esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation  
### Results of Performance Tests  
The purpose of the performance tests was to evaluate the designed architecture in terms of efficiency. Initially, prediction (inference) latency was measured in the edge-nodes. Additionally, the effect of model switching on prediction latency was measured. The executed tests are presented in detail in Appendix B.  
The results of the prediction tests are presented in Figure 8. It can be observed that Jetson Nano achieved \(\sim\)2.2 s lower latency (99 th perc. latency= 6.97 s) than RPi 4 (99 th perc. latency= 9.16 s).  
Figure 7: UML sequence view illustrating model loading at the edge-node based on the received weight file.  
Figure 9 presents latency of prediction, when a model was switched once per minute. Particularly, new model weights were transferred to edge nodes for switching of a model (see steps 18-28 in Figures 6 and 7), while predictions were provided simultaneously to simulated end users. It can be observed that prediction latency and deviation is higher with both edge-nodes, when the results are compared to prediction without model switching (Figure 8). Figure 10 presents switching latency with both edge-nodes. The switching latency is significantly larger with RPi 4 than with Jetson Nano. Finally, statistics of the tests have been summarised in Table 2. In overall, model switching led to ~2.1-3.7 s increase in prediction latency.  
Figure 8: Results of prediction tests.  
Figure 9: Results of prediction tests with model switching.
# An Abstract Framework for Non-Cooperative Multi-Agent Planning
## 5 Case Study  
In this section, we present a case study in which the abstract framework introduced in this work with a specific instantiation is applied. Particularly, this case study shows each of the phases of the framework using a specific technology or system.  
This case study consists of a logistical problem in which a series of trucks must carry packages from the point of origin of each package to its corresponding destination. Trucks represent self-interested agents with the particularity that they all belong to the same company, which implies that they have common interests because they do not seek to hurt others, but receive a reward depending on the packages delivered, which is understood as contrary interests. Thus, the nature of the problem determines that it is necessary to use a non-cooperative MAP system in a non-strictly competitive setting, since the profit of one agent does not necessarily imply a loss in the other agents, but all have their private interests.  
The problem to be solved in this case study consists of three trucks that are self-interested agents that we will call A, B, and C, respectively. These trucks must deliver three packages, taking them from their place of origin to their corresponding destination. Figure 4 shows the distribution of the truck agents and packages in a representation of a city map. Concretely, a square represents the package origin position and a dotted circle represents the destination of the package. The lines in different colors represent the possible routes to deliver the packages. Therefore, the goals for which the agents compete in this case are the packages, since completing a goal gives them a reward, while movements without cargo only incur costs but not profits. In this way, agents will try to minimize movements without cargo, while trying to solve as many goals as they can. In this case study, we also take into account the congestion caused by using the same road at the same instant of time. Note that we make a simplification of this to have an illustrative example, as only two trucks should not cause congestion in the general case. In addition, in more complex problems where the use of petrol stations or electric vehicle charging stations is included, other types of congestion could appear due to concurrent use. However, in this case study, we want a simple example to show how the framework works with a prototypical problem.  
Figure 4: Case study example.
