esa-sceva/satcom-chunk-collection huggingface dataset:
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 5 Critical Analysis and Open Research Challenges
### Comparative Evaluation of IMI Techniques  
While various IMI strategies have shown promising results in B5G networks, their deployment viability varies based on system constraints. A comprehensive evaluation of these techniques requires considering key trade-offs in terms of implementation complexity, scalability in dense network environments, and practical feasibility.  
Table 11 summarizes this comparison across several widely studied IMI techniques. For example, POC and eICIC are relatively simple and feasible for existing infrastructure but offer limited adaptability in UDNs. On the other hand, CoMP and BF deliver superior performance at the cost of high complexity and CSI dependency. AI-based methods like RL and FL show high adaptability and scalability but face deployment challenges related to training time, hardware acceleration, and explainability.  
These trade-offs underscore the need for hybrid, context-aware IM frameworks capable of dynamically selecting or combining techniques based on RT network conditions.  
This comparison demonstrates that no single IMI strategy is universally optimal. Future systems must dynamically balance these trade-offs, potentially through hybrid or AI-driven orchestration mechanisms adapted to deployment context.
## 3 System Design  
### Conceptual Design  
In general, a sink node occupies an important position in WSNs because all the data and commands have to pass through it. For this reason, the sink node is considered as an ideal place to translate protocols or perform some manipulations on sensing data. In our design, TinyONet is located on the sink node and maintains virtual sensors which perform as virtual counterparts [16] of physical sensors. A virtual sensor caches the latest sensing data from its corresponding physical sensor and behaves as a proxy of real sensor to external sensor applications. As shown in Figure 1, when an external sensor application issues a data-centric request, TinyONet dynamically organizes virtual sensors into a virtual functional entity named _Slice_ which serves the request. The _Slice_ has a socket connected to the requesting external sensor application and provides actual WSN services. By maintaining concurrent _Slices_, TinyONet can provide different kinds of data access services to multiple sensor applications using the same cached data. For example, the virtual sensors' temperature data collected from a building can be repeatedly used to serve a large number of applications. The cached data can be simultaneously reused not only by many building residents' PDA for smart environment, but also by some building management applications such as wine reservation.  
The proposed architecture is quite suitable for the periodic and the event-driven reporting models. In these models, the latest sensing data continuously flows into a virtual sensor when a new event occurs on a sensing field. It means that a cache miss will be resolved within a few seconds or a single  
Figure 1: Conceptual design of TinyONet.  
period. However, the situation is somewhat different in the case of the query-driven model. In the query-driven model, when the query is delivered to a _Slice_, some virtual sensors' data can be out of date, comparing to physical sensors' current data. In order to resolve this issue, we designed the system to provide the synchronous access to physical sensors' current data when the query driven-model is needed. Besides, _Slice_ should provide the node-centric communication method for sensor applications which need to directly communicate with physical sensors.  
Although _Slice_ has a role to interact with sensor applications, it is not desirable for _Slice_ to have actual processing functions. If the processing code is written in a _Slice_, the _Slice_ has to be replaced whenever a new service is needed. For this reason, we designed the system to offer a convenient method with which _Slice_ can delegate its actual processing codes on-the-fly.
## 3 Applications with J-KAREN Laser System
### Laser-Accelerated Electrons from Gas Targets  
Laser wake field acceleration (LWFA) [80], based on the effect of plasma waves excitation in the wake of an intense laser pulse, is now regarded as a basis for the next-generation of charged particle accelerators, competing with conventional accelerators in the energy gain per unit length. In order to generate a bunch with high quality, required for applications, the electrons should be duly injected into the wakefield and this injection should be controllable. Optical injection [81; 82; 83; 84] has an advantage in using a regular pattern wakefield excited by a driver laser pulse. The most simple optical injection scheme is based on the utilization of two counter-propagating laser pulses [82]. In the case of the collision of two counter-propagating laser pulses, the first laser pulse (the driver pulse) generates the wakefield in a plasma, and the second laser pulse (the injecting pulse) colliding with the driver pulse provides the conditions for the electron trapping by the wake wave.  
