esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Existing AI Techniques in SVAD  
Because of a lack of knowledge sufficient to generalize their characteristics and correctly classify them as outliers, most machine learning (ML) and expert systems frequently need help detecting and classifying these anomalies. These rare events make identification challenging and contribute to an imbalanced data classification [13; 14].  
Once predictive models are built and sufficient data labels are provided, anomaly detection is challenging, considering the binary classification problem. However, the data available for training a model are restricted to containing few or no anomalous events, and such labels are frequently infrequent or cumbersome [15].  
SVAD aims to find abnormal frames or pixel parts that contain various spatial and temporal data [16]. Spatial features can be collected from a single frame, whereas temporal features can be collected from the data on object movement and the order of frames. Generally, there are three methods for estimating abnormalities in SVAD [17]: (1) The characteristics of both regular and irregular events are reflected in a shared space, and the anomaly is identified based on the margin of the spatial distribution. (2) A dictionary was trained using the semantic properties of the event patterns. This dictionary is then used for anomaly calculation. (3) Anomalies are found through errors made during the prediction and reconstruction of prior or subsequent frames using various feature extractors trained to do so.  
There are also various studies [18; 19] on creating video synopses from surveillance cameras. These studies allow for a more compact view to select only active activities instead of whole frames and to achieve efficient video browsing. As a result, this contributes to focusing on the area of interest in SVAD and reducing false negatives more accurately.  
The majority of methods in this domain have a variety of restrictions [20] such as: (1) The features used in many methods are handmade. (2) Most techniques demand a time-consuming stage for building models, having expertise that might not be useful for practical applications. (3) Perceiving deviations from normality as abnormal has been the subject of numerous earlier studies. In literature, hand-crafted descriptors for anomaly detection are particularly common. These traits continue to significantly contribute to research on anomaly identification [21].  
Researchers have been inspired to use ML or DL techniques for abnormal event detection due to the success of similar techniques in computer vision and image processing [22]. Many challenging cognitive tasks, such as finding anomalies in surveillance video, have been solved using ML or DL [23].  
Our review of the literature offers a comprehensive road map for SVAD. The published works in this field are grouped based on the learning method and algorithms.
## 8 Analysis
### Comparison and Contribution  
Table 9 provides a comparative overview of previous digital identity models based on blockchain technology described in Section 2.3; and the last row represents this research. Each row corresponds to a cited work, and the table is structured across ten columns. The presence or absence of specific features in each work is denoted using the following symbols: yes (), no (), and partially (). The first column lists the referenced works. The second column (Reg. in BC) indicates whether the model includes user registration directly on the blockchain. The third column (Auth) specifies if the approach is designed as an authentication mechanism. The fourth column (User types) denotes whether the system supports differentiated user roles or types. The fifth column (Self-sovereign) assesses whether the model follows a self-sovereign identity (SSI) paradigm, granting users full control over their identity data. The sixth column (Authority certifier) indicates the presence of a legal or trusted authority acting as an identity certifier. The seventh column (SC) identifies whether the work provides implementation details or logic related to smart contracts. The eighth column (Pluggable) shows if the identity architecture is modular or supports integration and substitution of identity components (i.e., is pluggable). The ninth column (Costs) denotes whether the model provides transaction costs. Finally, the tenth column (BC Platform) specifies the blockchain platform utilized in each respective work.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Works**} & **Reg.** & \multirow{2}{*}{**Auth**} & **User** & **Self** & **Authority** & \multirow{2}{*}{**SC**} & **Pluggable** & **Costs** & **BC** \\  & **in BC** & & & & & & & & **Platform** \\ \hline [PERSON] [10] & ✓ & ✓ & ✗ & ✓ & ✗ & ✗ & ✗ & Not mentioned \\ [PERSON] and [PERSON] [12] & ✓ & ✓ & ✗ & ✓ & ✓ & ✓ & ✗ & ✗ & Ethereum \\ [PERSON] et al. [11] & ✓ & ✓ & ✗ & ✗ & ✗ & ✓ & ✗ & ✗ & Ethereum \\ [PERSON] et al. [2] & ✓ & ✓ & ✗ & ✓ & ✓ & ✗ & ✗ & Hyperledger Iroha \\ This research & ✓ & ✓ & ✓ & ✗ & ✓ & ✓ & ✓ & Ethereum \\ \hline \hline \end{tabular}
\end{table}
Table 9: A comparison of digital identity models using blockchain with our proposal. Symbols used: yes (), no (), and partially ().
