esa-sceva/satcom-chunk-collection huggingface dataset:
# RF-Access: Barrier-Free Access Control Systems with UHF RFID
## 4 Evaluation
### Detection of Motion Direction  
#### 4.2.1 Experimental Methods  
We invite some volunteers to conduct experiments in a general indoor environment for evaluating the performance of our system. Volunteers carry Impini) H47 RFID tags that reflect their identities at different locations and then execute traffic movements at different speeds with a 40 cm distance from the antenna, simulating movement in an 80 cm wide access control passageway. As shown in Figure 13, five tags are placed on the chest, left arm, right arm, left pocket, and right pocket, respectively. Three movement speeds including 1 m/s, 1.5 m/s and 2 m/s are adopted, which mimics three situations: constant speed passing, fast passing and running passing, respectively. Passage behaviors are further divided into two categories: (1) \"moving in\" behavior: from A to B; (2) \"moving out\" behavior: from B to A. The volunteers execute each behavior 20 times by carrying the tags at the same locations.  
Figure 12: System deployment of RF-Access.  
First, we study the performance of the moving direction. Figures 14 and 15 show the detection accuracy of the signal-based model and the slot-based model under different tag positions and different moving speeds. For the signal-based sensing model, the motion direction (in or out) has a small impact on sensing accuracy, but the moving speed has a great impact on the detection accuracy. The detection accuracy generally decreases as the moving speed increases. This is consistent with our intuition that high speeds reduce the collected data, which makes the signal-based analysis hard, leading to relatively low accuracy. Additionally, even when the speed is the same, the accuracy of different tags is different. For example, the right arm's tag has less than 70% accuracy as the tag moves out. The reason is that the human body blocks the line-of-sight signals, and the reflected signals caused by multi-path will give rise to estimation errors of the distance. This indicates that the signal-based solution can function properly in a relatively ideal scenario but easily suffers from environmental changes.  
Figure 14: Detection accuracy of signal-based sensing model.  
Figure 13: Tag positions.  
Compared with the signal-based solution, the time slot-based model has much better performance. As shown in Figure 15, the accuracy almost remains stable at a high level, regardless of the tag's position, moving direction, and moving speeds. This well validates the good robustness of the slot-based solution. The reason is that we use the reading records rather than signals to detect the direction, which has only two states in a time slot, making it robust to different environmental factors. Next, we study the impact of other factors on accuracy. The following experiments were carried out based on the time-slot-based sensing model.
# Characterization of Ultra-High-Dose Rate Electron Beams with ElectronFlash [PERSON]
## 2 Materials and Methods
### Dose Measurements with Gafchromic Films  
The absorbed dose distribution in a sample has been evaluated in a PMMA phantom in terms of percentage depth dose (PDD) and beam dose profiles. PDD and beam transverse profiles at different depths were investigated by inserting Gafchromics films [25] EBT-XD (Ashland Inc., Wilmington, DE, USA) in a PMMA slab phantom. The film response was calibrated with 10 EBT-XD films exposed in the range 0-30 Gy in CONV mode with a pulse width of 4 us, using a circular field of 120 mm in diameter, at 10 mm depth in a PMMA solid phantom for both 5 MeV and 7 MeV beams. To determine the dose applied to the calibration films, an advanced Markus ionization chamber (PTW, Breisgau, Germany) was used. For the ionization chamber read-out, a PTW UNIDOS webline electrometer with 400 V polarization was used. The conversion of the ionization chamber signal to the absorbed dose follows the general TRS-398 [26] formalism. For PDD FLASH measurements, films placed parallel to the beam axis in the PMMA phantom were irradiated with both  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Output energy & 5 or 7 MeV \\ Pulse repetition frequency & 1–250 Hz \\ Pulse width & 0.5–4 μs \\ Maximum peak beam current 7 MeV & 120 mA \\ Maximum peak beam current 5 MeV & 108 mA \\ \hline \end{tabular}
\end{table}
Table 1: Main features of ElectronFLASH.  
5 MeV and 7 MeV beams, with applicators A-d120 and A-d30 and a pulse width of 4 \(\upmu\)s. In addition, three films were irradiated for each energy-applicator combination in correspondence with the dose peak value depth (R100), 80% depth (R80), and 50% depth (R50). Twenty-four hours after the irradiation, the films were scanned using an Epson 800, 48-bit color scanner, and the dose maps were analyzed with FilmQA Pro software [25], with triple-channel and reference dose protocols.
