esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of Temporal and Spatial Trends in ANPP  
From 2001 to 2018, 24.28% of the total area in the study region showed a significant increase in ANPP (Figure 6). In Yunnan province, the distribution was found in the southeast part, the western areas of Ailao and Wuliang mountains, the southern side of the northeast's Wulian peak, and across both southern and northern sides of Wumeng mountain, which spans Yunnan and Guizhou provinces. In Sichuan province, the significant increase was mainly concentrated on the southeast side of the Daliang mountains in the southern area. Guizhou province exhibited a centralization on the eastern side of Wumeng mountain and within the southwest karst ecological protection area. Due to its proximity to Sichuan and Guizhou, Chongqing city showcased a distribution at the junction of these  
Figure 5: Trend of ANPP, PNPP, and HNPP from 2001 to 2018.  
three provinces, notably in the northern Daliang mountain and the ecological protection area of the Three Gorges Reservoir in Chongqing. Shaanxi province was characterized by a significant distribution on the northern side of the central section of Daba mountains and in the southern part of Qinling mountainous ecological protection area. In Hubei province, the areas of notable increase encompassed Wushan and both the southern and northern flank of the eastern segment of Daliang mountain, the central part of the South-to-North Water Diversion Project source ecological conservation area, and the Dabie mountainous ecological protection zone, in addition to the northern region of Dahong mountain and the northern part of southern Mufu mountain. The neighboring provinces of Henan and Anhui demonstrated a dense distribution at the junction where these provinces meet with Hubei, primarily focused in the central region of Henan and the Huai River ecological protection area, with Anhui showcasing a concentration mainly on both sides of Zhangba Ridge and in the central and northern parts of the province. In Hunan province, which is situated in the central part of the study area, the main distribution areas were both sides of the Wuling mountains and the northern section of the eastern part of Xuefeng mountains. In contrast, only 4.89% of the area experienced a significant decrease (Figure 6), which was primarily found in the middle and lower Yangtze River basin, the Pearl River Delta urban cluster, and the central part of Taiwan.
# Cognitive Aspects-Based Short Text Representation with Named Entity, Concept and Knowledge
## 4 Experiments
### Result Analysis
#### 4.5.1 Multiple Sources vs. Single Source  
CNN uses the single source of words as its input, multiple kernels with different sizes are employed on it. From the result in Table 4, we can see CNN performs best among the baseline methods which only use a single source. This is because different window sizes of convolution operation is employed to extract the local features which can enhance the text representation. Our model performs  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Twitter** & **Google Snippet** & **AG News** \\ \hline BoW+TFIDF & 94.25 & 61.84 & 72.7 \\ CNN & 95.14 & 85.21 & 83.97 \\ LSTM & 94.99 & 81.54 & 83.18 \\ Bi-LSTM & 95.10 & 84.86 & 83.5 \\ GRU & 94.99 & 80.92 & 82.98 \\ Attention & 94.43 & 84.73 & 83.34 \\ KBSTC & - & 72 & 67.9 \\ WCCNN & 95.09 & 85.83 & 85.57 \\ ECKA(proposed) & 95.76 & 87.59 & 86.93 \\ \hline \end{tabular}
\end{table}
Table 4: Text classification comparison of different models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variants** & **Twitter** & **Google Snippet** & **Ag News** \\ \hline ECKA with word only & 95.19 & 83.70 & 84.13 \\ ECKA with word and entity & 95.60 & 86.4 & 86.75 \\ ECKA with word and concept & 95.50 & 86.28 & 85.15 \\ ECKA with word and KG & 95.65 & 86.62 & 84.22 \\ ECKA with word and entity, concept and KG & 95.76 & 87.59 & 86.93 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The text classification comparison of ECKA variants.  
better than all the baseline models. The reasons that our model achieves better result than the others are listed as follows--(i) The model handles the ambiguous term by using named entity technique. Base on the named entity, the model can get more precise representation on the entity, concept and knowledge levels. (ii) We enrich the short text representation from different sources. The model learns the superordinate representation through concept. And the latent semantic representation is obtained through knowledge entity and its linked entities within the knowledge graph. (iii) We use the CNN to extract the local features and the attention network to capture the attentive representation from multi-level features respectively, which better captures category-related informative features for short text classification.
