esa-sceva/satcom-chunk-collection huggingface dataset:
# Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications
## 4 Simulation Results and Discussion
### Large Area UAV Deployment Simulation  
In a rectangular area of 4000 m \(\times\) 4000 m, we arrange 10 UAVs randomly and set up 10 target deployment points for the deployment of the UAVs. Within the area, there are 200 ground users in need of rescue. We employ the SAIBFGS algorithm, LAESGA algorithm, and SGA algorithm to compute the fitness function for the deployment. We set the maximum iteration count \(\gamma_{2}\) for the three algorithms as 4000. In order to ensure user QoS during the large-area deployment, we set the communication radius of the UAVs to be 750 m.  
As depicted in Figure 7, the pink circle represents UAV communication radius. All the targets presented in Tables 5 and 6 lie within the coverage range of the UAVs, illustrating that all three algorithms can facilitate large-scale redeployment of UAVs. In terms of connectivity and coverage, all three algorithms achieve 100% connectivity. The SAIBFGS algorithm attains a user coverage rate of 92%, the LAESGA algorithm a user coverage rate of 86.5%, and the SGA algorithm a user coverage rate of 85.5%. Concerning deployment accuracy, the SAIBFGS algorithm exhibits a deployment accuracy of 45.87%, the LAESGA algorithm a deployment accuracy of 90.36%, and the SGA algorithm a deployment accuracy of 89.26%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indicators** & **Initial** & **SAIBFGS** & **LAESGA** & **SGA** \\ \hline Connectivity & 100\% & 100\% & 100\% & 100\% \\ Coverage of & & & & \\ ground users & 66.5\% & 92\% & 86.5\% & 85.5\% \\ awaiting rescue & & & & \\ Accuracy of & / & 45.87\% & 90.36\% & 89.26\% \\ deployment & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of three algorithms for large area deployment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicators** & **Initial** & **SAIBFGS** & **LAESGA** & **SGA** \\ \hline Connectivity & 100\% & 100\% & 100\% & 100\% \\ Coverage of & & & & \\ ground users & 65.5\% & 91\% & 92\% & 88.5\% \\ awaiting rescue & & & & \\ Accuracy of & / & 89.85\% & 90.85\% & 90.83\% \\ deployment & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of three algorithms for small-area deployment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coordinate** & **SAIBFGS** & **LAESGA** & **SGA** \\ \hline (2807, 1496) & (2807.36, 1496.75) & (2807.33, 1495.58) & (2825.22, 1478.14) \\ (2673, 113) & (2465.86, 692.99) & (2608.97, 282.28) & (2678.63, 108.52) \\ (2508, 640)
# Evolutionary Game for Content Cache in a mm-Wave-Based Vehicular Fog
## 5 Experiments
However, it still takes time to converge into \(p_{c}^{*}\), so for implementation, \(p_{c}\) can be chosen among possible discrete values, \(k/|S|\) instead of continuously increasing values. Figure 12 shows \(p_{c}\) evolution based on local replicator dynamics that has no global information of other vehicles' selection. All vehicles conduct mixed strategies based on own probability \(p_{c_{s_{i}}}(t)\) for each iteration. Each vehicle updates the \(p_{c_{s_{i}}}(t)\) using the replicator dynamics of Equation (34) based on its history. There are many possible ESSs for this heterogeneous players' game; all combinations of 1 or 0 for the \(p_{c}\) of each sector are candidates or \(p_{c}\) when \(\overline{\mathrm{U}_{s_{i}}}(c,p_{-s_{i}})=\overline{\mathrm{U}_{s_{i}}}(p)\). For example, the sector 1, 2, 4, 7 and 11 choose cooperation strategy with \(p_{c}=1\) while others choose defection as shown in Figure 12a, where evolution speed \(\eta\) is 2, cost \(\tau\) is 0.3 and initial \(p_{c}\) is 0.5. Coincidently, above ESS is the same as the result of centralized approach shown in Figure 11a,b, but it could be varying according to strategies chosen at initial phase of the iterations. Figure 12b shows proportion of cooperative sectors over total 12 sectors with different \(\tau\) from 0.1 to 0.9. At an initial iteration where vehicles pursue a mixed strategy, the proportion \(p_{c}\) changes dynamically, but it converges into a stable state as individual \(p_{c_{s_{i}}}\) increases or decreases into 1 or 0. In contrast to the global algorithm that leads to a single ESS \(p_{c}^{*}\) regardless of the initial state, the distributed algorithm can cause different stable points according to the initial \(p_{c}\) as shown in Figure 12c. When each vehicle has own initial \(p_{c_{s_{i}}}\) such as 0.75, 0.5 and 0.25 with cost \(\tau=0.3\), we can observe that cooperation proportion reaches into the stable point similar with the individual initial \(p_{c_{s_{i}}}\). With higher initial \(p_{c}\), each vehicle has higher probability to choose a cooperation strategy and experience better payoff, which increases the \(p_{c}\) into 1. Otherwise, a vehicle  
Figure 10: Payoff and average payoff for defection and cooperation. may have better average payoff from defections rather than only a few cooperation because other cooperators raise the overall payoff at each iteration, and finally its \(p_{c_{s_{i}}}\) goes to 0.
