esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Reinforcement Learning
#### 4.3.2 Caching/Computing  
In [107], a DRL MDP-based algorithm is proposed to enhance caching and computing capabilites in cache-aided MEC networks. This approach lead to resource allocation optimization with low complexity and thus is able to achieve quasi-optimal performance under various system setups, and significantly outperform the conventional methods. In [108], the authors propose a deep actor-critic reinforcement learning based model for caching (centralized and decentralized). For centralized edge caching, the model aims at the maximization of cache hit rate, where both the cache hit rate and transmission delay are addressed as performance metrics that need optimization. Results show that the proposed approach outperforms previously applied conventional approaches, such as least frequently used (LFU), least recently used (LRU, etc. In [109], a Multi-Agent Multi-Armed bandit (MAMAB) approach is proposed for caching in 6G networks. The proposed model learns online the caching strategy in various environments (stationary and non-stationary), whereas conventional approaches first estimate the users preference and need and then tries to optimize the caching. Results show great accuracy and performance results of the proposed algorithm. Table 8 reports the RL models used in 6G for optimization and caching problems.
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 1 Introduction
### Unsolved Problems  
Military aviation maintenance faces a distinct set of challenges that set it apart from civilian maintenance operations. One prominent issue is the lack of precise guidelines from manufacturers for the durations and sequences of maintenance tasks [23; 24; 25]. This ambiguity often results in inefficient scheduling and organizational delays, which affect technical readiness and overall operational efficiency. Research suggests that these issues are not isolated to specific aircraft models but are widespread across various military aviation platforms. The distinctive operational demands of military aviation often deviate from the assumptions made by manufacturers, further complicating maintenance workflows [26]. Addressing these challenges requires innovative and tailored solutions that align with the specific requirements of military operations.  
Recent studies emphasize the potential of mobile technologies in maintenance contexts. Mobile applications specifically designed for maintenance management, like those of [15; 27], provide a valuable solution for documenting task durations directly at the worksite, offering a new level of accuracy for reliability assessments. By enabling real-time recording and analysis of maintenance activities, mobile applications contribute to a more detailed understanding of task efficiency and can highlight inefficiencies or deviations in standard procedures. This approach supports the broader ITS objectives of optimizing resources and reducing operational downtimes, aligning with sustainability goals in aviation.  
In line with global sustainability trends, studies such as [28; 29; 30] emphasize the importance of developing maintenance systems that enhance efficiency and minimize environmental impact. Sustainable maintenance in aviation depends on reducing resource waste and energy consumption by optimizing the planning and execution of maintenance tasks. The integration of ITS principles with digital tools can facilitate more eco-friendly operations by minimizing unnecessary downtimes, optimizing resource allocation, and extending the lifespan of critical components.
## 5 Discussion
### Greening 1: Crop Diversification  
Monitoring of compliance to CAP's Greening 1: Crop Diversification requirement is one example of the proposed scheme's direct application. Crop diversification entails the growing of different crop types based on farmers' total land area, aiming to improve biodiversity and reduce soil erosion. Farmers owning land of less than 10 ha are automatically exempted from the rule. If however, their land extends between 10 and 30 ha, at least two different crop types must be cultivated, with the main one not exceeding 75% of the total land. Similarly, arable land larger than 30 ha should involve at least three different crop types, with the main one occupying up to 75% and the main two occupying less than 95% of total land [44]. Based on the SVM crop type classification, 42.6% of farmers were exempted by having total arable land smaller than 10 ha, while only 2.2% were found to not comply with the requirement  
The Greening 1 requirement considers the total arable land owned by the farmer in order to decide on their compliance. Since our knowledge was limited to this particular dataset, we assumed that every farmer's total land is in fact encompassed within the borders of the study area. Therefore, the results are not representative of local farmers' compliance to Greening 1 but merely exhibit the ease of decision making based on the crop identification product.
