O-TiO\({}_{2}\) catalyst are suspended in a doubly distilled aqueous solution (V = 0.3 L), at fixed pH and concentration of the sacrificial species. In particular, to evaluate the effect of the sacrificial species on the photocatalytic activity, a fixed concentration (2.5 M) of each scavenger (i.e., methanol, ethanol, ethylene glycol, formic acid, glycerol, lactic acid) was used. The effect of the organic concentration was assessed by fixing methanol concentration between 0 and 2.5 M. A N\({}_{2}\) stream is bubbled into the solution starting 40 min before inserting the photocatalyst to prevent the interaction between dissolved oxygen and copper species or photogenerated electrons. The effect of the temperature was assessed by increasing the temperature of the system up to 80 \({}^{\circ}\)C (at this temperature a condenser was located on the reactor to prevent the evaporation of the organic species). To test the effect of pH, in some runs the solution pH is corrected to selected values by adding a diluted solution of KOH or HClO\({}_{4}\).  
The suspension, containing a fixed concentration of the sacrificial agent (2.5 M) and a fixed amount of the photocatalytic material (700 ppm), was exposed to solar radiation (See the Figure 1B for the schematic illustration of the experimental set-up) to evaluate the photoefficiency of the system under solar light irradiation in selected runs. These runs were carried out in early September 2021 in Naples (40\({}^{\circ}\)50\({}^{\prime}\)0\({}^{\prime}\) N, 14\({}^{\circ}\)15 0\({}^{\prime}\) E), between the hours 11.00 and 13.00 under clear sky conditions. To evaluate the separate contribution of the UV and visible radiation ranges in hydrogen production, some photocatalytic runs are carried out by employing a cut-off filter to eliminate radiations with wavelengths less than 400 nm. In some cases, the recovery of the material was performed following the procedure reported previously [42].  
Hydrogen estimation is carried out as reported elsewhere [42; 44]. The pH of the solution is measured using an Orion 420 p pH-meter (Thermo). Shimadzu UV-2600 UV/vis spectrophotometer was used for UV-vis diffusive reflectance analysis, with an integrating sphere attachment and BaSO\({}_{4}\) as the reflectance standard. A Quantachrome Autosorb 1-C instrument analyzer was employed to evaluate the textural properties of catalysts [42].
###### Abstract  
This study proposes a novel receiver structure for underwater vertical acoustic communication in which the bias in the correlation-based estimation for the timing offset is learned and then estimated by a deep neural network (DNN) to an accuracy that renders subsequent use of equalizers unnecessary. For a duration of 7 s, 15 timing offsets of the linear frequency modulation (LFM) signals obtained by the correlation were fed into the DNN. The model was based on the Pierson-Moskowitz (PM) random surface height model with a moderate wind speed and was further verified under various wind speeds and experimental waveforms. This receiver, embedded with the DNN model, demonstrated lower complexity and better performance than the adaptive equalizer-based receiver. The 5000 m depth deep-sea experimental data show the superiority of the proposed combination of DNN-based synchronization and the time-invariant equalizer.  
underwater acoustic communication; deep learning; timing offset estimation; human-occupied vehicle +
Footnote †: journal: Applied Sciences  
10 November 2020; 1 December 2020; Published: 3 December 2020
# Prediction of Cutting Parameters of HVOF-Sprayed Stellite 6
## 3 Experimental Work  
The aim of this part was to investigate the influence of specifically defined combinations of cutting parameters in the machining of selected materials. These are EN S235 JRG1, Stellite-6 and Carbide alloy WC-Co. These are interesting materials in the framework of research focusing on quality, economy, stability, and durability of Stellite 6 coatings for the base steel EN S235 JRG1. New predictions and experiences can be used theoretically and practically in engineering production, especially in the automotive and aerospace industries. The parameter \(Ra\) as a fully integrated data is fully sufficient to determine other technology functions derived from it. Therefore, in the first phase of the research, the analysis of roughness \(Ra\) in [\(\mu\)m] after machining is the most important.
