esa-sceva/satcom-chunk-collection huggingface dataset:
# Compressive Sensing-Based IoT Applications: A Review
## 4 CS-Based IoT Applications
### Sensing Layer
#### 4.1.1 Adaptive Measurements  
The intuitive solution is to minimize the number of the transmitted samples from the sensors to the processing unit by selecting a high CF. However, using a high CF can degrade the performance of data reconstruction. In addition, the optimal value of CF depends heavily on the signal sparsity which may vary in time (at the level of each sensor) and in space (sensors may have different readings for the same signal), hence CF can be high as long as it assures an acceptable recovery performance to meet the desired quality of service (QoS) for a specific application. Therefore, determining the optimal CF has been the quest of different studies by exploring different ideas and approaches. [PERSON] et al. [76] proposed an adaptive framework for selecting CF value to overcome the signal sparsity level variations from data-block to another. The proposed framework consists of a network of sensors, where each sensor transmits its data to a central node (CN). First, each sensor transmits a part of its data which can be seen as a training set to the CN. The latter starts a compression/decompression operations with several values of CF to determine the signal sparsity along with its best CF based on a predefined QoS metric (error of reconstruction). In addition, the CN creates a profile that assigns to each range of sparsity the best CF that renders the minimum reconstruction error. Furthermore, [PERSON] et al. [77] proposed a new approach to detect the changes in the signal sparsity using change point methods (CPM) [78] to update the CF value each time the signal sparsity level changes.
## 2 Materials and Method
### Numerical Model for Stability Analysis  
In order to understand the effect of the geological structures on the stability and failure mechanism of the deep-seated landslide, a numerical analysis was conducted using the Fast Lagrangian Analysis of Continua (FLAC), which was a commercially available software. The FLAC analysis utilizes explicit finite difference method to analyze the two-dimensional stress-strain field of a continuum, and is widely used for the analysis of geotechnical related fields, such as underground excavation, tunneling, slope stability, foundation, and seepage in earth dam [30]. The two-dimensional continuum field is discretized into grids, and the boundary conditions and constitutive law of material are assigned. The equation of motion is adopted to calculate the unbalanced force for each grid point in sequential time step. The strain increment and stress are calculated from the unbalanced force following each time step, and the resulting new set of unbalanced force is applied in the subsequent time step calculation. The iterative procedures continue until the differences in unbalanced forces become negligible. The final stress and displacement condition of the continuum are thus determined. In this study, the two-dimensional profile of the slope along the main sliding direction was taken for stability analysis. The DTM5 derived in 2003 is used to construct the model slope to properly present the slope topography before the 2005 event as illustrated in Figure 5. The lateral boundaries of the model were assumed to have no lateral displacement, and the base boundary was assumed fixed with no displacement. The boundaries are set at a sufficient distance so that the main slope body will not be affected by the boundary constraint. The material was assumed to behave elasto-plastically, and the Mohr-Coulomb constitutive model was adopted. The model profile was constructed following the investigated results of ground layers, geological structures, and ground water table. The material properties were assigned following data of previous research and results of laboratory tests. The analysis procedures are carried out by: (1) forming the grid of slope model, and assigning constitutive law and material  
Figure 4: Illustration of the identified lineaments of geological structure using shaded relief map derived from DTMH at eight different azimuth angles: \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), \(135^{\circ}\), \(180^{\circ}\), \(225^{\circ}\), \(270^{\circ}\), and \(315^{\circ}\), with a light incidence angle of \(45^{\circ}\) (WGS 84 coordinates). The yellow dash lines marked the lineaments on the main slope face observed at the azimuth angles of \(0^{\circ}\), \(90^{\circ}\), \(270^{\circ}\), and \(315^{\circ}\); and purple dash lines marked the lineaments on opposite slope face observed at the azimuth angles of \(0^{\circ}\), \(135^{\circ}\), \(180^{\circ}\), and \(225^{\circ}\).  
properties; (2) set up boundaries and initiate geo-static stress field; (3) apply ground water condition assumption; and (4) solve for stress-strain field of the model slope.
