esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast and Efficient Union of Sparse Orthonormal Transforms via DCT and Bayesian Optimization +
###### Abstract
In our method, we exploited the edge detection method in the DCT domain [11]. Then, the DCT matrix is also used to prevent increasing the computational times of the usage of multiple dictionaries. In sparse coding, using an appropriate value of \(\lambda\) is also important. This value affects the performance. However, because the optimal value is not convex and varies for target sparsity, finding the optimal value is difficult. Thus, efficient implementation requires finding \(\lambda\) quickly. In our previous work [12], we used an exhaustive method (or greedy search) to find it for each sparsity level. In [4], the authors proposed a method to gradually reduce the step size for iterations. In this study, we resolved the problem via Bayesian optimization [13; 14]. As a type of global optimization method, Bayesian optimization can find a near-optimal value in a non-convex function. Our contributions can be summarized as follows:  
* The SOT provides powerful transform, which outperforms KLT, but it is limited because of the computation time and dictionary size. * We propose an extension of the SOT to address the limitation of orthonormal dictionary learning, which is based on the union of orthonormal bases. However, in contrast to [10], we formulated an orthogonal sparse coding in several orthogonal sparse coding problems with subdatasets. * We classified input data and allocate each orthogonal dictionary and its coefficients to each classified input data to help to make the input data sparse representation. * To prevent time and iteration increase by the number of dictionaries, we used a double-sparsity structure proposed in [15] with a DCT matrix as a fixed base dictionary. * To find the optimal value of \(\lambda\), which is non-convex and continuous, we adapted Bayesian optimization in our proposed method and set the optimal parameter with fewer iterations. Thus, we propose a framework to design transforms that outperform the SOT in a short time.
# Development and Prospect of UAV-Based Aerial Electrostatic Spray Technology in China
## 4 Analysis and Prospects
### Accumulation of Aerial Electrostatic Spray Technology Research Based on Agricultural UAVs  
During the early stage of China's agricultural aviation electrostatic spray technology research, the design and test were carried out on manned fixed-wing aircraft and helicopter platforms. In recent years, plant protection UAVs have provided Chinese growers with a lower barrier to entry and a higher level of applicability. Chinese scholars have carried on beneficial exploration for UAV-based electrostatic spray technology according to different UAV models, field crops, operating parameters, and electrostatic nozzle parameters, but the main research emphasis is still on the contrast test of electrostatic spray and non-electrostatic spray to verify the operation effect of charged droplets. The research accumulation of airborne electrostatic spray technology based on agricultural UAV platforms is still less. It is not mature at the application level because the morphological characteristics of various crops and the farmland environment need more experimental data support. For example, (1) Relevant studies on the system construction, composition, and weight control of the electrostatic spray system lack continuity; (2) whether the operation with aerial electrostatic spray system is affected by surrounding facilities such as high-voltage lines, or whether it is incompatible with the flight control system of high-precision and fully autonomous plant protection UAV, is still unknown; (3) at present, the practical application of aerial electrostatic spray system in the field is limited to spraying water and water-based pesticides, and there is a lack of in-depth discussion on the research of the special electrostatic liquid pesticides and the electrical conductivity of pesticides for aerial spraying application; (4) working parameters of the spray system, such as charging voltage, system pressure and spraying speed, selection of flight speed and altitude, characteristics of aviation agents, and environmental factors such as temperature, humidity, and wind speed, all affect the settling process of charged droplets. Therefore, it is necessary to observe the droplet characteristics by studying their interaction effects; (5) the development trend of most commercial agricultural UAVs in China is integrating pesticide spraying, seed souring, and fertilizer spreading together with fully autonomous and high-precision operation. Therefore, it is necessary to consider whether the integration of aerial electrostatic spray system, UAV working systems, and flight control system will cause mutual interference.
