esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Deep Neural Networks for Real-Time Object Classification on Embedded GPUs +
## 5 Accelerated Matrix Multiplication for GPU-Only ConvMM
### Shared Memory Based Accelerated Matrix Multiplication Convolution (ConvSAMM)  
GPUs are equipped with different memory hierarchies which offer various performance characteristics. For the GPU platforms having compute capability less than 3.5, shared memory-based accelerated matrix multiplication (SAMM) is employed in the proposed flow of the deep architectures. Shared memory is much faster than the global memory used by the current version of GPU-only ConvMM layer. A profitable way to perform the computations on the GPU is to divide the data into the subsets or the tiles that fit into the shared memory and copying these tiles from the global memory to the shared memory. Using this technique, memory level parallelism can be achieved and the global memory access can be reduced which is a performance bottleneck.  
There are many redundant global memory accesses in the naive matrix multiplication approach as shown in Figure 10a. It can be analyzed that the same row elements of matrix A are required for every row element of resultant matrix C and similarly to compute the every column element of the resultant matrix C, all the column elements of matrix B are required from the global memory. Therefore, shared memory can be used to eliminate this redundancy and reduce the global memory accesses.  
Using the shared Memory, input matrices (A and B) are divided into the tiles (sub-matrices) to assign to the thread blocks, and multiplication of these tiles is performed independently as shown in Figure 10b. Shared memory based division of input matrices and resultant matrix into tiles is represented by different colors. By summing up the results of these multiplications, elements of the resultant tile (of matrix C) are computed. It can be noted that there is a lot of data redundancy within a tile. To compute the all elements on the same row of the submatrix C, same data of the submatrix A is required and hence it is in shared memory, so it can be accessed faster. Performance of this matrix multiplication is depended on the appropriate selection of the tile size. This tile size should be kept variable and selected according to size of matrices.
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 5 Fire Detection Results
### Model Training  
The forest fire detection system underwent training using various YOLO models on a platform equipped with a powerful AMD RyzenTM 5 5600 H (Santa Clara, CA, USA) processor featuring six cores and 12 threads, complemented by an Nvidia RTX 3060 GPU boasting 6 GB of RAM. The optimization process utilized the stochastic gradient method (SGD) to iteratively update the model parameters. As a result, the key hyper-parameters, including the initial learning rate, batch size, and number of epochs, were configured at 0.003, 16, and 200, respectively. Additionally, we applied specific trainingstrategies designed to expedite convergence and enhance the model accuracy, which were substantiated by the empirical results. Remarkably, the model exhibited convergence and stability at 100 epochs, as depicted in Figure 3, underscoring the effectiveness of the training parameters for the YOLOv8 model. The detection outcomes across various test dataset scenarios are visually presented in Figure 4. As can be seen in the majority of cases, the system effectively identified and classified fire, non-fire, and smoke regions. However, it's noteworthy that certain minute fire objects remained undetected, primarily attributable to their limited discernibility owing to both distance and image resolution constraints.  
Figure 4: Example of the confidence score diversity using the YOLOv8 detector.  
Figure 3: Training metrics using the YOLOv8 detector.
## 3 Results and Discussion
### XRD and EDS  
TiO\({}_{2}\) amorphous structure can be crystalized by calcination at high temperature [20]. Figure 5a represents the XRD of TNF that made up the membrane material. The peaks are marked as R for the rutile structure and A for the anatase structure as identified in the literature [21; 22]. The anatase structure peaks are (101), (004), (200), and (105) which correspond to 2 theta degrees of 25.19, 38.4, 48.35, and 52.39, respectively, whereas the rutile structure peaks are (101) and (110), which correspond to 2 theta degrees of 29.4 and 35.08, respectively. The XRD of TNF matches that in the literature which proves the formation of the TNF structure from its precursor [20]. Figure 5b represents the EDS analysis of the membrane, showing the chemical elements (Ti, C, and O) of the new membranes that are made of titanate and carbon nanofibers.
