esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Our Modeling Approach
### The Power of Hierarchical Modeling  
Instead of producing an entire model, the CPN formalism allows to \"break\" the system into autonomous units, which are modeled separately and then joined in one model. To implement a hierarchical model, we return to EST and CRT tables of Sections 4.2 and 4.3. The information of Table 1 shows clearly that events \(E_{6}-E_{8}\) do follow directly \(E_{5}\), which, in turn, follows events \(E_{2}-E_{4}\). In addition, there is a feedback from \(E_{6}\) to \(E_{2}\). In this regard, we can separate the entire set of events into two separate parts, which we name _nests_. The first nest includes events \(E_{1}-E_{4}\) and the other nest includes \(E_{5}-E_{8}\). The central connecting point between the two nests is \(E_{5}\) (the central connecting point is an event of one nest, which has the most link-arrows to events of another nest). Figure 6 shows the graphical representation of the connections between the two nests. Notice that we do not show the connections among the event-members of each nest here. Our purpose is to show the \"external\" links between the two nests. Above these two links (displayed by arrows), we write the events that act as connecting points. The central connecting point is shown in boldface. The main advantages of hierarchical modeling are the following:  
1. It is extremely helpful in checking our model for errors. Instead of examining the whole model, we can concentrate on the part that seems to be working improperly. Figure 6: Graphical representation of the hierarchical modeling. 2. We can freely add more places and transitions in the proper nest, without having to consider the entire model. 3. Each nest can be checked independently for possible deadlocks (cases where there is no enabled transition and the system halts). When all the nests are free of deadlocks and properly connected, the entire model is free of deadlocks. This deadlock control is much easier if implemented on a nest basis than implemented for the entire model. 4. The hierarchy does not bother about the classes of objects and their particular instances. 5. **Case Study: Modeling and Studying a Courier Company Based on Actual Data**  
To illustrate how we can apply the ideas presented in Section 3, we show how to develop a model based on real data. For this case study, we have collected data from a branch of a big courier company (we do not provide the name here and the employee names are given by their initials). The data record all the transactions performed for a period of 1 month. The transaction data we received were in the following form (only a small part is obviously shown here).
# Feature Detection of Focused Plenoptic Camera Based on Central Projection Stereo Focal Stack
## 4 Experiments
### Simulated Experiments
#### 4.1.1 Comparison of Refocusing Algorithm  
The simulated parameters of the multi-focused pleonptic camera are shown in Table 1. As all parameters are known in advance, including the positions of micro-lenses, the refocused images can be generated strictly in accordance with the traditional refocusing algorithm [2] and ours.  
In order to display the results more clearly, the checkerboard plane is used as the imaging target, which is set at the position 0.9m away from the main lens and perpendicular to the optical axis. The simulated raw images are shown in the Figure 6a,b. During the experiment, the traditional refocusing algorithm [2] is used to render the refocused image at planes with virtual depth \(v_{F}\in\{-6,-12\}\), as illustrated in Figure 6c,d. According to Equation (4), the corresponding value of plenoptic disc radius is \(R_{F}\in\{-5.64,-10.54\}\). The rendered images based on our central projection refocusing algorithm are shown in the Figure 6e,f.  
It can be seen that the imaging range of traditional refocused image becomes smaller with the increase of \(|v_{F}|\), while that of the proposed method maintain the same regardless of the change of \(R_{F}\). What is more, this results in the fact that the coordinates of the imaging center corresponding to the same object point in our refocused image remain the same in spite of the position of the refocus plane, while that is not the case for traditional method. As one can see, the experimental results are consistent with the analysis in Section 2.  
During the actual process of calculating refocused images or total focus images, most methods [9; 13; 14; 15; 16; 17] use the center of micro-image to approximate the center of micro-lens. Essentially, this is equivalent to refocusing based on Equation (5) other than Equation (1). If the concepts of virtual depth and orthographic coordinates are still used during reconstruction without further rectification, it will bring errors to the final reconstructed result. To verify this, the Equation (5) is used to calculate the total focus image (\(R_{F}=-6.55\)) corresponding to the simulated raw image. Then, the checkerboard corners on the total focus image are converted to real space based on the concept of traditional model and ours. The reconstruction results are shown in the Figure 7. It is obvious that the results of traditional methods deviate from the ground truth while ours are not.  
The experimental results demonstrate that the central projection refocusing model in this paper is more consistent with the actual results.
## 2 Background
### IoMT Needs: The Patient  
From the point of view of patients, IoMT technology provides convenience by reducing the time spent at the hospital. By collecting, analyzing, and transmitting patient data in advance, IoMT devices address the inconvenience of undergoing tests at each hospital visit, thereby reducing the number of unnecessary hospital visits, waiting times, and periods of hospitalization. As a result, financial and time burdens may be reduced.  
