esa-sceva/satcom-chunk-collection huggingface dataset:
# Collision Avoidance Adaptive Data Rate Algorithm for LoRaWAN
## 2 Technological Background
### LoRa Adaptive Data Rate  
The LoRaWAN standard employs an integral feature known as the Adaptive Data Rate, which serves multiple essential purposes. The ADR is designed to prolong the battery life of EDs, optimise quality of service requirements, enhance overall network capacity, and maximise data throughput. The achievement of these objectives relies on the dynamic management of the spreading factor and transmission power. The study [30] presents a synopsis of the LoRaWAN ADR framework. The ADR algorithm operates at both the end node and network server sides. EDs play a role in determining whether ADR should be activated by setting a specific bit. Once this bit is set, the NS takes charge of regulating the transmission settings and issuing the necessary ADR commands to the ED. Periodically, the ED verifies whether the network server is successfully receiving uplink packets, which is confirmed through SNR link margin feedback sent by the NS. In cases where uplink trans  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Req\(\backslash\)Int** & **SF7** & **SF8** & **SF9** & **SF10** & **SF11** & **SF12** \\ \hline SF7 & 6 & \(-16\) & \(-18\) & \(-19\) & \(-19\) & \(-20\) \\ SF8 & \(-24\) & 6 & \(-20\) & \(-22\) & \(-22\) & \(-22\) \\ SF9 & \(-27\) & \(-27\) & 6 & \(-23\) & \(-25\) & \(-25\) \\ SF10 & \(-30\) & \(-30\) & \(-30\) & 6 & \(-26\) & \(-28\) \\ SF11 & \(-33\) & \(-33\) & \(-33\) & \(-20\) & 6 & \(-29\) \\ SF12 & \(-36\) & \(-36\) & \(-36\) & \(-36\) & \(-36\) & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Interference thresholds per spreading factor [29].  
missions fail to reach the gateway, the primary objective of the ED is to improve the status quo by incrementing the spreading factor, hence decreasing the data rate. Additionally, if a downlink packet is not confirmed within a programmable measurement of packets, the ED takes proactive action by increasing the SF for the ensuing uplink packet [15]. This results in an improvement in the transmission range and the possibility of communicating with the GW. The specified procedure followed to adjust to optimal SF is shown in Figure 1. By dynamically adjusting these transmission parameters, LoRaWAN's ADR effectively adapts to varying environmental conditions, ensuring optimised performance and efficient resource utilisation in the network.
# Cooperative UAV Scheme for Enhancing Video Transmission and Global Network Energy Efficiency
## 2 Related Work
### Network and System Model
Each UAV \((v_{i})\) flies with a given speed \(s_{i}\) ranging between a minimum (e.g., \(s_{min}\)) and a maximum (e.g., \(s_{max}\)) speed limit towards a given trajectory (\(traj_{i}\)). We consider that UAVs fly following the Paparazzi mobility model [22] since it enables UAVs to adapt to any type of mission, and also groups most possible UAV movements by changing the probability of each movement type as needed [23]. Particularly, this mobility model considers five possible trajectories (\(traj_{i}\)): Stay-At (i.e., UAV flies in a circle), Way-point (i.e., UAV flies following a straight line to a destination position), Eight (i.e., UAV trajectory has the 8 form around two fixed positions), Scan (i.e., UAV performs a scan in an area defined by two points along the round trip trajectories); and Oval (UAV trajectory has an oval form). Each UAV \(v_{i}\) has a battery with initial energy (\(E_{0}\)), and it spends energy to transmit a packet (\(E_{tx}\)), to receive a packet (\(E_{rx}\)), to retrieve video frames (\(e_{frame}\)), and to fly with a given speed \(s_{i}\) (\(E_{fly}(d,s_{i})\)). Figure 2: SDN architecture. Each UAV \(v_{i}\) is able to measure the current energy (\(E_{t}\)) at any time, and thus it can compute the remaining energy (\(RE_{i}\in[0,1]\)) as follows:  
\[RE_{i}=\frac{E_{t}}{E_{0}}. \tag{1}\]  
The energy consumed in a straight flight \(E_{fly}\) to move a distance \(d\) at a given speed \(s_{i}\) can be computed as the integral of the power \(P(s_{i})\) in function of the given speed \(s_{i}\) along the time [24]:  
\[E_{fly}(d,s_{i})=\int_{t=0}^{t=d/s_{i}}P(s_{i})dt=P(s_{i})\frac{d}{s_{i}}. \tag{2}\]  
The energy required to transmit (\(E_{tx}\)) and to receive (\(E_{rx}\)) a packet depends on the transmission power (\(P_{t}\)). The energy consumed by the UAV \(v_{i}\) to transmit a packet of length \(n\) bits over the wireless link with a bit rate equal to \(r\) bps in a time \(T\) is computed as [25]:  
\[E_{tx}=(P_{ct}+\frac{P_{t}}{epa})T=(P_{ct}+\frac{P_{t}}{epa})\frac{n}{r}, \tag{3}\]  
where \(P_{ct}\) is the power required to run the transmitting circuit, and \(e_{pa}\) is the efficiency of the power amplifier. The energy consumed by the UAV \(v_{i}\) to receive a packet \(E_{rx}\) is computed as [25]:  
\[E_{rx}=\frac{P_{cr}}{r}n, \tag{4}\]  
where \(P_{cr}\) is the power required to run the receiving circuit. It should be highlighted that UAV movements require much more energy than for packet transmissions. In our scenario, we consider a battery replacement location \(L(X_{r},Y_{r})\) that a given UAV \(v_{i}\) could fly to replace/recharge the battery, and then return to the monitoring environment. Table 1 summarizes the main symbols used in this article.
