esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 4 Societal Benefit
### Transportation  
Satellite observations have successfully helped transportation in the following aspects: (1) environmental impact assessment; (2) infrastructure management; and (3) safety, hazards and disasters assessment [134]. These were achieved using hyperspectral and high-resolution optical observation, and light detection and ranging (LIDAR). The development of a transportation network, such as a highway system, is always accompanied by urban growth, and both of them would introduce environmental impacts, such as land use and land cover change, ecosystem, and air quality.  
Transportation infrastructure is composed of planning, design, and maintenance of transportation assets, including pavements, bridges, pipelines, rail-lines, harbors, and airports. Remote sensing, together with Geographic Information System (GIS) and Global Positioning System (GPS) may provide information of location and their change through time, include general road centerline, precise road width, traffic count (cars, airplanes, _etc._), parking [135], bridge location, corridor location, _etc._  
Apart from safety and hazards, which are more relating to real-time application, remotely sensed transportation disasters generally come from extreme events (e.g., hurricane, gale), which are identified and separated from the normal condition determined from related CDRs. Now CDR related technology and data products are advancing our awareness and ability to monitor the transportation assets and forecast disastrous events, thus improving the public's avoidance and mitigation responses.
## 5 Discussion  
### Evaluation and Adoption of The System  
Our evaluation shows that the system is valuable to the users, with users grading the importance of the top 20 outliers to be on average 4.55, on a scale from 1 to 5. The quantitative evaluation was limited by the number of labeled time series, but the conclusion is strengthened by the clear coincidence between quantitative and qualitativeresults described in Section 4.2.1. Because of the positive evaluation, the system is being further tested as part of DMGs production system and we are continually getting feedback with interesting findings based on top outliers.  
In the production system, users request songs with the most surprising trends during the last week and for each top 3 outlier song, the user is presented with the trend together with the expected behavior (level 1 explanation) (Figure 5).  
Even though end-users found it hard to grasp level 2 explanation, DMG has decided to keep it in the presentation, but only present the explanation when the users hover the expected values.  
When the users hover a prediction, the most influential contextual features are highlighted with dots (level 2 explanation). Instead of coloring the dots according to feature importance, the most important explanatory features are all highlighted with black dots, reducing the amount of information presented to the user.  
The current system is designed to find outlier songs, but--without changes to the outlier detection method-the system can also be used to identify outlier artists, outlier countries, etc., which will be a natural extension in the future. DMG will conduct experiments with different sources of outlierness.
## 4 An Engineering Application  
### General Description  
A deep foundation pit is located in Beijing and the average excavation depth is 21 m. Due to the geological condition being very complicated, the pile-anchor joint supporting system was decided on. The length of the support pile in the foundation pit is 25 m, which means the buried depth is 4 m. The foundation pit is 450 m in circumference, and is 10,660 m\({}^{2}\) in area. Investigation data show that a power pipeline is buried 2 m below the ground surface on the east side of the foundation pit. The positional relationship can be seen in Figure 13. The pipeline consists of concrete with a strength grade of C30. The pipeline structure is circular arch-vertical wall, and the clear section size is 2.0 \(\times\) 2.3 m including a clear width of 2 m, clear height of 2.3 m, and rise of arches of 0.45 m. A  
Figure 11: Effect of diameter on the pipeline settlement.  
Figure 12: Effect of elastic modulus on the pipeline settlement.  
composite lining is adopted, including an initial lining thickness of 0.25 m, and a secondary lining thickness of 0.25 m. The section of the pipeline is shown in Figure 14.  
The main physical parameters of the soil layer in the area where the foundation pit is located are shown in Table 1.  
A total of nine measuring points are arranged along the length direction of the pipeline, and the monitoring length is approximately 150 m. In Figure 13, the measuring point numbers are GX01\(\sim\)GX09, with a spacing of 20 m between GX02\(-\)GX08, 12 m between GX01\(-\)GX02, and 15 m between GX08\(-\)GX09. Additionally, CX4 and CX5 are two inclinometer holes on the east side of the pit wall, respectively. The distance between the measuring points and foundation pit side is shown in Table 2.  
Figure 14: Power pipeline tunnel section.  
