esa-sceva/satcom-chunk-collection huggingface dataset:
# Envisat RA-2 Individual Echoes: A Unique Dataset for a Better Understanding of Inland Water
## 4 Selected Case Studies
### Semliki River  
The second demonstration regards the Semliki River (Congo, central Africa). There are seven passes with RA-2 IE data available, over the Semliki River (0.136\({}^{\circ}\)S, 29.582\({}^{\circ}\)E). The altimeter tracks cross the river just 2 km from its mouth on Lake Edward, close to the Congo, Uganda border. Semliki flushes water out of Lake Edwards, to Lake Albert which is about 150 km north. Figure 15 shows the seven altimeter passes crossing the river. The river is approximately 150 m wide at this point. Despite the much larger size of this river (compared to Rio Nahuapa) the echoes appear to be specular in every pass. The tracks start just before the river, come into the radar view and continue after a brief run over land into Lake Edward, which (interestingly) does not exhibit any specular return, as expected by its dimensions and the resulting surface roughness.  
Figure 14: River slope estimate in: (**a**) radargram; (**b**) Doppler velocity; (**c**) range bins.  
Figure 15: Seven altimeter passes crossing the river. Color bar indicates the power of the returned echoes (coherently summed).  
Figure 16 plots the waveform of echoes over the river, and for comparison an overlay of the lake waveform. The river has a strong specular component with a weak underlying Brownian waveform, while the lake exhibits a classical Brownian waveform. The Brownian waveform implies that the lake surface is rough, which is not surprising since Lake Edward is large enough to produce \"ocean-like\" wind waves. The small peak on the leading edge of the Brown waveform, at range bin 109, can be presumably due to a small vessel on the lake. In any case, its hyperbolic feature in the radargram (not shown) represents the classical peculiarity of a point-like structure.  
The theoretical RCS (dashed black) overlaid on the seven altimeter passes is illustrated in Figure 17, highlighting a good agreement between theory and data as expected. Compared to the case study of Nahuapa River, the RCS narrows in Semliki River. In Nahuapa the side lobe at 500 m distance is 20 dB down from the peak. At the same distance in Semliki the side lobe is down 30 dB. This difference is explained by the nature of specular returns as discussed in Section 2.2. In addition, Figure 18 shows a good agreement of the theoretical Doppler velocity with the data. It is especially noteworthy that theory inferred from Equation (1) predicts the Doppler leveling to zero when the altimeter is directly over the water and this is confirmed by the collected data.  
Figure 16: Specular waveform of Semliki echoes (blue), compared with the Brownian Edward Lake (red).  
Figure 17: Theoretical RCS (dashed black) overlaid on the seven altimeter passes. Different colors are used to distinguish between the different satellite passages.
## 4 Simulation Results and Analysis
### Effects of Static Displacement Eccentricity
Thus, the frequency components become complex due to the effects of the bearing forces. It can be seen that the main frequencies are \(\Omega\) and \(2\Omega\), which means the vibration of a bearing is largely affected by the rotor. The other frequencies are the combination of VC frequency and rotating frequency due to the coupling effects. The spectra of the dynamic responses show that the coupling effects affect both the bearing and rotor. Figure 8: _Cont_. Figure 7: Time history of the dynamic responses for different eccentricities. (**a**) Time history of rotor dynamic responses; (**b**) Time history of bearing dynamic responses.
## 4 Current Issues and Challenges of Realising the Tactile Internet
### Challenges Related to Kinematic Devices
#### 4.12.2 Kinematic Actuators  
Kinematic actuators are used at the teleoperator end. Here, a robot will be placed at the teleoperator end to copy the action from the operator. However, there could be a variation in the Degrees of Freedom (DoF) of movement between an operator and a robot. In such instances, the reverse version of kinematic equations is applied. These equations calculate the link angle of the robot to guarantee that the final stance of the robot intently resembles that of the operator.  
The reverse versions of kinematic equations are responsible for solving basic kinematic conditions for robot links. If the number of robot links increases, these kinematic equations fail and become tedious to unravel. Therefore, numerical solvers are computationally concentrated, which can increment the activation time and therefore increment the RTT. Subsequently, one solution could be to transfer all the computational work to the cloud and save computational time.
