esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
###### Abstract  
Java Island, located in Indonesia, is the country's main island, with a population of 150 million, more than half the population of the country. There are at least four big cities located on the island that have seen fast development in the last 30 years. The land subsidence (LS) issue caused by groundwater overexploitation, compaction, and geological setting, has been known on the island for more than 20 years. However, past studies have mostly focused on one particular important area, while the big picture of LS on the whole island is often overlooked. This study utilized Sentinel-1 Synthetic Aperture Radar (SAR) data from 2017 to 2023, analyzed using Small Baseline Subset (SBAS) interferometry, to map LS across Java Island. We used DEMNAS to eliminate the topographic signal. We found ten regions with a noticeable LS rate, affecting nearly 60 million people who reside in the LS zones, namely, Serang, Greater Jakarta, Cianjur, Bandung, Cirebon, Brebes and Tegal, Pekalongan, Greater Semarang, Surabaya, and Sidoardjo. The highest rates and the large coverage of LS were observed in Greater Jakarta (up to 150 mm/year), Bandung (200 mm/year), Semarang (160 mm/year), and Pekalongan (up to 110 mm/year). LS was also detected in smaller areas or districts, such as Serang, Cianjur, Cirebon, Brebes, Tegal, Surabaya, and Sidoarjo, with rates ranging from 60 to 140 mm/year. The two areas of Cianjur and Brebes, which have never been mentioned in previous studies, show LS rates of about 80 mm/year and 70 mm/year, respectively. The LS rate in all areas was shown to be linear over time, except in Pekalongan, which shows rate deflation after 2021. We also found that most affected regions are urban and industrial zones, indicating a strong correlation with anthropogenic activities. LS leads to widespread socioeconomic and environmental impacts, including damage to infrastructure, increased flooding, and reduced groundwater capacity.  
land subsidence; InSAR; recursive least squares +
Footnote †: journal: Applied sciences
## 2 Ultrafast Vortices and Previous Results
The caustic surface of maximum fluence was shown to be determined by relation \(\rho_{F}^{2}=|l|/2\omega(\rho_{F})\), where  
\[\bar{\omega}(\rho)=\frac{\int_{0}^{\infty}|\hat{E}_{\omega}(\rho)|^{2}\omega d \omega}{\int_{0}^{\infty}|\hat{E}_{\omega}(\rho)|^{2}d\omega} \tag{7}\]defines, as in [37], the mean frequency of the pulse on the caustic \(\rho\). On this caustic surface the pulse temporal bandwidth and the topological charge were shown to be restricted by inequality \(\Delta\omega(\rho_{F})/\bar{\omega}(\rho_{F})<2/\sqrt{|l|}\), where  
\[\Delta\omega^{2}(\rho)=4\frac{\int_{0}^{\infty}|\hat{E}_{\omega}(\rho)|^{2}( \omega-\bar{\omega}(\rho))^{2}d\omega}{\int_{0}^{\infty}|\hat{E}_{\omega}(\rho) |^{2}d\omega} \tag{8}\]  
is the so-called Gaussian-equivalent half-bandwidth on a caustic \(\rho\) (yielding the \(1/e^{2}\)-decay half-bandwidth for a Gaussian-like spectral density \(|\hat{E}_{\omega}(\rho)|^{2}\)). Defining correspondingly the Gaussian-equivalent half-duration  
\[\Delta t^{2}(\rho)=4\frac{\int_{-\infty}^{\infty}|E(\rho,\tau)|^{2}[\tau-\tau (\rho)]^{2}d\tau}{\int_{-\infty}^{\infty}|E(\rho,\tau)|^{2}d\tau}\,, \tag{9}\]  
where  
