esa-sceva/satcom-chunk-collection huggingface dataset:
# DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks
## 4 A Delay-Constrained Duty-Cycle Scheduling Algorithm
### Duty Cycle Interval Determination
Thus, \(\mathrm{r}_{j}\) is bounded by \(T\) with \(2\leq j\leq g\):  
\[|\mathrm{r}_{j}|=|\mathrm{h}_{j}-\mu_{j}|\leq T \tag{18}\]  
For \(\delta=1\), the left side of the Lyapunov condition in Equation (17) can be expressed as follows:  
\[0\leq\sum_{k=2}^{g}\dfrac{1}{s_{g}^{3}}E\big{[}|\mathrm{r}_{k}|^{3}\big{]} \leq\sum_{k=2}^{g}\dfrac{E\big{[}|\mathrm{r}_{k}|^{2}T\big{]}}{s_{g}^{3}} \tag{19}\]  
\[\Rightarrow\lim_{g\to\infty}0\leq\lim_{g\to\infty}\sum_{k=2}^{g}\dfrac{1}{s_ {S}^{3}}E\big{[}|\mathrm{r}_{k}|^{3}\big{]}\leq\lim_{g\to\infty}\sum_{k=2}^{g }\dfrac{E\big{[}|\mathrm{r}_{k}|^{2}T\big{]}}{s_{g}^{3}} \tag{20}\]  
By Equation (15), \(s_{g}\to\infty\) as \(g\to\infty\). Therefore, \(\lim_{g\to\infty}\frac{T}{s_{g}}=0\). We have:  
\[\lim_{g\to\infty}\sum_{k=2}^{g}\dfrac{E\big{[}|\mathrm{r}_{k}|^{2}T\big{]}}{s_ {g}^{3}}=\lim_{g\to\infty}T\dfrac{s_{g}^{2}}{s_{g}^{3}}=\lim_{g\to\infty} \dfrac{T}{s_{g}}=0 \tag{21}\]  
According to Equations (20) and (21), the limit of the left side of the Lyapunov condition in Equation (17) becomes:  
\[0\leq\lim_{g\to\infty}\dfrac{1}{s_{g}^{3}}\sum_{k=2}^{g}E\big{[}|\mathrm{r}_{ k}|^{3}\big{]}\leq 0 \tag{22}\]  
Therefore, the Lyapunov condition holds when \(\delta=1\). Since the statement in Lemma 1 is satisfied, Equation (16) holds. Let us define the random variable \(\mathrm{z}\) that is a function of the required DSR \(\gamma\) as follows:  
\[ \begin{split}\mathrm{z}&=\sum_{k=2}^{g}\dfrac{ \mathrm{r}_{k}}{s_{g}}=\sum_{k=2}^{g}\dfrac{(\mathrm{h}_{k}-\mu_{k})}{s_{g}}\\ &=\dfrac{\mathrm{d}-\sum_{k=2}^{g}\mu_{k}}{s_{g}}=\dfrac{ \mathrm{d}-T\sum_{k=2}^{g}\psi_{k}}{T\sum_{k=2}^{g}\omega_{k}}\end{split}  \tag{23}\]  
Thus, \(\mathrm{z}\to\mathcal{N}(0,1)\), in which \(\mathrm{z}\) is derived from the standard normal table with a given required DSR \(\gamma\), e.g., with \(\gamma=95\%\), \(\mathrm{z}=1.65\). Replacing \(\mathrm{d}\) by the delay bound \(\theta\), in order to satisfy the delay bound requirement with the given probability, the duty cycle interval is expressed as follows:  
\[T\leq\dfrac{\theta}{\sum_{k=2}^{g}\psi_{k}+\mathrm{z}\sum_{k=2}^{g}\omega_{k}} \tag{24}\]  
The maximum value of the duty cycle interval, \(T_{max}\), is obtained as follows:  
\[T_{max}=\dfrac{\theta}{\sum_{k=2}^{g}\psi_{k}+\mathrm{z}\sum_{k=2}^{g}\omega_{k}} \tag{25}\]  
where \(\psi_{k}\) and \(\omega_{k}\) are calculated from Equation (11).
