esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data
### Study Datasets
#### 2.2.3 Alos Worldd 3D 30 m (AW3D30) Dem  
The Advanced Land Observing Satellite (ALOS) was developed by JAXA (Japan Aerospace Exploration Agency, Tokyo, Japan), and launched on 24 January 2006. ALOS was developed to contribute to the fields of mapping, precise regional land coverage observation, disaster monitoring, and resource surveying. The Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) sensors was an optical instrument onboard the ALOS and it was in operation between 2006 and 2011, which consisted of three along-track panchromatic radiometers at nadir (NDR), forwards (FWD)  
Figure 1: (a) Digital elevation map and the filtered footprints of Ice, Cloud, and land Elevation Satellite-1 (ICESat-1) reference data points (black points). (b) The map of land cover types and the footprints of the selected Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) altimetry data (black points).  
and backwards (BWD), and has a 2.5 m spatial resolution. From the 2.5-m ALOS along-track stereo observation images, a global DEM/DSM dataset named Advanced World 3D (AW3D) was generated with a 5 m grid spacing [35; 36]. The 5 m resolution AW3D dataset is distributed commercially, but the 30 m resolution DEM dataset AW3D30 is freely available. Since its release, the quality of AW3D has been progressively improved.  
In March 2017, Japan Aerospace Exploration Agency (JAXA) released the AW3D30 DEM Version 1.1. It has filled the void pixels in version 1.0, which are caused by the cloud and snow in the area between 60\({}^{\circ}\) N and 60\({}^{\circ}\) S. Version 2.1, released in April 2018, has removed the absolute offset errors and relative striping errors along satellite orbits in the area between 60\({}^{\circ}\) N and 60\({}^{\circ}\) S and part of the arctic in the first version. The latest version, version 2.2, was made available in April 2019, which has improvements over the old versions, especially for the northern region over 60\({}^{\circ}\) N. This version adds the missing tiles over the land of no-data or low-quality areas, and updates the coastline. We downloaded the AW3D30 version 2.2 from the Earth Observation Research Center JAXA ([[https://www.eorc.jaxa.jp/ALOS/en/aw3d30/data/index.htm](https://www.eorc.jaxa.jp/ALOS/en/aw3d30/data/index.htm)]([https://www.eorc.jaxa.jp/ALOS/en/aw3d30/data/index.htm](https://www.eorc.jaxa.jp/ALOS/en/aw3d30/data/index.htm))).
## Materials and Methods
### Data Processing  
The results of the chemical reference parameters (TSS, TA and pH) and the optical data obtained by the device were analyzed in Matlab(r) environment, version 2021a (MathWorks, Inc., Natick, MA, USA) using the PLSToolbox package (Eigenvector Research, Inc., Manson, WA, USA).  
The first part of data processing involved the creation of matrices showing the optical data and the execution of a first principal components analysis (PCA). This first unsupervised exploratory analysis allowed the identification, and then the removal, of some outliers using the 'Hotelling T2 computation' function. The data matrix used was averaged and then reduced to obtain a new matrix (similarly representative of the sampling campaign) made up of 24 samples for each sampling date (except only for the time 0 of 2020, which consists of 8 samples as mentioned above), switching from a matrix with 880 rows to a matrix with 176 rows. After performing the exploratory PCA analysis and removing outliers, reference parameters (TSS, pH and TA) were used for the calculation of three different predictive models. The partial least square (PLS) regression technique was used to create the models, with only autoscaling as pretreatment.. Cross-validation was used with the leave more out technique, venetian blinds with 5 cancellation groups. Concerning test-set validation, the dataset was randomly divided into a training set, corresponding to the calibration set, and into a test set. A total of 60% of the samples were used for the training set and 40% for the test set. To evaluate model accuracy, root mean square error  
Figure 1: (**a**) Prototype clamp wrapping the sample; (**b**) prototype layout.  
(RMSE) and coefficient of determination (R\({}^{2}\)) were used; the better the model performs, the lower the error is, and the higher the R\({}^{2}\) (as maximum equal to 1) is.  
