esa-sceva/satcom-chunk-collection huggingface dataset:
# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 3 Methodology
### Hybrid Loss Function for Knowledge Distillation and Class Separation
#### 3.2.1 Loss Function for Knowledge Distillation  
In this paper, the knowledge distillation loss \(L_{KD}\) is used to retain the knowledge of old targets. It is capable of making the current model simulate the old model trained on the old classes. The output value of the old model is set as an additional supervised signal to assist the training of the new model [35].  
\[L_{KD}(x)=-\frac{1}{n}\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{c}d(p_{ij})log[d (q_{ij})] \tag{3}\]  
where \(q_{ij}\) denotes the probability of \(i\)-th sample belonging to \(j\)-th class predicted by the model, \(p_{ij}\) is the one-hot class label vector of \(j\)-th class corresponding to \(i\)-th sample. \(d(p_{ij})\) and \(d(q_{ij})\) are modified versions of \(p_{ij}\) and \(q_{ij}\)[12]:  
\[d(p_{ij})=\frac{exp(\widetilde{o}_{i}(x)/T)}{\sum\limits_{j=1}^{n}exp( \widetilde{o}_{j}(x)/T)} \tag{4}\]  
\[d(q_{ij})=\frac{exp(o_{i}(x)/T)}{\sum\limits_{j=1}^{n}exp(o_{j}(x)/T)} \tag{5}\]  
\(T\) is the distillation parameter. \(o(x)\) represents the output logits of the current model, \(\widetilde{o}(x)\) represents the output logits of the old model in the previous increment process. \(T=1\) denotes the common Softmax output probability. However, when \(T>1\), the classification probability distribution is more moderate, making the remaining classes have a greater impact. Their higher loss function values must be reduced to a minimum. The model needs to learn more fine-grained classification. Therefore, the model learns a more distinctive representation of the class to retain the knowledge of the old classes. \(T\) is empirically set to 2 for all experiments in this paper.  
In this paper, the cross-entropy loss is used to learn to recognize new targets. The cross-entropy loss is used as classification loss \(L_{CE}\) to predict the difference or closeness of the output to the true sample labels.  
\[L_{CE}(x)=-\frac{1}{n}\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{c}p_{ij}log[q_{ ij}] \tag{6}\]  
where \(n\) and \(c\) denote the number of samples and the number of classes, respectively. When the difference between the expected probability and the true sample label increases, their cross-entropy increases.
## 4 FRA, Statistical Analysis  
### FRA Regions  
To ease and automate the interpretation of FRA signatures, statistical indicators are used within multiple frequency ranges of the measured FRA signature. FRA regions for power transforms are standardized by the Cigre WG A2.26, IEC60076-18 Ed.1 and IEEE Std C57.149-2012 [19; 20; 21]. However, this division is not applicable for IM FRA signatures. Therefore, this paper proposes new regions for the IMs FRA signatures based on the significant changes and related effects of IM parameters. The low-frequency (LF) range is normally impacted by the resistive elements and magnetic flux, the middle frequency (MF) range is dominated by the inductive elements while the capacitive components affect the signature in the high-frequency (HF) range. At a very high-frequency range (VHF), the signature is mainly due to external wires and other parameters. The common FRA regions for the transformers and proposed regions for IMs are presented in Table 6.
## 1 Introduction
Therefore, according to the previous research results and engineering practice, this paper proposes coating the fire-retardant coatings on cable trays to delay the failure time of the cables in trays. There are few relevant regulations and reference data about the type and thickness of fire-retardant coatings used in this fire protection method. Although fire-resistant cable trays coated with fire-retardant coatings have been produced and sold by some manufacturers, there is little research on the protective effect of such trays on cables. The relevant standards GA 479-2004 \"Fire resistance cables tray\" and GB 29415-2013 \"Fire-resistant cable trunk\" do not specify the thickness of a fire-retardant coating; they only require that the performance of a fire-retardant coating meets the requirements of GB 14907-2002 \"fire-retardant coating for steel structure\" [24; 25]. Therefore, the standards in this respect need to be further improved [26; 27; 28]. In this research, for the ISO 834 standard fire condition, a vertical fire resistance test furnace was used to test cable trays coated with different types and thicknesses of fire-retardant coatings. The surface temperature and the insulation resistance changes of cables in trays were collected, and the fire resistance effect was analyzed in order to provide a reference for the fire resistance transformation of the cable tray in service.