# Energy Performance of LR-FHSS: Analysis and Evaluation
## 4 Current Consumption Model of an LR-FHSS ED
### Unconfirmed Transmission
\[T_{lifetime\_unTx}=\frac{C_{battery}}{I_{avg\_unTx}} \tag{12}\]  
Finally, we can also calculate the energy cost of data transmission per bit, \(EC_{unTx}\), which refers to the amount of energy consumed by an LR-FHSS ED to transmit one bit of \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **DR8/DR10** & **DR9/DR11** & **DR0** & **DR5** \\ \hline \(T_{postTx}\) (ms) & 10.40 & 12.40 & 0.676 & 0.676 \\ \(T_{Rx1}\) (ms) & 99.20 & 49.50 & 198.40 & 16.40 \\ \(T_{header}\) (ms) & 700.4 & 466.9 & 401.4 & 12.54 \\ FRM Payload Maximum Size (bytes) & 50 & 115 & 51 & 242 \\ \hline \(T_{payload}\) (ms) & 1 byte & 870.4 & 435.2 & 753.7 & 33.79 \\ \(T_{payload}\) (ms) & 3379.2 & 3353.6 & 2228.2 & 387.1 \\ \hline \(T_{freqHops}\) (ms) & 1 byte & 2.475 & 1.350 & N/A & N/A \\ \(T_{Rx}\) (ms) & 7.875 & 7.650 & N/A & N/A \\ \hline \(T_{Tx}\) (ms) & 1 byte & 1573.3 & 903.5 & 1056.7 & 65.50 \\ \(T_{Tx}\) (ms) & \(Max\) & 4087.5 & 3828.2 & 2793.5 & 399.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Transmission variables depending on the DR and the FRM Payload size. For the latter, two cases are considered: the minimum one (i.e., 1 byte) and the maximum one permitted for each DR.  
application data, as shown in Equation (13). \(V\) and \(L_{data}\) respectively denote the supply voltage and the application-layer protocol data unit (i.e., the FRM Payload size). \[EC_{unTx}=\frac{I_{avg\_unTx}\times V\times T_{period}}{L_{data}} \tag{13}\]
# Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review
## 4 Results
### Data and Evidence Mapping  
Integrating data-driven models and decentralized systems enhances the effectiveness, scalability, and public acceptance of LEZ policies, ensuring that emission reduction strategies remain targeted, efficient, and equitable. Data and evidence mapping enable real-time policy adjustments and precision-based interventions. High-resolution emission mapping using Land Use Random Forest (LURF) models allows policymakers to identify pollution hotspots, optimize LEZ boundaries, and adapt strategies dynamically. Similarly, blockchain-powered access control ensures secure, transparent, and decentralized data management for adaptive LEZ operations.
