esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Moving Objects Behavior Analysis: Region Speed Limit Rate Measure
## 3 Results and Discussion
### Third Experiment  
Analogously to the second experiment, in Figures 14-17 we show the results for cell 6 for the SPI at 100 km/h.  
Figure 13: Calculated speed limit rates for the weekdays, cell 6 with MaxSpeed = 50 km/h.  
Figure 16: Calculated SPI for the weekends, cell 6 with Vmax = 100 km/h.  
Figure 14: Calculated SPI for the entire month, cell 6 with Vmax = 100 km/h in the morning.  
Figure 15: Calculated SPI for the entire month, cell 6 with Vmax = 100 km/h in the afternoon.  
Note that increasing \(Vmax\) will change the SPI value proportionally. For example, if in cell 6 between 14:00 and 16:00 on 3 August 2013, \(Vavg=50\) km/h then, at \(Vmax=60\), 80, and 100 km/h, we obtain:  
\[\mathrm{SPI}=100*\left(\frac{50}{60}\right)\qquad\qquad\mathrm{SPI}=100*\left( \frac{50}{80}\right)\qquad\mathrm{SPI}=100*\left(\frac{50}{100}\right)\]  
\[\mathrm{SPI}=83.33\;\mathrm{SPI}=62.5\;\mathrm{SPI}\;=50\]  
The results of the SPI were consistent with our speed limit rate measurements: weekdays were a little slower than weekends; on average, Sundays were the slowest days, as were afternoons when compared to mornings, a weekday with an unusual behavior was due to a holiday (Thursday15 August 2013), and extreme time-ranges, (00:00, 02:00), (02:00, 04:00), (20:00, 22:00), and (22:00, 00:00), were the fastest; this makes sense because at extreme time-ranges there is usually less traffic, which makes it easier for vehicles in circulation to move at higher speeds. However, in general, the differences between mornings-middays and afternoons were not greater than 0.2; see, e.g., the peak hours (08:00, 10:00) and (18:00, 20:00) in Table 4, where the speed limit measure ranges in the interval (0.4, 0.6).
# Analytic Form Fitting in Poor Triangular Meshes
## 3 Methodology
### Preprocessing  
#### 3.1.1 Spatial Hashing  
Spatial hashing on the input triangle set \(M\) is used in this work for accelerating the construction of explicit triangle neighborhood information (boundary representation). The I/O specification of the spatial hashing preprocessing follows.  
**Input:**  
1. \(M(\mathcal{T},\mathbf{P})\): Triangle (two-manifold) set with geometry \(\mathbf{P}\) and topology \(\mathcal{T}\).
2. \(\Omega\): Rectangular prism in \(R^{3}\), orthogonally oriented w.r.t. coordinate axes, equipped with a regular grid of voxels with dimension \(delta_{x}\), \(delta_{y}\), \(delta_{z}\).
3. \(N_{v}\) : A predefined number of voxels per dimension of \(\Omega\).  
**Output:**  
1. \(h\): Hashing function: \(h:P\to N\times N\times N\), which maps each vertex of the input triangle set \(M\) into the (i,j,k) indices of the voxel \(v_{ijk}\subset\Omega\), which contains such a vertex.
2. \(H\): \(\Omega\to T^{2}\), which maps each voxel \(v_{ijk}\) of Omega to the set of triangles of \(M\) which contain a vertex inside voxel \(v_{ijk}\). \(2^{T}\) denotes the power set of \(T\) (all sets made with triangles from \(T\)).  
The algorithm divides the prism \(\Omega\) into a set of rectangular voxels. These voxels are represented by a three-dimensional array where each cell contains the indices of the triangles with vertices in that voxel. Figure 3 shows a graphical representation of the voxel subdivision for an input triangle set.  
Figure 2: Fitting process. **Left** (red) block: segmentation/fitting decoupled. Real-time identification of \(g()\). **Right** (blue) block: codependent segmentation and fitting.  
The hash function \([i,j,k]=h(p_{i})\) maps a point \(p_{i}\in\Omega\) to a position \([i,j,k]\) in the hash table \(H\).  
\[\begin{split} i&=floor((p_{i}.x-\Omega.x_{min})/ \delta_{x}),\\ j&=floor((p_{i}.y-\Omega.y_{min})/\delta_{y}),\\ k&=floor((p_{i}.z-\Omega.z_{min})/\delta_{z}). \end{split} \tag{1}\]  
where \(\Omega.x_{min}\), \(\Omega.y_{min}\), \(\Omega.z_{min}\) are constants corresponding to the minimal positions of the prism \(\Omega\). \(\delta_{x}\), \(\delta_{y}\), \(\delta_{z}\) are the \(x\), \(y\), and \(z\) voxel side dimensions.  