# [PERSON]: Extreme Plastic Pollution Pathways in Riparian Waterways
## 3 Materials and Methods
### Technologies and Development  
The project employed a plastic bottle tracker to monitor plastic pollution behaviors, chosen for its prevalence as one of the most common single-use plastics found in rivers. The tracker was designed to provide daily updates during Plastics Awareness Month (see Figure 2).  
The core of the system was a 3 Dr Pixhawk autopilot, selected for its affordability, open-source flexibility, and autonomous functionality. This controller was equipped with sensors including a UBlox M6 GPS and Compass module, an MPU6000 accelerometer and gyroscope, and a barometer. The Pixhawk was imaged with Ardurover v4.2.3 firmware, which is part of the open-source Ardupilot ecosystem and specifically supports autonomous ground and water vehicles. Communication between the tracker and the research team was achieved using a SiK telemetry radio, later augmented with Drone Engage, a 4G-based cloud telemetry platform utilizing Vietnam's cellular network for real-time data logging and remote internet access. To integrate Drone Engage, a Raspberry Pi Zero was used as a companion computer, connected to the Pixhawk via an industrial grade 4G dongle. This setup facilitated seamless communication with the Drone Engage server and allowed real-time telemetry data to be accessed and logged remotely. The final hardware configuration is illustrated in Figure 3, and an image of the prototype setup is shown in Figure 4.  
The tracker components were housed in a watertight three-liter plastic bottle, weighted with 1.25 kg of ballast. To further protect the electronics, they were sealed inside an additional waterproof container within the plastic bottle. This design ensured the system remained operational under diverse environmental conditions. The plastic bottle's watertightness was tested under hydrostatic pressure (9.81 kPa for three hours at a depth of one meter). A 13 dBi monopole antenna, tuned to 1800 MHz, was mounted on the top of the bottle to ensure optimal communication. Multiple iterations of the tracker were developed and tested, culminating in the release of the final prototype into the river. Photographs documenting the development process and the final prototype are presented in Figure 5a.b.  
Figure 2: Day 2 daily update (screenshot).  
Figure 4: Final technology arrangement before installation in bottle (photo).  
Figure 5: (**a**): Plastic tracker development with final ballast and sensor, housing arrangement. (**b**): Plastic Tracker final prototype as released into the river (photos).  
Figure 3: Technology arrangement and data flow (diagram). Arrows indicate data flow direction.
## 2 Study Area and Materials
### Data Resources
#### 2.2.2 Indices Related to the VWC  
In this paper, six indices related to VWC in biological and meteorological mechanisms, i.e., NDVI, NDWI, NDII, gross primary productivity (GPP) [42], leaf area index (LAI) [43], and precipitation are used to evaluate their potentiality as auxiliary datasets through correlation analysis [6; 44; 45].  
NDVI, representing greenness, is computed from the Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance in the Red and NIR bands. GPP refers to the total organic carbon fixed by photosynthesis in unit time and area, including autotrophic breathing and heterotrophic breathing. LAI refers to the ratio of total leaf area to land area, representing the density of vegetation. NDWI and NDII, representing water content, are calculated from the MODIS reflectance in the Red and SWIR1 (SWIR2) bands. All the vegetation indices mentioned above can be downloaded from the NASA Land Processes Distributed Active Archive Center (LP DAAC) ([[http://ladsweb.nascom.nasa.gov](http://ladsweb.nascom.nasa.gov)]([http://ladsweb.nascom.nasa.gov](http://ladsweb.nascom.nasa.gov))). The specific products used in this study are listed in Table 1.  
Precipitation is a very important meteorological parameter for the understanding of land surface processes and global climate change and plays a key role in the growth of vegetation. Therefore, we also added the precipitation variable into the experimental process. The TRMM_3B42 RT_Daily product produced by the NASA GES DISC was chosen for the analysis [46]. We analyzed these potential indices related to VWC in Sections 3.1 and 4.1 in details to determine the model input.