In addition, the ratio between the standard deviation of the response variable and RMSE (RPD) was calculated. An RPD between 1.5 and 2 means that the model can discriminate low from high values of the response variable; a value between 2 and 2.5 indicates that coarse quantitative predictions are possible, and a value between 2.5 and 3 or above corresponds to good and excellent prediction accuracy, respectively [23].  
Finally, a significance test (Student's _t_-test) was performed between the reference and predicted data along the ripening evolution monitoring for each year considered.
## 2 Architecture of the Multi-Communication-Based DR System
### Design for Customer Side Architecture and Functions  
The architecture of the customer side is shown in Figure 4. As mentioned in the overview description about the system before, the regional agents are divided into two categories: LA and HEMS, where the former is like a slimmed DRAS, and the latter manages the physical loads directly.  
In the future scenario of the proposed DR system, LAs and HEMSs could be also developed by third-party organizations to fully spur the influences of market arrangements. The messages sent to LAs from upper blocks can be holistic quantities, electricity prices, or incentives. The specific DR task distribution mechanisms are decided by the corresponding LA. For LAs, there could be many layers, where the lower layer LAs manage HEMs, and distribution work will enter the last procedure (the strategies of load action to meet requirements), which was studied by many researchers [13].  
As for the selection of communication method, public internet connections are suggested to be applied among LAs to ensure a high data transmission rate. Because lower LAs that are not linked to DRAS are likely be settled by a third party, which means they are newcomers to original power system blocks, spreading a more dedicated wired communication line may not be justifiable and economical. Hence, it is a better choice to use public internet connections built by telecommunication suppliers.  
For the implementation of HEMSs, embedded devices combined with IoT gateways form the main body of the HIG. Connections can be more flexible since better solutions for mass terminal access and large-scale wireless transmission are provided by IoT technologies. The difficulties related to terminal identification, communication congestion, system investment, and energy cost in internet applications could be highly overcome by IoT communication methods. Lora was chosen in the implementation, the performance of which was verified in many cases [14]. In addition, the HIG needs to have internet connection ability, where a Wi-Fi chip is integrated to communicate with the HEMS monitor platform, which is achieved via internet cloud services.
# Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET
## 4 Proposed M-LBS Scheme
### Cache Freshness and Reset  
A significant consideration in cache management is the period of validity or freshness of the cached data. This duration represents when the data are still considered valid before it should be removed from the cache. Different factors can influence the freshness such as dynamism in location-based services, user mobility, and data updates/maintenance from LBS providers, among others, also depending on what type of knowledge is being represented by a cache entry. For instance, details about a menu for a restaurant may only last a few hours, while availability/pricing information for rooms in hotels could be relevant up to several days with reference to particular times during the year; without any doubt, longer obsolescence periods equate higher hit rates. In order to deal with stale caches, we need some mechanism to reset them.  
Commonly known as TTL or time-to-live (cache expiration time), past research works have shown (such as [2; 11; 13; 14; 29]) that cached items become stale or lose their freshness after staying in storage for a specific period of time after which they should be deemed no good and deleted either manually or automatically. If this does not happen, then subsequent requests will keep getting served outdated contents; hence, it such should never be allowed to happen.  
The cache reset process can be implemented using various approaches, such as the following:  
* _Time-based Reset_: Cached data are assigned a TTL value, and the cache is flushed or invalidated after the TTL expires. The TTL can be set based on the expected freshness requirements of the LBS data or the user mobility patterns [2].  
* _Event-based Reset:_ The cache is reset or invalidated in response to specific events, such as data updates from the LBS provider [13] or significant changes in user locations or context [14].
* _Hybrid Approach:_ A combination of time- and event-based reset mechanisms can be employed, where the cache is reset based on which condition is first met (TTL expiration or relevant event occurrence) [11; 29].  
In this work, we use a hybrid approach that combines the cache expiration time and event-based scheme (when a user moves into a new cell).
