esa-sceva/satcom-chunk-collection huggingface dataset:
# UV3D: Underwater Video Stream 3D Reconstruction Based on Efficient Global SFM
## 6 Experiments  
### Lts-L1 RA Confirmation Experiment  
In order to prove the advantage of the LTS-L1 RA in terms of efficiency, we provide a comprehensive evaluation of the accuracy and efficiency, which shows the performance of the L1 RA-IRLS (\(l1\)-norm rotation averaging-iteratively reweighted least squares) and LTS-L1 RA methods.  
We use the 'Notredame' dataset as experimental data, which was published by [PERSON] and [PERSON] [13] in 2014, and includes 64,678 relative rotations and 715 cameras.  
We set 5 indicators to analyze and compare the algorithms, measuring the running time in second, mean errors in degrees, RMS error in degrees, processing rate, and mean error/processing rate. The error in degrees calculation formula is shown in Equation (10), where \(R_{j}R_{i}{}^{-1}R_{ij}{}^{-1}\) is the discrepancy between observations \(R_{ij}\) and the global estimate \(R_{i},R_{j}\), \(\Delta\theta\) is the error in degrees, and the Equation (11) is the mean error in degrees. Equation (12) is the RMS error in degrees, where the number of camera pairs divided by the running time is the processing rate. The experiment was performed on Ubuntu 16.04 (CPU frequency: 2.50 GHz) and the result of test is shown in Table 1:  
\[\Delta\theta=\arccos(\frac{tr(R_{j}R_{i}{}^{-1}R_{ij}{}^{-1})-1}{2}) \tag{10}\]  
\[\Delta\theta_{mean}=\sum_{i=1}^{k}\Delta\theta_{i}/k \tag{11}\]  
\[\Delta\theta_{RMS}=\sqrt{\sum_{i=1}^{k}\Delta\theta_{i}{}^{2}}/k \tag{12}\]  
Due to the experimental data shown above, we can see that the LTS-L1 RA method has a shorter running time compared to L1 RA-IRLS, with a slightly higher mean error and lower RMS error. We can see that the processing rate is increased by 24.96%. In order to evaluate the algorithm synthetically, we include a new parameter in our experiment to measure the overall efficiency of the algorithm, the mean error/processing rate. We can see that it is 19.11% lower than for L1 RA-IRLS, meaning our method performs better in efficiency than L1 RA-IRLS.
# Ocean Wind Retrieval Models for RADARSAT Constellation Mission Compact Polarimetry SAR
## 2 Materials and Methods
### CoVe-Pol Model for Right Circular-Vertical (RV) Polarization
#### 2.2.1 Sensitivity Analysis
It is apparent that the coefficients can be divided into three groups according to their  
Figure 5: The mean sensitivity factors vs. coefficients: (**a**) radar incidence angle is 25 degree; (**b**) radar incidence angle is 35 degree; (**c**) radar incidence angle is 45 degree. The colors represent different wind directions (\(15^{\circ}\), \(30^{\circ}\), \(45^{\circ}\), \(60^{\circ}\), \(75^{\circ}\), \(90^{\circ}\)). degrees of influence. The \(SAF\) values corresponding to coefficients \(c_{1}\), \(c_{7}\), \(c_{9}\) are more than 1. The \(SAF\) values corresponding to coefficients \(c_{2}\), \(c_{3}\), \(c_{10}\), \(c_{11}\), \(c_{19}\), \(c_{21}\), \(c_{27}\), and \(c_{28}\) are in the range from 0.1 to 1, and the remaining \(c_{i}\) values are under 0.1. It is notable that the coefficients with higher \(SAF\) magnitudes play a part in the adjustment process of the coefficients \(c_{i}\), for lower orders of magnitude; but this influence does not work, if we put things the other way around. For example, assuming a \(SAF\) value of 1.3219, the corresponding \(c_{i}\) coefficients cannot influence the computed NRCS values above \(10^{0}\) order of magnitude, but these \(c_{i}\) coefficients do influence NRCS values below \(10^{1}\) magnitude such as \(10^{0}\) and \(10^{-1}\) magnitudes, as reported in this study. Moreover, because \(SAF\) values for typical wind directions have similar orders of magnitude, we give the average \(SAF\) values for the \(c_{i}\) coefficients in Table 1.