# Multi-WiIR: Multi-User Identity Legitimacy Authentication Based on WiFi Device
###### Abstract
This limitation results in the system's inability to recognize intruders, thereby diminishing its practicality. Subsequent research efforts have addressed the issue of identity legitimacy authentication [14; 15; 16]. However, there exists an issue with the variability of the threshold for determination, which changes with variations in experimental groups, lacking adaptability. Furthermore, existing research only focuses on single-user scenarios. Therefore, their practical application poses greater challenges. In prior studies [17], we effectively addressed the influence of changes in experimental group on judgment thresholds by introducing a particle swarm optimization (PSO) algorithm [18], yet this remained limited to single-user scenarios. To tackle the issue of multi-user legitimacy, this paper introduces a multi-user legitimacy authentication system named Multi-WiIR. The central idea leverages a multi-branch deep neural network, WiIR-Net, to extract features pertinent to individual users, and employs binary classifiers for each user. Ultimately, the legitimacy of each user is ascertained based on predefined thresholds derived from the model's output, thereby converting the multi-user legitimacy authentication issue into a single-user legitimacy authentication issue. This paper contributes to the field in the following ways:  
* We propose and implement Multi-WiIR, a multi-user legitimacy authentication system that detects the presence of trespassers in a scenario through the use of commercial WiFi devices. We evaluated the system in a real-world environment and showed that the system can concurrently authenticate the legitimacy of up to four users with an accuracy of 85.11%. * We propose a multi-branch deep learning model termed WiIR-Net. It employs convolutional neural networks (CNN) as the backbone, with multiple BiLSTM [19] branches for feature extraction. Each branch is equipped with a binary classifier, and the legit imacy of each user is determined by comparing the output of each classifier with a predefined threshold. Consequently, the multi-user legitimacy authentication problem is transformed into a single-user legitimacy authentication problem. * We conducted comparative experiments of operational efficiency and evaluation metrics between Multi-WiIR and various other models. The experimental outcomes demonstrated that the multi-branch architecture of Multi-WiIR enhanced the operational efficiency by 35.8% over multi-model multi-user legitimacy systems, and it surpassed the other classic models by nearly 5 percentage points in terms of performance. The remaining sections of this paper are organized as follows: Section 2 provides an overview of related work. Section 3 introduces the fundamental principles of WiFi-based technology and the knowledge required for WiIR-Net. Our Multi-WiIR framework and its associated processes are presented in Section 4. Section 5 presents the experimental results. In Section 6, we discuss the limitations of our work and potential solutions. Finally, we conclude our work in Section 7.
## 3 Design of Optimal Distribution Strategy of Damping Force Controller
### Optimal Distribution Strategy of Damping Force
Such force needs to meet or approach the constraint equation of Equation (16) as much as possible to ensure the rollover stability of the vehicle. The limitation characteristics of CDC is indicated as follows:  
\[\text{sat}(F_{Dij})= \begin{cases}F_{Dmin}/F_{Dij}\big{|}<|F_{Dmin}|\\ F_{Dij}|F_{Dmin}|<|F_{Dij}|<|F_{Dmin}|<|F_{Dmax}|\\ F_{Dmax}|F_{Dmax}|<|F_{Dij}|\end{cases}  \tag{35}\]  
where \(F_{Dmin}\) and \(F_{Dmax}\) are the lower and upper limits of the CDC shock absorber, they can be shown in Figure 6.  
Figure 5: Feasible region of optimal distribution of damping force. When the theoretical solution of Equation (34) does not meet the condition of Equation (16), the compensation damping force of the other shock absorber is optimally distributed according to the performance index of Equation (23). The expected damping force considering the shock absorber limit and rollover stability constraints is obtained as follows:  
\[\left\{ \begin{array}{l}F_{Dlj-d}=sat\left\{\frac{-\frac{2\sqrt{\alpha_{l}!}}{B} M_{\lambda Dn-nall}+\frac{3\sqrt{\alpha_{l}!}}{\Sigma}\sum\beta_{lj}-\beta_{lj} \sum\sqrt{\alpha_{l}!}}{\Sigma\sqrt{\alpha_{lj}}}\left[\frac{1-Sign(M_{xDn- roll})}{2}\right]\right\}\\ F_{Dnj-d}=sat\left\{\frac{-\frac{2\sqrt{\alpha_{l}!}}{B}M_{\lambda Dn-nall}+ \frac{3\sqrt{\alpha_{l}!}}{\Sigma}\sum\beta_{lj}-\beta_{lj}\sum\sqrt{\alpha_{ l}!}}{\Sigma\sqrt{\alpha_{l}!}}\left[\frac{1+Sign(M_{xDn- roll})}{2}\right]\right\}\end{array} \right. \tag{36}\]  
When the expected resistance force is not within the regulation range, the saturation constraint of the shock absorber should be considered and compensated by the shock absorber on the other side. This condition meets the expected moment constraint of anti-rollover preferentially. The distribution of the CDC damping force is optimized, and it effectively avoids the problem regarding the optimization of online solving constraints. The real-time performance of the control algorithm is improved.
