esa-sceva/satcom-chunk-collection huggingface dataset:
# Sparse Indoor Camera Positioning with Fiducial Markers
## 3 Proposed Method  
This section presents the proposed methodology for estimating the poses of sparse fixed indoor cameras using fiducial markers. In our previous work, a solution is proposed [5], assuming that some fixed cameras are close to each other, so they share part of their field of view. Thus, a large area can be covered by many fixed cameras, creating a path of shared fields of view. In that use case, markers placed on the ground are employed to obtain the pairwise relationship (pose) between the cameras and, thus, their global pose on a map. However, in many scenarios, this option is not possible, and we only have a small subset of sparse fixed cameras completely unconnected from each other.  
Our solution to the sparse camera pose estimation problem relies on the idea that the pose of a camera with a global reference system can be easily obtained from an image showing one or more markers whose poses are known. The problem then becomes placing markers on a large area and accurately estimating their poses. This problem can be solved by the method proposed in MarkerMapper [4], in which a set of markers are placed in the environment, and their poses are estimated from images, i.e., structure from motion using markers instead of key points. The problem with that approach is that it requires an extremely large number of unique markers, which is unfeasible in even relatively small scenarios.  
Our approach, which is illustrated in Figure 2, consists of placing a small set of markers on the ground and taking several images with a moving camera (i.e., a phone camera). The images, showing the markers from several viewpoints, are employed to obtain a reconstruction of the markers related to one of them; this is called a group. Then, a subset of the markers is moved while the rest are left in their positions, and the operation is repeated, creating another group. This second group is connected to the previous one by the fixed markers. The process is repeated until the whole area to map is covered, making sure we place markers under the fixed cameras we aim at locating. Thus, our set of images contains not only images from the moving camera but also from the fixed cameras.  
We first estimate the poses of the markers and the images involved within a group using a local reference system for each group (e.g., centered at one of the markers). Then, since groups are connected, it is possible to find the transformation between any group and one of them, which acts as a global reference system. In doing so, we obtained the poses of fixed cameras with that global reference system.  
The rest of this section explains the proposed method in detail. First, Section 3.1 provides some mathematical definitions necessary for the rest of the paper. Second, Section 3.2 explains how groups are analyzed, and Section 3.3 presents how they are merged into a metagroup to solve the proposed problem. Finally, Section 3.4 explains how to automatically determine the connection between groups.
# Propagation of Non-Linear Lamb Waves in Adhesive Joint with Micro-Cracks Distributing Randomly
## 1 Introduction
[42] performed finite element simulations on the necessity of phase velocity matching and group velocity matching. Their results verified that matching of the phase velocity is necessary for generating the non-linear Lamb waves with cumulative second harmonics, while the matching of the group velocity is not a necessary condition. In this paper, the propagation of non-linear Lamb waves in a tri-layer adhesive structure model with micro-cracks distributing in a random way is systematically investigated using the numerical simulation method. Based on the dispersion curves of the tri-layer structure, the Lamb wave mode pair matching the phase velocity is chosen to investigate the interactions of the micro-cracks and the Lamb waves. The relationships between the acoustic non-linear parameters and the propagating distances within the micro-crack damage areas, as well as other factors related to the micro-cracks, such as crack density and the friction coefficients of the micro-crack surfaces, are obtained quantitatively. This study is expected to provide a deeper understanding for the characterization of the damage of the adhesive layer with micro-cracks for the adhesive joints by effectively employing the non-linear Lamb waves.
# Post-Quantum Delegated Proof of Luck for Blockchain Consensus Algorithm
## 5 Conclusions  
This paper introduced the post-quantum delegated proof of luck (PQ-DPoL): a blockchain consensus algorithm designed to counter the growing threat posed by quantum computing. By integrating the Falcon and Dilithium post-quantum signature algorithms within a delegated consensus mechanism, PQ-DPoL strikes a balance between security, performance, and fairness. The system is designed to ensure resilience against quantum attacks, safeguarding blockchain networks as quantum computing advances.  
Our experiments highlight the trade-offs associated with post-quantum cryptography, particularly in terms of transactions per second (TPSs). While Falcon provides robust quantum resistance, its larger signatures reduce TPS values compared to traditional ECC-based solutions. To mitigate this, PQ-DPoL employs efficient compression algorithms, reducing block sizes without compromising security. Dilithium, with faster signing and verification times, also presents performance benefits but suffers from larger transaction sizes, impacting TPS values in high-throughput environments.  
