esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Medium Access  
We denote by \(N_{1}\) and \(N_{2}\) the total number of SNs in each class. Let us assume that RN_c_, \(c=1,2\), is active in a given cycle, and let \(k\) denote the number of C_c_ SNs, different from RN_c_, that are also active in the same cycle, \(0\leq k\leq N_{c}-1\). We denote by \(P_{s,k,c}\), \(P_{f,k,c}\), and \(P_{s/k,c}\), the probabilities that RN_c_ transmits a PF successfully, with failure (collision), or just transmits a PF either with success or failure, respectively, when contending with other \(k\,Cc\) SNs. Also, we denote by \(BT_{s,k,c}\), and \(BT_{f,k,c}\) the average backoff times generated by RN_c_, conditioned on it transmits a PF with success or failure (collision), respectively, when contending with other \(k\,Cc\) SNs. Please refer to [23] for details on how these parameters are determined.
## 3 Proposed Model
### Question Reasoning  
In this section, the aim is to build the correct query graph \(\mathcal{G}=(\mathcal{P},\mathcal{C})\) in the knowledge network based on the simple question generated by the question decomposition, where \(\mathcal{P}\) is the optimal path to the answer and \(\mathcal{C}\) is the constraint. Based on the question information, we propose the HBHR model to perform hop-by-hop reasoning to determine the optimum path \(\mathcal{P}\). The model is made up of four components, including question encoding, path encoding, similarity computation, and path generation. Figure 5 depicts the model structure.  
Figure 5: The structure of HBHR.  
The model's input is a simple question \(S_{E}=\{w_{1},w_{2},\ldots,w_{n}\}\) and the topic entity \(e_{t}\). To eliminate information interference and limit the likelihood of ambiguity during path selection, the placeholder \(\langle e\rangle\) has been used in place of the topic entity and constraint information in \(S_{E}\). To decrease the search space and increase path recall, the model employs beam search rather than typical breadth-first search for path selection, and a pseudo-siamese network based on the pre-trained model is used to calculate the similarity between questions and paths. Algorithm 1 gives the execution flow of the HBHR model.  
```
1:Knowledge Graph \(\mathcal{G}\), Topic Entity \(e_{t}\), Simple Question \(S_{E}\)
2:Optimal Path \(\mathcal{P}\)
3:initialization Max Heap \(B\) with Size \(K\)
4:initialization Similarity Calculate Function \(Sim\) with random weights
5:initialized new Path Set \(D=\{\}\)
6:\(\mathbf{e}^{g}\LeftquestionEncoder(S_{E})\) // question embedding
7:time step \(t\Leftarrow 0\)
8:whileTrue do
9:if\(t==0\)then
10:\(D=selectNextPath(e_{t})\) //select all path that start from node \(e_{t}\)
11:for\(\mathcal{P}\in D\)do
12:\(\mathbf{e}^{p}=pathEncoder(\mathcal{P})\) //path embedding
13:\(score=Sim(\mathbf{e}^{g},\mathbf{e}^{p})\)
14:\(B_{t}.push(\{\mathcal{P},score\})\)
15:endfor
16:else
17:for\(\mathcal{P}\in B_{t-1}\)do //browse all path in max heap \(B_{t-1}\)
18:\(D=selectNextPath(\mathcal{P}.endnode)\)
19:for\(\mathcal{P}\in D\)do
20:\(\mathbf{e}^{p}=pathEncoder(\mathcal{P})\)
21:\(score=Sim(\mathbf{e}^{d},\mathbf{e}^{p})\)
22:\(B_{t}.push(\{\mathcal{P},score\})\)
23:endfor
24:endfor
25:if\(B_{t-1}==B_{t}\)then
26:break //beam search end
27:endif
28:endif
29:\(t++\)
30:\(D=\{\}\)
31:endwhile
32:return\(B_{t}.top()\)
```  
**Algorithm 1** the process of HBHR  
After receiving \(S_{E}\), the model vectorizes it in order to obtain \(\mathbf{e}^{g}\). This step prepares the question and candidate paths for the next step of similarity calculation. We use the similarity computation with the question and the candidate paths.
# Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
## 5 Discussion  
All POFDs for the four dust detection algorithms were high, ranging between 71.83% (DST was the lowest at 02:00 UTC) and 96.85% (NDDI was the highest at 06:00 UTC). However, effective dust detection is still a major challenge. The first reason for high POFDs for the four algorithms is that most of the PM10 stations are distributed in central and eastern China while in the dust-prone northwestern region, PM10 stations are relatively few. Consequently, low YY values lead to high POFD. Second, PM10 concentrations greater than 600 \(\upmu\)g-m\({}^{-3}\) were set as the threshold value for identifying dust, which may result in high NY values and therefore high POFD. Additionally, the high POFD for NDDI was due to its failure to distinguish between bare ground from dust over the Tibetan Plateau region and the Taklamakan Desert and to misclassify dark ground surfaces in the northeast and red vegetation in the southwest as dust. We also found that from 02:00 UTC to 08:00 UTC, the POCD for all algorithms increased before 06:00 UTC and then decreased after 06:00 UTC, with a generally higher POCD at 06:00 UTC, which may be related to the solar angle.  
The POCDs for the four dust detection algorithms were relatively low partly due to discrepancies between the satellite dust identification results with the ground-based PM10 observations. First, the satellite seemed to detect severe dust events with a wide spatial coverage, while some high PM10 concentrations may not represent dust events. PM10 concentration in the urban environment is significantly affected by local sources. Local anthropogenic sources also affect the atmospheric PM10 concentration. For example, some studies have shown that traffic [60, 61] and road dust resuspension [62] are the main sources of PM10 in urban areas. Satellite, however, is much better at detecting dust events in the ambient areas of dust sources where dust concentrations are relatively high [63]. In this study, most of the used sites are located in eastern China, which could increase the influence of anthropogenic dusts. Second, unavoidable mismatch could also lead to differences in dust identification using two approaches. This is because satellites are not able to detect dust under clouds from the top of atmosphere, and FY-4A, with a spatial resolution of 4 km, seems to be impossible to detect small local dust events.  
Finally, we also tried to combine the BTD and IDDI algorithms for accuracy verification. The combined algorithms could reduce the misjudgment of bare land, similar to the Qinghai Tibet Plateau. As the result shows, the POFD for the fused algorithm was lower than those for the selected four single algorithms.  
In this study, based on the FY-4A imagery, the selected thresholds for different algorithms were close to those in previous studies. This is because there are various factors that would affect the setting of thresholds such as surface roughness, sensor wavelength ranges, and solar and viewing zenith angle [64, 65]. Additionally, the atmospheric effects are also an important influencing factor [58].
## 2 Related Works
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & **Method** & **Images’ Modality** & **Approach** \\ \hline \multirow{4}{*}{**Classification**} & [PERSON] et al. COVID-Net [17] & X-ray & A deep residual CNN based model \\ \cline{2-4}  & [PERSON] et al. GSEN architecture [18] & X-ray & A novel Gray-Scale spatial exploitation learning Net (GSEN) for COVID-19 \\ \cline{2-4}  & [PERSON] et al. Deep transfer learning [19] & CT-scans & ResNet18 pre-trained transfer learning-based model \\ \hline \multirow{4}{*}{**Infection Segmentation**} & [PERSON] et al. Inf-Net [20] & CT-scans & DL approach based on three reverse attention modules connected to a paralleled partial decoder \\ \cline{2-4}  & [PERSON] et al. VB-Net [21] & CT-scans & A modified 3D CNN that combines V-Net with the bottle-neck structure \\ \cline{2-4}  & [PERSON] et al. (CNN-VGG19) [22] & X-ray & DL approach based on fusion algorithm using N5 CT with deep learning VGG19 \\ \hline \multirow{4}{*}{**Diagnosis System**} & [PERSON] et al. JCS [23] & CT-scans & **Classification:** Res2 Net: **Infection segmentation:** VGG16 backbone + Enhanced Feature Module + Attentive Feature Fusion \\ \cline{2-4}  & [PERSON] et al. [24] & CT-scans & **Classification:** Resnet-50—2D deep CNN; **ROI extraction:** Unet; **Infection detection:** Grad-cam technique and a commercial off-the-shelf software for lung pathology detection \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of the recently published studies on COVID-19 detection approaches. [PERSON] et al. [24] presented a system that detects cases suspected to have COVID-19 features from CT images, using a Resnet-50--2D deep CNN architecture [28] with 94% sensitivity and 98% specificity; then, for cases classified as positive, an abnormality localization module will be executed to extract, using a Grad-cam technique [29], the network's activation maps that contributed most to the decision. An infection analysis will be further held using commercial off-the-shelf software to detect nodules and small opacities to reinforce the infection localization.