## 2 Materials and Methods
### Dynamic Light Scattering (DLS) Characterization  
Lipid-oligonucleotide conjugates (LONs) were analyzed at room temperature by dynamic light scattering (DLS) using a Zetasizer Nano ZS90 (Malvern Instruments Ltd., Malvern, UK). Size was measured in a DTS0012 cuvette (Malvern Instruments Ltd., Malvern, UK). All the scattered photons were collected at a 173\({}^{\circ}\) scattering angle. Measurement conditions were: material protein (RI: 1.450; Absorption: 0.001), dispersant water (Viscosity: 0.8872 cP; RI: 1.330) temperature at 25 \({}^{\circ}\)C and equilibration time was 3 s. Each test was triplicated. Nanoparticle size was determined in deionized water at 5 \(\upmu\)M oligonu  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Designation** & **Oligonucleotide Sequence, 5\({}^{\prime}\)–3\({}^{\prime}\)1** & \multicolumn{2}{c}{**Molecular Mass, Da2**} \\  & ODN & agtctcgacttgctac 3  & 5121.25 & 5120.98 \\  & ODN-\(F\)4  & agtctcgacttgctac-Flu & 5688.74 & 5687.93 \\ \cline{2-4}  & 18 & a\({}^{\circ}\)gttctcgacttgctac & 5248.86 & 5426.08 \\  & 28 & a\({}^{\circ}\)g\({}^{\circ}\)g\({}^{\circ}\)ktcgacttgc & 5736.36 & 5733.28 \\ \cline{2-4}  & 18-\(F\) & a\({}^{\circ}\)g\({}^{\circ}\)g\({}^{\circ}\)ktcgacttgc & 5996.35 & 5993.21 \\ \cline{2-4}  & 28-\(F\) & a\({}^{\circ}\)g\({}^{\circ}\)g\({}^{\circ}\)ktcgacttgc & 6303.85 & 6300.41 \\ \cline{2-4}  & 1\(\upmu\)n & a\({}^{\circ}\)gttctcgacttgc & 5408.87 & 5406.11 \\  & 2\(\upmu\)n & a\({}^{\circ}\)g\({}^{\circ}\)g\({}^{\circ}\)ktcgacttgc & 5696.38 & 5693.34 \\ \cline{2-4}  & 1\(\upmu\)n-\(F\) & a\({}^{\circ}\)g\({}^{\circ}\)ktcgacttgc & 5976.36 & 5973.24 \\  & 2\(\upmu\)n-\(F\) & a\({}^{\circ}\)g\({}^{\circ}\)ktcgacttgc & 6263.87 & 6260.47 \\ \hline  & EGFP & \begin{tabular}{c} _Sense_ CCUACGGCAAGCUGACCCUGA \\ _Antisense_ AGGGUCACGUUGCCGUAGG\UGGUGG\end{tabular} & 6698.10 & 6695.84 \\ \cline{2-4}  & RGMa & \begin{tabular}{c} _Sense_ CCAUACUUUCAAGAACUUGC \\ _Antisense_ AAGUUCUUGAAAGAUGGUGGUGG\end{tabular} & 6812.10 & 6811.57 \\ \cline{2-4}  & 2\(\upDelta\)-RGMa & \begin{tabular}{c} _Sense_ CCAUACUUUCAAGAACUUG\({}^{\circ}\)C\({}^{\circ}\)-p \\ _Antisense_ AAGUUCUUGAAGAUGAC\({}^{\circ}\)C\({}^{\circ}\)-p \\ \end{tabular} & 6563.99 & 6565.38 \\ \cline{2-4}  & 4\(\upDelta\)-RGMa & \begin{tabular}{c} _Sense_ CCAUACUUUCAAGAACUUG\({}^{\circ}\)C\({}^{\circ}\)-p \\ _Antisense_ AAGUUCUUGAAGAUGGAUGGUGG\end{tabular} & 6806.20 & 6811.30 \\ \cline{2-4}  & 2H-RGMa & \
\begin{tabular}{c} _Sense_ CCAUACUUUCAAGAACUUG\({}^{\circ}\)C\({}^{\circ}\)-p \\ _Antisense_ AAGUUCUUGAAGAUGGAUGGUGG\end{tabular} & 7260.00 & 7220.40 \\ \cline{2-4}  & 4H-RGMa &
\begin{tabular}{c} _Sense_ CCAUACUUUCAAGAACUUG\({}^{\circ}\)C\({}^{\circ}\)-p \\ _Antisense_ AAGUUCUUGAAGAUGGAUGGUGG\end{tabular} & 7501.20 & 7502.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structures and molecular masses of oligonucleotides and their lipid conjugates (LONs).  
cleotide concentration. The scattering intensity data was processed using the instrumental software to obtain the hydrodynamic diameter (Dh).
