esa-sceva/satcom-chunk-collection huggingface dataset:
# Devising Mobile Sensing and Actuation Infrastructure with Drones
## 2 Designing DaaG
### Structure of DaaG
#### 2.2.1 Task Management Module  
The task management module has three functionalities; sensor data retrieval, smart data processing and data transfer method selection. A sensor data retrieval function collects the data from the sensors through communication modules. These data are basically stored in the storage module. A smart data processing function uses these data in storage to generate the actuator command message. To generate the message, DaaG firstly classifies problematic sensors, which have to be monitored, and calculates the coefficient value between problematic sensors and actuators. Then, it generates the command messages for actuators to efficiently monitor problematic sensors. This component guarantees the smartness in the features. A data transfer method selecting function considers the status of the sensor network and the actuator network. Transferring data at a long range can be carried out in two ways with drones: multi-hop network and data ferry. This function selects one method that is suitable for sensing and actuating in the network environment. This component guarantees the connectivity of the features.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 2 Principles of Grounding Systems
### Key Parameters Influencing Grounding Performance  
Grounding performance is influenced by several parameters, including soil resistivity, electrode material, and system voltage levels. These parameters must be considered during the design phase to optimize the grounding system's effectiveness.
# Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series
## 3 Data and Methods  
### MODIS Data  
MODIS is one of the key instruments aboard the Sun-synchronous Terra and Aqua satellites. For our study, we used the vegetation index products MOD13Q1 and MYD13Q1 Collection 6 [55,56]. The products contain 12 layers at 250-m spatial resolution, of which we used the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), red (620-670 nm), Near-Infrared (NIR, 841-875 nm), blue (459-479 nm), Mid-Infrared (MIR, 2105-2155 nm) and pixel reliability layers. The 16-day composites have been derived from the atmospherically-corrected eight-day composite products MOD09A1 and MYD09A1, which themselves have been generated from the daily reflectance products based on the constrained view angle maximum value composite technique and corrected for gases, clouds and aerosols [57, 58, 59, 60]. Only the red and NIR band were originally acquired at 250-m spatial resolution, whereas the other surface reflectance bands were acquired at 500-m spatial resolution and resampled to 250-m for the MOD13Q1 and MYD13Q1 products.  
MODIS datasets are delivered in sinusoidal projection and a tiling system, where each tile covers an area of approximately 1,440,000 km\({}^{2}\). Continental China is covered by the tiles H25V03, H26V03, H23V04, H24V04, H25V04, H26V04, H27V04, H23V05, H24V05, H25V05, H26V05, H27V05, H28V05, H25V06, H26V06, H27V06 and H28V06. For each year and tile, 46 MOD13Q1/MYD13Q1 products were available, except for 2002, for which 35 products were available due to the Aqua satellite being launched in May 2002. We acquired all available MOD13Q1 and MYD13Q1 products for these tiles and the years 2002, 2005, 2010 and 2014 from the USGS Data Pool [61]. We chose the years 2002 and 2014, as they were the first and last years with available MOD13Q1 and MYD13Q1 at the time of our study, and the years 2005 and 2010, due to their alignment with the creation of China's National Land Cover Dataset.  
Figure 1: Rice production in mainland China 2014. Based on data from [2].  
We extracted all surface reflectance and vegetation index layers from the datasets and created an additional Land Surface Water Index (LSWI\({}_{2130}\)) from the near-infrared and mid-infrared layers with the equation:  
\[LSWI_{2130}=\frac{\text{NIR}-\text{MIR}}{\text{NIR}+\text{MIR}} \tag{1}\]  
where NIR is the Surface Reflectance Band 2 with a wavelength from 841 nm-876 nm and MIR is the Surface Reflectance Band 7 with a wavelength from 2105 nm-2155 nm, centered on 2130 nm. Using the pixel reliability layer, we masked all values that are not classified as _Good Data_, therefore excluding data classified as _Marginal Data_, _Snow/Ice_ or _Cloudy_. Throughout our processing chain, the MODIS data were kept in the original sinusoidal projection.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 5 Evaluation
### Testbed Results
#### 5.3.2 Testbed Validation  
Testbed experiments were carried out using the nodes of scenario 2 on the hardware testbed, using the proposed slot bonding implementation. Figure 13 shows the results for the heuristic (with \(\delta=0.8\)), the multi-PHY GA and the single-PHY GA that can only use the 50 kbps PHY.  