\[\tau(\rho)=\frac{\int_{-\infty}^{\infty}|E_{(}\rho,\tau)|^{2}\tau d\tau}{ \int_{-\infty}^{\infty}|E_{\omega}(\rho,\tau)|^{2}d\tau}\,, \tag{10}\]  
and on account that \(\Delta t(\rho_{F})\Delta\omega(\rho_{F})\geq 2\) for any pulse shape, it follows that the pulse duration on the bright ring of any ultrafast vortex always satisfies \(\Delta t(\rho_{F})\geq\sqrt{|l|}/\bar{\omega}(\rho_{F})\), which settles a lower bound to the duration of an ultrafast vortex with \(l\) units of OAM. It has been shown later [5,36] that the mean frequency on \(\rho_{F}\) is approximately equal to the mean frequency of \(\hat{a}_{\omega}\), which is directly related to the ultrashort laser source spectrum, i.e., \(\bar{\omega}(\rho_{F})\approx\bar{\omega}\), where  
\[\hat{E}_{\omega}(r,\phi,z)=\hat{a}_{\omega}D(z,\phi)\left[\frac{\sqrt{2}r}{s_{ \omega}(z)}\right]^{|l|}e^{\frac{i\omega r^{2}}{2\omega(z)}}\,, \tag{2}\]0  
With \(\bar{\omega}(\rho_{F})\approx\bar{\omega}\) independent of the topological charge \(l\), the approximate lower bound \(\Delta t(\rho_{F})>\sqrt{|l|}/\bar{\omega}\) with the right hand side growing monotonously with \(|l|\) implies that the duration in the bright ring of the synthesized ultrafast vortex must necessarily increase with \(|l|\)[5], if the laser source spectrum \(\hat{a}_{\omega}\) is fixed. These effects of the coupling between the OAM and the temporal degrees of freedom at the most energetic ring have been recently reviewed in [36], where they are also demonstrated to be substantially the same for other types of ultrafast vortices, as OAM-carrying nondiffracting X-waves [4,6,36].
## 4 The Scheduler Architecture  
The packet scheduling architecture in the 5G NR system is depicted in Figure 4. The system is characterized by selecting a beam, a queue management system, deadline possibility check, and RB allocation before the packet is finally transmitted. During the initial stage, the system picks the ideal beam that will serve the UE. A queue management system is organized in which priority scheduling is decided based on the priority of users (priority level 1 (PL1) and priority level 2 (PL2)). For instance, in smart grid communication, when PL2 users occur, regardless of their channel quality, they will occupy the system resources first before users are placed at PL1. Priority Level 1 users will be scheduled when there are no PL2 users in buffer. Data are transmitted by gNodeB through the downlink time-varying channel. Data packets from the upper layer of gNodeB are taken through the queue management system before the packets are delivered to the several UEs based on priority levels with the aid of the resource block allocation controller. Just before scheduling, the system also goes through a deadline possibility check to determine whether to transmit the packet or not. To ascertain which UE is to be scheduled in the scheduler, the scheduler can employ a scheduling technique coupled with additional information, including channel quality information or any other important information. After the packet is transmitted, the reward is, however, collected if the deadline is met. The UE measures the SNIR of the DL channel between the gNodeB and the UE as the basis of the state of the quality of the channel at the next allocation of resources.