## 4 Discussion
### Statistical Significance Analysis and Model Comparison  
To ensure the reliability of the predictive models, 30 independent runs were conducted for each model (MLR, GP, XGBoost, and GP-XGBoost) with different random seeds to account for variability in data splitting and model initialization. The performance metrics--\(R^{2}\), MSE, and MAE--were calculated for each run. This approach allows for a comprehensive assessment of each model's performance. The mean and standard deviation of these metrics were computed to quantify the models' central tendency and variability, as shown in Table 15.  
These results indicate that the hybrid GP-XGBoost model consistently outperformed other models with the highest mean \(R^{2}\) and the lowest MSE and MAE.
# Analytical Model for Enhancing the Adoptability of Continuous Descent Approach at Airports
## 5 The Applied Queueing Model
### Traffic Intensity  
Queuing theory presents a key parameter known as the traffic intensity, also referred to as the utilization factor, which is denoted by the Greek letter \(\rho_{ss}\) (\"rho\"), which is defined here as the average hourly demand rate of the stacking space divided by the average hourly capacity (or _service_) of the stacking space. If the average demand rate (the rate at which aircrafts arrive at the stacking space, i.e., the aircraft arrival rate) is denoted by \(\lambda_{ss}\) and the average service rate is denoted by \(\mu_{ss}\), then the _utilization of airspace factor_, \(\rho_{ss}\), for the stacking space within the TMA is as follows:  
\[\rho_{ss}=\frac{\lambda_{ss}}{\mu_{ss}} \tag{21}\]  
where the demand rate is expressed in terms of the number of aircrafts that arrive per hour at the stacking space, and service rate is expressed in terms of the number aircrafts per hour that may enter the stacking space. The value of the service rate, \(\mu_{ss}\), is conceptually equivalent to the airport arrival rate (AAR). However, because \(\lambda_{ss}\) is expressed in our examples per 15-min time period, the value of the service rate, \(\mu_{ss}\), represents the number of aircrafts that can be processed (served) per 15-min time period, and thus, it is one fourth of the value of AAR. The symbol \(\mu_{ss}\) is typically used in publications on queueing theory, while AAR is commonly used in air traffic management. Observe that the service rate,\(\mu_{ss}\), will be lower when CDA is assumed than when SDA is assumed due to the longer separation distances required between aircrafts using CDA.
###### Abstract  
Sensor-less adaptive optics (SLAO) based on stochastic parallel gradient descent (SPGD) is effective for the compensation of atmospheric turbulence in coherent free-space optical communication (CFSOC) systems. However, SPGD converges slowly and easily falls into local extremes. Therefore, we propose a novel NadamSPGD algorithm for efficient wavefront correction that combines Nesterov-accelerated adaptive moment estimation (Nadam) and SPGD. Specifically, Nesterov's accelerated gradient momentum (NAG) and adaptive gain coefficients are integrated to conventional SPGD to accelerate its convergence speed and avoid converging to extremum points. Theoretical analysis, numerical simulations and experimental results demonstrate that NadamSPGD can increase the convergence speed by \(\sim\)50% and significantly improve the robustness of parameters, and thus more efficiently suppress the negative effects of atmospheric turbulence on mixing efficiency (ME) and bit error rate (BER). Our algorithm also presents better dynamic performance under strong turbulence and high Greenwood frequency conditions, and it is more suitable for real-time SLAO systems. This study proves that the NadamSPGD algorithm is suitable for SLAO in the CFSOC system and is a viable substitute for SPGD to improve the quality of optical communications.  
