esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 3 Overview of Available Datasets
### Image Collections
For specialized tasks, SemanticPOSS focuses on 3D semantic segmentation, while WoodScape offers a unique fisheye camera perspective. Datasets like Lost and Found and ROAD Anomaly are tailored for obstacle detection and anomaly identification. Several datasets emphasize stereo vision and uncertainty estimation. Fishyscapes, PandaSet, and KITTI Road supply sensor-rich data and road/lane benchmarks, respectively, while Talk2 Car facilitates natural language grounding in visuals. Other contributions include event-based data (MVSEC), urban navigation aids (KAIST Urban), and comprehensive 3D annotations (Cityscapes 3D). Niche datasets such as RailSem19 and EuroCity Persons cater to rail scene understanding and urban pedestrian detection. Emerging datasets like DOLPHINS and PSI explore V2X network testing and pedestrian intent predictions. TICaM and Zenseact focus on vehicle interior monitoring and extensive sensor data across Europe. Anomaly instance segmentation is addressed by OoDIS, while real-world interaction scenarios are captured in LOOK (three citations). The diversity and thematic grouping of these datasets underscore their relevance and application in developing robust autonomous vehicle systems. The presented datasets showcase diverse strengths and weaknesses targeted towards various autonomous driving research applications. Data richness in terms of sample size is notable in datasets such as Waymo, KITTI-360, and WoodScape, enabling comprehensive model training and validation. KITTI-360 and Cityscapes offer extensive 2D and 3D semantic annotations, making them robust for developing versatile algorithms. Conversely, subsets like ROAD Anomaly and PSI are limited by their smaller sample sizes, potentially constraining their utility for generalized model training. Annotation depth varies significantly, with WoodScape providing the maximum class diversity (40 classes) compared to more focused datasets like EuroCity Persons and DOLPHINS. Sensor diversity is a prominent advantage seen in several datasets, with combinations of cameras, LiDAR, GNSS/IMU, and stereo cameras (e.g., KAIST Urban and Waymo), providing a step towards achieving comprehensive environmental perception. However, datasets focused on specific conditions or sensors, such as SemanticPOSS (solely LiDAR) and Fishyscapes (cameras only), may limit cross-modal insights. Citation metrics reveal the broader academic acceptance and utility of some datasets, where Cityscapes and KITTI-360 are highly referenced, underscoring their significance and reliability within the research community. Paying balanced attention to sensor variety, annotation detail, and data volume is crucial for advancing autonomous driving technologies.
# FPGA Implementation of ECT Digital System for Imaging Conductive Materials
## 5 System Implementation
### Software Implementation  
The software phase of the design containing the ECT-C unit attached to the generated AX14 interface model is implemented by the Embedded Coder and a code is generated. Finally, the generated HDL IP code for the IR unit is integrated into the proposed ECT system by utilizing Qsys tools in Quartus II CAD system. Moreover, the generated C code of the ECT-C is ready to be integrated with the other software components of the ECT system.  
On the other hand, the generation of the ECT-C corresponding C code for the ARM processor is handled by the Embedded Coder toolbox so as to be connected to the generated AX14 interface model. The compiled and bit-stream configuration files of the generated C and HDL-code parts, respectively, of the system, can be deployed to the FPGA platform directly from within the HDL Workflow Advisor  
Figure 5: Flow chart of the ILBP algorithm.  
tool. The generated C code the rest of the software components will be integrated into the final ECT system. Using the Qsys tools of the Quartus II CAD system, the generated HDL IP core for the image reconstruction subsystem can be reused within other related ECT systems.  
The VGA peripheral has two functions, controlling the VGA and reading/writing the SDRAM. The VGA peripheral controls the SDRAM beside communicating with the software processor and accepting data from it. Initially, the IDE processor sends a one-pixel image as a header to the VGA controller. This image alerts the peripheral that a new image needs to be written on the SDRAM since it is defined that there is no image with width 1. At this time, the display will be blanked out so the user will not notice the writing process on the SDRAM. In addition, a flag is set to signify the change of the background \"complexion\" is needed. The background color is chosen as white to match the picture. Afterwards, the address where the image will be written in the SDRAM is introduced by the software processor. The controller will keep sending the SDRAM addresses followed by SDRAM until the entire image is displayed.