## 2 Regression Tree Ensembles
### Boosting Regression Tree  
Boosting can be characterized as an enhancement of bagging that involves multiple base models by shifting the focus toward cases that encounter challenges in performing well [24; 25; 30]. In contrast to bagging, boosting regression trees serially construct simple tree models with improvement from one tree model to the other and fuse them to boost the model performance. Each tree is grown from a training dataset \(D=\{(x_{1},y_{1}),(x_{2},y_{2}),\dots,(x_{N},y_{N})\}\), utilizing knowledge from previously grown trees. A relevant algorithm is applied to fit the training datasets \(D^{(t)}\), \(t=1,2,\dots,\ T\), employing a sequence of varying weights \(w^{(1)}\), \(w^{(2)}\), \(\dots\), \(w^{(T)}\), returning the tree's predictions \(\hat{f}^{(1)}(x)\), \(\hat{f}^{(2)}(x)\), \(\dots\), \(\hat{f}^{(T)}(x)\) for each input vector \(x\) and their corresponding weight vector \(w\). The weight vector is usually originated by implementing an initial weight \(w^{(1)}\) and continuously adjusted in each created base model depending on perceived errors. The weight is increased for cases in which the base model generates big errors and reduced for situations in which the model produces low errors. The final output of the models is a weighted sum of the individual model outputs, with the weights being larger for the better models.  
One of the best-known boosting approaches is the LSBoost (least-squares boosting) algorithm, which is adopted in this work. It begins from the null model with residuals \(r_{i}=y_{i}\) for all \(i\) in the training dataset \(D=\{(x_{i},y_{i}),i=1,\ \dots,N\}\), where \(y\in\mathbb{R}\). Then, it fits a decision tree to the residuals from the model instead of the outcome \(y\). Sequentially, the algorithm updates the residuals by adding the newly generated decision tree into the fitted function. Each of these trees can be small to a certain extent by controlling the parameter \(d\) (number of splits) in the algorithm. By fitting small trees to the residuals, the \(\hat{f}\) is slowly boosted in areas where the performance is weak. The shrinkage parameter (learning rate) \(\lambda\) slows down the learning process further. For a small value of \(\lambda\), the iteration number needed to attain a certain training error increases. The output of the boosted model is presented by Equation (2).  
\[\hat{f}_{LSboost}(x)=\hat{f}(x)=\sum_{t=1}^{t}\lambda\hat{f}^{t}(x). \tag{2}\]
## 5 Performance Evaluation
### Results
#### 5.2.1 Attestation Efficiency  
Figure 5a,b shows that with the same number of attestations, our scheme achieves a higher efficiency than the baseline scheme. Our scheme can detect more attacks after a time duration by updating the risk value of each node. As we can see from the figure, our proposed scheme can detect all compromised nodes in the network after evaluating risk values of individual nodes and performing the verification. In contrast, the scheme with the fixed attestation rate cannot change the verification interval dynamically, so that the performance remains constant, leading to poor efficiency. As shown in Figure 5c, with the same number of verifications, our proposed scheme is capable of arranging more verification of nodes that have a high risk value.  
Figure 5: The attestation efficiency comparison. (**a**) Successful attestation; (**b**) number of attestations; (**c**) number of undetected attacks.
