esa-sceva/satcom-chunk-collection huggingface dataset:
# The RUNE Experiment--A Database of Remote-Sensing Observations of Near-Shore Winds
## 4 Results
### Actual Availability  
The availability of measurements depends on the ability of the lidar to measure an accurate LOS velocity, which is mostly a function of the laser power and the presence of aerosols. We define 'actual availability' when all samples within a 10 min interval fulfilled the thresholds discussed in Section 3 and a reconstruction of the wind vector could be obtained. Figure 6 shows the actual availability of all lidars performing VAD, dual setup and RHI scans. It should be noted that only the PPI scans during phase 2 and 3 have been used to obtain reconstructed wind speeds.  
For the PPI scans during phases 2 and 3, and for the dual setup during phases 2-4, all data where a range at any distance fulfilled the CNR criteria have been marked as available; data where all range gates fulfil that criteria are more rare. The lidar buoy was not available for a large portion of the experiment, but measured also during March. The other lidars only measured until February.  
Figure 7 shows the actual availability of the reconstructed horizontal wind speed components from the PPI and dual setup scans during phases 2 and 3 as a function of distance by assuming an operational availability of 100%. The actual availability decreases with distance (west-east) from the devices. The highest scanned points in the vertical coordinate, level 3, generally have the lowest availability. Furthermore, availability from the dual setup is slightly lower than that of the PPI scan because Koshava (pos. 1) and Sterenn (pos. 3) were located further away from the positions where the wind components are reconstructed compared to Vara (pos. 2). The points over land with low availability are due to the laser beam hitting objects.  
Figure 5: The wind speed (**a**) and the wind direction (**b**) at 100 m from m easurements of the Hovsere meteorological mast during RUNE.  
Figure 6: Actual availability of all reconstructed wind speeds in 10 min intervals that fulfilled the filtering criteria
## 3 Experimental Data and Estimation Algorithms
### Ampere-Hour Counting  
As mentioned earlier, this method provides a simple way to estimate the SOC. Starting with a known initial value of SOC (\(SOC_{0}\)), the value is then increased or decreased based on the current profile. The ampere-hour counting formula is depicted below [3]:  
\[SOC=SOC_{0}-\frac{1}{C_{n}}\int_{t_{0}}^{t}I\,d\tau \tag{7}\]  
where \(SOC_{0}\) is the initial SOC, \(C_{n}\) is the nominal capacity of the battery, and \(I\) is the discharge current.
## 3 Tree-Based Relative Support Distance  
In order to cope with large-scale SVM problems, we propose a novel selection method for support vector candidates using a combination of tree decomposition and relative support distance. We aim to reduce the training time of SVMs for the numerical computation of \(\alpha_{i}\) in (2) which produces \(w^{*}\) and \(b^{*}\) in (1) by selecting good support vectors in advance that are a small subset of the training data. To illustrate our concept, we start with a simple example in Figure 1, where the distribution of the iris data is shown: for the details of the data, refer to [PERSON] [34]. In short, the iris dataset describes iris plants using four continuous features. The data set contains 3 classes of 50 instances as Iris Setosa, Iris Versicolor, or Iris Virginica. We decompose the input space into several regions by decision tree learning. After training an SVM model for the whole dataset, we mark support vectors by filled shapes. Each region has its own majority class label, and the boundaries are between the two majority classes. The support vectors are close to the boundaries. In addition, we notice that they are located relatively far away from the center of the data points with the majority class label in a region.  
Figure 1: The construction of a decision tree and SVMs for the iris data shows the boundaries and support vectors (the filled shapes). The support vectors in the regions are located far away from the majority-class centroid.  
In light of this, we describe our algorithm to find a subset of support vectors that determine the separating hyperplane. We divide a training dataset into several decomposed regions in the input space by decision tree learning. This process brings each decomposed region to have most of the data points with the majority class label by the tree learning algorithm. Next, we detect adjacent regions in which the majority class is opposite to that of each region. We define this kind of region as distinct adjacent region. Then we calculate a new distance measure, relative support distance, with the data points in the selected region pairs. The procedure of the algorithm is as follows:  
1. Decompose the input space by decision tree learning.
2. Find distinct adjacent regions, which mean adjacent regions whose majority class is different from that of each region.