# Robust Transceiver Design for IRS-Assisted Cascaded MIMO Communication Systems
### Organization  
The rest of the paper is organized as follows. Section 2 outlines the system model and introduces its characteristics. The three different tasks for employing the IRS are also given in this section. In Section 3, the optimization problem belonging to the FD scenario is formulated, and its proper solution is presented. In addition, analytical bounds for the performance of the IRS are given and their corresponding proofs are provided. Section 4 provides the optimization problem for the HD scenario along with the solution. In Section 5, the effectiveness of the proposed algorithm is evaluated and verified by performing numerical simulations over various aspects. Finally, the paper is concluded in Section 6, and technical proofs of the theorems are given in the Appendices A-E.
# Performance of Satellite-Based Evapotranspiration
###### Abstract  
Farmers in the temperate zone of southern Chile have started to irrigate historically rainfed pastures during recent years to reduce dairy productivity losses against increasingly severe summer droughts. The lack of information on pasture water requirements (i.e., evapotranspiration), however, hampers the implementation of efficient irrigation programs. Here, we use in-situ observations to evaluate the skill of four remote sensing Surface Energy Balance (SEB) models and two satellite-based global evapotranspiration products (PML_V2 and GLEAM) to estimate actual evapotranspiration (ETa) of pastures in southern Chile during 2014-2017. Daily ETa measured at an evaluation site over the period ranges between 1.2 mm and 6.2 mm day\({}^{-1}\) during the growing season (October-March), with an annual maximum of about 4.8 mm day\({}^{-1}\) in January and a minimum 0.6 mm day\({}^{-1}\) in June. Only the Simplified SEB (SEBS) model and its operational variant (SSEBop) and the PML_V2 global evapotranspiration product perform well, capturing 63-79% of the variance of in-situ evapotranspiration with an error between 0.75 mm day\({}^{-1}\) and 1.1 mm day\({}^{-1}\). The readily available PML_V2 product can be used as a convenient way to determine average water footprint of pastures and the two SEBs models can be implemented to monitor irrigation requirements in near-real time from field to regional scales. These results demonstrated a high potential of satellite observations for monitoring evapotranspiration and quantify the water footprint of pastures in southern Chile for a sustainable irrigation practice.  
evapotranspiration; GLEAM; PML_V2; pasture; grassland; remote sensing; Chile +
Footnote †: journal: Water
## 2 Proposed LED Driver Circuit
### Boundary Condition of Input Inductor \(L\)  
The boundary condition for \(L\) is described as  
\[\left\{\begin{array}{l}2I_{L}\geq\Delta i_{L}\Rightarrow L\text{ works in the CCM}\\ 2I_{L}\leq\Delta i_{L}\Rightarrow L\text{ works in the DCM}\end{array}\right., \tag{12}\]  
where \(I_{L}\) and \(\Delta i_{L}\) are DC and AC components of \(i_{L}\).  
For analysis convenience, it is assumed that the input power is equal to the output power, and hence, the input current \(I_{in}\) can be represented by  
\[I_{in}=\frac{1}{1-D}\times\frac{V_{o}}{R_{\text{eq}}}, \tag{13}\]  
where \(R_{\text{eq}}\) is the equivalent output resistance.  
The average current of \(i_{L}\), \(I_{L}\), can be expressed as  
\[I_{L}=I_{in} \tag{14}\] \[I_{L}=\frac{1}{1-D}\times\frac{V_{o}}{R_{\text{eq}}}. \tag{15}\]  
Furthermore, the current ripple of \(i_{L}\), \(\Delta i_{L}\), can be represented by  
\[\Delta i_{L}=\frac{V_{L}\Delta t}{L}=\frac{V_{in}DT_{s}}{L}. \tag{16}\]  
Therefore, as \(2I_{L}\geq\Delta i_{L}\), the input inductor \(L\) will operate in the CCM, and hence, the following equation can be obtained:  
\[\begin{split}& 2I_{L}\geq\Delta i_{L}\\ &\Rightarrow 2\times\frac{1}{1-D}\times\frac{V_{o}}{R_{\text{eq}}} \geq\frac{V_{in}DT_{s}}{L}\\ &\Rightarrow\frac{2I_{L}}{R_{\text{eq}}T_{s}}\geq\frac{V_{in}D(1 -D)}{V_{o}}\\ &\Rightarrow\frac{2I_{L}}{R_{\text{eq}}T_{s}}\geq(1-D)^{2}D\\ &\Rightarrow K_{L}\geq K_{crit,L}(D)\end{split}, \tag{17}\]  
where \(K_{L}=\frac{2L}{R_{\text{eq}}T_{s}}\) and \(K_{crit,L}(D)=(1-D)^{2}D\).  