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 3 Meta-Heuristic Algorithms for Energy-Efficient IoD Path-Planning Description
### IoD Path Representation and Obstacle Modeling  
#### 3.2.1 IoD Path Representation  
In this study, we assume that the information about the environment is known in advance. In specific, the locations and sizes of obstacles are precisely known. The UAVs fly from starting positions (SPs) to the target positions (TPs) as safely as possible. The goal is to find the optimal paths for all drones in the IoD from SPs to TPs. To accomplish this, several factors should be considered, such as the formation of the IoD and terrain obstacles. The IoD formation involves multiple drones flying together in an environment where each drone should be equipped with an efficient path-planning technique to ensure that drones arrive at their destinations safely. The 3D path of the drone is represented by a set of waypoints that are connected to form a path from the starting point to the destination.  
Let \(M\) be the number of waypoints ranging from 1 to \(M\); then, the path can be represented as follows:  
\[Path=(P_{1}(x_{1},y_{1},z_{1}),P_{2}(x2,y2,z2),...,P_{M}(x_{m},y_{m},z_{m})). \tag{6}\]
## 3 Prototype Description and Implementation
### Prototyping of Software Components
#### 3.4.3 IPFS Prototyping
Thus, in the context of the proposed system, the usage of IPFS is justified by the possibility to offer a decentralized manner to store information related to the data collected by the UAV that will be saved in a JSON-formatted file once these data have been encased in the blockchain the system is built into. The files then will be distributed among the peers that belong to the IPFS network they take part in. Since those files (a) contain blockchain-related information and (b) become distributed among each of the IPFS participants, each of the IPFS nodes effectively becomes a blockchain node. In this way, IPFS provides an additional layer of improvements related to security and distribution, due to the following facts:  
1. It provides replicas of the information collected by the system, both included in the blockchain (as explained before, the data collected is stored as JSON objects in files keeping a blockchain-based structure like the one described in the next subsection and in Section 4) and, in case they are left out, as files to be shared in the IPFS network. 2. Eliminates the need to create a specific system server and having to keep it up and running at every moment so that availability of the content is guaranteed. Since information is saved in every IPFS node, it will be available even if just one of the IPFS nodes is still online and can be reached. 3. It makes more difficult to alter the information stored, since not only any data tampering will be noticed whenever the blockchain data validation is taking place, but also because it would have to be changed in each of the IPFS/blockchain nodes that share the data, which would require a very significant effort in terms of time, funds, and computational resources. As it can be seen, IPFS provides significant usefulness in the context of livestock monitoring, especially when the animals will be used for as part of commercial purposes (i.e., meat, dairy or wool production) and information about their activities, feeding grounds and location can come in handy for regulators. Files containing information about the cattle are available for all the participants in the system, enhancing transparency with regard to cattle care and grazing habits, and even if the node that provided them to the IPFS network in the first place becomes unavailable, they can still be used by the other participants in the system that downloaded them. Therefore, IPFS was included as one of the main software components in the UAV-based system that is put forward in this manuscript.
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 2 Related Works
### Edge-Based IoMT  
The IoMT is expected to enable cost-effective application in the healthcare field, especially for effective monitoring of dependent or chronic patients [4]. The combination of IoT and Deep Learning (DL) enables highly effective health monitoring systems. For instance, the health information generated by IoT devices are preprocessed and classified at edge computing and data transmission to the cloud server [5], or even primary disease prediction and patient life extension at home [6].  
Edge computing is considered as local processing, where the data are computed closer to the network edge than where the data are generated. Edge computing optimizes cloud computing systems by processing data at the edge of distributed networks and effectively reduces the amount of data that must be transferred, reducing the network traffic and the distance of data transmission [7]. For large-scale IoT edge computing, considering real-time, reliability, and accuracy, lightweight trust algorithms are more reliable for dealing with the attacks [8]. Accordingly, as the edge devices can spontaneously identify the data, it is an important target for healthcare applications in edge-based IoMT [9]. Figure 1 shows a conceptional edge-IoMT network architecture.