In the home, IoMT technology can be applied to the personal emergency response system (PERS), remote patient monitoring (RPM), and telemedicine. The PERS integrates wearable devices and relay devices with real-time medical call center services to increase self-reliance for the elderly at home or those with limited mobility, thus allowing healthcare professionals to quickly communicate with patients and provide emergency medical services. RPM utilizes home monitoring devices and sensors used in chronic disease management, which continuously monitor physiological parameters to manage patient disease at home and support long-term care. Consumer health wearables include physical activity trackers or consumer-grade devices for personal health or fitness, such as health bands, bracelets, sports watches, and smart clothing. Although most consumer health wearable devices are not sanctioned by regulatory authorities, they may be approved by experts for specific health applications following informal clinical validation or consumer research. In acute home monitoring, users are provided with medication alerts and dosing information to improve compliance and facilitate medication management, and discharged patients are observed to accelerate recovery and prevent readmission.  
Telemedicine includes virtual counseling to help patients manage their condition and obtain a prescription or recommended treatment plan; for example, counseling and evaluation of symptoms and lesions may be conducted through video observation or digital testing [13].
## 4 Proposed Method  
### Overall Framework of the Model  
In the field of radar-based human activity recognition, previous lightweight models often avoided using multi-domain feature fusion methods that significantly improve accuracy, because such fusion requires parallel structures, which substantially increase the number of parameters and FLOPs. However, our method constructs an efficient and lightweight feature extraction module using GhostV2 [24] convolution. This allows the model to remain lightweight, even when the number of parameters and FLOPs are doubled. Thus, we can utilize multi-domain feature fusion technology to achieve high recognition accuracy, without increasing the computational cost.  
We propose a lightweight through-wall radar human activity recognition model named TG2-CAFNet. Specifically, we built a lightweight CNN feature extraction module, TG2, suitable for radar spectrograms, based on GhostV2 convolution. Using a parallel structure, we extract the features of the RTM and TDM separately, without sharing parameters. Since a TDM is another domain representation of an RTM, we cannot treat the two spectrograms as completely different types during feature fusion; they have certain correlations. Considering that traditional fusion methods find it challenging to capture this relationship, we employ AFF [25] to adaptively fuse the features of the spectrograms after the parallel feature extraction module has finished. Furthermore, given the physical significance of the spatial distribution of radar spectrograms, we improve AFF using coordinate attention (CA) [26], so that the weighted fusion can extend to a spatial distribution, effectively enhancing the expression capability of the fused features. Figure 4 shows the overall framework of TG2-CAFNet.
## 3 Background
### Matching Strategy  
The purpose of the matching strategy is to understand customer needs and ensure satisfaction not only for customers but also for competitors, while adapting to market trends. By fulfilling these conditions, companies can make decisions regarding pricing, promotions, and overall strategies. The goal for businesses is to align product sales effectively, which leads to increased opportunities and maximized profits. The implementation of the matching strategy is suitable for both small and large companies. Creating competitive prices and strengthening customer trust contributes to improving price adjustments and overall product sales [25,26].  
Advantages of the matching strategy are [25,26]:  
* Higher Accuracy: Focused on information about product sales. Sales data must be accurate, up-to-date, and relevant. The results reveal the trend in product sales development.
* Improved Competitiveness: Enables companies to study and evaluate competitors and specify market trends. The collected data and information are used to make decisions about product pricing and promotions, ultimately leading to maintaining a competitive advantage.
* Increased Sales Efficiency: Relying on manual or online product promotion. Online promotion frees up time and resources for other important tasks.
* Specification of Pricing Strategies: By examining and comparing product sales, sellers can make decisions about pricing strategies, which ultimately ensures the company's competitiveness in the market.
* Improved Product Development: Sellers understand what competitors offer. Competition contributes to decisions about pricing strategies, and which features to include in their own products, ensuring competitiveness and attractiveness for customers.
* Adequate and Reliable Customer Information: By tracking and analyzing sales data, sellers can gain better insights into customer behavior, including buying habits and preferences. This information is important for product development and marketing.
* It is essential to note that clearly defined prices increase consumer confidence. This implies that customers have certainty in obtaining the best offer. The response to acquired trust contributes to higher customer loyalty as well as repeat business. Appropriately set prices are more appealing to price-sensitive customers, resulting in a higher conversion rate and increased sales. Consumers are more likely to make a purchase only when they are certain they are receiving the best price.