## 3 Our Proposal
### Disentangling Flows for Semantic Dimensions  
Based on the previous formulation, we can reintroduce the _semantic tags_ in the model by expanding latent factors \(\mathbf{z}\) with a categorical variable \(\mathbf{t}\). Hence, we define the generative process \(p_{\theta}(\mathbf{x}|\mathbf{t},\mathbf{z})\) where \(p(\mathbf{t})=Cat(\mathbf{t}|\boldsymbol{\pi})\) and \(p(\pi)\) is the prior distribution of the tags. We define the inference model as \(q_{\phi}(\mathbf{z},\mathbf{t}|\mathbf{x})\) and assume that it factorizes as \(q_{\phi}(\mathbf{z},\mathbf{t}|\mathbf{x})=q_{\phi}(\mathbf{z}|\mathbf{x})q_{ \phi}(\mathbf{t}|\mathbf{x})\). In order to handle the fact that tags are not always observed, we define a model similar to [18]. When \(\mathbf{t}\) is unknown, it is considered as a latent variable over which we can perform posterior inference  
\[\mathcal{L}_{u}=-\mathbb{E}\left[\log p_{\theta}(\mathbf{x}|\mathbf{t}, \mathbf{z})+\log p_{\theta}(\mathbf{t})+\log p_{\theta}(\mathbf{z})\right]- \mathbb{E}\left[\log q_{\phi}(\mathbf{t},\mathbf{z}|\mathbf{x})\right]\]  
When tags \(\mathbf{t}\) are known, we take a rather unusual approach through the idea of _disentangling flows_. As we seek to obtain a latent dimension with continuous semantic control, we define a tag pair as a set of negative \(\mathbf{t}_{-}\) and positive \(\mathbf{t}_{+}\) samples. We define two _target_ distributions \(p(z_{\mathbf{t}_{-}})\sim\mathcal{N}(-\mu_{*},\sigma_{-})\) and \(p(z_{\mathbf{t}_{+}})\sim\mathcal{N}(+\mu_{*},\sigma_{+})\) that model samples of a semantic pair as opposite sides of a latent dimension. Hence, we turn the treatment of tags into a _density estimation_ problem, where we aim to match tagged samples \(\mathbf{t}_{*}\) densities to given explicit target densities by minimizing \(\mathcal{D}_{KL}\left[q_{\phi}(z_{\mathbf{t}},|\mathbf{x})\|p(z_{\mathbf{t}_{ *}})\right]\). To solve this, we consider that \(q_{\phi}(z_{\mathbf{t}},|\mathbf{x})\) is parameterized by a normalizing flow \(f_{k}\) applied to the latent \(\mathbf{z}\), leading to our final objective  
\[\mathcal{L}_{o}=\mathcal{D}_{KL}\left[q_{\phi}(z_{\mathbf{t}_{*}})\|p(z_{ \mathbf{t}_{*}})\right]=\mathbb{E}\left[\log p(\mathbf{z})-\sum_{i=1}^{k}\log \left|\text{det}\frac{\partial f_{i}}{\partial\mathbf{z}_{i-1}}\right|-\log p (z_{\mathbf{t}_{*}})\right] \tag{13}\]  
This formulation enforces a form of _supervised_ disentanglement, where some of the latent dimensions \(\mathbf{z}\) are transformed to provide controls with explicit semantic target properties. The final bound is defined as the sum of both objectives \(\mathcal{L}=\mathcal{L}_{o}+\mathcal{L}_{u}\) and the complete model is obtained by integrating _regression_ and _disentangling_ flows together.
