esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 An Overview of Lattice Structures Built from Polygonal Hollow Shapes  
This section gives a brief review of lattice architectures built with different polygon hollow shapes. This is conducted to draw attention to often-used polygon hollow shapes in the design of lattice structures.  
Lattice structures that incorporate polygonal hollow shapes do so by building a configuration of interconnecting beams or struts structured in a grid-like pattern. These polygonal hollow shapes, which may comprise triangles, hexagons, rectangle/squares, and circles or other types of polygons, are used as the basic building blocks of lattice structures. The strut-and-node structures differ from the polygonal hollow cells that are the subject of this paper in that the latter have walls, while the others do not. Figure 1 shows polygon hollow shapes typically used in engineering applications [16; 17].  
Figure 1a shows struts being used as the basic building blocks for polygonal hollow structures, whereas Figure 1b displays beams or plates as the alternative basic building blocks. Given that such polygon hollow structures can be built with either one of the two types of basic building blocks, it is imperative to make an informed decision on the best choice using analytical models that describe their respective behaviour [17; 18]. As a result, it is crucial to conduct a comparative examination of the behaviour of polygon hollow shapes built using the two building blocks in order to rank them with regard to their mechanical efficiency. Additionally, for lattice structures with the same dimensions, it is important to determine the difference in the amount of material used to design these structures based on the two types of building blocks [18]. Polygon hollow structures built with struts are predicted to use less material than the ones built with beams or plates [16; 18]. This is due to the fact that the latter type of building blocks requires the use of more material as compared to adopting struts [17].  
In its entirety, selecting between struts and beams in the design and manufacturing of polygon-based lattice structures is determined by the engineering application's particular requirements [18]. When simple, straight-line load pathways are preferred and efficiency in the usage of materials and axial stiffness are crucial, struts are typically used [16]. Beams, in contrast, provide a higher degree of flexibility in terms of how to apply loads and have the capacity to carry loads with more complex distributions [17]. However, their manufacturing could prove more challenging in terms of the selection of materials, speed of manufacturing, and scaling of parts, in addition to requiring more material [15]. The choice between struts or beams/plates must therefore be based on a careful analysis of the primary structural specifications, cost, and desired design outcomes of a project.
# Image-to-Image Translation Using Identical-Pair Adversarial Networks
## 4 Experiments and Results
### Experimental Setup  
We used datasets that have been used in previous existing approaches such as pix2 pix [35], BicycleGANs [37], ID-cGANs [32] and cycleGANs [16]; and we keep the size of their training and test sets the same their settings to make a fair comparison.  
We worked on such datasets as:  
* Dataset from pix2 pix for Edges\(\rightarrow\)images, Aerials\(\rightarrow\)images, Labels\(\rightarrow\)facades
* The dataset from BicycleGANs for Night\(\rightarrow\)day
* The dataset from ID-cGANs for De-raining  
Our experiments were trained on NVIDIA Titan X (Pascal) GPUs, and we used Python for our implementations. We set our optimizers such as Adam solver with learning rate 0.0002, bias1 0.5, and bias2 0.999. We set batch size and training epochs depending on the size of the dataset and tasks. We trained our tasks with an iteration of around 10 k and selected only the best results.  
To illustrate the performance of our tasks, we conducted qualitative and quantitative experiments to evaluate the performance of the transformed image directly for the qualitative experiments. We used such metrics as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) [38], Universal Quality Index (UQI) [39], and Visual Information Fidelity (VIF) [40] to evaluate the performance over test sets.
# Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone
## 2 Materials and Methods
### Hardware Architecture
#### 2.3.2 Power System Architecture  
The RPSC power system was designed with a priority-based distribution scheme to ensure a stable voltage supply to the autopilot. By placing the Pixhawk power module at the first stage of the circuit, it receives the cleanest possible voltage, even during transient drops caused by high current demands from the motors (Figure 5).  
The system is powered by an 11.1V LiPo battery (3s1p), serving as the primary energy source. A kill switch ensures safety by instantly cutting power to all components in an emergency or during maintenance. The Pixhawk Power Module regulates the battery voltage, delivering a stable 5.1 V to the Pixhawk while monitoring power consumption. The system distributes power efficiently through parallel bifurcation: a Universal Battery Eliminator Circuit (UBEC) regulator steps down the voltage to 5 V, supplying the Raspberry Pi and alarm system. At the same time, the Motor Driver Power Supply directly utilises the 11.1 V to drive the RPSC motors, ensuring reliable operation of all components.
