esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method  
The proposed method synergizes several aspects of a website as different views to detect phishing websites, including the website's URL, attribute information, content information, and behavior information. Specifically, we use the following methods: (1) Weutilize a small amount of an unlabeled URL corpus and adversarial post-training learning to transform a 12-layer English BERT model into a URL feature extractor. This allows us to obtain the structural and semantic features of URLs at high quality and low cost. (2) Regarding attribute information, we primarily examine the domain registration information of the website. This is based on the consideration that phishing websites often have shorter registration times than benign websites. (3) Content information includes checking pop-ups, overlays, and unusual requests for personal information. (4) The page loading time and redirect times are recorded as the website's behavior information. The statistical results of the aforementioned website's attribute information, content information, and behavioral information are all encoded into vectors of the same scale. Additionally, the features of the URLs are extracted using our well-trained BERT-based encoder. Once all the information from different views has been encoded, we input this information into a Transformer network with a mixture-of-experts mechanism to further learn features relevant to the phishing detection task. This network comprises two key feature weighting components: the multi-head self-attention layer and the mixture-of-experts network. The multi-head self-attention layer captures dependencies between different views and allows the model to simultaneously attend to different semantic information. On the other hand, the mixture-of-experts network combines multiple expert models, which selectively contribute based on the input data's characteristics, thereby enhancing the model's performance and adaptability. The joint utilization of these two components facilitates the effective integration of features from diverse views, enables a comprehensive exploration of diverse information sources, and enables the adaptive extraction of highly expressive features. The model's architectural design is illustrated in the accompanying diagram in Figure 1. To provide a clearer exposition of our method, the contents of this paper are organized as follows: Firstly, we introduce the proposed URL feature learning method. Next, we present the features from perspectives other than the URL, along with their respective feature processing methods. Finally, we describe the multi-view fusion learning architecture.
## 4 Discussion
### The Influence of Different Responses of GPP and SIF to the Environment on the GPP-SIF Relationship  
We have found that the consistency of GPP and SIF light responses was influenced by EF and Ta (Section 3.1). When temperature and water status were not suitable for the vegetation growth (lower EF and Ta), the consistency between GPP and SIF was destroyed and the correlation between GPP and SIF was lower, as shown in Table 1. With the improvement in Ta and EF, the correlation between GPP and SIF was improved because the consistency of the SIF and GPP light responses gradually improved. The dynamics in the GPP-SIF correlation further indicated that, when the environmental conditions are not suitable for the vegetation growth, directly using SIF to quantify GPP causes greater uncertainty.  
The strong linear and non-linear GPP-SIF relationship has been reported in many studies. Here, we found that the half-hourly and daily GPP-SIF relationship at the DM site was non-linear. For the non-linear GPP-SIF relationship at the DM site, the slope values of the fitted GPP-SIF relationship curves increased with increasing Ta and EF, mostly caused by the different sensitivities of GPP and SIF to temperature and water status. As shown in Section 3.1, GPP was more sensitive to EF and Ta than SIF, and in any radiation bins, GPP varied with EF and Ta faster than SIF.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 2 Technology Background of Physical Layer
### Frequency Bands in 5G
#### 2.1.2 5G FR2 mmWave Band  
The FR2, on the other hand, utilizes mmWaves and refers to frequency bands above 24.25 GHz and up to 52.6 GHz in order to achieve low latency and wide bandwidth to achieve high data throughput. This frequency band is typically used in small femto-cells to cover small areas, typically building floors.
## 7 Discussion
### Advantages of the New Formula  
The industry clearly benefits from the spectrum usage fee reduction offered by the new formula. Some of the advantages for regulators are as follows:  
1. Regulators treat the reduced fee as a subsidy using the readiness of each industry according to INDI 4.0. The spectrum usage fee value of the new formula is lower than that of the old formula, which can help service operators survive. Various 5G-mmWave local micro-operators or 5G-mmWave private network service operators are not directly covered by the Indonesian regulator. There is therefore a demand for a new spectrum authorization design to allow new micro-operators to participate in 5G. The regulator grants exclusive licensing for service operators to provide long-term, usually 10 years, for a spectrum access license.