Figure 4 represents a block a diagram of the algorithm and how the vertices are stored in the hash table. As the input triangles sets are representation of two-manifold shells, \(H\) is a sparse array.  
Notice that the spatial hashing \([H,h]\) ensures that all triangles incident on (EDGEs of) a given triangle are stored in at most three voxels of the hash array \(H\), and they are directly accessible via the function \(h()\). Due to this reason, the spatial hashing accelerates the construction/weaving of the boundary representation of the input triangle set \(M\).
## 1 Introduction
However, the modeling method of the soil-rock mixture meso-structure model considering the influence of the irregular rock blocks remains insufficiently precise, and the mechanism by which rock content and soil-rock interface strength influence UCS are not yet fully understood. To address these gaps, this study aimed to analyze the mechanical characteristics of soil-rock mixture specimens under varying rock contents and soil-rock interface strength using both experimental methods and numerical simulations. First, several uniaxial compression tests were conducted on soil-rock mixture specimens with different rock contents. The influence of rock content on the strength response and failure characteristics of specimens was discussed at the macro scale. Then, to further analyze the meso- mechanism of the internal composition inside the specimen, numerical simulations of uniaxial compression tests on soil-rock mixture were carried out using the particle discrete element method (PDEM). An innovative approach to generating irregular rock block models randomly based on real rock block images was proposed, ensuring that the effect of internal irregular particles is fully considered during the model establishing. The failure characteristics of the soil-rock mixture specimens with varying rock contents and soil-rock interface strengths were identified. Finally, the mechanisms by which strength-influencing factors affect UCS values were discussed from both macro and micro perspectives.
## 3 Improved LEACH Protocol
### Parallel Transmission Based on Scheduling for Cluster Head Nodes and Sensor Nodes  
Since the transmission rate between two nodes in underwater acoustic networks is usually very low due to the narrow bandwidth of the underwater acoustic channel, increasing the transmission parallelism among network nodes is one of the most effective ways to improve the performance of underwater acoustic networks [22]. Parallel transmission in the underwater environment can be implemented by using time division multiple access (TDMA), code division multiple access (CDMA), and frequency division multiple access (FDMA), among which TDMA is the most widely used method due to its convenience and ability to exploit the long propagation delay. In this context, our study specifically adopts the TDMA approach for transmission scheduling. This choice was motivated by its inherent ability to exploit temporal parallelism through differential propagation delay among nodes, a feature that is critically dependent on strategic transmission scheduling. In cluster-based networks, data transmission involves two phases: sensor nodes and cluster head nodes. Effective scheduling requires careful timing coordination between these entities to ensure efficient data exchange.
## 3 Results and Discussion
### Mineralogical Analysis
Sd— siderite, Ank—anxrite, Dol—ddomite, Scbt—sphaerocobalite, Cal—cclite, Mag—magnetite, Sbn—stibnite, Anti—anilite, Bit—bismuthinite, Sm—stembergetic, Pgi—paraquanajautite, Lö—lollingite, Skt—skutterudite, Csaf—clinosafflorite, Trnt–tennantirite, Kou—koutekite, SyI—sylvanite, Knn—krennerite, Clc—chlinochore, Nim—mimite, Prl—pyrophyllite, Hys—findleyite, Hys—halloyite-7A, Cor—corrensite, Bt—bitoite, Ms—muscovite, Pch—plancheite, Ompg—omphacite, Fau—faujasite, StbCa—stilbite-Ca, At—actinolite, Ath—anthophyllite, Cum—cummingtonite, Fglin—terrorglaucophene, Qz—quartz, Cdt—claudette, Htc—hydrolatcite, Pjrs—plombojarosite, Spe—sergienite, Dev—devilline, Ske—starkeyite, Ksr—kieserite, Cksr—cobalt kieserite, Apw—alpowite, Hys—hexahdrite, Gp—gypsum, Vbz—vonbezingrite, Trn—nithemite, Tim—talmesite, HI—halite, Pata—paratacamite, Yuk—yukonite, Pyc—pyrochroite, Cyc—chalcocyanine, Rbe—ramsbeckite. ascending order of resistance to alteration in oxidized tailings. These iron sulfide minerals were not found by XRD conducted on bulk-tailing samples. Iron oxides (magnetic and hematite), oxyhydroxides and chalcopyrite are omnipresent in all polished sections. The grain sizes of all minerals of the TSF tailings are homogeneous, since the granulolometry used to carry the polished sections from the dense fractions is between 150 and 300 \(\upmu\)m of the TSF1 and TSF2 tailings and over 75 \(\upmu\)m for TSF3 tailings. Some grains consist of complex associations of several sulfides, iron oxide, oxyhydroxide and gangue minerals (Figure 12C,D,H). These minerals are present in the form of mixed grains (Figure 12F,J,L,M) in the TSF tailings. Liberated grains of several metalliferous phases were observed like chalcopyrite with irregular forms in the tailings of TSF1 (Figure 12A,B,E) and TSF2 (Figure 12G). The TSF3 tailings shows both liberated grains of chalcopyrite, arsenopyrite and magnetite (Figure 12K,N). Figure 12: Mineralogy of the TSF1 (**A–F**), TSF2 (**G–J**) and TSF3 (**K–N**) mine tailings determined by metallographic observation (polarized light) of polished sections. Cpy: chalcopyrite, Po: pyrrhotite, Asp: arsenopyrite, Mag: magnetite, Hem: hematite, Oxy-Hyd: oxyhydroxides.