# Antiderivative Antialiasing for Stateful Systems +
## 2 Antiderivative-Based Aliasing Reduction for Memoryless Nonlinear Systems
### The Higher-Dimensional Case  
Before turning to stateful systems, we shall briefly discuss how to lift the restriction to systems with one scalar input \(u(n)\) and one scalar output \(y(n)\) quietly assumed so far. To keep the notation concise, we collect the inputs \(u_{1}(n),u_{2}(n),\dots,u_{K_{\text{u}}}(n)\) of a system with \(K_{\text{u}}\) scalar inputs into an input vector \(\mathbf{u}(n)=\begin{pmatrix}u_{1}(n)&u_{2}(n)&\dots&u_{K_{\text{u}}}(n)\end{pmatrix}\) (using bold letters to indicate vectors here and in the following). Likewise, multiple outputs are collected into an output vector \(\mathbf{y}(n)=\begin{pmatrix}y_{1}(n)&y_{2}(n)&\dots&y_{K_{\text{y}}}(n)\end{pmatrix}\). We may first notice that the extension to vector-valued output is straightforward: the above derivation does not depend at all on the fact that the non-linear function (or its antiderivative) is scalar-valued.  
For vector-valued input, on the other hand, things do not work out so nicely anymore. In particular, we can no longer rewrite (2) as (3). So unless the integral in (2) happens to have a closed solution, to avoid numerical solving at run time, we are almost stuck with precomputing and tabulating the solution for all combinations of \(\mathbf{u}(n)\) and \(\mathbf{u}(n-1)\) at a certain density. Thus, if the input is \(K_{\text{u}}\)-dimensional, having to use two subsequent input vectors stacked on top of each other to form the lookup (index) vector, we would require a \(2K_{\text{u}}\)-dimensional lookup table. A small improvement is possible by realizing that we always integrate along lines, and a line can be parameterized by \(2K_{\rm u}-2\) parameters in a \(K_{\rm u}\)-dimensional space. Along a line, we can precompute the antiderivative with respect to the scalar coordinate along the line. This antiderivative with a one-dimensional argument and \(2K_{\rm u}-2\) parameters could be stored in a \(2K_{\rm u}-1\)-dimensional lookup table. While this may be reasonable for \(K_{\rm u}=2\), it quickly becomes infeasible for larger \(K_{\rm u}\) due to prohibitive memory requirements.
# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
## 4 Experiments
### Evaluation Indicators  
The post-training evaluation of the model encompasses the harmonic mean F1 score (\(F1-score\)), recall (\(Recall\)), precision (\(Precision\)), average precision (\(AP\)), detection speed (frames per second, \(FPS\)), and model size. These metrics collectively provide a comprehensive assessment of the model's performance. The calculation formulas for \(F1\), \(Precision\), and \(Recall\) are delineated below, offering a quantitative measure of the model's accuracy, reliability, and efficiency in processing. This multifaceted evaluation approach ensures a thorough understanding of the model's capabilities and limitations.  
\[Precision=\frac{TP}{TP+FP}\times 100\% \tag{11}\]  
\[Recall=\frac{TP}{TP+FN}\times 100\% \tag{12}\]  
Figure 10: Weighted fusion image processing. (**a**) The original image1; (**b**) The original image2; (**c**) The Image after weighted fusion processing.  
\[AP_{i}=\int_{0}^{i}P_{i}(R_{i})dR_{i} \tag{13}\]  
\[mAP=\frac{1}{n}\sum_{i=1}^{n}AP_{i} \tag{14}\]  
\(TP\) (true positive) denotes the count of accurately identified positive samples, while \(FP\) (false positive) signifies the quantity of falsely identified positive samples, and \(FN\) (false negative) indicates the count of incorrectly identified negative samples. \(F_{1_{0.5}}\) represents the harmonic mean of model precision and recall at an intersection over union (\(IoU\)) threshold of 0.5. \(AP\) (average precision) is defined as the area under the precision-recall (\(PR\)) curve, with values ranging from 0 to 1; \(AP_{0.5}\) calculates the average precision at various recall levels when \(IoU\) is set at 0.5. The \(mAP\) (mean average precision) metric, representing the average of these average precision values, serves as a comprehensive indicator of the overall accuracy of object detection algorithm models. This metric is crucial for evaluating the effectiveness of the model across different detection scenarios.