## 4 Results
### Spatial Pattern of UBLGUE  
To better study the spatiotemporal characteristics of UBLGUE, cities are divided into four categories in terms of the UBLGUE value, utilizing the natural breaks function of minimizing intragroup differences and intergroup differences in the ArcGIS 10.5 software. The efficiency values are categorized into four types, namely high, medium-high, medium-low, and low. A high level of efficiency means that the UBLGUE value exceeds 1. A medium-high level of efficiency means that the UBLGUE value ranges from 0.76 to 1. A medium-low level of efficiency means that the UBLGUE value ranges from 0.51 to 0.75. A low level of efficiency means that the UBLGUE value is below 1.  
Figure 4 displays a map of the spatial pattern of UBLGUE in 2003 and 2020. As illustrated in Figure 4, there are certain geographical differences and significant spatial polarizations in terms of UBLGUE in 2003 and 2020. Specifically, cities that have a high and medium-high-efficiency level are distributed in the southeastern CPUA in 2003. In particular, Xuchang, Luohe, Zhoukou, and Bozhou present high levels of UBLGUE. In addition, cities with high values are scattered across Jincheng, Hebi, and Puyang. Cities with low values (i.e., Xinxiang and Zhengzhou, Luoyang, Nanyang, and Shangqiu) are distributed in the priority development region, where the aggregation of economic production factors, such as the floating population and financial foundation, support urban development. However, the economic development of these cities has resulted in a high demand for built-up land. As a leading city in the CPUA, Zhengzhou has been a driving force in terms of investment scale, urban development, and per capita disposable income. However, the radiation of its driving role has certain limitations. Overall, the cities that have low-level efficiency are mainly distributed in the western CPUA, while the central area is dominated by cities with low-level efficiency, and cities with medium-low efficiency are scattered mainly in the edge of CPUA. The cities with medium-low values include Xingtai, Liaoheng, Yuncheng, Zhumadian, Xinyang, and Suzhou. In 2020, most cities with higher-level efficiency are located in the south-central region of CPUA, whereas cities with lower UBLGUE are distributed in the north-central region of CPUA. The main reason for the dissimilarity of these two groups of cities is that developed regions have higher economic efficiency and thus achieve the same output with fewer input resources than do less developed regions. The total number of medium-high-level cities is 15, accounting for 50% of the total. Cities with low values include Yuncheng, Changzhi, Handan, Xingtai, Anyang, Jiaozuo, Liaoheng, Heze, and Shangqiu, accounting for 30% of the total.  
Figure 3: The temporal trends in urban development and green use efficiency from 2003 to 2020.
## 4 Experiments
### Evaluation of Segmentation and Counting Performance
#### 4.3.1 Comparison of Different Down-Sampling Methods  
In this part, we compare the effect of different down-sampling and skip connection approaches for segmentation. In our proposed PID-Net, pixel interval down-sampling and max-pooling operations are concatenated to combine the dense and sparse feature maps after convolution operations. Then, in the process of hierarchy skip connection, max-pooling is applied to combine the high-level features and low-level features directly, which is beneficial to reduce the effect of resolution loss while up-sampling and help rebuild the segmentation result.  
To show the effectiveness and reasonability of the proposed method, we change the approaches of down-sampling and hierarchy skip connection as PID-Net Modified-1 (PID-Net-M1) and PID-Net Modified-2 (PID-Net-M2). In PID-Net-M1, max-pooling operations are only applied in the process of hierarchy skip connection and not in down-sampling. The down-sampling block of PID-Net-M1 is illustrated in Figure 8. In PID-Net-M2, all down-sampling operations are realized using pixel interval down-sampling without max-pooling. The segmentation evaluations and counting performance of those approaches are shown in Table 3.  
