esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Stitching the Reconstructed ECG Segments and Cross-Correlation Alignment  
* **Stitching the reconstructed ECG segments.** The output of the model is composed of 1000 samples of reconstructed ECG segments. Each sample is 8 s long. Therefore, they must be spliced together to form a continuous reconstructed ECG signal. When combining two ECG segments, the second ECG segment is placed after the first ECG segment. The spliced signal is used as the first segment, and the subsequent signal segments are further merged as the second segment. This step is repeated until all test segments in the recording are connected together. Finally, a completely reconstructed ECG signal is formed. In this study, we selected 600 records. When segmenting the data, 360 records were used as training sets, 120 records were used as validation sets, and 120 records were used as test sets. The length of each record is 8 min. When segmenting into 8 s, each record is divided into 60 signal segments, and each signal segment is 8 s long. When splicing the reconstructed ECG signal, sixty segmented signals of 8 s need to be spliced into 8 min signals, and finally, there are 120 reconstructed ECG signals spliced into 8 min.
* **Cross-correlation alignment.** Cross-correlation alignment is used on the spliced reference ECG signal and the reconstructed ECG signal to better assess the similarity between them and the referenced ECG signal reduces the error between them. In this study, cross-correlation alignment was used for the spliced reconstructed ECG signals and the reference ECG signals.
# Visual-Saliency-Based Abnormality Detection for MRI Brain Images--Alzheimer's Disease Analysis
## 2 Proposed Methodology
### Wavelet Decomposition  
Wavelet transforms are a powerful mathematical tool for image analysis. They provide simultaneous information about the image characteristics of frequency and time localization. Therefore, they are very helpful for classification tasks. Wavelet transformation produces results with less computation and no implementation complexity [25].  
The input images \(m(x,y)\) are decomposed into multiresolution sub-bands using a wavelet transform. The decomposed input images are represented as follows:  
\[m(x,y)\rightarrow(m_{0}m(x,y),\epsilon_{1}(x,y),\epsilon_{2}(x,y),\epsilon_{3} (x,y)\dots\dots) \tag{1}\]  
where \(m_{0}m(x,y)\) is the low-frequency component approximation, and \((\epsilon_{1}(x,y),\epsilon_{2}(x,y),\epsilon_{3}(x,y)\dots\dots)\) is the high-frequency component approximation. Unlike orthogonal cases, biorthogonal wavelet scaling functions are synthesizable. Biorthogonal wavelets were chosen with respect to the input MR images. They were also used to analyze the low-frequency images well. In the proposed method, a biorthogonal 9/7 wavelet filter was utilized for the decomposition. The MRI images were decomposed at different levels using wavelet analysis. A lower decomposition level provides less information, and a higher decomposition level provides more information to the classification unit. However, overfitting is a major concern when selecting the decomposition level. A five-level decomposition level was used in this method to prevent overfitting issues. The MRI axial view image obtained after wavelet decomposition is shown in Figure 3.  
Wavelets divide the input images into different frequency components. The 3D volume of the MRI data is decomposed into multi-resolution sub-bands at different levels.  
Figure 3: (**a**) Wavelet decomposition of MRI image, (**b**) input image, (**c**) lower frequency component, and (**d**) higher frequency component.