## 2 Materials and Methods  
### Study Area  
Two typical permafrost distributed regions are selected in this study, namely Wenquan (WQ) and Budongquan-Qingshuihe (BQ) (Figure 1). WQ (35.19\({}^{\circ}\)-35.70\({}^{\circ}\)N, 99.09\({}^{\circ}\)-99.60\({}^{\circ}\)E) is a typical transitional area between permafrost and seasonally frozen ground in the northeastern part of the QTP [36; 37]. It covers an area of about 2572 km\({}^{2}\), 76% of which is covered by permafrost. The altitude varies from 3570 m to 5060 m. As a cold temperate continental climate area, it is semi-humid and semi-arid [38]. The mean annual precipitation is 500-600 mm, and the mean annual temperature is \(-\)3.2 \({}^{\circ}\)C. WQ is primarily a grassland ecosystem, and the dominant vegetation types are alpine meadow and alpine steppe. The parent materials are slope deposits and residual deposits [36].  
BQ (33.34\({}^{\circ}\)-35.73\({}^{\circ}\)N, 93.39\({}^{\circ}\)-97.65\({}^{\circ}\)E) is located in the transition from the hills in the central to the low mountains in the eastern permafrost region [36]. The permafrost zones transit from the large extent of continuous warm and ice-rich permafrost to the alpine and mountainous isolated permafrost in the east [36; 37]. It covers an area of about 31,270 km\({}^{2}\). Its altitude varies from 3770 m to 5190 m. The annual mean air temperature is \(-\)4.4 \({}^{\circ}\)C, and the mean annual precipitation is 435 mm, with less precipitation from the east to the west. The climatic conditions change from the semi-arid cold in the west to the semi-humid cold regions in the east. Consequently, the primary vegetation types vary from the alpine meadow, alpine steppe to alpine desert. BQ embodies all the soil types of the permafrost region in the eastern Qinghai-Tibet Plateau, and mainly developed on the alluvial and fluvial deposit and slope deposit [36; 38].  
Figure 1: Study areas and sampling locations. (**a**) Identifies the location of two study area on the Qinghai–Tibet Plateau, (**b,c**) present the sampling sites at Budongquan–Qingshuihe and Wenquan, respectively.
# A Robust Heuristics for the Online Job Shop Scheduling Problem
## 3 Preliminaries
### The Basic Job Shop Scheduling Problem  
The formal definition of the basic JSSP is as follows. A finite set of \(n\) jobs \(\mathcal{J}=\{I_{1},J_{2},\ldots,J_{n}\}\) and a finite set of \(m\) machines \(\mathcal{M}=\{M_{1},M_{2},\ldots,M_{m}\}\) are given. Each job \(J_{j}\) consists of a set of operations \(O_{j,1},O_{j,2},\ldots,O_{j,m}\) which need to be processed in a specific order (known as precedence constraints). Each operation has a specific machine that it needs to be processed on and only one operation in a job can be processed at a given time (machines have capacity one). The operation \(O_{jk}\) takes time \(\tau_{jk}\) and may not be interrupted. The task to be solved is to assign the starting times \(t_{jk}\) for all the operations such that all the precedence and capacity constraints are satisfied. The total time in which all operations for all jobs are completed is referred to as makespan \(C_{max}\), and the optimal solution is the minimal makespan of a feasible schedule, formally:  
\[C^{*}=\min\{C_{max}\}=\min\{\max(t_{jk}+\tau_{jk})\mid\text{all jobs, all operations, schedule feasible}\}\,.\]  
This is the basic version of JSSP. Due to practical importance of the problem, many variants have been studied in the literature.  
Dimensionality of an instance of JSSP is the number of operations that have to be scheduled. When each of the \(n\) job needs to use each among \(m\) machines, the dimensionality is \(m\times n\).  
Obviously, instead of makespan, one can optimize other objectives. Some other objectives may be closer to practical needs, and in fact, multiobjective versions of the problem may be the models of choice in industrial applications. We wish to note that our approach is robust in the sense that it can be applied to other objective functions either readily or with some natural changes. However, for comparison of the results, we use the makespan objective, as it is used for the majority of benchmark instances.  
