esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Vehicular Edges Flash Storage  
Flash storage demonstrates compelling advantages for automotive technological applications. Its robust mechanical resilience, characterized by superior shock and vibration resistance, renders it particularly suitable for the rigorous operational environments encountered in vehicular systems. Moreover, flash storage exhibits optimal characteristics including low power consumption, high-performance data throughput, and rapid access times--critical attributes for real-time data processing in automotive computing architectures.  
Contemporary flash solid-state drives (SSDs) encompass a performance spectrum with read speeds ranging from approximately 500 MB/s to an impressive 7000 MB/s in PCIe 4.0 NVMe configurations. Vehicular edges generate substantial data volumes through complex sensor networks, necessitating storage technologies capable of expeditiousdata writing to prevent computational bottlenecks. For advanced deep learning models integral to autonomous driving systems, the capability to rapidly access and process extensive datasets is paramount [16]. The high-velocity read capabilities of flash storage fundamentally support the microsecond-level data retrieval essential for instantaneous decision-making processes in autonomous vehicular contexts.  
Vehicular edge flash storage systems exhibit intricate performance and reliability characteristics profoundly influenced by diverse environmental parameters. Critical factors, including temperature fluctuations, humidity levels, mechanical vibration, and physical shock--ubiquitous in automotive operational environments--can significantly modulate the functional integrity and operational longevity of storage devices [13].  
Temperature variation represents a particularly consequential environmental challenge for vehicular flash storage technologies. Consumer-grade flash drives typically maintain operational parameters within a temperature range of 32 \({}^{\circ}\)F to 158 \({}^{\circ}\)F [14]. Vehicles inherently experience extreme thermal diversity, from the frigid conditions of winter environments to the intense heat of summer landscapes and engine compartments. Elevated temperatures accelerate degradation mechanisms within flash memory cells, precipitating diminished lifespan and compromised reliability [15]. Empirical research indicates that each 10-degree temperature increment can potentially reduce flash drive life expectancy by approximately 50% [17]. Conversely, extremely low-temperature environments can substantially impair data read/write operational efficiency, potentially introducing latency in mission-critical automotive computing applications.
## 2 Materials and Methods
### Type of Activity  
Senior falls are mainly caused by weak muscles, slow reaction and movement disturbances [3, 8, 17]. For mimicking seniors' motor control conditions, the young subjects with eyes closed were tested at relaxed standing (simulating weak muscles and slow reaction) through a push-fall method (movement disturbances). The relax-falls were protected by foam mats. A test subject was first twisted (rotated) and randomly selected the push direction (forward, backward, downward/sit, left or right). The push-direction and closed eyes simulated the sudden loss of balance. Additionally, an eyes-closed, slow walk was applied for a trip-fall test. The six fall tests (Figure 2) would represent common fall situations among seniors.  
Seniors' falls often happened during a'switch' between physical activities, such as slip/trip, sit down/stand up, up-/downstairs or reaching for something [1, 18]. Hence, these daily activities, i.e., sitting, standing from a chair, walk with eyes-closed (mimic seniors' weak muscles for their motor control), going upstairs, walking downstairs, and reaching movement, were simulated, captured, and analyzed (Figure 3). Through comparisons between fall and non-fall situations, the number and locations of sensors for a reliable detection could be determined.  
Figure 2: Motion analysis/quantification of the common type of falls among seniors: (**a**) a forward fall; (**b**) a backward fall; (**c**) a sit fall (downward); (**d**) a left fall; (**e**) a right fall; (**f**) a trip fall.
###### Abstract  
Direct Displacement-Based Design (DDBD) is currently a widely used displacement-based seismic design method. DDBD accounts for the torsional response of Reinforced Concrete (RC) frame buildings by using semi-empirical equations formulated for wall-type buildings. Higher-mode responses are incorporated by using equations obtained from only a few parametric studies of regular planar frames. In this paper, there is an attempt to eliminate torsional responses by proportioning frames' secant stiffnesses so that the centers of rigidity and supported mass (the mass on and above each story) coincide. Once the torsional rotations are significantly reduced and only translational motions are achieved, higher-mode responses are included using a technique developed by the authors in their recent paper. The efficiency of the proposed design procedure in fulfilling the intended performance objective is checked by two plan-asymmetric 20-story RC frame building cases. Case-I has the same-plan configuration while Case-II has a different-plan configuration along the height. Both cases have different bay widths in orthogonal directions. Verification of the case studies by Nonlinear Time History Analysis (NTHA) has shown that the proposed method results in designs that satisfy the performance objective with reasonable accuracy without redesigning members. It is believed that a step forward is undertaken toward rendering design verification by NTHA less necessary, thereby saving computational resources and effort.  