# A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning
## 3 Proposed Strategy for Defect Depth Estimation  
In this section, a defect depth estimation strategy has been proposed to detect and identify the depth of each defect in thermal images as indicated in Figure 7. This design of detection system originated from the GRU neural network. The infrared thermal module and simulations are provided by COMSOL (FEM simulation) to examine the depth of defects in pulsed thermography (PT). First, the synthetic thermal sequences are acquired from FEM (COMSOL) based on the heat transfer modeling in FEM. Then, several thermal contrast feature vectors are extracted from each defect region to feed into the input of GRU. The output of GRU consists of a unique node which estimated the depth during the training. The mean absolute error is chosen as the loss function with the GRU model in the equation  
Figure 6: (**a**) 3D printed defects geometrics are highlighted in training samples; (**b**) the corresponding synthetic colorful thermograms in t = 3 s.  
as below Equation (5). In the end, the predicted depth output from the GRU is based on the feature extraction of thermal contrast vectors.  
\[L_{\text{cost}}=\frac{\sum_{i=1}^{m}(y-\hat{y})^{2}}{m} \tag{5}\]  
The GRU takes each vector at a time point in the input. This learning model was trained for 2500 epochs of each process. The training loss converged to the optimistic value and then flattened. In addition, the number of training curves (batch size) is set m. \(y\), \(\hat{y}\) are the ground truth and estimated depth respectively.
# An Upgraded Version of the Binary Search Space-Structured VQ Search Algorithm for AMR-WB Codec
## 2 ISF Quantization in AMR-WB
### Linear Prediction Analysis  
The 16 th-order LPC, \(a_{i}\), of a linear prediction filter is evaluated using a Levinson-Durbin algorithm, defined as  
\[\frac{1}{A(z)}=\frac{1}{1+\sum\limits_{i=1}^{16}a_{i}z^{-i}}, \tag{1}\]  
and the LPC parameters are then converted into the immittance spectral pair (ISP) coefficients for the purposes of parametric quantization and interpolation. The ISP coefficients are defined as the roots of the following two polynomials:  
\[F_{1}^{\prime}(z)=A(z)+z^{-16}A(z^{-1}), \tag{2}\]  
\[F_{2}^{\prime}(z)=A(z)-z^{-16}A(z^{-1}). \tag{3}\]  
\(F_{1}^{\prime}(z)\) and \(F_{2}^{\prime}(z)\) are symmetric and antisymmetric polynomials, respectively. It can be proven that all the roots of \(F_{1}^{\prime}(z)\) and \(F_{2}^{\prime}(z)\) lie and alternate successively on a unit circle in the \(z\)-domain. Also, \(F_{2}^{\prime}(z)\) has two roots at \(z=1\) (\(\omega=0\)) and \(z=-1\) (\(\omega=\pi\)), which can be eliminated by the introduction of the following polynomials, with 8 and 7 conjugate roots on the unit circle respectively, represented as  
\[F_{1}(z)=F_{1}^{\prime}(z)=(1+a_{16})\prod_{i=0,2,\ldots,14}(1-2q_{i}z^{-1}+z ^{-2}), \tag{4}\]  
\[F_{2}(z)=\frac{F_{2}^{\prime}(z)}{(1-z^{-2})}=(1-a_{16})\prod_{i=1,3,\ldots,13 }(1-2q_{i}z^{-1}+z^{-2}), \tag{5}\]where the coefficients \(q_{i}\) are referred to as the ISPs in the cosine domain, and \(a_{16}\) symbolizes the last predictor coefficient. Both Equations (4) and (5) can be solved using a Chebyshev polynomial. Subsequently, the 16 th-order ISF coefficients \(\omega_{i}\), derived from the ISP coefficients, can be acquired via the transformation \(\omega_{i}=\arccos(q_{i})\).
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 4 Data Plane Programmability Using P4
### P4 Processing Pipeline  
A P4 program defines a pipeline for packet processing which is visualized in Figure 2. It is structured into the parser, the ingress pipeline, the egress pipeline, and the deparser.  
The parser reads packet headers and stores their values in header fields. They are carried through the entire pipeline together with packets and standard metadata, e.g., the port on which the packet has been received. In addition, user-defined metadata may store values calculated during processing, e.g., flags required for decisions later in the pipeline. The ingress and egress pipeline may modify header fields, add or remove headers, clone packets, and perform many more actions useful for flexible packet processing. Packets or clones may be even processed several times by the ingress or egress pipeline, but we do not go into details here as we do not leverage these features for ABC. The ingress pipeline typically determines the output port for a packet. After completion of the egress pipeline, packets are deparsed, i.e., their headers are assembled from the possibly modified header fields, and sent.
