esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 HMBAC Design and Implementation
### HMBAC Architecture Design
#### 4.2.3 Hierarchical Blockchain Infrastructure  
The _Proxy Blockchain_ (PBC) receives user requests through the Frontend API and implements three main services through smart contracts. User requests along with the provided attribute certificate(s) are handled by _Proxy Smart Contract_ (PSC). The PSC will first trigger a certificate validation process, executed by _Trust Management Smart Contract_ (TMSC). The TMSC will validate the long-term (and possible temporal) attributes assigned to users via a typical challenge-response signature verification process. Note that users can access the HMBAC services only via an _authenticated channel_ (the Frontend API) and after successful _attribute authentication_ (by the PBC). If user attributes are verified, the PSC will then forward the request to the relevant Domain Blockchain for further processing and wait to receive the response via _Inter-BC API_. The response will eventually be sent back to the user via the Frontend API. The transaction history is recorded on the PBC. The _Logging Smart Contract_ (LSC) creates a log for each incoming user request until the transaction is completed.  
_Domain Blockchains_ (DBC) may receive user requests only from the PBC via the Inter-BC API and implement the following services. Each DBC contains an _Access Control Smart Contract_ (ACSC) that enforces the access control policy of the particular domain. This includes both intra-domain and cross-domain access policies that control access to data maintained by the domain's stakeholders. The ACSC checks if the user attributes (already validated in the PBC) are sufficient for the specific request, according to the predefined access policy. In this case, the request is forwarded to the relevant database(s) via _Database API_. Note that depending on the request, the API can retrieve ABE-encrypted data from multiple sources, for example, when a user is requesting data from multiple stakeholder databases.  
When the encrypted data return from the Data Layer, the Database API passes them to _Key Store Smart Contract_ (KSSC), which has access to the relevant domain's attribute public/private key pair (e.g., \(PK_{H},SK_{H}\) in the case of the Hospital DBC). The KSSC will first use the private key \(SK_{H}\) to generate the hospital domain attribute key (\(K_{hospitals}\)) based on the GID of the requesting user, to partially decrypt the data. The partially decrypted data will be forwarded to the PBC (via the Inter-BC API) and eventually to the user (via the Frontend API). The user must finally apply his attribute keys to fully decrypt and access the data (i.e., the attribute keys \(K_{doctor}\) and \(K_{onDuty}\) in our example).
###### Abstract  
As society evolves and technology advances, increasing transportation demands have heightened safety risks near schools and on mixed-traffic roads. While traditional studies on pedestrian evasive behavior have mainly focused on general traffic environments and used image-based features to predict trajectories, few have specifically addressed the behavior of pedestrians in school zones. This study fills that gap by analyzing pedestrian evasive actions near school zones in Pudong New Area, Shanghai, using real-time video data. In contrast to previous approaches, our research leverages key traffic variables--such as vehicle speed, pedestrian proximity, and traffic density--to predict whether pedestrians will engage in evasive behavior. We independently apply three predictive models: the traditional BP (Backpropagation) neural network, an improved GA-BP(genetic algorithm-backpropagation) neural network, and the XGBoost (Extreme Gradient Boosting) ensemble learning method. Our findings show that the improved GA-BP model outperforms the others, achieving an accuracy of over 79%. Furthermore, this study identifies crucial traffic factors influencing pedestrian behavior, offering valuable insights for road safety decision-making in school zones. This research demonstrates the potential of advanced predictive models for forecasting pedestrian evasive behavior. It enhances safety in school zones by highlighting the key traffic variables affecting pedestrians.  
