esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Throughput Line Buffer Microarchitecture for Arbitrary Sized Streaming Image Processing
## 4 Logic Implementation of D-SWIM
### Logic of Controller
#### 4.2.2 Buffer-Read Logic  
As introduced previously, the BRAM read address was synchronized to the write address of the LB being written. Thus, the Controller generates the AddrSel signal to indicate the LB index that stores the input block. The RdData loaded from multiple LBs are processed by the buffer-read logic to form the output 2D pixel window (Out2 Dwin) from multiple image lines. The buffer-read logic reverses transformation performed during the buffer-write process, and the circuit is shown in Figure 11. The logic contains three stages to transform the RdData into Out2 Dwin. The first stage performs line-wise reordering that changes the line-order of the LBs' output blocks to the spatial order of the image. As per the line-rolling behavior in Figure 6, (\(H\)-1) blocks of Out2 Dwin are read from the LBs, and the last block is directly sourced from InBlk with delay logic. The second stage performs a circular left-shift (CLS) which reorders the pixels from different BRAMs. The third stage removes several pixels at the beginning and the end of results from the previous stage, which ensures the output blocks are spatially aligned to the InBlk. Subsequently, the pixel blocks after the three-stage processing are concatenated with the delayed InBlk to construct the Out2 Dwin.  
Figure 10: Buffer-write logic.
## 4 Design of Multimedia Independent Multipath Routing Algorithms
### Direction Control Algorithm  
The path is controlled by the expansion angle, and the expansion angle is determined by the path length. The direction control algorithm consists of two parts: calculating the length of the curve and calculating the expansion angle.  
As shown in Figure 5, \(n_{i}\) represents the current node, \(n_{i+1}\) represents the next hop node. \(v_{i}\) and \(v_{i+1}\) represent virtual nodes of \(n_{i}\) and \(n_{i+1}\), respectively. \(v_{i+1}\) is the ideal next hop node of \(n_{i}\) node. The nearest node at this location is \(n_{i+1}\), so \(n_{i+1}\) is chosen as the next hop node. \((x_{t},y_{t})\) represents the coordinates of \(v_{i}\), \(\Delta L_{t}\) represents \(v_{i}\) path increment. \((x_{t+\Delta t},y_{t+\Delta t})\) denotes the next node of the spline curve. Then \(\Delta L_{t}\) can be calculated by the following formula:  
\[\Delta L_{t}=\sqrt{(x_{t+\Delta t}-x_{t})^{2}+(y_{t+\Delta t}-y_{t})^{2}} \tag{25}\]  
Cumulative, then the formula of the total length of the path can be calculated by the following formula:  
\[L=\sum_{j=0}^{\frac{1}{2\pi}}\Delta L_{j} \tag{26}\]  
where the smaller the value of \(\Delta t\), the closer the length of path calculation is to the length of ideal curve of B-spline formula. In practice, the curve length \(L=GetLength(\theta,\lambda)\) is calculated by self-defining integral function.  
Given \(L_{\text{max}}\), \(\theta_{\text{max}}\) can be calculated by Algorithm 1, and \(\Delta\theta\) can be calculated by Equation (24):  
Figure 5: Forwarding packets to the next hop.  
```
1.\(\theta=0\);
2.\(L^{\prime}\)=GetLength(\(\theta\),\(\lambda\));
3.while\(|L^{\prime}-L|>e\)do
4.\(\theta=\theta+\tau\);
5.\(L^{\prime}\)=GetLength(\(\theta\),\(\lambda\));
6.end while
7.return\(\theta\)
```  
**Algorithm 1** Get Spreading Angle (\(\tau\), \(L\)).
# Development and Application of Digital Twin-BIM Technology for Bridge Management
## 4 Building Information Management
### Digital Twin of Comoro Bridge for Structural Performance Evaluation  
#### 4.4.1 Digital Twin-BIM Technology and Finite Element Analysis Interoperability  
This study involved the interoperability between digital twin-BIM technology and finite element analysis (FEA), as depicted in Figure 13. The process of interoperability between digital twin-BIM technology and FEA is outlined as follows:  
1. The complete Comoro Bridge model was developed using BIM tools.
2. A simplification process was conducted to remove secondary elements such as railings, electrical poles, pedestrians, and other nonessential components.