## 4 Proposed Method and Problem Formulations  
Figure 2 presents the glimpses of the migration of a starting network (Figure 2a) to the target network (Figure 2f). We assume that the ISP network initially consists of multiple autonomous domains, in which few ASes (e.g., AS1 and AS2) are already SoDIP6 capable and other ASes (AS0, AS2, and AS3) are still running with legacy IPv4. AS0 is considered as a transit AS running the legacy IPv4 network. It is interconnected with other ASes. The purpose of this study is that the transit AS has to be smoothly migrated to the SoDIP6 network and run the multi-domain SoDIP6 networks with its legacy network integration. We consider that gateway router 4 (GR4) in Figure 2a is the foreign transit gateway. Following our previous work [7], we consider the choice of shortest path (SP) from the customer endpoint router to a foreign gateway for router replacement. In the beginning, it looks like a transit network migration, and we consider AS0 as a transit AS; however, this approach is applicable to any other ASes having multiple customer gateways (CGs) attached to it. For example, AS3 in Figure 2a is a legacy IPv4 network, where we suppose that services are provided to customers via multiple CGs attached with AS3, and then gateway router GR3 can be considered as the FG. We start the longest span shortest path (LSSP) between CG and FG for the migration.  
**LSSP:**: _The shortest path consisting of the highest number of nodes between source and destination among the set of other shortest paths obtained from different CGs to a FG is defined as the longest span shortest path (LSSP)._  
The gateway routers GR1, GR2, and GR3 are treated as CG routers, while GR4 is the FG router. During migration, AS0 becomes a hybrid SDN with dual-stack IPv6 operation. We consider minimum control path latency (MCPL) between switch-controller communications. MCPL provides the propagation latency in terms of control communication between the switch and the controller.  
**MCPL:**: _The shortest path from switch to controller with minimum propagation delay for control communication is termed as the minimum control path, and the propagation latency over the minimum control path is termed as minimum control path latency (MCPL)._  
The propagation latency and the controller processing capacity [32] are the two major driving parameters that need to be considered for the suitable placement of controllers to achieve better control communications in the SDN. To achieve this, a median router in the LSSP is selected for controller attachment. The migration sequence of other routers in the shortest path is generated using breadth-first traversal (BFT) after finding the median router as a starting point for traversal.  
Figure 2f is an example of a multi-domain SoDIP6 network, in which AS0, AS1, and AS2 are SoDIP6-capable but AS3 and AS4 are still operating as legacy networks. Our proposed approach is domain specific for migration and controller placement. Hence, the remaining legacy ASes can be migrated following the same proposed approach.
## 2 Background and Motivation  
### Cache Performance Cliff  
As shown in Figure 1b, the performance cliff can be divided into stable and performance areas. In the stable area, the miss rate decreases slowly with the capacity increase. In contrast, and vice versa in the performance area, a slight change in the capacity causes a significant decrease in the miss rate. It reflects the uneven variation between the miss rate and the capacity. The reason for the cliff is closely related to the application access mode.  
Existing studies use partition to remove the cache performance cliff. As shown in Figure 2a, the overall cache is divided into two independent regions, \(S_{1}\) and \(S_{2}\), and the  
Figure 1: The example of MRC in multi-cache storage system.  
request flows into the two partitions, respectively, according to the proportion \(\rho\). Each partition has its own request flow and capacity; therefore, it also has own miss rate function, which is represented by \(m_{1}(S_{1})\) and \(m_{2}(S_{2})\). Due to the self- similarity of the request flow [14], the miss rate of the partition is related to the overall miss rate. Suppose that the sampling rate of the request flow is \(\rho\), the \(m^{\prime}(S^{\prime})\) of the partitions, and the original. The miss rate has the formulation shown in Equation (1) [19]:  
\[m^{\prime}(S^{\prime})\approx\rho m(S^{\prime}/\rho). \tag{1}\]  
Based on (1), the cache miss rate of each partition and the whole can be further calculated, as shown in Equation (2) [19]:  
\[m_{1}(S_{1})\approx m(S_{1}/\rho)\]  
\[m_{2}(S_{2})\approx m((S_{1}-S_{2})/(1-\rho))\]  
\[m(S)\approx\rho m_{1}(S_{1})+(1-\rho)m_{2}(S_{2}). \tag{2}\]  
On the basis of single-level cache-removal, [PERSON] et al. [7] propose a multi-level cache performance cliff removal method, and its partition is shown in Figure 1(b). This method improves on the performance of the cliff area, but it ignores the performance potential benefit behind, which will be analyzed in detail below.
