esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Vision
#### 2.2.2 Dataset and Training  
For our application, we expanded our custom dataset from a list of five [Apple, Ball, Box, Bottle, Strawberry] to nine object classes [Apple, Banana, Ball, Box, Bottle, Lemon, Orange, Strawberry, Tomato]. In our previous paper [32], we were not able to identify strawberries as the dataset did not include them from a certain distance; hence, we downloaded custom images from Google Open Image [37] that were manually labelled for detection and segmentation using [38], as shown in Figure 4.  
Figure 4: Vision dataset with custom labelling: (**a**) apple, (**b**) orange, (**c**) tomato.  
Figure 3: Comparison of YOLO models: (**a**) latency, (**b**) object detection performance (“Image Size = 640), (**c**) instance segmentation of the models (“Image Size = 640).  
The YOLOv8 model was trained for 300 epochs with a batch size of 4 and an image size of 640 \(\times\) 480. The optimizer was set to auto, with an initial learning rate of 0.01, a learning rate final factor of 0.01, and momentum set at 0.937. The overlap mask was enabled with a mask ratio of 4.0, and Non-Maximum Suppression (NMS) was set with an IoU threshold of 0.7 to refine object detection. The training was performed on a single GPU (device: 0) with 8 worker threads to optimize data loading. The model was fine-tuned on this dataset, achieving [EMAIL_ADDRESS]--0.566 and F1--0.55. Figure 5a-c show instances per class, dataset, and training loss.  
During training, challenges were encountered when handling a higher number of instances per class, as the system became unstable and frequently crashed due to increased computational demands. To address this, batch sizes and data-loading strategies were optimized, ensuring a balance between memory efficiency and model performance. However, this limitation influenced the dataset composition, requiring adjustments to instance distributions across object categories. While the trained model demonstrated strong generalization, particularly in segmenting well-defined objects, misclassifications persisted for visually similar objects, such as the Bottle, Lemon, and Tomato.
# Conductive Textiles for Signal Sensing and Technical Applications
## 4 Testing for Conductive Textiles
### Electromagnetic Shielding (EMS) Testing
#### 4.2.4 Coaxial Transmission Line Method (Transverse Electromagnetic Cell Method)  
This method is the most popular and widely used as it is more suitable for measuring small-sized and thin conductive textile samples and extended frequency ranges [285]. One of the main advantages of this method is that the results produced in different labs are comparable. The ASTM D4935 test method is the most preferred method for measuring EMSE of textile materials (planar materials) because of a frequency range of 30 MHz to 1.5 GHz operating in a far-field EM wave as well as a plain wave [278]. The measurements can be executed at a particular frequency range using a modulated signal generator, crystal detector, and tweaked amplifier [281]. The set-up usually consists of coaxial adaptors, signal attenuators, and network analyzers [286]. The net shielding effectiveness originated from absorption and reflection is determined for far-field values. Near-field values may also be measured for electric (E) and magnetic (H) sources from far-field values determined prior [282]. This method has a positive side in that it does not require the thickness measurement of the sample.  
A dual TEM (transverse electromagnetic wave) cell method is also used, which allows for measuring the electric and magnetic polarizabilities of test specimens simultaneously [278].
# Unveiling LoRa's Oceanic Reach: Assessing the Coverage of the Azores LoRaWAN Network from an Island
## 1 Introduction
### Document Structure  
This paper is structured as follows. The next section presents a literature review on LoRa and LoRaWAN. This is followed by the materials and methods section, and subsequently by the results and discussion. Lastly, concluding remarks are provided in the final section. The main contributions of this work are:  
* A series of measurements on the deployed network which focus on signal quality and maximum distance of communication.
* Despite packet loss, we establish that communicating the vessel position at five-minute intervals is sufficient to map the vessel location for practical use cases.
* We investigate the impact of line of sight (LOS) vs. non line of sight (NLOS).
* We gained traction to develop the network further and perform more robust testing.
# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 3 State of the Art and Related Products  
Remote sensing satellite data have found application in a plethora of different business intelligence fields. In this paper, we focus on the ones highly correlated with the Greek economy, such as the tourism and agriculture industries. We pay particular attention to applications related to nighttime lights and urbanization, which are good proxies of the economy.
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 3 Proposed Method
### Proposed Model  
The innovative model in Figure 4 proposed herein represents a nuanced evolution of the widely acclaimed U-Net architecture, specifically honed to address the intricate challenges inherent in satellite imagery segmentation. In this tailored adaptation, a meticulous amalgamation of state-of-the-art techniques is employed, synergizing the computational efficiency offered by separable convolutions with the profound contextual awareness instilled by the self-attention mechanism.  
