esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The jumping ability of an individual is a predictor for performance in various types of sports in elite and recreational athletes [1; 2; 3], including sprint performance [4]. Besides sports performance, the ability to jump was shown to be a predictor for functional capacity [5] and the risk of falling [6]. Although there is evidence that muscle [7] or tendon stiffness [8] can be a predictor for sprint performance, there is not much evidence relating muscle or tendon stiffness to jump performance.  
The most commonly used jump tests are the countermovement jump (CMJ), the squat jump (SQ), and the drop jump (DJ). All jump tests involve the participant being asked to jump as high as possible [9]; however, during a DJ, the participant should also have a contact time below 250 ms [10]. Therefore, a DJ is often associated with a fast stretch-shortening cycle (SSC), whereas it is a slow SSC for a CMJ [10].  
Due to the high time pressure while performing a DJ, having stiffer tendons and/or muscles might be beneficial with regard to effective force transmission from the contractile element via the tendon to the bones [11; 12]. Consequently, moderate positive correlations have been reported between gastrocnemius medialis muscle stiffness and jump height, as well as the reactive strength index (RSI = jump height/contact time), but not with contact time [12]. A further study reported that tendon stiffness is moderately related to contact time during a DJ [11]. However, [PERSON] et al. [11] did not measure muscle stiffness, whereas [PERSON] et al. [12] did not measure tendon stiffness. Hence, to obtain a clear picture of the relationships between tissue stiffness and DJ performance, there is a need to conduct a study which measures both muscle and tendon stiffness, especially of the calf muscles [13], and to relate this to DJ performance parameters.  
When it comes to CMJs, the quadriceps muscles play a major role [13; 14; 15]. [PERSON] et al. [16] showed a positive correlation between the stiffness of the musculotendious structure of the vastus lateralis muscles and CMJ height (r = 0.55). There is also evidence that quadriceps power is significantly negatively related to muscle stiffness [17]. However, to date, there is no evidence linking muscle stiffness (of all muscles) and/or isolated tendon stiffness of the quadriceps muscle-tendon unit (MTU) with CMJ performance.  
Therefore, the purpose of this study was to investigate the relationships between jump performance parameters and tissue stiffness (i.e., muscle stiffness, tendon stiffness) of the main muscles involved. According to previous studies, we hypothesized that DJ performance (i.e., ground contact time, RSI, jump height) will be related to Achilles tendon stiffness, gastrocnemius medialis stiffness, and gastrocnemius lateralis stiffness. Moreover, we assumed a relationship between CMJ performance (i.e., jump height, squat depth, RSI modified) to patellar tendon stiffness, rectus femoris stiffness, vastus medialis stiffness, and vastus lateralis stiffness.
## 4 Conclusions  
The specific activities of the naturally radioactive elements \({}^{238}\)U, \({}^{232}\)Th and \({}^{40}\)K were measured in 47 soil samples collected from the Lisava uranium mining sector. According to the data obtained, these varied significantly from 32.12 to 396.23 Bq/kg for \({}^{238}\)U and from 497.32 Bq/kg to 615.32 Bq/kg for \({}^{40}\)K, whereas the specific activity of \({}^{232}\)Th varied insignificantly from 7.23 to 48.16 Bq/kg. In this study, \({}^{232}\)Th and \({}^{40}\)K presented averages lower than or comparable with the worldwide average, while the \({}^{232}\)U average was higher than the median value (35 Bq/kg). The radiological hazard indexes ranged considerably for Raeq (from 118.33 to 450.31 (Bq/kg)), DR (from 57.55 to 214.55 (nGy/h)), Hex (from 0.32 to 1.239) and RIL (from 0.877 to 3.148). The distribution of the risk assessment indices associated with the investigated soils exceeded the median values given by UNSCEAR. The investigated area had a moderate to elevated level of natural radioactivity. The very strong correlation between \({}^{238}\)U and the radiological hazard indices suggested that uranium was the dominant contributor to the radiological hazards and risks assigned to the gamma rays emitted from the \({}^{238}\)U decay series in the soil. The uranium mineral phases identified in the waste rock samples were brannerite, coffinite and pitchblende, usually forming crusts on rutile or cementing it.  
All the data indicated that the investigated area had a moderate to high level of natural radioactivity, and the high concentration of uranium in the soils reflected the richness of uranium in the parent rocks from which the soil was derived as well as uranium enrichment due to previous uranium mining activities.  