## 6 Evaluation Experiment of the Proposed Approach
### Relatedness of Crowdsourced Requirements  
The crowdsourced requirements have an averaged assessment score equal to 4.29 in terms of relatedness. It was evident that the crowd contributed requirements related to fitness mobile apps. This satisfies our assumption that users and developers of mobile apps in a certain domain--fitness in the case of the evaluation experiment--would most likely be the best-fitting participants to contribute related requirements. We believe that the domain knowledge they possess by using or developing an app can foster their abilities in generating fitness-related ideas compared to random users.  
It is worth mentioning that the relatedness factor could have received a higher overall assessment score. The reason behind having some requirements rated low to medium considering relatedness is that these requirements might not be directly related to fitness. They are more general features that can exist in any app but are preferred to be integrated in a fitness app to enhance its functionality. An example of this kind of requirement is _\"It would be useful to set an exercise reminder or the ability to add such a reminder to the google calendar on the phone\"_, that was written by a crowd member.
# An Overview of Key Technologies in Physical Layer Security
## 4 Challenges and Promising PLS Solutions
### Promising PLS Solutions
#### 4.2.2 Machine Learning (ML)-Based Channel Estimation  
The development of channel estimation techniques has been investigated in [101; 102; 103; 104]. From these investigations it is evident that tackling imperfect CSI in PLS is one of the problems still open for research. The conventional channel estimation methods based on channel modeling have been proved to be insufficient for providing accurate and timely CSI. There has been a recent surge in research directed towards the feasibility of tackling some of the various communication problems using ML [101]. Most of the research is devoted towards developing efficient and reliable algorithms for channel estimation in communication networks. The performance of the existing channel estimation algorithms can be augmented through the use of ML to achieve close-to-optimal algorithms with reduced complexity on the implementation [47]. ML has emerged as an effective tool for channel estimation in wireless communication systems, especially under some imperfect environments.
## 4 Results and Discussion
### RBC Classification  
For RBC classification into healthy and anemic categories, we trained the 3-TierDCFNet by fusing three state-of-the-art CNN models, i.e., DenseNet, EfficientNet, and ShuffleNet. This classification acted as a base for further severity detection of anemia. Thus, the role of CNN models has become crucial. The key advantage of this fusion is that DenseNet performs deep analysis on inputted images at the pixel level. Due to the RBC elements' high density and overlapped structure, we need to preserve the pixel-level semantic information of each image. EfficientNet preserves the semantic information of each image at tier-II, while ShuffleNet maintains accuracy with the least computational power. Individual and global training, validation, test accuracies, and losses of each tier were calculated to evaluate the proposed model.  
Accuracy and Loss Convergence  
After completing each tier training, three different accuracies were computed, i.e., training, validation, and test accuracy with relevant losses. So a total of twelve accuracies were calculated, three of each tier and three of the whole 3-TierDCFNet model, along with relevant losses shown in Table 4. Results show that all of the tiers outperformed regarding the recall value and got 93.49%, 95.98%, 95.96%, and 98.95% for true positive results, respectively. The recall, F1-score, and specificity of each tier were also given in Table 4.  