## 3 Results
### Optimizing Electrode Selection for EEG-Based TEAS Frequency Classification Using Saliency Maps  
In this section, the goal was to refine the electrode selection for EEG-based classification of TEAS frequencies by identifying the optimal subset of electrodes that maintain high classification accuracy. The selection of electrode combinations was provided by a saliency map analysis, which identified the most salient electrodes for phase discrimination across four different stimulation frequencies of 2.5 pps, 10 pps, 80 pps, and Sham. The topographic distributions of aggregated saliency maps over all the 12 phase combinations for various TEAS frequencies are shown in Figure 5. From Figure 5, electrodes Fp1, Fp2, Fz, F8, F7, T4, and T3 had the highest scores, in terms of differentiating the three phases--pre-stimulation, during-stimulation, and post-stimulation--across all the TEAS frequencies. Beginning with the two most significant electrodes, Fp1 and Fp2, the accuracy scores for each TEAS frequency were determined for various combinations of sorted electrodes, as indicated in Table 6. The line chart of the accuracies versus the combinations is shown in Figure 6. From Figure 6, the first combination (comb1) included the prefrontal electrodes, Fp1 and Fp2, providing a pre-stimulation accuracy between 47.6% and 51.7% across the TEAS frequencies. By expanding this set to include the frontal midline electrode Fz (comb2), associated with the midline frontal cortex, there was a noticeable improvement in classification accuracy, highlighting the significance of this region in processing TEAS phases. Further additions of electrodes, such as F7 and F8 in comb3, which were over the left and right dorsolateral prefrontal cortex, and T3 and T4 in comb4, over the left and right temporal  
Figure 4: Averaged classification accuracy at each EEG frequency band for different TEAS frequencies across time.  
areas, led to a substantial improvement in classification accuracy. Notably, the seven-electrode combination (comb4), which encompassed the prefrontal, midline frontal cortex, dorsolateral prefrontal cortex, and temporal areas, achieved performance levels close to that of the full 19-electrode set (all electrodes), which spanned the entire scalp. This finding indicates that the additional electrodes beyond the seventh did not contribute significantly to the model's performance, emphasizing the efficiency of the chosen subset of electrodes. This has profound implications for the application of EEG in clinical and research settings, suggesting that a reduced set of electrodes can provide efficient and reliable classification performance, potentially streamlining EEG protocols and improving patient comfort.
# Optical Interference Suppression Based on LCD-Filtering
## 4 MIMO Communication
With one transmitter active at a time encoding the source bits in the spatial domain and as conventional signal constellation, a transmit rate of \(\log_{2}(N_{t})+\log_{2}(M)\) can be achieved. Since a zero power constellation point would not allow the receiver to estimate the spatial information, the \(M\)-PAM constellation is altered in the case of SM to avoid the zero symbol. This modification reduces the minimum distance compared to conventional \(M\)-PAM but enables the use of the spatial domain. Results on the upper bounded BER at fixed efficiencies of \(4\,\mathrm{bits/s/Hz}\) and \(8\,\mathrm{bits/s/Hz}\) are shown in Figure 8. Generally speaking, the LCD filter offers a strong decorrelation of the signal sources at the cost of a decrease in signal power and by this a relative increase in receiver-side noise. For SM and SMP, the decorrelation gain by using the filter clearly outweighs this signal power reduction. Compared to the conventional MIMO receiver with a transmitter distance of \(d_{t}=0.4\,\mathrm{m}\), the SNR gain is approximately \(11\,\mathrm{dB}\) for SM and \(27\,\mathrm{dB}\) for SMP. With a lower distance of \(d_{t}=0.2\,\mathrm{m}\), these gains increase to \(18\,\mathrm{dB}\) for SM and \(40\,\mathrm{dB}\) for SMP. The higher gains for SMP show that the impairment of the modified constellation diagram for SM is outbalanced by the additional degree of freedom. At low transmitter distances, the conventional receiver performance deteriorates because of decreasing channel decorrelation. In the case of \(d_{t}=0.6\,\mathrm{m}\), the assumption of a very narrow receiver-side FOV in [13] leads to a perfect separation of the different sources without any filtering necessary. This result is questionable since diffuse signal components introduced by reflections are not taken into account and will destroy the assumed ideal scenario. The similar applies to a change of position/orientation of transmitter/receiver. Figure 8: _Cont._