BP neural network; genetic algorithm; ensemble learning; machine learning; pedestrian behavior; road accidents; school routes +
Footnote †: journal: Applied Sciences
## 4 Multimedia-Based Cross-Layer Optimization in VSNs
### Image-Based Cross-Layer Optimization
#### 4.1.2 Wavelet-Based Image Coding
The work in [37] presents some execution and compression characteristics and discuss energy consumption in visual sensors. We cannot forget that less-relevant information actually contributes to the final quality of the application. The fact that many works consider discarding of packets containing less-relevant information or reduction of the transmission rate of such packets may give a wrong feeling that such information is useless. Note that any data loss produces some degradation of the final image quality. What have been proposed are low and acceptable levels of degradation for an increased performance of the overall application in energy consumption, congestion control, error recovery and in-network compression. Table 1 summarizes the surveyed image-based cross-layer optimization solutions. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Approach** & **Coding** & **Processing** & **Optimization** \\ \hline [PERSON] _et al._[20] & Progressive & Source & Transmission rate adjustment. \\ \hline [PERSON] and [PERSON] [22] & Progressive & Source & Transmission rate adjustment. \\ \hline [PERSON] and & & & Multipath routing. \\ [PERSON] [23] & Progressive & Source & Error recovery by correction codes. \\ \hline [PERSON] _et al._[24] & Wavelet-based & Source & Congestion control. \\  & & & Energy preservation. \\ \hline [PERSON] and [PERSON] [26] & Wavelet-based & Source & Congestion control. \\ \hline [PERSON] _et al._[19] & Wavelet-based & Source & Transmission rate adjustment. \\ \hline [PERSON] _et al._[29] & Any & Source & Energy preservation. \\ \hline [PERSON] _et al._[30] & Wavelet-based & Source & Error recovery by correction codes. \\ \hline [PERSON] and [PERSON] [33] & Wavelet-based & In-network & In-network multimedia compression. \\ \hline [PERSON] _et al_[34] & Wavelet-based & In-network & In-network multimedia compression. \\  & & & Transmission rate adjustment. \\ \hline [PERSON] and [PERSON] [35] & Wavelet-based & In-network & Redundancy-based error recovery. \\  & & & Error recovery by correction codes. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Image-based cross-layer optimization.
# Streaming-Based Anomaly Detection in ITS Messages
## 5 Conclusions  
Detecting anomalies is a subjective task that relies on the problem domain, data traits, and the kinds of anomalies present. In this work, we studied the anomaly detection issue and applied it to messages generated by cars in C-ITS to detect anomalies characterised by obstacles on roads. Our approach involves utilising data to actively detect anomalies through unsupervised methods that target local contextual anomalies. A robust ensemble for anomaly detection is proposed that improves variance reduction by using heterogeneous base detectors with different hyperparameters. Detector selection based on local competency helps identify base detectors with a conditionally low model bias.  
Our future work will concentrate on updating the calibration of outlier scores by incorporating dependent loss functions, as a false negative in the C-ITS scenario might cause some troubling difficulties. We also recommend introducing automated parameter calibration in order to increase the algorithm's chances of being used by road infrastructure operators. Another essential component will be process optimisation in order to increase complexity while fine tuning the ensemble learning decision rules for efficiency enhancement. It will also be fascinating to investigate various scenarios based on actual traffic incident situations. We also suggest transforming the ELSCP approach into a tool for the real-time identification and analysis of data streams.  
Conceptualisation, J.C.M., S.C., C.d.R., H.F. and J.M.W.; methodology, J.C.M., S.C., C.d.R. and H.F.; software, J.C.M.; validation, J.C.M., C.d.R. and H.F.; formal analysis, J.C.M.; investigation, J.C.M.; resources, J.C.M.; data curation, J.C.M.; writing--original draft preparation, J.C.M.; writing--review and editing, S.C., C.d.R., H.F. and J.M.W.; visualisation, J.C.M.; supervision, S.C., C.d.R., H.F. and J.M.W.; project administration, S.C., H.F. and J.M.W.; funding acquisition, J.C.M., S.C., C.d.R., H.F. and J.M.W. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the French Embassy in Kenya.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Experiments  
All of the system's components were tested in a number of scenarios through a series of experiments. To determine the baseline demand of the sensing layer, we begin with an ESP32 duty-cycle test that separates the active-transmit and deep-sleep currents of the microcontroller. The heavier gateway, a Raspberry Pi 4 with a 5G modem, is covered in Section 3.3. The test measures both peak and steady-state requirements by profiling their joint draw during boot, idle, and full-throughput uplink. Lastly, end-to-end round-trip times are recorded by the 5G delay experiment.  