The experiments were conducted in a simulated environment with certain limitations such as smaller block sizes and computational constraints that restrict testing at larger-scale, real-world scenarios. Consequently, the current TPS measurements may not fully represent PQ-DPoL's performance under larger block sizes or higher node counts, emphasizing the need for further exploration in more extensive environments. Despite these limitations, Falcon demonstrated competitive performance, making it a viable candidate for blockchain systems prioritizing long-term security.  
Future work will focus on overcoming the limitations of the simulation environment, particularly by testing PQ-DPoL in larger-scale networks and with varying block sizes to assess its scalability and efficiency. Additionally, further research will explore optimizingcompression algorithms to improve TPS values without compromising security. Investigating other PQC algorithms with improved performance characteristics and refining the data compression techniques in PQ-DPoL will also be key areas of future exploration.  
Software, H.K. ([PERSON]); Investigation, W.K. and Y.K.; Writing--original draft, H.K. ([PERSON]) and H.K. ([PERSON]); Writing--review & editing, H.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2018-0-00264, Research on Blockchain Security Technology for IoT Services, 50%) and this work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2022-0-00627, Development of Lightweight BIoT technology for Highly Constrained Devices, 50%).  
The authors declare no conflict of interest.
## 2 Related Works
In the first stage, design and dispatch variables (e.g., solar collector area, SOFC stack size, battery capacity) are optimized via an \(\epsilon\)-constraint formulation--minimizing annualized cost while bounding CO\({}_{2}\) emissions and exergy efficiency at discrete \(\epsilon\)-levels--to generate a Pareto frontier. Candidate solutions are then ranked using LINMAP (Euclidean distance to an ideal point), TOPSIS (relative closeness to positive/negative ideals), and Shannon entropy-derived weights. In the second stage, a subset of Pareto solutions is evaluated through the Analytic Hierarchy Process (AHP) and the Gray Relational Analysis (GRA). The AHP employs [PERSON]'s 1-9 scale and enforces consistency ratios < 0.10 to derive criterion weights ([PERSON] et al., 2019). These weights feed into the GRA, where gray relational coefficients (distinguishing coefficient 0.5) produce a composite grade for each alternative. Applied to a solar-assisted SOFC system across residential and commercial building archetypes in three climate zones, the framework uses a MINLP model with temperature-dependent SOFC curves and hourly solar data, solved in GAMS/DICOPT (tolerance \(10^{-6}\)). Results show 12-18% cost savings and 20-25% emissions reduction versus baseline, demonstrating robust, multi-objective decision support. [PERSON] et al. (2019) apply GA with binary-real encoding and tournament selection to optimize distributed energy systems (DESs) in shopping malls, office buildings, and hotels. Seven dispatch strategies--FTL, FEL, FHL-ns, FHL-ms, FHL-nst, FMLR, and FSLR--are evaluated using hourly load profiles and energy simulations. Optimal CHP capacities range from 623 kW to 1 782 kW, with electric cooling ratios (electric chiller capacity/total cooling load) between 0.5 and 0.9. Performance is measured by primary energy saving  
\[\eta_{\text{PES}}=1-\frac{E_{\text{DES}}}{E_{\text{ref}}},\]  
and CO2 reduction  
\[\Delta\text{CO}_{2}=\frac{M_{\text{ref}}-M_{\text{DES}}}{M_{\text{ref}}},\]  
reaching 29.8% and 49.5%, respectively. FMLR proves most effective in malls and hotels, while FHL-nst excels in office buildings by time-shifting heat pump operation (COP \(\approx\) 3.5). Sensitivity analysis shows that higher coal-to-gas fuel mixes degrade both \(\eta_{\text{PES}}\) and \(\Delta\text{CO}_{2}\), underscoring the GA's utility in complex, multi-objective DES design. [PERSON] et al. [30] evaluate countries' logistics performance using the Logistics Performance Index (LPI) and address its limitations by introducing a GA-based model for criteria weighting. Employing 11 Multi-Criteria Decision-Making (MCDM) techniques across 33 indicators ensures robust performance analysis. Missing data are imputed using methods like mean, median, linear regression, and KNN, with linear regression identified as the most effective. GA outperforms traditional weighting methods like CRITIC and Entropy, highlighting key criteria such as goods exports, road quality, and GDP per capita. These weights align more closely with actual LPI scores. The approach enables more frequent performance evaluations, supporting countries in improving logistics rankings.