# Learning-Based Optimisation for Integrated Problems in
## Appendix A  
Transport problems and their OR formulations in transport science categorised into six main problem classes related to IFT operations, defined in Section 2.2. We additionally present the most conventional solution techniques from the optimisation point of view associated with these problems. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Optimisation Problem Class** & **Transport Problems** & **Related Problems in OR and Computer Science** & **Solution Techniques** \\ \hline \multirow{8}{*}{Network Optimisation} & \multirow{4}{*}{Network/infrastructure planning, facility location planning, strategical network flow planning, long-term rail-corridors and terminals, assignment of delivery zones or customers to facilities.} & Linear Assignment Problem (LAP) and Quadratic Assignment Problem (QAP), Facility Location Problem (FLP), Location Routing Problem (LRP), Hub-Location, Arc-Routing Problem, Clustering, Hub location Problem, Covering, Centre and Median Problem. & Graph optimisation, MIP/MILP, heuristics and metaheuristics, econometric models, aggregated transport models, land-use models, Multi Criteria Decision Analysis (MDCA), analytical methods, Geographic Information System (GIS). \\ \cline{1-1
## 7 Discussion
_Expressiveness/Control_: by supporting processing of individual result at the EM, we enable greater control over the results that are sent to the user as a result of contacting the group members. Currently we support sending all replies as a list, or combining the results in one arithmetic value (minimum, maximum, or average). We also support the user to specify the number of members that should reply before an answer is composed and sent back to the requester. Additional features to improve the control over entity behavior can be easily added by extending the EM functionality without the need to extend the members themselves. 9. _Security_: by using only standard CoAP unicast messages for communication, we make it possible to apply whatever used transport security mechanisms (such as DTLS) to these messages as well. It is clear from the discussion above, that our solution is capable of addressing the requirements we have put forward for group communication in IoT scenarios. Of course, the gain in flexibility comes with some drawbacks as well. Compared to a multicast solution, a unicast-based approach in general leads to more packets and to increased latency, with the exact values strongly depending on the topology. Of course, the experimental evaluation also reveals that a multicast-based solution is inferior in cases where packet loss cannot be tolerated, caching is relevant, security is required or heterogeneous groups need to be supported.
## 2 A Simple Dynamic Programming Solution
In contrast, \(f(i+1,j,k)\leq f(i,j,k)=t\). Therefore, in this case, we have \(f(i,j,k)=f(i+1,j,k)\). (1.2) If \(y_{j}\
eq z_{1}\), then in a similar manner, we can prove that \(f(i,j,k)=f(i,j+1,k)\) in this case. Combining the two subcases, we conclude that when \(x_{i}\
eq y_{j}\), we have  
\[f(i,j,k)=\max\left\{f(i+1,j,k),f(i,j+1,k)\right\}\]  
(2) When \(x_{i}=y_{j}\) and \(q<r\), there are also two subcases to be distinguished. (2.1) If \(x_{i}=y_{j}\
eq z_{1}\), then \(z=z_{1},\cdots,z_{t}\) is also a common subsequence of \(X[i+1:n]\) and \(Y[j+1:m]\) that excludes \(P\) as a substring of \(P[1:k]\oplus z\) and, thus, \(f(i+1,j+1,k)\geq t\). In contrast, \(f(i+1,j+1,k)\leq f(i,j,k)=t\). Therefore, we have \(f(i,j,k)=f(i+1,j+1,k)\) in this case. (2.2) If \(x_{i}=y_{j}=z_{1}\), then \(f(i,j,k)=t>0\) and \(z=z_{1},\cdots,z_{t}\) is an LCS of \(X[i:n]\) and \(Y[j:m]\) that excludes \(PIf \(q=\sigma(P[1:k]\oplus x_{i})\), then \(P[1:q]\) is the longest suffix of \(P[1:k]\oplus x_{i}\) that is also a prefix of \(P\). It follows that \(P[1:q]\oplus z^{\prime}\) is a suffix of \(P[1:k]\oplus x_{i}\oplus z^{\prime}\). Therefore, a sequence that excludes \(P\) as a substring of \(P[1:k]\oplus x_{i}\oplus z^{\prime}\) is also a sequence that excludes \(P\) as a substring of \(P[1:q]\oplus z^{\prime}\). It follows from the fact that \(z^{\prime}=z_{2},\cdots,z_{t}\) is a common subsequence of \(X[i+1:n]\) and \(Y[j+1:m]\) that excludes \(P\) as a substring of \(P[1:k]\oplus x_{i}\oplus z^{\prime}\) that \(z^{\prime}=z_{2},\cdots,z_{t}\) is also a common subsequence of \(X[i+1:n]\) and \(Y[j+1:m]\) that excludes \(P\) as a substring of \(P[1:q]\oplus z^{\prime}\). In other words:  