Figure 3: Workflow for semantic segmentation of aerial imagery.
###### Abstract  
Mobility is a significant robotic task. It is the most important function when robotics is applied to domains such as autonomous cars, home service robots, and autonomous underwater vehicles. Despite extensive research on this topic, robots still suffer from difficulties when moving in complex environments, especially in practical applications. Therefore, the ability to have enough intelligence while moving is a key issue for the success of robots. Researchers have proposed a variety of methods and algorithms, including navigation and tracking. To help readers swiftly understand the recent advances in methodology and algorithms for robot movement, we present this survey, which provides a detailed review of the existing methods of navigation and tracking. In particular, this survey features a relation-based architecture that enables readers to easily grasp the key points of mobile intelligence. We first outline the key problems in robot systems and point out the relationship among robotics, navigation, and tracking. We then illustrate navigation using different sensors and the fusion methods and detail the state estimation and tracking models for target maneuvering. Finally, we address several issues of deep learning as well as the mobile intelligence of robots as suggested future research topics. The contributions of this survey are threefold. First, we review the literature of navigation according to the applied sensors and fusion method. Second, we detail the models for target maneuvering and the existing tracking based on estimation, such as the Kalman filter and its series developed form, according to their model-construction mechanisms: linear, nonlinear, and non-Gaussian white noise. Third, we illustrate the artificial intelligence approach--especially deep learning methods--and discuss its combination with the estimation method.  
mobile intelligence; navigation; tracking; Kalman filter; estimation; tracking models; interacting multiple model; adaptive model; deep learning Article
## 3 Real-Time Implementation of FCS-MPC Techniques
### Implementation of the Real-Time Study  
The analysis of the predictive control schemes of the three-phase, two-level VSI feeding an isolated linear and non-linear load (both shown in Figure 2) was designed in MATLAB/Simulink 2021a, and the performance and waveforms were tested with the aid of RT simulation using OPAL OP5600 and RT-LAB 2022.1 software, as shown in Figure 5. The parameters of the system are detailed in Table 2, and the analysis was conducted with a sampling frequency of 20 kHz, in addition to other sampling frequencies specified for each respective case.  
Real-time experimentation in this study was conducted using the OPAL-RT OP5600 simulation platform. This state-of-the-art apparatus, located in the FlexElec Laboratory within the Energy Technologies Building at the University of Nottingham, provides a robust environment for advanced power electronics research. The complete test configuration, including the real-time setup, is illustrated in Figure 6.  
Figure 5: Real-time implementation concept.  
In the real-time implementation and validation of the proposed control strategies, it is assumed that the power semiconductors of the two-level voltage source inverter (2L-VSI) function as ideal switches. This assumption simplifies the mathematical model of the converter by disregarding non-linearities such as switching losses, conduction voltage drops, and dead-time effects. This study aims primarily to validate the performance and logic of the predictive control algorithm itself rather than to examine the performance variations associated with specific semiconductor technologies such as IGBT or SiC-MOSFET.  
The parameters for the simulations performed in real time are shown in Table 2.
## 2 Numerical Modeling
### Analysis Cases and Mesh Sensitivity
The boundary layer cells are allocated to the fluid-solid-coupled boundary, with 20 layers of graded fluid cells and 5 layers of graded solid cells. The mesh sensitivity analysis was conducted using Scheme A1 as an example, as presented in Table 3. Three different mesh resolutions were considered: coarse (3.26 million), medium (5.33 million), and fine (11.18 million). Monitoring variables were the outer-wall temperature at the leading edge (\(T_{\text{w,le}}\)) and the pressure drop between the inlet and outlet (\(\Delta P\)). To evaluate the mesh quality, the deviation (\(\epsilon\)) was defined as the relative error between the results computed from the fine mesh and those computed from the other mesh. It can be observed that deviations in \(T_{\text{w,le}}\) for coarse and medium meshes are 1.00% and 0.18%, respectively, while deviations in \(\Delta P\) for corresponding meshes are 6.47% and 0.36%, respectively. Based on these findings, it can be concluded that the medium mesh exhibits  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Type** & \begin{tabular}{c} **Branch** \\ **Configuration** \\ \end{tabular}  & **Test Round** & **Branch Total Flow Rate** & \begin{tabular}{c} **Single-Channel** \\ **BC Conditions** \\ \end{tabular}  & **Candidate Schemes** \\ \hline \multirow{6}{*}{ \begin{tabular}{c} Temperature \\ test \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} Series \\ arrangement \\ \end{tabular}  } & First & 200 g/s & \begin{tabular}{c} \(\hat{m}_{\text{in}}=2\) g/s, \\ \(T_{\text{in}}=400\) K \\ \end{tabular}  & Schemes A1, A2, B1, \\  & \multirow{2}{*}{ \begin{tabular}{c} Parallel \\ arrangement \\ \end{tabular}  } & \multirow{2}{*}{Second} & \multirow{2}{*}{100 g/s} & \(\hat{m}_{\text{in}}=400\) K & B1-M, B2, B2-M \\  & & & & & \\ \cline{1-1}  & \multirow{2}{*}{\ \begin{tabular}{c} Parallel \\ arrangement \\ \end{tabular}  } & \multirow{2}{*}{Second} & \multirow{2}{*}{100 g/s} & \(\hat{m}_{\text{in}}=0.5\) g/s, \\  & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & \multirow{2}{*}{ \begin{tabular}{c} Thermal– \\ mechanical test \\ \end{tabular}  } & \multirow{2}{*}{-} & \  
sufficient density for Scheme A1, and similar-scale meshes will be adopted for simulations of other schemes.