The confusion matrix of healthy and anemic RBC classification is given in Figure 9, while a visual comparison among training, validation, and test accuracies of each tier is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Accuracy**} & **Tier-I** & **Tier-II** & **Tier-III** & **Global** \\ \hline \multirow{2}{*}{Training} & Accuracy & 0.8563 & 0.9163 & 0.9685 & 0.9137 \\  & Loss & 0.1437 & 0.0837 & 0.0315 & 0.0863 \\  & Accuracy & 0.8453 & 0.8756 & 0.9528 & 0.8885 \\  & Loss & 0.1547 & 0.1244 & 0.0472 & 0.1115 \\  & Accuracy & 0.8132 & 0.8675 & 0.8929 & 0.8606 \\ \multirow{2}{*}{Test} & Loss & 0.1868 & 0.1325 & 0.1071 & 0.1394 \\  & Recall & 0.9349 & 0.9598 & 0.9596 & 0.9895 \\  & Fl-Score & 0.9257 & 0.9562 & 0.9587 & 0.9812 \\  & Specificity & 0.9165 & 0.9436 & 0.9634 & 0.9726 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Global and individual training, validation, and test accuracies with relevant losses, Recall, F1-Score, and Specificity of 3-TierDCFNet.  
given in Figure 10. The error bars show the difference between accuracy and relevant loss values.
## 3 Framework Design and Deployment
### Blockchain for Secure Data Management  
Blockchain technology provides a secure and transparent mechanism for managing data access and updates in FL environments, as depicted in Figure 2. Its implementation in the proposed framework relies on two key components:  
* Smart contracts: Smart contracts are utilized to govern data transactions and access controls. These contracts enforce predefined rules, ensuring data traceability and auditability. The state of a smart contract represents its current condition, while transactions dictate changes to this state. When a transaction meets the specified conditions of the contract, it transitions to a new valid state, maintaining the integrity and compliance of data governance processes. This ensures secure and transparent data management, as illustrated in Figure 2.
* Digital twin integration: Each physical asset in the system is represented by a corresponding digital twin that simulates its real-time status. The relationship \(T_{i}=f(D_{i})\) expresses that real-time data from an IoT device (\(D_{i}\)) updates its associated digital twin (\(T_{i}\)). Here, \(T_{i}\) represents the digital twin of the \(i^{th}\) physical asset, and \(D_{i}\) represents the data collected from the \(i^{th}\) IoT device. This ensures that the digital twin accurately reflects the current state of its physical counterpart, facilitating real-time monitoring and management.  
Blockchain is a critical component of the proposed framework, addressing key challenges in FL and IIoT environments. Its integration ensures secure and decentralized data management through an immutable ledger that records data transactions and model updates, using Merkle Trees to verify data integrity efficiently. Smart contracts play a pivotal role by validating model updates with cryptographic signatures, ensuring that only authenticated updates are integrated into the global model. Additionally, blockchain enhances privacy by enforcing role-based access control through smart contracts, in con junction with HE, to maintain the confidentiality of sensitive data. Lightweight consensus mechanisms, such as Delegated Proof of Stake (DPoS), are employed to balance security and computational efficiency, making the implementation scalable for resource-constrained IIoT environments. Moreover, blockchain's inherent transparency provides a verifiable audit trail of all operations, meeting regulatory compliance requirements and enhancing trust among stakeholders. These features collectively validate the inclusion of blockchain as an indispensable element of the framework, directly addressing security, scalability, and privacy challenges.
## 3 Rolling Optimization Algorithm
### Trajectory Generation  
During UAV flight, the algorithm generates a real-time collision-free trajectory for the next several intervals by predicting and avoiding possible collisions. The 3D state space is utilized, which makes the situation more complicated than that in 2D. One scenario is presented in Figure 4. In the figure, the UAV with solid lines represents the UAV's current position, while the UAV with dashed lines shows the last position. The gray triangle is forbidden for UAVs because it violates the turn degree limitation, which is that in this paper the degree between adjacent heading directions is no more than 45 degrees. On the contrary, the black rectangle represents the allowed position of the UAV in the subsequent move. Therefore, \(N\) UAVs would have \(26^{N}\) possible states in the ensuing interval if the limitation of heading direction change degree is not considered. In this paper, the state space has removed a large number of possible waypoints, and lowered the computation complexity greatly. For the possible feasible waypoints, we set a weight for each position, which is relevant to three factors.  