In general, it was observed that the testbed results approximate the theoretical values shown in Figure 11b. As expected, with the longer slotframe length, the mean PDR values also increased compared to the shorter slotframe length. More specifically, in the case of the 261 ms slotframe length, the GA performed better than the heuristic with PDRs of 0.88 and 0.86, respectively, meaning there was only a 2.3% decrease in performance. The GA that can only use the 50 kbps PHY was outperformed by its multi-PHY counterparts as it only achieved a PDR of 0.33 (i.e., the GA and the heuristic increased on this performance with 166.7% and 160.6%, respectively). When using a 423 ms slotframe length, the heuristic actually performed slightly better than the GA with PDRs of 0.94 and 0.88, and both again outperformed the single-PHY GA that had a PDR of 0.75 (i.e., the GA and the heuristic increased on this performance with 25.3% and 17.3%, respectively). The heuristic performing better than the multi-PHY GA is explained by the observation of link reliability values during these multi-hop experiments, which differed from the reliability values observed during the monitoring phase (on which the parent, PHY and slot allocation was based). More specifically, the monitoring results showed better performance mainly for the 50 kbps PHY compared to the values observed during the multi-hop experiments. As the GA more often uses 50 kbps PHY than the heuristic does, the PDRs of the GA was more negatively affected as a result. As such, these results actually motivate the integration of the heuristic in a routing protocol's objective function with real-time link reliability monitoring to dynamically adapt to varying link conditions, as discussed in Section 3.6. Overall, the results clearly show the efficiency of the proposed parent and PHY selection heuristic.  
Figure 13: The PDRs values for the testbed experiments, showing the results for the heuristic, the GA and the GA that can only use the 50 kbps PHY. The \(x\) denotes the mean value. The diamond shapes represent outliers that are values smaller than the minimum (i.e., the lower whisker), or larger than the maximum, value (i.e., the top whisker).
## 1 Introduction
The results show that the facet scattering of waves are stronger than the background of the sea surface, and the waves exhibit clear symmetrical features [26]. These studies have provided a preliminary understanding of the InIRA performance. However, great efforts are still required to extend the application of this new sensor. In particular, studies on InIRA wave retrieval are still lacking, and the InIRA SWH is necessary for correcting the sea state bias of InIRA-derived SSH measurements [27; 28; 29; 30; 31; 32]. Moreover, the SWH can supplement the products from other spaceborne wave sensors. Accordingly, this study focuses on the SWH retrieval from InIRA data and its validation. From the perspective of radar geometry, the InIRA is the closest to the SAR [21; 22; 24]. Methods of SWH retrieval from SARs may be referred to when using the InIRA. In SAR studies, the transfer relation or empirical models were used to retrieve the SWH. This study adopts a similar empirical model to retrieve SWHs from InIRA data, for avoiding nonlinear effects. This model is proposed based on the analysis of the ocean wave modulation theory at low incidence angles. The SWH retrievals using the proposed model are validated by a collocated model and traditional altimeter data. The manuscript is organized as follows: Section 2 introduces the datasets; Section 3 provides the modeling process; Section 4 shows the results for SWH retrieval and validation using the proposed model; Section 5 discusses the features and limitations for SWH retrieval by InIRA. Finally, Section 6 concludes the results and the future work.
###### Abstract  
The influence of quantum-well (QW) number on electroluminescence properties was investigated and compared with that of AlN electron blocking layer (EBL) for deep ultraviolet light-emitting diodes (DUV-LEDs). By increasing the QW number, the band emission around 265 nm increased and the parasitic peak around 304 nm was suppressed. From the theoretical calculation, the electron current overflowing to the p-type layer was decreased as the QW number increased under the same injection. Correspondingly, the light output power also increased. The increment of output power from 5 QWs to 10 QWs was less than that from 10 QWs to 40 QWs, which was very different from what has been reported for blue and near-UV LEDs. The parasitic peak was still observed even when the QW number increased to 40. However, it can be suppressed efficiently by 1 nm AlN EBL for LEDs with 5 QWs. The simulation showed that the insertion of a thin EBL increased the barrier height for electron overflow and the electron current in p-type layers decreased significantly. The results contributed to the understanding of behavior of electron overflow in DUV-LEDs.  