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
## 3 Results
### Spatial Distribution and Land Subsidence Rate
#### 3.1.7 Pekalongan  
Pekalongan (6.8936\({}^{\circ}\) S, 109.6665\({}^{\circ}\) E) refers to two administrative areas located in Central Java Province, which are located within an alluvial plain area. This location has been renowned as the center of Batik industry (Indonesian cultural fabrics) for centuries, and there is a long history of its active harbor. The industry areas are located mainly in the northern part of the city, which is also adjacent to the Java Sea. According to the 2020 census, Pekalongan is populated by 1.25 million residents.  
The LS in Pekalongan has also been considered significant for more than a decade, as studied by [7; 47] using the geodetic method. The result of this study shows that the LS rate in Pekalongan is up to 140 mm/year in several areas (Figure 9(b.1)). Figure 9a shows that the rate has not slowed since 2017.  
Groundwater overexploitation by the Batik industries has been identified as a key contributor to LS in Pekalongan [47]. This argument is supported by the fact that the area with a relatively high LS rate is predominantly occupied by Batik industries, where the volume of groundwater extraction is not well recorded. The other primary factor that contributes to LS in Pekalonganis is the geological condition, as it is located within a thick deposit of alluvial sedimentation [17], which is prone to LS [48].  
Figure 8: LS in Brebes and Tegal: (**a**) Map of LS rate in mm/year; (**b**) LS time series retrieved from InSAR from 2017 to 2023 in Brebes (**b.1**) and Tegal (**b.2**); and (**c**) land use in black box area (**a.1**) corresponding to residential housing and industrial area in Brebes (**c.1**) and (**a.2**) corresponding to industrial complexes in an agricultural zone in Tegal (**c.2**).  
Generally, LS in Pekalongan encompasses almost the entire city, with the most affected areas being densely populated residential areas, as shown in the satellite image of Figure 9(b.2).
## 1 Introduction
A number of software-defined receivers have already been developed for GNSS signal reception and processing [13; 14; 15; 16; 17]. Most of these receivers are capable of processing GPS, GLONASS and Galileo signals. Recently, a PC-based BeiDou software receiver was introduced in [18] albeit with limited algorithmic details on how to acquire, track and process a NH code modulated BeiDou signal. Therefore, in this paper, the authors discuss the implementation issues of a software-defined BeiDou receiver considering the challenges introduced by the existence of NH code modulation in case of BeiDou D1 signal and the higher data rate in case of BeiDou D2 signal. The rest of this paper is organized as follows: Section 2 discusses the main functionalities of a software-defined GNSS receiver in view of BDS. In Section 3, a live data collection scenario, front-end configurations and positioning results are presented and discussed. Finally, conclusions and future work are discussed in Section 4.
## 4 VSG Applications of Power Electronics-Based Units
### Bidirectional EV Charger  
As the sales of electric vehicles (EV) and plug-in hybrid electric vehicles (PHEV) rise around the world [46], the use of EV can deeply impact current power systems not only as load, but also as potential bulk storage for the distribution network. Vehicle-to-grid (V2G) technology can provide services such as frequency power regulation and load-shifting [47; 48; 49]. Also, when the bidirectional EV charger is utilized, in addition to energy storage, EV can also be controlled as virtual synchronous motors (VSMs) to provide inertia emulation and virtual spinning reserves, or even feed local systems in standalone mode, as shown in Figure 11. In a low-voltage distribution network, the VSG-based converters are implemented in single-phase [50; 51].