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 7 Emerging Research Areas and Future Directions
### Deep Learning and Advanced AI Applications  
In a study performed in 2020, [PERSON] et al. [127] concluded that while deep models can be effectively applied to traffic data for crash occurrence classification and risk prediction, simpler models can often achieve comparable or even better performance. Specifically, they found that for crash detection, CNNs with dropout outperformed some shallow models and, for crash prediction, deep models showed comparable performance to shallow models.  
Building on this, [PERSON] et al. [128] utilized a state-wide live traffic database that provides crowdsourced probe vehicle data to develop real-time traffic crash prediction models. The crash prediction models use machine learning models to predict crash risk according to pre-crash traffic dynamics and static freeway attributes. The results of the study reveal a significant relationship between rear-end crashes and pre-crash traffic dynamics. Additionally, the study ranks traffic speed factors in terms of feature of importance, finding the speed variance and speed reduction prior to crashes to be most important, both of which are positively related to rear-end crash risk. Random forest models emerged as the most effective among various machine learning approaches, highlighting significant relationships between rear-end crashes and pre-crash traffic dynamics. Key predictive factors included speed variance and reductions prior to crashes, offering actionable insights for traffic safety interventions.  
Together, these studies underscore both the potential and limitations of AI-driven methodologies in crash analysis and risk prediction, emphasizing the necessity of aligning model selection with specific data and research objectives.
# Global Transcriptomic Response of _Staphylococcus aureus_ to Viulent Bacteriophage Infection
## 2 Materials and Methods
### Bioinformatics Analysis  
The genome assembly of _S. aureus_ strain SA515 was performed using Unicycler (v0.4.8) with the default settings (GeneBank accession no. JAKRSL00000000) [26]. The annotation of the assembly was performed with the prokka (v1.14.6) [27]. The sequenced reads were mapped to the _S. aureus_ SA515 genome using HISAT (v2.2.1) [28]. SAMtools (v1.11) [29] software was used to compress mapped SAM files into BAM and for their subsequent sorting and indexing. Mapping quality and coverage along genes were assessed with QualiMap (v2.2.2) [30], and individual reports were merged with MultiQC (v1.9) [31]. Mapped reads were assigned to genes with featureCounts (v2.0.1) [32]. Differential gene expression analysis was performed using the edgeR (v3.36.0) [33] package for R. Genes, with a false discovery rate (FDR) cutoff of 0.001 and with a fold change log\({}_{2}\)(FC) threshold of \(\mid\)11 (i.e., \(\geq\) \(\mid\)2 \(\mid\)-fold change) considered to be differentially expressed. Further functional classification of DEGs into Gene Ontology (GO) categories was done using the Panther database ([[http://www.pantherdb.org](http://www.pantherdb.org)]([http://www.pantherdb.org](http://www.pantherdb.org)), accessed on 11 January 2022); categories were considered enriched with \(p_{\text{adj.}}\leq 0.05\). Plots were generated within R using ggplot2 (v3.3.2) [34], ggpubr (v0.4.0) [35], ggalt (v0.4.0) [36], ggpvent (v0.1.9) [37], lemon (v0.4.5) [38], cowplot(v1.1.0) [39], and pBrackets (v1.0.1) [40] packages. The prediction of the prophage sequences in the SA515 genome was performed using PHASTER [41].
# Occupational Risk Prediction for Miners Based on Stacking Health Data Fusion
## 4 Experimental Results
### Improvement of Stacking Fusion Algorithm Based on the Network Search Algorithm
Subsequently, hyperparameter tuning and cross-validation are performed on the meta-model (logistic regression) to identify the optimal hyperparameters. The model is then trained on the training set and evaluated on the test set. K-fold cross-validation is employed, where the training set is divided into K subsets. Each time, one subset is used as the validation set, and the remaining subsets are used as the training set. Subsequently, the performance metrics of the model are computed on the validation set. The cross-validation procedure is outlined below:  
\[CV=-\frac{1}{k}\sum_{k=1}^{K}\sum_{i\in V_{k}}logp(y_{i}\mid x_{i},\theta_{k}) \tag{14}\]where \(\sum_{i\in Vk}\) denotes the validation set for the k-th fold, and \(p(yi\,|\,xi,\,\theta_{k})\) denotes the model predictions on the kth fold. The parameter \(\theta_{k}\) denotes the hyperparameters of the model on the k-th fold. The cross-validation performance under different combinations of hyperparameters is obtained through cross-validation to find the optimal hyperparameters. The hyperparameters of the meta-model are then searched using the grid search technique as follows:  
\[\lambda^{*}=argmin_{\lambda\in\Lambda}CV(\lambda) \tag{15}\]  
where \(\lambda\) denotes the range of hyperparameter search and \(CV(\lambda)\) denotes the cross-validation performance under the hyperparameter \(\lambda\). Finally, using the found optimal hyperparameters \(\lambda^{*}\), the Stacking model is fitted on the training set and predicted on the test set. As demonstrated in Table 6 and Figure 11, the accuracy of the optimized Stacking model algorithm has increased from 89% to 90%. Additionally, the precision, recall, and F1-score show significant improvement compared to the pre-optimization period. As illustrated in Figure 12, the ROC curve and AUC value have also improved from 0.87 to 0.89, further enhancing the reliability of the model evaluation.
