esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Checking Resiliency and Sustainability of In-Vehicle Network for Real-Time Authenticity
## 4 MAuth-CAN
### Countermeasures of MAuth-CAN
#### 4.3.2. Countermeasure to DoS and Bus-Off Attacks  
Figure 4 shows a scenario that MAuth-CAN performs CAN message authentication under BoA, and consequently sends all AFR messages to ECUs when a message cannot pass the authentication.  
**AFR Flooding**  
Every time an unauthenticated message comes in, the authenticator instantiates and broadcasts an AFR. Ideally, if the authenticator always dominates CAN bus over any nodes including the attacker, every ECU under masquerade attack must receive the AFR message within 4 \(\times\) T\({}_{\text{tx}}\) time units according to Figure 4. In order for the attacker not to be able to infer anything from AFR messages or reuse the previous AFR messages, the authenticator uses the reversed hash chain. An AFR message consists of two packets. Thus, the AFR message for the first adversary message can reach all nodes within 3 \(\times\) T\({}_{\text{tx}}\) time unit, but the AFR message for the second adversary message can be delayed by the first AFR message. For this reason, the waiting time of ECU for authentication needs to be 4 \(\times\) T\({}_{\text{tx}}\) time units.  
**BO Avoidance**  
Figure 4: MAuth-CAN resistant against flooding.  
Figure 3: Basics of MAuth-CAN.  
Both authenticator and attacker attempt to dominate CAN bus at the same time if the highest priority CANID is open. If they send messages simultaneously with the same ID, the transmission error occurs and increases TEC of message senders i.e., the authenticator and the attacker, here. If either of authenticator or attacker's TECs steps over a threshold of Active Error mode, it transits to Passive Error mode. When TEC goes over the limit of Passive Error mode, the ECU in Passive Error mode is enforced to leave CAN bus for a while.  
To avoid this situation, the authenticator is equipped with two CAN controllers using two TECs of each CAN controllers. Consequently, both CAN controllers of the authentication cannot be enforced to enter into Passive Error mode at the same time.  
MAuth-CAN is resilient to masquerade attacks if the authenticator leaves no missing message to verify at all. It is also sustainable under BoA because the attacker is disabled to send adversary messages faster than authenticator using two TECs in dual-CAN controllers.
## 2 Methodology  
### Database Assembly Overview  
Field measurements of CDOM, DOC, remote sensing reflectance, \(R_{rs}(\lambda)\), and ancillary data and metadata were downloaded from SeaBASS and the Hansell/Carlson collection ([[https://hansell-lab.rsmas.miami.edu/research/data-collection/index.html](https://hansell-lab.rsmas.miami.edu/research/data-collection/index.html)]([https://hansell-lab.rsmas.miami.edu/research/data-collection/index.html](https://hansell-lab.rsmas.miami.edu/research/data-collection/index.html))) in April 2013. Coincident, Level 2 (L2) SeaWiFS and MODIS Aqua and Terra satellite imagery at all field stations were downloaded from the NASA Ocean Color website ([[http://oceancolor.gsfc.nasa.gov](http://oceancolor.gsfc.nasa.gov)]([http://oceancolor.gsfc.nasa.gov](http://oceancolor.gsfc.nasa.gov))). Due to the size of aggregated datasets for each of the key constituents (e.g., 117,291 raw CDOM records, 31,474 raw DOC records, 115,773 in situ reflectance records, and \(\sim\)177,000 matching satellite scenes), extensive automation in theprocessing, quality control, and merging of the databases was a necessity. A station-by-station analysis (or field experiment-specific analysis, as in [33]) of the data for establishing the customized spatial and temporal thresholds for matching coincident inherent and apparent optical properties and satellite imagery was not feasible. Relatively broad guidelines conducive to automation were established, as described in detail in the following sections. We assume, for example, that geospatial and temporal variability of CDOM and DOC is higher in coastal and shelf waters (defined here as samples collected in waters of 1000 m depth or less) than in the pelagic.