## 1 Introduction
Advanced instrumental equipment such as GC-MS/MS and LC-MS/MS is indispensable for laboratories to meet the challenges posed, allowing for precise, accurate, selective, and simultaneous determination of substances in diverse food matrices. Methods of implementing such analysis include optimizing extraction procedures to efficiently recover pesticides from oil matrices, fine-tuning the chromatographic conditions for optimal separation, and calibrating the mass spectrometric detection parameters to achieve the desired sensitivity and selectivity [7; 8; 9]. The analysis of pesticide residues is an extremely difficult task in view of the low concentrations of these compounds in plant matrices, which hinder their detection. Determining pesticide residues in matrices with high oil contents and low to moderate water contents poses a challenge due to signal suppression in chromatographic analysis, often resulting in poor recovery and inadequate precision for certain pesticide residues. The QuECherRS method successfully enables analyte extraction from products with high water contents, but it encounters limitations when applied to items with high oil contents [10]. To enhance detection and extraction methods, various pre-treatment techniques have been employed to concentrate analytes, significantly reducing solvent consumption. Among the innovative methods for the quantitative determination of pesticide residues, solid-phase extraction (SPE) [11], liquid-liquid extraction, dispersive liquid-liquid microextraction (DLLME) [12], solid-phase microextraction (SPME) [13], and single-drop microextraction (SDME) have been utilized [14]. In the case of sample preparation for determination using LC-MS/MS, the normative analytical method QuOil was employed [14]. Moreover, many research works utilized the Quick, Easy, Cheap, Effective, Rugged, and Safe (QuECherRS) technique for extracting pesticide residues from plant products with high oil contents [15]. The objective of this study was to develop an analytical method successfully enabling the determination of 273 pesticides, to meet the requirements outlined in document SANTE/11312/2021 V2, and with the requisite analytical scope in accordance with 396/2005. The developed method aligns with the latest European guidelines for pesticide residue analysis, as outlined in document SANTE/11312/2021 v2 [16].
# Vehicle Lateral Control Based on Augmented Lagrangian DDPG Algorithm
## 1 Introduction
Existing Safe DRL methods primarily employ approaches such as safety shielding layers [22], finite-state controllers [23], semi-supervised reward learning [24], and control barrier functions [25]. However, these solutions often suffer from high implementation complexity and overly conservative constraint conditions. This study innovatively introduces the augmented Lagrangian method to construct a dynamic constraint optimization model. By adaptively adjusting the penalty factor in real-time, it balances the threshold between safety and performance, thereby reducing safety violation rates. This approach provides a novel methodological framework for safe control in complex dynamic environments. This paper investigates four-wheel independent steering control for vehicles, applying deep reinforcement learning algorithms to address trajectory tracking challenges. The study aims to enhance vehicle stability and accuracy during high-speed maneuvers and complex trajectory scenarios. A DDPG-based approach is employed to optimize the neural network architecture, with specific designs for the Critic and Actor network structures and parameters. A novel replay-separated buffer method is proposed to improve the efficiency of experience replay, accelerating the training process while ensuring robustness. By leveraging the augmented Lagrangian method, the vehicle trajectory tracking problem is modeled as a CMDP, and a new reward function is designed to enhance training efficiency. Experimental results demonstrate that the proposed algorithm achieves significant improvements in key performance metrics, including training safety, tracking precision, and ride comfort.
## 3 Development of a 3 \(\upmu\)m-Thick Laser Structure Grown on InP-SiO\({}_{2}\)/Si
### Material Quality Evaluation  
In order to confirm the observations made from in situ measurements, extensive ex situ characterizations were done on both structures by Atomic Force Microscopy (AFM), Scanning Transmission Electron Microscopy (STEM), High-Resolution X-ray Diffraction (HRXRD) and Photoluminescence (PL) measurements. The roughness measured by AFM from a 5 \(\times\) 5 \(\upmu\)m\({}^{2}\) surface is of 0.3 nm for the structure grown on InPSi (Figure 3a). The latter is slightly higher than the one measured for the structure grown on InP (0.2 nm). The visible atomic steps are a signature of high crystal quality. The cross-sectional STEM image demonstrates planar regrowth of the full laser stack on InPSi (Figure 3b). The PL measurements were done using laser excitation at 1064 nm and an InGaAs detector (Figure 4a). The PL signal peak at 1515 nm measured on InPSi is 5 times more intense than the one measured for the structure grown on InP. This observation was already made before [20; 23; 36]. The difference of intensity can be explained by the enhancement of increased excitation and collection induced by the resonant cavity InP/SiO\({}_{2}\)/Si. The HRXRD profiles are presented in Figure 4b. The satellite peaks are sharp and intense which is a signature of high crystal quality. Altogether, these characterizations demonstrate the excellent material quality of the 3 \(\upmu\)m-thick laser structure grown on InPSi as compared to the one grown on InP as a reference.  