# Optimal Control Strategy for Floating Offshore Wind Turbines Based on Grey Wolf Optimizer
###### Abstract  
Due to the present trend in the wind industry to operate in deep seas, floating offshore wind turbines (FOWTs) are an area of study that is expanding. FOWT platforms cause increased structural movement, which can reduce the turbine's power production and increase structural stress. New FOWT control strategies are now required as a result. The gain-scheduled proportional-integral (GSPI) controller, one of the most used control strategies, modifies the pitch angle of the blades in the above-rated zone. However, this method necessitates considerable mathematical approximations to calculate the control advantages. This study offers an improved GSPI controller (OGSPI) that uses the grey wolf optimizer (GWO) optimization method to reduce platform motion while preserving rated power output. The GWO chooses the controller's ideal settings. The optimization objective function incorporates decreasing the platform pitch movements, and the resulting value is used to update the solutions. The effectiveness of the GWO in locating the best solutions has been evaluated using new optimization methods. These algorithms include the COOT optimization algorithm, the sine cosine algorithm (SCA), the African cultures optimization algorithm (AVOA), the Harris hawks optimization (HHO), and the whale optimization algorithm (WOA). The final findings show that, compared to those caused by the conventional GSPI, the suggested OSGPI may successfully minimize platform motion by 50.48%.  
floating offshore wind turbines; metaheuristic optimization; grey wolf optimizer; pitch control
# A Fast Hole-Filling Method for Triangular Mesh in Additive Repair
## 4 Preliminaries  
### Basic Concepts  
As shown in Figure 2a, a triangular mesh is composed of a set of vertices and triangles, in which a boundary edge is an edge that only has one adjacent triangle, and a boundary vertex is a vertex that is adjacent to a boundary edge. In addition, all triangles and vertices who share one common vertex are called 1-ring triangles and 1-ring vertices respectively. In order to make the notations appeared in this paper uniform, a group of symbols are defined: \(v_{i}\) is a vertex, \(e_{i,j}\) is an edge from \(v_{i}\) to \(v_{i}\), \(f_{i,j,k}\) is a triangle with vertices (\(v_{i}\), \(v_{j}\), \(v_{k}\)) in counterclockwise order and its normal is \(n_{i,j,k}=\overrightarrow{e_{j,i}}\times\overrightarrow{e_{k,j}}\), \(h_{i,j}\) is a halfedge from \(v_{j}\) to \(v_{i}\), and \(n_{i}\) is the normal at vertex \(v_{i}\). In this work, a halfedge data structure of triangular mesh is adopted for quick query, as shown in Figure 2b, in which two triangles \(f_{0,1,2}\), \(f_{0,3,1}\) share a common edge \(e_{0,1}\), so we call them as the adjacent triangles of \(e_{0,1}\). Since the startpoint of the edge \(e_{1,2}\) is the same as the endpoint of \(e_{0,1}\), \(e_{1,2}\) is regarded as the next edge of \(e_{0,1}\). Similarly, its previous edge is the edge \(e_{2,0}\). Furthermore, two vectors \(h_{0,1}\) and \(h_{1,0}\) are defined as the halfedges of \(e_{0,1}\). This data structure enables us to efficiently and rapidly access its topological elements for one selected vertex or edge. Assuming that all mesh models used in this paper are manifold, and especially there are no islands inside the detected holes.
