esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Test Organisms and Exposure Experiment Conditions  
_M. japonicus_ measuring 2.7 cm (\(\pm\)0.1 cm) in size and 7.4 g (\(\pm\)0.9 g) in weight were collected from a fisherman who lives in Suncheon Bay, Korea. The collected _M. japonicus_ were selected as healthy individuals with no appendages missing, and 30 individuals each were placed in the control and exposure tanks. Exposure was performed in three replicates to reduce experimental error. All _M. japonicus_ were exposed after a 24-hour acclimatization period to account for individual adaptation to environmental changes. The conditions of acclimatization were 22 \(\pm\) 1 \({}^{\circ}\)C (room temperature), 30.6 \(\pm\) 1% salinity, 17:7 photoperiod (light:dark), and an oxygenator was installed to provide oxygen to the organisms. Individuals were fed Tetramin (Tetra-Werke, Melle, Germany) at 0.95 g (\(\pm\)0.5 g) every 24 h.  
Exposure solutions were prepared with rigarol (Sigma Aldrich, St. Louis, MO, USA). The stock solution was prepared with a 10 mg L\({}^{-1}\) rigarol concentration by dissolving the substance in 99 percent analytical-grade acetone. The 1, 10, and 30 \(\upmu\)g L\({}^{-1}\) concentrations of solutions for exposure were prepared to dilute in artificial seawater with a stock solution. Five groups of crabs (corresponding to 1, 10, and 30 \(\upmu\)g L\({}^{-1}\) solutions, seawater, and solvent controls; \(n=225\)) were divided. Subsequently, these groups were divided into four groups, exposed to three concentrations of rigarol with control, and used for tissue sampling to analyze gene expression at various time intervals. The experiments were conducted in triplicate. The rigarol concentrations were 1, 10, and 30 \(\upmu\)g L\({}^{-1}\), considered within the range of environmental concentrations; artificial seawater was employed as a control.
## 2 Modified Hammerstein-Like (MHL) Model and Its Identification Method
### Rate-Dependent Hysteresis Model  
On the basis of the Madelung model, a rate-dependent term \(\Psi(t,s)\) is introduced to correct the effect caused by the increase in input frequency [23], as shown in Figure 3. The rate-dependent model is composed of the Madelung model and a rate-dependent correction term in parallel. Mathematically, the hysteresis model \(H_{D}(v(t))\) can be described as follows:  
\[H_{D}(v(t))=H_{S}(v(t))+\Psi(t,s) \tag{3}\]  
\[\Psi(t,s)=(k_{1}\dot{v}(t)+b_{1})\odot\frac{1}{k_{2}s+b_{2}} \tag{4}\]  
where \(H_{S}(v(t))\) is the rate-independent model based on [PERSON]'s rules, and its major hysteresis curves can be represented by Equations (1) and (2). \(\Psi(t,s)\) is the rate-dependent correction term, which is composed of two modules connected in series (\(\odot\) is the cascading symbol). \(\dot{v}(t)\) is the derivative of the input, \(s\) is the Laplace operator, and \(k_{1}\), \(b_{1}\), \(k_{2}\), and \(b_{2}\) are parameters to be identified.
## 3 Beamforming Optimization with AN
### Beamforming Optimization with AN in the MISO SWIPT CR-NOMA Network
Based on the properties \(\left\|\textbf{x}\right\|^{2}=\textbf{x}^{H}\textbf{x}\)\(\operatorname{Tr}\left(\mathbf{AB}\right)=\operatorname{Tr}\left(\mathbf{BA}\right)\), and \(x=\operatorname{Tr}\left(x\right)\), we can derive the following expressions: \(\left\|\mathbf{v}_{l}\right\|^{2}=\operatorname{Tr}\left(\mathbf{V}_{l}\right)\), \(\left\|\mathbf{w}_{l}\right\|^{2}=\operatorname{Tr}\left(\mathbf{W}_{l}\right)\), \(\left\|\mathbf{f}\right\|^{2}=\operatorname{Tr}\left(\mathbf{F}\right)\), \(\left\|\mathbf{g}_{l}^{H}\mathbf{v}_{l}\right\|^{2}=\operatorname{Tr}\left( \mathbf{G}_{l}\mathbf{V}_{l}\right)\), \(\left|\mathbf{g}_{l}^{H}\mathbf{w}_{m}\right|^{2}=\operatorname{Tr}\left( \mathbf{G}_{l}\mathbf{V}_{m}\right)\), and \(\left|\mathbf{g}_{l}^{H}\mathbf{f}\right|^{2}=\operatorname{Tr}\left( \mathbf{G}_{l}\mathbf{F}\right)\). Moreover, a matrix variable, \(\mathbf{V}_{l}=\mathbf{v}_{l}\mathbf{v}_{l}^{H}\), is equivalent to \(\mathbf{V}_{l}\) being a rank-one symmetric positive semidefinite (PSD) matrix, i.e., \(\mathbf{V}_{l}\succ 0\), and \(\operatorname{rank}\left(\mathbf{V}_{l}\right)=1\). Thus, we can express problem (11) in the following equivalent form:  
\[\min_{\{\mathbf{V}_{l},\mathbf{W}_{m},\theta_{l}\},\mathbf{F}} \sum_{l=1}^{L}\operatorname{[PERSON]}\left(\mathbf{V}_{l}\right)+\sum _{m=1}^{M}\operatorname{[PERSON]}\left(\mathbf{W}_{m}\right)\] (12a) s.t.