2. The new formula supports harmony and mutual support between industrialization and digital transformation. The Ministry of Communication and Informatics, as the regulator in the field of telecommunications, must work closely with other ministries to determine an overall general roadmap for the success of new technologies because the 5G-mmWave private network covers a wide range of industries and involves close coordination between various government departments. Without coordinated standards and policies, the true benefits of 5G-mmWave private networks will not be realized. Such collaboration will help drive the innovation ecosystem with customized offerings that meet the specific needs of the industry.
3. Because the coverage of this private network is limited to specific sites with a restricted coverage area, the 5G-mmWave private network usage scenarios will create immense opportunities to increase service operators' infrastructure and provide more significant economic benefits. The ongoing technological expansion of 5G-mmWave private networks has opened up opportunities for various new spectrum applications. Although frequently making spectrum use more efficient, these expansions have pushed significant interest and demand for the limited spectrum resource.
4. It will be easier to reuse these frequencies in other locations. Indonesia, which has advanced telecommunications technology, not only pays attention to increasing non tax state income annually but also aims to optimize the mmWave frequency with a broader bandwidth, which will enhance Indonesia's value. The application of spectrum usage fees with the new formula in industrial areas will have a multiplier effect on the output of the national economy and supporting Industry 4.0 and encourage digital economic growth and national digital transformation, especially for vertical industries in Indonesia.
# Generation of Complete SAR Geometric Distortion Maps Based on DEM and Neighbor Gradient Algorithm
## 3 Experiments and Results
### Comparison Between the SGD and the Interferometric Products  
The interferometric coherence, estimated from two radar recordings within the time interval between the master and slave images, reflects the similarity among echoes [21]. It is used as a metric of the accuracy of measured amplitude and phase. However, low-coherence areas are commonly found in relief mountainous areas affected by the SGD [22]. In addition, both temporal and spatial baselines lead to decorrelation [23; 24; 25]. Therefore, the constraints on the temporal and spatial baselines are required for observation of echo correlation to minimize these effects. In this study, since the temporal and spatial baselines are both small and the L-band ALOS PALSAR data has good penetrability, the decorrelation is mainly contributed by these SGD effects.  
The coherence maps retrieved from the ALOS ascending and descending pairs are shown in Figure 7. The red and yellow points represent the layover and shadow distortions, respectively, which are superimposed on the coherence, the value of which ranges from 0 to 1.0. As expected, the layover/shadow distortions are basically distributed in the low-coherence regions, which confirms that the areas of the layover/shadow distortion are decorrelated. However, a few layovers have a slightly high coherence where they have tensile distortion. Still, a small part of the layover/shadow points falls in the normal areas. This could be ascribed to the errors of the geo-location of radar scatterers [26]. As shown in Figure 8, the interferometric fringes cannot be well produced in the corresponding layover and shadow regions. This indicates that the SGDs are non-negligible undesirable effects in the InSAR application.  
Figure 6: Spatial interactions among the simulated distortions. The white points represent passive layover, while the black points represent passive shadow. (**a–c**) display the passive layover interacting with the foreshortening (“F”), the active shadow (“AS”), and the passive shadow (“PS”); (**d–f**) display the passive shadow interacting with the foreshortening (“F”), the active layover (“AL”), and the passive layover (“PL”).  
Figure 8: Interferograms derived from the ALOS ascending data (**a**,**c**) and the ALOS descending data (**b**,**d**); the red points represent the layover and the yellow points represent shadow.  
Figure 7: Coherence maps derived from the ALOS ascending data (**a**,**c**) and the ALOS descending data (**b**,**d**); the red points represent the layover and the yellow points represent shadow.
# Healthchain: A Privacy Protection System for Medical Data Based on Blockchain
## 5 System Design
### Module Design
#### 5.2.1 Transaction Encryption  
When a diagnostician completes the diagnosis or treatment of a patient, they need to enter the transaction upload page through the system. The physician needs to enter the patient's name and identity card number first. The two will then be linked to create the hash value, which will serve as the patient's unique identity number (i.e., serial number). They will then enter the time of treatment and medical information.  
Before each transaction is published, the diagnostician gets an AES key to encrypt the transaction. In addition, in order to improve the security without increasing the additional system burden, the system generates different AES keys ('one key at a time') by generating random numbers every time.