* The matching strategy, though conceptually outlined, aligns quantitatively with the demand tracking strategy applied over the study period of three years. The developed models, by integrating time-series analysis and regression techniques, demonstrate the connection between this demand strategy and the selection of appropriate models. These models effectively map demand fluctuations to sales outcomes, illustrating how strategic adjustments in product positioning can impact market performance. It is noted that seasonal components are observable within the models; however, a comprehensive analysis and evaluation of these is needed.
## 4 Experimental Datasets and Results
### Results Discussion  
In all the above-shown experiments, the performance of the proposed pipeline is being evaluated using a set of experiments i.e., first, we analyze several sample selection methods for MLR-LORSAL classifier. Later this work compared several classifiers for the same sample selection methods. In all these experiments, the size of the training set is fixed to the maximum of 50 samples randomly selected from all the classes while giving equal representation to each class. In each iteration, 100 new samples are selected based on sample selection method and their spatial locations i.e., the newly selected samples should not be spatially close to the previously selected samples. This phenomenon significantly helps to limit the redundancy factor among the training samples.  
The experiments are repeated on more complicated and nested region datasets such as Pavia University dataset. The number of training samples selected in each iteration is shown in the above figures. Based on the results, one can conclude that the fuzziness-based sample selection method is a competitive process that slightly worked better than other well-know sample selection methods. One can conclude from the results shown in Tables 2-4 and Figures 2-7 that all the sample selection methods performed well, however, BT and Fuzziness-based samples boost the accuracy for MLR-LORSAL classifier followed by MI. From several observations with a different number of training samples, there is a slight improvement in performance using MBT however, fuzziness and BT improve the generalization in impressive fashion. Fuzziness-based sample selection process works better due to the fact that these samples are usually close to the classification boundary.  
This work started evaluating the hypotheses with a 50 number of randomly selected samples; it is a well-known fact that the spectral information-based randomly adding samples back to the training set does not increase the accuracy as desired. Therefore, this work explicitly fuses the spatial information while considering the new samples for a training set which significantly boosts the accuracy and generalization performance of a classifier. Furthermore, this work also validates the sufficient number of samples required to train a classifier;  
**Author Contributions:** Conceptualization, M.A. ([PERSON]) and A.M.K.; methodology, M.A. ([PERSON]); validation, M.A. ([PERSON]), A.M.K. and M.M.; formal analysis, M.A. ([PERSON]), S.D., M.A. ([PERSON]), M.S.S., R.A.R., A.S.; investigation, M.A. ([PERSON]); resources, M.M., A.M.K.; writing--original draft preparation, M.A. ([PERSON]), A.M.K., S.D., M.A. ([PERSON]), M.S.S., R.A.R., A.S.; writing--review and editing, M.A. ([PERSON]), M.M., A.M.K., S.D., M.A. ([PERSON]), M.S.S., R.A.R., A.S.; visualization, M.A. ([PERSON]); supervision, M.M., A.M.K., S.D.; funding acquisition, M.M., A.M.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Lightweight CoAP-Based Bootstrapping Service for the Internet of Things
## 3 The Bootstrapping Service: CoAP-EAP
### Additional Considerations
#### 3.6.4 Authorization Aspects  
Authorization is also an important part of the bootstrapping process. The authorization information related to the bootstrapping service (_bootstrapping authorization information_) determines, for example, whether the Smart Object can join the security domain and under what conditions. Since our bootstrapping solution is based on AAA framework, the authorization information is carried to the Controller in the form of AAA attributes. RADIUS and Diameter define a myriad of attributes(e.g., NAS-Filter-Rules, Framed-MTU, Session-Timeout, _etc._) to carry this information. For more fine-grained authorization, [87] has recently specified how to transport SAML in RADIUS attributes, though more constrained authorization information (in term of message size) could be expressed in JSON [88] or CBOR [89] format. However nothing has yet been defined in this regard.  
We consider that the set of attributes already defined in RADIUS or Diameter could be a good starting point for a basic and correct authorization of the bootstrapping service. In particular, we have paid attention to the lifetime associated to the bootstrapping state or the service type. Nevertheless, if other types of information are required (for example, certain domains may require a hardware profile of the Smart Object to take an authorization decision regarding the Smart Object, even though it is successfully authenticated), this may require the definition of new IoT-based AAA extensions, such as [90]. Nevertheless, the AAA framework is prepared to be easily extended so giving the required flexibility to provide any application-specific authorization information needed for IoT applications.  
Additional authorization information may be required _after_ the bootstrapping for the operation in the security domain. For example, this is required for accessing services provided by other Smart Objects, the Controller, border routers, _etc._, in the security domain. This information is also application specific and considered as part of future research. Nevertheless, the IETF ACE WG is investigating solutions for this post-bootstrapping authorization process.  