A possible solution to the high level of radioactivity observed is the rehabilitation of the waste dump and the surrounding land that is exhibiting signs of degradation. Any work that will be undertaken needs to stabilize the uranium waste and reduce the contamination of surrounding areas. Furthermore, further investigations should be carried out to establish the level of contamination in the surface and ground waters in order to clarify any issues related to uranium mobility.  
Conceptualization, A.I.; methodology, A.I., A.C. and V.V.E.; software, A.I. and A.C; validation, A.I., A.C. and V.V.E.; formal analysis, A.C.; investigation, A.I., V.V.E. and A.C.; resources, A.I.; writing--original draft preparation, A.I.; writing--review and editing, A.I.; visualization, A.C. and V.V.E.; supervision, A.I.; project administration, A.I.; funding acquisition, A.I. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Romanian Ministry of Research, Innovation and Digitalization under the projects PN-19.45.02.03/28N/2019.  
Not applicable.  
The authors would like to thank [PERSON] for discussions and suggestions about the geology and mineralogy of radioactive elements.  
The authors declare no conflict of interest.
## 3 Challenges in FSO Communication
### FSO System and Channel Models
#### 3.2.3 FSO System Model Considering Jammer Interference  
The FSO system model described in Equation (1) can be expanded to include a jamming source. In this scenario, a jammer, which is uncorrelated and synchronized with the user's transmission time interval, emits random pulses with a certain level of jamming. Both the jamming and user signals travel through the FSO channel and reach the receiving photodetector. At the Rx, after signal conversion, the relationship between the input and output can be mathematically expressed as [46; 86]  
\[r= \eta_{e}hx+\sqrt{Y_{j}}h_{j}\Phi \tag{32}\]  
where \(h_{j}\) represents the irradiance of the jamming source at the destination, \(Y_{j}\) denotes the electrical power of the jammer, and \(\Phi\) indicates the jamming state defined as  
\[\Phi=\left\{\begin{array}{ll}1&\quad\text{In the presence of a jammer}\\ 0&\quad\text{otherwise.}\end{array}\right. \tag{33}\]
# Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artificial Neural Network
## 3 Results
### Pitch Response  
The ANN model was used to validate the pitch response results and FEM results for a wave height of 9.65 m and a wave period of 9.94 s. Figure 10 presents the convergence of the predicted values from the ANN training data and the FEM response of the spar hull. As shown, the outcomes forecasted by the ANN model results and the FEM results follow similar trends.  
Table 5 displays the ANN's statistical pitch response and the FEM results for a wave height of 9.65 m and a period of 9.94 s. The results of the ANN technique indicate accurate pitch response predictions.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Pitch Response (m)** & **Max** & **Min** & **Mean** & **Standard Deviation** \\ \hline FEM & 0.055 & \(-\)0.057 & 0.00026 & 0.021 \\ Resp-Pred & 0.057 & \(-\)0.057 & 0.00019 & 0.021 \\ Difference (\%) & 3.56 & 0.287 & 24.038 & 0.173 \\ \hline \hline \end{tabular}
\end{table}
Table 5: ANN and FEM statistical pitch responses for wave height of 9.65 m and the wave period of 9.94 s.  
Figure 10: The pitch response time history predicted by the training data of ANN and FEM results for wave height of 9.65 m) and the wave period of 9.94 s.  
Figure 9: Heave response time history predicted for wave height of 6.65 m and wave period of 8.25 s.  
The average prediction error was less for the prediction of the pitch response of the spar hull based on the newly chosen mechanical parameters and environmental forces (Figure 11). The response for a time history of 3000 s, wave height of 12.65 m, and wave period of 11.39 s was predicted by the ANN, and the outcomes were compared with the FEM model results. The pitch response exhibited a peak value of 0.8 rad.
## 5 Conclusions  
This paper analyzed the spatial and temporal evolution of land desertification in the Urat front flag during the last decade from the perspective of time and space, utilizing the Albedo-NDVI feature space method. It intends to conduct a scientific evaluation of the effect of ecological restoration projects and ecological security status implemented in the Urat front flag by investigating the spatial and temporal evolution laws of desertification, as well as to serve as a technical reference for national desertification control research. The study discovered that:  
1. The overall desertification status in the Urat front flag has improved, and the desertification land area for each grade has been altered to different degrees.
2. Between 2010 and 2020, the desertification recovery area in the study area is primarily driven by the conversion of extremely serious to moderate, serious to non-desertification, and moderate desertification to non-desertification, whereas the desertification aggravation area is primarily driven by the conversion of serious desertification to moderate desertification.
3. In descending order, the conversion rate of each type of desertification land area is as follows: extremely serious desertification > moderate desertification > weak desertification > serious desertification > non-desertification.