# CPF-UNet: A Dual-Path U-Net Structure for Semantic Segmentation of Panoramic Surround-View Images
## 1 Introduction  
In the field of autonomous and assisted driving, the utilization of panoramic surround view technology and Bird Eye View (BEV) perception capabilities is essential for ensuring safe vehicle operation in complex environments. This technology is particularly significant in underground parking lots with intricate structures and unstable lighting conditions. The panoramic surround view system captures continuous images of the surrounding environment through a multi-camera system deployed around the vehicle. These images are stitches them into a 360 degree panoramic view using algorithms such as distortion correction and inverse perspective transformation, providing a field of view coverage with no blind spots. This enables the vehicle to effectively monitor and identify both dynamic and static objects around it, such as pedestrians, other vehicles, road signs [1], and parking spaces [2]. Consequently, the vehicle's peripheral perception capabilities are significantly enhanced, leading to improved navigation accuracy and safety.  
With its excellent ability to understand and analyze images in detail, deep learning technology has shown broad application prospects in many disciplines, effectively promoting research progress in various fields [3; 4; 5; 6; 7]. Although semantic segmentation technology has demonstrated its effectiveness in panoramic surround-view applications [8; 9; 10], it still faces many challenges. First, the lighting conditions in underground parking lots are usually complex, ranging from very dim to suddenly bright areas, posing extremely high demands on the dynamic range and adaptability of the visual processing system. Second, the spatial constraints and high scene complexity in the parking lot require the algorithmto accurately segment and recognize tightly arranged vehicles and irregular obstacles. Additionally, the special layout and reflective surfaces in underground parking lots can lead to image distortion, which further increases the difficulty of semantic segmentation.
## 3 Results and Discussion
### Technical and Economic Evaluation by Process Stage
#### 3.1.3 Technical and Economic Evaluation for the Drying Stage  
After the secondary harvest, it is necessary to dry the biomass. In this stage, it was considered that 100% of the biomass fed is collected at the end of drying in both cases analyzed. However, this might not be true in reality, since there could be losses due to natural effects of technology in the process, causing lower biomass production. On the other hand, let us remember that the biomass production after the drying stage will depend on the feed. This is because the flocculation stage causes different removal percentages depending on the case considered. Something similar happens in the secondary harvest. To evaluate the drying technologies, the biomass from the technology in case D1 is considered as feed.  
According to the results (Table 5), energy consumption and operating cost is higher for spray drying (29% and 94%, respectively). The literature reports that this is due to the high energy demand to generate the hot air used in the drying process [34]. The integration of the residual heat generated by the thermoelectric provided by the CO\({}_{2}\) in the drying stage can reduce energy consumption. Some authors mention that renewable energies can also be integrated into the drying stage to reduce energy demand [17; 58]. Some existing methods of obtaining bioenergetics or bioproducts from microalgal biomass are carried out considering wet biomass, which would avoid the drying stage in the process. Authors report that drum dryers can exceed the denaturation temperature of proteins. This is not suitable if you want to dry biomass to produce high-quality proteins [41]. However, if the purpose of biomass is to produce biofuels, this technology can be considered, due to its lower energy consumption compared to the spray dryer and that, even if the protein denaturation temperature is exceeded, this does not affect the extraction or composition of the lipids. The selection of drying technology depends on both the scale of production and the purpose for which the dry biomass is intended [62].
# ARCFIRE: Experimentation with the Recursive InterNetwork Architecture
## 5 Discussion of Experimental Results
### Network Management Experiments  
Results presented in Section 4 provide substantial experimental evidence that the consequent application of RINA abstractions leads to significant, we argue drastic, improvements in configuration management, especially for performance, management software complexity, and automation. The applied RINA abstractions are:* The separation of mechanism and policy is the key abstraction, especially for network management. Mechanism' and 'policy' are relative and related to the scope of the system. In other words what is 'the mechanism' in one system (and scope) can be 'policy' in another (larger) system and scope.
* There is only one application protocol (CDAP, the Common Distributed Application Protocol) with only six operations: create, delete, read, write, start, and stop. CDAP operations are executed on well-defined objects in the Resource Information Base (RIB).
* Management is monitoring and repair, everything else is most likely control. The term management and its activities (often called management functions) are relative, i.e., more a continuum than a specific box in a system architecture. We can therefore use'monitoring and repair' on different system levels (with associated scope).