# A Non-WSSUS Channel Simulator for V2X Communication Systems
## 6 Simulation Examples
### Results of Changes of the Frame Length
Furthermore, for these short frame lengths, the performance of the LS estimator and STA technique with \(\alpha=20\) reached 1% for Eb/No regimes greater than 17.06 dB and 39.99 dB, respectively. Finally, Eb/No levels greater than 5.41 dB and 10.7 dB reached BER performances lower than 10% employing the LS estimator for frame lengths of 64 and 128, respectively. For the STA technique, considering frames with more than 64 OFDM symbols, the BER performance always remained greater than 1% when using \(\alpha=20\). As observed, the STA estimator performed well assuming short data frames and low mobility conditions. However, this technique demanded larger Eb/No values than the LS estimator. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Scenario I**} & \multicolumn{4}{c}{**Scenario II**} & \multicolumn{4}{c}{**Scenario III**} \\ \hline Frame length & \multirow{2}{*}{16} & \multirow{2}{*}{32} & \multirow{2}{*}{64} & \multirow{2}{*}{128} & \multirow{2}{*}{16} & \multirow{2}{*}{32} & \multirow{2}{*}{64} & \multirow{2}{*}{128} & \multirow{2}{*}{16} & \multirow{2}{*}{32} & \multirow{2}{*}{64} & \multirow{2}{*}{128} \\ \(F\) & & & & & & & & & & & & & & & \\ \hline Estimator & BER & \multicolumn{8}{c}{Required Eb/No (dB)} \\ \hline LS & \(10^{-1}\) & 4.00 & 4.61 & 5.08 & 7.92 & 3.98 & 4.52 & 5.37 & 10.70 & 3.87 & 4.55 & 5.41 & 10.31 \\ \hline STA (\(\alpha=2\)) & \(10^{-1}\) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \\ \hline STA (\(\alpha=20\)) & \(10^{-1}\) & 4.48 & 7.05 & NA & NA & 4.50 & 7.15 & NA & NA & 4.43 & 6.98 & NA & NA \\ \hline LS & \(10^{-2}\) & 15.37 & 17.06 & NA & NA & 15.34 & 16.63 & NA & NA & 15.18 & 16.79 & NA & NA \\ \hline STA (\(\alpha=2\)) & \(10^{-2}\) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \\ \hline STA (\(\alpha=20\)) & \(10^{-2}\) & 16.10 & 34.38 & NA & NA & 15.66 & 39.99 & NA & NA & 15.79 & 32.36 & NA & NA \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Required Eb/No levels to attain 10% and 1% BER over Scenarios I, II, and III for different frame lengths by considering initial speeds of \(\
u_{T}=\
u_{R}=40\) km/h and acceleration components of \(a_{T}=a_{R}=20\) m/s\({}^{2}\) (NA—not achieved). Figure 9: System performance by considering different frame lengths with initial speeds of \(\
u_{T}=\
u_{R}=40\) km/h and acceleration components of \(a_{T}=a_{R}=20\) m/s\({}^{2}\) for Scenario I.
# Multibody Simulation for the Vibration Analysis of a Turbocharged Diesel Engine
## 2 Problem Description and Modeling Approach
### Models of Increasing Complexity  
For the analysis of the vibration behavior of the internal combustion engine under examination, three models of increasing complexity were realized:  
Figure 3: Rayleigh damping vs. frequency (Hz) for the crankshaft.  
Figure 2: Graphic representation of bodies and connections (**a**,**b**) and highlight of crankshaft points used as master nodes for the reduction process (**c**).  
* The first approach, termed SOL1, realizes the simulation of the engine behavior using a rigid body model (Figure 4) and provides for the trend of the motion irregularities at low frequencies, with a first evaluation of the vibrations of the power train support brackets. The powertrain suspension brackets in the engine compartment are three: engine bracket, differential bracket, gearbox bracket (Figure 5a-d). These components are usually simulated in AVL/Excite as joints and treated as massless material points. The joints simulating the brackets were of Table Force/Moment type. This constraint allowed introducing the nonlinear behavior of the dowels stiffness's; stiffness and damping values were assigned (retrieved from the car company database) in the three directions x, y, z for each bracket and for different relative displacement values. The stiffness and damping values for the power train mounts were provided by the supplier and consist in a nonlinear relation between stiffness/damping and mount deformation.
* The second model, termed SOL2, is based on FE modeling of the crankshaft, with pulley and flywheel, in order to consider the flexibility of the crankshaft that, once appropriately condensed, is introduced into the MB calculation code. The aim is to obtain the values of the motion irregularities in addition to the support brackets vibrations. Comparing Figures 6 and 2b, it is possible to see an added condensation node (number 2) as requested by the splitting of pulley in two independent parts (capable of relative motion), the first (node number 1) referring to the shaft end and the second (node number 2) referring to the seismic mass. Now it is possible to point out the impact of torsional modes (in case they are excited) on the motion irregularities and support bracket vibrations. This will be analyzed in the next paragraph.