# Retrieval of Biophysical Crop Variables from Multi-Angular Canopy Spectroscopy
###### Abstract  
The future German Environmental Mapping and Analysis Program (EnMAP) mission, due to launch in late 2019, will deliver high resolution hyperspectral data from space and will thus contribute to a better monitoring of the dynamic surface of the earth. Exploiting the satellite's \(\pm\)30\({}^{\circ}\) across-track pointing capabilities will allow for the collection of hyperspectral time-series of homogeneous quality. Various studies have shown the possibility to retrieve geo-biophysical plant variables, like leaf area index (LAI) or leaf chlorophyll content (LCC), from narrowband observations with fixed viewing geometry by inversion of radiative transfer models (RTM). In this study we assess the capability of the well-known PROSPECT 5B + 4 SAIL (Scattering by Arbitrarily Inclined Leaves) RTM to estimate these variables from off-nadir observations obtained during a field campaign with respect to EnMAP-like sun-target-sensor-geometries. A novel approach for multiple inquiries of a large look-up-table (LUT) in hierarchical steps is introduced that accounts for the varying instances of all variables of interest. Results show that anisotropic effects are strongest for early growth stages of the winter wheat canopy which influences also the retrieval of the variables. RTM inversions from off-nadir spectra lead to a decreased accuracy for the retrieval of LAI with a relative root mean squared error (rRMSE) of 18% at nadir vs. 25% (backscatter) and 24% (forward scatter) at off-nadir. For LCC estimations, however, off-nadir observations yield improvements, i.e., rRMSE (nadir) = 24% vs. rRMSE (forward scatter) = 20%. It follows that for a variable retrieval through RTM inversion, the final user will benefit from EnMAP time-series for biophysical studies regardless of the acquisition angle and will thus be able to exploit the maximum revisit capability of the mission.  
EnMAP; hyperspectral; PROSAIL; multi-angle; canopy; biophysical variables; agriculture; spectroscopy +
Footnote †: journal: Remote Sensing
### Contributions  
There have been considerable contributions in the literature to FSO communications. Nevertheless, the usual space constraints for publications prevented comprehensive research work in which assiduous efforts towards FSO communications development can be presented. Therefore, an all-inclusive survey in which the trends in FSO communication systems can be studied is highly imperative. Based on this, apart from being a good complement to contributions of the existing work, this paper also offers in-depth clarifications regarding different related concepts and architectures. In this regard, the main contributions of this tutorial paper are as follows:  
1. Comprehensive Survey: The paper provides an extensive review of the enabling technologies critical to advancing FSO communication systems, including AO, modulation schemes, and error correction codes. This thorough examination aids in understanding how these technologies address the unique challenges of FSO links.
2. Identification of Challenges: It identifies and discusses significant challenges that hinder the deployment of FSO systems, such as atmospheric turbulence, weather-induced signal degradation, and alignment issues. This highlights the need for targeted solutions and informs future research directions.
3. Integration of Hybrid Solutions: The paper explores the integration of FSO with radio frequency, millimeter-wave, and Terahertz technologies, emphasizing how hybrid approaches can enhance reliability and coverage in next-generation networks.
4. Emerging Trends: The research highlights emerging trends in FSO communication, particularly the integration of artificial intelligence and machine learning techniques. This focus on AI-driven optimization provides insights into how these technologies can enhance the performance of FSO systems in dynamic environments.
5. Assessment of Future Prospects: The paper evaluates the potential of FSO to meet the increasing demands for high-speed, low-latency communication in the context of 5G and beyond. It provides a forward-looking perspective on scalability, reliability, and deployment strategies that can support the evolution of FSO communication systems.
* Research Directions: Finally, the paper identifies open challenges and outlines future research directions critical to maximizing the effectiveness of FSO technology. This contributes to the broader understanding of the role FSO can play in the future of wireless communication systems.