Figure 3: Characterizations done on the laser structure grown on InPSi: (**a**) AFM image done of a 5 \(\times\) 5 \(\upmu\)m\({}^{2}\) surface; (**b**) cross-section STEM image.
## 4 Experimental Results
### Methodology  
The loop closure detection stage, a critical component of visual SLAM algorithms, typically relies on image data obtained from a visual SLAM front-end, which often involves image pre-processing and keyframe selection. In order to make a better fit for mainstream visual SLAM algorithms, we used DSO [30] as the front-end visual odometry to obtain photometrically calibrated and representative keyframe data for evaluating loop closure detection algorithms. DSO is a typical direct visual odometry approach that operates on the raw intensity values of image pixels, without relying on feature point extraction and matching.  
In the feature extraction stage of our proposed method, the maximum pixel size of each image block is set to 80, the core distance \(\epsilon_{c}\) is set to 0.02, and the reachability distance \(\epsilon_{r}\) is set to 0.05. In the incremental BoW model, the maximum radius of a leaf is set to 0.005, and the maximum capacity of a node is set to 10. In the loop closure detection stage, the minimum normalized similarity score is set to 1.2, and the minimum consecutive time index interval is set to 5.  
We evaluated our algorithm using precision and recall metrics, with precision being defined as the ratio of the number of correct detections to the total number of detections, and recall being defined as the ratio of the number of correct detections to all real loop closure events.
## 5 Conclusions  
Forest area and canopy cover estimates are among the most essential information in any forest management practice. One of the approaches to derive such information is through visual image interpretation techniques using satellite images. The technique is particularly quick, less costly, and helpful when the technical capacities to use digital image classification and mapping are limited. In this study, visual image interpretation was applied, and the use of PlanetScope, RapidEye, and Sentinel-2 satellite images for forest area and canopy cover estimation was evaluated. PlanetScope and RapidEye images produced similar estimates for all of the study sites and all of the LULC classes. Sentinel-2 image estimates varied significantly from the two other types of images in study sites with relatively denser forest cover, but resulted in similar estimates in sites with less dense forests. In visual image interpretation practices, very high-resolution images should be given priority. The choice of image type can be influenced by the condition of the forest on the ground as well as the costs of the images. In case of using expensive and very high-resolution images, a sampling approach could reduce the overall costs compared to wall-to-wall acquisitions. Furthermore, the precision of the estimate is dependent on the sample size. Therefore, by taking the purpose of the assessment as well as the available resources into account, one should aim for sample sizes that balance the inventory costs and the required precision of an estimate.  
[PERSON] and [PERSON] performed the experiments, analyzed the data and wrote the paper; [PERSON] conceived and designed the experiments, supervised all the research work that led to this paper and reviewed the manuscript; [PERSON] supervised the research work that led to this paper and reviewed the manuscript; [PERSON] reviewed the manuscript; E.N. provided inputs to the experiments and reviewed the manuscript.  
The first and second authors of the study are funded as a Ph.D. student by the Norwegian Government through support of the project \"National MRV capacity Building towards Climate Resilient Development in Ethiopia\".  
Special thanks should go to the Norwegian University of Life Sciences and Wondo Genet College of Forestry and Natural Resources (Hawassa University). The authors would also like to thank the Planet Company for allowing to freely download and use PlanetScope and RapidEye images in this study.  
The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## Materials
### Sentinel-2 Optical Time Series  
We obtained 15, 22 and 17 S2 optical images over Montpellier, Tarbes and Catalonia, respectively, covering the same period of the S1 acquisitions (Section 2.3). The optical cloud-free images were downloaded over each study site with a frequency of approximately one image per month. These optical data were obtained via the Theia website ([[https://www.theia-land.fr/](https://www.theia-land.fr/)]([https://www.theia-land.fr/](https://www.theia-land.fr/))) which provides S2 images corrected for atmospheric effects (Level-2A). Optical images were used to calculate the NDVI values. The NDVI values were integrated as an additional post-processing filter in the proposed change detection method.