From Table 3, we find that the proposed PID-Net achieves the best counting performance. By comparing with the PID-Net-M1 and PID-Net-M2, the average accuracy is increased by 0.1% to 0.6%; the improvement of average Dice value is 0.1% to 1.1%; the average Jaccard is improved by around 0.3% to 1.6%. Furthermore, the mean Hausdorff distance of PID-Net is the shortest, which indicates the similarity between the predicted images and GT images is the highest. Finally, the counting accuracy achieved 96.97%, which  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Definition** & **Metric** & **Definition** \\ \hline Accuracy & \(\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}}\) & Dice & \(\frac{2\times|V_{pred}|V_{GT}|}{|V_{pred}|+|V_{GT}|}\) \\ \hline Jaccard & \(\frac{|V_{pred}|\cap V_{GT}|}{|V_{pred}|\cup V_{GT}|}\) & Precision & \(\frac{\text{TP}}{\text{TP}+\text{FP}}\) \\ \hline CA & \(1-\frac{|N_{pred}-N_{GT}|}{N_{GT}}\) & HD & \(d_{H}(X,Y)=max(sup_{x\in X}inf_{y\in Y}d(x,y),sup_{y\in Y}inf_{x\in X}d(x,y))\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The definitions of evaluation metrics. CA and HD are abbreviations of the Counting Accuracy and Hausdorff Distance, respectively.  
Figure 7: The illustration of TP, TN, FP and FN between the predicted image and GT image.  
shows the satisfactory counting performance of the PID-Net. Hence, the segmentation and counting performance of PID-Net is the best by referring to all evaluation metrics.
## 3 Results
### FEM Test Case
#### 3.1.2 Condition One  
In simple terms, the magnitudes of each bin in the 10 dB BW of the iterations' spectra must increase when moving toward the centre frequency. Due to dispersive nature and multimodality of guided-waves, this condition is not true for many iterations as a combination of waves will be received with different centre frequencies. Nonetheless, this condition must be true in the case of the torsional wave as it will have the same characteristic of the excitation waveform. Figure 7 shows an example of an outlier detected based on this condition. As can be seen, in the region between 26.5 to 27.5 kHz, the magnitude of three frequencies are actually decreasing, and a local minima is created. This suggests that the iteration is actually two separate wave modes with two different centre frequencies.  
Figure 6: Example of an outlier case detected using Condition Zero (C0), where (**a**) shows the time domain of the iteration window and (**b**) is its respective power spectrum. The red lines (dotted, +) show the references achieved from excitation sequence and the black lines (x, solid) show the results from each iteration.  
In this condition, in order to avoid marginal errors due to the limited number of samples in the window, a safety variable (\"_diffValue_\"), so the condition holds true if a slight decrease in the magnitude of each consecutive bin, is detected. In these tests, this value is kept at zero which means strictly positive increase of the magnitudes must be observed when moving toward centre frequency. The reason is, in case of guided-waves testing, if the flexural waves are strong enough that the frequency characteristics of the excitation waveform are changed, the signal must not be marked as a defect; since, in effect, the iteration is representing a strong existnace of a flexural wave.
# Cooperation-Assisted Spectrum Handover Mechanism in Vehicular Ad Hoc Networks
## 2 Related Work
### Cooperative Assistance Delivery Mechanism  
In recent years, cooperative communication technology is applied for improving the transmission performance. The cooperative communication has two modes, one is the relay mode, and another is the co-transmission mode, as shown in Figure 3. The relay mode is mainly because the relay point is located between the transmitting end and the receiving end, so the relay point can use a relatively high transmission rate to help transmit data and reduce the overall transmission time. In the co-transmission mode, the cooperating node and the transmitting end form a virtual cluster through synchronization, and then transmit the same piece of data with less power, to save power. There have been  
Figure 2: The traditional spectrum handover.  
many documents [17; 18; 19; 20] using these two methods to improve the performance of network transmission. In terms of the mobility management mechanism, the studies [21; 22; 23; 24] propose the cooperative mechanisms at the network layer. These mechanisms help mobile devices obtain new network IP addresses in advance through relay node. The most time-consuming process is to obtain the IP address in the network layer, because the IP address must be unique and cannot be the same as that of other people. [PERSON] et al. [25] proposed a group-based mobility management scheme in VANETs, but they do not consider the spectrum handover issue.