* The third model, termed SOL3, shows the FE modeling for both the previously considered crankshaft and the clutch unit.  
For all the models, the remaining powertrain components were considered as rigid, with assigned characteristics such as weight, mass center position and inertia. The gearbox was not modeled, neither was present in the experimental bench test (an engine brake was adopted).  
Therefore, the purpose of model SOL1 is purely methodological, while models SOL2 and SOL3 are those on which the analyses of interest and outcomes comparison were carried out.
## 4 Simulations and Results Analysis
### Double-Lane Change Maneuver  
The handing stability of the 4 WS vehicle is closely related to the intention and operation of the driver, so the driver-vehicle closed-loop system is considered in this paper. The double-lane change maneuver is commonly applied to study the performance of the driver-vehicle closed-loop system, and here this maneuver was simulated in a Matlab/Simulink environment. The initial speed of the double-lane change maneuver was 72 km/h, and the road friction coefficient was set as 0.85. The simulation results are shown in Figure 26.  
In Figure 26a, we can see that the sideslip angle of the 4 WS vehicle with the proportional controller was the highest. As can be seen in Figure 26b, the 4 WS vehicle with the H2/H\(\infty\) controller was able to track ideal yaw rate well, while those of the H\(\infty\) controller and the proportional controller had yaw rate tracking errors. In Figure 26c, although the 4 WS vehicles with the proposed three kinds of controllers can track paths, the 4 WS vehicles equipped with the H\(\infty\) controller and the proportional controller showed oscillations in the back straight path. Hence, considering the driver model, the 4 WS vehicle with the H2/H\(\infty\) controller had the best path tracking capability.  
Based on the analysis of the simulation results of the four steering maneuvers, it was concluded that (1) the H2/H\(\infty\) controller can improve the 4 WS vehicle handing stability, which includes the yaw rate tracking ability and the path tracking performance, and that (2) the 4 WS system with the H2/H\(\infty\)  
Figure 25: The fishhook maneuver simulation results under disturbances: (**a**) sideslip angle response; (**b**) yaw rate response.  
controller outperformed those of the H\(\infty\) controller and the proportional controller in terms of vehicle handing stability and robustness.  
Figure 26: The double-lane change maneuver simulation results: (**a**) sideslip angle response; (**b**) yaw rate response; (**c**) lateral displacement response.
## 5 Validation II: Simulation of Open-Hole Tension Tests with GA-Based Uncertainty
### Information from Last GA Generation of CT Simulations  
Figure 5 presented the best FE simulation within the 17 generations of the GA algorithm to replicate the load vs. displacement curves from experiments. The optimal input settings were validated in Section 4.  
In addition to this optimal set of FE input parameters, all of the 24 sets of input parameters obtained from the last generation of the GA algorithm are considered. Figure 11 shows the load vs. displacement graphs (in blue colour) from these 24 FE simulations of the last GA generation. It can be seen that the FE simulations cover the range of experimental results, motivating the consideration of all sets of input parameters to account for the inherent uncertainty of the wood veneer laminates subjected to progressive damage. Table 7 demonstrates that the previously determined optimal input data lie well within the resulting ranges of the four FE input parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \(\mathbf{\varepsilon_{1}^{i}}\) (-) & \(\mathbf{\varepsilon_{2}^{i}}\) (-) & \(\mathbf{\varepsilon_{1}^{s}}\) (-) & \(\mathbf{\varepsilon_{2}^{s}}\) (-) \\ \hline Optimal results & 0.0059 & 0.0065 & 0.084 & 0.081 \\ Last GA generation & 0.0049–0.0096 & 0.0032–0.0082 & 0.077–0.102 & 0.075–0.127 \\ \hline \end{tabular}
\end{table}
Table 7: Range of damage input parameters found by GA within last generation of calibration compared to optimal FE input parameters.  