Figure 13: Plan view of the foundation pit excavation and buried power pipeline.
## 3 Results and Discussion
### Photocatalytic Performance Results
#### 3.5.2 The Reusability Study  
The study of photocatalytic applications places significant importance on reusability. This aspect is crucial, as it enables us to determine whether a photocatalyst can be effectively reused in wastewater treatment. Given this, to evaluate the reusability of the TiO\({}_{2}\)/zeolite composite, three consecutive tests of degradation of MO were conducted under the same reaction conditions. As shown in Table 6, the TiO\({}_{2}\)/zeolite photocatalysts retain their ability to degrade MO solutions at 10 ppm even after three uses, showing a slight difference from the initial experiment. However, for 20 ppm solutions of MO, the sample presents a considerable decrease in its degradation efficiency after two cycles. The loss might be attributed to the catalyst leaching that inevitably occurs during the recovery and washing process, as well as the blocking of the active site by the surface coverage with the dye and its degradation intermediates [61].
# Research on the Robustness of Interdependent Networks under Localized Attack
## 3 Results and Analysis
### Artificial Network
#### 3.1.3 Degree Distribution Is an Important Factor Affecting Network Robustness  
For WS networks, both isolated and coupled, the order of attack strategies according the destruction on networks is TA > RA > TLA > PDLA; but for BA networks, the order is TA > TLA > RA > PDLA; for NN networks, the order is TA = RA > TLA > PDLA. Obviously, the robustness under TA is the worst, and robustness under PDLA is the best. The differences only exist in the robustness under RA and TLA. The difference between WS and BA networks is due to the degree distribution: the degree distribution of the WS network is relatively uniform; this makes it possible for WS networks to break into more clusters under random attack, which means the interdependent WS-WS network is more vulnerable under RA than TLA. However, the degree distribution of the BA network is a power law distribution, which is to say, there are a few hub-nodes in this kind of network. In the TLA scenario, the probability of hub-nodes being attacked increases faster than that in the RA scenario with the expansion of the attack area, resulting in BA networks being more vulnerable under TLA than RA. This is consistent with the conclusion of [21] that \"there is a higher probability that higher degree nodes will be within the attacked hole, which accelerates the fragmentation of the BA network\". Note that both the isolated NN network and interdependent NN-NN network show the same failure process under TA and RA. This is because the degree of all nodes in the NN network is the same, so the failure possibility of them is identical under targeted attack and random attack.
## 5 Verification  
The classical incompressible fluid flow around a cylinder is used to verify the correctness of the program. The flow around a cylinder refers to the flow pattern of low-speed, steady flow around a two-dimensional cylinder. This flow pattern is only determined by the Reynolds number (\(Re=\rho v\mathrm{L}/v\)), where \(v\) represents the typical macroscopic velocity of the flow and \(\mathrm{L}\) is a characteristic length of the fluid domain. If \(Re\) exceeds 100, it will result in turbulent flow in the channel.  
Assume a two-dimensional region with dimensions of 12 cm in length and 4 cm in width is established on the \(xoy\) plane. A cylinder with a radius of 0.5 cm and a central coordinate located at the point (\(x=4\) cm, \(y=2\) cm) is positioned within this plane, as illustrated in Figure 4. The left and right boundaries of the region are set as periodic boundaries,while the upper and lower boundaries are designated as no-slip boundaries. The viscosity of water \(\
u\) is 1 \(\times\) 10\({}^{-6}\) m\({}^{2}\)/s, and the maximum flow rate is 5 cm/s. The resolution of the cylinder diameter is 30 cells. The calculation results for different Reynolds numbers depicting the flow around a cylinder are shown in Figure 5. At low Reynolds numbers, laminar flow occurs, as shown in Figure 5a,b. As the Reynolds number increases, there is no separation behind the cylinder. Instead, a pair of symmetric vortices appear, and then the vortices begin to detach due to instability, as shown in Figure 5c,d. Furthermore, the finite element method was implemented to compare the fluid dynamics around a cylinder, as shown in Figure 6. There are altogether 14,652 finite element meshes. The numerical results demonstrate that for Reynolds numbers lower than 100, the fluid flow presents a relatively stable pattern. When the Reynolds number surpasses 100, a significant amount of turbulence emerges, similar to the coupled simulation results witnessed in LBM-DEM analysis. The turbulence results calculated using the LBM method are essentially consistent with existing conclusions [36], confirming the accuracy of the previous formula derivation.  