# A Heterogeneous Battlefield Situation Information Sharing Method Based on Content
## 3 Entity Oriented General Description of Battlefield Situation Information
### Description and Organization of Internal Information about Entities Based on a Template
To describe this relational network, we constructed a semantic link network of entity relationships. The semantic link network is a self-organized semantic network that consists of semantic nodes, semantic links, and rules on semantic links; where a semantic space defines the semantics of semantic nodes, semantic links, and rules [13; 14; 15], and the interactive semantics and the semantic base were proposed for establishing the semantic basis [16]. The was originally used to support intelligent web applications by extending hyperlinks [17; 18], and the basic theory was developed systematically [19; 20]. Currently, it is extended to simulate the basic structure of a cyber-physical society [21; 22; 23]. Thus, to describe the relationships among entities effectively, we constructed a semantic link network, which is denoted as \(<\)_N,L,R\(>\)_; where \(N\) represents the collection of semantic nodes, i.e., the entities existing in the entity space, \(L\) represents the collection of semantic links, i.e., the relationships  
Figure 8: Structure of the entity class classification tree. between entities, and \(Rl\) represents the collection of reasoning rules based on \(L\), which is closely related to the properties of entity relationships. When storing the information of entity relationships in an SLN, an adjacency matrix called the Semantic Relationship Matrix (SRM) is used. For example, suppose there are \(n\) entities {e\({}_{1}\), \(e_{2}\),..., \(e_{n}|n\in\mathbf{N}\)} in the entity space, then the corresponding SLN can be represented by an SRM as follows. \[\left( \begin{array}{cccc}r_{11}&r_{12}&...&r_{1n}\\ r_{21}&r_{22}&...&r_{2n}\\...&...&...&...\\ r_{n1}&r_{n2}&...&r_{nn}\end{array} \right)\]  
where \(r_{ij}\) (\(i,j\in[1,n]\)) represents the semantic links from \(e_{i}\) to \(e_{j}\), \(r_{ii}=e_{i}\), and \(r_{ij}=r_{ji}^{-1}\). If there are no semantic links between \(e_{i}\) and \(e_{j}\), then \(r_{ij}=r_{ji}^{-1}=Null\). Generally, an element in SRM should be a set of semantic indicators as there may be multiple semantic links between the same pair of nodes, and a three-dimensional tensor can be used to represent a semantic link network [24; 25]. Moreover, since semantic links are dynamically evolved, based on SRM and the reasoning rules in \(Rl\), the semantic links between any two entities can be obtained, which is useful in the search for entities meeting specific semantic constraints.
# Sparse Indoor Camera Positioning with Fiducial Markers
## 4 Experiments  
This section details the experiments conducted to validate our approach. To the best of our knowledge, there are no specific public datasets available to test our method, necessitating the creation of our own. Although our method can be applied to the dataset of our previous work [5], where cameras have overlapping fields of view, these datasets do not contain sparse cameras. Thus, it is a more straightforward problem. The reverse is impossible, i.e., the previously proposed method cannot solve the problem we tackle in this paper. Consequently, we recorded three datasets in our building with different levels of complexity. We employed fixed cameras located on the ceiling with no overlapping in their field of view and a moving camera (a phone camera). The datasets present no loop closure. The ground-truth positions of the fixed cameras were manually verified to establish a baseline for accuracy. The control points were obtained using the available map of the building. Please note that it is only possible to establish control points in very salient regions of the environment, such as a door or the intersection between two walls. Lastly, the number of fixed cameras in each dataset was selected to maximize camera coverage while ensuring no overlap in their field of view. This setup was chosen to have as many error measures as possible for evaluation purposes. However, our method could have been applied with only two cameras per environment, obtaining similar results.  
To achieve good precision in pose estimation using fiducial markers, it is crucial that the markers cover a significant area in the images and are distributed throughout. Adequate coverage and sufficient angle between the camera plane and the normal to the marker plane ensure accurate pose estimation.  
The experiments were conducted on a system equipped with an Intel(R) Xeon(R) Silver 4316 CPU @ 2.30 GHz, running Ubuntu 20.04.6 LTS. The average processing time per dataset was approximately 3 hours and 32 minutes with a non-optimized implementation. Please note that due to the usage of sparse graph optimization techniques, we obtain a solution that, despite a high number of variables, tends to be efficient.  
This section is divided into five subsections. In Section 4.1, we show our first dataset in which we reconstruct a single corridor. Section 4.2 presents our second dataset, employed to reconstruct a complete building floor of our laboratory, comprised of four corridors and a large hall. Section 4.3 presents our most complex dataset, an entire building comprised of two floors Section 4.3 interconnected by stairs. Then, Section 4.4 evaluates the method proposed to find static markers between groups, i.e., the method proposed in Section 3.4. Section 4.5 performs a comparative analysis of the proposed method with prior works. Furthermore, Section 4.6 discusses potential limitations and the adaptability of our system to various environmental conditions.