# SoK: Delegated Security in the Internet of Things
## 4 Formalisation of the Security Properties  
In this section, mathematical formalisations of CIA security properties are introduced (see Section 3.2) for each application domain of delegation defined in Section 2 with respect to the adversary model from Section 3. All formalisations are represented as security games:  
* \(\mathsf{AC}\) is an Access Control/Authorisation delegation game.
* \(\mathsf{AD}\) is an Authentication delegation game.
* \(\mathsf{RD}\) is a Rights delegation game.
* \(\mathsf{CD}\) is a Computation delegation game.  
An adversary \(\mathcal{A}\) is considered to win the security game if they successfully violate the targeted security property (confidentiality, integrity, or availability).  
In order to be able to provide mathematical formalisations and clearly define the required elements, the following collections are introduced:  
* \(PL:=\left\{\mathsf{id}=0,1,\ldots\mid\mathcal{P}_{\mathsf{id}}\right\}\) is a list of all system participants.
* \(RL:=\left\{\mathsf{i}=0,1,\ldots\mid\mathcal{R}_{\mathsf{i}}\right\}\) is a list of all system resources.
* \(M\subseteq PL\times RL\) denotes the access matrix. More precisely, if \((\mathsf{id},i)\in M\) then party \(\mathcal{P}_{\mathsf{id}}\) has access to resource \(\mathcal{R}_{\mathsf{i}}\). It contains all system participants.
* \(F\subseteq PL\) includes all \(\mathcal{P}_{\mathsf{id}}\) which are accessible from outside of a system.
* \(H:=\left\{i=0,1,\ldots\mid\mathsf{h}_{\mathsf{i}}\right\}\) includes all elements of a system that are expected in case the system operates without deviations. It can be parties, passwords, messages, etc.
* \(L:=\left\{\mathsf{id},\mathsf{id}_{\mathcal{D}},i\in M\mid\mathcal{RP}( \mathsf{id},\mathsf{id}_{\mathcal{D}},i)\right\}\) specifies all valid proofs of rights \(\mathcal{RP}\) related to an owner party \(\mathcal{P}_{\mathsf{id}}\), a delegate \(\mathcal{D}\) with \(\mathsf{id}_{\mathcal{D}}\) and a resource \(\mathcal{R}_{\mathsf{i}}\) for each party involved in a system.
* \(S:=\left\{\mathsf{id}\in M\mid\mathsf{s}_{\mathsf{id}}\right\}\) contains all secrets of each party in a system.  
In all of the models described below, one of the initial steps for an adversary is to receive an access matrix \(M\) as input. However, just as in the case of oracles, some adversaries, depending on their characteristics, may not receive \(M\), for example, an adversary with \(\mathcal{RS}=Ex\) receives only \(F\).
## 2 Problem Formulation
### Supervising Methodology of RSTT
As in (1), both impulsive and non-impulsive disturbances can be characterized by the statistics of pulse amplitude and width. Therefore, statistical parameters APD and PDD are jointly used to describe and characterize the interferences within the operating frequency bands of RSTT. Figure 2: Example of the on-site measurement of disturbances in the Global System for Mobile Communications-Railway (GSM-R) band in the time-domain, (**a**) the pantograph arcing emission disturbance with data from [24] and (**b**) the interference from the GSM signal in the adjacent channel with data from [26]. Generally, the interference effect on RSTT depends both on the MCS of the transmitted signal and the statistical characteristics of the received disturbance. Considering the diversity of disturbances as well as RSTT signals, especially with the coding gain of FEC codes, the impact on radio transmission differs widely. Therefore, a universal method is required to evaluate the real-time transmission quality in such a complex situation.
# Coffee-Leaf Diseases and Pests Detection Based on YOLO Models
## 3 Materials and Methods
### YOLO Family
#### 3.5.4 YOLOv11  
YOLOv11 [35] redefines what is possible in terms of accuracy, speed, and efficiency. Building on the advances of previous versions of YOLO, YOLOv11 brings significant improvements in both architecture and training methods, making it a highly versatile choice for a wide range of computer-vision tasks.  