Figure 4: Schematic diagram of a cylindrical flow model.  
Figure 5: Simulation of channel flow with a cylindrical obstacle.
## 3 Results and Discussions
Figure 6: Variation of transmitted wave pressure with time in the air cavity (sensor-2) in the presence of various density foams and, without any foam (baseline). The extent of cell wall perforation reduces with increasing foam density. This is to be expected as the foams of higher density have thicker cell walls and hence, a greater resistance to cell wall perforation. In the early literature [5; 6], plastic wave transmission and shock Hugoniot conditions were studied in dense porous aluminum with interconnected porosity, which is in contrast to the present work in which shock loading of closed cell aluminum foams containing 70-90% porosity, made by gassing liquid aluminum, was studied. Due to the high porosity and the high order of regular isotropic cell structure, the foam strength was several times lower in the current work. Thus, the behavior of the foams with respect to shock attenuation was observed to be different in the present study. Recent work by [PERSON] et al. [17] presented the effects of blast waves generated by 100 g TNT on two layers of aluminum foams sandwiched between steel plates. In the present work, a shock tube was used for shock wave generation and direct loading of single layer foams, which was amenable for the analysis of shock-material interactions and reflection waves. Therefore, here, densification was observed in the distal region of the foams, which was unseen in previous studies.
# Pedestrian Fall Detection Methods for Public Traffic Areas: A Literature Review
## 3 SOTA Methods for Fall Detection
### CV-Based Methods  
With the increasing richness of video image data and progress in CV technology, fall detection has been achieved with CV technology. Compared with other methods, such as sensor-based fall detection, CV-based methods have been found to eliminate the burden of carrying sensors. Moreover, with the widening of CV applications, in addition to health monitoring of the elderly, some studies have applied CV to PTAs and manufacturing. [PERSON] et al. [6] presented a fall detection method that includes a human detection algorithm for infrared videos and a fuzzy-based model for fall detection and inactivity monitoring. Fuzzy logic is used to increase flexibility, avoid limitations in the representation of human figures, and smooth the limits of the evaluation parameters. They determined
###### Abstract  
This study investigates whether electromyography (EMG) analysis can reflect ergonomic improvements for commuter bike users by assessing muscle activity differences between comfort- and performance-oriented saddle positions. A cohort of 30 city bike riders underwent a comprehensive fitting procedure, where one position was individually established based on a comfort perception questionnaire and adjusted by a bike fitter. The aim was to compare the EMG activity of muscles involved in propulsion and lumbar spine stabilisation across different positions. The Statistical Parametric Mapping (SPM) method was also used to analyse muscle activity throughout the pedalling cycle. The results revealed that the customised position significantly reduced EMG activity in muscles, particularly during key pedalling phases, significantly improving rider comfort and muscle efficiency. SPM analysis highlighted decreased strain in the vastus lateralis and tibialis anterior, indicating improved muscular efficiency and enhanced comfort for urban cyclists. This underscores the importance of personalised bike fitting in promoting comfort and reducing the risk of injury, suggesting that EMG analysis is a valuable tool in both clinical and recreational bike-fitting practices. Future research should explore the long-term effects and potential applications of the proposed fitting method for other bicycle geometries whenever comfort and lower back stability are priorities.  
e lectromyography; biofeedback; bicycling; ergonomics; muscle fatigue; physical comfort; urban health 2020
## 3 Results and Discussion
### Data Processing and Arithmetic
#### 3.3.2 Distribution and Collection of Questionnaire  
The end-use customers of the \"Fresh Networking\" supply chain were selected as the target of the survey, and through data statistics and analysis, the quality level of the supply chain services provided by the \"Fresh Networking\" project could be directly perceived. The questionnaire survey was directed at the consumers of fresh agricultural products distributed by the \"Fresh Networking\" project, and field research was the main surveying method, supplemented by online platform research.  