3. The IFC format was utilized for seamless interoperability between BIM and FE analysis (FEA).
4. After achieving interoperability, the bridge model was prepared for the FE analysis (FEA).
5. The finite element analysis (FEA) was performed.
6. Results and evaluations are conducted.
## 3 Results and Discussion
### Adopted Strategies for Adaptation and Mitigation
#### 3.4.2 Policy-Driven Agricultural Adaptation in Europe and the United Kingdom  
[PERSON] et al. [112] present a framework for designing effective European agri-environmental policies that support climate mitigation, adaptation, and biodiversity conservation. They identify various agricultural practices that enhance ecosystem services, particularly in mountainous regions of Europe. These practices are organised into several categories, each encompassing strategies to improve environmental resilience.  
The first category, \"Vegetation and Landscape Elements\", includes measures like maintaining semi-natural vegetation, grasslands, small plots, and traditional land features (hedges, terraces, water points, drove roads, and tracks). Retaining these elements supports biodiversity and stabilises ecosystems. \"Crop and Species Management\" covers strategies like crop diversification, cultivating locally adapted varieties, and genetic selection for high productivity. Practices here also involve rotational cropping with legumes, planting nectar-rich species to support pollinators, and implementing cover crops and green fallows to maintain soil health. Other categories include \"Input Management\", \"Grazing\", and \"Silviculture\", each offering ways to improve soil, plant, and animal resilience in response to changing climate conditions.  
Similarly, [PERSON] [95] explored how agricultural practices in the United Kingdom can be adapted to address climate risks, focusing on insights from farmers and stakeholders.  
The study highlights strategies such as improving infrastructure, enhancing soil health, diversifying risks, and streamlining business practices. Farmers also emphasise adjustments to grazing and feed management, operational timing, and selecting resilient crop varieties and livestock breeds to improve adaptability and productivity. These actions collectively enhance resilience, supporting both environmental stability and farm viability in the face of extreme weather and climate variability.
# Key Management Scheme Based on Route Planning of Mobile Sink in Wireless Sensor Networks
## 3 The Scheme Design
### Establishment of Session Keys  
After the MS movement route is determined, the MS can interact with cluster heads for data transmission. Data interaction is under the premise of building secure session keys. In this article, since the MS contains relatively more abundant resources, the cluster head and MS can establish a session key with the improved ECDH algorithm. However, the member nodes and cluster head could establish a session key using a binary symmetric function due to the limited energies of the member nodes.
When considering the datasets of historical newspapers with Gothic typography, such as the German-Brazilian Newspaper (GBN) Dataset created in the study of [6] with annotations for layout analysis and OCR and the Printed BlaLet GT dataset [28], no references were found investigating layout analysis. Thus, there is a gap in the literature to be filled when it comes to the use of deep learning techniques in German-Brazilian historical newspapers. This work proposes a new annotated dataset for layout analysis of historical Brazilian newspapers and also makes a comparative analysis between the performances of the YOLOv8 to YOLOv11 architectures on a combined dataset to investigate which is the best for the OCR task. Thus, this study provides an original dataset to enrich the area of layout analysis of historical documents, which has a scarcity of available data--especially those with annotations--for layout detection studies. In addition, the results achieved reveal the performance of current YOLO models on a dataset compiled for this application, showing the potential of the models for further research and development of more robust OCR technologies for recognizing historical documents. In summary, the main contributions of this study are:  
* Creation and availability of a dataset of historical Brazilian newspapers for layout analysis, named \"Brazilian Historical Newspaper\" (BHN). As one of the main contributions of this paper, the first dataset of historical Brazilian newspapers with annotations for layout detection was created. * Development of a combined dataset, combining the BHN, GBN, and Printed BlaLet GT datasets, consisting of 459 images of historical documents annotated for layout analysis. After organizing the data and reviewing the annotations, the dataset was made publicly available on the _Roboflow_ (www.roboflow.com, accessed on 25 September 2024) website. * Comparative analysis of the performance of the YOLOv8, YOLOv9, YOLOv10 and YOLOv11 architectures in the layout analysis task, trained on the combined dataset. As Section 2.3 explains, these architectures were selected because of their speed, ease of implementation, and suitability for the task of layout analysis. This paper highlights the potential of these architectures in layout analysis, where significant results were obtained that not only demonstrate the effectiveness of the architectures, but also motivate new scientific research in the area. This paper is organized as follows: Section 2 addresses the general methodology used in the research; Section 3 presents the results and discussions; and finally, Section 4 contains the conclusions about the research and suggestions for future studies.