4. The study area is arid with little rainfall and a water scarcity as a consequence of its geographic location and climatic environment, and the degree of desertification in the region is dominated by serious, weak, and non-desertification land types.
5. The dynamic change of vegetation NPP is the consequence of the combined effects of climate change and human activities. Annual NPP per unit area in Urat front flag reached 306.3 gC/m\({}^{2}\) in 2010 and 354.5 gC/m\({}^{2}\) in 2020, a rise of 48.2 gC/m\({}^{2}\) each year since 2010, and the overall distribution is growing in a band from west to east.  
Conceptualization, Y.F., S.W., M.Z. and L.Z.; methodology, Y.F. and S.W.; Investigation, Y.F., S.W., M.Z. and L.Z.; data curation, L.Z.; formal analysis, Y.F., S.W. and M.Z.; writing--original draft preparation, Y.F.; writing--review and editing, S.W., M.Z. and L.Z.; resources, Y.F., S.W., M.Z. and L.Z.; project administration, Y.F. and S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Project No.: 31700369).  
Not applicable.  
Data Availability Statement:Data is contained within the article.  
Conflicts of Interest:The authors declare no conflict of interest.
# Object Retrieval in Microscopic Images of Rocks Using the Query by Sketch Method
## 1 Introduction  
Modern technological development contributes to the continued growth of multimedia databases in various areas of life. As a result, it raises problems in the management of large libraries of such data, in particular in relation to automated search, interpretation and indexing of digital images or video sequences. Non-automated retrieval and interpretation are undoubtedly the most effective form of data analysis; however, in the case of very extensive databases (often comprising hundreds of thousands or millions of objects), this method is hard to implemented. A similar situation is observed in the case of imaging data analysed for the needs of geological sciences. The universal access to cameras and digital video recorders results in a radical increase of archives and multimedia databases. Unfortunately, such sets are frequently labelled incorrectly, and thus searching for appropriate information in these databases (minerals, inclusions, structures, textures, etc.) is time-consuming and sometimes impossible. This situation makes it necessary to develop and verify techniques that would allow automated management of imaging data sets. These include, for example, image retrieval techniques which have been actively developed in recent years [1; 2; 3; 4; 5]. Image retrieval methods use both verbal and image information. In this paper, we describe a relatively new methodology, namely on the retrieval of images or their fragments based on a manually created drawing (sketch) [6; 7; 8]. The study describes the suggested methods of the Query by Sketch (QBS) and outlines the possibilities of their application for the needs of geological research. The methods described allow for full or partial automation of searching through databases of microscopic images of rocks obtained in the course of documenting and testing of deposits.
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 3 Hardware Design
### Alternative Hardware Setups
The NeSDR uses comparatively high power, has small receiver bandwidth, limited dynamic range (8-bit IQ), unstable clocking, and has no processor on board (e.g., FPGA for signal conditioning), but it is low-cost. A classic and popular board is the HackRF One (Great Scott Gadgets, Lakewood, CO, USA, [[https://greatscottgadgets.com/hackrf/one/](https://greatscottgadgets.com/hackrf/one/)]([https://greatscottgadgets.com/hackrf/one/](https://greatscottgadgets.com/hackrf/one/)), accessed on 23 March 2023), with an improved sample rate of 20 MHz, but it is larger and more expensive than the NeSDR. The CariboulLite, Haifa, Israel (CariboulLabs, [[https://www.crowdsupply.com/cariboulabs/cariboulite-rpi-hat](https://www.crowdsupply.com/cariboulabs/cariboulite-rpi-hat)]([https://www.crowdsupply.com/cariboulabs/cariboulite-rpi-hat](https://www.crowdsupply.com/cariboulabs/cariboulite-rpi-hat)), accessed on 23 March 2023) is a newer board with a pHat, making it convenient. It also has an onboard FPGA for basic signal processing. Therefore, it is an exciting new board that could make the interference station more compact and streamlined. The LimeSDR Mini (Lime Microsystems, Guildford, UK, [[https://limemicro.com/products/boards/limesdr-mini/](https://limemicro.com/products/boards/limesdr-mini/)]([https://limemicro.com/products/boards/limesdr-mini/](https://limemicro.com/products/boards/limesdr-mini/)), accessed on 23 March 2023) has more dynamic range with 12 bits, a receiver bandwidth of 40 MHz, and a larger FPGA. It has superior performance, but is also more expensive. These three boards are interesting candidates for improving the system, but there are many other COTS SDR RFFEs [93]. An RFFE with more dynamic range, larger bandwidth, or better clocking stability leads to superior detection and classification capabilities or combining it with other sensors [57; 94].