oeherent free-space optical communication; sensor-less adaptive optics; stochastic parallel gradient descent; atmosphere turbulence +
Footnote †: journal: _Photonics_
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 7 Network, System, and Channel Model
### Channel Model  
In order to delineate the conditions of signal propagation, the concept of LoS probability is introduced to quantify the likelihood of the LoS component's presence. The 3 GPP channel model [45] provides specific LOS probability models, with antenna heights of 3 m for indoor scenarios, 10 m for Urban Micro (UMi) environments, and 25 m for Urban Macro (UMa) settings. With respect to this work, the UMa scenario is chosen. This can be written as  
\[P_{\text{{r}}\text{{L}}\text{{O}}}=\begin{cases}1&,d_{\text{3D-out}}\leq 1 \text{m},\\ \left[\frac{18}{d_{\text{3D-out}}}+\exp\left(\frac{-d_{\text{3D-out}}}{63} \right)\left(1-\frac{18}{d_{\text{3D-out}}}\right)\right],\\ \left[1+\text{C}^{{}^{\prime}}(h_{UT})\frac{5}{4}\left(\frac{d_{\text{3D-out}} }{100}\right)^{3}\exp\left(\frac{d_{\text{3D-out}}}{150}\right)\right]\end{cases} \end{cases}, \tag{11}\]  
where  
\[C^{{}^{\prime}}(h_{UT})=\begin{cases}0,&h_{UT}\leq 1 \text{3m}\\ \left(\frac{h_{UT}-13}{10}\right)^{1.5},&13\text{m}<h_{UT}\leq 23\text{m} \end{cases} \tag{12}\]  
Pathloss presents a strong correlation with the channel modelling and is influenced by distance. Since UAVs operate in a three-dimensional environment, the pathloss is given by  
\[PL_{1}=28+22\ log_{10}(d_{3D})+20\ log_{10}(f_{c}) \tag{13}\]  
where \(d_{3D}\) is the location of the UAVs 3D distances and \(f_{c}\) is the operating frequency.  
This algorithm adopts a narrow-band geometric channel model, as described in [12]. With this model, the downlink channel from gNB \(\mathcal{B}\) to the UAV in gNB \(\mathcal{L}\) can be expressed \[c_{L,b}=\frac{\sqrt{M}}{\rho_{L,b}}\sum_{p=1}^{N_{l,b}^{p}}\alpha_{l,b}^{p}a^{*}( \varphi_{l,b}^{p}) \tag{14}\]  
where  
\(\alpha_{l,b}^{p}\): path gain of the p-th path;  
\(\varphi_{l,b}^{p}\): angle of departure(AoD);  
\(a(\varphi_{L,b}^{p})\): array response vector associated with AoD;  
\(N_{l,b}^{p}\): no. of channel paths.  
This model accounts for both LOS and NLOS scenarios. For the LOS case, \(N_{l,b}^{p}=1\) is assumed.  
The received power at the UAV is measured over a set of PRBs at a given time t and is given by  
\[P_{\text{UAV}}^{L,b}[t]=P_{\text{TX},b}[t]\Big{|}c_{\text{t,b}}^{*}[t]h_{b}[t] \Big{|}^{2} \tag{15}\]  
where \(P_{\text{UAV}}^{l,b}[t]\) is received downlink power, \(P_{\text{TX},b}[t]\) is power from gNB \(\mathcal{B}\).  
Some additional losses are also suffered due to penetration and oxygen absorption.
## Appendix A  
In Reference [19], the nonlinear model contains five equations, which are torque equations, mechanical equations, navigation equations, motion equations, and ground driving motion equations. The nonlinear mathematical model formula of air-ground platform hovering in flight mode is obtained, where \(\theta,\phi\), and \(\psi\) are attitude angle, \(x\), \(y\), and \(z\) are the offset from the origin position of the initial ground coordinate system, \(F\) is lifting force, \(\Omega_{i}\) is rotor speed, \(I\), is moment of inertia, \(\omega\) is angular velocity in body coordinate system, \(p\), \(q\), and \(r\) are linear speed of body coordinate system, \(l\), \(b\), and \(d\) are the distance from the center of mass of the air-ground platform to the rotor shaft, rotor lift coefficient and rotor drag coefficient, respectively, \(\Delta\)s is wheel travel distance, and \(\Delta\theta\) is the steering angle increment.  