## 3 An Improved DPC Algorithm Based on Chebyshev Inequality and Differential Privacy
### Analysis of DP-CDPC Algorithm Complexity  
This section provides an analysis of the complexity of the DP-CDPC algorithm. Compared with the DPC algorithm, the DP-CDPC algorithm introduces additional processes such as dichotomy for determining the truncation distance, addition of noise to the local density, and adjustment of parameters for determining the clustering centers. Let \(N\) represent the size of the dataset, the time complexity of the DP-CDPC algorithm is mainly composed of the following components:  
(1) Calculation of statistics. The time complexity of data standardization processing is \(O(N^{2})\). The time complexity of calculating the distance \(d_{ij}\) between samples is \(O(N^{2})\). The time complexity of dichotomy method for determining the cutoff distance \(d_{c}\) is \(O(\log N)\). The time complexity of calculating the local density of each sample \(\rho_{i}\) is \(O(N^{2})\). Adding noise to the local density can obtain the local density of each sample after adding noise \(\rho_{i}\), and the time complexity is \(O(N)\); The time complexity of calculating the center offset distance of each corresponding sample \(\delta_{i}^{\prime}\) is \(O(N^{2})\); For the calculation of the mean and standard deviation of \(\rho_{i}^{\prime}\) and \(\delta_{i}^{\prime}\), the time complexity is \(O(N^{2})\) respectively. The total time complexity is: \(O(N^{2})+O(N^{2})+O(\log N)+O(N^{2})+O(N)+O(N^{2})+4O(N)\ \sim O(N^{2})\).  
(2) Selection of clustering center. The time complexity of getting the threshold of \(\delta_{i}^{\prime}\) and \(\rho_{i}^{\prime}\) is \(O(N)\). The total time complexity of getting the clustering centers is \(O(N)\).  
(3) Assignment of non-clustering centers. Assigning non-clustering center points to the closest points within the cluster based on the nearest neighbor principle has the same time complexity as the DPC algorithm, which is \(O(N)\).  
In summary, the overall time complexity of DP-CDPC algorithm is consistent with that of DPC algorithm, which is \(O(N)\).
## 3 Materials and Methods
### Procedure  
The evaluation experiment was conducted in a specialized university laboratory dedicated to fNIRS research, maintaining controlled noise and lighting conditions. Upon arrival, participants were provided with a concise overview of the experimental protocol  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic** & **Details** \\ \hline Sample Size & 18 \\ Age (M \(\pm\) SD) & 22.61 \(\pm\) 0.98 \\ Age Range & 21–25 years \\ Gender Distribution & Male: 50\%; Female: 50\% \\ Health Status & All participants reported no psychiatric or neurological disorders. \\ Left-hander/right-hander & All participants were right-handed. \\ Experience with Hair Dryers & 100\% of participants reported prior experience using hair dryers. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Participant demographics and characteristics.  