# Two-Way Transmission for Low-Latency and High-Reliability 5G Cellular V2X Communications
## Appendix A Derivation of (13)
\begin{split}\xi_{1}&=\sum_{j=0}^{K-1}\bigcup_{ \tilde{r}}\binom{K-1}{j}\frac{K\mu_{h_{2}}^{m_{h_{2}}}(-1)^{j}\mathcal{A}_{j^{ \prime}}\mathcal{B}_{j^{\prime}}}{\Gamma\left(m_{h_{2}}\right)}\\ &\quad\times\left[\frac{\Gamma\left(m_{h_{2}}+\tilde{j}\right)}{ \mu_{h_{2}}^{j}(1+j)^{m_{h_{2}}+\tilde{j}}}-\sum_{i=0}^{N}\sum_{q=0}^{\tilde{ j}}\bigcup_{\tilde{r}}\binom{\tilde{i}}{q}\binom{N}{i}\ \ \frac{(-1)^{i}\rho^{\tilde{i}-q}\delta^{i}\mathcal{A}_{j^{ \prime}}\mathcal{B}_{j^{\prime}}\Gamma\left(m_{h_{2}}+\tilde{j}+\tilde{i}-q \right)e^{-\mu_{h_{1}}i\delta}}{\left(\mu_{h_{2}}\left(1+j\right)+\mu_{h_{1}}i \delta\rho\right)^{m_{h_{2}}+\tilde{j}+\tilde{i}-q}}\right].\end{split}  \tag{12}\]  
In the similar manner, \(\xi_{2}\) is defined and expanded as  
\[\xi_{2}\triangleq\Pr\left(\left|g_{2\mathcal{L}^{\kappa}}\right|^{2}>\chi \delta\right)=\Gamma_{\left|g_{2\mathcal{L}^{\kappa}}\right|^{2}}\left(\chi \delta\right)=\sum_{l=0}^{K}\bigcup_{l^{\prime}}\binom{K}{l}(-1)^{l}\mathcal{A }_{l^{\prime}}\mathcal{B}_{l^{\prime}}\mathcal{X}^{\tilde{l}}\delta^{l}e^{-\mu _{h_{2}}l\chi\delta}. \tag{13}\]  
Plugging (12) and (13) into (10), we obtain  
\[ \begin{split}\mathcal{P}_{2}^{SIC}=& 1-\left\{\sum_{j=0}^{K-1}\sum_{l=0}^ {K}\bigcup_{\tilde{r}}\bigcup_{\tilde{r}}\binom{K}{l}\binom{K-1}{j}\frac{K\mu_{h _{2}}^{m_{h_{2}}}(-1)^{j+l}\mathcal{A}_{j^{\prime}}\mathcal{B}_{j^{\prime}} \mathcal{A}_{i^{\prime}}\mathcal{B}_{j^{\prime}}\mathcal{A}_{i^{\prime}} \mathcal{B}_{i^{\prime}}\chi^{\tilde{l}}e^{-\mu_{h_{2}}l\chi\delta}}{\Gamma \left(m_{h_{2}}\right)}\right.\times\\ &\left.\left[\frac{\Gamma\left(m_{h_{2}}+\tilde{j}\right)}{\mu_{h_{ 2}}^{\tilde{l}}(1+j)^{m_{h_{2}}+\tilde{j}}}-\sum_{i=0}^{N}\sum_{q=0}^{\tilde{i}} \bigcup_{\tilde{r}}\binom{\tilde{i}}{q}\binom{N}{i}\frac{(-1)^{i}\rho^{\tilde{i}- q}\delta^{i}\mathcal{A}_{i^{\prime}}\mathcal{B}_{j^{\prime}}\Gamma\left(m_{h_{2}}+ \tilde{j}+\tilde{i}-q\right)e^{-\mu_{h_{1}}i\delta}}{\left(\mu_{h_{2}}\left(1+j \right)+\mu_{h_{1}}i\delta\rho\right)^{m_{h_{2}}+\tilde{j}+\tilde{i}-q}}\right] \right\},\end{split}  \tag{14}\]  
which is (13).