* A network in the RINA sense is a set of nodes and DIFs plus the IPCPs in the DIFs. This seems to imply that we need more than one dimension to show and explore a Recursive InterNetworking Architecture (RINA) network, i.e., a simple 'topology' is not enough.  
During the execution of the experiments we have identified and studied a number of further abstractions, namely:  
* A node can be created and validated independent of any other node. Its configuration only requires its DIFs and their connections (plus the Distributed IPC Facility (DIF) QoS cubes). This has been demonstrated in the Rumba tool.
* A network can be created and validated only using individual node configurations. There is no further information required.
* All aspects of a RINA network can be shown and explored using four different graphs: a network (nodes) graph with the individual nodes and their configuration, a PtP graph with point-to-point connections between nodes, a DIF graph showing all defined DIFs and their connections, and a network graph (or IPCP graph) showing all IPCPs with their DIF and their connections. Some of these graphs are trees (in the mathematical sense), the PtP graph can be any type of graph.
# Seamless Handover Scheme for MEC/SDN-Based Vehicular Networks
## 4 Novel Seamless Handover Scheme for MEC/SDN Vehicular Networks
### Calculating Packet Error Rate  
One of the main performance indicators is the packet error rate (PER), which is an indicator for failed transmission. The PER represents the ratio of undelivered packets to the total transmitted packets. It is measured during a sliding window. Figure 5 indicates the methodology of evaluating the PER. The total interval during which the PER is calculated is divided into k windows (\(\omega\)) of equal time intervals (\(\tau\)). The total PER of a vehicle \(\rm v_{i}\), \(\rm PER_{T}^{vi}\), is calculated by Equation (3).  
\[PER_{T}^{vi}=\sum_{j=1}^{k}PER_{\omega j}^{vi} \tag{3}\]  
where \(\rm PER_{\omega j}^{vi}\) is the PER of the jth window and can be calculated as the ratio of undelivered packets to the total transmitted packets during the window. PER is an important performance metric, since it indicates two main packet losses; the loss of packets missed during queuing due to the arrival of a newer basic safety message (BSM) and the packet loss in the channel due to collision or weak signal.
# Deep Learning-Based Crack Detection on Cultural Heritage Surfaces
## 2 Materials and Methods
### Deep Learning Models  
#### 2.3.1 GoogleNet Model  
GoogleNet, introduced by researchers at Google in 2014, was inspired by the Network in Network (NiN) architecture proposed by [43]. It secured first place in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) image classification competition, surpassing VGGNet [44] in accuracy. Evolving from earlier architectures such as LeNet-5, GoogleNet enhanced the capability of convolutional neural networks (CNNs) to address complex  
Figure 1: Cracks in images converted from color to grayscale. (**a**,**c**,**e**) denote color images while (**b**,**d**,**f**) indicate grayscale images.  
image classification tasks, including datasets such as MNIST, CIFAR, and ImageNet. Key advancements included increasing network depth and layer size, integrating dropout techniques to mitigate overfitting, and incorporating max-pooling layers to enhance feature extraction, despite the potential loss of spatial information. These improvements rendered GoogleNet effective not only in image classification but also in related tasks such as localization, object detection, and face recognition.  
A notable innovation of GoogleNet was the introduction of the inception module, which facilitated multi-scale feature extraction by integrating convolution operations of varying kernel sizes within the same layer. This design reduced the number of parameters (weights and biases) while improving the network's representational capacity. [PERSON] et al. [12] further refined this approach by employing \(1\times 1\) convolutions for dimensionality reduction, enabling deeper and wider architectures without a proportional increase in computational complexity. This strategy, known as the bottleneck design, contributed to the development of highly efficient deep learning models capable of learning intricate feature representations.  
The GoogleNet architecture comprises 22 layers, or 27 layers when pooling layers are included. It processes RGB images with a resolution of 224 \(\times\) 224 pixels, leveraging 1 \(\times\) 1 convolutions prior to 3 \(\times\) 3 and 5 \(\times\) 5 convolutions to optimize dimensionality reduction and computational efficiency (Figure 2a). The rectified linear unit (ReLU) activation function is utilized throughout the network. During training, auxiliary classifiers are incorporated into intermediate layers to address the gradient vanishing problem and provide additional regularization.  
Figure 2: Diagram of architecture for (**a**) GoogleNet and (**b**) SegNet.