# Time-Frequency Attribute Analysis of Channel 1 Data of Lunar Penetrating Radar
###### Abstract  
The Lunar Penetrating Radar (LPR) carried by the Chang'E-3 (CE-3) and Chang'E-4 (CE-4) mission plays a very important role in lunar exploration. The dual-frequency radar on the rover (DFR) provides a meaningful opportunity to detect the underground structure of the CE-3 landing site. The low-frequency channel (channel 1) maps the underground structure to a depth of several hundred meters, while the high-frequency channel (channel 2) can observe the stratigraphic structure of gravel near the surface. As the low-frequency radar image is troubled by unknown noise, time-frequency analysis of a single trace is applied. Then, a method named complete ensemble empirical mode decomposition (CEEMD) is conducted to decompose the channel 1 data, and the Hilbert transform gives us the chance for further data analysis. Finally, combined with regional geology, previous studies, and channel 2 data, a usability analysis of LPR channel 1 data provides a reference for the availability of the CE-4 LPR data.  
lunar penetrating radar; data analysis; complete ensemble empirical mode decomposition (CEEMD); Hilbert transform +
Footnote †: journal: Applied Sciences
## 3 Results
### Detailed Analysis of Developments in Selected Specialties
#### 3.7.6 Telemedicine in Gerontology
The field remains exploratory, emphasizing development and feasibility, and further clinical validation is needed. 3.7.9. Telemehabilitation  
In Poland, rehabilitation has seen significant growth in telerehabilitation technologies for neurological and musculoskeletal conditions accelerated by COVID-19. During Early Development (2016-2018), efforts focused on hand telerehabilitation systems for post-stroke patients using computer vision [147] and mobile applications for monitoring activities [148]. During the pandemic (2021-2023), telerehabilitation programs for Duchenne muscular dystrophy (DMD) proved feasible [149,150], with surveys exploring respiratory telerehabilitation for DMD patients [149,150]. Reviews of multiple sclerosis rehabilitation have highlighted the role of telemedicine while emphasizing clinical validation needs [151]. Studies have compared patient satisfaction between telerehabilitation and traditional therapies [152]. Recent Advances (2022-2024) include meta-analyses of cognitive telerehabilitation effectiveness [153] and evidence-based guidelines for Physical and Rehabilitation Medicine [154]. This field has progressed from feasibility studies to clinical evaluations, particularly in neurological rehabilitation. Research shows telerehabilitation's effectiveness for stroke, DMD, MS, and cognitive impairment. The pandemic has accelerated its implementation when in-person care is limited. Studies indicate that telerehabilitation matches traditional methods in terms of patient acceptance. Despite these positive findings, additional large-scale trials are needed to refine the protocols and verify long-term outcomes.
## 3 Results and Discussion  
### Variation Patterns of GPP, PAR and ELUE on Different Time Scales  
#### 3.1.1 Diurnal Characteristics  
Hourly averaged daytime GPP, PAR and ELUE during the growing seasons were shown in Figure 2a-d. A trough of ELUE occurred at around noon and peaks appeared at sunrise/sunset. ELUE varied from 0.07 to 0.13, 0.04-0.10, 0.07-0.14 and 0.05-0.10 mol C MJ\({}^{-1}\) in rice, soybean, maize and wheat fields, respectively. A synergistic relationship was found between GPP and PAR, of which the trends were opposite to ELUE. [PERSON] and [PERSON] (2008) found that leaf LUE exhibited a bimodal behavior, which peaked in the early morning and late afternoon in wheat fields [43]. The midday depressions in net CO\({}_{2}\) assimilation have been reported because stomatal control of photosynthesisresults from high T\({}_{\text{a}}\) or VPD [44]. Besides, the assimilation of 1 mol of CO\({}_{2}\) requires 8 moles of photons at the leaf scale under ideal conditions [45]. However, the measured ELUE values are much lower than the theoretical ELUE values because they are constrained by many photosynthetic reactions [21; 46]. Photosynthetic productivity usually saturates far below the maximum solar light intensity; in those conditions, many absorbed photons and the resulting electronic excitations of the pigment molecules can no longer be utilized for photosynthesis. To avoid photodamage, various protection mechanisms are induced that dissipate excess excitations, which otherwise could lead to the formation of harmful molecular species such as singlet oxygen. For example, non-photochemical quenching (NPQ) of chlorophyll fluorescence is thought to be an indicator of an essential regulation and photoproduction mechanism against high-light stress in photosynthetic organisms [47]. [PERSON] et al. (2018) revealed that the depression of light interception is a strategy for avoiding the interception of excessive irradiance on whole-crown and leaf scales [48]. These processes may be the other reasons causing a decline in ELUE at midday.