Figure 13: **(a)** Whole view of etch pit distribution on the rear surface of the 70 \(\times\) 140-mm wide CR-39; **(b)**,**(c)** Typical images of etch pits on the rear surface.  
The experiments of the optical injection [17] have been performed with J-KAREN laser system [63]. The 40 fs driver laser pulse with 0.4 J energy is focused onto a 1-mm-diameter helium gas jet by an off-axis parabolic mirror (OAP). The peak irradiance is I\({}_{0}\) = 3.0 \(\times\) 10\({}^{18}\) W/cm\({}^{2}\). The 50 fs injecting pulse with 30 mJ energy is focused onto the focus point of the driver pulse by an OAP. Its peak irradiance, I\({}_{1}\), is about 8.0 \(\times\) 10\({}^{16}\) W/cm\({}^{2}\).  
In order to demonstrate the optical injection, we must use plasma with a density below the self-injection threshold. When the plasma density, n\({}_{\rm e}\), is 1.0 \(\times\) 10\({}^{19}\) cm\({}^{-3}\), the reproducibility drops to 0%. Figure 14 shows the result of the optical injection for n\({}_{\rm e}\) = 1.0 \(\times\) 10\({}^{19}\) cm\({}^{-3}\). The collision of the two laser pulses produces a monoenergetic electron bunch with the energy of about 134 MeV and a 3.5% root-mean-squar (RMS) energy spread. Using the sensitivity of the phosphor screen, calibrated with the help of a conventional electron accelerator, we estimate that the total charge of the monoenergetic electron bunch is 8.7 pC.
## 3 Results and Discussion
### Interpretation of CB Model
#### 3.6.1 Feature Importance (FI)  
In this part, the means of absolute SHAP values were used to measure the FI values for the entire database (globally) based on the CB model prediction. Figure 12 provides a rich visualization of the importance of input features. In the figure, the vertical axis represents the input features, which were sorted based on their importance in prediction, and the horizontal axis represents their corresponding importance values. If the FI value is far away from zero, it means the feature has a significant impact on the ML model prediction. Conversely, if the FI value is close to zero, it indicates the feature has less impact. The FI value has the same unit as the CB model output (kN).  
Based on Figure 12, the most critical input features that significantly impacted the SS of RCDBs are \(b\) and \(f_{c}^{\prime}\). This observation aligns with mechanics-driven models outlined in Appendix A, which indicated that the SS of RCDBs strongly depends on \(f_{c}^{\prime}\) and \(b\). The second critical input features are the geometrical dimensions such as \(a/d\), \(d\), \(h\) and \(l_{0}/h\). This is logical given that these features are relevant to the SS mechanism, as RCDBs' behavior (failure mode) is strongly dependent on cross-sectional properties. Surprisingly, the feature \(\rho_{l}\) has a more significant impact on the SS than features \(\rho_{v}\) and \(\rho_{h}\). This is due to the fact that horizontal reinforcements mainly act as shear ties, which prevent buckling of vertical bars and improve the cohesion. Features \(f_{yl}\) and \(\rho_{v}\), which are related to the horizontal reinforcement and web reinforcement, respectively, have relatively similar impacts. Features \(a\), and \(\rho_{h}\) are considered less important. Other input features such as \(f_{yv}\)  
Figure 12: Feature importance of CB model using SHAP.  
\(l_{e}\), \(f_{gh}\), \(s_{v}\), and \(s_{h}\) are not that important since their FI values are small. However, deciding whether to exclude a specific feature from the model or not requires a comparative analysis with extensive optimization.