## 2 Experimental Section
The dispersed signal is collected by a CCD array camera (Princeton Instruments, PIXIS 100F) that is synchronized with the laser at 1 kHz repetition rate to achieve shot-to-shot spectral acquisition. A phase-stable optical chopper (Newport 3501) in the Raman pump beampath at 500 Hz (also synchronized with the laser) ensures that one Raman spectrum can be collected within 2 ms through dividing the Raman probe profile with \"Raman pump on\" by \"Raman pump off\" (see Equation (1)). Therefore, the recorded FSRS signal strength is typically expressed in the stimulated Raman gain:  
\[\text{Raman Gain}=\text{Probe\_spectrum}_{\text{pump-on}}/\text{Probe\_spectrum} _{\text{pump-off}}-1 \tag{1}\]  
We routinely collect the Raman spectrum with 3000 laser shots per point and 100 sets, so 150,000 Raman spectra are averaged to yield the final Raman spectrum as shown in Figures 3a and 4a with much improved signal-to-noise ratio. Experimentally we do not observe sharp noises that affect the detection sensitivity of the system, and the highly efficient data averaging within \(\sim\)3 s for each recorded data trace largely removes the broad baseline fluctuations of the probe pulse (e.g., mostly up and down in intensity profile, not left and right along the frequency axis). All the experiments are performed at room temperature (21.9 \({}^{\circ}\)C) and ambient pressure (1 atm). To investigate the resonance Raman enhancement effect, we use the previously developed tunable FSRS in the visible to generate the ps Raman pump pulse (at 487 and 550 nm) in conjunction with an fs Raman probe pulse (to the red side of the pump) based on supercontinuum white light generation in a 2-mm-thick \(Z\)-cut sapphire plate followed by prism compression. Figure 3a displays the detailed comparison between ground-state FSRS spectra collected at various Raman pump-probe wavelengths, wherein the intensity noise level does not increase significantly as Raman pump wavelength approaches the electronic absorption peak. Figure 3b shows the computed Raman modes from density functional theory (DFT) B3 LYP calculations in the electronic ground state using 6-311G+(d, p) basis sets for LD390 in methanol solution and the integral equation formalism polarizable continuum model (IEFPCM-methanol), performed by the _Gaussian 09_ program [26].
# Transfer Learning-Based Detection of Pile Defects in Low-Strain Pile Integrity Testing
## 3 Methodology  
### Dataset  
The dataset used in this study consists of 328 reflectogram images collected from real construction sites, ensuring the authenticity and relevance of the data to practical scenarios. The dataset includes reflectograms from bored reinforced concrete piles installed at real construction sites. All piles were solid without any previous intentional defects, representing typical cases encountered in standard pile integrity testing procedures. The dataset is imbalanced, comprising 246 intact and 82 defective reflectograms. To address this imbalance and improve model performance, oversampling was applied by duplicating the defective reflectograms three times, resulting in a balanced dataset with 246 intact and 246 defective images. Oversampling was chosen over undersampling due to its demonstrated ability to improve classification performance by retaining valuable information from the majority class while enhancing the representation of the minority class [36]. Oversampling was applied exclusively to the training set using random duplication of defective samples, followed by randomized shuffling to mitigate potential bias. Random oversampling via duplication was explicitly employed to balance classes due to the small number of defective  
Figure 1: Types of pile defects: bulge imperfection, neck defect, weak concrete, crack, and broken pile (adapted from [12]).  
cases. Although the synthetic minority oversampling technique (SMOTE) was not utilized in this study, its use could be explored in future work to enhance data diversity. All images were preprocessed and resized to 300 \(\times\) 100 pixels, ensuring uniformity and compatibility across different pretrained CNN architectures. While oversampling addressed class imbalance, the limited dataset size remained a challenge. Advanced data augmentation strategies, such as Generative Adversarial Network (GAN)-based synthetic sample generation, could be employed in future studies to further enhance model generalization capability. Original reflectograms were recorded as 1D time-series acceleration data. These were converted into standardized grayscale 2D images (300 \(\times\) 100 pixels) representing reflectogram amplitude versus time, ensuring consistency across samples. The dataset was split into training (80%) and testing sets (20%).  