## 4 Experimental Setup
### Evaluation Metrics
#### 4.1.1 Imperceptibility  
In order to evaluate the imperceptibility of the proposed method, several metrics were used to measure the amount of distortion introduced by the embedding process. This distortion can be measured geometrically or perceptually. The maximum root mean square error (MRMS) proposed in [22] was used to calculate the objective distortion between the original mesh and the watermarked ones.  
The MRMS, which refers to the maximum between the two root mean square error (RMS) distances, is calculated by  
\[d_{MRMS}=max(d_{RMS}(M,Mw),d_{RMS}(Mw,M)) \tag{9}\]  
\[d_{RMS}(M,Mw)=\sqrt{\frac{1}{|M|}\int\int_{p\in M}d(p,Mw)^{2}dM} \tag{10}\]  
where \(p\) is a point on surface \(M\), \(|M|\) represents the area of \(M\), and \(d(p,Mw)\) is the point-to-surface distance between \(p\) and \(Mw\). It is worth noticing that surface-to-surface distance, as the MRMS metric, does not represent the visual distance between the two meshes [21]. Therefore, another perceptual metric is needed to measure the distortion caused by the watermark insertion.  
The mesh structural distortion measure (MSDM) metric is chosen to measure the visual degradation of the watermarked meshes [21].  
The MSDM value is equal to 0 when the original and watermarked 3D objects are identical. Otherwise, the MSDM value is equal to 1 when the objects are visually very different.  
The global MSDM distance between the original mesh \(M\) and the watermarked mesh \(Mw\) having \(n\) vertices respectively is defined by  
\[d_{MSDM}(M,M_{w})=\left(\frac{1}{n}\sum_{i=1}^{n}d_{LMSDM}(a_{i},b_{i})^{3} \right)^{\frac{1}{3}}\in[0,1). \tag{11}\]  
Figure 9: The imperceptibility performance in terms of MRMS using several quantization steps for Bimba, Horse, and Bunny models.  
\(d_{LMSDM}\) is the local MSDM distance between two mesh local windows \(a\) and \(b\) (in meshes \(M\) and \(Mw\), respectively), which is defined by  
\[\begin{split} d_{LMSDM}(a,b)&=(0.4\times Curv(a,b)^ {3}+0.4\times Cont(a,b)^{3}\\ &+0.2\times Surf(a,b)^{3})^{\frac{1}{3}}.\end{split} \tag{12}\]  
\(Curv\), \(Cont\), and \(Surf\) refers to curvature, contrast, and structure comparison functions, respectively.
# Experimental Analysis of Driver Visual Characteristics in Urban Tunnels
## 3 Experiment Design  
### Experiment Locations  
The fast inner ring road in Nanjing was selected as the experimental section, as shown in Figure 1. The Nanjing fast inner ring road comprises four expressways in the core area of the main city, with an overall length of 33.06 km in the shape of a well, among which the length of the elevated section is 18.19 km, while the tunnel section is 14.87 km. There is no signal light in the whole journey. The design speed of the elevated section is 80 km/h, and the speed limit of the tunnel section is 60 km/h. Caochangmen Tunnel, Qingliangmen Tunnel, Shuiximen Tunnel, and Jiqingmen Tunnel are located on the western inner ring road, whereas the south inner ring road is elevated to Yingtian Avenue. The inner ring north line has Mofanmalu Tunnel and Xuanwuhu Tunnel. Jiuhuashan Tunnel, Xianmen Tunnel, and Tongjimen Tunnel are located on the eastern inner ring road. Xuanwuhu Tunnel is the tunnel under the lake, whereas Jiuhuashan Tunnel is the mountain tunnel, and the others are intersection overpass tunnels. All tunnels are designed with six two-way lanes, and the entrances and exits are equipped with enhanced lighting. The experimental tunnel length is between 0.59 and 2.78 km, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Tunnel** & **Length (km)** \\ \hline Xuanwuhu Tunnel & 2.66 \\ Mofanmalu Tunnel & 1.44 \\ Caochangmen Tunnel & 0.73 \\ Qingliangmen Tunnel & 1.66 \\ Shuiximen Tunnel & 1.28 \\ Jiqingmen Tunnel & 0.59 \\ Tongjimen Tunnel & 1.40 \\ Xianmen Tunnel & 1.77 \\ Jiuhuashan Tunnel & 2.78 \\ \hline \end{tabular}
\end{table}
Table 1: Experimental tunnel length statistics.