# A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly
## 5 Case Study for the Healthcare Sector
### Assessment Results  
**Interdependency Graph:** Ten tangible assets and three intangible (e.g., data and personnel) are included with different connections among them. Figure 4 illustrates the interdependency graph of the scenario. In the Patient Registration processing activity the engaged assets are the Client Application, PC002, Secretary, HospitalServer1, the SQLServer and the Personal Data (red color), while in the Patient Monitoring the included assets are the Glucose Meter, Blood Pressure Monitor, Doctor, PC001, Client Application, HospitalServer1, SQLServer and Health Records (green color). As can be seen, the Health Records and Personal Data nodes isStoredOn on the SQLServer. The latter IsInstalledOn the HospitalServer1, while a ClientApplication IsConnectedTo to the same server. The Doctor interacts with the PC0001 based on the physical interaction IsUsedBy. It becomes obvious that several interconnections can be defined as a result of the actual dependencies of cyber assets, data sources and actors. Especially for large scale or dynamic environments, our approach can be proved beneficial as it offers a cartography that can assist the assessor to understand how the interconnected assets facilitate the data flows of the procession activities. In this way, the interdependency graphs contribute, not only to the uncovering of privacy risks on individual assets, but crucially, they ease in highlighting privacy risky paths which are formed by chains of assets. **Privacy scoring system:** As aforementioned, our proposed asset-centric scoring system incorporates the privacy impact as contributing factor for quantifying the impact that a vulnerability may have on user's privacy. Table 6, provides an overview of the vulnerabilities of core assets which are engaged in the processing activities of the organisation. As can be seen, independently of the vulnerability characterisation score, which reflects the cybersecurity criticality against the CIA triad, based on the introduced privacy scoring system the risk assessor can have the impact reflection of this cyber threat to the privacy dimension. Considering the peculiarities of each case, i.e., the sensitivity of the affected asset, the vulnerability type, the type of processed data and the number of the affected processing activities (scope of impact), our scoring system calculates the Privacy Impact score and the Privacy score. In this way, in the context of a privacy impact assessment, where mitigation actions should be driven by setting the privacy preservation as the main goal to achieve, the assessor can take advantage of the privacy impact and score to clearly identify the potential privacy risks and prioritise the mitigation actions accordingly.
## 4 Complexity and Performance  
This section details the performance evaluation of the proposed S-EFB-LNMS and P-EFB-LNMS LDPC decoders by Monte-Carlo simulations, including decoding performance, decoding complexity, and space overhead, as well as the potential benefits and limitations of these techniques in practical applications.
###### Abstract  
TH-2 is China's first short-range satellite formation system used to realize interferometric synthetic aperture radar (InSAR) technology. In order to achieve the mission goal of InSAR processing, the relative orbit must be determined with high accuracy. In this study, the precise relative orbit determination (PROD) for TH-2 based on global positioning system (GPS), second-generation BeiDou navigation satellite system (BDS2), and GPS + BDS2 observations was performed. First, the performance of onboard GPS and BDS2 measurements were assessed by analyzing the available data, code multipath errors and noise levels of carrier phase observations. The differences between the National University of Defense Technology (NDT) and the Xi'an Research Institute of Surveying and Mapping (CHS) baseline solutions exhibited an RMS of 1.48 mm outside maneuver periods. The GPS-based orbit was used as a reference orbit to evaluate the BDS2-based orbit and the GPS + BDS2-based orbit. It is the first time BDS2 has been applied to the PROD of low Earth orbit (LEO) satellite formation. The results showed that the root mean square (RMS) of difference between the PROD results using GPS and BDS2 measurements in 3D components was 2.89 mm in the Asia-Pacific region. We assigned different weights to geostationary Earth orbit (GEO) satellites to illustrate the impact of GEO satellites on PROD, and the accuracy of PROD was improved to 7.08 mm with the GEO weighting strategy. Finally, relative orbits were derived from the combined GPS and BDS2 data. When BDS2 was added on the basis of GPS, the average number of visible navigation satellites from TH-2A and TH-2B improved from 7.5 to 9.5. The RMS of the difference between the GPS + BDS2-based orbit and the GPS-based orbit was about 1.2 mm in 3D. The overlap comparison results showed that the combined orbit consistencies were below 1 mm in the radial (R), along-track (T), and cross-track (N) directions. Furthermore, when BDS2 co-worked with GPS, the average of the ambiguity dilution of precision (ADOP) reduced from 0.160 cycle to 0.153 cycle, which was about a 4.4% reduction. The experimental results indicate that millimeter-level PROD results for TH-2 satellite formation can be obtained by using onboard GPS and BDS2 observations, and multi-GNSS can further improve the accuracy and reliability of PROD.  