## 4 Experiments
### Comparison Experiments  
#### 4.5.1 Comparative Experiments with FFT and DCT  
In order to verify the advantages of the wavelet transform in the rain removal task, we have designed comparison experiments with commonly used frequency domain methods (fast Fourier transform (FFT) and discrete cosine transform (DCT)). These experiments will help us further validate the effectiveness of the wavelet transform in high-frequency raindrop trace removal and background detail recovery.  
1. Experimental design This section compares three methods: multi-scale wavelet transform (MSWT), fast Fourier transform (FFT), and discrete cosine transform (DCT). Experiments used the Rain1200 and Rain1400 synthetic datasets for training and the RealRain dataset for unsupervised testing. All methods were trained with the same deraining network architecture and consistent parameters (learning rate, batch size) for fairness. Each method transforms rainy images in the frequency domain before processing with the derain network.  
For evaluation, PSNR, SSIM, NIQE, and PIQE were used. PSNR and SSIM evaluate image quality, while NIQE and PIQE assess unreferenced image quality, particularly for  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Title 1**} & \multicolumn{2}{c}{**Rain1200**} & \multicolumn{2}{c}{**Rain1400**} \\ \cline{2-5}  & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline Q1 & 32.17 & 0.915 & 31.04 & 0.895 \\ Q2 & 33.64 & 0.922 & 32.38 & 0.913 \\
**Q3** & **34.88** & **0.963** & **32.57** & **0.956** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiment results for the loss function. (The bold words indicate the experimental results of the algorithm proposed in this paper.)real rain images. The experiments compare the effectiveness and limitations of each method on synthetic and real datasets.  
2. Experimental results and conclusions  
The quantitative comparative experimental results of multi-scale wavelet transform (MSWT), fast Fourier transform (FFT) and discrete cosine transform (DCT) on Rain1200, Rain140, and RealRain datasets are shown in Table 4. This table provides the PSNR, SSIM, NIQE, and PIQE indicators of each method on each dataset.  
As can be seen from Table 4, the wavelet transform method outperforms the FFT and DCT methods in terms of PSNR and SSIM on both the Rain1200 and Rain1400 datasets. Also, on the RealRain dataset, the wavelet transform method significantly outperforms the FFT and DCT in terms of NIQE (11.52) and PIQE (9.13) values, which indicates that the wavelet transform is able to remove the raindrop traces while retaining the background details of the image in a better way, and generates a higher quality image. Therefore, compared with the FFT and DCT methods, the wavelet transform method has a significant improvement in rain removal effect, background detail recovery, and image quality, especially in real rain images.
# Road Infrastructure Challenges Faced by Automated Driving: A Review
## 4 Identification of Challenges
### Design  
Historically, the development of road infrastructure has been constructed considering humans. With the infiltration of ADS and ADAS, the perception of how road infrastructure is experienced changes. However, in the foreseeable future, until full automation is reached, both \"worlds\" should be satisfied. Moreover, the physical road infrastructure and onboard sensors are currently the priority and the most desirable forms. With advances in ADAS, however, the need for digitalisation will increase. Today, digital maps are only a supplement to physical infrastructure and onboard sensors, but this could change in the future. The questions we could ask to help us define and explain challenges regarding the design of traffic signs and road markings are the following:  
* What are the current designs that challenge ADAS? * Which of those design options are viable and can be changed in the short term? To recap the design limitations of traffic signs and road markings, it is worth looking, once again, at the list of design limitations in Tables 5 and 7 as well as the list of advances in Tables 6 and 8. As we can see, ADAS have a problem in interpreting collocated traffic signs, textual messages or irregular and worn-out road markings. This problem can be solved partially with sensor fusion and digitalisation. Moreover, some of the problems are already solvable. Flickering of the electronic signs is reported only in some cases and depends on the sign manufacturers and, therefore, could be solved through regulating the refresh rates of electric signs. Other examples are related to increasing the width of road markings from 100 to 150 mm, which is already adopted in the US [79]. Implementation of RFID, infrared barcodes and other smart solutions could be a solution for the transition period [59; 60; 61; 62]. However, more drastic measures such as replacing dashed with solid lines to achieve better continuity are not viable in the foreseen future, and solving these issues would therefore require some support from digitalisation. Another more drastic measure is dedicated lanes for AVs. There are mainly two ways for this approach, either to use current infrastructure and assign some of the lanes to AVs only or to build completely new lanes. The latter is a less viable solution due to high costs and space limitations. Be that as it may, the interaction between human drivers and AVs is inevitable, and extensive traffic flow simulations and simulator studies are necessary to see a whole picture and real
## 3 Materials and Methods
### Analysis of Thin Section, SEM, and X-ray Energy Spectroscopy  
Thin sections were observed on a Leica DM4500P microscope. Before thin-section observation was performed, the light-source intensity of the microscope should be adjusted to the maximum. Use the 50\(\times\) objective to observe the entire rock section in both reflected and polarized light. Reflectance and polarized light images of the flakes were obtained using the ZENcore operating software v3.47.  