The field research was conducted mainly in relation to customers in the three towns of Wuhan, including Qingshan, Hongshan, Jianghan, and Hanyang, where electronic vegetable boxes were installed. The online questionnaire was sent to the customers who most often bought fresh agricultural products from the \"Fresh Networking\" e-commerce platform and had experience of its supply chain service.  
The combined online and offline survey process issued a total of 460 research questionnaires (400 offline and 60 online), of which 450 questionnaires (392 offline and 58 online) were returned, and of which 408 questionnaires were true and valid. After a simple calculation, the final efficiency of the questionnaire was deemed to be 88.69%.
# Enhancing Voice Cloning Quality through Data Selection and Alignment-Based Metrics +
## 2 Related Work
With regard to the corpora, numerous many-to-one voice cloning corpora are available in the community, primarily designed for TTS approaches. These corpora prioritiise the measurement of dataset quality across various dimensions. Regarding signal quality, the SNR holds significant importance, both during the content filtering [36; 37] and data recording stages [38; 39; 40; 41]. Linguistic considerations also come into play, with some researchers emphasising the need for balanced phonemic or supraphonemic units within the dataset [38; 39; 41; 42]. Additionally, text preprocessing techniques are employed to ensure accurate alignment with the uttered speech and to reduce variability in pronuncations [36; 39; 40; 41; 42; 43; 44]. Lastly, the quantity of audio data generated by each speaker is a critical aspect in corpus creation, particularly in datasets with a low number of speakers [36; 38; 39; 40; 41; 42; 43; 44]. Finally, to evaluate the quality of voice-cloning systems, various approaches have been devised within the research community. Objective metrics that compare a degraded signal to a reference signal, such as PESQ, PEAQ, or POLQA [45], are widely available for assessing audio fidelity in scenarios involving, e.g., signal transfer or speech enhancement. However, these metrics exhibit limitations when applied to voice-cloning systems, as the degraded signal may not be directly related to the original one. Consequently, an emerging trend focuses on estimating the MOS using the cloned signal alone. MOSnet [4; 5] and NISQA [9; 46] are examples of systems that employ deep learning models for MOS estimation in this context. In line with this trend, the VoiceMOS Challenge [47] was introduced to address the specific issue of MOS estimation in voice-cloning systems. The challenge served as a platform for the development of this type of system, fostering advancements in the field and facilitating the resolution of this particular challenge. Our study focused on evaluating the quality of a particularly complicated voice-cloning dataset featured in [3] with the aim of identifying and removing audio files that contribute to heterogeneity within the overall data. The impact of these reductions was assessed within a TTS framework utilising a Tacotron-2 model as the acoustic model, paired with a HiFi-GAN-based vocoder. Furthermore, the quality of the resulting audio was evaluated using MOSnet and NISQA as the MOS estimator systems. Additionally, two distinct open voice cloning corpora in English and Spanish were also processed for contrasting purposes.
## 2 Literature Review
### EEG Signals  
An electroencephalogram (EEG) is a graphical record of the electrical activity of the brain recorded from the scalp. This method can be performed in two different ways: (1) Record invasive EEG directly from the brain through surgically implanted electrodes; (2) Record non-invasive EEG from the surface of the human brain [7; 8]. A sufficient amount of information about brain activity can be extracted from the EEG signal. Feature extraction involves transforming the original signal into a related data structure by removing noise and highlighting important data, called a feature vector [9].  
The features extracted from biological signals can be divided into three types [10]: (1) Time-domain features (TDF) are calculated based on the signal amplitude, and the result value gives a measure of the waveform amplitude, frequency, and duration within some limited parameters; (2) Frequency domain feature (FDF), based on the estimated power spectral density (PSD) of the signal; and (3) Time-frequency domain characteristics (T-FDF), using time-frequency representation to explain the energy that can locate the signal in time and frequency [11; 12].  