deep ultraviolet light-emitting diodes; AlGaN; electroluminescence; electron overflow; electron blocking layer +
Footnote †: journal: Applied Physics Letters
## 2 Experimental Methodology
### Experimental Equipment and Instrumentation
#### 2.2.3 Particle Transport Tracking: IMU Embedded Instrumented Particle  
Specifically, the MEMS sensor embedded in a purpose-built 3D-printed shell offers an in-house designed instrumented device (similar to [46]) that is deployed herein (Figure 6) to study sediment transport processes with a comprehensive set of flume experiments. The MEMS sensors used are sensors logging data at a high frequency (200 Hz), encapsulated in a spherical shell. The casing of the instrumented particle involves a 3D-printed plastic shell with a diameter of 7.33 cm (Figure 6). The IMU sensor fitted in the instrumented particle yields a Lagrangian view of its transport by saltation, using sensor high resolution data. This can be an off-the-shelf IMU (e.g., 3-SpaceTM Data Logger, Yost Labs Inc., Portsmouth, OH, USA) or a custom-made sensor (as designed and developed by [46]).  
Figure 5: Demonstration of the experimental setup. (**a**) Top view of one of the beds roughnesses showing particle at rest. (**b**) Side view of one of the beds roughnesses showing the particle at motion phase.  
A magnetometer for orientation sensing relatively to the earth's gravitational field, a triaxial accelerometer for measuring particle accelerations (\(\pm\)16 g), and gyroscopes recording the rotations around the axis of the particle's local coordinate system (at a range of \(\pm\)2000\({}^{\circ}\)/s) comprise the IMU. All these data can be fused (as shown in [48]) and are recorded at 200 Hz. A microcontroller is used to take data from the sensor and writes these to the external memory (microSD) from where the data can be downloaded to a personal computer for further analysis. The sensor benefits from accurate calibration routines, checking for the orthogonality of the axes of the sensor and a full 360\({}^{\circ}\) rotation test.  
Figure 6 shows the used instrumented particle with its internal components, including a 3D-printed holder, the IMU sensor, and the upper and lower half hemispheres. The lightweight casing of the sensor as well as the IMU size, along with the relative size of the casing (limited by the size of the IMU sensor), mean that the instrumented particle casing and sensor are having a solid density lighter than that of water (specific density <1). In order to achieve solid densities that will allow the instrumented particle to be transported as bedload in saltation mode, rectangular stripes of lead sheet shaped to fit the lower half casing (placed around the inner part of the lower half casing, like an O-ring, in Figure 6) and inside the 3D-printed holder, which also fits the IMU sensor. By fitting an appropriate number of stripes symmetrically (so as to balance the particle) within the holder (within the narrow gaps), specific instrumented particle densities can be achieved, in a repeatable fashion.
## 2 Study Area and Datasets
### Study Area  
In this paper, the upstream agricultural lands of Mahabad city were selected for evaluating the proposed methodology. The study region is in the northwest part of Iran and lies south of Lake Urmia in a fertile plain (Figure 1a,b), approximately 1400 m above sea level (Figure 1c). The average annual temperature and rainfall of this region are 12 \({}^{\circ}\)C and 390 mm, respectively [49]. Considering the population living in the surrounding areas of the study site, about 200,000 people are influenced by the agricultural products of this region. Additionally, this region's croplands directly impact the water dynamics of Lake Urmia, contributing to its gradual desiccation [50]. Figure 1d illustrates the 10-year average (2013-2023) of Normalized Difference Vegetation Index (NDVI) derived from L8/9 data. The surrounding regions of the area are mainly covered by rocks and mountains. Therefore, the inside region of the boundary depicted in Figure 1c,d was chosen as the study site. The majority of agricultural lands are situated in the central parts, whereas the surrounding regions are mainly related to bare lands and urban areas. So, there is a diverse ecosystem within the area, encompassing agricultural lands with various crop types, bare soil, and urban areas. Various agricultural products are cultivated in this region, including both autumn and spring crops. Wheat is the main autumn crop, while beet, alfalfa, corn, and onion are the main spring crops. Additionally, there are extensive garden lands of apple in this region. Following the agricultural calendar, the cultivation of autumn crops begins in November, while the harvesting of spring crops continues until the end of December. Consequently, the cropping year in this region spans from November of the previous year to December of the following year.  
Figure 1: (**a**) Location of the study area in Iran, (**b**) S1, S2, and L8/9 scenes and orbits over the study site, (**c**) Digital Elevation Model (DEM) of the study site, and (**d**) Average NDVI of the past 10 years (2013–2023), derived from L8/9.