# Transaction Mechanism Based on Two-Dimensional Energy and Reliability Pricing for Energy Prosumers
## 1 Introduction
It means that an information-driven transaction is required in active prosumer environments [18]. In this study, a novel transaction mechanism based on dynamic pricing is proposed to pursue improved utility grid operation. The basic concept of the proposed mechanism increases each benefit of utility and prosumers by exchanging more information. For this purpose, it is described as the proposed mechanism and the operation sequence. As a first step, the utilities can announce multiple uncertainty pricing bands beforehand that may vary depending on time, and the prosumers can select a reliability band appropriate for their situation. In the proposed mechanism, prosumers commit both the amount of generation along with the uncertainty of that amount. Thus, utilities have access to more detailed information about generation uncertainties than under the current system. A case study shows that, under the proposed mechanism, prosumers achieve more revenue and grid reliability is improved for utility compared to the conventional pricing case. This is because both utilities and prosumers have an opportunity to maximize their benefits via additional information about uncertainty. In addition, by performance characteristic and economic sensitivity analysis, it is discussed that the proposed mechanism is adaptively worked and has an economically optimal point related to resource characteristics. This mechanism is described from the generation point of view, but it works in the same way for demand. The rest of this paper is organized as follows. Section 2 describes the proposed dynamic-pricing-based transaction mechanism and compares it to conventional energy-based dynamic pricing. Section 3 discusses how to implement the proposed mechanism in energy systems. Section 4 demonstrates a measurement study using real wind generation data applied to the proposed dynamic pricing mechanism. Section 5 concludes the paper.
# Optical-OFDM VLC System: Peak-to-Average Power Ratio Enhancement and Performance Evaluation
## 4 PAPR Reduction Methodology and Proposed Model
We found that the precoding techniques effectively minimize PAPR without compromising BER efficiency, requiring no additional side information or extra processing unlike various precoding methods like Discrete Cosine Transform (DCT), Discrete Hartley Transform (DHT), Walsh-Hadamard Transform (WHT), Vandermonde-Like Matrix transform (VLM), Zadoff-Chu Matrix Transform (ZCT), and precoding with Discrete Fourier Transform (DFT). Furthermore, the VLM approach demonstrated the highest reduction in PAPR compared to other methods. Therefore, we applied the VLM precoding technique to the various OOFDM approaches to mitigate high PAPR issues.
## 3 Methodology
### Materials  
Two different lithologies of igneous rock from the Sines Massif, Portugal were sampled and used in the experiments: (i) Experiment 1 with a cumulate gabbro (CG) from a cliff near Praia do Norte and (ii) Experiment 2 with a gabbro-diorite (GD) sampled at quarry Monte Chaos [11].  
The CG displays a medium to coarse cumulate texture and is formed of clinopyroxene (45-55%), olivine (15-20%), brown amphibo (10-15%), plagioclase (5-10%) and primary ilmenite (5%); it occasionally shows accessory alteration products (e.g., chlorite, actinolite, serpentine). The GD exhibits a layered medium to coarse texture and is composed of plagioclase (50-60%), clinopyroxene (20-25%), subordinate olivine (5-10%), biotite (10-15%) and ilmenite (5-10%). Despite some fractures with chlorite and incipient sericitization, the sample has no significant alteration.  
The coarse-grained CG sample was cut into 40 subsample cubes of 27 cm\({}^{3}\) each and four of 1 cm\({}^{3}\). They were divided in four run sets and one reference set (0-day set). For Experiment 1, each set of specimens had seven cubes of 27 cm\({}^{3}\), two parallelepipeds of 27/2 cm\({}^{3}\) and one of 1 cm\({}^{3}\) (Table 1). The coarse-grained gabbro-diorite sample was cut into 80 subsamples cubes of 27 cm\({}^{3}\) each and four of 1 cm\({}^{3}\). For Experiment 2, each set of specimens had 15 cubes of 27 cm\({}^{3}\), two parallelepipeds of 27/2 cm\({}^{3}\) and one of 1 cm\({}^{3}\) (Table 1).  
The brine used in the experiments is a natural brine sampled from an old borehole in a saline aquifer (see Section 4.2).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Experiment** & **Sample** & **Samples** & **Samples of 27 cm\({}^{3}\)** & **Samples of 1 cm\({}^{3}\)** & **Brine (cm\({}^{3}\))** & **CO\({}_{2}\) (cm\({}^{3}\))** & **Run (Days)** \\ \hline
1 & Cumulate gabbro (CG) & 7 & 2 & 1 & 600 & 1184 & 1, 4, 16, 64 \\
2 & Gabbro-diorite (GD) & 15 & 2 & 1 & 1350 & 218 & 1, 4, 16, 64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental conditions of rock-brine-CO\({}_{2}\) exposure in autoclave.  