# Intelligent Detection of Parcels Based on Improved Faster R-CNN
## 2 Method
### Loss Function  
The proposed method follows the loss function in the Faster R-CNN with the addition of an object edge detection loss, which is defined as shown in Equation (6).  
\[\text{Loss}=L_{cls}+L_{box}+L_{edge} \tag{6}\]  
where \(L_{cls}\) is the classification loss, defined as in Equation (7), \(p_{i}\) denotes the probability that the \(i\)-th candidate box is predicted to be a parcel, \(p_{i}^{*}\) denotes the label of the parcel, \(p_{i}^{*}=1\) when it is a positive sample and \(p_{i}^{*}=0\) when it is a negative sample.  
\[L_{cls}(p_{i},p_{i}^{*})=-\sum\log[p_{i}^{*}p_{i}+(1-p_{i}^{*})(1-p_{i})] \tag{7}\]  
\(L_{box}\) is the localization loss function, using \(\text{Smooth}_{L1}\) loss, which has the advantage of being more robust to outliers and has little impact on the training process when the predicted value differs significantly from the true value, as defined in Equation (8).  
\[L_{loc}(x,l,g)=\sum_{i\in(cx,y,w,h)}^{N}x_{ij}^{k}\text{Smooth}_{L1}\Big{(}l_ {i}^{m}-g_{j}^{m}\Big{)} \tag{8}\]  
where  
\[\delta_{j}^{cx}=\frac{g_{j}^{cx}-d_{i}^{cx}}{d_{i}^{m}} \tag{9}\]  
\[\delta_{j}^{cy}=\frac{g_{j}^{cy}-d_{i}^{cy}}{d_{i}^{h}} \tag{10}\]  
\[g_{j}^{h}=\log(\frac{g_{j}^{w}}{d_{j}^{w}}) \tag{11}\]  
\[g_{j}^{h}=\log(\frac{g_{j}^{h}}{d_{i}^{h}}) \tag{12}\]  
\[\text{Smooth}_{L1}(x)=\left\{\begin{array}{ll}0.5x^{2}&if\ \ |x|<1\\ |x|-0.5&otherwise\end{array}\right. \tag{13}\]  
Here, \((\delta_{j}^{cx},\delta_{j}^{cy},\delta_{j}^{w},\delta_{j}^{h})\) denotes the Ground Truth coordinates of the parcel, \((d_{j}^{cx},d_{j}^{cy},d_{j}^{w},d_{j}^{h})\) denotes the coordinate of the prediction frame of the parcel obtained from the network prediction, and \((l_{j}^{cx},l_{j}^{cy},l_{j}^{w},l_{j}^{h})\) denotes the offset of the prediction frame from the corresponding Ground Truth.  
\(L_{edge}\) is the edge detection loss, which uses a root mean square error loss to calculate the edge difference between the wrapped candidate frame and Ground Truth, as defined in Equation (14).  
\[L_{edge}(y,\hat{g})=\frac{1}{N}\sum_{i=1}^{N}(|y_{i}-\hat{y}_{i}|)^{2} \tag{14}\]  
where \(y_{i}\) denotes whether the \(i\)th pixel value is an edge of the parcel, if it is an edge \(y_{i}=1\), otherwise \(y_{i}=0\), \(\hat{y}_{i}\) represents the probability that the \(i\)th pixel value is predicted to be an edge. \(N\) is the total number of samples.
## 2 Data Description  
### HY-2 Altimetry Data  
All level 2 products of HY-2 series missions were administrated and distributed by China's National Satellite Ocean Application Service (NSOAS, [[http://www.nsoas.gov.cn/](http://www.nsoas.gov.cn/)]([http://www.nsoas.gov.cn/](http://www.nsoas.gov.cn/))). The specific FTP address is osdds-ftp.nsoas.org.cn and a user account is required. We obtained 7 GM cycles of HY-2A, 88 ERM cycles of HY-2B, 56 ERM cycles of HY-2C and 23 ERM cycles of HY-2D sensor data records (SDRs) in standard NETCDF format. The general information of the acquired HY-2 datasets is listed in Table A1 in Appendix A.  