\[\left\{\begin{array}{l}\begin{array}{l}\begin{array}{l}m\ddot{x}=(\cos \phi\sin\theta\cos\psi+\sin\phi\sin\psi)\sum\limits_{i=1}^{4}F_{i}\\ \\ m\ddot{y}=(\cos\phi\sin\theta\sin\psi-\sin\phi\cos\psi)\sum\limits_{i=1}^{4}F_ {i}\\ \\ m\ddot{z}=(\cos\phi\cos\theta)\sum\limits_{i=1}^{4}F_{i}-mg\\ \\ \left\{\begin{array}{l}\dot{\omega}_{x}=\frac{I_{y}-I_{z}}{l_{y}}\omega_{y} \omega_{z}+\frac{I}{l_{y}}b(\Omega_{4}^{2}-\Omega_{2}^{2})+\frac{I_{x}}{l_{x}} \omega_{y}(-\Omega_{1}+\Omega_{2}-\Omega_{3}+\Omega_{4})\\ \dot{\omega}_{y}=\frac{I_{x}I_{y}}{l_{y}}\omega_{x}\omega_{z}+\frac{I_{y}}{l_{y }}b(\Omega_{3}^{2}-\Omega_{1}^{2})-\frac{I_{x}}{l_{y}}\omega_{x}(-\Omega_{1}+ \Omega_{2}-\Omega_{3}+\Omega_{4})\\ \\ \dot{\omega}_{z}=\frac{I_{x}-I_{y}}{l_{z}}\omega_{y}\omega_{x}+\frac{I_{z}}{l_{ z}}(-\Omega_{1}^{2}+\Omega_{2}^{2}-\Omega_{3}^{2}+\Omega_{4}^{2})\\ \\ \left\{\begin{array}{l}\dot{\phi}=\omega_{x}+(\omega_{z}\cos\phi+\omega_{y} \sin\phi)\tan\theta\\ \dot{\theta}=\omega_{y}\cos\phi-\omega_{z}\sin\psi\\ \dot{\psi}=\frac{1}{\cos\theta}(\omega_{z}\cos\phi+\omega_{y}\sin\phi)\\ \\ \dot{\theta}=\omega_{y}\cos\phi-\omega_{z}\sin\psi\\ \\ \dot{\psi}=\frac{1}{\cos\theta}(\omega_{z}\cos\phi+\omega_{y}\sin\phi)\\ \\ \left\{\begin{array}{l}\dot{x}=-p\cos\theta\cos\psi+q(\sin\theta\sin\phi\sin \psi-\cos\psi\cos\phi)\\ \\ \dot{\psi}=p\cos\theta\sin\psi+q(\sin\theta\sin\phi\sin\psi+\cos\psi\cos\phi)\\ \\ r(\sin\theta\cos\phi\cos\psi-\cos\psi\sin\phi)\\ \\ \dot{z}=-p\sin\theta+q\sin\theta\cos\theta+r\cos\theta\cos\phi)\\ \\ \left[\begin{array}{l}x_{1}\\ y_{1}\\ \theta_{1}\end{array}\right]=\left[\begin{array}{l}x_{0}\\ y_{0}\\ \theta_{0}\end{array}\right]+\left[\begin{array}{l}-\Delta s\sin(\theta_{0}+ \Delta\theta/2)\\ \Delta s*\cos(\theta_{0}+\Delta\theta/2)\\ \Delta\theta\end{array}\right]\\ \end{array}\end{array}\right. \tag{1}\]
# Q-LBR: Q-Learning Based Load Balancing Routing for UAV-Assisted VANET
## 4 Proposed Q-LBR Design  
In this section, we describe the Q-LBR design in detail. Q-LBR is designed to maximize the network utilization of a URN through load balancing. Q-LBR introduces new mechanisms in UAV-assisted VANET, as described in Figure 2.  
The Q-LBR protocol consists of two phases, as described in Figure 3. During the first phase, a URN collects a ground network congestion identifier (GNCI) to the GN messages through broadcast and unicast overhearing to determine the congestion level of the ground network. Through this phase, the URN can recognize the congestion level of the ground network based on the collected GNCI and UAV relay congestion identifier (URCI) information. During the second phase, the URN disseminates URPA information corresponding to the action of the Q-learning. Specifically, the URN substitutes the GNCI and URCI into the Q-learning states and feeds the appropriate reward value back based on an RCF calculation. Finally, the result of the RCF determines the URPA value, which is divided into upper and lower values, and shares it with a Hello message.  
Figure 1: UAV relay coverage model.
# Privacy-Preserving Data Mining on Blockchain-Based WSNs
## 2 Related Work
### Onion Routing in WSNs  
Onion routing has a long tradition in enabling anonymous communication in public networks, first using layered objects to establish anonymous connections [18], and now being implemented in the TOR network to construct anonymous connections one hop at a time using a key-agreement protocol [19].  