# SALMA: A Novel Middlebox Infrastructure System Based on Integrated Subnets
## 2 Motivation
### Utilization of Middleboxes  
The collected statistics showed that multiple duplicate middleboxes of the same type, (i.e., IP firewall, IDS/IPS, or load balancer) were installed inside the same network. For example, when we closely looked at one of the investigated service-provider networks, we found that every interface that was associated with an untrusted network was connected to a firewall and an IDS. In some cases, duplicate middleboxes are installed in parallel for high availability. Thus, the utilization of the firewalls and IDSs relies on the activities of the customers in the connected branches. To demonstrate our claim, we collected the utilization statistics of four middleboxes (two firewalls and two load balancers) installed in various locations inside a service-provider network, as shown in Figure 3. In fact, we noticed that every common service chain is grouped in an independent set, so firewalls must exist in every set. This means that the maximum utilization of every set could not exceed the firewall utilization. Additionally, the datasets provided have multiple firewalls and load balancers; their utilization was similar to that displayed in the figure. The statistics were collected during October.  
In reality, middlebox duplication is done not only to achieve high availability, but also due to topology-dependent conditions. Typically, middleboxes are scattered all over the network in order to implement the network policy on the right traffic. This duplication  
Figure 3: The utilization of four independent middleboxes installed in different locations inside a service-provider network. (**a**) Firewall 1 (FW1). (**b**) Firewall 2 (FW2). (**c**) Load balancer 1 (LB1). (**d**) Load balancer 2 (LB2).  
practice results in low middlebox utilization as well. We found that the firewalls studied showed a serious utilization gap because of the topology-dependent conditions and routing protocol stiffness. This is due to the fact that the traffic of untrusted domains has to be monitored and examined by the firewall/IDS policies regardless of its volume. Furthermore, the available routing protocols are not flexible enough to provide a sufficient degree of control. In fact, when we analyzed the middleboxes' utilization statistics, we found variability during a small period of time (i.e., one or more days), which is aligned with previous findings [4]. However, when these groups of firewalls are integrated into a single firewall and all the in-scope traffic is forwarded to that box, the utilization will be increased, and all the overall cost (management, operation, and others) will be reduced.
## 4 Results  
### Analysis of Speed Profiles by Vehicle Pair  
Descriptive statistics of driving speed are presented with examples of speed profiles for each car-following pair, as shown in Figure 8. In the AV pair, the average speed and the standard deviation of speed were observed to be approximately 95 km/h and 0.11-0.38 km/h, respectively. Additionally, the results of the speed ranges of 0.43 and 1.13 km/h of \(\rm AV_{L}\) and \(\rm AV_{F}\) showed that the driving behaviors of the two vehicles are quite similar. The mechanical and deterministic movement, which resulted from the maneuvering control logic, of the preceding \(\rm AV_{L}\) also had an effect on the movement of the following \(\rm AV_{F}\). In the case where an MV follows an AV, which is the first scenario of the mixed pair (\(\rm AV_{L}-MV_{F}\)), the difference in the standard deviation of the speed between \(\rm MV_{F}\) and \(\rm AV_{L}\) was significantly large; although, the average driving speeds were similar, indicating that the driving behavior was meaningfully different. In addition, the speed range of \(AV_{L}\) was 0.42 km/h, whereas the speed range of \(\rm MV_{F}\) was 40.00 km/h. Another interesting finding was observed with the second scenario of the mixed pair (\(\rm MV_{L}-AV_{F}\)). The standard deviation of speed for the \(\rm AV_{F}\) following \(\rm MV_{L}\) was observed to be 19.80 km/h, which indicates that the standard deviation of speed was larger than that of the AV pair. The stochastic movement resulting from the human driver also had an effect on the movement of the following \(\rm AV\). Therefore, it can be said that there is a significant difference in driving behavior between \(\rm AV\) and \(\rm MV\) that follow the preceding \(\rm AV_{L}\). In the MV pair, the average driving speed was 100 km/h, which is the highest speed among all vehicle pairs. As expected, the largest speed variation was found in the MV pair. Because MVs are driven according to the human driver's awareness and decisions, they are likely to make more frequent changes in driving speed with greater acceleration rates. When a vehicle pair consists of the same vehicle type, stable interaction between vehicles is found due to similar driving behavior [29]. The difference in the standard deviation of speed is 0.27 km/h in the \(\rm AV_{L}-AV_{F}\) pair and 0.18 km/h in the \(\rm MV_{L}-MV_{F}\) pair. On the other hand, in the case of mixed pairs, the standard deviations of speed were 6.55 km/h and 1.04 km/h for \(\rm AV_{L}-MV_{F}\) and \(\rm MV_{L}-AV_{F}\), respectively.