## 3 Methodology
### Efficient Power Transmission Mechanism  
In the pursuit of a modern and sustainable energy infrastructure, the efficiency of power transmission within a smart grid stands as a paramount objective. This mechanism is designed to optimize the movement of electricity across the grid, effectively delivering power while minimizing losses, enhancing reliability, and accommodating the integration of renewable energy sources. Through the integration of cutting-edge technologies and innovative strategies, the smart grid achieves an intelligent and efficient power transmission system. At the heart of this mechanism lies the utilization of high-voltage direct current (HVDC) transmission, which facilitates the seamless conveyance of electricity over extended distances with reduced energy losses compared to traditional alternating current (AC) methods. In tandem with HVDC, flexible AC transmission system (FACTS) devices, powered by sophisticated control systems, regulate voltage levels and augment power flow control. This dynamic adjustment alleviates line congestion, optimizing power transmission pathways as shown in Figure 10.  
In the realm of real-time monitoring and control, wide-area monitoring systems and phasor measurement units (PMUs) offer a comprehensive understanding of grid dynamics. These systems provide synchronized measurements, enabling grid operators to make informed decisions that optimize power flow, enhance stability, and mitigate power oscillations. Further reinforcing the efficiency paradigm are demand response initiatives,  
Figure 10: Block diagram of transitioning from the traditional to the smart grid system.  
Figure 9: Improvement in round-trip latency due to traffic scheduling functions.  
empowering consumers to adjust their energy consumption patterns based on grid conditions. Additionally, energy storage systems play a pivotal role by capturing excess energy during periods of low demand and releasing it during peak times. This process smooths energy supply variations and reduces transmission losses, enhancing the overall grid efficiency. Smart transformers and substations integrate advanced monitoring and control capabilities, fine-tuning voltage levels and optimizing power quality. By dynamically rating transmission lines based on real-time data, dynamic line rating systems enhance safety while maximizing line capacity, particularly during challenging weather conditions.
# Finite Element Modeling of the Soil-Nailing Process in Nailed-Soil Slopes
## 3 FE Model of Nailed-Soil Slopes
### Finite Element Mesh  
The mesh generator needs an optimum meshing parameter, which represents the average element size, (\(L_{e}\)). [PERSON] [38] have studied the sensitivity of the mesh parameter, considering very coarse to very fine mesh, on the reinforced soil wall analysis results, and they found that meshing has minimal influence on analysis results of reinforced soil wall. The \(L_{e}\) parameter is calculated from the outer geometry dimensions (\(X_{min}\), \(X_{max}\), \(Y_{min}\), \(Y_{max}\)) and global coarseness setting (\(n_{c}\)) in PLAXIS as follows.  
\[L_{e}=\sqrt{\frac{(X_{max}-X_{min})(\gamma_{max}-\gamma_{min})}{n_{c}}}. \tag{5}\]  
The number of generated triangular elements is around 250 elements, considering the \(n_{c}\) value as 100. The discretized domains of the soil slope during the construction stage and working stage are portrayed in Figure 4. It is observed from discretized domain figures that the meshes are deformed in sequential construction and loading stages. The deformation is more on the slope face during construction and on the backfill during the loading stages.  
Figure 4: Mesh plots of the soil-nailing process during the construction and overburden pressure stages. (**a**) Initial mesh. (**b**) Mesh at stage 1. (**c**) Mesh at stage 3. (**d**) Mesh at stage 4. (**e**) Mesh at stage 5. (**f**) Mesh at stage 6. (**g**) Mesh at stage 8. (**h**) Mesh at stage 9.