## 1 Introduction
Other studies reported the formation of chlorite or non-swelling minerals in hydrothermal systems and consequently, the reduction of the swelling capacity of smectite [47; 48]. The formation of magnetite and siderite as corrosion products was also predicted by a carbon steel-bentonite interaction model [48]. It is accepted that reservoir conditions will evolve with time, with temperatures at the canister-bentonite interface expected to increase to \(\sim\)100 \({}^{\circ}\)C for the first 10-100 years after DGR post-closure [1]. This temperature is estimated to be maintained for up to 1000 years [49], after which the temperature is estimated to gradually decrease to 80-50 \({}^{\circ}\)C, over a period of 1000 to 10,000 years [37]. Numerous studies, performed either at laboratory scale or long-term in situ experiments and geochemical modeling, reported the migration of Mg in the bentonite to the hottest zones near the canister surface [18]. That caused the enrichment in Mg at the canister-bentonite interface, allowing interaction with ions in solution like Fe\({}^{2+}\), Fe\({}^{3+}\), Na\({}^{+}\), K\({}^{+}\) or Ca\({}^{2+}\), and forming minerals like saponite, chlorite group or ankerite (Ca(Fe\({}^{2+}\),Mg)(CO\({}_{3}\))\({}_{2}\)) [37; 44; 50]. Mg enrichment in bentonite has also been observed by diffusion towards the alkaline interface of cement-based materials used as engineered barriers in some DGR concepts [6; 51; 52]. Despite all these studies, the interaction between magnesium bentonite and carbon steel under conditions of saturated and depleted Mg DGR is not well known. This research aims to improve the understanding of the interaction between carbon steel and Mg-bentonite under hydrothermal conditions at an evolved scenario in a DGR, where Mg-montmorillonite is assumed to be the dominant mineral. The research employed compacted bentonite that were hydrated with a saline solution and heated at 100 \({}^{\circ}\)C from a steel plate, creating a hydrothermal gradient in opposite directions (heat and hydration). Laboratory-scale experiments were conducted for periods of 1, 6 and 22 months using FEBEX Mg-bentonite, pretreated with MgCl\({}_{2}\), with the objective to examine the behavior of this bentonite and the development of corrosion products at the steel plate surface.
###### Abstract
If a fault occurs in one phase of the motor, the state is as shown in Figure 1. The authors of [31] showed the results of a study on the improvement of power density through fifth and seventh harmonic injection using the space-vector pulse-width modulation (SVPWM) control method in a six-phase PMSM. In addition, from a design point of view, the winding coefficient of a six-phase motor with the same pole slot combination is equal to or higher than that of a three-phase motor, which is advantageous in terms of efficiency, power density, and costs [24]. It is also advantageous for balancing the spatial harmonics of the magnetomotive force that causes torque pulsation, and it is easy to obtain stable torque-output and speed-control characteristics [27]. Figure 1: Phase switching from six to three phases. The optimal design of an IPMSM or PMa-SynRM was determined by analyzing the magnetic equivalent circuit (MEC) using the lumped-parameter method (LPM), which is characterized by fast analysis and finding optimal candidate models through the genetic algorithm [21]. The optimal design of this method was established for a five-phase motor with a two-layer structure, but has not been previously attempted for an asymmetric six-phase PMa-SynRM. In addition, since the fault-tolerance capability of the six-phase motor presented in [30] was not experimentally verified, it was necessary to evaluate its feasibility. Therefore, this paper investigated the optimal design of a 3 kW six-phase down-scaled model of a PMa-SynRM type, which could respond flexibly to fault situations, had excellent traction, and reduced PM usage; it was conducted using LPM and the genetic algorithm, and fault-tolerant capability was confirmed by analyzing the load characteristics for six- and three-phase operations. This paper is organized as follows. Section 2 describes the optimal design process of PMa-SynRM with asymmetric winding of a six-phase two-layer rotor structure. Section 3 analyzes the LPM and FEM results at the test point of the optimally designed motor, and Section 4 compares and analyzes the steady-state load test results of the PMa-SynRM. Section 5 analyzes the transient-state during switching from six to three phases.
# Fast Object Detection in Light Field Imaging by Integrating Deep Learning with Defocusing
## 2 Methodology
### Refinement of the Depth Estimation  
By taking the estimated depth by defocus response as input, multi-baseline stereo matching can be performed to further improve the accuracy of the depths of the object. When considering relatively narrow baseline of the multi-view images, phase shift based stereo depth estimation algorithm is  
Figure 4: The Architecture of the convolutional neural network.  
