esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Position and Target Rate for Covert Communication in UAV-Assisted Uplink RSMA Systems
## 6 Conclusions  
In this paper, we investigated the covert communication in UAV-assisted uplink RSMA system, where U1 adopts an RS strategy with random transmit power to guarantee U2's covert and reliable transmission. Specially, we studied the optimal power allocation factor for \(\mathbf{s}_{11}\) and \(\mathbf{s}_{12}\). Then, we derived closed-form expressions of minimum DEP, ESR, and UAV's optimal target rate that maximizes ESR. Constrained by the minimum DEP and ECR, we maximized the ESR by optimizing the position and target rate of the UAV. Numerical results showed that the proposed scheme outperforms NOMA systems in terms of the ESR constraint by the same DEP and ECR.  
Conceptualization, Z.D. and X.Y.; methodology, Z.D.; software, Z.D.; validation, Z.D.; formal analysis, Z.D.; investigation, Z.D.; data curation, Z.D.; writing--original draft preparation, Z.D.; writing--review and editing, Z.D, X.Y. and L.W.; visualization, Z.D.; supervision, X.Y.; project administration, L.W. and T.Z.; funding acquisition, L.W., T.Z. and X.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant No. 62271412, 62271399, and partly supported by Natural Science Basic Research Plan in Shaanxi Province of China under grant No. 2022 KJXX-39.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Application to Real Data
### CMP Inversion  
The geometry of the receiver positioning is illustrated in Figure 19(a). In this survey each source line passes between two receiver lines, therefore there are no purely inline data. This configuration obligates us to apply algorithms that handle broadside data as a full 3D inversion, as well as our proposed method. The survey was conducted in an area for which several seismic sections were already available. Figure 19(b) shows the locations of the seismic lines in relation to the MCSEM receivers. The frequencies used in the inversion were 1.3 and 5.0 Hz.  
Figure 20: Geometry of the MCSEM survey.  
Due to the sparsity of data points in the area, the columns corresponding to the CMPs have 300 m sides. This implies low spatial resolution and limits the capacity of the inversion to detect sub-vertical bodies, such as resistivity variations associated with fractures. However, it allows for the identification of regional tendencies in the resistivity structures. The mesh in depth has increasing thickness to account for variations in sensitivity, and the bathymetry is addressed through regularization that connects the lateral cells.  
A data sample illustrating the small noise level in this dataset and the fit obtained by the generated synthetic data is shown in Figure 21, which shows curves for the \(E_{x}\) and \(E_{y}\) components in one receiver for one transmitter pass. Observe that the signal to noise ratio is large for many of the curves, but that the noise overwhelms the signal in the longest offsets. The synthetic data closely matches the field data in areas where the earth is layered, but less so in areas strongly influenced by 3D structures.  
The inversion results are integrated to 2D seismic sections for comparison with the stratigraphy, with the structural behavior of the area, and with the positioning of the _Bottom Simulating Reflector_ (BSR), which is usually associated with the base of the hydrate accumulation zone.  
The CMP inversion was able to locate a resistive layer below the BSR and a shallow zone of resistive bodies. These two anomalous resistive zones are clearly delineated in the inverted resistivity sections shown in Figure 22, superimposed on the corresponding seismic sections indicated by white lines. In the first interpretation, the shallow and less resistive layer is associated with gas hydrate accumulation and the intermediate zone is associated with free gas accumulation underneath the BSR. The results match the stratigraphy overall, and we can highlight some higher anomalies close to the faults.  
Figure 21: Example of the data in a receiver superimposed with the synthetic data obtained from the inverted model. (**a**) \(E_{x}\) and (**b**) \(E_{y}\).  
This inversion runs in parallel in 62 core processors with 128 GB/node. The execution time was approximately 10 hours with 45 iterations, which is about the same time as two iterations of a true 3D Gauss-Newton inversion for this data set in the same machine.
## 5 Context Aware CL Optimization on MAC and NET Layers
### Context-Aware DR-MAC  
The DR-MAC is a contention-based MAC protocol based on unslotted CSMA/CA algorithm. The original DR-MAC allows three state settings to control the number of backoffs and backoff exponential (BE) according to frame loss rate and latency, as shown in Figure 6. To incorporate AmI context information, DR-MAC is modified as shown in Figure 7.  