# Evaluation of Global Precipitation Products over Wabi Shebelle River Basin, Ethiopia
## 2 Study Area Description and Dataset
### Datasets  
#### 2.2.1 Rain Gauge Data  
There are about 74 meteorological stations within and around the basin which are not evenly distributed spatially, clustered in upper and urban areas. The rainfall dataset for WSRB was taken from the National Mereological Agency (NMA), covering the period 1983 to 2014. Long-term meteorological data for the WSRB are more complete in upstream parts of the basin, and these stations were taken to analyze precipitation in the area as shown (Figure 2) below.  
Rain gauge stations for this study were carefully chosen on the basis of their quality control process for climate data (verification of in situ station's geographical coordinates, checking for false zeros, checking for the presence of outliers, and homogeneity testing) using the Climate Data Tool (CDT) [[https://github.com/rijaf-iri/CDT](https://github.com/rijaf-iri/CDT)]([https://github.com/rijaf-iri/CDT](https://github.com/rijaf-iri/CDT)) (accessed on 27 June 2020). Twenty-seven out of 74 gauging stations with a percentage of available (non-missing) and continuous data greater than 80% were selected for the comparison of different GPPs in the study area.
# Energy Efficient Constellation for Wireless Connectivity of IoT Devices
## 5 Energy Entropy and Energy Capacity  
In this section, we show how the proposed signal constellation, i.e., zero energy symbol mapping, can reach benefits from the energy consumption point of view. To show this we have derived the source entropy and the channel capacity of the Z-BPSK and compared it with the BPSK known results.
# Differences Research on Human Overall Comfort Under Low Pressure
## 4 Discussion
### Single-Environment Differences Under Low Pressures
#### 4.2.3 Acoustic Environment Differences Under Low Pressure  
The lower the acoustic environment noise level, the more comfortable it was. Both domestic and international regulations set an upper noise limit. In accordance with the regulations for the thermal comfort zone, acoustic comfort was classified into Level I and Level II, as shown in Table 10.  
The noise levels under standard atmospheric pressure in Table 10 were basically consistent with references [42; 43; 44; 45; 46; 47]. Moreover, the noise limits recommended in Table 10 were more suitable for high-background-noise areas, such as train carriages or aircraft cabins. The noise limits increased by 1-2 dB at both acoustic comfort level I and level II under low pressure. This also indicated that lower pressure had an impact on the acoustic environment evaluation and enhanced the subjects' tolerance to noise. According to the analysis in Section 4.1.3, when the atmospheric pressure decreased, the air density decreased, and the speed of sound remained basically unchanged. However, the air characteristic impedance decreased. The human middle ear also plays a role in acoustic impedance matching in sound transmission. The sound energy transmitted from the middle ear to the inner ear was calculated under standard atmospheric pressure and low pressure, respectively. The sound energy under low pressure was 15.6% less than that under standard atmospheric pressure [65; 66]. The low-pressure factor had an impact on the human ear's physiological function. Low-pressure hypoxia hinders the conversion of sound energy to nerve impulses, thereby increasing the auditory threshold.  
Therefore, there were two aspects to explain why the acoustic comfort votes and tolerance were higher under low pressure than under standard atmospheric pressure. First, the sound energy transmitted from the external auditory canal to the inner ear was reduced under low pressure. Second, the hypoxia caused by lower pressure led to disorders in the physiological mechanisms of the inner ear and a decrease in the auditory threshold.