The microscopic composition of the minerals was observed with a Quanta 250 FEG desktop scanning electron microscope (SEM) from the FEI Company, Hillsboro, OR, USA. The maximum magnification is 100,000 times. In high-resolution mode, it has a resolution of 3 nm/30 kV. The working temperature of the instrument was 20 \({}^{\circ}\)C, and the working humidity was 50%. The accelerating voltage of the SEM was 10 kV. The emission current was 10 uA, and the working distance was 15 mm. A backscattered electron receiving probe was used as a signal receiver. SEM follows standard operating procedures (SOPs) for mineralogy analysis.  
Quantax 200x flash X-ray energy spectrometer from Brooke Company in Kassel, Germany, was used for the rapid and accurate qualitative and quantitative analyses of mineral constituent elements. The error of the analysis result was less than 5%. The composition of a mineral was identified by its elemental composition and atomic ratios. The X-ray excitation voltage was 20 kV. The dead time was 35% to 40%. Point analysis (100 s), line scan (1800 s), and area scan (3600 s) were performed on the sampled locations, respectively. Data processing followed the principle of 3 times the standard deviation.
## 3 The Proposed Method
### The DQN with the CBAM  
Research [22] has proven that the introduction of the experience replay and target network has greatly improved the performance of the DQN. In this system, the state data are converted into a square matrix to train the CNN. On this basis of the DQN, the CBAM is added, which consists of two important submodules: channel and spatial. The channel submodule adopts both average-pooling and max-pooling outputs, processed by a shared network. The outputs are then processed by the spatial submodule. In the spatial submodule, passing through the pooling layer, the output data of the channel submodule are put into a convolution layer. The following details the operation of the submodules.  
Let \(\mathbf{F}^{c}_{avg}\) and \(\mathbf{F}^{c}_{max}\) represent the average-pool and max-pool operations, respectively. Two different descriptors are generated after the pooling operation, acting on the UAV states. The channel attention data \(\mathbf{M}_{c}\in\mathbf{R}^{C+1+1}\) can be obtained after the pooling results are both processed by the following shared network. The shared network consists of a multi-layer perceptron (MLP) that has one hidden layer, and the size of the hidden activation is \(\mathbf{R}^{C/r*1+1}\), where \(r\) is the reduction ratio. The output feature data are summed element-wise after the shared network. So, the channel attention operation can be expressed as  
\[\begin{split}\mathbf{M}_{c}(\mathbf{F})&=\sigma(MLP( AvgPool(\mathbf{F}))+MLP(MaxPool(\mathbf{F})))\\ &=\sigma(\mathbf{W}_{1}(\mathbf{W}_{0}(\mathbf{F}^{c}_{avg}))+ \mathbf{W}_{1}(\mathbf{W}_{0}(\mathbf{F}^{c}_{max}))),\end{split} \tag{20}\]  
where \(\sigma\) represents the sigmoid function, \(\mathbf{W}_{0}\) and \(\mathbf{W}_{1}\) are the weights of the shared network and they are shared for both inputs' pooling results, \(\mathbf{W}_{0}\in\mathbf{R}^{C/r*r}\) and \(\mathbf{W}_{1}\in\mathbf{R}^{C*C/r}\).  
The spatial attention graph is generated by using the spatial relationships of the features that focus on 'where' the useful information is. The spatial attention is supplement to the channel attention. For the spatial attention, the average-pooling and max-pooling operations are applied first to concatenate the channel attention outputs to a resultful characteristic descriptor. Then, the descriptor is processed by a convolution layer to obtain the spatial attention data \(\mathbf{M}_{s}(F)\in\mathbf{R}^{HW}\). So, the spatial attention operation can be expressed as  
\[\begin{split}\mathbf{M}_{s}(\mathbf{F})&=\xi(f([ AvgPool(\mathbf{F});MaxPool(\mathbf{F})]))\\ &=\xi(f([\mathbf{F}^{s}_{avg};\mathbf{F}^{s}_{max}])),\end{split} \tag{21}\]  
where \(\xi\) represents the sigmoid function and \(f\) denotes a convolution operation.