Figure 10: Qualitative comparison of damage between simulations and experiments in open-hole tension tests with varying hole diameter.
## 2 Materials and Methods
### Inactivation of Conidial Germination  
To study the effect of GA, TC and DBD plasmas on the inactivation of conidial germination, the conidia of _C. lactucae-sativa_ were collected from inoculated lettuce leaves and adjusted to a concentration of 10\({}^{6}\) conidia/mL using a hemacytometer under a compound microscope. Some 100 \(\mu\)L of conidial suspension was dropped onto a slide culture before exposure to the GA, TC and DBD plasmas for 5, 10, 15 and 20 min. The slide culture was incubated at room temperature (25 \(\pm\) 3 \({}^{\circ}\)C). Conidial germination was observed at 4 and 24 h after incubation. The non-treated conidia were used as a positive control. Each treatment contained three replicates (20 conidia/replicate), and the experiment was repeated three times. The percent inhibition of conidial germination was calculated according to the formula of [26] (Equation (10)), as follows:  
\[\mathrm{Inhibition\,\,of\,\,conidial\,where \(gc\) is the number of germinated conidia in the non-treated control and \(gt\) is the number of germinated conidia in each plasma-treated case.
# Modified Local Linear Embedding Algorithm for Rolling Element Bearing Fault Diagnosis
## 2 LLE Algorithm  
LLE algorithm is a local linear algorithm, which is mainly used linear weight coefficient to represent the contribution of each data point in data reconstruction [16]. With condition of remaining the geometric relations in neighborhood being invariant, the data in high-dimensional observation space is mapped to low-dimensional space by linear mapping such as scaling, rotation and translation [17]. In this way, it can preserve the inherent geometry relationship among data points. Suppose that \(Y\subset R^{d}\) is a low-dimensional manifold embedded in the high-dimensional space \(R^{D}\), where \(d\ll D\). There are \(n\) data points in the high dimension space: \(x=\left\{x_{i}|i=1,\ldots,n\,x_{i}\in R^{D}\right\}\). The details of the LLE algorithm are as following:  
1. For each D-dimensional data point \(x_{i}\), k-nearest neighbor criterion is used to filter the nearest \(k\) points \(N_{i}=\left\{x_{i}^{1},x_{i}^{2},x_{i}^{3},\ldots,x_{i}^{k}\right\}\). Euclidean distance is adopted to construct the neighborhood graph \(G\);
2. Each data point can be represented by a weighted linear representation of its surrounding neighborhood points, and minimizing the following objective functions to get the smallest reconstruction error: \[\begin{split} min\varphi(w)=min\left(\sum\limits_{i=1}^{n} \left|\left|x_{i}-\sum\limits_{j=1}^{k}w_{ij}x_{j}\right|\right|^{2}\right)\\ s.t.\Bigg{\{}\begin{array}{l}w_{ij}=0,\ x_{ij}\
otin V\\ \sum_{j}w_{ij}=1,\ x_{ij}\in V\end{array}\end{split}\] (1) where the weight \(w_{ij}\) represents the reconstructed contribution of the \(j\)-th sample point to the \(i\)-th sample point. \(V\) represents a neighborhood which is the number of neighborhood points is \(k\). If the sample \(j\) is the neighborhood of the sample point \(i\), \(w_{ij}\) is set to 1, otherwise it is set to 0;
3. Use \(y_{i}\) to build the d-dimensional embedded space, it is generated by this cost function: \[\begin{split}\phi(Y)=min\Sigma_{i}\left|y_{i}-\sum_{j}w_{ij}y_{j} \right|^{2}\\ s.t.\ YY^{\Upsilon}=I\end{split}\] (2)  
Based on step 2, the cost function is minimized by the optimal weight \(w_{ij}\), and it can obtain the optimized low-dimensional coordinate \(y_{i}\). Where, \(Y=\left\{y_{i}|i=1,\ldots,n\,y_{i}\in R^{d}\right\}\), and \(I\) is the unit matrix of \(d\times d\). To minimize the value of cost function, it takes \(Y\) as the eigenvectors which are corresponded to the minimum \(d\) nonzero eigenvalues of \(M=\left(I-W\right)\left(I-W\right)^{T}\).