Several other features of the model can be naturally relaxed or generalized. For example, the capacity of machines may be more than one, and operations may be performed on a set of machines. Below, we discuss some variations.
## 4 Adaptive Hybrid GA-SAA Algorithm  
Due to the advantages of hybrid algorithms [40; 41; 42; 43; 44; 45; 46], in this paper, we design an adaptive hybrid GA-SAA algorithm, where the Metropolis strategy in SAA is incorporated into GA to further improve the neighborhood searching efficiency. The algorithm includes the following procedures: (a) initial source population generation and evaluation, (b) solution preservation, selection, and recombination, (c) neighborhood searching based on SAA, and (d) end-condition examination. In this algorithm, the solution is optimized based on adaptive principles according to the problem characteristics and optimization degree, which is beneficial to ensuring the improvement direction.
# Diversity and Functional Evolution of Terpene Synthases in Rosaceae
## 4 Material and Methods
### Expression Analysis of TPSs in Rosaceae  
For the three representative plants (_F. vesca_, _M. domestica_, _P. persica_), RNA-seq data of different tissues and development stages (ripe fruit, immature fruit, leaf) was retrieved from the fruitENCODE project [37]; each tissue consisted of two replicates. The fruitENCODE project aims to generate a comprehensive annotation of functional elements in seven climacteric fruit species (apple, banana, melon, papaya, peach, pear, and tomato) with sequenced reference genomes. The data were deposited in the SRA database of NCBI with accession number PRJNA381300. Transcriptome analysis was implemented by the protocol in a previous study [38]. The clean reads of RNA-seq data from each sample were mapped against the genome reference with HISAT2 [39]; each SAM file was converted into a BAM file, and sorted. Duplicates were removed with SAMtools [40; 41]. Further transcript assembly and quantification of the read alignments were performed using Stringtie [42]. Gene expression levels were measured by FFKM (fragments per kilobase of transcript per million mapped reads) and normalized with the row-scale method. Heatmaps with all samples were plotted using the \"HeatMap\" function in TBtools.
## 2 Design Concept of Smart Transfer Planer
### Signal Transform  
Parallel coupled transmission lines can be designed as microwave filters [17]. In this scenario, a filter operating in the Ku-band is coupled from outdoors through window glass as a dielectric. First, the inductance and capacitance of the prototype circuit of the bandpass filter at the cutoff frequency are calculated, and alternative transmission line lengths for the inductance and capacitance are identified. These alternative segment combinations form a stepped LC filter. Knowing that indoor and outdoor structures are identical and symmetrical, where the glass thickness and dielectric constant are both variable within a range, it is observed from the simulation process that thicker glass leads to greater transmission loss in the coupler and there is significant variation in the dielectric constant of different glass types. The glass dielectric constant used in this design simulation is 3.7. Figure 8 depicts a schematic diagram of the filter coupler, designed in the form of a vertical coupled microstrip line stepped filter. The size of the single-sided stepped filter is calculated according to reference [18]. The outdoor and indoor sides of the filters are simulated in HFSS. As shown in Figure 8, the left side of the outdoor area is set as the input port, and the right side of the indoor area is set as the output port, while the remaining two ports are open-circuit, forming a bandpass filter. Figure 9 presents the S-parameter results of the filter coupler, where Port 1 represents the outdoor end filter and Port 2 represents the indoor end filter. Due to the symmetrical structure, the outdoor and indoor structures can be interchanged. By observing the simulation results from Port 1 to Port 2, it is observed that the reflection coefficient is below \(-10\) dB within the operating frequency band of 12 GHz to 13 GHz, and the penetration coefficient can be considered as the coupling amount to the other side, controlled between \(-2\) dB and \(-3\) dB within the operating frequency band.  
Figure 7: Radiation pattern of multiple antenna arrays.
