esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
## 3 From the Synthesis of a 3D Scene to Its Safe Reception: Process Description
### Decryption and Visualization of the Scene Stored in the Cgh  
Decryption (Figures 1d and 3b) involves undoing the steps from the previous section. In the decryption process, this random phase contribution is subtracted from the SPCV to recover the SPV, as follows:  
\[SPV[i]=SPVC[i]-PRV[i]\quad\forall\quad i\in\{1,\dots,Z\} \tag{3}\]  
The original order is then restored to obtain the CGH-PV using the indices stored in the RSV (Figure 3b). Negative values resulting from this subtraction are adjusted by adding the modulo to ensure that they remain within the allowed range of phase values.  
From this vector, the phase matrix of the CGH is obtained to recover an image file (png format). With the correctly decrypted CGH, the original scene can be recovered. This step corresponds to Figure 1e. Figure 5 shows the results for phase holograms encoded with integer values [0-255] corresponding to images in PNG format. In our case, since the phase information is encoded with integer numbers, no information is lost.
# _Sci_
## 3 Results
### Technical Challenges of Employing Lasers versus Radio Frequency Communications
#### 3.2.2 Acquisition, Tracking, and Pointing  
While a narrow beam is advantageous for thwarting detection, interception, and exploitation (discussed later), and for lowering transmitting powers, it does necessitate higher precision in aligning sending and receiving stations. With a small beam divergence (relative to a traditional radio frequency signal), laser communications must address \"leading\" the receiver by a point-ahead angle. This angle is required due to the finite speed of light and the relative motion of the satellite. This is not a negligible value and may amount to tens of microradians [37]. Just as beam width is dependent on wavelength and the diameter of the optical aperture of the transmitting telescope, so too is pointing accuracy, where accuracy is cited in [37] as the wavelength divided by twenty times the aperture diameter.  
In some cases, the pointing accuracy of a centimeter-class antenna must be within an accuracy of one micro-radian [37]. Laser beam pointing error is indicated by a special vibration term, while the receiving optical platform error maintaining pointing towards the transmitter are dealt with in the signal's budget for power [59].  
Bifocal Relay Mirror Spacecraft  
Substantial developments in acquisitions, tracking, and fine pointing evolved in the twenty-first century and continue to this day [60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72], including substantial development in target acquisition [61; 62; 63; 64; 65; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81], target tracking [62; 63; 75; 76; 78; 81; 83; 84; 85; 86; 87; 89; 90; 91], and fine pointing [60; 73; 74; 77; 79; 80; 82; 88] at the Naval Postgraduate School, among several other institutions achieving microradian pointing accuracy and closely pursuing nano-radian accuracy.
# Deriving Controllable Local Optimal Solutions through an Environment Parameter Fixed Algorithm
## 4 Experiments
### Additional Validation of the CLS
Compared to the initial MD especially, when the initial value for SD was 101 deg, we can see that MD is much higher, even if it moves only 20 deg. In Figure 10, each heading displays its initial value. The size of the graph indicates the shape of the graph in which the initial value of the environment parameter is reflected. By conducting random tests for various scenarios, EPFA-CLS demonstrates its ability to be derived from initial values. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Initial Value (Random)**} & \multicolumn{4}{c}{**Final Value (CLS)**} \\ \hline
**No.** & **SD [deg]** & **SV [m/s]** & **WD [deg]** & **WV [m/s]** & \(\mathbf{MD}_{initial}\) **[m]** & \(\mathbf{CLS_{SD}}\) **[deg]** & \(\mathbf{CLS_{SV}}\) **[m/s]** & \(\mathbf{MD}_{maximum}\) **[m]** \\ \hline
1 & 101 & 8 & 15 & 2 & 79.3849 & 180.9525 & 15 & 387.5419 \\
2 & 117 & 11 & 322 & 6 & 57.7895 & 188.0334 & 15 & 279.0536 \\
3 & 142 & 14 & 242 & 5 & 308.4076 & 174.3665 & 15 & 381.5254 \\
4 & 171 & 4 & 237 & 1 & 187.3034 & 149.0326 & 12.1728 & 416.7096 \\
5 & 209 & 12 & 356 & 1 & 250.1607 & 172.6729 & 15 & 401.1164 \\
6 & 220 & 4 & 281 & 7 & 109.1765 & 195.6159 & 15 & 262.6963 \\
7 & 232 & 7 & 231 & 7 & 203.9565 & 196.0379 & 15 & 319.9787 \\
8 & 238 & 6 & 161 & 3 & 120.9584 & 148.9174 & 15 & 424.4204 \\
9 & 252 & 9 & 148 & 3 & 141.9169 & 148.0669 & 15 & 411.2176 \\
10 & 254 & 7 & 223 & 15 & 332.7779 & 196.8761 & 15 & 586.8834 \\
11 & 263 & 1 & 232 & 7 & 107.3001 & 195.9918 & 15 & 319.0679 \\
12 & 311 & 7 & 256 & 6 & 70.8144 & 189.7698 & 15 & 335.1269 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Table illustrates the results of CLS derivation for 12 random initial values using EPFA-CLS. Figure 9: The figure represents the distribution of optimized results obtained by setting all values from the Boston Housing dataset as initial values and introducing four random control parameters. The histograms depict the control parameters before and after optimization using each of the NOX, RM, DIS, and TAX control parameters, compared to the initial values shown in black. The distribution of Boston Housing Prices is shown in black to observe the changes in housing prices after optimization. The red color represents NOX, yellow represents RM, blue represents DIS, and green represents TAX.