# Mapping with Pleiades--End-to-End Workflow
## 4 Methods
### Ortho-Rectification  
Standard direct ortho-rectification is performed for each image. In the presented workflow, the rectification is embedded in the DSM resampling step during DSM generation. There, the multi-spectral images are available as epipolar rectified products. In the step of spatial point intersection, those points get their 3D coordinates and their multi-spectral values. Thus, the DSM resampling step is also applied to generate the ortho-images. For mosaicing, the ortho-images are sorted w.r.t. to their global incidence angles such that the more nadir ones are prioritized. During fusion, a radiometric block adjustment is performed based on a least squares adjustment.
# Functions of Representative Terpenoids and Their Biosynthesis Mechanisms in Medicinal Plants
## 7 Regulation of Terpenoid Biosynthesis by Transcription Factors
It was further confirmed that SmERF11 can bind directly to the promoter of _SmDXR_, regulating the biosynthesis of tanshinone. In addition, TcERF15 was found to bind and activate _TcTASY_, encoding a key enzyme in _Taxus_ species, thereby enhancing paclitaxel biosynthesis [140], as well as CsAP2L1, which can promote the transcript levels of _CsCBCAS_ and _CsTHCAS_, suggesting that it promotes cannabinoid biosynthesis [128]. The bHLH TFs have been identified in the regulation of plant growth and development as well as secondary metabolism. Among them, the MYC type in bHLH is the most thoroughly studied transcription factor. In _A. annuua_, AabHLH1 and AaMYC2 are positive regulatory factors for artemisinin biosynthesis. Overexpression of _AabHLH1_ and _AaMYC2_ can significantly increase the levels of genes associated with artemisinin biosynthesis, such as _AaADS_, _AaCYP71 AV1_, and _AnDBR2_[140; 141]. Overexpression of SmbHLH10 and SmbHLH148 in _S. milliorrhiza_ can enhance the tanshinone content. Yeast one-hybrid results indicate that SmbHLH10 and SmbHLH148 can directly bind to G-box elements on the promoter of enzyme genes to activate their expression, thereby controlling the tanshinonebiosynthesis. Further qRT-PCR analysis of the overexpression lines revealed that _SmDXS_, _SmDXR_, _SmCPS_, and _SmJRB_ were greatly induced [142; 143]. In contrast, some bHLH TFs suppress terpenoid biosynthesis; SmbHLH3 negatively control tanshinones biosynthesis and downregulated related genes, such as _SmDXR_, _SmDXS3_, _SmHMGR1_, _SmCPS1_, _SmKSL1_, and _SmCYP6 AH1_[57]. Similarly, TcJAMYC1, TcJAMYC2, and TcJAMYC4 negatively regulate _Taxus_ species paclitaxel biosynthesis [144]. There are several other TFs implicated in terpenoid biosynthesis in medicinal plants. Overexpression of AaNAC1 has been demonstrated to increase the artemisinin levels in _A. annuua_[43]. AaHY5 and AabZIP1, which belong to the bZIP family, are reported to exert positive control on artemisinin biosynthesis in _A. annuua_[145; 146]. SmJAZ3 and SmJAZ9, which belong to the JAZ family, can inhibit the expression of tanshinone biosynthesis genes [147]. In _S. miltiorrhiae_, SmGRAS1 and SmGRAS2, which encode GRAS TFs, can upregulate the transcription of _SmKSL1_ to increase the tanshinone levels [148; 112]. An increasing number of TFs were identified that are involved in the regulation of terpenoids biosynthesis in medicinal plants. These TFs can act independently or cooperatively to simultaneously regulate terpenoids biosynthesis. However, validating their function and determining their substrate specificity are also significant challenges.
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 3 Mathematical Formulation of the Model  
As discussed earlier, we aim to develop a hub-and-spoke location model, which includes determining the location of domestic facilities (as spoke) and upgrading a number of those as the hub locations. We also determine the inter-hub links to be established, as well as the routing decisions among different O-Ds. We present the primary model in Section 3.2, adding the congestion balance constraints in Section 3.3, and the complete model (primary model + congestion balance + flow-based cost function) in Section 3.4.  
In the literature, a set of MIP models are proposed by [PERSON] et al. [46] for urban transportation. Another set of models is presented by [PERSON] et al. [47] and [PERSON] and [PERSON] [24]. In this work, we use the modeling approach of the latter to develop the base model. There are several related research and formulations on this topic, but [PERSON] and [PERSON] [24] were able to demonstrate the superiority of the developed model in terms of having less constraints and variables, as well as less computational time, on a benchmark dataset.