## 5 Proposed Path Planning Algorithm
### Introduction of Path Propagation Model  
The propagation model of MGPF-Hybrid A* is similar to that of Hybrid A*. It has two node types; one is a grid map node (GMN) and another is a propagated node (PN). The two types of nodes are located in Cartesian coordinates consisting of \((x,y)\). The GMN is allocated 'Opened' and 'Closed' states. When the proposed path planner is operated, the size of the GMN is assigned. The other node, named PN, is the candidate of the generated path. When we operate the proposed path planner, the number of nodes that are PNs increase step-by-step from the start node. If the PN reaches the end point of the given reference path, the traced organization of the final PN to the start node is the local path point of MGPF-Hybrid A*.  
Since this is a step-by-step propagation, it is related to operating time. Assuming that it uses a tree model, in which the start node is the root node, and the target node is one of the branch nodes far from the root node, if the PNs behave like the breadth-first-search method, it takes a great deal of time to converge on the global optima. On the other hand, if the behavior is similar to that of the depth-first search method, and it selects the correct sub-tree direction, it will take a small amount of time. Therefore, to induce the second method, APF is applied. This APF generates a potential valley toward the reference path which the PN should follow. This valley indicates the correct sub-tree direction of the depth-first search method. The height caused by the valley indicates the cost. The closer to the valley, the smaller the cost, and the farther away from the valley, the larger the cost. If the valley pulls the PN, real-time performance will be guaranteed. The potential field depicting a valley is shown in Figure 4a.
## 6 Results
### Ionospheric Perturbations of November-December 2006
Etna area during September–December 2006, with OLR average values for 2006 (black), OLR anomalies for 2006 (red column), OLR anomalies for 2007 (yellow columns), atmospheric chemical potential (ACP) for 2006 (pink), ACP for 2007 (cyan), Etna 2006 eruptions (grey boxes), and DEMETER orbits (dash green). (b) Daily maps for 6 November 2006 representing the OLR anomalies’ spatial extent over Etna regions (with black star—Etna volcano, red solid lines—plate boundaries, brown lines—fault systems). (c) Daily maps for 6 December 2006 (see previous point). For cross-comparison with the ion density increase observed by DEMETER at the latitude of Mt. Etna, a local network of 17 ground-based GPS receivers in the volcano region was used to derive the ionospheric TEC during 9:30-10:00 p.m. LT on 4 November 2006 (see Figure 8a). Figure 8b shows that the TEC prominently increased between 37\({}^{\circ}\) and 42\({}^{\circ}\) N, in good agreement with the ion density increase probed by DEMETER. Similarly, the ion density recorded along the DEMETER satellite orbit over Mt. Etna area prominently increased on 6 December 2006 at around 8:45 p.m. UT, in concomitance with the increase of the ground-based TEC derived by the local GPS receiver network (Figure 9a) at the volcano longitude (see Figure 9b). Thus, DEMETER ion observations and ground-based GPS TEC simultaneously increased over the Mt. Etna area during the nighttime period on both eruption days. Figure 7: **(a)** Temporal dynamics of the chemical potential correction \(\Delta U\) over the Etna volcano (37.755N, 14.995E) for the period 10 October–30 December 2006; red arrow indicates the 4 November 2006 eruption; **(b)** 2D distributions of the chemical potential correction for the period 30 October–01 November 2006.
# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 5 Tomography Experiments and Results
### Influences of Ground-Based Station Distribution  
To study the influence of station distribution on the tomographic results, three experiments were carried out and compared, i.e., CS04, CS05, and CS06. All the GNSS station locations in these three experiments are simulated at evenly distributed grid points with different horizontal resolution, i.e., 1\({}^{\circ}\), 0.5\({}^{\circ}\) and 0.2\({}^{\circ}\) in CS04, CS05 and CS06, respectively, resulting in 45, 153, and 861 stations in the tomographic region as summarized in Table 1. The heights of the stations are interpolated from the ERA5 surface geopotential product. Comparisons of tomographic results for these three experiments are presented in Figure 8.  