# On End-to-End Intelligent Automation of 6G Networks
## 5 Research Innovation Opportunities for Intelligent Automation
### Dynamic Data-Driven Orchestration  
Although ML-based and ML-assisted network orchestration has been recently proposed in the literature, additional work can be done. This is because 6G networks are highly dynamic and thus are prone to constant change. Hence, there is a need for ML algorithms that can adapt to these changes. For example, ML algorithms need to consider the different QoS requirements and corresponding traffic priority levels that arise from network slicing and multi-tenancy, particularly with the continuous network service metric reporting, as well as the continuous resource slicing and monitoring. To that end, advanced intelligent paradigms and techniques such as reinforcement learning (RL) and federated learning (FL) can be viable solutions [110; 111]. In particular, RL's ability to learn optimal policies through experience makes it a prime solution to be deployed as the basis of data-driven orchestration in 6G networks. This is because RL models can learn optimal orchestration policies and implement them while having a lower run-time complexity compared to other conventional ML techniques. In a similar fashion, FL's ability to develop a decentralized and privacy-preserving global model by relying on a highly distributed system makes it another prime potential solution towards the data-driven orchestration of 6G networks. Therefore, adopting such advanced paradigms and techniques will pave the way towards highly intelligent and automated 6G network orchestration.  
Figure 6 is an illustrative example that shows an RL-enabled effective network orchestration framework. In this case, the RL agent collects data from the different terrestrial and non-terrestrial networks, particularly with respect to channel conditions and user density. Using this information, the RL agent can learn optimal resource allocation policies for the available networks. After learning these policies, the agent can then implement them for future decisions without being hindered by high computational complexity.  
Figure 5: Research opportunities for innovation for intelligent orchestration.
## 4 Solution Method
### Modeling Travel Time Uncertainty  
To model the stochasticity in UAV travel times, we classify each edge \((i,j)\) in the solution according to the characteristics of its end node \(j\). Edges connecting to nodes with even IDs or IDs divisible by three are designated as stochastic, while all other edges maintain deterministic travel times. This assignment is formalized as follows:  
\[\text{edge\_type}(i,j)=\begin{cases}1\text{ (stochastic)},&\text{if }j\bmod 2=0 \text{ or }j\bmod 3=0\\ 0\text{ (deterministic)},&\text{otherwise}\end{cases} \tag{15}\]  
Figure 7: Architecture of the proposed neural network.  