## 3 Results and Discussion
### Determination of the Crack Initiation Criterion Based on Strain
#### 3.2.1 Double-Strain Criterion of Crack Propagation
\begin{array}{l}x,x\geq 0\\ 0,x<0\end{array} \right. \tag{16}\]  
The boundary equation expression of the micro-crack zone at the crack tip is obtained as follows:  
\[r=\frac{\Big{(}1-v^{2}\Big{)}^{2}K_{I}^{2}}{E^{2}\pi\varepsilon_{e}^{2}}\cos ^{2}\frac{\theta}{2}\Bigg{[}\Bigg{(}\frac{1-2v}{1-v}\Bigg{)}^{2}+\Big{(}\frac{ 1}{1-v}\Big{)}^{2}\sin^{2}\frac{\theta}{2}\Bigg{]} \tag{17}\]  
Letting \(\theta=0^{\circ}\) and \(K_{I}=K_{IC}\) in Equation (17), the critical length of the micro-crack initiation zone at the crack tip on the x-axis can be obtained as follows:  
\[r_{0}=\frac{\Big{(}1-v^{2}\Big{)}^{2}K_{IC}^{2}}{E^{2}\pi\left[\varepsilon_{e} \right]^{2}}\Bigg{(}\frac{1-2v}{1-v}\Bigg{)}^{2} \tag{18}\]  
where \(\left[\varepsilon_{e}\right]\) is the crack initiation strain threshold and \(r_{0}\) is the critical length of micro-crack initiation at the crack tip when \(\theta=0^{\circ}\). When the crack initiation, the critical length of the micro-crack zone at the crack tip obtained from the equivalent strain theory is equal to the critical length obtained from the unified strength theory of concrete. Let Equation (12) be equal to Equation (18). The strain threshold of micro-crack initiation under the plane strain state can be obtained as follows:  
\[\left[\varepsilon_{e}\right]=\frac{\sqrt{2}(1+v)(1-2v)\sigma_{s}}{(1-2 av)E} \tag{19}\]When the concrete material is determined, the parameters \(\alpha\), \(\
u\), \(E\) and \(\sigma_{s}\) in Equation (19) are constant, so [\(\varepsilon_{e}\)] can be used as the criterion for crack initiation. [\(\varepsilon_{e}\)] is represented by \(\varepsilon^{ini}\). The theoretical relationship between the micro-crack initiation strain and unstable propagation strain Equation (19) during the propagation process is established. Reference [38] indicates that the maximum yield strain could be used as the critical state for the unstable propagation of concrete cracks. The yield strain is represented by \(\varepsilon^{un}\). Due to the limited level of strain testing in previous studies, the initiation stage of micro-cracks was often not observed. As concrete is a typical quasi-brittle material, the propagation progress of cracks can be subdivided into micro-crack initiation, stable propagation, and unstable propagation. In this paper, it is suggested that the crack-propagation state of concrete can be judged by two double-strain fracture parameters--micro-crack initiation strain \(\varepsilon^{ini}\) and unstable strain \(\varepsilon^{un}\). The double-strain model of frost-heave crack propagation can be described as follows:  
When the strain at the crack tip \(\varepsilon\leq\varepsilon^{ini}\), there is a stable micro-crack zone at the crack tip;  
When the strain at the crack tip \(\varepsilon^{ini}<\varepsilon\leq\varepsilon^{un}\), the micro-crack at the crack tip propagates steadily without macro-cracking. When the strain at the crack tip \(\varepsilon^{un}<\varepsilon\), macro-cracks emerged and continued to propagate in an unstable state.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Renewable Energy Sources
#### 4.6.5 Ashton Hayes Smart Village  
The facilitation of the connection of micro-generation technologies, like photovoltaic panels and wind turbines on the LV network, along with network monitoring and analysis, has been the topic of investigation of the \"Ashton Hayes Smart Village\" project, which was carried out in the UK. The scope was to reduce the CO\({}_{2}\) emissions and increase the integration of renewable energy sources by power quality instruments in LV network and secondary substation power monitoring [116].