# Recent Improvements in the Development of Soft Grippers
## 3 Dexterous Manipulation
### Achieving Dexterity for Soft Grippers
#### 3.2.2 Stable In-Hand Manipulation
The fingers contain programmable ligaments that are composed of conductive thermoplastic elastomers, whose stiffness varies when stimulated electrically or when heated. The LMPA-based soft robotic arm in [30] is omnidirectionally bendable and capable of performing dexterous opening and grasping operations with cable-driven fingers. The bio-inspired soft wrist developed in [74] was based on amulti-cable jamming approach. The wrist can perform hybrid motion and stiffness control through the actuation of one same muscle. According to the authors, their work is the first one to achieve both motion and stiffness control by driving cables, as their work inherits the advantages of existing jamming methods and easy deployment. The soft-rigid hybrid gripper in [75] is inspired by the human metacarpophalangeal joint and was designed with eight independent muscles. The authors showed that adding this joint enabled the hybrid gripper to perform in-hand manipulation, lateral passive compliance, and new control modes. The gripper had three major control modes that allowed it to achieve a large grasping force, dexterous motions, and stabilized grasping. In [76], they use an algorithm to reposition the gripper's fingertips on the surface of a grasped object. They then search for an optimal grasp state on the object's surface using a manipulability-based metric. The two-finger hand designed in [77] was experimentally proven to be highly adaptable and capable of grasping and manipulating heavy objects, thanks to its reinforced fingers. The soft pneumatic fingers have an embedded rigid skeleton that increases the torsional stiffness up to 300%. According to [78], soft fingertips more efficiently maintain stable contact than some other deformation control strategies. In [79], deformable finger pad designs that have different inner solid structures are proposed. Ref. [80] presents a novel method to model the contacts of soft fingertips in dexterous manipulation. The results of their experiments demonstrate that soft fingertips allow better manipulation performance. Figure 4: Soft dexterous grippers capable of stiffness variation: (**a**) Soft gripper with McKibben muscles for dexterous manipulations [54]; (**b**) Soft gripper with decoupled stiffness and shape control. Each finger has an articulable rigid backbone and is powered by pneumatic muscle actuators [18]; (**c**) Soft robotic manipulation system capable of stiffness variation and dexterous operations. The robotic arm is made of soft elastomers embedded with low melting point alloys [30]; and (**d**) Dexterous robotic finger with a sliding, rotating, and soft-bending mechanism that maximizes [21].
# Multi-Source Satellite Imagery and Machine Learning
## 4 Materials and Methods
### Dataset
#### 4.1.2 Data Preparation  
Creating a consistent and structured database is essential for training and validating machine learning models. In this study, data were compiled from multiple sources: satellite and water images (OLI, OR), existing geological maps, field observations, and derived parameters, such as spectral ratios, PCA, normalized indices (NDVI, IB), and morphometric attributes (slope, drainage density, altitude). These data were integrated into a tabular framework, where each observation represents a geographical point (field site), and each column corresponds to an explanatory or target variable.  
The input variables were classified into two categories:  
* Continuous variables: Spectral reflectance values, ratios (e.g., B5/B7, OR5/OR4), altitude, slope, morphometric indices, etc.
* Categorical variables: Soil types, lithological classes, and geological formations identified in the field or from existing maps.  
Particular attention was paid to data preprocessing to ensure their compatibility with machine learning algorithms. Missing data were imputed using appropriate statistical methods (e.g., spatial interpolation or local average). Quantitative variables were normalized to reduce the effect of scaling differences, while qualitative variables were encoded numerically using techniques such as one-hot coding.  
The database was divided into two subsets for the training and validation phases: 80% of the data (935 samples) were allocated to training the models. In contrast, 20% (234 samples) were being reserved for their validation. This distribution aims to guarantee analytical robustness, minimize sampling bias, and ensure the reproducibility of geological classifications. Since the data is unbalanced, some classes are significantly over-represented compared to others. A proportional distribution was maintained during the creation of the subsets, thus making it possible to keep the same distribution of classes in the validation set. This ensures that the validation set accurately reflects the actual distribution of classes in the database, thus avoiding evaluation biases.  
Figure 3 illustrates the compilation and integration of geospatial data from various sources (geological and soil maps, satellite imagery, radar, and topographic data) to analyze a dependent variable on a set of independent variables.  