For edges with stochastic travel times, we employ a lognormal distribution with a variance level parameter \(\lambda=0.3\). The mean parameter \(\mu\) of this distribution is set to the travel time predicted by our neural network model, establishing a direct connection between our predictive framework and the stochastic simulation. This parameter represents a moderate level of variability and was chosen based on empirical observations of UAV operations in semi-controlled environments. Given a mean travel time \(\mu\), we derive the parameters of the lognormal distribution as follows:  
\[\sigma^{2}=0.3\cdot\mu\text{ (variance)} \tag{16}\]  
\[\mu^{\prime}=\ln\left(\frac{\mu^{2}}{\sqrt{\sigma^{2}+\mu^{2}}}\right)\text{ (log-mean)} \tag{17}\]  
\[\sigma^{\prime}=\sqrt{\ln\left(1+\frac{\sigma^{2}}{\mu^{2}}\right)}\text{ (log-standard deviation)} \tag{18}\]  
The travel time for a stochastic edge is then sampled from \(\text{lognormal}(\mu^{\prime},\sigma^{\prime})\). This approach allows us to model realistic variations in travel times while maintaining computational tractability.  
During the Monte Carlo simulation phase, each candidate solution is evaluated across multiple runs (100 for the initial assessment and 1000 for the final refinement). For each run, the travel times of stochastic edges are randomly sampled from their respective distributions. If the total travel time of a route exceeds the maximum allowed duration, the entire reward for that route is forfeited, reflecting the operational reality that incomplete missions yield no benefit.
## 1 Introduction
### Internet of Things  
The pervasiveness of Internet among human beings reaches already more than 50% of the world population, i.e., almost 4 billion people [11]. A total of 90% of those Internet users access it via mobile phones [12]. The connection of things with other things and people via Internet, known as the Internet of Things (IoT), is also growing in economic importance (USD 17 billion in 2021 [13]). The number of IoT connected devices reached the 20 billion mark in 2018 [14]. The forecast is that this figure will reach USD 30 billion by 2030 [15]. An IoT device has at least one transducer (sensor or actuator) to interact directly with the physical world and at least one network interface (e.g., 5G, Ethernet, Wi-Fi, Bluetooth) to interface with the digital world [16]. They bridge the physical with the digital world. Oracles play a similar role in distributed blockchains.  
Already in 2016, the 3 GPP consortium agreed to further develop IoT protocols like NarrowBand Internet of Things (NB-IoT) and Long Term Evolution Machine Type Communication (LTE-M). 4G mobile networks already use them within the suite of standardised low power, wide area (LPWA) technologies [17; 18]. These two IoT protocols aim to meet low-cost, low-current, wide coverage and high capacity requirements. 5G is also being deployed in private networks, e.g., in industrial IoT [19]. Market analysts forecast a USD 3.6 trillion value generated in massive IoT developments up to 2035 [20].  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Generation** & **Tx Speed** & **Technology** & **Period** & **Details** \\ \hline
1 & 14.4 kbps & Analog Tx & 1970–1990 & Voice only \\
2 & up to 14.4 kbps & CDMA/TDMA & 1990–2000 & Digital Tx, SMS, GSM Standard \\
2.5 & 20–170 kbps & GPRS & 2000–2003 & First basic Internet browsing \\
2.75 & up to 236 kbps & EDGE & 2003 & \(\times\)3 GSM data capacity \\
3 & 144 kbps–3 Mbps & UMTS, CDMA2000 & 2004–2005 & First streamings \\
3.5 & 1–10 Mbps & HSPA & 2006–2010 & Higher speed over UMTS \\
4 & 144 kbps–100 Mbps & LTE & 2010–2020 & First streamings \\
5 & 1 Gbps & 3 GPP Standards & 2021– & Starting deployment \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mobile networks history in generations.
## 2 Materials and Methods
### Data Acquisition
#### 2.3.2 UAV Nano-Hyperspec  
Texo DSI [36] and HeadWall Photonics Incorporated provided a Nano-Hyperspec visible near-infrared (VNIR) Imaging Sensor on a six-rotor UAV [37]. This sensor had 12-bit depth imagery, a spatial resolution of \(\approx\)50 mm\({}^{2}\), and pixel dispersion at 2.2 nm/pixel (270 bands over 400-1000 nm). It was operated by a Texo unmanned aircraft system (UAS)pilot as a ground-based controller on 30 July 2020, at noon GMT over Wheat Maid. The flight path was pre-programmed to improve resolution and drone orientation. The GNSS Trimble R10 provided position data via ground control points (GCPs) for image ortho-rectification and stitching. Points that were >50 degrees in slope gradient were omitted from the sampling plan, as this exceeded the Nano-Hyperspec sensors. Eight data strips were collected. Due to the high precision of the UAV raster image using multiple GCPs, the UAV orthoreferenced Nano-Hyperspec image acted as the master image for co-registration of the remaining instrument images. All other instruments' images had varying georeferencing procedures during their post-processing, so to align all instruments, a common GCP system with the highest precision, to which all other images were corrected to, was used. A geo-referenced digital terrain model (DEM) of Wheat Maid Valley was created through overlapping aerial photographs in Geographic Tagged File Format (GeoTIFFs) [38]. This was delivered as a 2 GB, 11 mm\({}^{2}\) ground sample distance (GSD) TIFF file.  