The settings (_MinBE_, _MaxBE_, _Backoffs_) for transmitting high priority sensor data are chosen due to its having the lowest packet lost according to the results of original DR-MAC. It is important to ensure that important sensor data can arrive at its destination; otherwise it is impossible for the intelligent agents that subscribe to the sensor data to correctly perform any context inference processes. This packet loss improvement can also improve end-to-end frame latency as shown later in the evaluation section. The settings chosen for transmitting low priority sensor data is the default settings for the IEEE 802.15.4 standard.  
Figure 6: The original DR-MAC.  
Figure 7: Context-aware DR-MAC.
## 4 Measurements and Methods
### Estimation of the Equivalent Scattering Absorption Area \(A_{sc}\)  
The \(A_{sc}\) for the furniture configurations is estimated by Equation (39). The \(A_{sc}\) for the furniture in combination with the two ceiling treatments and for the two mounting heights, given in Table 2, were measured. No wall panels were present during these measurements. The results are presented in Section 5.1. The same number of microphone and loudspeaker positions were used, as for the measurements of the room acoustic parameters.
## 4 Experimental Setup
### Channel SNR and Optimal MODCOD Estimation
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**MODCOD**} & **Spectral Efficiency** & **Payload Size (\(p\))** & \multicolumn{2}{c}{**NF for a Single**} \\  & **[bits/s/Hz]** & **[bits]** & **SNR Measurement** \\  & & & **Period** \\ \hline BPSK-S 1/5 & 0.1 & 2896 & 0 \\ BPSK-S 11/45 & 0.12 & 3616 & 0 \\ BPSK 1/5 & 0.2 & 2896 & 0 \\ BPSK 4/15 & 0.27 & 4184 & 0 \\ BPSK 1/3 & 0.33 & 5056 & 1 \\ QPSK 11/45 & 0.49 & 3616 & 1 \\ QPSK 4/15 & 0.53 & 4184 & 1 \\ QPSK 14/45 & 0.62 & 4696 & 1 \\ QPSK 7/15 & 0.93 & 7216 & 2 \\ QPSK 8/15 & 1.07 & 8296 & 3 \\ QPSK 32/45 & 1.42 & 11,176 & 4 \\
8 PSK 8/15 & 1.6 & 8296 & 4 \\
8 PSK 26/45 & 1.73 & 9016 & 5 \\
16 APSK 7/15 & 1.87 & 7216 & 5 \\
16 APSK 8/15 & 2.13 & 8296 & 6 \\
16 APSK 26/45 & 2.31 & 9016 & 7 \\
16 APSK 3/5 & 2.4 & 9376 & 7 \\
16 APSK 32/45 & 2.84 & 11,176 & 8 \\
32 APSK 2/3 & 3.33 & 10,456 & 10 \\
32 APSK 32/45 & 3.56 & 11,176 & 10 \\ \hline \end{tabular}
\end{table} 
Table 1: An overview of the MODCODs used in the simulation framework and the corresponding spectral efficiencies, payload size and NF for a single measurement period.
## 4 Workflow Overview and Details
### Loss  
The loss of Seg2 pix is briefly given in Equation (3), which is the same as that of Pix2 pix [2], mostly based on the shape of the conditional GAN. The loss of the conditional GAN is written as Equation (1):  
\[\begin{split}\mathcal{L}_{cGAN}(G,D)=\mathbb{E}_{x,y}[logD(x,y)]+ \\ \mathbb{E}_{x,y}[logD(1-D(x,G(x,z))]\end{split} \tag{1}\]  
To encourage less blurring, we use the \(L_{1}\) distance:  
\[\mathcal{L}_{L_{1}}(G)=\mathbb{E}_{x,y,z}[||y-G(x,z)||_{1}] \tag{2}\]  
The final objective loss becomes:  
\[G^{*}=\arg\min_{G}\max_{D}\mathcal{L}_{cGAN}(G,D)+\lambda\mathcal{L}_{L_{1}}(G) \tag{3}\]  
\(x\) represents the input data, which are the concatenated data of the line art and trap ball segmentation data, and \(y\) represents the real colorized data of the ground truth. \(z\) is a random noise vector that prevents the generator from producing deterministic outputs. \(G\) and \(D\) define the generator and the discriminator  
Figure 8: The image on left shows the generated image without the attention layer, and the right shows the generated image with the attention layer. The two images show different qualities of the color distribution on the small details.  
Figure 7: The block diagram of the self-attention layer [38].  
The generator tries to minimize this objective against an adversarial discriminator, which tries to maximize it. As a final objective, we sum with the \(L_{1}\) distance loss to train the image's low-frequency content while the adversarial loss trains the high-frequency content. This makes the image sharper to avoid blurry features in the image.