effective modal superposition; direct displacement-based design; performance-based seismic design; plan-asymmetric RC frame buildings; torsion effects; higher-mode effects
# Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology
## 3 Results
### Time Series Comparison of Distinct Spatiotemporal Patterns
However, much more day-to-day variability in post-monsoon cloud cover is also present in the Jabal Al Qamar than Jabal Al Qara. Field observations confirmed this difference in post-monsoon cloud cover. Finally, the greening of vegetation in Jabal Al Qamar endmember regions is more intense, more persistent, and more frequently observed out of season than in Jabal Al Qara endmember regions. Figure 4: Jabal Al Qara EM annual cycle. Median (black) and 1 st & 3 rd quartiles (green) for each endmember region computed over 2000-2017. Forests reach higher maximum NDVI values and senesce slower than grasslands. Western regions of both grasslands and forests senesce slower and show more variability than eastern regions. Clouds clear earlier and are less consistent over grasslands than forests. Clouds are more consistent in the west than the east. On average, monsoon onset is more gradual than termination, especially in forests. Nejad values are low year-round, with a minor but consistent annual cycle due to solar geometry. Annual medians for each EM region shown as thin horizontal lines; July 1 (Julian Day 182) shown as a thin vertical line for visual reference. Figure 5: Jabal Al Qamar EM annual cycle. Median (black) and 1 st & 3 rd quartiles (green) for each EM region computed over the period 2000-2017. Opposite from Jabal Al Qara, forests in Jabal Al Qamar generally have higher maximum values and slower senescence in the east than the west. In some wet years, the regreening post-monsoon is prominent in both western and eastern endmember time series. Similar to Jabal Al Qara, onset of the monsoon is more gradual than termination. Nejad values are low year-round with barely visible east-west variations, predominantly due to variations in monsoon cloud cover. The solar illumination cycle is also faintly present. Annual medians for each EM region shown as thin horizontal lines July 1 (Julian Day 182) shown as a thin vertical line for visual reference. Figure 6: Full 18-year Jabal Al Qara EM time series. Each curve is the spatial average of the 1001 pixels that comprise each EM region. Substantial variability is observed among and within EMs. Grasslands vary from year to year in maximum greenness, rate of senescence, and relative similarity of western, central, and eastern regions. Some years show post-monsoon representing in one or both of the forest EM regions, but most years do not. Years with both strong and weak greening in both forests and grasslands are present at both the beginning and end of the time series. No clear trend is visually evident in duration or timing of monsoon, maximum greenness, or rate of senescence of any EM.
## 4 Data Mining Methods for Graduates' Cultivation
### Factor Selection and Classification  
#### 4.2.1 Feature Selection Algorithms  
In order to better uncover the key factors influencing graduate training, classical algorithms such as Lasso, Elastic Net, and Orthogonal Matching Pursuit are chosen to select features for the questionnaire data, respectively. The following is a brief introduction to these feature selection algorithms.  
**Least Absolute Shrinkage and Selection Operator** (Lasso) [34] is a method for compressing the estimated coefficients. It was proposed by statistician [PERSON] in 1996. The basic idea is to construct the L1 penalty function, so that some coefficients with no significant influence after compression are compressed to zero, thus realizing variable selection. Specifically, under the constraint that the sum of the absolute values of the regression coefficients (i.e., the L1 norm) is less than a constant, the sum of the squares of the residuals is minimized, so that some regression coefficients strictly equal to 0 can be generated, which can be explained.  
**Elastic Net** (EN) [35] is based on Lasso to add the L2 norm as a regression model for a priori regular term training. This combination allows for learning a model with only a few parameters that are non-zero sparse, just like Lasso, but because the impact of the penalty of L2 norm, it still maintains some regularity like Ridge regression (Hastie). Elastic networks are useful when multiple features are related to another feature. Lasso tends to choose one of them at random, while elastic networks prefer to choose two.  
**Orthogonal Matching Pursuit** (OMP) is a classical reconstruction algorithm in signal processing and compression sensing domain [36]. It decomposes the signal on the complete dictionary library. The essence is also the optimization problem of the L1 norm. It can effectively and quickly find the sparse solution and realize the selection of variables and features.  
Figure 2: Visual analysis interface for graduate training exploration system. The Factor Comparison View details data of the graduate training process based on the questionnaire (**a**) and shows the predicted results of factors influencing the publication of first-grade graduate students (**d**). The Ranking View shows the comparison of the ranking of the results derived from the three factor selection methods (**b**) and the ranking results of the synthesis of the factor selection methods (**c**). The Algorithm Analysis View shows the ROC curves for the five classifications based on different factor selection methods (**e**). The Algorithm Comparison View compares the AUC values of the five classification methods based on different factor selection methods (**f**).