## 3 Application of the Proposed MSPSO Algorithm to the IPTBK Problem
### Solving the IPTBK Problem Using the MSPSO Algorithm
#### 3.2.1 Data Pre-Processing Phase  
Data Collection  
Assume that the IPTBK problem must be solved using the MSPSO algorithm with an SPIE involving a Bloom filter and an auditing function of packet marking, as displayed in Figure 5. In the solution procedure, the associated routers store partial route information during the attack process (i.e., record packet digests of sampled packets, \(p\%\)). In the study, we used a small sampling rate \(p\) (3.0%) to achieve low storage and computational cost for the networks. The victim must use recursive lookup to reconstruct the attack path associated SPIE mechanism. The SPIE uses auditing techniques to support the attack path construction from traffic flow packets collected from the true source of the network. Notably, traffic auditing is accomplished by computing and storing 32-bit packet digests rather than storing the packets themselves in the SPIE. Additional detailed information regarding the format of 32-bit packet digests in the IP header is provided in [5].  
Figure 4: Basic concept of route construction with the revised PSO model.  
Figure 5: Packet marking mechanism with a Bloom filter and an auditing function in the Source Path Isolation Engine (SPIE) [5].  
Encoding of Particles  
To search each of the weighted graphs in terms of the routing cost, the network packets collected were encoded for examining each path, with the routing costs represented by weights. The system must list the connection sequence of all the routing paths and then select the appropriate path by comparing the costs of attack paths. An example of the format of particle encoding is presented in Figure 6. In Figure 6, \(x^{\mathbf{k}}_{i}\) represents the position and velocity information of a particle in subswarm \(k\).  
Intelligent Reconnaissance Strategy  
The position and velocity vectors of the particles in the PSO method are represented by real numbers of the form \(x^{\mathbf{k}}_{i}\), \(x^{\mathbf{k}}_{j}\). The path-search algorithm searches each of the weighted graphs with appropriate routing costs. First, the sequence of each attack path is examined with a sigmoid function to decide whether two neighbouring nodes are connected. In Equation (6), \(e_{ij}\) indicates whether the system has a path that connects node \(i\) to node \(j\). The parameter \(e_{ij}\) is affected by the constants \(c_{i}\) in Equation (6) if the \(c_{i}\)_value_ is higher than the minimum cost (threshold) to connect two nodes \(t_{c}\). As displayed in Figure 7, this node is connected on the path. Finally, the attack path is formed by linking all the connected nodes with a list of IP addresses.  
\[\mathit{Sig\ value}=\mathrm{array}\left(\frac{1}{1+e^{-(x-t_{c})}}\right) \tag{8}\]  
Figure 6: An example of the state update of the PSO particles in \(k\) subswarm movement.  
Figure 7: Sigmoid function.