The information extracted from EEG signals can be used for the diagnosis of brain diseases, such as disorders, coma, and epilepsy [8; 13]. The Fourier transform (FT) is an effective technique for signal processing used in feature extraction. The Fourier transform (FT) of the function \(f(x)\) is [14; 15]:  
\[F(w)=\int_{-\infty}^{\infty}f(x)e^{-iwx}dx \tag{1}\]  
Discrete Fourier Transform (DFT) maps a sequence into the frequency domain. It is used in the case of discrete and periodic signals [15]. Let the signal be \(q_{m}\) for \(m=0\ldots M-1\) and \(q_{m}=q_{m+j_{M}}\) for all \(m\) and \(j\). The discrete Fourier transform of \(q\) is [14]:  
\[Q_{k}=\sum_{0}^{M-1}e^{-i\frac{2\pi}{m}km}q_{m} \tag{2}\]
# The Generalized Skin Depth for Polarized Porous Media Based on the Cole-Cole Model
## 2 Calculation of the Generalized Skin Depth of Polarized Media
### Calculation of the Generalized Skin Depth with Polarized Media  
Generally, the skin depth \(\delta\) is defined as the depth at which the amplitude of the plane wave falls to 1/e (approximately 37%) of its surface value [15]. The mathematical expression of the skin depth is given by [16]:  
\[\delta=\sqrt{\frac{2}{\omega\mu\sigma}} \tag{8}\]  
where \(\omega\) is the angular frequency, \(\sigma\) is the conductivity, and \(\mu\) is the magnetic permeability tensor. As seen from Equation (8), the skin depth of the classical plane wave is related only to the conductivity and frequency, and the conductivity is usually considered constant. If we can obtain the underground conductivity, we can obtain the skin depth of the model according to the measured frequency. However, when considering the earth-induced polarization effect, the conductivity is not constant. Therefore, the classical definition of the skin depth is no longer applicable. It is necessary to redefine the generalized skin depth based on the conductivity model of polarized media.  
The wave number expression is as follows:  
\[k^{2}=\omega^{2}\mu\varepsilon-i\omega\mu\sigma(\omega) \tag{9}\]  
where \(\mu\) is the magnetic permeability tensor and \(\varepsilon\) is the dielectric constant. Substituting Equation (7) into Equation (9), the following results can be obtained:  
\[k^{2}=\omega^{2}\mu\varepsilon-i\omega\mu(\sigma^{\prime}+\sigma^{\prime\prime }i) \tag{10}\]  
With the definition \(k=\alpha+i\beta\) inserted into Equation (9), the imaginary and real parts can be separated.  
\[\alpha^{2}-\beta^{2}=\omega^{2}\mu\varepsilon+\omega\mu\sigma^{\prime\prime} \tag{11}\]  
\[2\alpha\beta=-\omega\mu\sigma^{\prime} \tag{12}\]  
The attenuation constant \(\alpha\) and phase shift constant \(\beta\) can be obtained:  
\[\alpha=\sqrt{\frac{\omega\mu}{2}\left(\sqrt{\left(\sigma^{\prime 2}+\left( \sigma^{\prime\prime}+\omega\varepsilon\right)^{2}}+\left(\omega\varepsilon+ \sigma^{\prime\prime}\right)\right)\right.} \tag{13}\]  
\[\beta=\frac{-\omega\mu\sigma^{\prime}}{2\sqrt{\frac{\omega\mu}{2}\left(\sqrt{ \left(\sigma^{\prime\prime}{}^{2}+\left(\sigma^{\prime\prime}+\omega \varepsilon\right)^{2}+\left(\omega\varepsilon+\sigma^{\prime\prime}\right) \right)}}\right.} \tag{14}\]  
According to the definition of skin depth, the generalized skin depth formula of porous polarized media can be obtained as follows:\[\delta_{\text{code-code}}=\frac{1}{\alpha}=\sqrt{\frac{2}{\omega\mu\left(\sqrt{\left( {\sigma^{\prime}}^{2}+\left(\sigma^{\prime\prime}+\omega\varepsilon\right)^{2} }+\left(\omega\varepsilon+\sigma^{\prime\prime}\right)\right)}} \tag{15}\]  
where the dielectric constant \(\varepsilon=8.85*10^{-12}\) F/m and the permeability of free space \(\mu=4*\pi*10^{-7}\) H/m. In the generalized skin depth formula, the term \(\omega^{2}\mu\varepsilon\) is not ignored. When the chargeability is 0 and the term \(\omega^{2}\mu\varepsilon\) is ignored, Equation (15) can be converted back to the traditional skin depth Equation (8).