# Improving the Manipulability of a Redundant Arm Using Decoupled Hybrid Visual Servoing
## 2 Methodology
### Decoupled Hybrid Visual Servoing  
The proposed DHVS method decouples the \(Z\)-axis of translational velocity and the three rotational velocities (the components that create the IBVS singularity and unnecessary motions for the robot) from the image-Jacobian matrix. The translational velocities of \(X\) and \(Y\) components will be measured in 2D, while the error of the other four parameters will be calculated by 3D estimation of the target. The control rule in the classic IBVS system is defined as bellow:  
\[\mathbf{\dot{s}}=\mathbf{L}_{i}\mathbf{v}_{cam} \tag{6}\]  
By decoupling the interaction matrix, the control law would be amended as follows:  
\[\mathbf{\dot{s}}=\mathbf{L}_{xy}\mathbf{v}_{xy}+\mathbf{L}_{r}\mathbf{v}_{r} \tag{7}\]  
where \(\mathbf{v}_{xy}=[\mathbf{v}_{x}\,\mathbf{v}_{y}]^{T}\) and \(\mathbf{v}_{r}=[\mathbf{v}_{z}\,w_{x}\,w_{y}\,w_{z}]^{T}\). In addition, \(\mathbf{L}_{xy}\) and \(\mathbf{L}_{r}\) are calculated as follows:  
\[\mathbf{L}_{xy}=\left[\begin{array}{cc}\frac{f}{Z}&0\\ 0&\frac{f}{Z}\end{array}\right] \tag{8}\]  
\[\mathbf{L}_{r}=\left[\begin{array}{cc}-\frac{u}{Z}&-\frac{u\bar{v}}{f}&\frac {f^{2}+u^{2}}{f}&-v\\ -\frac{v}{Z}&-\frac{f^{2}+v^{2}}{f}&\frac{u\bar{v}}{f}&u\end{array}\right] \tag{9}\]  
Hence:  
\[\mathbf{v}_{xy}=\mathbf{L}_{xy}^{+}\{\mathbf{\dot{s}}-\mathbf{L}_{r}\mathbf{v }_{r}\} \tag{10}\]  
since the time variation of the features are related to the feature errors \(\mathbf{\dot{s}}=-k\mathbf{e}\), therefore (10) will change to:  
\[\mathbf{v}_{xy}=\mathbf{L}_{xy}^{+}\{-k(\|e\|)\mathbf{e}-\mathbf{L}_{r}\mathbf{ v}_{r}\} \tag{11}\]  
To reduce the convergence time, the following adaptive representation of the controller gain has been implemented [30]:  
\[k(\|e\|)=(k(0)-k(\infty))e^{\frac{-k(0)}{k(0)-k(\infty)}\|e\|}+k(\infty) \tag{12}\]In (12), for small amounts (less than 0.005 m) of \(\|e\|\) the positive amount of gain is \(k_{0}=k(0)\), while for the high amounts (more than 0.005 m) of \(\|e\|\) the gain is \(k_{\infty}=k_{\|e\|\to\infty},k(\|e\|)\), and the slope of \(k\) at \(\|e\|=0\) is \(k_{0}^{\prime}\).  
The term \(\mathbf{L}_{r}\mathbf{v}_{r}\) will be calculated during each iteration, and the outcome of this term will be placed in (11). To determine \(\mathbf{v}_{r}\) in the PBVS method the same scenario for the IBVS method will happen as follows:  
\[\mathbf{s}_{p}=\mathbf{L}_{Pxy}\mathbf{v}_{xy}+\mathbf{L}_{Pr}\mathbf{v}_{r} \tag{13}\]  
where \(\mathbf{L}_{Pxy}\) is a matrix generated by the first and second columns of \(\mathbf{L}_{P}\) in (3), and \(\mathbf{L}_{Pr}\) is a matrix created by the last three columns of \(\mathbf{L}_{P}\) in (3). Therefore:  
\[\mathbf{v}_{r}=\mathbf{L}_{Pr}^{+}\big{\{}-k(\|e\|)\mathbf{e}_{p}-\mathbf{L}_ {Pxy}\mathbf{v}_{xy}\big{\}} \tag{14}\]  
The camera velocity vector can be calculated by solving (11) and (14), simultaneously.