Although combined applications in the context of PPDM are rare [20], onion routing remains a privileged technique to protect from external monitoring attacks, as is proven by existing work on the topic [21].  
[PERSON] et al. [22] used a layered object to retrieve data from a WSN without disclosing the identity of the queried sensor node. Although the technique preserves privacy effectively, it only allows for data retrieval from individual nodes. One important work related to query privacy over WSNs is the work of [PERSON] et al. [23], which proposes a similar solution, although with different underlying architectural choices (i.e., no region-based servers and a different trust model). The solution works by using source routing to privately route a declarative query to the aggregator nodes of a WSN. However, aggregator nodes are a point of failure for the network since they collect data from the sensor nodes in their proximity.  
In general, we noted that most related work adopts a strong separation between data collection/querying and data mining. This highlights the originality of our work, where the data remains on the sensors, with a direct decentralized implementation of the data mining process that involves using onion routing for the purpose of protecting the tasks together with the processed data.
# Thermal and Physical Properties of Crude Palm Oil with Higher Oleic Content
## 3 Results and Discussion
### Thermal Analysis of the Oils
Table 3 shows the melting peaks and the range temperature of the endothermic events for both compounds. HOPO exhibited lower temperatures for the endothermic peaks than APO. This behavior could be related to the fact that the APO has about 50% of unsaturated fatty acids composition, while HOPO has around 65%. In fact, unsaturated compounds present lower melting points than saturated ones [28; 32; 34; 35]. The DSC crystallization profiles for APO and HOPO obtained by the samples' cooling program are presented in Figures 1 and 2 and Table 3. APO showed three crystallization peaks (Tc), which are \(-49.09\)\({}^{\circ}\)C, \(1.55\)\({}^{\circ}\)C and \(15.61\)\({}^{\circ}\)C. A similar crystallization profile was noted for HOPO (Table 3). Additionally, in the case of APO, a third peak involving less energy, ranging from 5.88 to 17.99 \({}^{\circ}\)C, can be attributed to the presence of TAGs such as SSS and SSU triacylglycerglycerols [28]. The exotherm observed at lower temperatures, which in the case of APO ranges from 5.88 to 63.32 \({}^{\circ}\)C, and in the case of HOPO involves temperatures from 1.82 to 71.88 \({}^{\circ}\)C, is possibly related to unsaturated fatty acids (for example, UUU and SUU). These findings explain the broadest and most intense exotherm observed in HOPO, which showed a higher content of unsaturated fatty acids [33]. Similar behavior was previously reported for crude palm oil, involving two sharp peaks of crystallization obtained at temperatures slightly higher than those found here [8; 33]. The mass loss related to the decomposition of palm oils occurred in two steps (Figure 3). The thermal decomposition profile of both oils is similar, including a slow first step of mass loss from 180 to 300 \({}^{\circ}\)C, and a fast second step of mass loss from 180 to 440 \({}^{\circ}\)C, which accounts for more than 90% of the sample mass loss. Nevertheless, a difference in the kinetic of the first step of mass loss is noted among the oils (Figure 3). The first temperature range is related to the degradation of polyunsaturated fatty acids, followed by monosaturated and saturated fatty acids [36]. Even with a higher concentration of unsaturated fatty acid, HOPO is slightly more stable than APO, showing a slower first decomposition step. This thermal profile is probably due to the high amounts of antioxidant compounds, such as carotenes and tools. The same stability improvement was reported for several commercial edible oils, such as olive, soybean, and canola [34]. Figure 2: DSC curves in a heating and cooling program of APO and HOPO for run 2.
# A Review on Augmented Reality in Education and Geography: State of the Art and Perspectives
## 2 Historic Overview of AR
These technologies are not only actively used in educational settings but also represent the immersion levels and hardware requirements. Geography was chosen as the central educational domain for this study due to its intrinsic spatial, environmental, and physical dimensions, which make it ideally suited for immersive visualization. Among academic disciplines, geography uniquely combines physical processes, human interaction, and environmental dynamics, areas where AR can significantly enhance comprehension through experiential learning. Furthermore, contemporary education emphasizes learning, where tools like AR can bridge abstract geographical theory and real-world context. Thus, geography provides both an ideal use case and a pedagogical challenge that AR can meaningfully address. Regarding the criteria for selecting the AR technologies, factors included affordability, deployment in classrooms, ease of use for students and teachers, and the capability to simulate real-world geography. Additionally, peer-reviewed literature, adoption in educational pilot programs, and support from government or institutional bodies helpednarrow the selection. Moreover, the psycho-physiological effects of AR use in geography education call for deeper exploration. Early studies indicate that moderate AR exposure can increase cognitive engagement and memory retention while reducing disinterest compared to traditional methods. However, prolonged usage or poorly designed interactions may lead to cognitive overload or visual strain, especially among younger students.