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 3 How Technologies Can Fix the Current Healthcare System
### Various Health Advances with IoT  
The Internet of Things (IoT) is an abstraction of infinite, smart, physical and virtual objects that have unique identities connected to create an ultimate cyber-physical pervasive framework. These devices capture, monitor and transmit data to a public or private cloud to facilitate a new level of convenient and efficient automation. IoT-based solutions are very effective in terms of energy consumption, CPU and memory usage [18].  
We are witnessing the rise of the cellular Internet of Things (IoT), and it is expected that there will be 20 billion devices or things connected to the Internet by 2020 [19]. The IoT trend is a next-generation technology that can change the whole business spectrum with a variety of applications such as smart cities, industrial control, retails, waste management, emergency services, security and logistics. Most importantly, IoT is considered as an enchanting technology that can revolutionize the current healthcare system with a variety of cutting-edge and highly individualized solutions. These solutions include remote health monitoring, remote diagnostics, tele-auscultation, chronic diseases management, independent care for elderly and much more [20]. Patients compliance with medication and treatment by healthcare providers is another prominent potential application of IoT. In addition, IoT can also be used to authenticate medicine, monitor drug supplies and provide efficient scheduling of available resources to ensure their best use for more patients.  
In medical IoT, various medical sensors, devices, smartphones, imaging devices, personal digital assistant (PDAs) and EHRs act as core parts of the system. These devices monitor important health information, like physiological vital signs, changes in mood and behaviours and blood glucose, which can be effectively utilized by healthcare providers to improve the quality of care and health outcomes. Furthermore, IoT-based solutions have potential to reduce the required time for remote health provision and increase the quality of care by reducing costs with enriched user experience.
# Nucleation Mechanism and Rupture Dynamics of Laboratory Earthquakes at Different Loading Rates
## 2 Materials and Methods
### Diagnostic Method
#### 2.3.1 Full Field Displacement  
In this study, a Photron FASTCAM SA1.1 high-speed camera, with a capability to record at 50,000 frames per second (fps), was utilized to monitor the textured fault field of view (Figure 1). It possesses a buffer for 400 frames both before and after the trigger. The resolution at this speed stands at 1024 \(\times\) 112 pixels, which translates to a physical domain of 330 \(\times\) 36 mm. To quantitatively deduce the dynamic displacement during an earthquake simulated in the lab, digital image correlation (DIC) was used. Random speckle of monodispersed black dots was imprinted on a PMMA sheet's surface, positioned orthogonally to the fault's plane. DIC evaluations were calculated using the VIC-2D software version 6 by Correlation Solutions Inc., Irmo, SC, USA. by leveraging the \"Fill Boundary\" algorithm. This specific algorithm is adept at deciphering shear offsets at displacement discontinuities. For our calculations, a subset size of 21 \(\times\) 21 pixels, approximately 10.25 mm, was selected with an incremental step of three pixels (approximately 1.5 mm). No averaging was conducted across multiple subsets, maintaining a consistent DIC parameter set that provided a holistic view of the displacement field for all experiments. Detailed error analysis can be found in a separate study [37].  
Figure 2: Typical time history curves for displacement and load during experimental runs. The black line denotes the vertical load, while the red line denotes the horizontal load. Displacements from the upper jack and bottom jack are represented by the blue and orange lines, respectively.