## 3 Results and Discussion
### Mechanical Characterization
#### 3.2.2 Flexural Testing  
The ISO 178 flexural test is considered as a testing method allowing some of the limitations of tensile testing to be overcome because of the absence of severe constraints due to the clamping of the samples [32]. The flexural test investigated in this study was not applied to a tubular geometry as in [PERSON] et al. [32], but it was performed according to the standard ISO 178. The reason for this choice is that the tubular geometry limits the possibility of varying raster orientation in the printed sample. Therefore, the standard ISO 178 geometry was used, as reported in Figure 1, allowing us to use the same raster orientation as for the samples subjected to tensile load. The experimental curves obtained from the flexural test do not show any significant differences between specimens obtained from bars printed with different lengths (Figure 10). The readings of maximum flexural stress varied in the range between 77.48 MPa and 108.02 MPa for the two materials (Table S2). [PERSON] et al. [27] in their study reported flexural stresses varying from 52.89 MPa to 126.30 MPa. Although the material type seems to be the only relevant factor (\(p\)-value = 0.0379) in this experimental study (Table 5), the small R-squared = 0.22 and adjusted R-squared = 0.17 values obtained from the ANOVA analysis shows that only a small fraction of total variability measured in the flexural stress is due to the investigated factors. Similarly to the tensile test, this reveals a high level of noise affecting the flexural stress which dramatically affects the test sensitivity when applied to FDMM printed specimens. This result can be explained by the fact that the shear stresses developing within the specimen during a flexural test can influence its results. For this reason, its effect is minimized in the ISO 178 standard by fixing the ratio (r) of support length (L\({}_{\text{S}}\)) to the specimen height (h) to be equal to 16 [33]. Clearly, this condition is not favorable for the purpose of the mechanical characterization of FDMM samples where the interlaminar bonding--whose resistance can be tested by the presence of shear stress--plays a relevant role on the mechanical properties of the specimens.
## 5 Experimental Results and Analysis
### Ablation Experiments
#### 5.4.2 Different Neural Network Structures  
To verify the learning effect of the spatio-temporal hypergraph neural network, we conducted experiments on different hypergraph neural networks on CS (cross-subject) protocols and CV (cross-view) protocols of the NTU RGB+D 60 dataset, respectively. As shown in Table 3, for CS (cross-subject) protocols, the accuracy of action recognition was 81.2% when only using spatial convolutional blocks in the hypergraph neural network; 82.1% when using only temporal hypergraph convolutional blocks in the hypergraph neural network; and 91.2% when using both temporal and spatial hypergraph convolutional blocks. Compared to the former, the accuracy of action recognition was improved by about 10% and 9.1%, respectively. For CV (cross-view) protocols, the accuracy of action recognition was 86.3% when only using spatial convolutional blocks in the hypergraph neural network, 87.7% when only using temporal hypergraph convolutional blocks in the hypergraph neural network, and 96.7% when using both temporal and spatial hypergraph convolutional blocks, with an improvement of about 10.4% and 9%, respectively. This shows that the spatio-temporal hypergraph neural network can further improve the mining ability of temporal and spatial information in human actions, and achieve the purpose of improving the accuracy and model generalization ability.
# A Systematic Review of Defensive and Offensive Cybersecurity with Machine Learning
## 4 Discussion
### High Dimensionality of Network Traffic Data  
High dimensionality of network traffic data has made classification challenging as network traffic data usually comprise of many attributes and features [39]. This is mainly due to the computational complexity and resources required to process large and sparse matrices with many feature columns and observation rows. This challenge makes it difficult for researchers to train models in order to differentiate between anomalous and normal behavior [40]. As a result, many of the selected reviewed papers discussed the need for a reduction in the dimensionality of network traffic data as well as feature selection and the introduction of ML techniques when classifying such data. Some of the authors of the selected reviewed papers employed data mining techniques in a cloud-based environment, by choosing suitable attributes and features with the least relevance with regards to weight for the classification. In the majority of the reviewed studies, the standard strategy is to choose features with more desirable weights.
# Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network
## 6 Significance of Dataset and Proposed Approach  
The BoF feature extraction approach with ANN classifier is proposed in this study to detect and recognize non-standard traffic signs. To examine the significance of our proposed method, one baseline was created from the collected dataset for this research, namely, the histogram of oriented gradients (HOG) technique proposed in [67; 68]. In [67] hybrids of HOG and SURF features descriptor was used  
Figure 16: Classification accuracy rate; (**1**) SVM; (**2**) Ensemble Subspace \(k\)NN; and (**3**) ANN.  
with ANN classifier to select the most important and discriminative features. In [68] HOG based feature extraction technique with SVM and Random Forest, classifiers were used to recognize traffic sign images. Additionally, the performance of our proposed approach was also evaluated by using publicly available dataset (BTSD) of traffic sign images. To conduct the experiments with the BTSD dataset, 12 class of Belgian traffic sign image samples are taken into consideration because they are also commonly used in Malaysia. To compare, we have conducted nine additional experiments to show the significance of dataset and proposed approach. The findings of these experiments are shown in Figure 17 with confusion matrices.  
Figure 17: Confusion matrices of nine additional experiments.  
These experiments were conducted to measure the overall accuracy of all three classifiers using the baseline and collected dataset and BTSD dataset. The baseline accuracy was compared with the accuracy of the proposed method. The accuracy of the baseline approach and proposed approach is shown in Table 3. As shown here, the proposed method outperformed the baseline method in both datasets. Moreover, in classifiers ANN outperformed others.
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 4 Experimental Setup and Performance Evaluation  
In this section, we describe our experimental setup and the environment in which we deployed our proposed system. After conducting laboratory experimental testing in a controlled environment, the system was installed on-site to validate the functionality of the proposed system. This section presents the on-site implementation of our proposed system.
## 3 Experimental Results
### Varying Infrastructure Density Scenario
#### 3.2.2 Average End-to-End Latency
To sum up, the simulation results suggest that for the both investigated C-V2X technologies and their modes, the more dense infrastructure deployment is very beneficial. In particular, the upgrade from 2 BSs to 4 BSs brings the major improvement in the context of all the considered V2I services. The gain was higher for the 5G-based C-V2X as the transition from no coverage to the full support without the limitations is obtained for this technology and its both modes. Moreover, it is worth noting here that the density infrastructure increase from 4 BSs to 8 BSs does not provide any further benefit in any of the investigated cases. So, also taking into account the economical feasibility of this deployment, we can conclude that this deployment is not beneficial from the technical as well as economical perspective. Figure 18: E2E latency simulated for the both modes of the 4G-based C-V2X technology, both infrastructure density deployments and traffic intensity of 250 vehicles per hour. Figure 19: E2E latency simulated for the both modes of the 4G-based C-V2X technology, both infrastructure density deployments and traffic intensity of 1500 vehicles per hour. Figure 20: E2E latency simulated for the both modes of the 5G-based C-V2X technology, both infrastructure density deployments and traffic intensity of 250 vehicles per hour. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**C-ITS Service** & **2 Base Stations** & **4 Base** & **8 Base** \\  & **Stations** & **Stations** & **Stations** \\ \hline \multicolumn{3}{c}{**Low-frequency (1–2 Hz), low-latency (<100 milliseconds) services**} \\ \hline Slow and stationary vehicle warning & \(\diagup<600\) m and 1000 vehicles per hour & \(\diagup\) & \(\diagup\) \\ Weather condition warnings & \(\diagup<600\) m and 1000 vehicles per hour & \(\diagup\) & \(\diagup\) \\ Intersection management & \(\diagup<600\) m \& 1000 vehicles per hour & \(\diagup\) & \(\diagup\) \\ \hline \multicolumn{3}{c}{**Low-frequency (1–2 Hz), high-latency (<500 milliseconds) services**} \\ \hline Point of interest notification & \(\diagup<600\) m & \(\diagup\) & \(\diagup\) \\ Local electronic commerce & \(\diagup<600\) m & \(\diagup\) & \(\diagup\) \\ Media upload & \(\diagup<600\) m & \(\diagup\) & \(\diagup\) \\ Map updates & \(\diagup<600\) m & \(\diagup\) & \(\diagup\) \\ Cooperative flexible lane change & \(\diagup<600\) m & \(\diagup\) & \(\diagup\) \\ \hline \multicolumn{3}{c}{**High-frequency (10 Hz), low-latency (<100 milliseconds) services**} \\ \hline Electronic emergency brake light & \(\diagup\) & \(\diagup\) & \(\diagup\) \\ Emergency vehicle approaching & \(\diagup\) & \(\diagup\) & \(\diagup\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Communication requirements of common C-ITS services and their fulfillment by LTE-based C-V2X in the infrastructural mode and investigated context. Adapted and updated from [16,39]. Figure 21: E2E latency simulated for the both modes of the 5G-based C-V2X technology, both infrastructure density deployments and traffic intensity of 1500 vehicles per hour.