# Three-Dimension Inversion of Magnetic Data Based on Multi-Constraint UNet++
## 3 Method Testing
### Noise Resistance Comparison  
In actual production operations, there are always different degrees of noise in the collected geophysical signals, so the robustness of the geophysical inversion method is very important. To test the anti-jamming ability of the multi-constraint DL method, we added Gaussian random noise conforming to the normal distribution, the amplitude of which is 2% or 4% of the amplitude of the corresponding data. The experimental results of noise resistance of this method are shown in Figure 15. The inverse results before and after adding noise were compared to reflect the strength of noise resistance. Similarly, in order to quantitatively describe the strength of the noise resistance, we continued to use the two indicators of model-fitting difference and data-fitting difference.  
We chose a more complex synchine test model to test the noise resistance of the proposed method, and noted its inversion effect under an amplitude of noise interference of 2% and 4%. At the 2% maximum amplitude noise, the model-fitting difference of the inversion results is 0.0017, and the data-fitting difference is 1.5021 nT. Under the maximum amplitude noise of 4%, the model-fitting difference of the inversion results is 0.0018, and the data-fitting difference is 1.7145 nT.  
Figure 15: The noise resistance test results: (**a**) the magnetic anomaly data without noise; (**b**) the result of multi-constraint DL prediction; (**c**) the inversion results without noise; (**d**) the synchine prism; (**e**) the inversion results of magnetic anomaly data with 2% noise; (**f**) the forward magnetic anomalies of the inversion results under 2% amplitude noise; (**g**) the data-fitting difference under 2% amplitude noise; (**h**) the magnetic anomaly data with 4% amplitude noise; (**i**) the inversion results of the magnetic anomaly data with 4% noise; (**j**) the forward magnetic anomalies of the inversion results under 4% amplitude noise; (**k**) the data-fitting difference under 4% amplitude noise.
# Effects of Blade Extension on Power Production and Ultimate Loads of Wind Turbines
## 4 Dynamic Load Evaluations
### Ultimate Loads in All Operating Conditions  
In the above sections, the ultimate load variation and dynamic load response under the typical wind conditions including the NTM, ETM, EOG, ECD and EWS were analyzed. The main ultimate loads influenced by the blade extension on the blade root and tower bottom for all the design load cases (DLC) are shown in Tables 15 and 16. For comparison, the loads for the turbine with different blade extension lengths and the original design are computed. The ultimate loads of blade root and tower bottom show an obvious increase with the blade length. The load increase is higher than 5% after blade extension. In particular, when the blade is extended by 2 m, the loads such as \(M_{y}\) of the blade root and tower bottom, etc., are increased over 10%.  
Steady loads at blade root and tower bottom are also shown in the tables for comparison. It is found that dynamic loads are generally more affected by the blade extension than the steady loads. The ultimate thrust at the hub for all the design load cases are shown in Table 17, and a similar load increase percentage as that of the blade root and tower bottom can be observed.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**37.5 m**} & \multicolumn{2}{c}{**38.5 m/37.5 m**} & \multicolumn{2}{c}{**39 m/37.5 m**} & \multicolumn{2}{c}{**39.5 m/37.5 m**} \\ \hline  & **Dynamic** & **Steady** & **Dynamic** & **Steady** & **Dynamic** & **Steady** & **Dynamic** & **Steady** \\ \hline \(M_{y}\) & 24,004 & 12,249.6 & 105.49\% & 102.43\% & 111.84\% & 103.88\% & 115.43\% & 104.42\% \\ \(F_{x}\) & 415.5 & 209.888 & 98.53\% & 102.18\% & 104.02\% & 103.55\% & 107.20\% & 103.99\% \\ \hline \hline \end{tabular}
\end{table}
Table 16: The main ultimate loads affected by blade extension on the tower bottom.  