## 2 Materials and Methods
### Sweet Pepper Part Classification Process  
When growing sweet pepper, to promote its growth, reduce damage caused by pests and diseases, and increase fruit yield, it is necessary to remove old leaves at the lower part and suckers that occur between central stems and nodes [28]. In order to automate agricultural tasks such as pruning and harvesting, it is important to detect the branching point of the central stem and nodes, the sucker that is a shoot of new growth from the nook where a branch splits in two and a piece of the plant that gardeners remove in the pruning process. In this study, local features of sweet pepper were used for the classification of plant parts such as fruits, stems, leaves, and nodes from obtained images to differentiate them by each part. First, the harvesting image was pre-processed through the NDVI to remove noise such as the background image. Fruit, stem, leaf, and node were segmented through k-means clustering. Skeletonization of each segmented part was performed through a morphological method. Local features from the skeletonized image were then figured out using SIFT and SURF algorithms. Numbers of local features of fruits, stems, leaves, and nodes were then compared to confirm local features. The extracted local features were classified through the SVM and BP classifier algorithms. Their classification performances were compared. In addition, a CNN (Convolutional Neural Network), a type of deep neural network, was implemented to differentiate fruits, nodes, leaves, and suckers of sweet pepper. Its classification performance was compared to that of the SVM or BP classifier algorithm. Figure 4 shows the whole classification process for plant parts.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Useful Range** & **FWHM 1** & **Tolerance** & **Peak Transmission** \\ \hline BP470 & 435\(-\)495 nm & 85 nm & \(+/-10\) nm & \(>\)90\% \\ BP500 & 440\(-\)555 nm & 248 nm & \(+/-10\) nm & \(>\)85\% \\ BP505 & 485\(-\)550 nm & 90 nm & \(+/-10\) nm & \(>\)90\% \\ BP525 & 500\(-\)555 nm & 80 nm & \(+/-10\) nm & \(>\)90\% \\ BP635 & 610\(-\)650 nm & 65 nm & \(+/-10\) nm & \(>\)90\% \\ BP660 & 640\(-\)680 nm & 65 nm & \(+/-10\) nm & \(>\)90\% \\ BP695 & 680\(-\)720 nm & 65 nm & \(+/-10\) nm & \(>\)90\% \\ BP735 & 715\(-\)780 nm & 90 nm & \(+/-10\) nm & \(>\)90\% \\ BP800 & 745\(-\)950 nm & 315 nm & \(+/-10\) nm & \(>\)90\% \\ \hline \hline \end{tabular} 1  
\end{table}
Table 2: Specifications of the band-pass filter used by the system.
# Combined Use of 3D and HSI for the Classification of Printed Circuit Board Components
## 4 Discussion
This inevitably leads to changes and shifts in the spectral signatures as can be seen in Figure 17 for one of the samples in the used dataset. That our proposed approach is robust to such influences can be seen from the achieved results. This is due to two main facts: First, we use spectral features that describe the shape of spectral signatures, even if the expression of absorption or reflectance peaks is low due to the weak reflectance, the shape of the spectral signature remains the same. Thus, the defined rules based on the shape apply in both cases. The second point is due to the 3D component. Through the 3D information we can group components in clusters and for the rule check we refer to the average spectral signature of this cluster. Thus, the influence of dirty areas is only significant if the object is completely soiled. Figure 16: Small components such as metals (red and blue box), resistors (green box) and capacitors (yellow box) are difficult to distinguish because of limits in resolution. Nevertheless, they can be classified as components of a PCB due to the topological relationships provided by the 3D data. The processing time of the data is also a factor that needs to be taken into account. The 3D processing and point-by-point checking for conditions using MATLAB on a machine with an Intel(R) Core(TM) i7-10700 CPU @ 2.90 GHz and 16 GB RAM was 110.29 s for the background removed dataset (752.253 3D points and 224 bands) shown in Figure 4. For comparison, processing the same dataset (only HSI) on the same machine using SVM (LIBSVM for MATLAB) requires 2025.07 s.  
For a practical application, 110.29 s is considered high. However, in this context it must also be considered that a full spectral and high-resolution 3D point cloud serves as the data basis. Optimizations in terms of processing time is conceivable and could be achieved by reducing the data basis (e.g., reduction of spectral resolution), by using more powerful hardware and by more efficient programming (e.g., parallel computing). Figure 17: Influence of dirt on the reflectance of spectral signatures. (**a**) Spectra of a heavily polluted area (red box). (**b**) Spectra of areas without dirt (green box).