With the focus on our IoT system's power consumption, there was a series of operational tests designed and performed in order to evaluate the energy requirements of ESP32 node, the Raspberry Pi, and the 5G cellular module. The main goal is to identify the energy usage of different scenarios and data transmission rates and determine if the needed power requirements could be fulfilled with sustainable power sources such as solar panels in order to be able to provide the needed functional power to the system for long-term deployment in remote environments. Finally, the methodology, setup, and results are analyzed.
## 8 Security and Privacy Risk Assessment
### Security and Privacy Risk Assessment at the Requirements Analysis Phase
#### 8.3.2.3 Calculate Risk Score  
The aim of this stage is to calculate the risk score based on the impact and likelihood of threats and vulnerabilities. Appendix I of NIST 800-30 details calculating the risk score by multiplying impact times likelihood [23]. Alternatively, the team can use the CVSS risk score calculator to calculate the risk score [47]. A sample risk score matrix using a qualitative assessment approach is presented in Table 9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Qualitative Values** & **Semi-Quantitative Values** & **Likelihood Score Definition** \\ \hline - & **Scale** & **Bins** & - \\ \hline Very Low (1) & 0–4 & 0 & Highly unlikely the threat event \\  & & & occurs or exploits the vulnerabilities \\ \hline Low (2) & 5–20 & 2 & Unlikely the threat event occurs or exploits the vulnerabilities \\ \hline Medium (3) & 21–79 & 5 & Somewhat likely the threat event \\  & & & occurs or exploits the vulnerabilities \\ \hline High (4) & 80–95 & 8 & Highly likely the threat event occurs \\  & & & or exploits the vulnerabilities \\ \hline Very High (5) & 96–100 & 10 & Almost certain the threat event \\  & & & occurs or exploits the vulnerabilities \\ \hline \hline \end{tabular}
\end{table}
Table 7: Assessment scale for likelihood.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Likelihood Factor**} & \multirow{2}{*}{**Likelihood Description**} & \multicolumn{3}{c}{**Likelihood Level**} \\ \cline{3-5}  & & **Qualitative** & **Semi-Quantitative** \\ \cline{3-5}  & & **Scale** & **Bins** \\ \hline Adversary intent & \begin{tabular}{c} Make the whole \\ application unavailable \\ \end{tabular} & Very High & 100 & 10 \\ \hline Adversary skill & \begin{tabular}{c} Requires medium level skill \\ to launch the attack \\ \end{tabular} & High & 90 & 8 \\ \hline Affected asset & \
\begin{tabular}{c} All assets that depend on the \\ web server including the web \\ server itself \\ \end{tabular} & Very High & 100 & 10 \\ \hline Historical & Very common attack for web \\ evidence &
\begin{tabular}{c} Server \\ \end{tabular} & Very High & 100 & 10 \\ \hline  & Average & Very High & 97.5 & 9.5 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Likelihood analysis for DoS attack on a web server.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Impact**} & \multicolumn{5}{c}{**Likelihood**} \\ \cline{2-5}  & **Very Low (1)** & **Low (2)** & **Medium (3)** & **High (4)** & **Very High (5)** \\ \hline Very High (5) & 5 & 10 & 15 & 20 & 25 \\ \hline High (4) & 4 & 8 & 12 & 16 & 20 \\ \hline Medium (3) & 3 & 6 & 9 & 12 & 15 \\ \hline Low (2) & 2 & 2 & 6 & 8 & 10 \\ \hline Very Low (1) & 1 & 2 & 3 & 4 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Risk score matrix for qualitative approach.