## 2 Experimental Load History Data Collection  
An experiment was conducted under operating conditions in order to determine the actual load history of a single stanchion. Two pairs of standions with a nominal load of 50 kN were used in tests on local roads. Figure 1 shows the timber truck before the cargo was loaded, and Figure 2 shows the truck during timber loading. Direct measurements of the forces acting on particular points of the stanchion are rather problematic. Therefore, it was decided to measure the transverse displacement of the free end of the stanchion and determine the forces based on the assumed distribution of masses and dimensions of the logs. The measuring system consisted of a displacement transducer mounted on a support fixed to an additional unloaded stanchion and a rigid beam mounted on the loaded stanchion as shown in Figure 3.  
distribution per stanchion determined for a static load case is shown in Figure 4. Loads caused by logs that were not in direct contact with the stanchion were also included. Forces for dynamic load cases were determined by multiplying the forces obtained for the static load by the coefficient related to the transverse displacement of the free end of the stanchion. This coefficient is defined as \(k=x_{dyn}/x_{stat}\), where \(x_{dyn}\) stands for the transverse displacement under dynamic load and \(x_{stat}\) stands for the transverse displacement under static load. The load history obtained from the experiment is shown in Figure 5.
## 6 Simulation Results
### Stability Analysis
At that time, it is possible to arbitrarily select the input physical value domain to get good control results without fear of instability because there is no excessive increase of driving force. Numerical experiments are carried out when control accuracy is almost the best. In this case, when expanding the physical value domain of the driving force by 5 times, the result shows that there are some early points of the control process, when the system is in the start-up phase, that the driving force increases by about (35%\({}^{\circ}\)+45%). However, with the entire control process on the operating trajectory, the driving force has no significant change and control accuracy is guaranteed. Thus, with the experiment mentioned above, it is feasible to select a value domain smaller than the upper bound limit. In addition, through the mentioned numerical simulation results, the controller can return the system to trajectory when there are errors by disturbance. Compare the fuzzy controller (FZ) with a fuzzy controller integrated with PID (FZ + PID) shown in Figures 19 and 20. The result shows that the accuracies of Fuzzy and Fuzzy + PID controllers are almost the same. On the other hand, with noise increasing the external load (cutting force) within 50%, the accuracy is still guaranteed and the errors are negligible. Figures 19 and 20 also show the simulation results of the Inverse Dynamics + PD controller (no perturbation, Figure 19), and Inverse Dynamics + PID (with perturbation, Figure 20). The adjustment capability of the Inverse Dynamics + PID controller is found to be slower. Figure 19: Tool path position errors between desired value and simulation result in normal cutting force mode. Figure 18: Membership function value of the fuzzy set u(t) corresponding to the physical value domain of u(t) is chosen to be 3 times larger than the value in Table 6.  
Thus, among many methods of ensuring the stability and accuracy of the fuzzy controller, it is possible to say:  
* the fuzzy controller has an integrated PID control unit,
* the fuzzy controller without integrated PID unit can still be stable and ensure the accuracy, by choosing a good fuzzy rule and physical value domain of appropriate input and output variables.
# Robust Sampling Frequency Offset Estimation for OFDM over Frequency Selective Fading Channels
## 3 Conventional Estimation Scheme  
In this section, we briefly revisit the conventional SFO estimation algorithms used \(n\) the DRM system [12; 13; 14] and focus on the MSE performance of the considered estimation schemes. In [12; 13], the differential relation among FRCs is used, while [14] exploits two adjacent OFDM blocks where the sets of pilot subcarriers are located symmetrically and distributed equally around the DC carrier. In order to benchmark the proposed estimation scheme, a robust estimation algorithm which archives frequency estimates in closed-form [22] can be applied to the DRM system.
# TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
## 4 Experiment
### Results of Different TD-LSTM Variants
#### 4.3.2 Impact of Temporal Closeness, Period and Trend  
To prove the effect of time dependence on the performance of the model, we choose the closeness length \(l_{c}\) from 2 to 11 and the periodic length \(l_{p}\) from 0 to 2 to compare the performance of their various combinations. The results at depths of 0 m, 50 m, 100 m, 200 m and 500 m are shown in Figure 5, respectively. We use the x-axis to indicate the number of closeness lengths, three colors to indicate the number of periodic lengths from 0 to 2, and the y-axis to the corresponding MSE value. Figure 6(a) shows the model performance of each parameter combination on the sea surface (0 m). By comparison, at a depth of 0 m, \(l_{c}=2\), \(l_{p}=1\), the model obtains the best MSE. As shown in Figure 6(b), the best performance of the model obtained when \(l_{c}=10\), \(l_{p}=1\). Figure 6(c) reflects the results at 100 m, \(l_{c}=10\), \(l_{p}=1\) bring the best performance. At a depth of 200 m, \(l_{c}=7\), \(l_{p}=1\), the model obtained the best MSE. In the case of a depth of 500 m, \(l_{c}=4\), \(l_{p}=1\) was the best selection.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Methods** & **P1** & **P2** & **P3** & **P4** & **P5** & **Average of MSE** \\ \hline TD-LSTM & 0.0525 & 0.0319 & 0.0158 & 0.0305 & 0.0232 & 0.0308 \\ 8-LSTM & 0.0576 & 0.0293 & 0.0129 & 0.0287 & 0.0280 & 0.0313 \\
24-LSTM & 0.0611 & 0.0314 & 0.0162 & 0.0297 & 0.0227 & 0.0322 \\ TD-SVR & 0.0368 & 0.0269 & 0.0296 & 0.0321 & 0.0348 & 0.0321 \\
8-SVR & 0.0414 & 0.0500 & 0.0203 & 0.0536 & 0.0403 & 0.0411 \\
24-SVR & 0.0389 & 0.0229 & 0.0294 & 0.0455 & 0.0267 & 0.0327 \\ TD-MLPR & 0.0361 & 0.0316 & 0.0353 & 0.0428 & 0.0288 & 0.0349 \\
8-MLPR & 0.0622 & 0.0580 & 0.0302 & 0.0689 & 0.0411 & 0.0521 \\
24-MLPR & 0.0617 & 0.0282 & 0.0503 & 0.0715 & 0.0295 & 0.0482 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of SST predictions in the coral sea area.  
Figure 6: Comparison of performance at different depths.  
Figure 7: Impact of temporal dependences at different depths.  
With the same comparison method, the choices obtained at different depths are different. This result tells us that at different depths, time-dependent parametric matrix fusion should be performed for different depths and selecting appropriate parameters. Only in this way can we get a better model.
## 2 Materials and Methods
### Procedures  
There is an inverted U-shaped relationship between exercise intensity and improvement of cognitive function. The peak of the cognitive function corresponds to moderate-intensity exercise [38; 39]. Moderate-intensity aerobic exercise is defined as that requiring 50% of the peak oxygen consumption (VO\({}_{2}\)), based on the classification of the American College of Sports Medicine exercise levels [40]. Research has shown that moderate-intensity aerobic exercise has a significant effect on cognitive function [41; 42]. A comparison of different durations of exercise revealed that exercise of medium duration (20 min) was more effective in improving cognitive function than short (10 min) and long durations (40 min) [43]. Therefore, we utilized a 20 min moderate-intensity exercise protocol to examine improvements in cognitive function.  
Peak VO\({}_{2}\) was determined via a cardiopulmonary exercise test (CPET), which was conducted before the main experiment. Our protocol consisted of a 4 min rest, 4 min warm-up, cardiopulmonary exercise, and 2 min cool-down. A ramp program with an incremental increase in the workload of 20 W/min was employed using stationary bicycles (Aerobike 75 XLIII; Konami, Tokyo, Japan) and an exhaled gas analyzer (AE-310S; Minato Medical Science, Osaka, Japan). All subjects were instructed to maintain a cadence of 50 rotations per minute (rpm) during the cardiopulmonary exercise test [44]. Exhaustion was defined as follows [45] (1): a plateau in oxygen consumption (VO\({}_{2}\)); (2) respiratory exchange ratio >1.1; (3) HR values near the age-predicted maximal heart rate, calculated as \(220-(0.65\times\text{age})\); (4) a decrease in the cycling cadence to <50 rpm, despite strong verbal encouragement. The highest value obtained for VO\({}_{2}\) was considered the VO\({}_{2}\) peak. An interval of more than 1 week was maintained between the CPET and main experiment.  
Participants sat on a bicycle ergometer in a natural sitting position, and then performed the pedaling exercise; the composition of respiratory gas was monitored simultaneously. After 4 min each of rest, the exercise intensity of the warm-up was set at 20 W and lasted 4 min. The 20 min exercise session was initiated at a workload corresponding to 50% peak VO\({}_{2}\), followed by 20 min of rest post-exercise. Participants performed a 2-back test three times during the pre-exercise period (pre), immediately after the exercise period (post1), and after the 20 min post-exercise rest period (post2). Their reaction times were recorded. fNIRS was used to monitor the differences in the fluctuation of oxygen concentration in the PFC during the entire experiment.