Generally, each cycle of HY-2A had 4630 pre-designed passes in the geodetic mission phase and the ground tracks were nearly identical if their pass numbers were the same. HY-2B, HY-2C and HY-2D have 386, 274 and 274 passes, respectively, for each cycle in the  
Figure 1: Flowchart for evaluating HY-2 altimetric data’s performance in marine gravity research.  
exact repeat mission phase. Although the spatial coverage rate of HY-2 series missions (HY-2A GM \(\sim\)16 km at equator; HY-2B ERM \(\sim\)200 km; HY-2C and HY-2D ERM \(\sim\)300 km) was obviously lower than for the Jason (\(\sim\)4 km) and SARAL/AltiKa (\(\sim\)5 km) geodetic missions, the ground tracks could still be a great supplement to providing different track orientations of multi-satellite materials for the recovery of marine gravity fields. The ground tracks of HY-2 missions are shown in Figure 2. In Figure 2a, HY-2B C015, HY-2C C012 and HY-2D C028 were selected to show how HY-2 samples the globe. A magnification over Region 1 was executed for better contrast effects and the ground tracks of measurements from HY-2A GM C004, Jason2 C180 and SARAl/AltiKa C026 are also plotted in Figure 2b,c.  
The SDR products provided certain items to calculate along-track SSH, such as altitude, Ku band range and corrections. The correction items were divided into three groups: instrumental corrections, path-delay range corrections (dry and wet tropospheric and ionospheric correction and sea state bias) and geophysical environmental corrections (geocentric ocean and pole tide height, solid earth tide and inverted barometer correction). Among them, instrumental corrections were taken into account in the provided range items, while the latter two groups need to be considered further when calculating SSH. All the above-mentioned correction items are provided in level 2 products. Detailed sources of applied corrections for estimating SSH are listed in Table 1.  
Figure 2: Global distribution of HY-2 altimetry data and magnification in a 30\({}^{\circ}\)\(\times\) 30\({}^{\circ}\) region (Region 1, North Pacific; (**a**): global distribution of one complete ERM cycle from HY-2B (red), HY-2C (green) and HY-2D (blue); (**b**): regional distribution of one complete ERM cycle from HY-2B (red), HY-2C (green), HY-2D (blue), SARAL/AltiKa (yellow) and Jason-2 (purple) in Region 1; (**c**): regional distribution of one complete GM cycle from HY-2A (black) and one complete ERM cycle from SARAL/AltiKa (yellow) and Jason-2 (purple) in Region 1).
###### Abstract  
This paper proposes an energy market management framework to address the day-ahead optimization of a distribution network (DN) with a multi-microgrid (MMG). In this framework, an economic optimization based on a dynamic game is designed to ensure the reliability and stability of the DN operation while setting the internal transaction price to minimize the operation cost of the distribution network operator (DNO). The DNO, as a leader, sets the power flow constraints and microgrid operators (MGOs), as followers, respond to the internal transaction price and in turn affect the DNO's price decision. The price incentive mechanism of the DNO is to encourage MGs to participate in energy market management, to promote joint operation of MGs and to reduce the operation cost of MGOs. The differential evolution algorithm and mixed integer programming method are applied to find the equilibrium solution to the dynamic game. Numerical simulations of a modified IEEE 33-bus system show the effectiveness of the proposed energy market management framework.  
Keywords:distribution network; multi-microgrid; economic optimization; dynamic game; transaction price +
Footnote †: journal: Applied Sciences
## 2 Key Steps in a FRB Search Pipeline
### Candidate Classification
#### 2.4.2 Machine Learning Techniques  
Even after employing effective candidate sifting and clustering strategies, strong RFI environments can still result in a large false positive rate, thereby increasing the latency of a real-time FRB detection pipeline. This is crucial as often, in the event of a detection of a FRB by the pipeline, the complex voltage data from the radio telescope are saved to a disk for offline processing (Section 2.5). When the false positive rate is too high, the pipeline latency can be large enough such that it will not save the complex voltage data in time to do any further analysis. Furthermore, too many hits will lead to too many candidates and too much voltage data filling up the storage capacity on the machine. Artificial intelligence is an important tool to rapidly classify real candidates from the spurious ones with excellent precision. Very recently, convolutional neural networks (CNNs) trained over extensive datasets from several radio telescopes have been implemented on FRB search pipelines [31; 32; 33]. These algorithms are extremely effective in identifying real FRBs among the vast amount of candidates resulting from the detection pipeline.