## 4 Results and Discussion
### Case Study Results
#### 4.1.1 Application of Guidelines to Coco Shapes
For LAA12, the learning activities included sound effects and music that accompanied the instructions provided to the user. It is important to mention that the school director recorded the instructions played in the learning activities, as she represented a familiar voice for the students due to her excellent pronunciation of English. This was conducted to evoke positive emotions and hold the students' interest. For LAA13, Coco Shapes' figure and color activities include a physical object with geometric figure slots and primary/secondary color buttons, mirroring the transition students' curriculum. Interacting with these objects stimulates tactile and visual senses, enhancing the learning experience by allowing children to touch, manipulate, and explore, thus boosting topic retention and comprehension. For LAA15, as part of the learning activities of Coco Shapes, students press buttons, insert shapes into well-defined spaces, and select options on a tablet screen, depending on the challenges defined for each theme. This diversity of interaction styles involves children more actively in the multimedia experience. The student's commitment and attention to the challenge are encouraged by offering several interaction options. The guidelines LAP1, LAP2, LAP3, and LAP4 were considered when applying the guidelines related to student progress, as described below. For LAP1, the number of successes and failures recorded based on the student's interaction with Coco Shapes is displayed on the dashboard, which displays a line graph of the student's performance over time (see Figure 6) in comparison to the course average. With this information, teachers can identify long-term trends, patterns, or improvements in student performance. For LAP2, the web app visualizes each course's average time spent on Coco Shapes based on student usage (see Figure 8), indicating the exposure duration to the second language. This usage time reflects student engagement, allowing teachers to assess interest and participation levels. For LAP3, Figure 4 shows an example that includes the number of successes and failures, the student progress as a percentage, and the course average throughout the system's use. The graph follows the LAD5 guideline and allows the student's performance to be displayed and compared with the average results for the course. This allows the teacher to identify opportunities for the improvement of the teaching strategy for students who show low performance over time. For LAP4, the Coco Shapes web app logs student interactions with learning activities, allowing access to historical data by course. This enables stakeholders to refine pedagogical strategies and compare student cohorts for trend analysis and improvement areas. Teachers can also control course visibility within their school's assigned course list.
# Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat
###### Abstract  
The emergence of urban heat as a climate-induced health stressor is receiving increasing attention among researchers, practitioners, and climate educators. However, the measurement of urban heat poses several challenges with current methods leveraging either ground based, in situ observations, or satellite-derived surface temperatures estimated from land use emissivity. While both techniques contain inherent advantages and biases to predicting temperatures, their integration may offer an opportunity to improve the spatial resolution and global application of urban heat measurements. Using a combination of ground-based measurements, machine learning techniques, and spatial analysis, we addressed three research questions: (1) How much do ambient temperatures vary across time and space in a metropolitan region? (2) To what extent can the integration of ground-based measurements and satellite imagery help to predict temperatures? (3) What landscape features consistently amplify and temper heat? We applied our analysis to the cities of Baltimore, Maryland, and Richmond, Virginia, and the District of Columbia using geocomputational machine learning processes on data collected on days when maximum air temperatures were above the 90 th percentile of historic averages. Our results suggest that the urban microclimate was highly variable across all of the cities--with differences of up to 10 \({}^{\circ}\)C between coolest and warmest locations at the same time--and that these air temperatures were primarily dependent on underlying landscape features. Additionally, we found that integrating satellite data with ground-based measures provided highly accurate and precise descriptions of temperatures in all three study regions. These results suggest that accurately identifying areas of extreme urban heat hazards for any region is possible through integrating ground-based temperature and satellite data.  
urban heat; satellite; ground-based; land use and land cover; machine learning Article
# Multivariate Network Layout Using Force-Directed Method with Attribute Constraints
## 4 System
### Attribute Selection View  
As shown in Figure 2b, the user needs to select the attribute used for the layout and the attribute value under that attribute. For **numerical attributes** and **non-numerical attributes**, we have designed different attribute community distribution methods.  