## 3 Results and Discussion
### Effects of Fire Locations
Consequently, the heat accumulation from the thermal smoke decreased, thus, the maximum temperature in the stairwell decreased. In general, an increase in the height of the fire location contributes to the safety of the environment with respect to public evacuation and the tolerance limit for high temperatures [38; 39; 40]. The scattered data presented in Figure 11 were obtained from the fourth and sixth columns of Table 5, and their distribution trend is in accordance with Figure 10. Similarly, the relationship can be fitted using the least squares method, as expressed by Equation (9). Moreover, Equations (8) and (9) differ with respect to the slope. Figure 11 reveals that the distance from the fire source determines the amount of thermal smoke and the HRR. In particular, the fresh air through the entrainment movement loses heat during its upward movement; thus, there is a gradual decrease in the temperature of the thermal smoke in accordance with an increase in the height of the fire location. \[T_{m}=-0.34\times Z_{Tm}+148.2 \tag{9}\]  
Figure 10: Relationship between maximum temperature (\(T_{m}\)) and fire location factor (\(h^{*}\)). The scattered data presented in Figure 12 were obtained from the third and fifth columns of Table 5, thus representing \(t_{m}\) (time required to reach maximum temperature) at different fire locations and an HRR of 1.5 MW. The majority of the scattered data was found to be distributed in the area between the two red lines in Figure 11, i.e., the range of \(t_{m}\) was 53-64 s for different fire location factors. Hence, to an extent, the fire location has a slight influence on the duration of the thermal smoke flow. However, when the fire was in the atria of Floor 1 (\(h^{*}\) = 0.002), \(t_{m}\) was significantly longer. This is because in comparison with the others, the fresh air required to support combustion flowed only from the first floor and upper space. However, with an increase in the height of the fire, the fresh air could be obtained from the space below and above. In particular, with an increase in \(h^{*}\), the fire developed at a higher rate with increased air entrainment, which transferred more oxygen from the outside. Moreover, below the colored area, there are two data points (\(h^{*}\) = 0.76 and \(h^{*}\) = 0.86 with corresponding fire locations on Floors 17 and 19), which indicate a shorter \(t_{m}\) in the simulation. This may be because the shorter path of the _stack effect_ resulted in faster smoke flow, and therefore, a shorter time was required to reach the maximum temperature. Figure 11: Relationship between height of maximum temperature (\(Z_{Tm}\)) and maximum temperature (\(T_{m}\)). Figure 12: Time required to reach maximum temperature for different fire location factors.
## 1 Introduction
This is achieved by storing in the BESS only the amount of energy which is estimated to be needed during the night. This yields lower depth of discharges and hence, reduced capacity losses for the BESS. However, in case of inaccurate estimations for the needed energy, decreased economic profit might be observed. Furthermore, in [14] is proposed a cooperative energy management between a utility and households with PV battery systems. The energy management is examined under RTP tariff and aims to operate the BESS at minimum cost for each household. The objective of this work is to develop EMSs for a residential BESS which operates in a ToU tariff scenario. In this work the operation of the BESS is examined without a RES. The novelty in this paper comes in the achievement of the maximum possible economic profit for the consumer indirectly by decreasing the degradation effects to the BESS and hence, extending its lifetime expectancy. The goal is to prove that it is possible to secure increased economic profit for the consumer even in a ToU tariff and with the absence of a RES. In order to achieve that, the EMSs utilize a widely used battery mathematical model which is experimentally validated for a specific BESS and a battery degradation mathematical model from the literature. Furthermore, dynamic optimization methodologies which take under consideration forecasted profiles for the household's energy consumption are developed. This paper is organized as follows: Section 2 presents a specific residential BESS and a seasonal ToU tariff in which the EMSs are applied. Section 3 presents the kinetic battery model (KiBaM) and a battery degradation model for lithium iron phosphate batteries. Section 4 outlines the development of EMSs for application in residential BESS without RES at the seasonal ToU tariff. Section 5 demonstrates the operation of the aforementioned premature-ageing preventing strategies and analyzes their performance. Finally, at Section 6 the concluding remarks of this work are presented.