Figure 1: The hair dryer pictures used in the experiment. **(a)** hair dryer 1; **(b)** hair dryer 2; (c)** hair dryer 3. All the hair dryers are grasped by right hands, and each picture was paired with three sound levels: no sound, low sound, and high sound.  
and completed a questionnaire containing demographic information such as age and gender. Afterward, they were seated in a comfortable position facing a 22-inch LCD monitor with a resolution of 1440 \(\times\) 900 pixels and a refresh rate of 60 Hz, through which the experimental stimuli were displayed. Participants were then equipped with the fNIRS experimental equipment. Clear instructions were provided to ensure participants understood the task, following which stimuli were presented in a randomized sequence. Each stimulus was displayed for 6 s [18], accompanied by the product sound played through a Bluetooth stereo connected to the experimental computer, while participants observed the hair dryer product. Prior to each stimulus, a blank screen with a cross was shown for 10 s to allow participants' blood oxygen signals to return to baseline [44]. Baseline regression is a critical step in ensuring the reliability of neural activity measurements [45]. Following each stimulus, participants were instructed to evaluate the overall impression of the hair dryer using a numerical scale ranging from 1 to 7 on the keyboard, where higher numbers denoted higher perceived quality. The experiment lasted approximately 40 min, including the pre-experimental setup. During the experiment, each stimulus was repeated six times to ensure reliable data collection and to account for variability in participants' responses. The six repetitions of each stimulus were presented in a randomized order to minimize potential order effects and habituation biases. The experimental procedure was programmed and controlled using E-Prime 2.0 (Psychology Software Tools, Inc., Pittsburgh, PA, USA), and the experimental design is shown in Figure 2.
## 3 Processing Methods
### Training Method of MDISE Model  
The design of the model's structure plays a key role in its modeling capability, and the training method and hyperparameter settings also influence the model's learning ability. In regression tasks, the mean squared error (MSE) loss function, also known as the L2 loss function, is commonly used. Compared to the linear loss function (L1), the L2 loss function accelerates model convergence but is more sensitive to outliers, which can lead to gradient explosion issues. The SmoothL1 loss function was introduced by [PERSON] in the Fast R-CNN paper [PERSON] (2016), combining the advantages of both L1 and L2 loss functions. It avoids gradient explosion caused by outliers while maintaining sensitivity to small errors, which helps the model make fine-grained adjustments. For input spectral data with poor consistency, the proposed MDISE model uses the SmoothL1 loss function to compute the error of the inversion results. The expression for the SmoothL1 loss function is as follows:  
\[SmoothL1(y,y^{\prime})=\begin{cases}\frac{0.5(y-y^{\prime})^{2}}{\beta}&|y-y^ {\prime}|<\beta\\ |y-y^{\prime}|-0.5\beta&\text{otherwise}\end{cases} \tag{8}\]  
where \(y\) represents the true concentration value of the sample, \(y^{\prime}\) represents the predicted concentration value, and the hyperparameter \(\beta\) controls the threshold for switching between L2 loss and L1 loss function, with a range of \(\beta>0\). When \(\beta\) is set to the default value of \(1\)it means that when the absolute error between the true and predicted gas concentrations is less than 1%, the L2 loss function is used, resulting in smoother optimization and faster convergence. When the absolute error is greater than or equal to 1%, the L1 loss function is used instead, ensuring more stable gradients in the presence of outliers. The model parameters are optimized using the stochastic gradient descent with momentum (SGDM) optimizer, where the momentum is set to 0.9, the L2 regularization coefficient is set to 0.0001, and the initial learning rate is set to 0.01. To improve training efficiency, the ReduceLROnPlateau learning rate decay strategy is employed to adaptively adjust the learning rate. If the validation loss does not decrease within 20 training epochs, the learning rate is halved, with a maximum of 200 training epochs. The Early Stopping strategy is used to prevent overfitting, and training is stopped early if the validation loss does not decrease within 50 epochs.
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 3 Results and Discussions  
This study develops advanced machine learning approaches--Artificial Neural Networks (ANNs) and Random Forests (RFs)--to enhance energy efficiency in HVAC systems by predicting energy demands for two distinct buildings: the Lucia building in Valladolid and the FUHEM facility in Madrid, Spain. Our research aims to validate these models' performance while analyzing their effectiveness in addressing different thermal requirements: comprehensive HVAC energy demands for Lucia versus specific heating needs for FUHEM. The methodology involved systematic data collection and preprocessing from both sites, followed by rigorous validation procedures including cross-validation to ensure prediction reliability. The Lucia dataset incorporated multiple HVAC demand drivers including temperature fluctuations, humidity levels, occupancy patterns, and insulation characteristics. In contrast, the FUHEM dataset focused primarily on heating demands influenced by external temperature variations and temporal factors (daily and seasonal cycles). Comparative analysis revealed notable performance differences between the models, reflecting the distinct energy consumption profiles of each building. These findings contribute to developing integrated energy management strategies adaptable to varying climatic conditions and operational requirements.