**Numerical attributes.** Taking the _age_ attribute of each node in a social network as an example, users are not very interested in a specific age most of the time. Therefore, we provide an interval division method for numerical attributes. The user can select the expected number \(m\) of attribute communities to be divided. Knowing that n nodes are waiting to be laid out in the network, the formula for the expected number of nodes \(n_{c}\) in each community is as follows:  
\[n_{c}=\frac{n}{m} \tag{49}\]  
Sort the numerical attributes of all nodes, and divide the sequence with \(n_{c}\) as the length. When the sum of the number of nodes corresponding to consecutive numerical attributes reaches \(n_{c}\), it is classified as an attribute community. The label of the attribute community is the mark from the minimum to the maximum value of the numerical attribute. Each node in the attribute community can be assigned this mark.  
Figure 2: Visual system for user interaction with FDAC. (a) The focus selection view with (a1) the automatic screening by the system and (a2) the search view. (b) The attribute selection view. (c) The layout result view.  
**Non-numerical attributes.** Take the _keyword_ attribute of each node in the paper citation network as an example. A paper often contains multiple keywords. When users explore the keyword attributes, they often choose multiple keywords as the focus of attention. Using a method based on perfect matching, the dataset is traversed, and the node attributes that exactly match the user's selection are labeled. For nodes that match multiple attributes at the same time, they contain multiple tags and are assigned to a new attribute community.  
**Node attribute community distribution.** Based on the choices, we need to assign attribute labels to nodes. For some nodes, its attribute value matches only one of the choices, then the label of this node is the attribute value. For other nodes, if its attribute value matches multiple attribute values, then the label of this node is the combination of the attribute values and this node will be assigned to a new attribute community where nodes have the same label also be in. For example, _keywords_ attribute may contain multiple values, like _graph layout_, _visualization_, _data mining_, etc. the combination of the attribute values is graph _layout+visualization_.
## 3 Actual Seismic Data Application
### Application Example in HD-YK Block  
In order to prove the effectiveness and stability of the method, it was applied to the HD-YK block of the research area. The geological characteristics of this block are more complex than those of the SHB-YM block. The target layer has a dip angle of 3-6 degrees, the maximum structure gradient is 15 degrees southeast, and the target layer has a structural drop of 450 m. The block was first discovered in 2015, and then five fractured-cavity units were depicted. The scheme design utilized seven appraisal wells and deployed 41 new development wells. Six wells and two test production wells were completed. In the past two years, researchers have been troubled by the fact that decision-makers cannot effectively compare and optimize well placement and drilling based on the characteristics of high-efficiency wells, resulting in a large gap between the number of wells and the planned total number of wells.  
In the early stage, according to the plan explained based on the best migration processing data that has been implemented, there are only two fault zones with a length of 16.25 km that can deploy the effective well positions required by the plan, and no large-scale SFCRs in the block can be demonstrated. The characteristics of the layers are the main reason for the lag in well deployment progress. From the previous interpretation results, we can conclude that the seismic profile across any line of the original seismic data can only distinguish the \"beaded\" reflections with strong reflected energy and multiple abnormal energies in the horizontal continuous track, and relatively clear SWFAs identified nine targets (Figure 9(a)). As indicated in the RMS amplitude attribute plan, the characteristics of the fault zone are not clear. Coupled with the interference of the sheet-like strong reflection background, the diffraction wave field anomalies produced by many relatively small-scale heterogeneous reservoirs are uniform. Submerged in the background (Figure 9(a)), it is difficult to delineate.  
Figure 10: An arbitrary profile in the HD-YK block. (**a**) the original seismic data; (**b**) the heterogeneous SWFA data.  
In view of the above situation, the method in this paper is used to carry out targeted research, and the data volume of the heterogeneous body wave field is obtained by calculation. The FVRs of the arbitrary profile using the heterogeneous SWFA data are clearly distinguishable, at least 14 sweet spots can be clearly identified, and the RMS amplitude attribute plan along the target layer can clearly distinguish the development of the fault zone. Distribution characteristics and heterogeneous reservoir spots (Figures 10b and 11b), the length of the identified fault zone reached 42.66 km, and the number of well sites available for batch deployment exceeded 53.