Figure 5: An illustration of depth estimation based on the defocus response of the focal stacks.  
adopted in the present study [12]. As discussed above, different 2D slices of the light field correspond to different views of the scene. Sub-pixel matching requires sub-pixel shift of the 2D slices to build the cost volume, and the corresponding phase shift in the 2D Fourier transform can be given by:  
\[\mathcal{F}\{I(\mathbf{s}+\Delta\mathbf{s})\}=\mathcal{F}\{I(\mathbf{s})\}e^{2\pi i\Delta \mathbf{s}} \tag{9}\]  
where the 2D shift vector \(\Delta\mathbf{s}\ \in\ \mathbb{R}^{2}\) denotes shift distance in 2D. The inverse Fourier transform is implemented to get accurate sub-pixel shift 2D slice \(I^{\prime}(\mathbf{s})\)  
\[I^{\prime}(\mathbf{s})=\mathcal{F}^{-1}\Big{\{}\mathcal{F}\{I(\mathbf{s})\}e^{2\pi i \Delta\mathbf{s}}\Big{\}} \tag{10}\]  
The cost volume \(C\) is built by two components, including the sum of the absolute differences (SAD) and the sum of the gradient differences (GRAD).  
\[C(\mathbf{s},l)=\alpha C_{A}(\mathbf{s},l)+(1-\alpha)C_{G}(\mathbf{s},l) \tag{11}\]  
where \(l\) is cost label, \(\alpha\) is weight coefficient of SAD and GRAD. The SAD cost component is defined as:  
\[C_{A}(\mathbf{s},l)=\sum\limits_{\mathbf{u}\in V}\sum\limits_{\mathbf{s}\in W}\min(|I(\mathbf{ u_{c}},\mathbf{s})-I(\mathbf{u_{c}},\mathbf{s}+lk(\mathbf{u}-\mathbf{u_{c}})|) \tag{12}\]  
where \(W\) is the window to compute disparity, \(\mathbf{u}\) denotes the view point on \(UV\) plane, and \(\mathbf{u_{c}}\) denotes the center view point. The GRAD cost components is defined as:  
\[\begin{array}{l}C_{G}(\mathbf{s},l)=\sum\limits_{\mathbf{u}\in V}\sum\limits_{\mathbf{s }\in W}\gamma(\mathbf{u})\min(|I_{x}(\mathbf{u_{c}},\mathbf{s})-I_{x}(\mathbf{u_{c}},\mathbf{s}+lk( \mathbf{u}-\mathbf{u_{c}})|)+\\ \big{(}1-\gamma(\mathbf{u}))\min(|I_{y}(\mathbf{u_{c}},\mathbf{s})-I_{y}(\mathbf{u_{c}},\mathbf{s} +lk(\mathbf{u}-\mathbf{u_{c}})|)\end{array} \tag{13}\]  
where the \(I_{x}\) is the \(x\)-directional gradient and \(I_{y}\) is the y-directional gradient, \(\gamma(\mathbf{u})\) is the weight coefficient of the two directions. Obviously, the closer to center view, the better the image is. Hence, \(\gamma(\mathbf{u})\) is defined as  
\[\gamma(\mathbf{u})=\frac{|u-u_{c}|}{|u-u_{c}|+|t-t_{c}|} \tag{14}\]  
Every cost slice is refined through weighted median filtering in accordance with the center view image. The disparity map is then computed by winner-takes-all strategy.
## 6 Algorithms and Frameworks in Federated Learning
### Frameworks and Tools
#### 6.7.2 PySyft  
In federated learning, PySyft distinguishes itself by not only providing tools for FL but also for encrypted and private machine learning. This framework expands upon popular deep learning platforms like PyTorch and TensorFlow, facilitating multi-party computation. What makes PySyft unique is its emphasis on decentralized deep learning. Its versatile API is designed with a focus on differential privacy and encrypted computation, ensuring that the privacy concerns inherent to FL are effectively addressed [94].  
PySyft's versatility is a standout feature. Beyond FL, it serves as a versatile tool for practitioners interested in privacy-preserving machine learning. Its integration with PyTorch, a favorite in the deep learning community, provides a familiar environment, which is always advantageous. However, PySyft, being relatively new compared to TensorFlow, may occasionally exhibit features that are in beta or still being developed [95]. Nonetheless, the PySyft community, led by OpenMined, is vibrant, passionate, and continually pushing boundaries.