## 4 Results and Discussion
Existingfamily libraries within BIM software may not adequately represent the architectural nuances of heritage structures like theAd Deir Tomb, necessitating the creation of custom families. This process adds complexity and may require specialized expertise in BIM modeling. Moreover, to address these technical challenges, several strategies can be employed. First are advanced computer vision techniques. Exploring advanced computer vision techniques, such as feature extraction algorithms and surface reconstruction methods, can improve the accuracy and efficiency of data acquisition and processing. These techniques can help automate the identification of corresponding points on irregular surfaces, reducing the need for manual intervention and streamlining the data collection process. Second is high-performance computing. Investing in high-performance computing infrastructure can accelerate data processing and registration tasks, reducing the computational burden associated with large-scale data sets. Parallel processing and distributed computing architectures can be leveraged to efficiently handle the processing demands of 3D laser scanning data. Finally are custom BIM libraries. Developing custom BIM libraries tailored to heritage projects can address the limitations of existing family libraries within BIM software [26]. These custom libraries can include predefined templates and components specifically designed to represent the unique architectural elements found in heritage structures like Ad Deir Tomb. Collaborative efforts among heritage practitioners, B  
Figure 11: Providing engineering information via Revit. Figure 12: The integration of Historical data within HBIM model. developers can facilitate the creation of comprehensive BIM libraries optimized for heritage conservation projects. Furthermore, heritage BIM serves as a foundational tool for documentation as well as the conservation planning. It facilitates the identification of areas with high salt content, a common issue in Petra's sandstone monuments [49]. Understanding the spatial distribution of salts within the monument is crucial for devising targeted conservation strategies. Additionally, the model aids in identifying vulnerable structural elements, such as eroded surfaces and unstable sections, which can inform preservation decisions [10].
# Numerical Simulation of Rockfill Materials Based on Fractal Theory
## 5 Conclusions  
Based on the fractal theory, a fractal model is established with the number of particles between different particle groups as the statistical number. The relationship between the fractal dimension \(D\) and different mechanical performance indexes is discussed. Several conclusions can be drawn as follows:  
1. With the use of the particle flow code in two dimensions and on the basis of fractal theory, a fractal model was established with the number of particles used as the statistics. Compared with the specimens obtained by equivalent substitution, which considerably disturbed the particle size distribution, the numerically simulated specimens obtained by the other three scale methods exhibited better fractal characteristics.
2. A great fractal dimension \(D\) corresponds to more obvious deviatoric stress curve softening and volumetric strain curve dilatancy and thus a larger peak deviatoric stress. The peak internal friction angle of the specimen \(\varphi_{\mathrm{P}}\), the secant modulus \(E_{50}\), and the Poisson's ratio \(v_{50}\) show better linear fitting with fractal dimension \(D\).
3. The fractal dimension \(D\) often changes the mechanical properties of the specimen with certain macro-responses, that is, with the increasing fractal dimension \(D\), the compactness indicators \(e_{max}\), \(e_{min}\), and \(e\) decrease little by little, the strength indicator \(\varphi_{\mathrm{P}}\) increases gradually, the deformation indicator \(v_{50}\) diminishes, and \(E_{50}\) rises. A meso-response is that the fractal dimension \(D\) increases with the coordination number.
4. Using the particle aggregate model, which is used to calculate the rockfill materials in accordance with the fractal theory, the relationship between fractal dimension, peak strength and confining pressure of rockfill materials is established. The limit value of particle breakage is given by using fractal dimension.  
Conceptualization, H.H.; Data curation, Y.M.; Writing-review, W.H. and W.Y.; Supervision, X.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded the National Nature Science Foundation of China (No. 51978540), the Doctor foundation of Xinxiang University (No. 1366020159).  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
Suppose, for example, that, instead of a traditional static PRO system, we have a PRO system that can rotate with a defined angular velocity. Depending on the membrane orientation, there will be effects related to centrifugal and Coriolis forces that could enhance mass transfer through the membrane. In this work, we present a theoretical system that allows a PRO process to include rotational kinetic energy, and we describe the mathematical method used for obtaining a new expression for a solution-diffusion model affected by rotational kinetic energy. A PRO system that incorporates rotational kinetic energy can induce turbulence through inertial effects on the feed and draw solutions throughout the entire volume of the membrane module. This turbulence could enhance water flow across the membrane, even in flow-dead zones within the module. In the proposed PRO system, the membrane module will rotate in the air, moving only the mass of the module and the volume of water inside. As a result, the efficiency of the electric motor may exceed that of the two hydraulic pumps, which must maintain a high flow rate throughout the entire hydraulic system of the feed and draw solutions in a typical PRO system. The magnitude of the effects will depend on parameters such as the turning radius, angular velocity, direction of rotation, and solution densities. These factors provide numerous options for controlling the performance of the system. This theoretical work provides a framework for advancing PRO technology by introducing the possibility of actively controlling CP through rotational kinetic energy. The technical challenges related to the rotating mechanism and couplings, large-scale implementation, and detailed energy consumption of the rotational components are beyond the scope of this study and will be addressed in future research.