## 5 Parameterization by Number of Vertex Deletions
### Hardness on Trees
The resulting tree has depth three, since every leaf has distance two to a vertex from \(V\) and these vertices are all adjacent to \(v^{*}\). Moreover, every color occurs at most twice. It remains to show the following equivalence to obtain a parameterized reduction. \[G\]  
has an independent set of size \[\kappa\Leftrightarrow G^{\prime}\]  
has a rainbow subgraph of order \[\ell:=n-\kappa\]. (11)  
\"\(\Rightarrow\)\": Let \(S\) be an independent set of size \(\kappa\) in \(G\). We show that the subgraph \(G^{\prime\prime}\) obtained by removing \(S\) from \(G^{\prime}\) is rainbow. First, none of the vertices in \(V\) is incident with an edge with a unique color, so these edges remain in \(G^{\prime\prime}\). Moreover, for each edge incident with \(v\in S\) in \(G\), there is another edge in \(G^{\prime}\) that has the same color. The endpoints of this edge are either not in \(V\) or they are adjacent to \(v\) in \(G\), so they are not in \(S\). This other edge thus remains in \(G^{\prime\prime}\). \"\(\Leftarrow\)\": Let \(S^{\prime}\) be a set such that \(|S|=\kappa\) and deleting \(S^{\prime}\) from \(G^{\prime}\) results in a rainbow graph \(G^{\prime\prime}\). The set \(S\) has the following properties: First, \(v^{*}\) is not in \(S\), since otherwise an edge with color \(c^{M}_{\{u,v\}}\) is missing in \(G^{\prime\prime}\). Second, the leaves of \(G^{\prime}\) and their neighbors are also in \(G^{\prime\prime}\), since the edges between these vertices have unique colors. Hence, \(S^{\prime}\subseteq V\). Clearly, \(S^{\prime}\) is an independent set in \(G\): If \(S^{\prime}\) contains two vertices \(u\) and \(v\) that are adjacent in \(G\), then both edges with color \(c_{\{u,v\}}\) are missing from \(G^{\prime\prime}\).
# The Development of a Prototype Solution for Detecting Wear and Tear in Pedestrian Crossings
## 3 Prototype
### Architecture  
The prototype architecture has been developed to offer an intelligent and effective solution for controlling the status of crosswalks in real time, thereby contributing to pedestrian safety and efficient urban traffic management. The core concept involves integrating Internet of Things (IoT) modules into municipal vehicles, such as garbage trucks or council cars, which regularly drive through the city's streets. These IoT modules are equipped with cameras and Global Positioning System (GPS) devices. The data collected are transmitted to servers, where they are processed and stored in a NoSQL database, in this case, Firebase [17]. In addition, an Application Programming Interface (API) hosted on the Render platform [18] is used to facilitate communication between the IoT devices and the web  
Figure 15: Comparison between the two approaches in two different examples: Example (**a**): (1) and (2) shows the detection using the first approach considered—YOLOv4-tiny and Adaptive Threshold. (3) shows the result obtained under the same conditions but using the second approach—YOLOv4-tiny with three classes. Example (**b**): (1) and (2) shows the detection using the first approach considered—YOLOv4-tiny and Adaptive Threshold. (3) shows the result obtained under the same conditions but using the second approach—YOLOv4-tiny with three classes.  
application. This architecture allows for continuous, real-time collection of information on the state of the crosswalks, providing a comprehensive view of the situation throughout the city where this prototype is applied.  
Figure 16 illustrates the architecture of the prototype developed, with its various components and the interconnection between them. It shows the presence of Internet of Things (IoT) devices in the vehicles, which are responsible for capturing and transmitting data on the status of crosswalks. This data transmission is carried out using data in JSON format [19]. The use of an Application Programming Interface (API) facilitates communication between the IoT device and the web application. It is also important to note that the IoT device requires an API key [20] to communicate with it, ensuring that only authorized devices can send information. The web application developed provides an accessible and interactive interface for viewing and analyzing the data collected, which is useful in a smart city context [21]. The following sections will detail the development of the various components present in the architecture presented.