One of the main features of YOLOv11 is improved feature extraction. The improved backbone and neck architecture offers superior feature-extraction capabilities, resulting in more accurate object detection and better performance in complex tasks. This improved architecture ensures that YOLOv11 is able to handle a wide variety of visual challenges with greater efficiency.  
YOLOv11 offers state-of-the-art object-detection capabilities through a number of architectural improvements. Building on its predecessors, YOLOv11 integrates transform-based modules and advanced feature-extraction techniques, providing greater accuracy and efficiency in defect detection. Its main improvements include:  
* YOLOv11 adopts a new backbone called CSP-DenseNet, which combines the dense connections strategy of DenseNet with CSP connections. This hybrid design improves feature reuse and gradient flow while reducing computational overhead, making the backbone highly effective for detecting intricate and overlapping features in extrusion defects.
* The YOLOv11 neck incorporates a transform-based Feature Pyramid Network (T-FPN), which uses self-attention mechanisms to improve feature aggregation at multiple scales. This architecture improves the model's ability to detect objects of varying sizes and shapes, especially in cluttered or noisy environments. In addition, the neck has DCNs, which dynamically adapt the receptive field to different defect geometries.
* The model head has an anchorless mechanism, combined with a lightweight dynamic prediction module, eliminating dependence on predefined anchor boxes. This improves detection efficiency and flexibility, while reducing computational complexity. The head is optimized for multi-scale prediction, ensuring the accurate regression and classification of bounding boxes, especially for subtle and complex defects such as ropes and spars.
# Artificial Intelligence Enabled Routing in Software Defined Networking
## 4 Performance Evaluation  
### Simulation Settings  
The parameters and their values used in the simulation are summarized in Table 4. The routing module in the application plane uses the Dijkstra algorithm. The communication interface between the data and control planes uses OpenFlow Protocol V1.3. The network topology of the data plane, as illustrated in Figure 9, consists of three source nodes, one destination node, and 9 available transmission paths. Therefore, a total of 729 path configuration outcomes are obtained. We use the Iperf [41] tool to generate UDP flows at data rates varying from 70 Mbps to 150 Mbps. The bandwidth of each link is 250 Mbps. The buffer size of each OpenFlow switch is 200 packets. The monitoring period is fixed at 3, 5, or 10 s.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **C Congestion** \\ **Flag** \\ \end{tabular} } & \multicolumn{2}{c}{**Data Rate (Mbps)**} & \multicolumn{2}{c}{**Path Configuration**} & \multicolumn{2}{c}{
\begin{tabular}{c} **Congestion** \\ **Probability** \\ \end{tabular} } \\ \cline{2-7}  & **S\({}_{\textbf{1}}\)-D\({}_{\textbf{1}}\)** & **S\({}_{\textbf{2}}\)-D\({}_{\textbf{1}}\)** & **S\({}_{\textbf{3}}\)-D\({}_{\textbf{1}}\)** & **S\({}_{\textbf{1}}\)-D\({}_{\textbf{1}}\)** & **S\({}_{\textbf{2}}\)-D\({}_{\textbf{1}}\)** & **S\({}_{\textbf{3}}\)-D\({}_{\textbf{1}}  
A multilayer perceptron (MLP), which consists of an input layer, an output layer, and at least one hidden layer, is used as an ANN model in the control plane. Figure 10 depicts multiple nodes for the input layer and only one node for the output layer. The number of neutrons at the two hidden layers varies from 100 to 200, which are used to evaluate the accuracy of the ANN model. First, we collect 65,000 data records to train the ANN model. The training data are 80% of the 65,000 data records, and the remaining 13,000 data records are the validation data. The accuracy of the trained model with 120 and 140 neutrons at the first and second hidden layers, respectively, can be approximated at 82%. Considering both performance advantages and computational complexity [42], 120 and 140 neutrons at the first and second hidden layers, respectively, are adopted for the ANN model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Simulator & Mininet 2.3.0 \\ SDN protocol & OpenFlow V1.3 \\ Packet generator & Iperf \\ Traffic type & UDP \\ Link bandwidth & 250 Mbps \\ Data rate & 70 Mbps \(\sim\) 150 Mbps \\ Buffer size & 200 packets \\ Routing module & Dijkstra algorithm \\ Monitoring period & 3, 5, or 10 s \\ No. of source nodes & 3 \\ No. of destination nodes & 1 \\ No. of available paths & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters and values used in the simulation.  