## 2 Theoretical Foundations
### DSCN Principle and Design
\[\mathbf{y}^{1}(i_{0},\,j_{0})=\sum_{g=1}^{G}\sum_{j=0}^{K_{g}-1}\mathbf{w}\mathbf{x}(i_{0 },j_{0}\,+j+\triangle p_{j}^{g}) \tag{4}\]  
\[\mathbf{y}^{2}(i_{0},\,j_{0})=\sum_{g=1}^{G}\sum_{i=0}^{K_{h}-1}\mathbf{y}^{1}(i_{0} \,+i+\triangle p_{j}^{g},\,j_{0}) \tag{5}\]  
Secondly, DCNv3 employs bilinear interpolation for sampling, which is a relatively intricate interpolation technique entailing high computational overhead. Bilinear interpolation necessitates two linear interpolations and one weighted summation operation. Assuming the presence of three spatial coordinates \((x_{0},y_{0})\), \((x_{1},\,y_{1})\), and \((x_{2},y_{2})\), \(f(x_{0},y_{0})\) can be computed using the bilinear interpolation formula with \(f(x_{1},\,y_{1})\) and \(f(x_{2},y_{2})\). \[ \begin{split} f(x_{0},y_{0})=f(x_{1},\ y_{1})(x_{2}-x_{0})(y_{2}-y_{0}) \\ +f(x_{1},\ y_{2})(x_{0}-x_{1})(y_{2}-y_{0})\\ +f(x_{1},\ y_{2})(x_{2}-x_{0})(y_{0}-y_{1})\\ +f(x_{2},\ y_{2})(x_{0}-x_{1})(y_{0}-y_{1})\\ \end{split}  \tag{6}\]  
The DSCN, in contrast, employs linear interpolation for sampling, which is a simpler interpolation method with reduced computational complexity. It only necessitates two linear interpolation operations. \[f^{\prime}(x_{0},y_{1})=f(x_{1},\ y_{1})(x_{0}-x_{1})+f(x_{2},\ y_{1})(x_{2}-x_ {1}) \tag{7}\]  
\[f(x_{0},y_{0})=f^{\prime}(x_{0},y_{1})(y_{0}-y_{1})+f^{\prime}(x_{1},\ y_{2})(y_ {2}-y_{1}) \tag{8}\]  
The DCNv3 model employs a modulation mask to regulate the sampling weights, thereby increasing both the parameter count and the computational overhead. In contrast, DSCN eliminates the need for a modulation mask and instead utilizes a straightforward weight matrix to control the sampling weights, resulting in a significant reduction in complexity as well as a decreased parameter count and computational cost. In summary, the input feature map undergoes a depthwise convolution (DWConv) initially to reduce dimensionality in the channel dimension. This is followed by a linear projection layer that generates offsets through a linear transformation. These offsets are utilized to control subsequent sampling positions, allowing the convolutional kernel to adaptively adjust the receptive field according to features of varying shapes and sizes. Subsequently, the input feature map is interpolated linearly based on these generated offsets using a linear function for estimating values between sampling points. The sampled feature map encompasses feature information surrounding the target object, thereby aiding in improved recognition and localization by the network. Finally, this sampled feature map passes through another DWConv operation to increase dimensionality in the channel dimension and restore its original number of channels within the feature map, ensuring effective connectivity with subsequent layers. Consequently, the output feature map contains DSCN-processed features specific to the target object.