# High-Frame-Rate 3-D Vector Flow Imaging in the Frequency Domain
## 2 Materials and Methods
### Simulations  
Simulations were carried out in Matlab with Field II [36; 37]. As sketched in Figure 1, a numeric phantom was developed to simulate a parabolic flow inside a straight, cylindrical vessel with 4 mm radius (R). Its axis was maintained at z\({}_{0}\) = 16 mm, while the beam-to-flow angle resulted from different combinations of \(\beta_{D}\) and \(\beta_{A}\) angles, as summarized in Table 1. The density of the randomly distributed scatterers was set at 27 scatterers per resolution cell. For each setup configuration, simulations were run to simulate a 1 s long acquisition (i.e., 500 consecutive RF data volumes were produced).  
Figure 3: 3-D vector flow imaging (VFI) elaboration scheme. The beamformed volume samples are sequentially elaborated per plane in both directions (panels **A** and **B**). The corresponding components are finally grouped to obtain the velocity estimation (**C**).
## 4 Numerical Simulation
The control error at steady state, i.e., the angle between the solar vector and the \(-O_{b}Y_{b}\) axis, is shown in Figure 8. The angle begins to increase gradually when the satellite flies into the shadow zone of the orbit and decreases when the satellite flies into the sunlit zone of the orbit. The error angle is basically within \(10^{\circ}\) in the sunlit zone of the orbit, which meets the basic flight mission requirements of most satellites, such as energy acquisition. However, in the shadow zone of the orbit, the maximum error angle reaches about \(20^{\circ}\). This is because the angle between the solar vector and the \(-O_{b}Y_{b}\) axis cannot be measured in the shadow zone. At the same time, it should be noted that the control error may vary with the satellite's characteristic parameters, orbital parameters, initial attitude, and control coefficients.
# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
###### Abstract  
Earth Observation (EO) data is a critical information source for mapping and monitoring water resources over large inaccessible regions where hydrological in-situ networks are sparse. In this paper, we present a simple yet robust method for fusing optical and Synthetic Aperture Radar (SAR) data for mapping surface water dynamics over mainland China. This method uses a multivariate logistic regression model to estimate monthly surface water extent over a four-year period (2017 to 2020) from the combined usages of Sentinel-1, Sentinel-2 and Landsat-8 imagery. Multi-seasonal high-resolution images from the Chinese Gaofen satellites are used as a reference for an independent validation showing a high degree of agreement (overall accuracy 94%) across a diversity of climatic and physiographic regions demonstrating potential scalability beyond China. Through inter-comparison with similar global scale products, this paper further shows how this new mapping technique provides improved spatio-temporal characterization of inland water bodies, and for better capturing smaller water bodies (\(<\) 0.81 ha in size). The relevance of the results is discussed, and we find this new enhanced monitoring approach has the potential to advance the use of Earth observation for water resource management, planning and reporting.  
surface water mapping; SAR and optical data fusion; logistic regression; water resource management; sustainable development Article  
An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China  
1 DHI GRAS, Agem Alle 5, 2970 Hersholm, Denmark; [EMAIL_ADDRESS] (C.M.M.K.); [EMAIL_ADDRESS] (K.G.); [EMAIL_ADDRESS] (C.T.) 2 Department of Geosciences and Natural Resource Management (IGN), University of Copenhagen, 1350 Copenhagen, Denmark; [EMAIL_ADDRESS] 3 Piesat Information Technology Co., Ltd., Beijing 100000, China; [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (T.G.) 4 Correspondence: [EMAIL_ADDRESS]
# Application of a Hybrid Model in Landslide Susceptibility Evaluation of the Western Tibet Plateau
## 3 Results
### ROC Curves  
The accuracy of landslide sensitivity analysis models is usually evaluated by the receiver operator curve (ROC) and area under curve (AUC) [66,67]. Evaluation results are shown in Figure 10. Evaluation accuracies of IVM, W\({}^{+}\), W\({}^{-}\), IVM-LR, W\({}^{+}\)-LR, W\({}^{-}\)-LR, IVM-MLP, W\({}^{+}\)-MLP, W\({}^{-}\)-MLP are 86.8%, 86.1%, 87.0%, 87.3%, 86.4%, 86.1%, 86.5%, 86.9% and 85.8%, respectively. The AUC values are 0.868, 0.861, 0.870, 0.873, 0.864, 0.861, 0.865, 0.869 and 0.858 respectively. The results indicate which method is more suitable for working areas at high altitudes. The accuracy rate obtained by the five calculation models was greater than 85%, and the accuracy rate of IVM-LR was the highest.