## 4 Results and Discussion
### Financial Prediction Regression Results  
This experiment aimed to evaluate the performance of different privacy protection strategies in financial prediction regression tasks, with a focus on analyzing the ability of each model to capture the dynamic characteristics in time-series data and their impact on privacy protection. Unlike medical image classification, time-series data emphasize the sequential and trend relationships between data points, posing higher demands on the balance between predictive accuracy and privacy protection. As shown in Table 4, the proposed method outperformed all other methods across all evaluation metrics (precision: 0.95; recall: 0.91; accuracy: 0.93; FPS: 54), demonstrating its strong adaptability and performance advantages in handling time-dependent data.  
Theoretically, the characteristics of time-series data require models to effectively capture trends and changes over time. Traditional methods, such as DP and MPC, exhibit weaker capabilities in preserving such information due to noise interference or communication overhead, which reduce their ability to represent temporal patterns. While FL improves local modeling and global aggregation, it struggles to maintain the integrity of global time-series features in non-independent and identically distributed (Non-IID) data scenarios. The proposed method leverages the probability density extraction module to model the global distribution characteristics of time-series data, effectively capturing trend  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** & **FPS** \\ \hline FHE [46] & 0.85 & 0.82 & 0.83 & 25 \\ DP [47] & 0.87 & 0.84 & 0.86 & 28 \\ MPC [48] & 0.89 & 0.86 & 0.88 & 35 \\ FL [49] & 0.92 & 0.89 & 0.90 & 45 \\ Proposed Method & 0.95 & 0.91 & 0.93 & 54 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Financial prediction regression results.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** & **FPS** \\ \hline FHE [46] & 0.82 & 0.79 & 0.80 & 23 \\ DP [47] & 0.84 & 0.81 & 0.83 & 25 \\ MPC [48] & 0.86 & 0.83 & 0.85 & 30 \\ FL [49] & 0.88 & 0.86 & 0.87 & 45 \\ Proposed Method & 0.93 & 0.89 & 0.91 & 57 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Medical image classification results.  
information. Combined with dynamic obfuscation adjustments through the information entropy fusion module, it ensures that the obfuscated data retain the ability to express critical temporal features. Furthermore, the performance loss term in the fusion loss function directly optimizes the prediction error for time-series data, enabling the model to balance the trade-off between privacy protection and temporal pattern preservation. These designs mathematically ensure the integrity of trend capturing in time-series data, providing a highly efficient and secure solution for financial prediction tasks.
## 5 Conclusions and Futures Extensions
### Future Extensions  
After presenting the main conclusions, we introduce possible future research extensions of this work. Specifically, the following ones are considered: (i) Adapt the optical channel to LEO and MEO scenarios, in order to evaluate the performance of the proposed relaying strategies at different satellite orbits; (ii) evaluate the benefits of using time-packing with optical feeder link in the reverse flow of information, from the IoT terminals to the satellite gateway; and (iii) study the viability of using time-packing techniques as a potential physical layer security scheme. In the first case, the aim would be to assess the benefits that LEO and MEO satellites could reap from the proposed relaying architectures. For both LEOs and MEOs, the slant range and elevation angle of the feeder link changes continuously with the time and, as consequence, the optical channel modeling has to be adjusted accordingly to consider these effects. The complexity of the proposed schemes should be also analyzed in detail, since LEO and MEO satellites have more energy constraints than GEO ones due to the Earth/Moon possibly blocking the sunlight that reaches their solar panels. In the second case, the reverse link should be evaluated by replacing the time-packing scheme with a frequency-packing one. It is well known that NB-IoT uses SC-FDMA in the uplink and, as consequence, solutions that increase the spectral efficiency by overlapping the subcarriers should be better considered. As a side effect, SC-FDMA can compensate in part the increase of PAPR that overlapping in the time and frequency domains introduce. Finally, in the third case, the ISI that the process of shrinking the pulses/subcarriers introduces could be used as an artificial noise signal, which can mask the desired information from potential eavesdroppers.  