# FPGA Applied to Latency Reduction for the Tactile Internet
## 3 Discrete Model of the Tactile Internet
Reaction signals grouped into \(\mathbf{o}(n)\) can be in the form of strength, touch, temperature, etc. Reaction signals are captured by the SD that turns this information into electrical signals from real or virtual sensors, if the SD is in a virtual reality environment. After capturing this information the SD transmits these signals to the HSD. In the model presented in Figure 1, the signals generated by the SD are expressed as  
Figure 1:0where \(g_{i}(n)\) is the \(i\)-th signal generated by the SD at the \(n\)-th instant of time, \(t_{s}\) and \(N_{SD}\) is the amount of signals. The HSD in turn processes this information and sends to the NW subsystem through the array \(\mathbf{h}(n)\), expressed by  
Figure 1:1  
where \(h_{i}(n)\) is the \(i\)-th signal generated by HSD at the \(n\)-th instant of time, \(t_{s}\) and \(N_{HSD}^{b}\) is the amount of signals. The signal received by the HMD through the backwards channel of the NW subsystem can be expressed as  
Figure 1:2  
where  
Figure 1:3  
in which, \(r_{i}^{b}(n)\) represents an added noise and \(d_{i}^{b}(n)\) represents a delay associated with the \(i\)-th information transmitted in \(\mathbf{q}(n)\) by the backwards channel. Similarly to the forward channel, noise can also be characterized as a random variable Gaussian of zero mean and variance \(\sigma_{rb}^{2}\) and delays are characterized as integers with \(t_{s}\) granularity. The HMD processes the \(\mathbf{q}(n)\) signal information and generates a set of control signals that will act on the MD and can be characterized as  
Figure 1:4  
where \(p_{i}(n)\) is the \(i\)-th signal generated by the HMD at the \(n\)-th instant of time \(t_{s}\) and \(N_{HMD}^{b}\) is the number of signals. The MD in turn will synthesize the reaction stimuli generated by the environment, i.e., the ENV subsystem. Based on the model, it is possible to characterize these reaction stimuli as a signal expressed by  
Figure 1:5  
where \(\partial_{i}(n)\) is an estimate of the \(i\)-th stimulus \(o_{i}(n)\) generated in the ENV subsystem. Examples of reaction stimuli generated or synthesized by MD are touch, strength and temperature. In addition to the latency associated with the NW subsystem that characterizes the communication medium between the OP and ENV subsystems, the MD, HMD, HSD, and SD subsystems also add latency to the system. Based on the work presented in [13; 14] these components represent 30% of total latency. The latency of the MD and SD subsystems are associated with sensors and actuators that can be mechanical, electrical, electromechanical and other variations. HMD and HSD latencies are associated with the processing time of the algorithms in these devices and depending on the type of hardware and implementation architecture this latency can be considerably reduced.
# Magnetohydrodynamic Nanoliquid Thin Film Sprayed on a Stretching Cylinder with Heat Transfer
###### Abstract  
The magnetohydrodynamic thin film nanofluid sprayed on a stretching cylinder with heat transfer is explored. The spray rate is a function of film size. Constant reference temperature is used for the motion past an expanding cylinder. The sundry behavior of the magnetic nano liquid thin film is carefully noticed which results in to bring changes in the flow pattern and heat transfer. Water-based nanofluids like Al\({}_{2}\)O\({}_{3}\)-H\({}_{2}\)O and CuO-H\({}_{2}\)O are investigated under the consideration of thin film. The basic constitutive equations for the motion and transfer of heat of the nanofluid with the boundary conditions have been converted to nonlinear coupled differential equations with physical conditions by employing appropriate similarity transformations. The modeled equations have been computed by using HAM (Homotopy Analysis Method) and lead to detailed expressions for the velocity profile and temperature distribution. The pressure distribution and spray rate are also calculated. The comparison of HAM solution predicts the close agreement with the numerical method solution. The residual errors show the authentication of the present work. The CuO-H\({}_{2}\)O nanofluid results from this study are compared with the experimental results reported in the literature showing high accuracy especially, in investigating skin friction coefficient and Nusselt number. The present work discusses the salient features of all the indispensable parameters of spray rate, velocity profile, temperature and pressure distributions which have been displayed graphically and illustrated.  