Average _Nw_ error is about 6.57 and 6.27 mm/km in CS05 and CS06 at the lowest layer, with improvements of about 11.2 and 15% compared to CS04, respectively. At the middle layers, for example, the 8 th layer with height of 2 km, _Nw_ error is almost the same in CS05 and CS06 (\(\sim\)3.36 mm/km), with improvement of about 11.3% compared to CS04. As can be seen in Figure 9, as the ground GNSS stations becomes denser, the puncture points of GNSS signals is generally more discretely distributed in space, which will result in better estimates of refractivity at the voxel nodes. There are almost no differences at upper layers due to the low water vapor content there. Compared to the influences of multi-GNSS observations as discussed in the previous section, we can find that increases in ground stations have larger impacts on the tomographic results than increases in GNSS observations.
# Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD
## 6 Conclusions  
The effective fault diagnosis of planetary gearboxes is indeed difficult and has always attracted the attention of researchers. The weak fault features are difficult to extract from a planetary gearbox because the signal components are nonlinear, nonstationary, and easily drowned out by noise. In this  
Figure 24: Classification result under condition 3.  
Figure 25: Classification result under condition 4.  
Figure 26: Classification result under variable conditions.  
paper, we propose a fault diagnosis method based on ICEEMD-time-frequency information entropy. The effectiveness of the method is verified by a case study where the proposed method outperforms the wavelet entropy method and the VMD-time-frequency information entropy method in extracting critical fault features. In regard to fault classification, the VPMCD algorithm is adopted to comprehensively consider the correlation about eigenvalues, which can effectively identify the characteristic information of various types of small samples. Moreover, experimental results show that the proposed method can not only accurately diagnose faults under multiple operating conditions, but also produces satisfactory diagnostic performance under variable operating conditions. Therefore, the ICEEMD-time-frequency information entropy and VPMCD method, with favorable robustness and diagnostic performance, has wide applicability in other similar fault diagnoses of rotating machinery. However, to some extent, the performance of the proposed method is limited by computer resources. Future work will focus on optimization of the algorithm to improve its performance.  
Conceptualization, Y.W.; Data curation, Y.W.; Methodology, Y.W., H.L., and Z.F.; Writing original draft, Y.W.; Writing review and editing, X.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Key R & D projects of Shanxi province (No. 2019 ZDLGY17-06), the Key Projects for Quality Reliability Design, and Analysis Technology Breakthrough of MIIT under Tender (No. TC190A4 DA/6).  
The authors declare no conflict of interest.
## 2 Materials and Methods
Then:  
\[u_{0}=0,\ v_{0}=0,\ f_{0}=0 \tag{13}\]  
Taking into account Equation (12), Equation (13) in the first approximation takes the form:  
\[\frac{\partial f_{1}}{\partial t}+bf_{1}-\
u\Delta f_{1}=0 \tag{14}\]  
Further, for convenience of notation, we omit the index 1 in Equation (14), as well as in the functions \(u_{1}\), \(v_{1}\), and \(P_{1}\) in the first approximation [32; 33]. The continuity equation and the boundary conditions of the hydrodynamic problem in the first approximation are determined by Equations (7)-(11). Differential Equations (1) and (14) with boundary conditions Equations (7)-(11) are consistent equations of motion of the rotor, foundation and weakly conductive viscous fluid [34]. To calculate the hydrodynamic force, the movement of the rotor and the foundation, it is convenient to represent them on the complex plane in the following form:  
\[z=Ae^{i\Omega_{0}t}+Be^{i\omega t} \tag{15}\]  
\[z_{2}=Ce^{i\Omega_{0}t}+De^{i\omega t} \tag{16}\]  
Taking Equations (14) and (15) into account, the equations of motion of a viscous fluid Equation (5) take the form:  