## 4 Simulation Analysis  
### Scenario Setup  
This paper adopts an Iridium-like satellite constellation [24] with an orbital altitude of 780 km, an inclination of 86.4\({}^{\circ}\), and seven orbits with seven satellites each. The constellation parameters are shown in Table 4.
# AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications
## 1 Introduction
Based on the control cost function and the schedulability constraint, our specific problem is to find the runnable periods that minimize the control cost while guaranteeing the schedulability constraint. Since the control cost function is a function of control period and delay, it should be transformed into a function of runnable periods. For that, we carefully investigate how runnable periods affect the temporal behavior, i.e., control period and delay, of a control system, and develop a generalized method for the transformation. After the transformation, the Lagrange multiplier method is used to find the optimal runnable periods. Note that the Lagrange multiplier is a well-known optimization method for constrained optimization problems. As it provides an analytical method without any problem space search, our method can find the optimal runnable periods regardless of the size and the complexity of a target system. The detailed optimization process is explained in three steps beginning from the most uncomplicated application model to the general DAG model for the explanation purpose. Although our solution cannot find the real optimal solutions due to a heuristic applied during the optimization, our evaluation results for small systems show that the performance loss is marginal compared with the real optimal solutions. Moreover, even for large systems, our method performs better compared with a state-of-the-art optimization method. This paper's contributions can be summarized as follows:  
* We formulate a problem of AUTOSAR runnable periods optimization in the context of control-scheduling codesign, which we consider to be one of the first such attempts;
* For the above problem, we present a Lagrange multiplier-based analytical method for DAG-based AUTOSAR control applications, which can find near-optimal solutions in polynomial times. The rest of this paper is organized as follows: Next section provides related work. In Section 3, the background is given and the problem is described. Sections 4 and 5 introduce our preliminary works for limited application models. Then, Section 6 describes our analytical solution for the general DAG model. Section 7 evaluates our method. Finally, Section 8 concludes this paper.
## 2 A Brief Discussion on LEACH, EESR and Sub-Cluster LEACH Protocols
### EESR Routing Protocol  
EESR protocol [17; 18] is a popular routing protocol mainly used for constructive infrastructure-based communication. It generally supports densely deployed sensor monitoring field. For implementation, the BS is situated at the center of network and the sensor nodes are scattered over the field. The field is divided into quadrants and there is a categorization into 4 quadrants: left upper, right upper, left lower and right lower. These quadrants are also denoted as (\(+\), \(+\)), (\(+\), \(-\)), (\(-\), \(+\)) and (\(-\), \(-\)). Each quadrant is further divided into equal parts as shown in Figure 2. The green node is denoted as BS, which is situated at the center. Manager nodes are shown in red color, and they are fixed at the diagonal axis in each quadrant. Yellow nodes are sensor nodes that are used for sensing the data [19; 20]. The field is further divided into sectors as shown in Figure 2 with blue lines. The sectors designate the distance from BS to field far apart. This division based on hop-wise distance [21; 22]. Either the CH node is 1 hop or more than 1 hop distant from BS; the data are collected accordingly. The manage nodes play vital roles in the network for collecting the data to their neighboring nodes and transferring data to the BS. The sensor nodes that are situated in each quadrant 1 hop distant to BS are not required to send data first to manager node; nodes can directly send data to the BS [23]. If sensor nodes are further than 1 hop, then sensor nodes must transmit their aggregated data to the neighboring manager nodes, and finally, manager nodes transfer the data to BS [24; 25; 26]. If sensor nodes are situated in the 2 nd sector but they are distant from the 2 nd sector manager node, then the sensor node has no need to send data first to the 2 nd position manager node, it can send data to a neighboring sensor node, which acts as an intermediate node, and then that node transmit the data to the neighboring manager node [27; 28]. Hence, it is not necessary to send the data to the manager node, thereby reducing the energy consumption [29; 30].