magnetohydrodynamic; nanoliquid thin film; Al\({}_{2}\)O\({}_{3}\)-H\({}_{2}\)O; CuO-H\({}_{2}\)O; spray; heat transfer; stretching cylinder; homotopy analysis method; numerical method; residual errors; skin friction coefficient; Nusselt number +
Footnote †: journal: Applied Physics Letters B
# Active RIS-Assisted Uplink NOMA with MADDPG for Remote State Estimation in Wireless Sensor Networks
## 3 MADDPG-Based Resource Allocation  
In this section, we first reformulate the optimization problem proposed in Problem 2 as a Multi-Agent Markov Decision Process (MDP) and then solve it using the MADDPG, Algorithm 2. As shown in Figure 2, the proposed system architecture follows a centralized training and decentralized execution (CTDE) framework, which is well suited to distributed wireless sensor networks. Specifically, the MADDPG algorithm is trained offline at a fusion center or an edge server, where both the actor and critic networks are optimized based on collected environmental data. After training, only the lightweight actor networks are deployed to individual sensor nodes, enabling each agent to independently make decisions based on its local observation without the need for online coordination. This ensures scalability and practicality in decentralized scenarios. Regarding the deployment of an active RIS, we assume it is installed at a fixed infrastructure location (e.g., a gateway node or controller hub), where an external or a battery-based power supply is available. Each active element is equipped with a low-power amplifier, and the number of elements is limited to ensure that the overall power consumption remains within practical bounds. The actions related to RIS beamforming are optimized jointly with the power control and sensor grouping through learned policies.  
These implementation considerations ensure that the proposed active RIS-assisted MADDPG architecture is both theoretically grounded and practically feasible for deployment in low-power, distributed WSN environments.  
Figure 2: Schematic of MADDPG.
# Non-Vertical Well Trajectory Design Based on Multi-Objective Optimization
## 3 Establishment of Optimization Model
### Selection of Optimization Index
\[Q=\int_{0}^{L}\Bigl{(}\tau^{2}(x)+\theta^{2}(x)\Bigr{)}\mathrm{d}x \tag{13}\]  
In the formula, variable is the deflection of the wellbore trajectory, reflecting the degree of deviation of the trajectory from the plane curve; \(\tau\) is the curvature of the trajectory with length \(L\). An arc-shaped deflecting trajectory is designed based on the minimum curvature  
Figure 5: Force diagram of a micro-segment of drill string. method. Its curvature is constant, and torsion is 0. For the vertical section or the stable section, the curvature and torsion are both 0. (5) Target accuracy  
The accuracy of the target directly determines the contact length of the effective reservoir. Under complex geological conditions (such as thin reservoirs, fault development areas), the accuracy of the target is a prerequisite for successful drilling. If the target area is not hit, the drilling hole needs to be supplemented or the trajectory needs to be adjusted, which significantly increases the time and cost. Deviation from the target area may lead into dangerous areas such as high-pressure layers and faults, causing blowout or wellbore instability. The target accuracy is usually quantified by the spatial deviation between the actual wellbore trajectory and the design target. The calculation expression of the target accuracy is as follows:  
\[\delta=\sqrt{\Delta N^{2}+\Delta E^{2}+\Delta Z^{2}}. \tag{14}\]  
Among the variables, \(\Delta E\) is the deviation between the actual target and the design target in the east coordinate direction; \(\Delta N\) is the deviation between the actual target and the designed target in the north coordinate direction; \(\Delta Z\) is the deviation between the actual target and the designed target in the vertical direction.