Figure 10: ANN model (a four-layer multilayer perceptron (MLP)).  
Figure 9: The SDN data plane used in the simulation.
# Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing
## 3 Methodology
### Evaluation Criteria  
Three evaluation criteria were defined in the simulation to evaluate the relocation model performances: car utilization ratio, reservation acceptance ratio, and relocation cost.  
The car utilization ratio is the percentage of total actual driving hours of rented cars over total possible driving hours of cars,  
\[Utilization\ ratio=\frac{\ vehicle\ hours\ of\ cars\ used}{\ available\ vehicle\ hours\ of\ entire\ fleet}100\%\]  
[PERSON] et al. has revealed that the utilization ratio is proportional to the average profit per day [27]. Thus, it is important that the carsharing company needs to ensure that all cars can be rented (fully operated) to increase profit.  
The acceptance ratio is the number of reservations that have been accepted compared to the total number of requested reservations,  
\[Acceptance\ ratio=\frac{accepted\ reservations}{total\ reservations}100\%\]  
An accepted reservation means that when a customer makes a reservation, the carsharing reservation system checks whether there is an available car at the departure station and free parking space at the destination station. In this study, we assumed that there were no cancelations for an earlier reservation, and that the customer drives the car from the departure station at the starting time and parks the car at the expected destination at the ending time of the reservation. Thus, the acceptance ratio probes customer satisfaction.  
Relocation cost provides information about the number of cars being relocated by the operator at the end of the day,  
\[Relocation\ cost=Total\ number\ of\ cars\ being\ relocated\]  
Carsharing companies normally want to reduce the relocation cost, and, hence, increase the total profit. The relocation will be triggered at the end of the day (midnight). The stations are located in the downtown area which are close to each other and the maximum relocation time from one station to another is no more than 1 h. Thus in this study, a 1 h relocation time was assumed at midnight, when cars would generally not be operated due to sparse reservations. The status of a relocated car should be a parked car, i.e., there was no existing reservation for the car.
## 2 Materials and Methods
### Equivalent Circuit Model Analysis
The value of \(C_{FSS,2}\) can now be computed by calculating the value of \(Z_{FSS,1}\) at \(\omega_{res,2}\) (i.e., the second parallel resonance frequency), when the series \(Z_{FSS,1}\) acts as an inductance:  
\[Z_{FSS,1}=j\omega L_{eq}\ at\ \omega=\omega_{res,2} \tag{9}\]  
Hence, \(C_{FSS,2}\) is determined from the second parallel resonance condition at \(\omega_{res,2}\):  
\[C_{FSS,2}=\frac{1}{\omega_{res,2}^{2}\cdot L_{eq}} \tag{10}\]  
Finally, the FSS impedance shows another series resonance at \(\omega_{res,3}\): (in correspondence with 7.2 GHz, Figure 3), so that \(C_{FSS,2}\) is positioned in series with \(L_{FSS,3}\), whose value is established from the third resonance condition at \(\omega_{res,3}\), and we can define \(Z_{FSS,2}\):  
Figure 3.0  
Figure 3.1  
The overall resulting \(Z_{FSS}\) deducted from the analyzed ECM can be expressed as:  
Figure 3.2  
where \(Z_{2}^{p}\) is specified in Equation (14):  
Figure 3.3  
The derived ECM is synthetized in Figure 4b. To validate the resulting equivalent circuit model, the comparison between the imaginary part of the overall \(Z_{FSS}\) derived from the full wave simulations and the imaginary part of the analytical \(Z_{FSS}\) reconstructed via ECM is presented in Figure 4a. Figure 4a features the superposition of the two curves, confirming the accuracy of the ECM retrieval procedure. This results in an FSS unit-cell impedance characterized by \(R_{FSS,2}=5\cdot 10^{3}\)\(\Omega\), \(L_{FSS,1}=0.4\) nH, \(L_{FSS,3}=0.12\) nH, \(C_{FSS,1}=0.2\) pF, and \(C_{FSS,2}=0.04\) pF.