Table 1 and Figure 4 present the data distribution according to the different types of geological formations.  
\begin{table}
\begin{tabular}{l r r r} \hline
**Geological Data Types** & **Training** & **Test** & **Total** \\ \hline Alluviums & 90 & 23 & 113 \\ Basalt & 260 & 65 & 325 \\ Gn + Pyro & 50 & 12 & 62 \\ Gnesis & 148 & 37 & 185 \\ Granites & 185 & 46 & 231 \\ Pyroclastics & 57 & 15 & 72 \\ Trachytes & 145 & 36 & 181 \\ Total & 935 & 234 & 1169 \\ \hline \end{tabular}
\end{table}
Table 1: Quantitative distribution of data by type of geological formation.  
Figure 3: Illustrates the compilation and integration of geospatial data from various sources.
###### Abstract  
This paper proposes \"An Integrated Self-diagnosis System (ISS) for an Autonomous Vehicle based on an Internet of Things (IoT) Gateway and Deep Learning\" that collects information from the sensors of an autonomous vehicle, diagnoses itself, and the influence between its parts by using Deep Learning and informs the driver of the result. The ISS consists of three modules. The first In-Vehicle Gateway Module (In-VGM) collects the data from the in-vehicle sensors, consisting of media data like a black box, driving radar, and the control messages of the vehicle, and transfers each of the data collected through each Controller Area Network (CAN), FlexRay, and Media Oriented Systems Transport (MOST) protocols to the on-board diagnostics (OBD) or the actuators. The data collected from the in-vehicle sensors is transferred to the CAN or FlexRay protocol and the media data collected while driving is transferred to the MOST protocol. Various types of messages transferred are transformed into a destination protocol message type. The second Optimized Deep Learning Module (ODLM) creates the Training Dataset on the basis of the data collected from the in-vehicle sensors and reasons the risk of the vehicle parts and consumables and the risk of the other parts influenced by a defective part. It diagnoses the vehicle's total condition risk. The third Data Processing Module (DPM) is based on Edge Computing and has an Edge Computing based Self-diagnosis Service (ECSS) to improve the self-diagnosis speed and reduce the system overhead, while a V2X based Accident Notification Service (VANS) informs the adjacent vehicles and infrastructures of the self-diagnosis result analyzed by the OBD. This paper improves upon the simultaneous message transmission efficiency through the In-VGM by 15.25% and diminishes the learning error rate of a Neural Network algorithm through the ODLM by about 5.5%. Therefore, in addition, by transferring the self-diagnosis information and by managing the time to replace the car parts of an autonomous driving vehicle safely, this reduces loss of life and overall cost.  
autonomous vehicle; Integrated Self-diagnosis System; In-Vehicle Gateway; Optimized Deep Learning Module; Edge Computing
## 3 Results
### Spatial Analysis  
The runs theory was applied to each region to identify drought events and characterize them in terms of duration, severity, intensity, and peak. Figures 7-9 display the drought count in each grid cell along with the mean duration and severity.  
Figure 6: Zones produced after regionalization.  
Figure 8: Mean duration of droughts that occurred from 1902–2015 at each grid cell.  
Figure 7: Drought count at each grid cell.  
From Figure 7, it is clear that there are remarkable variations in drought count across Pakistan. Zone 1 mostly experiences a low to high number of droughts with a few grid cells--bordering China --experiencing an extremely high number of droughts. Moving southwards into zone 3, the number of droughts decrease at first before increasing to high and then extremely high.  
High drought count mostly dominates zone 2, except for its southern part where an extremely high number of droughts occur. In zone 4, we again see a pattern of decreasing drought count moving westwards. The northern and western borders of zone 5 experience a high number of droughts, in contrast to the rest of that region that encounters an extremely high number of droughts.  
Moreover, an interesting pattern in contrast to Figure 7 can be observed in Figure 8, which displays the mean duration for grid cell from 1902-2015. The areas with high drought count have extremely short (in Zone 3) and short (in zone 5) duration of droughts. On the other hand, the zones experiencing an extremely low number of droughts (zone 4) have the longest duration. This observation makes sense as SPEI is a normalized index with approximately equal probability of SPEI being less than \(-0.5\) for each grid cell. The number of drought months is therefore approximately equal for all grid cells. Since the number of drought months is equal to the product of mean duration and drought count, drought duration and drought count have a strong negative correlation. From Figure 9, it can be concluded that regions that experience a low number of meteorological droughts are usually struck with longer and more severe droughts and vice versa. However, the most severe droughts are concentrated in west Baluchistan.  