Fe(III) iron band ratio surface reflectance images were provided by HeadWall Photonics and Texo DSI after their post-processing procedures were conducted. The 2 DRMS polygons were intersected with the Normalized Difference Water Index (NDWI) and Fe(III) iron rasters after the data strips had been mosaiced. UAV strips south of the central lower lagoon were missing or encompassed dead pixels due to the corrupted strip.
# Three-Dimensional Division of Visible Light Communication Irradiation Area
## 4 VLC Experiment Setup and Discussion  
### Experiment Setup  
In this study, we designed an experimental setup of VLC. In this communication system, an STM32F103C8T6 chip was used as the microcontroller unit (MCU), and a 10 m VLC system was achieved using the IM/DD technique. Figure 4 shows the entire transceiver system.  
Figure 4 shows how the signal was transmitted from the computer through an RF wireless module A to B, where A and B had the same frequency. The wireless operating frequency band was 433.4-473.0 MHz. It had 100 channels, with a 400 kHz incremental step between each channel. The data were then transmitted to the MCU of a transmitting circuit board through a serial port. The communication rate of the transmitter was 115,200 bps. The received data were transmitted by a single LED after being modulated and amplified. The LED power was 3 W, and the luminous flux was 190-210 lm. The lampshade half angle was 30\({}^{\circ}\) and the LOS channel was 10 m. At the receiver, the optical signal was detected by a Hamamatsu S6801 photodetector with an effective active area of 150 mm\({}^{2}\) and a half angle of 35\({}^{\circ}\), and the data signal was amplified, demodulated, and transmitted to the MCU.  
Figure 4: Transceiver system.  
The MCU in the receiver sent demodulated data to a wireless module C through the serial port. Finally, the data were sent from the wireless module C to D and displayed on the computer, where C and D had the same frequency. Figure 5 shows the experimental setup.  
Figure 5(a),b show photographs of the experimental scenarios when the lights are turned on and off, respectively. We can see from Figure 5(a) that the transmitter of the communication system was fixed and the receiver was moved longitudinally (10 m) and laterally (1 m) on the track. The transmitter and the receiver were at the same height (70 cm). We found from the analysis of the above communication indicators that use of the lampshade increased the communication distance significantly. Therefore, this study conducted a series of experiments to study the effect of the lampshade (the height was from 2 to 6 cm (Figure 5(c)) and the lampshade half angle was 30\({}^{\circ}\)) on the VLC performance. On the basis of the BER distribution of the communication system to determine the best position of the mobile receiver, the communication BER was recorded at 5 cm, 10 cm, 20 cm, 40 cm, 60 cm, 80 cm, 100 cm, 150 cm, 200 cm, 250 cm, 300 cm, 400 cm, 500 cm, 600 cm, 700 cm, 800 cm, 900 cm, and 1000 cm. The lateral distance varied from \(-\)20 cm to \(+\)20 cm, and one experiment was performed per 1 cm. Each experiment was repeated five times in order to ensure the reliability of the data. All experiments were conducted with the lights turned off to avoid the influence of fluorescent lamps and other light on the experiment.