3. Calculate the relative support distances for the data points in the found distinct adjacent regions.
4. Select the candidates of support vectors according to the relative support distances.
## 5 Results and Discussion
### Satellite Spectral Analysis
#### 5.2.1 Spectral Analysis of Vegetation
The remotely sensed structures (Installations **A–C**) at Hermopolis, shown in Figure 7, resemble these storerooms (**A**). Photos taken of exterior (**B**) and interior (**C**) of the mudbrick storerooms at the Ramseseum (photo taken by author). the structure, while only a small portion of the western section was identified. Around Installation F, there appears to be a wall encompassing much of the structure. Identifying this construction as a small enclosure wall for Installation F or an earlier (or later) structure built on/around Installation F would be possible, but for now unreasonable without some geophysical surveying or excavation. On the Deutschen Hermopolis-Expedition mission's final map of the sacred temple precinct, [PERSON], the director of the project, sketched a portion of the fragmentary building in the area in question [56]. There were no notes as to whether the expedition excavated this structure (highly unlikely) or if this was simply a structure seen above the surface (most likely). This finding is promising, as it corroborates that the eastern quarter of the tell did indeed have some ancient human building activity, be it domestic or industrial in nature. Ultimately, this is a form of validation for the results derived from the remotely sensed analysis of the site. While the Normalized Difference Vegetation Index (NDVI) is the most common spectral index used for archaeological prospection [57], through the present study it was found that the Infrared Percentage Vegetation Index (IPVI) was the most effective vegetation index for exploring the site of Hermopolis, specifically. In this case, the different parameters present at Hermopolis were simply much more receptive to the IPVI than to the NDVI or GNDVI, demonstrating that certain vegetation indices are more conducive to particular sites. This may be explained by the type of plant growth, as spectral signatures derived from terrestrial photosynthetic activity differ greatly depending on the type and density of the vegetation.
## 4 Experiments and Results
### Velocity of EM Wave Underwater  
The velocity of sound underwater is 1500 m/s, which is slow when compared to the velocity of EM signals in the same medium. Thus, EM waves are faster than acoustic waves, hence yielding less propagation delays and enabling fast communication. Figure 7 shows the velocity of propagation when an EM signal propagates inside water.  
Figure 7 evidently shows a significant decrease in the velocity of EM waves. This holds true because the quality of water is inconsistent everywhere. At every stage of the water flow, the water quality differs, and this difference is due to the amount of solid particle concentration in water that contributes significant changes in the water conductivity. As the water quality decreases, there is a significant increase in conductivity of water that is commonly found. A high conductive medium reflects the EM waves more than the low conductive medium, making it difficult for the EM wave to travel through it. Table 3 elaborates the possible water conductivities, and, based on that, categorizes  
Figure 6: Overall path loss in underwater environment.  
Figure 7: Velocity of EM waves at different conductivities of water.  
the nature of water. Thus, Figure 7 shows how the velocity of EM waves gets affected as water conductivity increases, which has a significant impact over EM waves.
## 2 Materials and Methods
### Data Treatment  
The SERS spectra were imported to MATLAB R2012a (7.14) software for preprocessing methods and analysis. The adaptive iteratively reweighted penalized least squares (airPLS) algorithm was used for fluorescent background removal [48]. The SERS spectra were normalized by total area and mean centered [49]. GA data analysis was performed using PLS_Toolbox 6.2 (Eigenvector Research, Inc.) available within the MATLAB environment [49] and the ANN analysis was performed using R-project software, ver. 3.4.4.
## 4 Discussion
### Impact of Dropout Positioning and Structure on the Networks  
By incorporating various advanced uncertainty quantification modules (Experiments A-E), the study investigates how different regularization strategies and uncertainty quantification modules collectively affect the performance of seabed terrain reconstruction. The experimental results indicate that a fixed dropout strategy, while effective in alleviating overfitting, fails to capture prediction uncertainty adequately, resulting in unstable local predictions. The layered progressive dropout strategy, which incrementally increases the  
Figure 8: Comparison of variance heat maps for different network structures.  
dropout rate in deeper layers to strengthen feature regularization, improves generalization overall--even though a high dropout rate in deep layers may compromise some local structural details and affect the SSIM metric. Furthermore, employing an activation-driven adaptive dropout that dynamically adjusts the dropout rate based on local activation values effectively differentiates between high- and low-activation regions, significantly enhancing detail recovery and structural fidelity despite some localized errors. Building on this, the integration of a dynamic gating network with an uncertainty quantification module further refines the process. The dynamic gating module uses \(1\times 1\) convolutions combined with normalization and activation functions to adaptively weigh the importance of channel features, thereby suppressing redundant information; the uncertainty quantification module, which integrates MC-Dropout with heteroscedastic noise modeling, provides a probabilistic interpretation of the predictions, effectively reducing overconfidence and artifact occurrence. These structural improvements enable Method E to achieve the best RMSE and MAE in experiments and significantly outperform other methods in PSNR--although it slightly lags in SSIM--thus overall enhancing reconstruction accuracy and robustness. In summary, the synergistic effect of different regularization strategies and uncertainty modeling modules substantially improves the detail recovery and structural fidelity in complex terrain DBM reconstruction tasks.