## 3 Materials and Methods
### Biological Activity
#### 3.6.2 Cytotoxic Activity  
To evaluate the cytotoxic activity of the bee pollen extract with the sulforhodamine B (SRB) colorimetric assay, 5 human tumour cell lines were used, as follows: AGS (stomach gastric adenocarcinoma), CaCo-2 (epithelial colorectal adenocarcinoma), HeLa (cervical 25 carcinoma), MCF-7 (breast adenocarcinoma), and NCI-H460 (non-small cell lung cancer); hFOB (human Fetal Osteoblastic) was also used, and it is a non-tumour cell line. The treatment solution was prepared from a 20 mg/mL hydroethanolic bee pollen extract, which was freeze-dried and then diluted to various concentrations (125 \(\upmu\)g/mL to 2000 \(\upmu\)g/mL).  
The cell lines' subcultures were performed in an RPMI-1640 medium enriched with 2 mM glutamine, 100 U/mL penicillin, 100 \(\upmu\)g/mL streptomycin, and 10% FBS, and they were kept in a humidified air incubator containing 5% CO\({}_{2}\) at 37 \({}^{\circ}\)C. After 24 h of incubation, the attached cells were then treated with different extract concentrations and incubated again for 48 h. Afterwards, the adherent cells were fixed with cold trichloroacetic acid (TCA 10%, 100 \(\upmu\)L) and incubated at 4 \({}^{\circ}\)C for 1 h. Subsequently, the cells were washed with deionized water and dried. An SRB solution (SRB 0.1% in 1% acetic acid, 100 \(\upmu\)L) was added to each well plate and incubated for 30 min at room temperature. The unbound SRB was removed with 1% acetic acid and the plates were air-dried. The bound SRB was solubilized with Tris (10 mM, 200 \(\upmu\)L). To measure the absorbance at 540 nm, an ELX800 microplate reader (Bio-Tek Instruments, Inc.; Winooski, VT, USA) was used [27]. Elipticine was used as a positive control, and the results were expressed as GI\({}_{50}\) values in \(\upmu\)g/mL (sample concentration that inhibited 50% of the net cell growth).
## 8 Discussion
### Recommendations
#### 8.2.5 Quantum Communications and Computing  
With the support of entire applications/scenarios, 6G will meet higher security needs than current technologies. With the application of a quantum key based on the quantum no-cloning theorem and the uncertainty principle, quantum communications can give extremely robust security [397]. Whenever eavesdroppers attempt to carry out observations, measurements, or copy activities in quantum communications, the quantum state is disrupted, and the eavesdropping activity may be easily identified. Theoretically, quantum communications have the potential to provide complete security. While Tb/s data transmission and full applications/scenarios provide obstacles for wireless computing in 6G, they also present opportunities [397]. When compared to traditional computing, which uses 0-1-b operations, quantum computing, which relies on quantum superposition and entanglement, can significantly increase computing capabilities by utilising unitary transformations in the form of qubits. As a result, quantum computing has the potential to greatly accelerate and improve artificial intelligence algorithms that require large amounts of data and extensive training. Furthermore, by combining quantum theory and machine learning, it is possible to construct more powerful and efficient machine learning algorithms to meet the requirements of 6G.  
2.6 Molecular Communications and the Internet of Nano-Things (Molecular Communications and the Internet of Nano-Things)  
Advanced nanotechnology has the potential to enable the production of nanodevices such as nano-robots, implantable chips, and biosensors, which have vital applications in areas such as nanoscale sensing and biomedicine [398]. The use of nanotechnology in biomedicine, in particular, has pliqued interest because it has the potential to accomplish tasks such as intelligent drug distribution through blood vessels and monitoring of body organs, both of which have the potential to enhance significantly human healthcare outcomes. It is possible to achieve effective communication and information transmission by connecting nanodevices to the Internet or by forming networks (i.e., the Internet of Nano-Things); in biomedicine, the Internet of Bio-Nano-Things (IoBNT) can enable the connection of nanodevices and biological entities. In the Internet of Things, molecular communication is an enabling approach for the IoBNT, in which biological molecules are used to communicate and transport information amongst nanodevices [398]. A further benefit of combining the IoBNT with body area networks, which are short-distance wireless networks made up of wearable monitoring devices/sensors and sensing devices embedded in or on the body, is that they can give comprehensive solutions for healthcare enhancements.