Figure 17: Load time series of \(M_{y}\) at blade root.
# IRONEDGE: Stream Processing Architecture for Edge Applications
## 5 Results and Comparison
### Loss Rates and Latency  
Consider now Figure 7, which presents the recorded loss rates and average latency.  
In Figure 7, for K0-WC, there was significant loss starting from only 2000 Data Reports per second, at 16.3%, and reaching a peak of 90.6% for 14,000 Data Reports per second. Contrary to K1-NC, no loss was recorded until the test case for 14,000 Data Reports per second, at 1.5%. As this marks the point at which both implementations exceeded a maximum acceptable level of 1% of loss for the Data Reports, this was chosen as the cut-off point for further test cases. For the average latency, as presented in Figure 7, once again,  
Figure 6: Resource usage for each implementation and Data Reports’ rate. (**a**) Average memory utilization. (**b**) Average CPU utilization.  
Figure 7: Implementation performance measured as loss rates and latency, for each implementation and Data Reports’ rate. (**a**) Loss rates. (**b**) Average end-to-end latency.  
K1-NC showed significant performance increases when compared with K0-WC. There was a decrease in average latency from 52.2 ms to 19.8 ms. This gap in latency increased significantly for higher Data Report rates. The collected data suggest, therefore, that the observed increase in pressure on the limited computing resources was sufficient to prevent the accurate processing of received Data Reports in a timely fashion. The average latency values for K1-NC never exceeded 275.2 ms, for 8000 Data Reports per second. While K0-WC showed a maximum of 55.042 ms, this was a full 55.042 s to deliver the Events resulting from each Data Report, for 14,000 Data Reports per second. The occurrence of latency peaks at 8,000 Data Reports per second is of particular interest. This effect may be explained by the internal batching size used in Apache Storm, which was set to 5000 records, as previously shown in Table 3.  
For 8000 Data Reports, the last 3000 had to wait for the eventual arrival of additional messages over the next second, increasing the average latency. For 10,000 Data Reports, two batches may be created in the same second, resulting in a decrease. This effect continued for 12,000 and 14,000 Data Reports, but the first 10,000 Data Reports that were successfully batched attenuated the effect when considering the average latency. From the loss rate and latency, it is clear that the removal of Kafka Connect as an interconnecting component offered significant performance improvements for the proposed architecture. This is in spite of the fact that Figure 6 shows that resource usage did not increase with higher Data Reporting rates.
# Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
## 4 Propulsion Systems in Nanosatellites
### Taxonomy Nanosatellites' Propulsion Systems
#### 4.2.2 Electric Propulsion Systems
It provides between 0.085 and 0.5 mN of thrust with a maximum specific impulse of 3200 s, making it well suited for long-duration missions that require precise attitude control [58]. Busek's micro-ion propulsion systems are also used in various CubeSat missions for orbital adjustments and station-keeping [60]. A recent innovation is the Miniaturized Micro Ion Thruster, which utilizes advanced microfabrication processes to achieve high performance in a compact form factor designed explicitly for CubeSats [66]. Hall effect thrusters generate thrust by ionizing a propellant gas, such as xenon, using an electric field. The ions are accelerated by the Hall effect, where electrons trapped in a magnetic field create an electric field that accelerates the ions out of the thruster [67]. Figure 6: Ion Thruster Propulsion System. Hall thrusters balance efficiency and thrust, making them suitable for various missions, including satellite station-keeping and interplanetary travel [68]. The schematic of a hall thruster propulsion system is shown in Figure 7. Hall thrusters require power to ionize the propellant and maintain the electric and magnetic fields to accelerate the ions. Also, additional power is needed to sustain the magnetic field. The power (\(P\)) required is similar to that of ion thrusters and is calculated as shown in Equation (11).
# Towards Collaborative and Dynamic Spectrum Sharing via Interpretation of Spectrum Access Policies
## 5 Policies
### Controller and SDR Platform  
As was shown in Figure 2, the radio platform includes two kinds of processing: RF and Digital. While the RF processing is responsible for receiving and transmitting radio signals, the Digital processing is responsible for receiving and sending out digital information converted from and to the radio signals, respectively. Finally, the digital information is exchanged with the radio user via dedicated I/O streams.  