To ensure reproducibility, we fixed the random seed to 7 throughout the training and evaluation process. The reported results are based on a single train-validation-test split using this fixed seed. While this configuration provides a consistent baseline for model comparison, future work may involve averaging results over multiple random splits to assess variability.
## 3 Results
### Results of Individual Studies
#### 3.3.2 Outcome: Intraoperative Use  
Fourteen studies were included (five pilot, one retrospective cohort, two retrospective comparative, two prospective comparative, two prospective comparative, one prospective case-control, two prospective cohort, one prospective observational) [11; 23; 24; 25; 26; 28; 29; 30; 31; 34; 35; 37; 38; 39]. The authors of these studies reported that AR efficiently aided surgeons in performing surgical operations. Moreover, it supported the surgeon in a broad range of aspects, enabling them to understand the 3-D anatomy, providing extremely accurate guidance and reducing surgery time and radiation exposure at the same time. AR has been demonstrated to be successful in different surgical procedures, including K-wire placement, verteboplasty and pedicle screw placement [2]. Only one study reported no differences in the results achieved by the AR group compared to the traditional group during total hip arthroplasty [34]. The hardware used in these procedures included head-mounted displays, such as HoloLens or Xvision (Augmedics). One of the articles mentioned MR [28] rather than AR. Different AR registration techniques were used in these studies. These include preoperative CT scans, also used for intraoperative capturing [3], or intraoperative computed tomography (iCT) imaging [25], which ensures high navigational accuracy. In other cases, an optically tracked reference plate was strapped to the patient's limb [28] or a tracked tool was used to perform registration of preoperative CT data directly on the anatomical landmarks of the patient [30]. Lastly, CT scan data were integrated with ultrasound imaging [23], and markerless AR was used to perform real-time registration and to update imaging data during surgery [9].  
According to MINORS, the overall quality of evidence in these studies was assessed in the range between \"low\" and \"high\". All the results are reported in Table 3.
# Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach
## 3 Green Computing for WBANs
### Link Efficiency Oriented Network Model
#### 3.1.2 Neighbor Table (NT)  
Each sensor node stores status information of its adjacent neighboring nodes. This information is collected from HPs received from each neighbor node. Each time a BSN receives HP from its  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline _SID_ & Source node identifier \\ _NID_ & Neighbor node identifier \\ _RE_ & Residual energy \\ _LE_ & Link efficiency between the nodes \\ _HC_ & Number of hop-counts to the BNC \\ \(d\) & Distance from source node to BNC \\ \hline \hline \end{tabular}
\end{table}
Table 1: HP header fields in ELR-W Protocol.  
neighbor node, it updates its information in NT. The procedure for constructing and updating NT is demonstrated in Algorithm 1.  
```
Notations:\(HP\) = Hello packet \(RE_{j}\) = Residual energy of neighbor node \(j\) \(LE_{i,j}\) = Link efficiency between node \(i\) and node \(j\) \(HC_{j,\text{BNC}}\) = Number of hop-counts from neighbor node \(j\) to BNC \(d_{i,j}\) = Distance between nodes \(i\) and \(j\) \((NT)\) = Information in neighbor table \((HP)\) = Information in Hello packets Input:\(H\)Ps from a neighboring node \(j\) Process:
1. start
2. for each HP do
3. if\(HP\Big{(}RE_{j},\ LE_{i,j},\ HC_{i,BNC},\ d_{ij}\Big{)}\
eq\text{NT}\Big{(}RE_{j},\ LE_{i,j},\ HC_{i,BNC},\ d_{ij}\Big{)}\)then
4. update record for neighbor information in neighbor table \(RE_{j}(NT)\ \leftarrow\ RE_{j}(HP)\) \(LE_{ij}(NT)\ \leftarrow\ LE_{i,j}(HP)\) \(d_{ij}(NT)\ \leftarrow\ d_{ij}(HP)\) \(HC_{i,BNC}(NT)\ \leftarrow\ HC_{i,BNC}(HP)\)
```  
**Algorithm 1**Neighbor table construction algorithm of ELR-W protocol at node \(i\)
# Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection
## 4 Discussion
### Performance Review of Potential Increased Use  
The performance of this platform was reviewed in preparation for increasing the number of devices and participants. As mentioned in the Results session, less than 0.5 s is needed to transmit the archived ECG data, execute the arrhythmia-detection algorithm, and save the results in the internal network. In this perspective, 180 ECG data can be obtained and processed sequentially. Because the DMZ server possesses 40-core CPUs and sufficient memory, ECG data of 40 participants can theoretically be processed at the same time. Then, a total of 7200 ECG data can be processed sequentially, and the amount of ECG data obtained for an hour is approximately 0.047 TB. While the number of participants increases, the network capacity in the AWS-VPC is adjusted with the autoscaling function. The storage capacity is another important issue. The internal server possesses 80 TB HDD and its actual usable capacity is approximately 60 TB. Under these conditions, over 1200 h of ECG data, obtained from 7200 participants, can be saved. If the HDD is running low on capacity, the performance of the platform might be degraded. Then, new HDDs should be added while minimizing data loss during downtime with a load balancer.