Figure 2: (a) Conceptual diagram of the reactive zones (Z1, Z2, Z3, Z4 and Z5) around the injection well according to [34] and [35]. (b) Sample preparation for mineralogical and geochemical analyses before and after SC CO\({}_{2}\) exposition. (c) Layout of the experimental setup. Reactor system used for the pressurized CO\({}_{2}\) injection (modified from. [36]).
## 3 Results  
The initial literature search resulted in finding 193 studies that were screened for title and abstract. After this screening, 109 studies were removed, and the remaining 84 papers were analyzed individually. Figure 3A displays a flowchart of the study selection.  
A total of 56 articles were selected for this review and are reported here. Thirty-eight studies (67.9%) focused exclusively on the automatic or semi-automatic segmentation of a structure of interest (e.g., vasculature or foveal avascular zone). The remaining 18 articles (32.1%) had a final goal of classifying the images into pathological or healthy or disease staging, either based on extracting hand-crafted features and then employing a machine learning technique, or end-to-end deep learning methods. A number of studies (n = 9, 16.1%) presented both a segmentation and a classification method, all of which employed a machine learning classification method based on extracted features that first required the segmentation of a structure of interest (e.g., vasculature parameters or the foveal avascular zone (FAZ) area). These 9 studies are included in both Section 3.1 on segmentation tasks and in Section 3.2 on classification tasks, hence making the final number of analyzed studies focusing on segmentation equal to 47. Studies that included the comparison of various segmentation or classification methods (e.g., thresholding vs. machine learning for segmentation) are included in each relevant section.  
The methods for segmentation were global or local thresholding (n = 23/47, 48.9%), deep learning (n = 11/47, 23.4%), clustering (n = 6/47, 12.9%), active contour models (n = 5/47, 10.6%), edge detection (n = 1/47, 2.1%), or machine learning (n = 1/47, 2.1%). For classification tasks, machine learning was the majority (n = 12/18, 66.7%) over deep learning techniques (n = 6/18, 33.3%). Figure 3B shows a pie chart of the segmentation and classifications tasks.
# Contrast Sensitivity Based Multiscale Base-Detail Separation for Enhanced HDR Imaging
## 2 Image Decomposition  
The iCAM06 model is an image appearance model that modifies the color appearance model for HDR image tone mapping. The color adaptation model, defined in the standard color model CIECAM02, and the nonlinear response functions of human visions, are used. Image decomposition methods are usually used in local tone mapping for edge preservation and enhancement. Detail information might be reduced when the whole dynamic range is largely compressed [11; 12]. The procedure of image decomposition is shown in Figure 1. The detail layer is processed separately for preserving and enhancing, whereas the base layer is tone-compressed through the tone mapping. After separated processing, the base layer is recomposed with the detail layer.  
Figure 1: Procedure for image decomposition.  
For predicting image appearance effects, the detail enhancement is applied to predict the Stevens effect, i.e., [1], an increase in luminance results in an increase in local contrast. According to the brightness function proposed by [PERSON], the changing ratio of brightness is increased when human vision perceives the luminance to be increasing.  
The iCAM06 applies the Stevens effect to enhance the detail layer, but the [PERSON] effect is not suitable for complex scenes such as images because it explains the relationship between brightness and luminance for a simple target [13]. [PERSON] and [PERSON] examined the brightness perception in a complex field. The result shows that the brightness perception in the complex field is affected by a luminance variation in local surrounding areas [14]. Moreover, the human contrast sensitivity functions operate a band-pass filter in luminance information and a low-pass filter in chromatic information [1]. In the human visual system (HVS), a local contrast is more sensitive than a global contrast in a real-world scene. When compressing the global dynamic range, the local details must be clearly preserved in the real-world scene. To accomplish this visual feature, the FBF corresponding to the filter in Figure 1 divides XYZ into base and detail layers [2]. The FBF is sped-up by using a piecewise linear approximation and nearest neighbor down sampling in the iCAM06 [4].  