## 4 Experimental Validation
### Case 2
#### 4.2.3 Image Comparison Experiments  
The comparative image experiments are shown in Table 14. The results indicate that the proposed MCR-KAResNet-TLDAF method achieved a higher average accuracy than the other three individual image processing methods.  
Although the MCR-KAResNet-TLDAF method did not achieve the highest accuracy in the 0-1 transfer task, it outperformed the other methods in the remaining five transfer tasks. The MCR-KAResNet-TLDAF method achieved an accuracy of over 99.5% across all the transfer tasks; a benchmark that the other three methods could not reach. It improved by 16.449% compared to the RP-KAResNet-TLDAF method, by 44.109% compared to the MTF-KAResNet-TLDAF method, and by 3.111% compared to the CWT-KAResNet-TLDAF method. Notably, in the 2-0 transfer task, the MCR-KAResNet-TLDAF method achieved an accuracy of 99.667%, breaking the bottleneck where the other three methods had accuracies below 87%. These results demonstrate the significant advantages of the MCR image processing method on the JNU dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Group** & **Methods** & **0-1** & **0-2** & **1-0** & **1-2** & **2-0** & **2-1** & **Average** \\ \hline \multirow{3}{*}{First group} & 0.2 RP-MTF-0.2 CMF & 72 & 88 & 71.33 & 83.33 & 86 & 83.67 & 80.72 \\  & 0.2 CMF-RP-0.2 MTF & 80.33 & 87.67 & 82.67 & 92.67 & 82.67 & 96 & 87 \\  & **0.2 MTF-CWT-0.2 RP** & 99.667 & 100 & 100 & 100 & 99.667 & 100 & **99.889** \\ \hline \multirow{3}{*}{Second group} & 0.2 MTF-0.2 RP-CWT & 100 & 100 & 100 & 100 & 83.67 & 100 & 97.28 \\  & CWT-0.2 MTF-0.2 RP & 100 & 98.33 & 100 & 100 & 98.33 & 100 & 99.44 \\  & **0.2 MTF-CWT-0.2 RP** & 99.667 & 100 & 100 & 100 & 99.667 & 100 & **99.889** \\ \hline \multirow{3}{*}{Third group} & 0.1 MTF-CWT-0.1 RP & 100 & 98.67 & 100 & 100 & 97.67 & 100 & 99.39 \\  & MTF-CWT-RP & 99.33 & 100 & 100 & 100 & 85.33 & 99.67 & 97.39 \\  & 0.2 RP-CWT-0.2 MTF & 100 & 95.67 & 100 & 100 & 99 & 99.67 & 99.06 \\  & **0.2 MTF-CWT-0.2 RP** & 99.667 & 100 & 100 & 100 & 99.667 & 100 & **99.889** \\ \hline \hline \end{tabular}
\end{table}
Table 13: Experimental results of image fusion method on the JNU dataset.
## 4 Simulation Results
### Error and the Speed of Mobile Nodes  
The speed of mobile nodes is an important factor affecting the localization error. Here the speed of mobile nodes is set to vary from 2 m/s to 20 m/s. The results of the simulation experiments as shown in Figure 12 indicate that the faster the moving speed, the bigger the localization error is. Due to using geometrical relationship to localize the mobile nodes coarsely, the localization error of AUV-aid is the maximum and changes stable without downtrend. AFLA used the geographical relationship of neighbor nodes to localize mobile nodes which did not need the information of anchors, so its precision is affected slightly by the speed of mobile node. Since the faster mobile nodes move, the less  
Figure 11: The percentage distribution.  
number of task anchors which can contact with them in three dimensional UASN can be acquired. So with the speed of mobile nodes increasing, the localization error of PCFL is almost equal to that of AFLA. And the overall varying trend of the localization error of PCFL or ACFL rises faster than the other three algorithms. When the speed is lower than 18 m/s, the localization error of PCFL is the minimum. As a whole, PCFL exceeds the other four methods with its average localization error of 3.92 m, much smaller than that of ACFL, 7.33 m, due to the projection technology.