# A Petri Net Approach for Business Process Modeling and Simulation
## 5 Basic Structures of Petri Nets Applied to Business Process  
In this section, we propose four Petri net structures for BP modeling. These structures are designed to represent the relationships among processes, and model various scenarios and behaviors within organizations.  
The simplest structure, depicted in Figure 1, represents a single activity. It consists of a transition (\(t_{1}\)) and its corresponding output place (\(p_{1}\)).  
Figure 1: PN structure for a single process.  
When two activities are executed sequentially, they are connected in the required order, as depicted in Figure 2.  
Figure 3 illustrates the PN for selecting an activity based on a certain condition. The token positioned in P1 can be moved either to place 3 or place 4 (disjunction). The choice of the activity to be carried out is determined by the process itself. For example, the process description could be as follows:  
* First Activity: \"generate invoice (1)\".
* Next Activity: \"deliver the invoice to the client personally (2)\" or \"send the invoice to the client via email (3)\".  
Figure 4 depicts the PN for executing activities in parallel. The token is initially placed in P1, and can be moved to both P3 and P4 simultaneously (conjunction). The activities are executed concurrently. For example, the process description could be as follows:  
* First Activity: \"buyer arrives at the checkout (1)\".
* Following Activities: \"cashier registers the sale of items (2)\" and \" the inventory in the system is updated (3)\".  
Figure 5 illustrates the Petri net for executing an activity triggered by different preceding activities. The token is observed in Activity 3, and can originate from either Activity 1 or Activity 2. The execution of Activity 3 occurs when the trigger comes from either Activity 1 or Activity 2. For example, the process description could be as follows:  
Figure 4: PN representing the execution of activities in parallel or concurrently.  
Figure 3: PN structure for the selection of the activity to be executed.  
Figure 2: PN structure for two chained process.  
Figure 6 depicts the PN for the synchronization of activities in parallel. In this structure, the token is present in both Activity 1 and Activity 2. Both activities must be completed in order for Activity 3 to be executed. For example, the process description could be as follows:  
* Activity 1: \"client pays the advance payment of a car\".
* Activity 2: \"financial company approves credit for car purchase\".
* Activity 3: \"manufacturing department assembles automobile\".  
Activity 3, which involves assembling the automobile, can only be executed when both Activity 1 (client payment) and Activity 2 (credit approval) have been completed.  
BPs can be broken down into subprocesses, and these subprocesses can be modeled using the six basic PN structures. Depending on the number of subprocesses, the resulting network may be more or less complex.
## 6 Extraction of System Information and Division of Subsystems and Second-Level Subsystems
### Extraction of System Information  
To detect the design patterns included in the system, the information of the system needs to be extracted first. In this study, the XML files of the UML class diagrams and the XML files of the UML sequence diagrams of the system are parsed to obtain information, and the information was stored. The information of the UML class diagrams includes classes, operations, abstract classes, abstract operations, generalization, composition, aggregation, and dependency between classes, creation relationship between classes, naming, and so on, while the information of the UML sequence diagrams mainly includes the calling relationship between the operations of the classes.  
For systems that lack UML design documents and only have source codes, this study converts the source codes into a UML model (this article mainly focuses on class diagrams and sequence diagrams) using the reverse engineering function of UML modeling tools such as Visio and programming language development environments such as IntelliJ IDEA, and then parses the converted UML model to obtain relevant information.
###### Abstract  
Embedded systems are increasingly applied in numerous security-sensitive applications, such as industrial controls, railway transports, intelligent vehicles, avionics and aerospace. However, embedded systems are compromised in the execution of untrusted programs, where the instructions could be maliciously tampered with to cause unintended behaviors or program execution failures. Particularly for remote-controlled embedded systems, program execution monitoring and instruction fault self-repair are important to avoid unintended behaviors and execution interruptions. Therefore, this paper presents a hardware-enhanced embedded system with the integration of a Security Processing Unit (SPU) in which integrity signature checking and checkpoint-rollback mechanisms are coupled to achieve real-time program execution monitoring and instruction fault self-repairing. This System-on-Chip (SoC) design was implemented and validated on the Xilinx Virtex-5 FPGA development platform. Based on the evaluation of the SPU in terms of the performance overhead, security capability, and resource consumption, the experimental results show that, while the CPU executes different benchmarks, the average performance overhead of the SPU lowers to 1.92% at typical 8-KB I/D caches, and it provides both program monitoring and fault self-repairing capabilities. Unlike conventional hardware detection technologies that require manual handling to recovery program executions, the CPU-SPU collaborative SoC is a resilient architecture equipped with instruction tampering detection and a post-detection strategy of instruction fault self-repairing. Moreover, the embedded system satisfies a good balance between high security and resource consumption.  