From (17), it can be seen that as \(K_{L}\geq K_{crit,L}(D)\), the input inductor \(L\) operates in the CCM; otherwise, in the DCM. Therefore, the operation boundary curve of \(L\) can be drawn as shown in Figure 5.
# Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM
###### Abstract  
Nowadays, bearings installed in industrial electric motors are constituted as the primary mode of a failure affecting the global energy consumption. Since industries' energy demand has a growing tendency, interest for efficient maintenance in electric motors is decisive. Vibration signals from bearings are employed commonly as a non-invasive approach to support fault diagnosis and severity evaluation of rotating machinery. However, vibration-based diagnosis poses a challenge concerning the signal properties, e.g., highly dynamic and non-stationary. Here, we introduce a knowledge-based tool to analyze multiple health conditions in bearings. Our approach includes a stochastic feature selection method, termed Stochastic Feature Selection (SFS), highlighting and interpreting relevant multi-domain attributes (time, frequency, and time-frequency) related to the bearing faults discriminability. In particular, a relief-F-based ranking and a Hidden Markov Model are trained under a windowing scheme to achieve our SFS. Obtained results in a public database demonstrate that our proposal is competitive compared to state-of-the-art algorithms concerning both the number of features selected and the classification accuracy.  
bearing faults; vibration signals; multi-domain features; relevance analysis; Hidden Markov Models +
Footnote †: journal: Applied Sciences
## 5 Benchmark Testing and Performance Analysis of MRSO
### Comparative Study of MRSO and RSO Using Standard Benchmarks
#### 5.2.2 Multimodal Functions (F8-F16)

Table 1: The MRSO vs. standard RSO performance on classical benchmark functions: average, standard deviation, and statistical significance. Regarding the standard deviation, the MRSO also exhibits better consistency in five out of the nine functions. For instance, in F12, the MRSO's lower standard deviation (4.465 \(\times\) 10\({}^{-2}\)) compared to the RSO's 1.055 \(\times\) 10\({}^{-1}\) indicates more stable outcomes across multiple runs. In F14, the MRSO shows improved consistency with a standard deviation of 8.072 \(\times\) 10\({}^{-1}\) versus the RSO's 1.787. Similarly, the MRSO performs better in F15 with a lower standard deviation (4.068 \(\times\) 10\({}^{-4}\)) than the RSO (6.153 \(\times\) 10\({}^{-4}\)), confirming its reliability. Although the RSO shows better consistency in F8 with a lower standard deviation (1.073 \(\times\) 10\({}^{+3}\)) than the MRSO (1.835 \(\times\) 10\({}^{+3}\)), the MRSO's superior consistency in other functions solidifies its advantage. These comparisons highlight the MRSO's overall improved stability and accuracy in solving multimodal functions.
## 2 Status and Literature Review  
### Review of Risk Assessment Technique  
Risk assessments refer to the process of determining the risk level by estimating the hazards in the safety management process and determining whether to allow the risks based on the results. In other words, risk assessment is a process in which hazards that may occur in a workplace are detected in advance, risks are estimated, and measures are taken according to the magnitude of the risks. In this process, the safety level is managed in the workplace through a process of establishing, reflecting, and documenting mitigation measures in accordance with the evaluation results of hazards that may occur in the workplace through the calculation of the risk level [1; 2].  
Risk assessment is divided into two types: quantitative and qualitative. For qualitative evaluation, there are driving analysis techniques, checklist techniques, and what-if. Quantitative evaluation methods include fault tree analysis (FTA), event tree analysis (ETA), and failure mode and effect analysis (FMEA). Among these, quantitative risk assessment is conducted in Korea [3; 4]. Quantitative risk assessment follows the evaluation model of MIL-STD-882E, a program used for safety management at the Pentagon [5]. It describes how to recognize risks required for quantitative evaluation. To discriminate the risk, the risk is expressed according to the severity coefficient of the hazard and a coefficient associated with the likelihood of occurrence per unit time. The FMEA method is the most commonly used among risk analysis methods.  