\[ \begin{array}{l}\frac{\partial u}{\partial t}-2\Omega_{0}v+b_{m}u-\frac{v} {r}\frac{\partial f}{\partial\phi}=-\frac{1}{\rho}\frac{\partial p^{\prime}} {\partial r}+\omega^{2}Be^{i(\sigma t-\phi)}\\ \frac{\partial v}{\partial t}+2\Omega_{0}u+b_{m}v+\frac{\partial f}{\partial r }=-\frac{1}{\rho r}\frac{\partial p^{\prime}}{\partial\phi}-i\omega^{2}Be^{ i(\sigma t-\phi)}\end{array}  \tag{17}\]  
\[P^{\prime}=P-A\Omega_{0}^{2}r\rho e^{-i\phi} \tag{18}\]  
where \(\sigma=\omega-\Omega_{0}\) is the frequency of oscillations of the free surface of the liquid (the velocity of wave propagation on the free surface of the liquid in the forward direction) and \(\omega\) is the frequency of free oscillations (self-oscillations) of the system [35]. It is advisable to use special functions to solve Equation (17) taking into account Equation (7) and boundary conditions Equations (8)-(11). Taking Equations (15)-(17) into account, we represent the velocity components of the liquid particle \(u\) and \(v\), as well as the pressure \(P^{\prime}\) and the function \(f\) in the form:  
\[G(r,\phi,t)=g(r)e^{i(\sigma t-\phi)} \tag{19}\]  
where we consider forced oscillations of the fluid, as free fluctuations of the liquid quickly decay due to external friction and the viscosity of the liquid [36]. Taking Equation (19) into account, we can represent the function \(f\) as:  
\[f(r,\phi,t)=R(r)e^{i(\sigma t-\phi)} \tag{20}\]After substituting Equation (20) into Equation (14), we will have the first-order Bessel equation with respect to \(R(r)\), the solution to which is written as:  
\[R(r)=z_{1}(\alpha r)=C_{1}I_{1}(\alpha r)+C_{2}N_{1}(\alpha r) \tag{21}\]  
where  
\[\alpha=\sqrt{-\frac{b_{m}+i\sigma}{\
u}} \tag{22}\]  
where \(I_{1}(\alpha r)\) and \(N_{1}(\alpha r)\) are Bessel and Neumann functions of the first order; \(C_{1}\) and \(C_{2}\) are constants of integration, which are determined from the boundary conditions.
## 1 Introduction  
### Electrical Signal in Plants  
In 1973, [PERSON] discovered the transmission of action potentials between cells of Dionaea muscipula [1]. Since then, many studies have been carried out on electrical signals generated by plants. Nowadays, it is known that plant electrical signals are generated by an ionic imbalance. The ions movement generates a potential difference commonly known as voltage [2]. This potential difference is very weak, in the order of millivolts, and is easily affected by the noise of electronic-signal acquisition systems. Electrical signals are the most important physiological responses of plants [3]; they can be generated by different stimuli [4], and abiotic and biotic factors [5]. The electrical response spreads throughout the plant to the different tissues [6], specifically through the xylem and phloem [7]. Electrical signals are classified according to the type of stimulus that causes them, as well as the way in which they propagate in the plant [8]. Different types of electrical signals have been reported in the literature, the most studied are the action potential (AP), the variation potential (PV), and the system potential (SP) [9]. Action potentials (AP) are generated when the plant is exposed to stimuli that do not cause severe damage. Some examples of this type of stimulus are changes in temperature, lighting, and humidity, among others. When this type of signal propagates through the plant, the amplitude of the electrical signal does not decrease [10]. Variation potentials (VP) are signals generated by stimuli considered destructive. Variation potentials (VP) are the signals generated by stimuli that can be considered destructive, for example, a burn caused by fire or crushing, among others. One important characteristic of this kind of signal is that the greater the damage caused to the plant, the greater the traveled signal distance [11].  
The system potential (SP) is a signal that is generated when the movement of ions has finished; its activation will depend on the location of the stimulus. For example, if heat is applied to the first leaf of a poplar tree, an SP signal is obtained, while if heat is applied to the fourth leaf, a VP signal is obtained [12]. The study of electrical signals in plants is very broad, since it also depends on the type of plant [13; 14], and the type of stress to which the plant is exposed, for example, a hydric stimulus [15]. Further studies should be conducted on the behavior of the electrical signal of plants to take into account its strong randomness and background noise [16].