## 2 Methodology
### Description of the Algorithms
#### 2.3.1 OCx Algorithm  
The OCx algorithm is one of the primary methodologies we employed to derive mChl-\(a\) from R\({}_{\mathrm{rs}}\) data. This algorithm is characterized by a polynomial relationship expressed as:  
\[\log_{10}\left(\mathrm{mChl}-a\right)= a_{0}+\sum_{i=1}^{4}a_{i}\mathrm{F}^{i}, \tag{1}\]  
where the \(a_{i}\) coefficients of this function are sensor-specific and based on global data [5; 25], and F is the logarithm of the blue/green ratio of remotely sensed reflectance (\(\mathfrak{N}\)), as seen below:  
\[\mathrm{F}=\log_{10}[\mathfrak{N}]=\log_{10}\biggl{[}\frac{max(Rrs(443,490))} {Rrs(555)}\biggr{]}, \tag{2}\]  
The OCx algorithm was assessed using the parameters associated with the different sensors (SeaWiFs, MERIS, MODIS, and OCTS [25]). The optimal fit was for the SeaWiFs parameters \(a_{0}=0.3272\), \(a_{1}=-2.9940\), \(a_{2}=2.7218\), \(a_{3}=-1.2259\), \(a_{4}=-0.5683\). Consequently, these parameters were selected for this study.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Year** & **January** & **February** & **March** & **April** & **May** & **June** & **July** & **August** & **September** & **October** & **November** & **December** \\ \hline  
\end{tabular}
\end{table}
Table 1: Dates of IMECOCAL campaigns where in situ Chl-\(a\) data were obtained are marked in blue.
## 2 Coding Metamaterials for RCS Reduction
### Development of Unit-Cell Metamaterial Structures  
Initially, a unit-cell selection process for the one-bit coding metamaterials was performed through a parametric study of several design structures. All the designs were constructed with similar dimensions of 5 \(\times\) 5 mm\({}^{2}\) substrate materials. The trial-and-errormethod was utilised to discover the optimised unit-cell metamaterial design for the coding metamaterial application. The focus on the designs was divided into four structures, namely circular, square, triangular, and unique shapes of SRR metamaterials. In each subsection, various substrate materials were used to analyse the outcomes, such as FR-4, Rogers RT6002, and Rogers RO3006, which were bought from Shenzhen Dihe Electronic Co., Ltd. (Shenzhen, China). The selected substrate materials have dielectric constants and tangent losses of 4.3, 2.94, and 6.5 and 0.025, 0.0012, and 0.002, respectively. These substrate materials were adopted because they possess large differences in dielectric constant values between the materials. Therefore, they will typically reveal whether the dielectric constant of each substrate material influences the RCS-reduction values. Moreover, these substrate materials are easily available in the local market.  
Three designs, as illustrated in Table 1, were adopted, and the bistatic scattering patterns at 8 GHz (C-band) of each design were tabulated as well in the same table. This analysis revealed that most of the design structures exhibit similar bistatic RCS peak points, which range from \(-\)55.1 dBm\({}^{2}\) to \(-\)58.9 dBm\({}^{2}\). The overall designs exhibited the lowest bistatic RCS values when an FR-4 substrate material was used compared to other materials. Moreover, the scattering patterns exhibited equivalent outcomes for all the design structures in spherical shapes. Figure 1 demonstrates the monostatic RCS values of each design with various substrate materials. There were only minor discrepancies that occurred between the designs and the substrate materials.  
Figure 1: _Cont._  
Figure 1: Monostatic RCS values for circular metamaterial unit-cell design with various substrate materials: (**a**) Design 1; (**b**) Design 2; and (**c**) Design 3.