The pseudo code of FBF is shown in Figure 2[4]. \"Image \(I\)\" in code means the log-image of respective XYZs. NB_SEGMENTS\(\left(\left(\max(I)-\min(I)\right)/\sigma_{r}\right)\) is the interval of intensity (stimulus) range for the piecewise-linear approximation of the original BF, \(\ast\) denotes the convolution and \(\times\) is simple multiplication. The standard deviation \(\sigma_{r}\) (default \(\sigma_{r}=0.35\)) decides the number of NB_SEGMENTS. Accordingly, the FBF causes the reduction of the original luminance information in the iCAM06 due to the log processing of \(Y\) (0 to \(10^{4}\)) of HDR images and the fixed edge-stopping function reduces the detail information in a specific region of the image according to the kernel parameters. The output image from the iCAM06 loses the edge and detail information.
## 2 Materials and Methods
### Quality Assessment of Apples Fumigated with PH\({}_{3}\) and MeBr  
The quality assessment of apples was based on two recommended PH3 fumigation methods, which both showed potential in controlling the larvae of _C. sasakii_, and two different temperature conditions as well as the MeBr treatment currently used in the industry. To assess fruit quality, we used _M. punilla_ var. Fuji fruits that had been stored for 4 weeks and were previously treated with 1-MCP (1 uL L\({}^{-1}\) for 24 h). After PH3 fumigation for 72 h at either 5 \(\pm\) 2 \({}^{\circ}\)C or 25 \(\pm\) 1 \({}^{\circ}\)C, and after MeBr fumigation for 2 h at 15 \(\pm\) 1 \({}^{\circ}\)C, the fumigated apples were stored for either 4 or 8 weeks prior to the evaluation of firmness, sugar content, and internal and external color change. Firmness was measured using a fruit firmness tester (53205 Digital Fruit Firmness Tester; [PERSON], Forli, Italy) equipped with an 8 mm steel plunger. Fruits were compressed by 6 mm at the equatorial zone at a rate of 0.5 mm s\({}^{-1}\), and the maximum number developed during the operation was recorded. Firmness was measured three times per fruit, and 10 fruits were analyzed per treatment. Results were expressed in kgf. Soluble sugar content was measured using a portable refractometer (Hand Refractometer ATC-1E; Atago Co., Tokyo, Japan). Total fruit was homogenized using a tissue homogenizer and filtered through a funnel covered with filter paper. Subsequently, 0.5 mL of the filtered liquid was dropped onto a refractometer, and the sugar content was measured by observing the scope of the refractometer. For each treatment, the sugar content of 10 apples was measured, and the results were expressed as Brix%. Surface color change was measured using a colorimeter (SpectroDens, Techkon, Konigstein, Germany), which expressed color as Hunter L\({}^{*}\) a\({}^{*}\) b\({}^{*}\) values. Tape was used to mark a 10 mm circle at three points along the equatorial zone of each fruit, and the internal (breakdown and flesh browning) and external colors were measured inside and on the surface of the marked circle, respectively. Flavor evaluations of apples from the individual PH3 and MeBr treatment groups were conducted by 10-12 staff members [18]. A hedonic scale from 1 to 9 (1 = strongly dislike, 9 = strongly like) was used for all sensory ratings.
## 3 Methodology and Data Collection
### Data Pre-Processing  
Data from the aforementioned main datasets were linked to the segment where they belonged via road segment ID (_Tmcld_). This link was carried out using the data on the road segment position in World Geodetic System (WGS84) or using special TMC location tables acquired directly from the data provider. Only such road segments which could be mapped to traffic information were used for the prepared analysis. The first issue concerning data understanding was its relatively vast scope, which would not allow the possibility of browsing through the individual segments, let alone the individual FCD measurements.