## 3 Conventional Data Detection Algorithm Based on Manifold Optimization
Since \(\text{trace}\Big{\{}\tilde{\mathbf{Y}}_{k}\tilde{\mathbf{Y}}_{k}^{T}\Big{\}}\) is independent of the transmitted signal \(\tilde{\mathbf{X}}_{k}\), the objective function can be further transformed into  
\[F(\tilde{\mathbf{X}}_{k})=-\text{trace}\bigg{\{}\tilde{\mathbf{Y}}_{k}\tilde{\mathbf{X}}_ {k}^{T}\Big{(}\tilde{\mathbf{X}}_{k}\tilde{\mathbf{X}}_{k}^{T}\Big{)}^{-1}\tilde{\mathbf{X }}_{k}\tilde{\mathbf{Y}}_{k}^{T}\Bigg{\}}. \tag{11}\]  
Since the transmitted signal \(\tilde{\mathbf{X}}_{k}\) can be approximated to lie on a sphere manifold when the data length is sufficiently long [15], the optimization problem is eventually written in the following form:  
\[ \begin{split}&\min_{\tilde{\mathbf{X}}_{k}}F(\tilde{\mathbf{X}}_{k})=- \text{trace}\bigg{\{}\tilde{\mathbf{Y}}_{k}\tilde{\mathbf{X}}_{k}^{T}\Big{(}\tilde\mathbf{{X}}_{k}\tilde{\mathbf{X}}_{k}^{T}\Big{)}^{-1}\tilde{\mathbf{X}}_{k}\tilde{\mathbf{Y}}_{k}^{ T}\bigg{\}},\\ &\text{s.t.}\big{\|}\tilde{\mathbf{X}}_{k}\big{\|}_{F}=\sqrt{N_{t}N_{f} \sigma_{x}^{2}}.\end{split}  \tag{12}\]  
Equation (12) can be further expressed as the Riemannian optimization problem:  
\[ \begin{split}&\min_{\tilde{\mathbf{S}}_{k}}F(\tilde{\mathbf{S}}_{k})=- \text{trace}\bigg{\{}\tilde{\mathbf{Y}}_{k}\tilde{\mathbf{S}}_{k}^{T}\Big{(}\tilde\mathbf{{S}}_{k}\tilde{\mathbf{S}}_{k}^{T}\Big{)}^{-1}\tilde{\mathbf{S}}_{k}\tilde{\mathbf{Y}}_{k}^{ T}\bigg{\}},\\ &\text{s.t.}\big{\|}\tilde{\mathbf{S}}_{k}\big{\|}_{F}=1.\end{split}  \tag{13}\]  
where \(\tilde{\mathbf{X}}_{k}=\sqrt{N_{t}N_{f}\sigma_{x}^{2}}\tilde{\mathbf{S}}_{k}\). After that, one can use the Riemannian conjugate gradient algorithm [16] to solve (13). Note that this algorithm needs the Euclidean gradient of \(F(\tilde{\mathbf{S}}_{k})\), computed as  
\[ \begin{split}\text{Grad}\,F(\tilde{\mathbf{S}}_{k})=& -2\Big{(}\tilde{\mathbf{S}}_{k}\tilde{\mathbf{S}}_{k}^{T}\Big{)}^{-1}\tilde{\mathbf{S}}_{k} \tilde{\mathbf{Y}}_{k}^{T}\tilde{\mathbf{Y}}_{k}\tilde{\mathbf{S}}_{k}^{T}\Big{(}\tilde\mathbf{{S}}_{k}\tilde{\mathbf{S}}_{k}^{T}\Big{)}^{-1}\tilde{\mathbf{S}}_{k}\\ &-2\Big{(}\tilde{\mathbf{S}}_{k}\tilde{\mathbf{S}}_{k}^{T}\Big{)}^{-1} \tilde{\mathbf{S}}_{k}\tilde{\mathbf{Y}}_{k}^{T}\tilde{\mathbf{Y}}_{k}.\end{split}  \tag{14}\]  
Proposed Manifold Optimization-Based Data Detection Algorithm for MIMO-OFDM Systems Under Time-Varying Channels  
The algorithm in Section 3 is only applicable to the scenarios where the channels are constant within one frame. However, in practice, channels are time-varying from one OFDM block to another. Therefore, considering time-varying channels, we propose a manifold optimization-based data detection algorithm for MIMO-OFDM systems in this section.
# Analysis of the Near-Ground Wind Field Characteristics during Typhoon Soulik
## 3 Quantitative Analysis of Mean Wind Properties
### Profiles of Wind Velocity
This indicates a higher level of agreement with the measured wind profiles. Meanwhile, there are errors of 1.01 m/s, 0.94 m/s, and 1.57 m/s in the root mean square of the estimated power law, the logarithmic law, and the Deaves-Harries model, respectively. The findings indicate that the power law and the logarithmic law provide a more precise description of the observed wind profiles, consistent with the findings of [PERSON] et al. [12] on wind profiles at the time of typhoon  
Figure 3: Comparison between measured results and theoretical results of wind profiles at different wind speeds: (**a**) Different wind profile, and (**b**) Measured and theoretical results. landfall. Conversely, the Deaves-Harries model demonstrates a poorer fit with the actual wind profiles.