## Appendix A Formal Definitions of TV-HARM
### A.2. Formal Definitions  
**Definition A1**.: _Threat Vector Combination. A combination of threat vectors is defined as_  
\[C_{i}=\{tv_{1},tv_{2},\ldots,tv_{i}\},\quad tv_{i}\in 2^{TV}\] (A1)  
_where \(tv_{i}\) is an element from the power set of all the threat vectors, \(TV\). In this context, \(C_{i}\) represents a set of threat vectors, and each \(tv_{i}\) is an individual element from the power set of the entire set of threat vectors, \(TV\)._  
**Definition A2**.: _TV-HARM Structure. TV-HARM is defined as a 3-tuple as follows:_  
\[TVH_{C_{i}}=(U_{C_{i}},L_{C_{i}},M_{C_{i}})\] (A2)_where \(M_{C_{i}}\) is the mapping between the upper- and lower-layer components:_  
\[M_{C_{i}}:U_{C_{i}}\to L_{C_{i}}\] (A3)  
_Specifically, \(M_{C_{i}}\) maps \(U_{C_{i}}\) (the attack graph, \(AG\)) to \(L_{C_{i}}\) (the attack tree, AT) for each \(C_{i}\)._  
**Definition A3**.: _Attack Graph. An attack graph is defined as_  
\[U_{C_{i}}=(H,E)\] (A4)  
_where \(H\) is a finite set of hosts in the satellite network, and \(E\subseteq H\times H\) is a set of edges that represent the relationships between pairs of hosts. Satellite networks comprise a complex, multilayered structure consisting of the ground, space, and user segments, which are interconnected through various protocols._  
**Definition A4**.: _Attack Tree with Protocol Vulnerabilities. To account for the limitations of the original TV-HARM, we extend the attack tree definition by incorporating protocol vulnerabilities:_  
\[L_{C_{i}}=(A,B,c,g,root)\] (A5)  
_where \(A\) is a finite set that includes both host-based vulnerabilities and protocol-based vulnerabilities. \(B\) represents the set of gates (internal nodes in the attack tree), defining logical conditions (AND/OR) between child nodes. The function \(c:B\to P(A\cup B)\) describes the children associated with each gate in \(B\). The function \(g:B\to\{AND,OR\}\) specifies the type of each gate. The root node represents the ultimate attack goal and belongs to \(A\cup B\)._
## 3 Results and Discussion
### Peat Chronology and Accumulation Rate  
Due to the fact that the distribution of \({}^{210}\)Pb over the studied section has a non-exponential character, due to the migration of some part of \({}^{210}\)Pb down the section, the authors used a triple approximation to the linear regression method for the entire data set \(\ln\) (\({}^{210}\)Pb\({}_{\text{uns}}\)) depending on the mass of each layer [35; 55]. Calculation of dating errors was performed by Monte Carlo simulation [56]. The results of dating the ISNO-1 peat profile are shown in Figure 5. The results showed that the 19-21 cm horizon, identified by the peak of \({}^{137}\)Cs activity and several other physicochemical parameters (in particular, ash content), corresponded to the age of 1963, which confirms the correctness and adequacy of the chosen peat core dating model. Typically, peaks in anthropogenic radionuclides in peat deposits correlate with specific major events in the history of radioactive fallout, such as the Partial Nuclear Test Ban Treaty of 1963 and the Chernobyl accident, providing a benchmark for geochronological studies [14; 34; 70]. However, due to its high migration and bioavailability in peat deposits, \({}^{137}\)Cs does not always mark radiation events in the section [12; 24]. However, in the considered case, the physicochemical conditions of the peat profile contributed to the retention at a depth of 19-21 cm of part of the \({}^{137}\)Cs that entered the surface of the peat as a result of the global fallout in 1963. The retention of \({}^{137}\)Cs in the horizon, which marks global atmospheric fallout, is also facilitated by vegetation at the ISNO-1 core sampling site, the root system of which limits the assimilation of \({}^{137}\)Cs to a depth of 14 cm.  
Figure 5: The results of \({}^{210}\)Pb chronology for the ISNO-1 peat core according to the CF model using the Monte Carlo method.  
The results of calculating the linear rate of accumulation \(s\) and the rate of accumulation of mass \(r\) in the studied peat deposit are shown in Figure 6. The \(s\) values varied from 0.09 \(\pm\) 0.02 to 1.3 \(\pm\) 0.05 cm/yr and averaged 0.48 \(\pm\) 0.08 cm/yr. The peat mass accumulation rate \(r\) ranged from 0.43 \(\pm\) 0.01 to 7.2 \(\pm\) 0.02 g/cm\({}^{2}\) year. The average value of \(r\) was 3.615 \(\pm\) 0.005 g/cm\({}^{2}\) yr. The data on the linear rate of peat accumulation corresponds to the previously obtained data for the Subarctic region of European Russia [38].  
The vertical distribution of the linear accumulation rate \(s\) decreases with depth, while the distribution of the mass accumulation rate \(r\) increases. Such distribution is natural since the peat deposit is compacted with depth. The linear norms of peat accumulation are congruent with the data [35].  
Based on \({}^{210}\)Pb dating, the \({}^{210}\)Pb atmospheric flux was estimated as 69.13 \(\pm\) 10 Bq\(\cdot\)m\({}^{-2}\cdot\)yr, which is congruent with the data on peatlands in Northern Europe [71] and the authors' earlier data on the Subarctic region of European Russia [38].