TH-2; precise relative orbit determination; GPS; BDS2; GEO satellite 2021, 13, 4487. [[https://doi.org/10.3390/ns13214487](https://doi.org/10.3390/ns13214487)]([https://doi.org/10.3390/ns13214487](https://doi.org/10.3390/ns13214487)) +
Footnote †: journal: remote sensing
## 2 Data Acquisition and Preprocessing
### Data Normalization  
To expedite the rate at which the data gradient declined during the training phase of the water quality parameter forecasting model, it was essential to preprocess the data. This involved normalizing all values to fall within the [0, 1] range. Subsequently, 70% of this normalized dataset served as the training set, while the remaining 30% was designated as the test set. The training set was employed to calibrate the model, optimizing the model parameters within the defined bounds. The test set was then utilized to evaluate the model's predictive accuracy. Upon completing the predictions, the data were reverted to their original scale. If \(X_{\text{max}}\) and \(X_{\text{min}}\) represent the highest and lowest values of the dissolved oxygen sequence, respectively, the normalized value for an original value \(X\) in the dissolved oxygen sequence is denoted as \(x\). The method of normalizing data was carried out using the following equation:  
\[x=(X-X_{\text{min}})/(X_{\text{max}}-X_{\text{min}}) \tag{2}\]
## 2 Research System Architecture
### Fault Testing Platform  
The gearbox fault testing platform constructed in this study is shown in Figure 2. A single-phase motor was used to drive the gearbox to simulate the blades driven by wind energy. The kinetic energy was transferred through the gearbox to the generator and exported by the generator as the load. Regarding the signal acquisition, the vibration signals of gearboxes were captured by vibration sensors.  
To discuss the abnormal vibration when the wind turbine gearbox fails, five models were set up based on the gearbox fault testing platform in Figure 2, including a normal gearbox (Type 1) and four fault types of gearboxes (Type 2-Type 5). The vibrations induced by different faults in the gearbox were analyzed.
# Efficient and Robust Arabic Automotive Speech Command Recognition System
## 4 Experiment Results and Discussion
### Preliminary Results
Furthermore, fine-tuning the Wav2 Vec model to learn the properties and characteristics of the Moroccan dialect requires a large and diverse dataset. However, the MASCD is relatively small, limiting the model's ability to learn the nuances of the dialect effectively. Consequently, the model did not generalize well to Moroccan commands, leading to lower accuracy. Since Arabic command recognition is a complex task due to language complexity, many words have the same rhythm. Therefore, as illustrated in Table 4, almost all previous works include a few commands covering simple commands (e.g., start, stop) that are easy to recognize. Only the study by [PERSON] et al. [8] achieved a good result with 40 command classifications. However, the dataset used in this research consists of simple commands, primarily formed from single words and digits that are easily recognized. While our dataset is not large and some commands consist of three words, the model we proposed was able to achieve notable results, outperforming other models in both clean and noisy environments. It is worth noting that for a fair comparison, we investigated the possibility of testing the models in the literature and our model on a standardized dataset. However, despite the efforts, we found that the existing research shared only the results of the model performances and did not provide access to either the models or the dataset used. As a result, the comparison could not be conducted under a standardized condition. Figure 10: Model-accuracy comparison: overall, clean, and noisy environments.