## 1 Introduction
For Curve Area in the machining path, the above curvature-constraint method can achieve good results, but since curvature is a unique property of curves, the feedrate-constraint value obtained by this method is not reasonable for Line Area. In addition, some scholars obtain the feedrate-constraint value for continuous small line segments based on the limitations of acceleration of each axis [16; 17; 18] or contour error [19], but the calculation result will be unstable when the length of the line segment is too short. In recent years, some other scholars inserted a transition curve such as PH [20] and B-Spline [21; 22; 23] at the corner of adjacent line segments. Then, the feedrate can beconstrained according to the curvature of the transition curve. At the same time, the machining path will be smoother. However, if the line segment is too short, the transition curve will be hard to insert. Fitting continuous small line segments to a curve such as Akima [24], B-Spline [25], and NURBS [26; 27] is another similar method, and the method is also used by Siemens 840D CNC [28]. However, the trajectory error between the fitting curve and the original line segments is inevitable, which will damage machining quality. Additionally, there are many difficulties [29] in the feedrate planning and interpolation of the transition and fitting curve. To address the above problems, this paper proposes a Feedrate-constraint method for the continuous small line segments based on the Nominal Acceleration (FNA). The FNA method obtains the predicted value of acceleration during machining process by the machining trajectory prediction and acceleration filtering, and the feedrate of the actual machining process is constrained according to the predicted acceleration. Compared with conventional methods, the FNA method is not sensitive to the angle of the corner and the length of the line segment, so it can be used for different machining paths consisting of long segments or continuous small segments, and it has no special requirements for the accuracy of the machining path. Therefore, the feedrate-constraint value obtained by this method is more reasonable and stable, which can improve the quality and efficiency of the CNC machining. In the following section, the FNA method will be introduced in detail and its effectiveness will be verified by simulation and experiment. This paper is organized as follows: Section 2 is the introduction of the FNA method; Section 3 is the simulation and experiment; and Section 4 is the conclusion.
# Influence of Vegetation Filter Strip on Slope Runoff, Sediment Yield and Nutrient Loss
###### Abstract  
It is an important branch of erosion research to control soil erosion on eroded gullies and slopes by using vegetation filter strip. Several simulated rainfall experiments were carried out in soil tanks filled with loess sandy loam taken from a typical eroded gully area with less vegetation coverage in Yanghe hilly basin in Xuanhua District, Zhangjiakou City, Hebei Province. The soil and water conservation effects of two different vegetation setting modes were compared under the same vegetation strip width and different rainfall intensities and slopes. During the rainfall process, the changes of runoff and sediment yield and nutrient loss were not stable, but the same erosion index had similar variation trends under different combinations of rainfall intensity, slope and vegetation coverage. Multiple regression results showed that runoff and sediment production in eroded gully can be effectively reduced through vegetation filter strips, which are jointly affected by rainfall intensity and slope. There was no significant difference in the amount of runoff and sediment yield between the two vegetation setting modes. Rainfall intensity and slope gradient showed different strengths of impact on nutrient loss. Through cluster analysis, the results showed that the impacts of rainfall intensity, slope gradient and vegetation setting modes on soil and water loss on slope can be equal or offset. In general, setting vegetation filter strips can offset the effects of rainfall intensity and slope, but vegetation regulation of erosion was not obvious under extreme rainfall and steep slope conditions. What's more, rainfall intensity had a dominant effect on erosion. The results in this research may provide reference for practical application of vegetation filter strips on eroded slopes.  
v +
Footnote †: journal: Applied sciences  
Article Influence of Vegetation Filter Strip on Slope Runoff, Sediment Yield and Nutrient Loss
## 2 Materials and Methods
### Cell Viability Assay  
Viability was assessed 48 h after miR-320a transfection in hOBs (\(n\) = 3), MG-63 (\(n\) = 3), and U20S (\(n\) = 3) using the CellTiter 96(r) AQueous One Solution Cell Proliferation Assay (Promega; Madison, WI, USA) according to manufacturer instructions. The absorbance was measured at 490 nm with a scanning multiwell spectrophotometer Infinite M200 (Tecan).
# A Robust Generator-Harvester for Independent Sensor Systems +
## 2 State of the Research Field and Topics Analyzed
### Problems Relating to the Electromagnetic Component of the Designed Harvester  
A specific area of research within the above power generation procedures encompasses the designing of the electromagnetic part of the device or apparatus, completed with associated steps and stages such as coupling to a motion source and interaction with the system that is being powered, namely, a sensor or a sensor system. A detailed discussion of linear machine concepts suitable for use in linear motor/generator configurations, including an interpretation in terms of the geometry (planar or tubular), is available in [22]. A comparison involving linear generators is detailed in [23].  