# Teenagers and Automated Vehicles: Are They Ready to Use Them?
###### Abstract
However, only a few studies investigate the views of teenagers towards AVs. In addition, these studies do not focus only on teenagers but on broader age ranges (e.g., young minors together with teenagers or teenagers with young adults), usually putting little emphasis on the variation in mobility patterns among the different age categories. In this context, the current paper investigates the views of teenagers aged 14-17 years in relation to AVs. This specific age group has continuously evolving and transforming travel patterns, and they make trips more independently compared to younger children. The data of this study were collected through focus groups (FG) and various engagement activities (namely, a real-life demonstration of an AV, a mood board activity, and an inspirational space activity), where the students involved could freely express their views on the topic. The engagement activities were then followed by a post-pre survey. This paper aims to capture teenagers' opinions, perceptions, and concerns about AVs and their possible future use based on their mobility needs. The study is particularly timely because according to the World Health Organisation (WHO), \"Road traffic injuries are the leading cause of death for children and young adults aged 5-29 years\" [15], and automated vehicles, especially in Europe, are considered one of the main technological tools to achieve Vision Zero, namely the reduction in road deaths to zero by 2050 [16]. The paper is structured in the following manner. Section 2 illustrates previous works supporting the present analysis. Based on the summary of the state of knowledge, we refine in this section our research questions. Section 3 defines the methodological approach undertaken. The results and their discussion are included in Section 4. Finally, the conclusions and possible further research steps are described in Section 5.
## 3 Diversity-Controlled MUST Technique
### Transmitter Design at the BS  
Figure 2 shows the overall structure of the transmitter at the BS in the proposed diversity-controlled MUST technique. Bit-streams \(\mathbf{b}_{B}^{i}=\left\{b_{B,1}^{i},\ldots,b_{B,j}^{i},\ldots,b_{B,m}^{i}\right\}\) and \(\mathbf{b}_{E}=\left\{b_{E,1},\ldots,b_{E,j},\ldots,b_{E,m}\right\}\) enter to the BS, where \(M\) denotes the modulation order, i.e., \(m=\log_{2}M\). Bit-streams \(\mathbf{b}_{B}^{i}\) and \(\mathbf{b}_{E}\) denote the bit-streams for the \(i\)-th MUST-based UE (\(i\in\{1,2,...,N\}\)) and the MUST-enhanced UE, respectively. The term \(j\) indicates the \(j\)-th bit in an \(M\)-QAM symbol. According to the bit-labeling rule (Gray-mapping) of the MUST category 2, the bits of the MUST-enhanced UE are coded by the following function, \(G(\cdot)\):  
\[G\left(b_{E,j},b_{B,j}^{i}\right)=\left(b_{E,j}\oplus b_{B,j}^{i}\right)=b_{E, j}^{i}, \tag{1}\]  
where \(\oplus\) denotes a bitwise XOR operator. The Gray-mapped bit-streams \(\mathbf{b}_{B}^{i}\) and \(\mathbf{b}_{E}^{i}\) are then modulated by \(M\)-QAM. The modulated symbols to be sent via the \(i\)-th sub-carrier for the MUST-based UE and the MUST-enhanced UE are denoted by \(x_{B,i}\) and \(x_{E,i}\), respectively. The \(x_{B,i}\) and \(x_{E,i}\) are assigned power by \(1-\alpha\) and \(\alpha\), respectively. In this paper, we assume that \(\alpha\) is the same value for all \(i\). Thus, the super-imposed signal in the \(i\)-th sub-carrier, \(s_{i}\), is given by  
\[s_{i}=\sqrt{1-\alpha}x_{B,i}+\sqrt{\alpha}x_{E,i}, \tag{2}\]  
where \(\mathbb{E}[|x_{B,i}|^{2}]=\mathbb{E}[|x_{E,i}|^{2}]=1\). Thus, \(\mathbb{E}[|s_{i}|^{2}]=1\).
## 4 Experimental Verification  
### Experimental Scheme  
To verify the accuracy of the above summarized simulation model, a comprehensive performance test bed for linear conjugate internal gear pump was constructed, as shown in Figure 10. It uses servo motor direct drive structure, built-in speed torque sensor to monitor input parameters in real time and sets the flow rate and pressure difference sensor in series at the inlet and outlet, respectively. The proportional valve loading system is used to simulate working conditions, and the auxiliary oil filling system is used to provide 2-3 bar oil suction pressure. The layout of the test bench is shown in Figure 10a, Composition of the piping system of the test bench is shown in Figure 10b, Composition of the electrical system of the test bench is shown in Figure 10c. The experimental system of power loss and thermal balance is shown in Figure 11, which integrates surface-mounted temperature sensor and shell opening oil film pressure detection module and uses a thermal imager to obtain the temperature field distribution of the shell under different working conditions. Flow sensors, speed torque sensors, pressure and other sensor data are connected to the controller through the expansion module, and the upper configuration software is used to record and store the data. The key sensor technical parameters are shown in Table 2.  