## 6 Implementation and Evaluation
### Accuracy of ADL Measurement  
Table 2 presents the accuracy of activity measurements using the proposed system. In this evaluation, we considered the following activities: passage through a door and usage of home appliances (faucet, gas stove, refrigerator, microwave, and rice cooker). The calculation of accuracy was made in comparison with user actions. Activity measurements for using a faucet, refrigerator, microwave, and rice cooker are accomplished by the tag device's BMI160 inertial measurement unit (IMU) that includes a three-axis accelerometer and a three-axis gyroscope. According to the tag device's implementation, the faucet should be the up-down type, and the direction of opening an item such as a faucet, refrigerator door, microwave door, or rice cooker should be aligned with the \(y\)-axis of the BMI160, a restriction we maintained. Most measurements are more than 95% accurate, except for the low accuracy of turning off the faucet, which, based on our analysis, may be caused by the physical vibration from the water flowing out when the faucet is turned on, due to which the MCU of the tag device is continuously interrupted by its accelerometer. To solve this, we hypothesized that it is appropriate to read the acceleration value at regular intervals to detect the faucet being turned off. For the detection of turning on the faucet, the method using an acceleration interrupt seems suitable as initially designed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**State of Tag Device** & **Maximum (mA)** & **Minimum (mA)** & **Average (mA)** \\ \hline Advertising & 12.245 & 0.800 & 2.755 \\ Standby & 5.690 & 0.803 & 0.895 \\ Active & 6.579 & 1.640 & 1.751 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Power consumption of tag device according to its state.  
Figure 9: Power-consumption waveform of the tag device.
## 2 Materials and Methods
### Experimental Characterization Setup  
The experimental set-up used to characterize the filters is shown in Figure 4. The optical input is the broadband ASE (amplified spontaneous emission) from an Erbium-doped fiber amplifier (EDFA). The ASE is first sent to an optical isolator, and then to a 1:99 fiber power splitter--1% of the light is used as a reference to monitor the ASE output, while the remaining 99% is sent to the DUT (device-under-test). 1D-grating couplers are used to couple the light from the ASE fiber-source to/from the Si-PIC with the integrated filters. The output signal from the DUT, together with the 1% monitor signal, are monitored by an optical spectrum analyzer (OSA), using a MEMS-activated optical switch to acquire both spectra. Using stabilized current generators, the resonance wavelengths of the individual rings in the filters can be precisely thermally-tuned.  
Figure 4: Experimental set-up. EDFA: erbium doped fiber amplifier; FPS: fiber power splitter; OSA: optical spectrum analyzer. The continuous lines represent the fiber optic connections.  
Figure 3: (**a**) Section view of the chip; (**b**) Microscope-image of the double-ring filter with tuning electrodes.
## 4 Functional Model of the Framework
### Level 3--Analysis of Historical Data
#### 4.3.2 Second Step--Data Processing and Cleaning  
This step aims to process and clean the data for standardization and normalization. First, we process null values by either deleting the entire record or replacing the null with the mean or median of the values of other records. Second, outliers are also treated in the same way as null values. They are deleted or replaced with the mean or median value of other records in order to isolate their negative impact on the accuracy of the results and learning. Third, we convert the nominal or categorical data into numbers to enable their processing. As an example, the level of congestion is expressed in numerical values ranging from 1 to 4, with 4 being highest level of congestion. Subsequently, scaling is performed so that the value of each column is scaled between 0 and 1. Furthermore, we delete dummy data, using the One-Hot Encoder algorithm [64]. For example, after encoding the column for the congestion level, one of the previously mentioned four columns can be omitted because it is possible to deduce its value without its presence. In other words, if the value of the mean, the extreme, and the stop are equal to zero, then this means that the normal is one, without the need for it to exist in the database. Next, we process the data for standardization. For this purpose, we can use one of two methods: normalize or standardize, as shown in Equations (3) and (4).  
\[N=\frac{[X-Min(x)]}{[Max(x)-Min(x)]} \tag{3}\]  
\[S=\frac{[X-Mean(x)]}{SD(x)} \tag{4}\]  
Figure 3: Schema for the data received by the cloud from different sources.  
Furthermore, we merge or delete similar data to prevent duplication. Finally, we ensure that the data are balanced so that the value of each class of the output variable (e.g., the congestion level) is as close as possible to the number of records of the other class. Consequently, some records in small categories may be duplicated or some records in larger categories may be omitted.