Furthermore, an interesting area lies in modeling specifically designed devices, as is presented in articles [24; 25].  
The decisive parameter for comparing the efficiency of the design and implementation of generators and their transformation characteristics, the parameters for the choice of the downstream concept, is the quantity effective volumetric power density \(p_{\text{efd}}\) [\(W/\text{m}^{3}\)], shown in Table 1[20]. Using this parameter, the efficiency rates of an energy source with respect to its volume are easily comparable, and such a procedure thus facilitates the comparison between selected concepts and designs, which may be dimensionally different and difficult to measure.
## 3 Material and Methods  
### Background and Challenges  
#### 3.1.1 The Use Case  
In the music streaming business, it is essential to know when streaming behavior changes for multiple reasons: Such information is an advantage in negotiation between stakeholders (artists, music managers, or record labels), it helps to focus marketing campaigns, and it may even help decide what the next song should sound like. DMG delivers a service with which artists and music companies upload songs to streaming services (Spotify, YouTube, iTunes, TikTok, etc.) and monitor streaming. The system aggregates streaming data from all of the streaming services and gives users a homogeneous interface that allows users to manually identify changing trends and anomalies. This task is hard and time-consuming, so in the ideal world, the system itself identifies and explains changing trends, failures in the system, etc., i.e., any anomaly that the user might find interesting. Our method addresses this use case.  
In data science, finding these anomalies is called outlier detection. The typical definition of an outlier is \"_an observation which deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism_\" [29].  
In the music streaming use case, the mechanism of \"the other observations\" is described by the distribution of the normal songs following expected trends. These distributions could, e.g., reflect that some songs are played more on the weekend or the dependency between number of streamings on consecutive days--the expected behavior. DMG is interested in surprises, i.e., outliers. Outliers differ from normal observations, e.g., when a song suddenly becomes more popular than expected or changes the pattern of when it is played. Such a change could be, e.g., the result of a concert or that the song is used in a YouTube video, in other words, changes to the mechanisms that generate the streamings.  
Because outliers and inliers are relative we are not interested in a binary decision, but rather in a ranking of the observations on how normal or outlierish the objects are. The task is to find a model that defines the grade of normality (and outlierness) that ideally reflects how interesting the deviation is to the user.
## 1 Introduction
### Abnormal Propagation Paths in the Power Grid During Tropical Cyclones
#### 4.2.2 Managing Islanded Microgrids  
Considering that system redispatch and power flow redistribution may cause the interconnected system to split into multiple islands during the abnormal propagation process, the following actions are taken based on the type of island to ensure independent operation.  
If the island contains no generators, all loads \(P_{\text{ds}}\) in the island should be shed:  
\[\Delta P_{\text{Id}}=P_{\text{ds}} \tag{30}\]  
If the island has only one generator and the total load demand \(P_{\text{ds}}\) is less than the generator's output \(P_{\text{g0}}\), the generator's reduced power \(\Delta P_{\text{gd}}\) is as follows:  
\[\Delta P_{\text{gd}}=\max\bigl{(}P_{\text{ds}}-P_{\text{g0}},P_{\text{g\,min}} -P_{\text{g0}}\bigr{)} \tag{31}\]  
Furthermore, if the generator power reduction still cannot balance the total load demand \(P_{\text{ds}}\), generator tripping will occur.  
If the island has only one generator and the total load demand \(P_{\text{ds}}\) exceeds the generator's output \(P_{\text{g0}}\), the generator's increased power \(\Delta P_{\text{gu}}\) is as follows:  
\[\Delta P_{\text{gu}}=\min\bigl{(}P_{\text{ds}}-P_{\text{g0}},P_{\text{g\,max}} -P_{\text{g0}}\bigr{)} \tag{32}\]  
Furthermore, if the generator's output increase still cannot balance the total load demand \(P_{\text{ds}}\), load shedding will be required.  
For islands with multiple generators, the calculation process follows the same steps as in Steps 1-3.