## 2 Materials and Methods
### Models and Analysis
#### 2.2.3 Rainfall Interpolation and Topographic Models  
A 2 m resolution digital elevation model (DEM) was obtained from Navarre's regional Government at [[ftp://ftp.cartografia.navarra.es/](ftp://ftp.cartografia.navarra.es/)]([ftp://ftp.cartografia.navarra.es/](ftp://ftp.cartografia.navarra.es/)). The downloaded tiles were mosaicked and slope and aspect were calculated from the DEM using the _slope_ and _aspect_ tools.  
The sum of the accumulated rainfall (mm) during the wheat growing season, from November to May, at every station was gathered in a georeferenced-points dataset. This dataset was used to interpolate rainfall using two strategies: inverse distance weighting (IDW) and kriging. All DEM, topographic feature and subsequent GIS processing were completed on ArcGis Pro 2.3.0.
## 2 Overview of the Approach
### Concept of Flexible Buses  
The concept of a flexible power bus refers to a specific point within the electrical network designed to centralize control over a set of small, geographically distributed electrical installations. These installations, often organized as microgrids, collectively contribute to the network's generation and demand capabilities. When multiple microgrids are interconnected and linked to the main grid, they create a centralized node known as the Point of Common Coupling (PCC). This PCC acts as a flexible power bus, where aggregated power adjustments can be managed for both active and reactive power. It provides the system operator with a single, centralized location to control the power flows from all connected installations downstream, thus facilitating power management capabilities [31; 32].  
This flexibility is possible through the integration of various distributed energy resources (DERs) and/or controllable loads within the network. For example, thermostatic loads, which are prevalent in many electrical installations, can be adjusted without violating user comfort limits, allowing for controlled variations in power consumption [33]. Similarly, photovoltaic panels and small wind turbines can be regulated by modifying their operating points, such as adjusting maximum power point tracking (MPPT) settings [34; 35]. Energy storage systems, whether stationary batteries or those integrated into electric vehicles, provide additional flexibility by allowing the injection or absorption of power based on network needs [36]. Moreover, flexible buses can take advantage of deferrable and controllable loads [37; 38], like dishwashers and washing machines, that can shift their operation to periods of lower demand or higher renewable generation. In this context, DG is represented within the flexible bus concept.  
When modeled, these buses differ from traditional PQ or PV buses, where power is typically fixed to a certain value. Instead, in this optimization problem, flexible buses are modeled as PQ buses, but the \(P/Q\) value can vary within predefined limits, represented as \(\{P_{max},P_{min}\}\) and \(\{Q_{max},Q_{min}\}\). This margin is given by the available generation and demand resources downstream of the PCC.  
In a practical implementation, when optimizing the system, the operator identifies potential flexible buses by collecting detailed information on the PCCs of microgrids, distributed installations, and controllable resources across the network. This process includes assessing each bus's capability to adjust power in terms of both generation and demand, as well as determining the specific power adjustment margins available.  
In Figure 1, the flexible power buses are highlighted in blue.