## 3 Methods
### Background Removal based on Improved K-means Clustering Algorithm  
The background removal step serves to separate the vegetation from the soil. This is done by using the improved self-adaption K-means clustering algorithm. The principle of this algorithm is to minimize the sum of the squares of the distance from each point in the cluster domain to the center of the cluster [18]. This procedure consists of the following steps:  
(a) As initial clusters, choose \(k\) data points at random from the dataset.  
Figure 2: Workflow of a semi-automated method used to estimate leaf coverage.  
2. Calculate the Euclidean distance from each data point \(x_{i}\) (\(i\) = 1,2,... \(k\)) to each cluster center \(m_{i}\) and assign each data point to its nearest cluster center.
3. Calculate new cluster centers \(m_{i}\) so that the squared error distance of each cluster is a minimum.
4. Repeat steps (b) and (c) until the lustering centers \(m_{i}\) remain constant.
5. Terminate the process.  
This method has two main disadvantages: First, the selection of these initial clustering centers may change the final clustering results, and second, it may cause the final result to become trapped in a local optimum. In this paper, we propose to solve these problems by using an improved K-means clustering algorithm based on Otsu multi-threshold segmentation. First, the Otsu algorithm was used for histogram multi-threshold segmentation, which divides each image into several classes and minimizes the variance between these classes. The improved algorithm is as follows:  
1. Take the threshold of the Otsu segmentation \(T_{1}\)-\(T_{k}\) as the initial clustering center of the K-means algorithm.
2. Calculate the Euclidean distance from each data point \(x_{i}\) (\(i\)=1, 2,..., n) to each cluster center \(T_{i}\) and assign each data point to the nearest cluster center.
3. Calculate the new cluster center \(t_{i}\) to minimize the squared error distance of each cluster.
4. Repeat steps (b) and (c) until the clustering centers \(t_{i}\) remain constant.
5. Calculate the arithmetic mean for \(t_{i}\) and \(T_{i}\) and then obtain the final segmentation threshold \(M_{i}\).
6. Use \(M_{i}\) to complete the image segmentation.
# Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe
## 3 Background on Communication Protocols  
Underwater communication protocols are specialized sets of rules and procedures that are designed to facilitate the transmission and reception of data between underwater devices, such as sensors, vehicles, and buoys. These protocols are necessary because the underwater environment poses unique challenges that are not present in other communication scenarios. One major challenge of underwater communication is the high attenuation of electromagnetic waves in water, which limits the range and bandwidth of wireless communication protocols. As a result, most underwater communication protocols use acoustic signals to transmit data. Acoustic signals have a longer range in water than electromagnetic waves and can travel through the water with lower attenuation [14].  
There are various types of underwater communication protocols, each with their own advantages and limitations. For example, some protocols, such as acoustic modems, are designed for high data rate applications and can transmit data at rates of several megabits per second over short distances. Other protocols, such as acoustic telemetry, are designed for long-range communication and can transmit data over distances of several kilometers, but at lower data rates. Underwater communication protocols also differ in terms of their operating frequency, modulation scheme, and error correction techniques. These parameters are important for optimizing the communication link and ensuring reliable data transfer between devices [15].
# Development and Application of Sub-Cycle Mid-Infrared Source Based on Laser Filamentation
## 2 Ultrafast Pump-Probe Spectroscopy  
The ultrabroadband MIR supercontinuum is very suitable for the detection of various vibrational modes of molecules and high-reflection bands caused by free carriers in solid materials. It is one of the ideal probe pulses for the studies of ultrafast dynamics in the fields of molecular science and solid state physics.  
Several groups demonstrated ultrafast pump-probe spectroscopy with the ultrabroadband MIR supercontinuum generated through filamentation. For example, structural dynamics of molecules in the femtosecond time scale was investigated with ultrafast pump-probe spectroscopy using the ultrabroadband MIR probe pulse [9; 10; 11], free-carrier dynamics in semiconductors were clearly observed in the very wide range of the probe energy [12; 13; 14], and two-dimensional spectroscopy with the ultrabroadband MIR probe was experimentally demonstrated [15; 16].