The first factor is the distance between the upcoming possible position and the destination point. The farther the distance is, the lower the weight of the candidate point would be. The factor is calculated as shown in Equation (7). Therein, the numerator \(K_{1}\) is considered to adjust the weight of the factor. \(dist(a,b)\) calculate the Euclidean distance between vectors \(a\) and \(b\).  
\[f(D)=\frac{K_{1}}{1+dist(g_{i},p_{i}^{t+1})} \tag{7}\]The second factor is the angle between the possible heading direction and direction from the possible point to the destination point. The degree describes the level of deviation of the UAV from the shortest distance path. It is shown in Equation (8). The 0.1 in the denominator is set in case the angle 0 would make the factor infinite.  
\[f(A)=\frac{K_{2}}{0.1+\arccos(\frac{(p_{i}^{t+1}-p_{i}^{t})*(g_{i}-p_{i}^{t+1}) }{\|p_{i}^{t+1}-p_{i}^{t}\|*\|g_{i}-p_{i}^{t+1}\|})} \tag{8}\]  
The last factor is the eigenvalue of the crossing points in the grid model. The initial eigenvalue of each crossing point is set to be 1. Eigenvalues of points would change with the generation of UAVs' trajectories. Eigenvalue is the key information in ROA, and will be introduced later.  
Based on the three factors above, the weight for each candidate point is determined as in Equation (9). The weight of each point depends on not only the distance, but also the angle by which the UAV has deviated from the planned path. \(f(P)\) stands for the information of the crossing points.  
\[W=f(D)*f(A)*f(P) \tag{9}\]  
According to the weights for each candidate position, the roulette method is used to determine the position of the UAV in the next interval. The reason we use the roulette method is that although it is more likely for the greatest value position to be the next waypoint, it still can be other points. It would help the algorithm solve the problem of getting into the local optimization solution to some extent.
## 3 Results
### Isotherm Behaviors  
The initial content of dye can have a significant impact on the adsorption efficiency because it affects the mass transfer of solute between the aqueous phase and the solid phase [44]. The influence of CV dye concentration on the elimination percentage and the adsorbed quantity of dye was investigated in the range of 4-250 mg/L (Figure 7a). It can be observed that the removal rate of the CV dye has decreased, but the adsorbed quantity has increased with increases in the initial content of dye. The enhanced driving force of mass transfer from solution to the DSD solid surface could explain the improved adsorption capacity. This causes CV dye molecules to diffuse into the internal porosity of the adsorbent and reach the active sites [46]. Figure 7: (**a**) Effect of initial CV dye concentration on percentage of elimination and adsorption capacity, and (**b**) and nonlinear relationship of [PERSON] and Liu isotherm models. Study of the adsorption isotherm provides information about the equilibrium behavior (mechanism and maximum adsorption capacity), affinity, binding energy between adsorbate/biosorbent, and mode of adsorption (monolayer or multilayer). In this work, [PERSON], [PERSON], [PERSON], and [PERSON] isotherm models were employed to investigate the adsorption behavior of CV dye onto DSD adsorbent. The adsorption isotherms at three temperature values (20, 35, and 50 \({}^{\circ}\)C) are presented in Figure 7b. As observed, the temperature remarkably influenced the dye-binding capacity, and the adsorption is favored at high values of temperature. This could be due to the increase in active sites for CV dye uptake as the temperature rises. The relevant parameters of the studied isotherm models were calculated and summarized in Table 5. According to the R\({}^{2}\) and \(\chi^{2}\) values, the equilibrium data were best fitted by the [PERSON] and Liu model compared with the other models (as shown in Figure 7b). The Langmuir equation suggests the saturation of the adsorbent surface at high concentrations, whereas the Liu model suggests that the adsorbent sites cannot have the same energy [26; 47]. Based on Langmuir isotherm, the monolayer adsorption capacity (Q\({}^{0}\)max) was found to be 183.6, 198.0, and 243.6 mg/g at 293, 308, and 323 K, respectively. The gain in adsorption efficiency at higher temperatures was linked to a rise in adsorption energy. Generally, the appropriate isotherm models are classified in the following order: Langmuir > Liu > Freundlich > Redlich-Peterson. As DSD is a natural material, its adsorption capacity was compared with other natural adsorbents such as clay, zeolite, biochar, etc. Table 6 summarizes the maximum adsorption capacity which is calculated from Langmuir from for some adsorbent materials. The results show the DSD powder has an excellent ability to remove CV dye from water media compared with many natural and synthetic materials and can be applied as a low-cost and eco-friendly adsorbent to remove dye colors from wastewaters.