Risk assessment represents risk through qualitative/quantitative evaluation methods and is a measure for managing human/physical disasters that may occur in the workplace. The design-for-safety review is a concept to implement more accurate and efficient safety management by preemptively managing the risks that may occur at the construction site by conducting risk assessment and risk estimation in the design stage. It provides a risk assessment template, a method for recognizinghazards, and examples of risk estimation so that safety inspectors can perform consistent and objective risk assessment. Work is therefore performed according to the risk level of the work based on the calculated risk level. However, the missing tasks and requirements should be supplemented through consultation with experts due to limitations in the specified items and contents [6; 7]. In this process, different evaluation results are displayed depending on the experience and supervision of the safety supervisor, and many efforts are being made at home and abroad to address this problem.
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 4 Proposed Protocol
### Link Expiration
#### 4.3.2 Ret  
\(RET\) specifies the minimum duration amongst all connections between the source and target node, whereas \(\mathit{LET}\) specifies the estimated duration for which the connection between any two nodes stays active [35]. A route with a higher \(RET\) is a considered a more dependable route. Figure 5 illustrates two nodes [36], i.e., where node \(N_{1}\) has \((x_{N1},y_{N1})\) coordinates along with the velocity \(v_{N1}\) which moves at an angle \(\theta_{N1}\) whereas node \(N2\) have \((x_{N2},y_{N2})\) coordinates along with the velocity \(v_{N2}\) which moves at an angle \(\theta_{N2}\). Furthermore, both nodes have same the communication range \(R\). Then, the \(\mathit{LET}\) between two nodes can be calculated using Equation (21).  
\[\mathit{LET}=\frac{-((\alpha\times\beta)+(\gamma\times\lambda))+\sqrt{(\alpha ^{2}+\gamma^{2})\times R^{2}-((\alpha\times\lambda)+(\gamma\times\beta))^{2}} }{\alpha^{2}+\gamma^{2}} \tag{21}\]  
where,  
\(\alpha=v_{N1}\times cos\theta_{N1}-v_{N2}\times cos\theta_{N2}\)  
\(\beta=x_{N1}-x_{N2}\)  
\(\gamma=v_{N1}\times sin\theta_{N1}-v_{N2}\times sin\theta_{N2}\)  
\(\lambda=y_{N1}-y_{N2}\)  
The \(RET\) is actually the lowest \(\mathit{LET}\) of all possible network links, determined using Equation (22).  
\[RET=min(\mathit{LET}_{1},\mathit{LET}_{2},\mathit{LET}_{3},\ldots,\mathit{ LET}_{n}) \tag{22}\]
## 1 Introduction
Since the 1980s, a study of anthropogenic and natural influence on the ionosphere has been carried out at the Institute of Ionosphere using the method of Doppler frequency shift (DFS) measurement on an inclined radio path [30; 31; 32]. The method was successfully applied to detect ionospheric anomalies both at preparation and during earthquakes and at the study of lithosphere-atmosphere-ionosphere coupling [33; 32]. This paper presents a complex investigation of the anomalous effects that were detected prior to an earthquake in the data time series on electromagnetic waves in the ULF/VLF bands, the flux of subsurface gamma rays, and the DFS of ionospheric signal. A comprehensive study of these effects was carried out at the Geophysical Observatory, which resides at the High Mountain Station of the Institute of Ionosphere and at the Tien Shan High Mountain Research Station of the P. N. Lebedev Physical Institute of the Russian Academy of Sciences. The observatory is situated in one of the most earthquake-proneregions of Northern Tien Shan (Kazakhstan), where the sources of the historical Verny (\(M7.3\), 1887), Chilik (\(M8.3\), 1888), and Kemin (\(M8.2\), 1911) catastrophic earthquakes are located. Long-term continuous monitoring of geophysical fields is performed here with the goal of disclosing the anomalies bound with earthquake preparation. In the present publication, attention is drawn to the seismo-electromagnetic phenomena registered in a ground-based experiment, namely to the anomalous effects in the ULF/VLF band, as a possible precursor of an \(M6.1\) earthquake that occurred on 4 March 2024. The anomalies of electromagnetic emission are considered here in complex with the variation of the flux of gamma rays measured among subsoil rocks and with the changes in DFS of the ionospheric signal. Interest in this work appeared when, a few days before the earthquake, we detected in real time a characteristic cove-like depression in the counting rate record of a gamma detector installed in a borehole. This observation indicated a high probability of an incoming earthquake, which did actually take place 8 days later. Earlier, similar anomalies in the gamma-ray flux have been repeatedly observed 8-2 days before several \(M<7.0\) earthquakes with epicenters situated within the Dobrovolsky circle [34].