In the case of SDR, the radio platform also has a Software Control module, which is used for setting RF and Digital hardware and software, as well as controlling the radio behavior. The Software Control module interacts with the Controller module (c.f. Figure 4) of the Cognitive Radio Engine through the Controller's API. This API handles both control messages, e.g., transmission requests, and data messages, e.g., packets, that the CRE needs to send to other radios. In addition to message handling, the Controller's API is also used for fetching policies from a Policy Authority, as needed.  
One of the primary responsibilities of the Controller is the translation between the language used to capture information in Software Control and the language used in the CRE. Since on the CRE's side, everything is expressed in OWL and SPARQL, and on the Software Control side, either in JSON or CORBA IDL, the Controller must support this \"adaptation\". The Controller passes policies to the Policy Manager component and allows the CRE to get _meters_ and set _knobs_, through which the radio platform is monitored and controlled.
# A Novel Reconfigurable Vector-Processed Interleaving Algorithm for a DVB-RCS2 Turbo Encoder
## 4 New Approach: Parallel Vector Processing Based Algorithm
### Bit to Word  
As in other DSP architectures, the CEVA-XC4500 DSP requires padding of the original input data for more efficient data manipulation. Hence, the first phase in the algorithm is padding every input data bit with 15 zeros. Consequently, every original input-data bit is now represented as one Word (16 bits) as shown in Figure 3.  
The CEVA-XC4500 DSP is capable of storing 512 bits in one store operation. Following that \(16\times 32=512\), loading 32 bits of the original input-data stream will resolve in 512 bits of padded data as the result of one iteration of this phase. The number of iterations needed for all the original input-data stream to be processed is \(\left\lceil\frac{2N}{N2}\right\rceil\), where N is the number ofcouplets in bits (see Figure 1). Hence, the output vector of this phase is of size \(16\times 2N\) [bits] or \(2N\) [words].  
Notes:  
1. The permutation stage is identical to every two bits of the original input couplet (A and B), and so we refer to every padded couplet as a Double-Word (DW). In addition, the architecture of the CEVA-4500 DSP is designed to processes DWs; therefore, the next two phases of permutation are performed on \(N\) DWs.
2. The Bit-to-Word phase is added as an input rearrangement requirement of the specific DSP implementation. Four other processors will/will not require different input rearrangements, which will affect the results.
# Mobility-Multihoming Duality
## 2 Combined Mobility and Multihoming
### Quic  
QUIC [5] is a comprehensive UDP-based transport protocol. It offers direct support for much of the functionality present within MP-TCP (e.g., congestion control, flow control, sub-flows, etc.) but with improved communication session control and integrated security. Support for multipath in QUIC is currently a work in progress [6]. However, mobility support in QUIC does not yet exist, though it is conceivable that similar mechanisms to those of MP-TCP could be designed.
## 3 Case Study
### Data Prediction and Analysis
This demonstrates that the spatiotemporal monitoring data between displacement sensors in different areas of the landslide are utilized under the framework of tensor complementary structure, and the deformation law characteristics of short-term correlation and long-term deformation consistency in the inherent deformation process of the landslide are fully considered.
# Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading
## 4 Experiments
### Comparison Experiments
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**MovieLens**} & \multicolumn{4}{c}{**Accuracy**} & \multicolumn{2}{c}{**Diversity**} \\ \cline{2-7}  & **P@100** & **R@100** & **F1@100** & **NDCG@100** & **H@100** & **I@100** \\ \hline \multirow{2}{*}{\begin{tabular}{c} Probs \\ Heats \\ HybridGCN \\ LGCNHS \\ \end{tabular} } & 0.038 & 0.491 & 0.072 & 0.042 & 0.368 & 0.380 \\  & 0.058 & 0.603 & 0.106 & 0.080 & 0.545 & 0.341 \\  & 0.059 & 0.618 & 0.108 & 0.082 & 0.579 & 0.332 \\  & 0.046 & 0.422 & 0.083 & 0.058 & 0.365 & 0.387 \\  & 0.065 & 0.674 & 0.119 & 0.092 & 0.720 & 0.298 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} **Douban** \\ Heats \\ HybridGCN \\ LGCNHS \\ \end{tabular} } & \multicolumn{4}{c}{**Accuracy**} & \multicolumn{2}{c}{**Diversity**} \\ \cline{2-7}  & **P@100** & **R@100** & **F1@100** & **NDCG@100** & **H@100** & **I@100** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Probs \\ Heats \\ HybridGCN \\ LGCNHS \\ \end{tabular} } & 0.002 & 0.025 & 0.004 & 0.002 & 0.989 & 0.163 \\  & 0.002 & 0.025 & 0.004 & 0.002 & 0.989 & 0.165 \\  & 0.004 & 0.045 & 0.008 & 0.005 & 0.993 & 0.189 \\  & 0.002 & 0.020 & 0.003 & 0.002 & 0.717 & 0.083 \\  & 0.005 & 0.056 & 0.009 & 0.006 & 0.977 & 0.091 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Compare the results of the test (@100). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**MovieLens**} & \multicolumn{4}{c}{**Accuracy**} & \multicolumn{2}{c}{**Diversity**} \\ \cline{2-7}  & **P@50** & **R@50** & **F1@50** & **NDCG@50** & **H@50** & **I@50** \\ \hline \multirow{2}{*}{ \begin{tabular}{c} Probs \\ Heats \\ HybridGCN \\ LGCNHS \\ \end{tabular}  } & 0.047 & 0.328 & 0.082 & 0.050 & 0.440 & 0.430 \\  & 0.084 & 0.460 & 0.142 & 0.110 & 0.642 & 0.382 \\  & 0.087 & 0.476 & 0.147 & 0.114 & 0.674 & 0.376 \\  & 0.063 & 0.301 & 0.104 & 0.076 & 0.423 & 0.425 \\  & 0.098 &These results indicates that LGCNHS can achieve excellent recommendation performance in various scenarios, further demonstrating the potential and effectiveness of combining spreading-based recommendation algorithms with deep learning methods. Additionally, the adaptability of LGCNHS is one of its advantages. Unlike traditional spreading-based recommendation algorithms, which are confined to processing user-item interaction data, LGCNHS is capable of effectively handling diverse types of complex data, including user-item interaction data, user features, and item features. This capability makes LGCNHS valuable in applications such as e-commerce recommendations, movie recommendations, and music recommendations, offering new perspectives for both future research and practical applications.
# A Method Based on an Autoencoder for Anomaly Detection in DC Motor Body Temperature
## 2 Materials and Methods  
### Anomaly Dataset for DC Motor Body Temperature  
The anomaly dataset was created by using DC motor experimental equipment, which was designed specifically for this study, shown in Figure 1. In the equipment, a 12 Volt 200 rpm DC motor was used.  
The DC motor was operated for certain periods (22 min) and the data consisting of temperature, speed, and current values were monitored as in Figure 2 and simultaneously recorded on an SD card every second.  
Due to the fact that temperature measurement is one of the most popular techniques for monitoring the condition of electrical motors [15; 16], this study concentrates on the detection of anomalies in the body temperature of a DC motor.  
The anomaly dataset for DC motor body temperature (Celsius, \({}^{\circ}\)C) includes two labeled time series data, normal and abnormal (faulty). The normal data were collected under the normal room conditions with a stable working regime while the faulty data were collected under external faults created randomly in the braking part of the experimental equipment in the same room conditions. Visualizations of a normal and an abnormal data sample are given in Figure 3.
# Evaluation of Positioning Accuracy of Smartphones under Different Canopy Openness
## 2 Materials and Methods  
### Test Devices  
Three smartphones and two GNSS receivers are used in this experiment. Smartphones: OPPO K9 (hereinafter referred to as K9), Realme GT NEO (NEO) and HUAWEI P50 Pro (P50). GNSS receivers: UniStrong G138 BD (G138 BD) and HiTarget iRTK2 (iRTK2). The three smartphones used in this paper support all constellation systems, K9 supports receiving single-frequency satellite signals, and the other two mobile phones support receiving dual-frequency satellite signals. The types of satellite systems and signal frequencies supported by each device are shown in Table 1.  
The tripod is installed and fixed on the test point to enable the device to stably obtain the positioning data. The tripod is connected with a base and an extension rod. Starfish iRTK2 is placed on the top of the extension rod. Other test devices are fixed on the extension rod in different directions (Figure 1). Each device is about 10 cm away from the extension rod and the placement direction is East: X70; West: NEO, G138 BD; South: P50; North: K9. The height between each device and the test point is iRTK2: 2 m; X70; 1.8 m; P50:1.7 m; K9: 1.6 m; NEO:1.5 m; G138 BD: 1.3 m. The absolute position of each device is calculated by using the distance and direction from the extension rod and the height to the test point.