# Method for Recognition of Communication Interference Signals under Small-Sample Conditions
## 5 Simulation and Analysis
### Experiment on the Impact of Different Generative Networks on Accuracy  
To further validate the advantages of the algorithm proposed in this paper, three types of generative networks--GAN, WGAN, and DCGAN--were selected to augment and expand the time-frequency diagrams of communication interference signals. The generator and discriminator in GAN consist of a series of fully connected layers; similarly, WGAN's generator and discriminator are composed of fully connected layers, but it introduces the Wasserstein distance into the original GAN framework. DCGAN, a Deep Convolutional Generative Adversarial Network, is a hybrid of CNN and GAN. Figure 10a-c, respectively, show the recognition accuracies after augmenting the real samples with 30, 60, and 90 generated samples per category using these four generative networks, followed by classification through C-ResNet.  
Analysis of the figures reveals that with an increase in sample size, the recognition rates of all four networks improve to varying degrees. However, since the GAN network's generator and discriminator are comprised of a series of fully connected layers, the quality of sample generation is relatively poor. Consequently, the recognition rates remain the lowest under all conditions of sample expansion, with 30, 60, or 90 samples per category. WGAN, which introduces the Wasserstein distance on the basis of GAN, improves the quality of sample generation to some extent. At a sample expansion of 90 and a JNR of 10 dB, its recognition rate reaches 85.47%, an improvement of approximately 3.5% over the GAN network. DCGAN, which replaces fully connected layers with convolutional layers to capture local features in images, produces higher-quality samples than the fully connected layer-only GAN model. The figures show that recognition rates after sample augmentation are about 8% higher compared to GAN and WGAN models. The proposed algorithm, WDCGAN-SA, achieves the highest recognition rates across different sample sizes, about 5% higher than DCGAN. This demonstrates that the data augmentation methods designed  
Figure 9: Recognition Accuracy with Different Quantities of Generated Samples.  
in this study effectively improve the accuracy of recognizing communication interference signals under small sample conditions.
## 3 Results and Discussion
### Back-Trajectory Footprints
#### 3.4.2 Sea Breeze Case Study: 23-24 July 2016
Overall, this case study demonstrates the differences in CO\({}_{2}\) mixing ratio values in the region during land breeze versus sea breeze events. In addition, it showcases the differences in the captured and simulated values between the CAV site near the city center and the SME site on the coast. For example, independent of the land breeze/sea breeze wind conditions, the CAV site captures enhancements that are much more variable compared to the SME site, with the enhancements at SME being more stable throughout. The model tends to underestimate the CO\({}_{2}\) enhancement seen at one of the sites under both land-breeze and sea-breeze conditions. On 23 July, under land-breeze conditions, the underestimation happened at CAV, while on 24 July, during sea-breeze conditions, the underestimation happened at SME. There may be several reasons for this mismatch, including underestimation of  
Figure 8: The left column shows log-scaled maps of the 12 h footprints from each of the CO\({}_{2}\) measurement sites on 23 July 2016 at 16:00 UTC (**top**) and 24 July 2016 at 16:00 UTC (**bottom**). The middle column shows a zoomed-in view of the CO\({}_{2}\) model/data comparison from Figure 3 during the period of this case study for CAV (**top**) and SME (**bottom**). The right column shows a zoomed-in view of the PBL comparison from Figure 5 during the period of the case study for CAV (**top**) and SME (**bottom**). emissions, overestimation of the biogenic fluxes, misrepresentation of the mixing, etc. The forthcoming ANR COoL-AMmetropolis project aims to use CO, 14C, Volatile Organic Compounds, and Black Carbon measurements to partition fossil fuel signals from biogenic sources, along with LIDAR measurements for better PBL characterization.