## 2 Related Works
### Existing Offloading / Caching Decision Algorithms  
In Reference [20], the author considers the multi-user computation offloading problem in MEC system as an evolutionary game model, and proposes an evolutionary game algorithm based on reinforcement learning to achieve efficient computing offloading. However, IoT devices need to adjust their own strategies through continuous evolution and trial and error to maximize the fitness value, which will cause the time complexity of the algorithm is relatively high. Similarly, there are also some problems in the research of literature [21; 22] based on game theory. In addition, a large number of mobile edge computing resources managements based on convex optimization (such as References [23; 24]), non convex optimization (such as References [25; 26]), combinatorial optimization (such as References [27; 28]), evolutionary algorithm (such as Reference [29]), etc. Although some of the authors, besides the basic algorithms, also provide approximate and simplified algorithms (such as Reference [24]), these algorithms still need to occupy a certain amount of computing time, especially in large-scale users and large-scale tasks environment, the algorithm cost is still the main problem that affects the real time of decision-making.  
The resource management algorithm determines the computing offloading requests and data caching requests in the execution terminal, and the algorithm's own overhead will directly affect the real-time performance of the decision, especially for the time delay sensitive task processing.
## Materials and Methods
### Measurements of Canopy-Scale Carbon Fluxes (Eddy Covariance)  
Eddy-covariance (EC, [26]) stations were operated in the center of two representative fields, one each for wheat and sugar beet to provide canopy-scale net CO\({}_{2}\) and water vapor fluxes. The core EC instrumentation consisted of a CSAT3 sonic anemometer (Campbell Scientific, Logan, UT, USA) and a Li7500 open-path gas analyzer (Li-COR, Lincoln, NE, USA), both logged at a frequency of 20 Hz and mounted 1.5-2 m. Covariances and net CO\({}_{2}\) fluxes were computed over half-hourly time intervals using the software packages TK2 [27] and ECpack [28]. Detailed descriptions of the measurements and data processing are given in [29; 30].  
Derivation of GPP\({}_{\text{EC}}\) from net CO\({}_{2}\) fluxes and smoothing were performed as described in [20], using the flux partitioning tool [31]. Actual photosynthetic light-use efficiency (LUE\({}_{\text{EC}}\)) was derived as the ratio of GPP\({}_{\text{EC}}\) and absorbed photosynthetically active radiation (APAR) obtained from spectroradiometric measurements (see Section 2.6).
## 3 Results and Discussion
### The Spatiotemporal Changes in Vegetation Indices (2001-2020)
The normalized difference water index (NDWI) was observed during the time frame spanning from 2000 to 2005, exhibiting low values in the northern regions of the White Nile district and being almost nonexistent in the far north (Figure 8). Conversely, a higher density of NDWI was noted in the southern regions, gradually increasing towards the far south. Between 2006 and 2010, a decline in the normalized difference water index (NDWI) was observed as one moves towards the northern areas. In  
Figure 8: The difference in NDWI over the time series from 2001 to 2020 of White Nile State. these regions, the NDWI might be absent, while its values rise as we move southwards, peaking in density in the far southern areas. From 2011 to 2015, a decrease in the NDWI was observed, particularly in the northern and far northern regions, with water density increasing as we approach the southern areas. As for the period spanning from 2016 to 2020, there were minimal quantities of the NDWI, possibly lacking in the northern regions, with an increase in NDWI values towards the south. The density of the NDWI peaks in the far south due to higher rainfall intensity (Figure 8). The Enhanced Vegetation Index (EVI) had low values in the White Nile State's northern regions from 2000 to 2005 but was higher in the southern regions. Between 2006 and 2010, EVI decreased moving north and increased moving south. From 2011 to 2015, EVI decreased in the north and increased in the south. From 2016 to 2020, EVI was minimal in the north  
Table 4 shows the areas that vary at regular intervals for every vegetation index. This draws attention to the variations in the planted cane areas between 2001 and 2020, which are a reflection of shifting climatic conditions, variations in rainfall, and the consequent availability of water needed for sugar cane irrigation. Since these regions were the least attainable during the period, we discovered that there was a significant fluctuation in the areas that were evident in the vegetation indices. The agricultural area declined between 2001 and 2005, then increased between 2005 and 2010; however, it was still greater than in the first era. From 2016 to 2020, the cultivated area increased once more.