\[f(i+1,j+1,q)\geq t-1=f(i,j,k)-1 \tag{3}\]  
In contrast, if \(P[1:q]\) is the longest suffix of \(P[1:k]\oplus x_{i}\), \(f(i+1,j+1,q)=s\) and \(v=v_{1},\cdots,\)\(v_{s}\in Z(i+1,j+1,q)\), then \(v\) is an LCS of \(X[i+1:n]\) and \(Y[j+1:m]\) that excludes \(P\) as a substring of \(P[1:q]\oplus v\). In this case, \(v^{\prime}=x_{i}\oplus v\) is a common subsequence of \(X[i:n]\) and \(Y[j:m]\) that excludes \(P\) as a substring of \(P[1:k]\oplus x_{i}\oplus v^{\prime}\), because \(P[1:q]\) is the longest suffix of \(P[1:k]\oplus x_{i}\) and \(q<r\). Therefore:  
\[f(i,j,k)\geq s+1=f(i+1,j+1,q)+1 \tag{4}\]  
Combining (3) and (4), we have:  
\[f(i,j,k)=1+f(i+1,j+1,q) \tag{5}\]  
Combining the two subcases, where \(x_{i}=y_{j}\) and \(q<r\), we conclude that the recursive Formula (2) is correct for this case. (3) When \(x_{i}=y_{j}\) and \(q=r\), we must have \(x_{i}=y_{j}\
eq z_{1}\); otherwise, \(P[1:k]\oplus z\) will include the string, \(P[1:k]\oplus x_{i}=P\). Similar to Subcase (2.1), we can conclude that in this case,  
\[f(i,j,k)=f(i+1,j+1,k)=\max\{f(i+1,j,k),f(i,j+1,k)\}\]  
The proof is complete. \(\blacksquare\)
# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 6 Cooperative Content Caching Framework
### Caching Techniques
#### 6.1.2 COOP with K-Means Clustering  
We also utilize K-means to cluster the users managed by RSU. As the user sends a request to RSU, it checks data in nearby clusters. If data are located in the first or third cluster, RSU stops searching and sends the caching node's information to the requesting node, as shown in Figure 4. In this technique, the requests are fulfilled by surrounding nodes through V2V communication, and no server is required. Algorithm 1 shows the working of Cooperative Content Caching.  
First, the number of vehicles and the total number of RSUs at time t within a single MBS are initialized. Popularity is calculated in MBS and, popular data are cached on RSUs. Each vehicle is handled in a parallel fashion. When the vehicle sends a request to the server, RSU searches the requested content in its clusters and the vehicle gets data directly from that node. Otherwise, RSU searches in its local cache; after finding the content in its cache, it is directly delivered to the node, elsewhere downloaded from MBS.  
Figure 4: COOP using K-means clustering.  
Figure 3: Cooperative content caching (COOP).
###### Abstract  
A photoelectric scanning measurement network is a kind of distributed measurement system based on the principle of angle intersection, in which transmitters and photoelectric receivers are the main parts. The scanning lasers in transmitters emit signals and they are obtained by receivers at the measured points. Then the coordinate of the receiver can be calculated by the optimization algorithm. Its outstanding static measurement performance and network scalability capacity give it great potential in large-scale metrology. However, when it comes to moving targets, the angle intersection failure will produce a dynamic error, which limits its further application. Nowadays the research on error modeling and compensation is also insufficient though it has been the crucial concern. In this paper, we analyzed error causes and constructed a dynamic error model. Dynamic error characteristics and the law of propagation were discussed. The measurement uncertainty at different movement speeds was quantized through simulation experiments. To verify the error model, experiments were designed and the dynamic error was evaluated in practice. It matched well with simulations. The model was tested to be reasonable, and provided theoretical support for error compensation.  