# Enabling Horizontal Collaboration in Logistics Through Secure Multi-Party Computation
## 1 Introduction  
As of 2020, more than 20% of all truck rides in the European Union were empty runs, as measured by the total amount of kilometers covered ([[https://trans.info/en/eu-empty-runs-267211](https://trans.info/en/eu-empty-runs-267211)]([https://trans.info/en/eu-empty-runs-267211](https://trans.info/en/eu-empty-runs-267211)) (accessed on 30 June 2025)), and a significant fraction of non-empty runs was underutilized--that is, they had unused deck space. The situation is even worse for domestic transport, where almost one out of four runs is an empty run, with even higher figures in certain countries, e.g., Cyprus (44% of empty runs), Greece (40%), or Bulgaria (39%). Given that the global freight traffic demand is expected to triple between 2015 and 2050, improving this situation is of critical importance for ecological, economic, and social reasons; cf. also Section 2.2.  
While the aforementioned empty runs are partially due to stringent European legislation ([[https://transport.ec.europa.eu/transport-modes/road/mobility-package-i_en](https://transport.ec.europa.eu/transport-modes/road/mobility-package-i_en)]([https://transport.ec.europa.eu/transport-modes/road/mobility-package-i_en](https://transport.ec.europa.eu/transport-modes/road/mobility-package-i_en))(accessed on 30 June 2025)), another reason is insufficient horizontal collaboration among competing transport companies. The potential benefits of collaborative transport networks (CTNs), e.g., for cost reduction [1] or decarbonization [2] have been well acknowledged. A recent study by [PERSON] et al. [3] defined the Collaborative Freighter Delivery Problem (CFDP) and provided a simplistic yet highly effective heuristic optimization algorithm to quantify the potential of collaboration.  
Focusing on the CFDP rather than actual deployments, their approach assumes all participating hauliers share all information about their orders with a trusted entity that carries out the optimization and assigns specific orders to specific hauliers. However, a recent study by [PERSON] et al. [4] identified a total of 31 barriers in 5 different domains for why CTNs do not see widespread adoption, including, e.g., competition law and trust in coordinators. While different in nature, one core aspect of these two barriers is the need for keeping all information strictly confidential, to avoid being accused of forbidden collaboration and to protect business secrets, respectively. Thus, the approach of [PERSON] et al. [3] fails to satisfy these central real-world requirements.
## 5 Conclusions  
In this chapter, we mainly focus on the study of RUL for bearings under a small amount of data and propose a CNN-GRU-MHA method for predicting the RUL of bearings.  
1. The method combines CNN and GRU and directly inputs the vibration signals processed by the maximum-minimum normalization method and the discrete wavelets as HIs into the model.
2. Local features of rolling bearing signals are extracted using CNN, and then the timing information is modeled and predicted using the GRU model; MHA is introduced for weighting, the L1 regularization method is added to reduce the number of features and reduce the computational complexity, and avoid overfitting, and a model-based migration learning method is also introduced to achieve the RUL prediction of rolling bearings with a small amount of data.
3. Experimental validation was carried out using the PHM2012 and XJTU-SY bearing datasets. The experimental results of PHM2012 data show that the average RMSE of the CNN-GRU-MHA model, with three sets of twelve migration experiments under variable load conditions, is 0.0443; and the results of the XJTU-SY data show that the average RMSE of two sets of four migration experiments under variable load conditions is 0.0691, which verifies the accuracy and good generalization of the model.
4. In future work, it will be necessary to further collect actual industrial production bearing vibration data to validate the model's performance.  
Conceptualization, J.Y. and Q.Y.; methodology, J.Y. and X.P.; software, X.P. and J.S.; formal analysis, J.S. and X.P.; data curation, X.P. and J.S.; writing--original draft preparation, J.S.; writing--review and editing, J.Y.; visualization, X.P.; supervision, Q.Y.; project administration, Q.Y. and J.Y.; funding acquisition, T.L. and J.Y. All authors have read and agreed to the published version of the manuscript.  
The National Natural Science Foundation of China [No. 52305465] and the Hunan Natural Science Foundation in China [No. 2021J]50054].  
Not applicable. This study does not involve research on humans or animals.  
Not applicable. This study does not involve research on humans.  
Data Availability Statement: Data are available on request from the authors.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.