## 2 Materials and Methods
### Model Limitations and Practical Implications  
The developed model offers a powerful tool for practical applications such as zoning recommendations, urban ventilation planning, and pollution sensor placement in cities with complex topography like Almaty.  
However, the model has several limitations: it does not incorporate chemical transformation of pollutants, uses a simplified turbulence scheme, and is limited to a single-hour simulation window.  
Future improvements may include coupling with chemical transport models, real-time sensor feedback loops, and adaptive meshing for localized emission zones. Despite these limitations, the current model remains highly informative for evidence-based urban environmental planning.  
The use of the full compressible Navier-Stokes equations, while computationally intensive, allows for accurate modeling of airflow in a complex urban environment. Although small-scale turbulence is not explicitly resolved (as would be done with LES), the large-scale convective and diffusive transport processes are adequately captured due to the fine spatial grid and physically justified boundary conditions.  
This approach is suitable for mesoscale urban applications, such as identifying ventilation corridors, stratification effects, and pollutant accumulation zones. Future research could explore coupling the current model with turbulence closure schemes (e.g., k-\(\varepsilon\) or LES) to enhance resolution in street-canyon scale flows.
# Analyzing Zone-Based Registration Using a Three Zone System: A Semi-Markov Process Approach
## 3 New Mathematical Model
### Notations and Assumptions  
We define the following notations to analyze the signaling cost on radio channels.  
* C\({}_{\rm p}\): Signaling cost for paging per cell on radio channels
* C\({}_{\rm u}\): Signaling cost for one location registration on radio channels
* \(\theta\): Probability of returning to the registered zone
* \(n\): Number of cells per zone
* \(T_{i}\): Interval between two incoming calls (r.v. (random variable), \(T_{i}\sim\)Exp(1/\(\lambda_{i}\)), E(\(T_{i}\)) = 1/\(\lambda_{i}\))
* \(T_{o}\): Interval between two outgoing calls (r.v., \(T_{o}\sim\)Exp(1/\(\lambda_{o}\)), E(\(T_{o}\)) = 1/\(\lambda_{o}\))
* \(T_{c}\): Interval between two calls (r.v., \(T_{c}\sim\)Exp(1/\(\lambda\)), E(\(T_{c}\)) = 1/\(\lambda\))
* \(T_{m}\): Sojourn time in a zone (r.v., E(T\({}_{m}\)) = 1/\(\mu\))
* \(R_{m}\): Interval between the arrival of the call, and the time when the UE moves out of the zone (r.v.)
* \(\rho\): Incoming calls to mobility ratio (CMR = \(\lambda_{i}/\mu=\rho\))
* \(f^{*}_{m}(s)\): Laplace-Stieltjes transform for \(T_{m}\) (= \(\int_{t=0}^{\infty}e^{-st}f_{m}(t)dt\))  
In addition, we assume the following for square zones, as shown in Figures 1 and 2, in order to analyze the registration load over 3 Zs.  
* The interval between two calls, \(T_{c}\), follows an exponential distribution with mean 1/\(\lambda\).
* The UE's sojourn time in a zone, \(T_{m}\), follows a general distribution with mean 1/\(\mu\).
* The probability that UE returns to the last zone is \(\theta\).
* The probability that UE moves to one of the three zones except the last zone is the same (in other words, \((1-\theta)\)/3).
* Call holding time is so short that it can be ignored.  
Note that, by the additivity of Poisson processes [20], incoming calls with rate \(\lambda_{i}\) and outgoing calls with rate \(\lambda_{o}\) form a total call rate \(\lambda\) (= \(\lambda_{i}\)+ \(\lambda_{o}\)).
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 6 Test Campaigns and Results
### DGNSS-Based Cooperative Positioning
#### 6.3.1 Material and Experimental Setup  
The following results and comments concern the use of two Xiaomi Mi 8 Pro smartphones referred to as Sm01 and Sm02, for which a pairwise cooperation was reciprocally ensured through the proposed framework. The devices were running Android(tm) 10 Q and Dual Frequency GNSS receiver embedded in the Broadcom BCM47755 chipset, enabled to guarantee access to both raw GNSS measurements and GFLP reference position estimates. The data transfer to the CPS was peformed through 4G/LTE civil networks locally available at the tests locations.