# RoPT: Route-Planning Model with Transformer
## 3 Problem Definition  
**Definition 1**.: Road Network. _The road network is modeled with an undirected graph \(G=(V,E)\), where \(V\) represents the set of nodes and \(E\) represents the set of edges. In this paper, a node \(V_{i}\in V\) represents a road section. An edge \(E_{ij}=\big{(}V_{i},~{}V_{j}\big{)}\in E\) is an intersection of two connecting sections, such as \(V_{i}\) and \(V_{j}\). Note that \(n=\mid\!V\!\mid\) is the number of nodes in the graph, and also corresponds to the number of sections._  
**Definition 2**.: Route. _A route is represented as \(R=(V_{1},~{}V_{2},~{}\ldots,~{}V_{L})\), where \(L\) is the length of a route._  
**Definition 3**.: Look Up. _The function \(\textsl{LookUp}(M,i)\) represents the search operation, which is used to search values in the matrix \(M\) with the index \(i\)._  
**Problem 1**.: Route Planning. _In this paper, a route planning task is decomposed into several tasks to recommend a next node. Given the current route \(R\) and destination \(D\), the next node \(v_{i+1}\) is predicted, and added into \(R\), as shown in Formula (1) and (2)._  
\[v_{i+1}=\textsl{RoPT}(R_{i},D,M) \tag{1}\]  
\[R_{i+1}=R_{i}\bigcup v_{i+1} \tag{2}\]  
For ease of reference, the symbols and interpretations are listed in Table 1.
## 1 Introduction
A review of the state of the art in robotic applications, particularly addressing the inverse kinematics of manipulator robots, is presented in Table 1. This review highlights that differential evolution (DE) and particle swarm optimization (PSO) are the most commonly used metaheuristic algorithms for solving the inverse kinematics problem. Other algorithms, such as Artificial Bee Colony (ABC), bees algorithm (BA), Beta Salp Swarm Algorithm (\(\beta\)-SSA), Wild Geese Migration Optimization (GMO), Gray Wolf Optimization (GWO), and Firefly Algorithm (FA), have also been implemented. Most studies focus on a single manipulator, with only two cases [15; 16] proposing more generalized methods. Typically, these studies address only the position problem, as incorporating orientation significantly increases the complexity of solving the inverse kinematics. Although joint limits are considered in all optimization processes, hard bounding is commonly applied to manage constraints, or candidate solutions are recalculated when they exceed the search space. This approach is necessary because metaheuristic algorithms are not inherently designed to handle constrained problems. In this paper, we propose a novel method to solve inverse kinematics using a metaheuristic algorithm combined with a Jacobian step. Unlike traditional approaches, the Jacobian step is applied neither in each iteration nor to a subpopulation; it is used selectively when a deadlock is detected, and only to the global best solution. This strategy reduces the implementation complexity of the algorithm. Our method effectively addresses both position and orientation in inverse kinematics, formulating the problem as a constrained optimization task where joint limits are represented as constraints. Since metaheuristic algorithms are not inherently designed for constrained problems, we employ penalty functions to manage these constraints. Our approach is generalizable to redundant robots with \(n\) degrees of freedom. The applicability of the algorithm is demonstrated using a 5 DOF KUKA robotic arm, showcasing its effectiveness and versatility. A comparative study is conducted using the following metaheuristic algorithms: the differential evolution algorithm \"DE\" proposed by [PERSON] et al. in 1997 [29], the particle swarm optimization algorithm \"PSO\" proposed by [PERSON] and [PERSON] in 1995 [30], the bees algorithm \"BA\" proposed by [PERSON] et al. in 2006 [31], the invasive weed optimization algorithm \"IWO\" proposed by [PERSON] and [PERSON] in 2006 [32] and the imperialist competitive algorithm \"ICA\" proposed by [PERSON] and [PERSON] in 2007 [33].