# Towards Client Selection in Satellite Federated Learning
## 4 Algorithm
### Client Selection  
We design a CS algorithm for SFL. We evaluate a client from the delay (which includes the communication delay and computational time cost), orbit characteristics, and data quality. These data can be provided directly in the form of hyper-parameters or calculated through a program. For example, the communication and computational ability is represented by channel rate and CPU frequency, respectively. They can be assigned reasonable values directly in the simulation. In this paper, we gave each client a random value range from 1 Gbytes/s to 10 Gbytes/s. The orbit characteristics include the satellites' position, direction, and velocity, all of which can be computed easily by STK. Data quality is a unique designed index in this study, and it is also a variate that is evaluated and updated in every round of training.
## 4 Results and Discussions
### The Effect of the Thermal Conductivity Ratio of Solid to Air  
Figure 5 shows the isothermals (on the left) and streamlines (on the right) for \(Ra=10^{5}\), \(\varepsilon=0\), \(\phi=0.5\) and \(Pr=0.71\), with \(Kr=10^{-1}\), \(10^{0}\), \(10^{1}\), \(10^{2}\) and \(10^{3}\). For \(Kr=10^{-1}\), the isothermals are dense in the solid walls and spare in the cavity. This is because the large  
Figure 4: Variations of the average Nusselt number as a function of \(Ra\).  
thermal resistance of the solid walls leads to a small temperature difference in the cavity. As a result, the buoyancy force becomes weak, and thermal conduction dominates the heat transfer in the cavity. The above conclusions are also proved correct by the streamlines, which are uniformly distributed in the cavity. As \(Kr\) increases, the isothermals in the solid walls become spare due to the reduction of the thermal resistance. In the cavity, the isothermals are most commonly horizontally distributed, but have a vertical distribution near the hot interface. This is because the boundary layer is going to be formed near the wall. From the streamlines, two vortexes are formed due to the enhanced buoyancy force and the maximum velocity of the air becomes greater. This indicates that, with the increase in \(Kr\), natural convection is strengthened and gradually occupies the leading role. In addition, from the isothermals for \(Kr=10^{0}\) and \(Kr=10^{1}\), the transition from conduction to natural convection of the air takes place and is almost accomplished as \(Kr\) increases to \(10^{2}\). In the case of \(Kr=10^{2}\) to \(Kr=10^{3}\), heat transfer is dominated by natural convection, judging from the patterns.  
Figure 5: \(Cont.\)  
Figure 6 shows the evolution of the average convective Nusselt number of the hot interface as a function of \(Kr\). It can be seen that \(\overline{Nn}_{\rm conv}\) increases with increasing \(Kr\) from \(10^{-1}\) to \(10^{1}\), and remains stable from \(10^{1}\) to \(10^{3}\). This is because the increasing \(Kr\) increases the temperature difference and thus causes stronger natural convection. From the figure, the growth rates of \(\overline{Nu}_{\rm conv}\) are as different as those of \(Kr\), in a different scope. From \(Kr=10^{0}\)  
to \(Kr=10^{1}\), the growth rate is the fastest, and from \(Kr=10^{1}\) to \(Kr=10^{3}\), the change of \(\overline{Nu}_{\rm conv}\) turns to be extremely small. This indicates that \(Kr\) has a positive effect on natural convection, but the effect is close to a limit as \(Kr\) exceeds \(10^{2}\).
## 1 Introduction
Likewise, the temperature of the surrounding environment can interfere with the focused information of the image. Therefore, noise removal and other pre-processing efforts are necessary. In view of the above research background and existing problems, this article proposes an RFR-GA-BLS fault diagnosis model. Firstly, a series of pre-processing such as grayscale map conversion, region of interest (RoI) acquisition, denoising and image segmentation are performed on the acquired infrared images. Then, the second-order statistical features are extracted using the grayscale co-occurrence matrix (GLCM). After that, they are imported into RFR-GA-BLS. The extracted features are ranked by composing Robust Feature Ranking (RFR) with robust feature properties. The optimal features are added one at a time, and a feature search is performed to derive the optimal and minimum number of features. The optimal features and the highest accuracy of each round are filtered out after each round of data input to BLS using the optimization-seeking property of the Genetic Algorithm (GA). The rest of the article is organized as follows: Section 2 introduces the relevant technical theory. Section 3 reviews the rolling bearing infrared image data acquisition experiments. Section 4 describes the model building. Section 5 gives the experimental results of fault diagnosis. Section 6 draws conclusions. The contributions of this article are as follows:  
1. The infrared image replaces the previous vibration signal and acoustic emission signal, so that the fault performance is visualized. 2. RFR uses the robustness of features to rank features in the RFR-GA-BLS fault diagnosis model. Instead of the previous way of filtering or rejecting features, it solves the problem of difficult feature selection by finding the optimal number of features and corresponding features in an iterative way. 3. The GA-BLS in RFR-GA-BLS fault diagnosis model solves the problem of difficult setting of BLS parameters and can find the best parameters and the highest accuracy rate. 4. RFR-GA-BLS has superior accuracy than machine learning and faster training time than deep learning.