## 2 Methodology
### Automated Processes
#### 2.2.1 Dichotomy Method  
The flow chart illustrated in Figure 4 provides the basic procedure of the dichotomy method. This automated solution flow chart mainly involves two parts. One is the iterative steps for stall margin searching, and the other is the loop for determining a stall point through the dichotomy method. With the powerful integration capabilities of the Isght platform, this process is automated by macro commands to invoke the Fine/Turbo solver. The initial parameters comprise the guess pressure p, the pressure increment step \(\Delta\)p, and the solution accuracy equations, which were 90 kPa, 10 kPa, and 100 Pa, respectively, adopted in the study. During stall margin searching as exhibited in Figure 4, boundary conditions are uniformly altered based on computational convergence, determined by the criteria outlined in Equation (5).  
\[\left\{\begin{aligned} \xi<10^{-4}\\ \big{|}\dot{m}_{out}-\dot{m}_{in}\big{|}/\dot{m}_{in}<0.1\%\\ \max(A(i))-\min(A(i))<0.1\%,i\in[N-\psi,N]\end{aligned}\right. \tag{5}\]  
In the formula, \(\xi\) indicates the global residual values of the numerical computation. \(\dot{m}\) is the mass flow rate. The subscript '\(in\)' denotes inlet parameters, while the subscript '\(out\)' denotes outlet parameters. In addition, '\(A\)' represents the convergence variables, which include mass flow rate, efficiency, temperature, etc. '\(i\)' is the iteration step during calculation. '\(N\)' signifies the maximum iteration step for the numerical simulation, and '\(\psi\)' is a constant between 300 and 500.  
After locating the range of the stall margin considering the above process, the initial interval includes the stall point denoted as 'left' and 'right', following which the calculation proceeds as per the steps below.  
(a). Calculate the midpoint of the pressure interval, noted as mid = (left + right)/2.  
(b). Perform a numerical simulation with'mid' as the outlet boundary.  
Figure 4: Automated solving flow chart employing dichotomy method.  
(c). If convergence occurs, the stall point pressure interval lies between [mid, right], update 'left' to mid, and repeat step (b).  
(d). If there is no convergence, the stall point pressure interval is [left, mid], update 'right' to mid, and repeat step (b).  
(e). Iterate steps (a) to (d) until either the accuracy criteria are met or a pre-determined number of iterations are reached.
## 2 Materials and Methods
### Climate DATA and Land Cover Map  
In this study, we utilized monthly the Standardized Precipitation Evapotranspiration Index (SPEI) to characterize the spatial and temporal dynamics of the 2018-2019 extreme drought event across Australia [(49)]. This dataset at 1\({}^{\circ}\) spatial resolution with 1-month, 6-month timescale (indicated as \(\text{SPEI}_{\text{1-month}}\), \(\text{SPEI}_{\text{6-month}}\)), representing the cumulative water status (deficit or surplus) over the preceding 1 or 6 months respectively, was downloaded from SPEI Global Drought Monitor (Available online: [[https://spei.csic.es/](https://spei.csic.es/)]([https://spei.csic.es/](https://spei.csic.es/)) (accessed on 20 July 2020)).  
Monthly air temperature (at 2-m height) and soil moisture content (surface 0-7 cm depth, root zone 28-100 cm depth) based on ERA-5 reanalysis data were downloaded from Copernicus Climate Change Service (Available online: [[https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)]([https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)) (accessed on 28 September 2020)) [(50)]. Gridded annual potential evapotranspiration (PET) and monthly mean decadal precipitation were collected from the Bureau of Meteorology (Available online: [[http://www.bom.gov.au/](http://www.bom.gov.au/)]([http://www.bom.gov.au/](http://www.bom.gov.au/)) (accessed on 5 April 2020)). Firstly, we converted monthly precipitation into annual scale, and then aggregated into 0.1\({}^{\circ}\) spatial grid consistent with _PET_. Secondly, aridity index was computed as annual precipitation normalized by annual _PET_. The Equation (3) is:  
\[AI=\frac{P}{PET} \tag{3}\]  
Finally, pixels within the entire study area were binned by AI (every 0.1 increment) into 6 groups from humid to arid region, as shown in Figure 1b.  
National Dynamic Land Cover Dataset (DLCD) was used in this research, obtained from Geoscience Australia and Bureau of Agricultural and Resource Economics and Sciences (Available online: [[http://www.ga.gov.au/scientific-topics/earth-obs/landcover](http://www.ga.gov.au/scientific-topics/earth-obs/landcover)]([http://www.ga.gov.au/scientific-topics/earth-obs/landcover](http://www.ga.gov.au/scientific-topics/earth-obs/landcover)) (accessed on 10 May 2020)). This dataset validated with abundant field sites was aggregated to 0.5\({}^{\circ}\) spatial resolution by most frequent values.
## 3 Results and Discussion
### The Analysis on the Vibration Isolation Effect of Test Groups
The vibration isolation effect in the Z-direction was of most concern because the vibration amplitude in the Z-direction was much larger than that in the other two directions, which had the greatest influence on the structure. The vertical vibration is considered as a vital parameter for measurement and construction control.