[PERSON] and [PERSON] conceived, designed, and analyzed the experiments; [PERSON] was oriented to DVB-S2(X) and NB-IoT signalling, time-packing generation, and its suppression, [PERSON] focused on modeling, and [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Agencia Estatal de Investigacion (AEI) and the European Regional Development Fund (FEDER) with grant number TERESA-TEC2017-90093-C3-1-R (AEI/FEDER, UE), and has been based upon work from COST Action CA19111 NEWFOCUS, supported by COST (European Cooperation in Science and Technology).  
Not applicable.  
The authors would like to express their own deepest gratitude to [PERSON] for his support in publishing this paper in this Special Issue.  
The authors declare that there is no personal circumstances or interest that may be perceived as inappropriately influencing the representation or interpretation of reported research results. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
Among the different filters that f-OFDM can offer, we adopt computing the Hann, Hamming and Blackman filters thanks to the great performance they shown in terms of BER and OOB reduction [21] and also to provide a better comparison. These schemes will be BER analysed for a V2X communication i.e., a high mobility channel. The multiuser detector will be based on Message Passing Algorithm (MPA ) [22]. It will be proved that LDS-F-OFDM outperforms the LDS-OFDM and LDS-UFMC by allowing the filter length to exceed the CP length of OFDM and designing the filter appropriately. The remainder of this paper is organised as follows. Section 2 introduces the system model of LDS-UFMC. Section 3 is devoted to the LDS-F-OFDM system model. Section 4 presents simulation results of LDS-UFMC and LDS-F-OFDM compared to LDS-OFDM in different environments. Finally, Section 5 concludes this work.
# Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)
## 7 Experimental Results
### Compass Accuracy Measurement  
Compass accuracy depends on the calibration before the USV sailing. If the calibration procedure is carried out correctly, direction disorientation cannot occur. The compass accuracy also depends on magnetic interference around the compass. This relates to the position of compass placement if the compass is placed on hardware with strong magnetic properties or devices with large electromagnetic radiation. Compass accuracy will be  
Figure 11: Ground Control Station (GCS).  
Figure 12: Military camp’s swimming pool.  
reduced if the interference is very large, it can cause disorientation heading of the USV. To determine the magnitude of interference in the compass is done by analyzing the graph that shows the magnitude of the Magnetic Field versus time; the interference is large if the deviation of the maximum Magnetic Field value from the average value on the graph is above 60%. In addition, the interference is negligible if the deviations occur below 30%. While for the range between 30% to 60%, the amount of interference is moderate [29]. Magnetic field data obtained when the USV is sailing with the autonomous mode are presented in Figure 13.  
According to Figure 13, it can be interpreted that the greatest magnetic field deviation from the mean is 487.76 mG, or around 21.23% of the mean. From the analysis results, it is concluded that the amount of interference that occurs in the compass is small due to magnetic field deviations, which are still below 30%. Additionally, from the study, the data rate of the compass is about 1.68 bytes/s.
# Energy-Efficient Amplifiers Based on Quasi-Floating Gate Techniques
## 4 Design Example: Super Class AB QFG Amplifier in a 130 nm CMOS Process  
To illustrate the different approaches that can be employed to design energy-efficient amplifiers, a novel topology is presented in this section. It combines various techniques to achieve high performance and very low power consumption. Some of these techniques have been described above and some others are introduced here.
# Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
## 4 False Data Injection Attacks Detection Modelling and Methods
### Detection Methods of FDIA
In [149] FDIA and stealth attack detections in wide area measurement in SG monitoring system is examined. ML and Deep Learning methods for intrusion detection examined in detail for different categories in [150]. Detection of electricity theft is discussed in [151]. ML techniques such as PCA [125; 145; 152; 153], game theory approaches [113], and the Stackelberg game [154] can be used for detecting energy theft. Five ML models k-NN, SVM, ANN, NB, and DT, and tested all proposed approaches on MATPOWER are used in [146]. Furthermore, [155] is used one-class SVM (OCSVM), robust covariance (RC), isolation forest (ISOF), and local outlier factor (LOF) as individual classifiers for detection FDIA. A feed-forward neural network (FFNN) is proposed in [111] for stealthy FDIA detection with used random forest for feature selection and compared the deep learning scheme with three methods as gradient boosting machines (GBM), generalized linear models (GLM), and the distributed random forests (DRF). Isolation forest (ISOF) is used in [155] to detect FDI attacks with simulated data; it reduces the data's dimensionality using PCA, to show that ISOF outperforms their findings using four ML methods: SVM, k-NN, NB, and MLP. It does not say how long it took to train the models, but the fact that ISOF outperformed the other models is surprising. On the same sample, supervised models do better than unsupervised models in terms of precision. Another FDIA detection method is presented in [171], a framework with anomaly and FDIA detectors. LSTM-based CNN are used for time series anomalies of attacks formulation. RNN with an LSTM cell is delivered to get the dynamic behaviour of cyber activities on IEEE 39 bus system. SAE, one of the DNN architectures with advanced feature extractor and LRC, used to detect anomalies caused by stealthy FDI attacks, is proposed in [179]. A classification scheme which is based on ERT algorithm and KPCA is presented in [17] and used for dimensionality degradation. A sparse PCA approximation-based method used sparse data sets to aim recovery functions which precision is inversely proportional to the sparsity of available data presented in [153]. A detection method using a semi-supervised ML technique known as the DRE is proposed [176]. It aims to validate a semi-supervised
# Prediction Method of Swirling Flame Lean Blowout Based on Flame Image Morphological Features
## 3 Prediction Method Based on Flame Image Morphological Features
### Random Forest Regression Model  
The random forest model is a machine learning method that uses the bootstrap method to randomly select samples from the original training set, build a CART regression tree based on these randomly selected samples, and then integrate multiple regression trees. This method has the characteristics of a simple and clear algorithm, strong practicability, good prediction accuracy, and strong noise robustness. Since its sample set selection and input feature selection are random, it can avoid the over-fitting problem in the process of model learning [39].  
By using the aforementioned methods, the swirling combustion blowout prediction problem is transformed into a high-dimensional regression problem, and the random forest regression model is used to deal with the problem so as to obtain the LBO prediction model. The random forest regression model is applied directly to predict the blowout states based on the extracted features with time-series information. The input variables are the extracted features with time-series information, and the output variable is the blowout states. Taking into account the number of input samples and the number of features, we set the number of regression trees in the model to 1000. To evaluate the predictive effect of the model, we use two indicators: root mean square error (RMSE) and coefficient of determination (\(R^{2}\)). They are calculated as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}{(\hat{y}_{i}-y_{i})^{2}}}, \tag{1}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}{(\hat{y}_{i}-y_{i})^{2}}}{\sum_{i=1}^{n}{(\hat{y }-y_{i})^{2}}}, \tag{2}\]  
where \(n\) is the number of samples in the data set, \(\hat{y}_{i}\) is the predicted value of the model, \(y_{i}\) is the corresponding true value, and \(\hat{y}=\sum_{i=1}^{n}{y_{i}}\) is the average of the true value.  
Figure 7: Temporal evolution of flame area \(S\) (red line with the left scale) and corresponding blowout state value (black line with the right scale) in Case 9 mentioned in Table 1.
# Spin-Orbital Transformation in a Tight Focus of an Optical Vortex with Circular Polarization
## 9 Conclusions  
The presented research shows that if an initial optical vortex with right-hand circular polarization has a total (averaged over the beam cross-section in the source plane) longitudinal SAM equal to the beam power and a total longitudinal OAM equal to the beam power multiplied by the beam topological charge, then while focusing, the sum of the total longitudinal OAM and SAM will be conserved and will be equal to the beam power multiplied by the topological charge increased by one. The total longitudinal SAM in the focus decreases and becomes equal to the power difference between the zero and second angular harmonics in the beam since the beam contains zero \(\exp(im\theta)\), second \(\exp(i(m+2)\theta)\), and first \(\exp(i(m+1)\theta)\)) angular harmonics. The total longitudinal OAM in the focus will increase and become equal to the beam power multiplied by the topological charge, minus the power of the zero harmonic, and plus the power of the second harmonic. It is also shown that the maximum value by which the total longitudinal SAM can decrease upon focusing is achieved at a constant initial beam amplitude and at a numerical aperture of 1 and is equal to half the beam power. In other words, no more than half of the beam power can be converted from the spin component to the orbital component due to SOC.  
The results obtained in the research can be used in the problems of microparticles manipulation [38; 39; 40].  
Conceptualization, V.V.K. and S.S.S.; methodology, V.V.K. and S.S.S.; software, S.S. and V.D.Z.; validation, V.V.K. and S.S.S.; formal analysis, V.V.K. and S.S.S.; investigation, S.S.S., V.D.Z. and E.S.K.; resources, V.D.Z. and A.M.T.; data curation, S.S.S.; writing--original draft preparation, V.V.K., S.S.S. and E.S.K.; writing--review and editing, V.V.K., S.S.S., E.S.K. and V.D.Z.; visualization, S.S.S. and V.D.Z.; supervision, V.V.K.; project administration, S.S.S. and E.S.K.; funding acquisition, S.S.S. and E.S.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Russian Science Foundation, grant number 23-12-00236 (in theory). This work was performed within the State Assignment of FSRC \"Crystallography and Photonics\" RAS (in simulation).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.