Finally, how the Controller will handle any authorization information will depend on the local security policies and the activity of the Smart Object in the security domain. Additionally, if some authorization related information needs to be carried to the Smart Object we will use our CoAP-based EAP lower-layer for the transport. The general framework to support this is using CoAP options containing the required information. For example, an _Authorization_ Option may be included in the CoAP message to notify the Smart Object with the bootstrapping authorization lifetime value, if this value is different from the default, which is 8 hours following the hint in [14]. Knowing the session lifetime will allow the Smart Object to arrange a new bootstrapping before the existing bootstrapping state expires.
# Regular Equivalence for Social Networks
## 4 Results
### Runtimes for Calculating Regular Equivalences  
First of all, we investigate practical running times for calculating regular equivalences. We generated 350 random complex networks (using the algorithm in Appendix A), having \(10^{4}\leq|V|\leq 1.5\times 10^{6}\) vertices, each having \(3|V|\leq|E|\leq 15|V|\) edges, which is representative for the real-life social networks studied in this paper. We then calculated the minimal regular equivalence relation using the algorithm above, and timed the execution, using Java 1.8.0_60 (x64) on an Intel Core i7-5600U 2.6 GHz with 16 GB RAM. Results can be seen in Figure 2. The quadratic polynomial \(y(x)=10^{-10}x^{2}+4\times 10^{-5}x\) was fitted to the data with a coefficient of determination \(R^{2}=0.93\), which means that the experimental values are statistically well explained by the quadratic polynomial. For an average randomly generated graph, the repeat-loop was executed only about four times, which explains the fact that the execution time is very low, although the computational complexity was shown to be quadratic in theory. Moreover, it turns out that for real-life social networks the execution time is very low as well. Our approach is thus a valid way to investigate real-life, large-scale, and complex networks, allowing deep analysis which was previously very demanding with respect to the required computational time.  
However, it is quite possible to construct a graph that needs \(O(|V|)\) iterations, resulting in the maximum number of colors \(|V|\) needed for the minimal regular coloring. Two such graphs are shown in Figure 3. For example, take some \(n\in\mathbb{N}\) such that \(6\leq n\), then one can define the circular graph on the left of Figure 3 as \(G=(V,E)\) with \(V=\{v_{i}\in\mathbb{N}|1\leq i\leq n\}\) and \(E=\{\{v_{i},v_{i+1}\}|1\leq i<n\}\cup\{\{v_{n},v_{1}\},\{v_{1},v_{3}\},\{v_{1},v_{4}\}\}\). The algorithm will then need \(O(|V|)\) iterations, after which all vertices will have received a unique color. A second example is shown on the right of Figure 3. The key to understanding that the algorithm takes many iterations in these cases follows from the fact that the vertices receive a new color one by one, each iteration changing the spectrum of only 2 other vertices (the neighbors). As such, the algorithm has to propagate through the graph linearly and that takes \(O(|V|)\) iterations. Clearly, these two examples are fabricated and do not likely appear in real-life, making them pathological situations with which we are able to stress-test the algorithm.
# SDR-Based 28 GHz mmWave Channel Modeling of Railway Marshaling Yard
## 4 Results
### Relationship between Parameters and Position  
#### 4.1.1 Relationship between Number of MPCs and Position  
As the index of the path increases, the quality of the sound data received from the path deteriorates, and the probability of extracting channel parameters from it decreases accordingly. If the number of decoded channel parameters exceeds 85% of the total decoding times for a specific path, then the path is determined as an effective path. The number of effective paths at each position is determined based on this principle, and the relationship between number of MPCs and position is shows in Figure 16.  
Figure 15: The channel parameters at the position of 12.4m. The horizontal axis coordinates 1# to 10# represent the index of MPC, which increases as the power decreases and the delay increases. (**a**) Delay. (**b**) Received Power.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Multipath Component** & **Delay (ns) (95\%)** & **Received Power (dBm) (95\%)** \\ \hline
1 & 165 & \(-\)21.15 \\
2 & 185 & \(-\)36.078 \\
3 & 210 & \(-\)43.687 \\
4 & 235 & \(-\)48.653 \\
5 & 260 & \(-\)52.216 \\
6 & 285 & \(-\)53.432 \\
7 & 365 & \(-\)56.995 \\
8 & 385 & \(-\)58.209 \\
9 & 405 & \(-\)58.767 \\
10 & 410 & \(-\)59.293 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The channel parameters at the position of 12.4 m on \(T_{1\&2}\).  
As the distance between Tx and Rx increases, there is no significant change in the number of MPCs, which fluctuates randomly between 7 and 11; the mean value \(\mu=9.55\) and the variance \(\delta=0.868\).