Drought management and mitigation in Region 1 should focus on moderate and severe drought, whereas, in Region 2, the focus should be on highly frequent droughts with moderate severity and duration. The south of Region 3 experiences an extremely high number of droughts with extremely short duration and slight severity. The north of Region 3, on the other hand, is struck with a low number of droughts but increasing severity and duration. Region 4 also shows spatial variability with the western part experiencing an extremely low number of droughts of extremely high duration and extreme severity. The southeast of Region 5 is distinct from the rest of the region with an extremely  
Figure 9: Mean severity of droughts that occurred from 1902–2015 at each grid cell.  
high number of droughts of extremely low duration and slight severity as compared to the rest of Region 5, which faces a high number of droughts of long duration and moderate severity.
## 2 Bottom-Up Assisted Pressure Drive Mechanism  
### Process Description  
A schematic of the bottom-up assisted pressure drive is presented in Figure 1. In this process, a group of horizontal wells are positioned near the bottom of the reservoir. Injectors and producers are placed alternatively. The distance between the injector and the producer can be varied based on the reservoir conditions. Before the steam injection, a high mobility zone is generated to build the communication between the injector and producer ([PERSON] et al., 2017; [PERSON] et al., 2021). Subsequently, steam injection is initiated. The operating strategies are similar to those in the SAGD process. High-quality steam (steam quality higher than 95%) is introduced in the reservoir. Different from the SAGD process, a flat steam chamber is developed in the bottom-up assisted pressure drive process. With the combination of gravity drainage and pressure drive, condensed water and heated oil are produced through the producer.  
By applying the bottom-up assisted pressure drive, a few main advantages over the conventional SAGD process are expected. As shown in Figure 1, a flat steam chamber is developed from the bottom in the early stage of the process. Because of the long distance between the injector and producer, a relatively large contact area between the steam and crude oil is established, leading to high oil production. Furthermore, the pressure drive is the main driving force in the bottom-up assisted pressure drive process. Due to the large distance between the injector and the producer, a relatively large pressure difference may be applied. Compared with the gravity force in the SAGD process, the pressure drive is more energetic, further improving oil production. The aforementioned features associated with the bottom-up assisted pressure drive process exhibit great potential in reservoirs with shale barriers, thin formation, or reservoirs with top water. In this study, the authors focus on the application of bottom-up assisted pressure drive in reservoirs with shale barriers.  
Figure 1: Schematic of the bottom-up assisted pressure drive process with respect to the well locations, steam chamber characteristics, and shale barriers distribution.
## 4 Case Study: The Grottalunga Viaduct
### Bridge Structural Scheme and FEM Model
In order to take into account the effects of the construction stages, initially the dead load was  
Figure 4: The Grottalunga viaduct; (**a**) longitudinal scheme; (**b**) pier transverse section. applied to the bridge, namely the self-weight of the piers and girders and the weight of the deck and superstructure. Then, the restraint reaction was evaluated together with the displacements of the deformed configuration reported in Figure 5c. The left cantilever showed a displacement on the pier top of \(43\,\mathrm{mm}\) and the right cantilever of \(-42\,\mathrm{mm}\). The setting of the nonlinear joint movement range was adjusted to consider that the restraints at the abutments and the connection between the cantilever and drop-in span are placed after the displacement due to the dead load, that is after the construction is completed. Thus, the force-displacement relationship for the support at the left abutment or at the joint between the right end of the left cantilever (RELC) and the left end of the drop-in span (LEDIS), where the longitudinal displacements are enabled, assumes the form represented in Figure 5d. The support where the relative displacements are restrained (as the joint between the left end of the right cantilever and the right end of the drop-in span) maintains the form depicted in Figure 5e. In Figure 6, periods, modal shapes, and mass participant ratios for the translational directions are depicted, showing that in order to capture the translational response, six modes should be taken into account. Regarding longitudinal behavior, the drop-in span and the constraints divide the structure into two substructures. Remarkably, fifth mode and sixth mode are characterized by the pier deformation shape with an inflection point along the height; they have to be taken into consideration in order to correctly estimate the bridge longitudinal behavior. Figure 5: Simplified modeling of the bearing: modeling scheme (**a**) at the abutments; (**b**) at the deck-drop-in span connections; (**c**) deformed shape due to dead load (horizontal displacement of \(+43\,\mathrm{mm}\) and \(-42\,\mathrm{mm}\) for the left and right cantilevers, respectively); force-displacement relationship for support with (**d**) unrestrained displacement; (**e**) restrained displacement.