## 5 Evaluation and Results
### Simulation Setup
#### 5.1.2 Simulation Workflow  
The evaluation was executed using a Python automation script:  
1. Registering 50 users with unique attribute profiles.
2. Creating ABAC rules for 30 mining licenses.
3. Evaluating 1500 user-resource access queries.
## 3 Results
### Simulation Results  
The microinverter circuits and components described in Section 2 were simulated using power electronics blocks in MATLAB Simulink. To simulate the solar panel, the \"PV  
Figure 23: Structure of the inverter task.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Maximum power, \(P_{max}\) & 150 W \\ Voltage at maximum power, \(V_{mp}\) & 18.7 V \\ Current at maximum power, \(I_{mp}\) & 8.02 A \\ Voltage in open circuit, \(V_{\infty}\) & 22.3 V \\ Short-circuit current, \(I_{sc}\) & 8.51 A \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the solar panel (Gi-Power GP-150P).  
Array\" block with the characteristics in Table 3 was used. The controllers were implemented digitally to achieve a better approximation to the real system by using the MATLAB function block. The simulation was carried out in discrete time with a sampling time of 1 \(\mu\) s. For the results of the DC-DC stage, the irradiance reference shown in Figure 24 was used to simulate the behavior during the day.  
Figure 25 shows the reference generated by the incremental conductance algorithm and the filtered and measured current signals of the panel. It was observed that the 2 PZZ control was able to follow the MPPT current reference without steady-state errors, but with some oscillations.  
Both the incremental conductance algorithm and current control managed to obtain the maximum power from the solar panel, approaching the ideal power, in the face of continuous irradiance variations. The resulting input power is shown in Figure 26.  
Figure 24: Reference used to model the behavior of irradiance during the day.  
Figure 25: Reference, measured, and filtered signals for the current of the solar panel.  
In the results of the DC-AC stage, an irradiance reference of 1000 W/m\({}^{2}\) was used. In Figure 27, the response of the 3P3Z current control is shown, where it is evident that the control is capable of following the reference (\(i_{Lref}\)), but with some oscillations. In addition, the current delivered to the grid--that is, the output current of the LCL filter \(i_{g}\)--is shown. The system's response to the anti-windup PI voltage control (Figure 28) showed oscillations; large parts of these components were eliminated with the digital filter. Furthermore, it was observed that \(V_{bus}\) quickly followed the 250 V reference with a settling time of approximately 0.35 s. In this case, the reactive power reference \(Q_{ref}\) was set to zero VAR.
## 9 Results and Discussion  
In order to evaluate the effectiveness of the proposed system in simulation of disaster response, the performance of the proposed model is calculated from: (a) the average of ratings between times, and (b) the number of objectives involved in selection of the appropriate service vehicle(s) based on the available routes in the emergency zone (the DOE). In this Section we set out the methodology with results and a discussion.  
To evaluate the overall performance of the proposed model with random [dynamic] obstacles (bicycles, cars, and other vehicles moving on the map (the DOE)), the performance of the proposed model was compared with a conventional method and Hed-DSS model as discussed in [41]. The proposed model, [including Hedge Algebra and _Kansei_ evaluation] are also used to quantify decision makers' sensibilities in emergency response.  
The approaches considered have performed well in evaluation and testing in the similar conditions (nodes) using the same data sets in the same map. The performance average of all cases (60 nodes)  
Figure 11: Priority vehicles in order to identify emergency locations in Japan on the Google map.  
Figure 12: The route of transportation vehicles was appeared on the Google map.  
in simulations) was calculated for variety of cases. The proposed model far outperforms the other conventional approach in short time for the evaluation of disaster response, as shown in Figure 13.  
In further evaluation and testing in simulations [the same number of 9 objectives with a variety of objectives], the experimental results show that the proposed model achieved increased computational efficiency (reduced computing time). Moreover, the results consistently show that the proposed approach in a comparative analysis [SOM integrated with the Hedge Algebra integrated _Kansei_ evaluation approach] demonstrates a consistent improvement of response time for the proposed model results achieved, as shown in Figure 14.  