Keywords:Photoelectric scanning; angle intersection; dynamic error modeling; large-scale metrology +
Footnote †: journal: Applied Physics Letters
## 5 Conclusions
### Applications and Limitations  
Concerning the actual satellite scheduling process, the real-coding PBIL algorithm might play a crucial part to help the Satellite Control Center make decisions precisely and quickly. Generally, users call for demands regarding earth observation and send these requirements to the Satellite Control Center. Then, the Center will aggregate all the demands to obtain the targets to be observed and make a reasonable scheduling plan within a specific period for satellites to meet the observation needs. The planning process is difficult to rely on manual completion, while the algorithm we proposed will be helpful. According to the information on targets, satellites and ground stations, it is easy to obtain the visible time window set of satellite to target. Just input the window set, and the algorithm will output the satellite scheduling result, that is, which target is to be observed by which satellite at which time.  
In this paper, we carry out simulations to verify the algorithm dealing with 10 satellites and 50-100 targets. The results show that the algorithm displays good performance in both optimal speed and quality. However, the scenarios in our paper are far from the large-scale scenario with vast quantities of satellites and targets as the reality. Whether the algorithm can deal with large-scale satellite scheduling problems well still needs further discussions and verifications. Another limitation of our algorithm is that it might not be suitable for multi-satellite scheduling problems requiring the revisiting observation of targets. Due to the real-coding format, each target can be observed, at most, once in a scheduling period. Our approach might be applicable to situations with a large number of observation demands while the satellite resources are limited, rather than the scenarios with repeated observation requirements for a specific area.
# Dialogue Enhanced Extended Reality:
## 3 Conceptual System
### Conceptual Workflow  
In this section, we describe an Interactive eXtended Reality (IXR) system that helps operators to carry out a certain task or process through the combination of XR technologies with the SDS process control logic (see Figure 2). The proposed work streamlines multiple input and output XR devices into the logical scheme of SDSs. As a result, we describe a framework that enhances both classical SDSs and XR devices as Human-Machine communication interfaces.  
A Device Control Layer (DCL) handles machine-dependent data transformations and communication protocols between devices. The IO API is a modular interface for the use of peripheral devices in industrial environments, for example, collaborative robots. Analogous APIs are available for, for example, aural and visual interaction devices. The DCL copes with the  
Figure 2: Architecture for the Interactive eXtended Reality (XR) framework.  
and the Response Generation Layer (RGL) to gather and send the exchanged data content and format from and to each involved device.  
Data processed by the DCL is accessed by the IL and transformed into a normalised semantic encoding, similar to the SLU module in Figure 1. The objective is to discretise the highly-variable data collected by multiple input devices and extract meaning out of it. For example, if the operator asks for the robot's malfunctioning parts, the semantic interpretation of both the operator's audio transcription and the robot sensors' data are extracted in this module.  
The Interaction Manager Layer (IML) processes the encoded information of every input device to decide the next system response and communicates it to the RGL. In order to decide the appropriate response to give to the operator, the IML takes into account the semantic intention of the operator (in a similar way to [44]), but also the status of the contextual knowledge stored in the Domain Knowledge Layer, such as data from the involved physical industrial devices, ontologies, operator profiles, and so on. The system response is represented as a set of semantic actions, which is then sent to the RGL. With each user turn, the IML updates the interaction state according to the processed input and the selected system responses.  
Finally, the RLG transforms the set of semantic actions in the system response to data formats usable by the target output devices so they can be communicated to the operator, for example, rendering commands, natural language audio or text, and device movement commands. These device-usable responses are then sent to the DCL to close the communication loop with the operator.
# Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach
## 4 Detection and Identification of a Drone Using a Machine-Learning Approach
### Preparing Data for Training and Evaluation  
For successful training of a neural network, a sufficiently large set of data is needed. The object of interest should be captured in various light, spatial or other conditions. It is also important that the data show all of the defined drone types in different situations, rotations, and environments. The amount of data collected should be sufficient to allow the network to be trained and to recognize the object of interest. In addition to the obtained data, it is also necessary to make annotations that specify the exact location of the object in the scene.