# Online Identification of Photovoltaic Source Parameters by Using a Genetic Algorithm
## 2 The Optimized SDM Parameter Identification Method Based on Genetic Algorithm
### Guess Solution Calculation  
The guess solution is obtained by means of the explicit equations shown in the following and is used as an approximated solution for the SDM parameters. More details are given in [11; 25] and the related references.  
\[I_{pkg}\simeq I_{sc} \tag{7}\]  
\[I_{sg}=C_{0}\cdot T^{3}e^{\left(-\frac{E_{g}}{kT}\right)} \tag{8}\]  
\[\eta_{g}=\frac{V_{oc}}{V_{t}\cdot\ln\left(\frac{I_{pkg}}{I_{sg}}+1\right)} \tag{9}\]  
\[R_{sg}=\frac{x\eta_{g}V_{t}-V_{MPP}}{I_{MPP}} \tag{10}\]  
\[R_{tkg}=\frac{x\eta_{g}V_{t}}{I_{pkg}-I_{MPP}-I_{sg}\cdot(e^{x}-1)} \tag{11}\]  
where \(V_{t}=\frac{kT}{q}\) is the thermal voltage of the PV junction, \(E_{g}\) is the material bandgap and \(C_{0}\) is the temperature coefficient. The latter quantity is computed using the following equations:  
\[C_{0}=\frac{I_{sc0}\cdot e^{\tau_{0}}}{T_{0}^{3}};\hskip 28.452756 pt\gamma_{0}=- \frac{V_{oc0}}{\alpha_{\rm p}-\frac{V_{q0}}{T_{0}}}\left(\frac{\alpha_{I}}{I_ {sc0}}-\frac{3}{T_{0}}-\frac{E_{g0}}{kT_{0}^{2}}\right)+\frac{E_{g0}}{kT_{0}} \tag{12}\]  
The subscript \"0\" stands for a reference condition, which usually corresponds to standard test conditions (STC).  
The auxiliary variable \(x\) is calculated by using the Lambert-W function again:  
\[x=W\left[\frac{V_{MPP}\left(2I_{MPP}-I_{pkg}\right)e^{\frac{V_{MPP}\left(V_{MPP }-2\eta_{0}V_{t}\right)}{\eta_{g}^{2}V^{2}}}}{\eta_{g}I_{sg}V_{t}}\right]+2 \frac{V_{MPP}}{\eta_{g}V_{t}}-\frac{V_{MPP}^{2}}{\eta_{g}^{2}{V_{t}}^{2}} \tag{13}\]  
As the previous equations show, datasheet information concerning the operation in standard test conditions (STC) and the thermal coefficients \(\alpha_{I}\) and \(\alpha_{V}\) is needed to calculate the SDM parameters. Moreover, the values of \(I_{sc}\),\(V_{oc}\)\(V_{MPP}\), \(I_{MPP}\) and the PV cell temperature \(T\) at the current environmental condition must also be provided.  
It is worth noting that \(I_{sc}\) and \(V_{oc}\) appear only in (7) and (9), respectively. In the proposed method, to avoid the measure of \(I_{sc}\) and \(V_{oc}\) in the actual operating conditions, the following approximation will be used for silicon-based PV panels:  
\[I_{sc} =\beta_{I}\cdot I_{MPP}\hskip 28.452756 pt\beta_{I}\in[1.05\div 1.20]\] \[V_{oc} =\beta_{V}\cdot V_{MPP}\hskip 28.452756 pt\beta_{V}\in[1.10\div 1.35] \tag{14}\]  
The ranges of \(\beta_{I}\) and \(\beta_{V}\) have been selected as suggested in [26; 27]. As a reference, average values for the silicon-based PV panels studied in [5; 9; 11] are \(\beta_{I}=1.10784\) and \(\beta_{V}=1.29541\). On the other hand, the dye-sensitized solar cells (DSSCs) and polymer PV modules (PPM) studied in [28] exhibit slightly higher values: \(\beta_{I}=1.13158\), \(\beta_{V}=1.48414\) for DSSCs and \(\beta_{I}=1.25053\), \(\beta_{V}=1.49043\) for PPM. Hence, the ranges of \(\beta_{I}\) and \(\beta_{V}\) should be slightly increased for non-silicon PV panels.
## 2 Methodology and Data  
### Satellite Data  
The satellite data used in this study include XCH4 data from the Sentinel-5P satellite TROPOMI sensor Level 2 products, XCH4 data from the GOSAT satellite Level 2 products, XCH4 data from TCCON ground stations, and GF-5.  
Information on the XCH4 products from satellites used in this study is provided in Table 1.  
It can be seen from the information of these satellites that the spatial resolution and revisit period of Sentinel-5P and GOSAT satellites are very different, which leads to a certain spatial and temporal variability between the TROPOMI data and the GOSAT data, and spatial and temporal matching must be carried out to screen out the data that can be used for fusion before the data are fused.