Experimental results demonstrate that identifying linguistically (semantically) and evaluating the factors to be considered in a decision enables support emergency response (in an emergency zone, the DOE) to decide on the optimal routes for support and emergency service vehicles to provide an effective and efficient response. Quantifying decision maker's reasoning, perception, and linguistic (semantic) information performs a significant role in decision making under uncertain environments.  
The evaluation based on experimental testing has been presented to demonstrate the efficacy of our proposed model. The results of the simulation show that the proposed model provides an effective basis for resource allocation and deployment in emergency response scenarios. Moreover, our proposed method may be used to provide decision-support in both emergency and disaster planning and in'real-time' adaptable management of emergency response in dynamic uncertain environments.
## 8 Comparison with Other Emerging Interconnect Technologies
### Fusion of Inter-Chip Wireless Interconnects with IoT  
With IoT, fifty billion devices are predicted to be connected to the Internet by 2020 [110]. Chip-to-chip wireless interconnects can provide a seamless interconnection infrastructure to directly integrate chips into the IoT. One way of achieving this is to integrate the MAC of the inter-chip wireless transceivers with that existing in the IoT. The emerging IEEE802.11 ad, IEEE802.11 ay standards and compatible transceivers, modems, and MAC units, if integrated on the chip can connect chips directly to the IoT eliminating multi-level communication hierarchies thereby reducing significant overheads. Thus, using wireless inter-chip interconnects, it is possible to interconnect chips to IoT directly and efficiently. However, a challenge is expected to stem from the fact that the IEEE802.11 standards are developed for home WiFi environments target sparse network usage. Typically, they employ carrier sense approaches which are not scalable to many concurrent links. On the other hand, inter-chip interconnections require dense interconnects and high bandwidth. Therefore, novel MACs and standards need to be developed to enable this fusion of inter-chip wireless communication and IoT.  
Wearable electronic devices such as smart wearable displays, watches, health monitoring equipment and vision devices are forming an integral part of modern IoT as all such devices will be equipped with IoT connectivity in the near future. These devices require integration of multiple ICs on a flexible substrate [111]. The design of active circuits and antennas on a flexible substrate require novel innovations in materials, circuit design as well as antenna design [112; 113; 114]. Due to constant bending of the substrate platform physical metallic wires could easily suffer from wear and tear. Therefore, inter-chip wireless interconnects can be used for power-efficient multi-chip integration without requiring physical waveguide-based interconnections.
## 2 Energy Management and Review of Surveys
### MG Components

Table 2: The MG components [15]. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**ESS Technology**} & **Cost of Energy** & **Cost of Power** & **Cost of Operation and** & **Environmental** \\  & **Capital (USD/kWh)** & **Capital (USD/kW)** & **Maintenance (USD)** & **Impact** \\ \hline Superconducting magnetic & & & & \\ energy storage (SMES) & 1000–72,000 & 200–489 & 0.001–18.5 & Low \\ Flywheel Energy Storage (FES) & 1000–14,000 & 250–350 & 0.004–20 & Very low \\ Pumped hydro storage (PHS) & 5–100 & 2500–4300 & 0.004–3.0 & High/Medium \\ Thermal Energy Storage (TES) & 20–60 & 200–400 & - & - \\ Compressed Air Energy & 2–120 & 400–1000 & 0.003–25 & Medium/Low \\ Storage (CAES) & & Different types of batteries & & \\ Lead acid (Pb-A) & 200–400 & 300–600 & 50 & High \\ Lithium-ion (Li-ion) & 600–3800 & 1200–4000 & – & Medium/Low \\ Sodium-sulfur (Na-S) & 300–500 & 350–3000 & 80 & High \\ Nickel–cadmium (Ni-Cd) & 800–2400 & 500–1500 & 20 & High \\ Vanadium redox (VR) & 150–1000 & 600–1500 & 70 & - \\ Zinc bromide (ZnBr) & 150–1000 & 400–2500 & – & - \\ Polysulfide bromide (PSB) & 150–1000 & 700–2500 & – & - \\ Capacitors & 500–1000 & 200–400 & 0.005–13.0 & - \\ Super-capacitors & 300–2000 & 100–450 & 0.005–6.0 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative costs and environmental impacts of different energy storage technologies [21; 22; 23].