# CoDA: Collaborative Data Aggregation in Emerging Sensor Networks Using Bio-Level Voronoi Diagrams
## 5 Simulation Results
Finally, the synchronization error and overhead of the CoDA and DDB protocol are analyzed. The time derivation is set as sending node broadcasts a message until it being receives by the destination node. Figure 12 shows the synchronization error comparison. Each error variable is independent and follows the Gauss distribution. In the early stage of synchronization, the synchronization error of the two protocols is not quite different. With the time elapse, it can be observed that CoDA demonstrate much better than DDB in aspect of synchronization error, and the variation range of error value is stable and narrow. Figure 13 shows the comparison of cumulative synchronization error of different protocols. In the experiment, the synchronization of 4 hops is set up, and the simulation time is limited to 120 s. In the initial stage, DDB can obtain relatively better precise synchronization than CoDA. Generally, the differences of cumulative synchronization error between DDB and CoDA are not significant. With the passage of time, the cumulative synchronization error demonstrates an increasing trend. Figure 11: Comparison of energy consumed by each node. Figure 12: Comparison of synchronization error. As shown in Figure 14, the CoDA and DDB algorithms are compared to the synchronization overhead. Before the burst packet is sent, the RTS packet is 20 bytes, CTS is 14 bytes, the data frame is 2346 bytes. Besides, the reply message is a fixed length. Therefore, the effect of RTS/CTS on the convergence rate is very small. From the experimental results, it can be seen that the number of synchronous information packets of CoDA can be controlled at a lower level with the increase of the node. That is because the inter-cluster communication by multi-hop mechanism in CoDA can reduce a large number of overhead packets compared to DDB, which can reduce the power consumption of cluster heads. In addition, the number of synchronization messages is related to the number of nodes in the network. The number of nodes is more, the greater the synchronization overhead is. In general, the CoDA has the advantages of less accumulation error, high synchronization accuracy and good availability.
# CTIMS: Automated Defect Detection Framework Using Computed Tomography
## 4 Data Pre-Processing  
CT data pre-processing is a critical step in CT inspection as it represents the initial operation to perform on the raw data collected from the CT scanner. This step prepares the 2D or 3D CT data for the next step, such as data storage or CT inspection. Thus, pre-processing plays an essential role in the whole inspection process. While dealing with 3D datasets, some challenges need to be considered while collecting data to improve the visualization and inspection algorithms. First is the image reconstruction, which converts the collected projection images into a 3D volume of the scanned object tool [35]. The reconstruction is performed using the FDK [36] reconstruction implementation. Second, the background subtraction or removal removes the unwanted noisy background or indications of the support materials, which are used to fix the tool during the scanning process. Third, the image registration corrects the geometrical misalignment and aligns the different scans. However, the registration becomes slow and inaccurate while registering big object tools because of the large variation in the CT scan volume sizes and appearance variances [37]. As large tools might not be scanned at once, in this case, the tool is divided into different parts to fit into the scanner. The scans of different tool sections are individually inspected for defects or combined to generate the whole tool scan. It is worth mentioning that data denoising is not included in the pre-processing because of the introduced smoothing by the registration. In addition, it is crucial to keep as much as possible all features/details of the scanned tool to improve the inspection accuracy. Therefore, the denoising/filtering step is performed at the level of inspection by refining the created mask using an erosion filter. The CT data need special treatment and preparation to handle all the previous challenges related to the raw data collected from the CT scanner. The data pre-processing module of the proposed toolbox integrates two main pre-processing functions: background removal and registration.  
These two functions play a key role in cleaning the raw CT data and making the data standardized for every scan. It takes two volume data as the input and returns the background cleaned and correctly oriented volumes ready to be used for further analysis. Figure 9 shows the general workflow of the pre-processing module. The pre-processed data are stored in the database for future use in fault/ defect inspection. More details about these two modules are presented in the following sections.
# Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
## 3 Method
### Classification Models  
#### 3.3.1 K-Nearest Neighbors  
The K-Nearest Neighbors (KNN) algorithm classifies based on the similarity between instances via calculating the distance between an unknown sample and each sample in the training set. It selects K samples with the closest distances and assigns their majority class as the predicted class for the unknown sample. The k-NN algorithm relies on the proximity relationship for classification, and it is highly sensitive to the distribution of classes in the dataset [35]. When a class is severely underrepresented in the dataset, the k-NN algorithm tends to exhibit bias, resulting in poorer classification performance for minority classes. Due to the imbalance in the apple rot dataset and the increased dimensionality after concatenating photonic and electronic features, distances between data samples became relatively uniform, causing a loss of differentiation in distance measurement. This weakened the ability of the KNN algorithm to rely on proximity relationships for classification.