###### Abstract
The physical indication of NEP indicators is clear and easy to calculate. Therefore, they are widely used in studies related to carbon uptake in regional and even global terrestrial ecosystems. Many factors affect carbon uptake in terrestrial ecosystems, including meteorological factors (temperature, precipitation, atmospheric radiation, etc. ), soil conditions, human activities, and land use changes [12]. Among them, meteorology is an important factor in vegetation growth. It affects vegetation carbon uptake by influencing vegetation growth cycles, the magnitude of photosynthesis and respiration, and the activity of microorganisms in the soil [13]. Different vegetation types exhibit different levels of carbon uptake, and land use patterns directly reflect vegetation types and their spatial and temporal distribution characteristics, thus having a direct impact on regional vegetation carbon uptake [14]. In addition, the driving factors affect vegetation carbon uptake with a complex interaction [15]. For example, the meteorological effect is the main external factor affecting vegetation growth. However, the process of land use change considerably alters the flux of heat and water interactions at the \"land-atmosphere\" interface, exerting a significant impact on local climate characteristics [16]. Under the influence of these complex interactions, it is difficult to quantify the effect and contribution of specific factors on vegetation carbon uptake, making it challenging to analyze the causes of carbon uptake in terrestrial ecosystems. The Dongting Lake basin is an important subtropical humid area in China's middle and lower reaches of the Yangtze River, which is one of the 200 global conservation priority eco-regions [17]. It is also a biodiversity conservation hotspot in the world, which has three Ramsar sites in the Dongting Lake Basin [18]. The Food and Agriculture Organization of the United Nations (FAO) and the Worldwide Fund for Nature (WWF) have utilized it as a research sample because of its abundance in natural resources, its high percentage of forest cover, and its substantial potential as a carbon sink. The carbon sink function of terrestrial ecosystems in the Dongting Lake basin not only plays an essential role in the sustainable development of the region but also provides a reference for the \"carbon peaking and carbon neutral\" strategy in China and other regions of the world and therefore has a substantial demonstration value [19]. This study aimed to explore the spatial and temporal patterns of net ecosystem productivity in the Dongting Lake basin and the influencingfactors, providing a scientific basis for understanding the characteristics of regional carbon fluxes and assisting in ecosystem management.
# Data-Free Area Detection and Evaluation for Marine Satellite Data Products
###### Abstract  
The uncertainty verification of satellite ocean color products and the bias analysis of multiple data are both indispensable in the evaluation of ocean color products. Incidentally, ocean color products often have missing information that causes the methods mentioned above to be difficult to evaluate these data effectively. We propose an analysis and evaluation method based on data-free area. The objective of this study is to evaluate the quality of ocean color products with respect to information integrity and continuity. First, we use an improved Spectral Angle Mapper, also called ISAM. It can automatically obtain the optimal threshold value for each class of objects. Then, based on ISAM, we perform spectral information mining on first-level Yellow Sea and Bohai Sea data obtained from the Geostationary Ocean Color Imager (GOCI), Moderate Resolution Imaging Spectroradiometer (MODIS) and Ocean and Land Color Instrument (OLCI). In this manner, quantitative results of information related to data-free areas of ocean data products are obtained. The findings indicate that the product data of OLCI are optimal with respect to both completeness and continuity. GOCI and MODIS have striking similarities in their quantitative or visualization results for both evaluation metrics. Moreover, a concomitant phenomenon of ocean-covered objects is apparent in the data-free area with temporal and spatial distribution characteristics. The two characteristics are subsequently explored for further analysis. The evaluation method adopted in this study can help to enrich the content of ocean color product evaluation, facilitate the research of cloud detection algorithms and further understand the composition of the data-free regional information of marine data products. The method proposed in this study has a wide application value.  
Keywords:evaluation; detection; data-free area; anomalous missing information; accompanying phenomena; marine satellite +
Footnote †: journal: Remote Sensing