Figure 10: Comprehensive performance testing test bench for internal gear pumps. (**a**) Layout of the test bench; (**b**) Composition of the piping system of the test bench; (**c**) Composition of the electrical system of the test bench.  
Figure 9: Internal gear pump total efficiency distribution cloud.  
The power loss and efficiency test data of the hydraulic pump are calculated based on the following equations:  
\[\eta_{m}=\frac{(p_{out}-p_{in})V_{g}}{2\pi T_{in}}\times 100\% \tag{17}\]  
\[\eta_{p}=\frac{q_{out}}{q_{in}}\times 100\% \tag{18}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor Type** & **Range** & **Accuracy** & **Manufacturer** & **Illustration Ref.** \\ \hline Speed-Torque sensor & 0-200 N-m & \(\pm\)0.2\% PS & ZEMIC (Beijing China) & \\ \hline Temperature sensor & 0-100 \({}^{\circ}\)C & \(\pm\)0.15 \({}^{\circ}\)C & Matsushima (Kitakyushu Japan) & \\ \hline Flow sensor & 5-150 L/min & \(\pm\)0.5\% of reading & Hydrotechnik (Limbaugh Germany) & \\ \hline Oil temperature sensor & \(-\)50-200 \({}^{\circ}\)C & \(\pm\)0.2\% PS & Hydrotechnik (Limbaugh Germany) & \\ \hline Pressure sensor & 0–250 bar & \(\pm\)0.2\% PS & Hydrotechnik (Limbaugh Germany) & \\ \hline Thermal imager & \(-\)20-350 \({}^{\circ}\)C & \(\pm\)2\% PS & Hikvision (Beijing China) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Power loss and thermal balance temperature experimental testing sensor parameter.  
Figure 11: Introduction to the experimental testing plan for power loss and thermal balance.  
The total efficiency of hydraulic pump is the product of volumetric efficiency and mechanical efficiency:  
\[\eta_{tol}=\eta_{m}\eta_{m}\times 100\% \tag{19}\]
## 5 Results
### Comparison with Other Published Methods  
This section presents a detailed comparison between the proposed method and other previously published approaches in the motor imagery field. This comparison is based on the classifiers' mean accuracy. Comparisons include SincNet [39], HSS-ELM [40], IFNet [41], TSLDA [42], TSFBCSP-GA [43], FBRTS [44], TWSB [45], multi-scale CSP and Riemannian [14], and our novel hybrid ER/MI three-dimensional fractal dimension (Katz, Petrosian, and Higuchi) pipeline. The results for each subject related to multi-scale CSP and multi-scale Riemannian, as well as the results achieved from this current study, 3D-FD, are rounded to the nearest tenth to fit into the comparison table, Table 10. The results per year are presented in Figures 4 and 5, along with the other methods used in different publications.  
After analyzing the subject accuracy for our method, 3D-FD, compared to the other approaches, the results show that 3D-FD achieves high and consistent classification accuracy across subjects, with six out of nine subjects showing an accuracy above 80%. The balanced performance, without extreme highs or lows, indicates that 3D-FD generalizes well across diverse subjects despite inter-subject EEG variability concerns. This stability is verified by the overall mean accuracy of 79.1%, which aligns with the best MI method year to date, the TWSB method [45].  
Student's t-tests were performed again to compare the 3D-FD method with various existing methods. A significant difference was found between 3D-FD and both HSS-ELM (\(p\) = 0.0212) and SincNet (\(p\) = 0.0001), with 3D-FD performing significantly better in both cases. No significant differences were found between 3D-FD and the other methods (all \(p\)-values \(>\) 0.05). These results indicate that while 3D-FD shows a noted advantage over certain methods, like HSS-ELM and SincNet, its performance is comparable to other existing methods (see Table 11).  
The significant differences observed between the 3D-FD method and both HSS-ELM and SincNet highlight that 3D-FD can offer advantages over these approaches, possibly due to its unique feature extraction capabilities in capturing complex data patterns from EEG signals. The lack of significant differences from other methods shows that 3D-FD performs comparably to existing approaches. The latter highlights the robustness of 3D-FD without significant statistical superiority.  
Figure 5: A heatmap of the classification accuracy of various methods across subjects and the overall mean.