embedded system; hardware security; security processing unit (SPU); program monitoring; instruction fault self-repairing +
Footnote †: journal: Applied Sciences
## Case Study
### Strategic Verification  
Quick DR ability is meaningful for power system control, but it has a high requirement for the control system. A \"local detection and action\" response strategy was proposed in order to achieve quick DR tasks based on public communication connections. However, this strategy is new to traditional power system control, and its influence and performance need to be checked. Therefore, frequency regulation was chosen as the scene of the proposed system and strategy participating in the quick DR task. A hardware-in-loop co-simulation platform was developed to undertake the experiment.
## 3 Model Design
### Classification Module  
The classification module is the final component of the EBLCS model, responsible for predicting the HS code for each good based on the refined text representation vectors. After the spatial attention mechanism enhances the feature vectors by emphasizing the most relevant words and word embedding dimensions, these vectors are fed into the classification module. This module typically consists of one or more fully connected layers that process the input vectors to generate a probability distribution over all possible HS code classes. The class with the highest probability is selected as the predicted HS code for the goods. The classification module leverages the enriched contextual features obtained from the previous layers to make accurate and robust predictions, thus improving the overall performance of the HS code classification task.  
The output layer of the classification module is a fully connected neural network that includes a hidden layer, which maps the feature vectors to the final classification results. The feature vector \(F^{\prime\prime}\) is subjected to maximum pooling over the word embedding dimensions to retain the maximum value for each dimension, thereby reducing computational complexity. Then, the fully connected layer is used as well as the softmax function to obtain the probability distribution vector \(P\in\mathbb{R}^{1\times s}\) of the goods text belonging to each HS code, in which S is calculated as shown in (17), where \(S\) is the number of HS codes contained in the training data. The HS code corresponding to the index of the maximum value is selected as the final classification result, and the calculation formula is shown in (18).  
\[P=softmax\big{(}WF^{\prime\prime}_{max}+b\big{)} \tag{17}\]  
\[y^{*}=argmax(P) \tag{18}\]
## 4 Conclusions  
The following conclusions can be drawn based on the experimental investigation conducted in this research:  
1. Experimental results showed that severe cracks mainly concentrated at the beam ends during the collapse. The initial stiffness of the frame beams reinforced with BFRP bars was obviously lower due to the low elastic modulus of BFRP bars. The collapse resistance presented wave-like increasing tendency with the vertical displacement at the failed middle column, which was significantly affected by structural damage, such as cracking or crushing of concrete and rupture of BFRP bars.  
2. Top longitudinal BFRP bars at the BESC and bottom longitudinal BFRP bars at the BEMCs kept tensile during the loading process, which played an important role in resisting structural collapse. Adjacent structures could provide horizontal reaction forces to confine the free deformation of residual sub-assemblages after the middle column failed, and it was beneficial to mitigate the collapse risk of the frame structures. The vertical deformation of the frame beams beside the failed column was nearly linear and proportional to the vertical displacement of the failed middle column. Comparing with the sub-assemblage without prestress, unbonded prestress had negligible effect on the initial stiffness of the residual structures after the removal of the middle column. However, applying prestress could remarkably improve the collapse resistance of the structures.  
3. DIF values of collapse load were discussed based on the energy conservation method, and a calculation method of DIF for prestressed frame structures was developed. It was suggested that DIF values for the non-prestressed frame structures reinforced with BFRP bars in the beams should be conservatively taken as 2.0, while those for the prestressed sub-assemblages can be taken between 1.44 and 2.0.  
[PERSON] designed the experiments and wrote the paper. [PERSON] and [PERSON] conducted the experiments and analyzed the data. [PERSON] audited the content.  
This research was funded by the National Natural Science Foundation of China (grant no. 51568005 and 51868004); Chinese Postdoctoral Science Foundation project (no. 43 XB3782 XB); and High-level innovation team and outstanding scholar plan of Guangxi high colleges.  
The authors would like to appreciate the research group members and the relevant staff of the structural laboratory of Guangxi University that make a great contribution to all the experimental tests.  
The authors declare no conflict of interest.