esa-sceva/satcom-chunk-collection huggingface dataset:
# Theory of AdaDelSPGD Algorithm in Fiber Laser-Phased Array Multiplex Communication Systems
## 3 Experiment
### Experiments on the Influence of the Algorithm on the Bit Error Rate of Communication  
The fiber laser-phased array communication system uses four channels of the same code to communicate at the same time, and each channel communication frequency is 8G. The purpose of multi-channel simultaneous communication is to increase the communication power and communication distance. Figure 7 shows the eye diagram of the experimental site.  
The experiments were carried out in an indoor close-distance environment and an outdoor long-distance environment. If the distance between the receiving end and the transmitting end was 20 m, the transmitting power of each channel was 1 mW, for a total of 4 mW, and an optical fiber collimator was used to receive the data. The waveform before and after applying the AdaDelSPGD algorithm was observed by an oscilloscope. Optical power was measured using a detector, and the bit error rate was obtained by FPGA. When the distance between the receiving end and the transmitting end was 3 km, and the transmitting power of each channel was 20 W, for a total of 80 W. Due to the large size of the synthetic spot, four collimators were used to receive data, and the optical power and bit error rate were recorded. Multi-channel reception is conducive to improving the bit error rate. Before and after applying the AdaDelSPGD algorithm, the optical power values and BER values were obtained for both the inner and outer field experiments and are shown in Table 4. As can be seen from the table, the optical power was improved by more than 30%, and the bit error rate decreased before and after the AdaDelSPGD algorithm was added, regardless of the close indoor conditions or the long distance outside the city environment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **20 m** & **3 km** \\ \hline The optical power value before algorithm & 347 \(\mu\)W & 13 \(\mu\)W \\ The optical power value after algorithm & 471 \(\mu\)W & 17 \(\mu\)W \\ Increase in optical power value & 35.7\% & 30.8\% \\ BER before algorithm & 5.83 \(\times\) 10\({}^{-9}\) & 7.64 \(\times\) 10\({}^{-6}\) \\ BER after algorithm & 4.35 \(\times\) 10\({}^{-9}\) & 5.90 \(\times\) 10\({}^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The changes in optical power value and bit error rate before and after adding the AdaDelSPGD algorithm at different distances.  
Figure 7: The eye diagram of experimental site at 8G communication frequency.
# An Energy-Efficient and Secure Routing Protocol for Intrusion Avoidance in IoT-Based WSN
## 5 Network Model and Discussion
### Packet Delivery Ratio  
Figure 8 demonstrates the performance measurement in terms of packet delivery ratio for ESR protocol against the existing work under varying nodes density topologies. In Figure 8, ESR has shown the highest data delivery ratio as an average of 30% improvement than existing solutions, especially in high nodes density topologies. This is due to the fact that ESR protocol considering multiple and optimal criteria for the selection of cluster head, and more priority was given to the more suitable nodes for generating stable and energy efficient clusters. In addition, after the formation of clusters, they remained fixed and ESR re-initiated inbound process for the selection of cluster heads. Moreover, the reliability of the data packets that were communicated between the base station (BS) and cluster head was achieved using the \((k,n)\) threshold-based Shamir secret sharing scheme, which results in reducing route breakages and ultimately increased data delivery performance.  
Figure 8: Packet delivery ratio in varying nodes density topologies.  
Figure 7: Network lifetime in varying node density topologies.
# Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
## 6 Analysis and Discussion
### Doppler Spectral Density  
An important metric to characterize dynamic channels is the Doppler spectral density (DSD). In Figure 16, we present the time-variant DSD estimated with MUSIC [43] and ESPRIT [44]. Both methods are so-called super-resolution algorithms and both manage to estimate the Doppler well. There is a model parameter in both algorithms that must be estimate which is related to how many sources (tones) are expected, and this will vary in scenarios such as in the ones presented here. Usually, the model order is estimated using, for example, the Akaike information criterion or minimum description length, but this study, we simply set the model parameter to two. The results showed that even in the challenging parts of the _loop_ scenario, both MUSIC and ESPRIT managed to find the dominant Doppler component.  
Figure 14: RMS Delay spread for the two scenarios, calculated using the local scattering function.  
Figure 15: The empirical CDF for the RMS delay spread for the two scenarios, calculated using the local scattering function.
# Semi-Supervised Semantic Segmentation Network for Point Clouds Based on 3D Shape
## 3 Method  
Point cloud data collected by LiDAR typically contain spatial coordinates, color, and reflection intensity. Although the properties may vary slightly between acquisition devices, the spatial coordinate property is always complete and essential for segmentation. The point cloud data to be segmented can be represented as \(P=\{pi\}\,|\,pi=\{x,y,z\}\), where \(p_{i}\) represents a point in 3D space with \(x,y,\) and \(z\) coordinates. Based on the inherent characteristics of the point cloud data, we can extract their shape feature set \(S=\{s_{i}|s_{i}=\{x,y,z,n_{x},n_{y},n_{z},k\}\}\). An unsupervised machine learning method and a neural network with shared parameters can encode the inputted point cloud data to generate the point cloud feature set \(F_{1}\). By grouping the original point cloud \(P\) and performing PointNet feature learning, we can obtain the feature set \(F_{2}\). Finally, by applying an attention mechanism to \(F_{1}\) and \(F_{2}\) for feature learning, we can produce the set \(C=\{Ci\,|\,Ci=\{x,y,z,c\}\}\), which contains the category information. Figure 1 illustrates the overall network.
## 2 Materials and Methods
### Heuristic Optimization Algorithms
#### 2.1.1 PSO Algorithm Principles  
To better understand this optimization algorithm, one can make an analogy between particles and a swarm of bees passing through an open field in which several groups of flowers can be found. The swarm tends to locate the position with the highest density of flowers for a minimum area in this field. The group of particles which, in this case, is represented by bees, initially has no knowledge of solving this problem. One can say that the optimization agents found in this algorithm learn based on a set of defined rules.  
A mathematical approach for this algorithm is shown below in Figure 5.  
From the figure presented above, the following components can be distinguished:  
* The inertia factor is represented by the result obtained during the previous iteration;
* The memory factor, for which the component is calculated according to the best result corresponding to that optimization agent (particle), is called the personal optimum;
* The cooperation factor, the value of which is calculated using the most favorable point determined by the whole particle group, is a point named global optimum.
# Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
## 2 Data-Driven Traffic Estimation and Prediction
### Toll Ticket Data  
Electronic toll collection (ETC) systems are widely applied in toll roads, HOV lanes, toll bridges, etc. For example, more than 90 percent of vehicles on expressways in China were equipped with the ETC system by the end of 2019. Toll ticket data (TTD) collected from ETC systems provide a high-coverage and low-cost approach for expressway traffic estimation.  
Based on the TTD from the Shandong Expressway Toll System in China, a simulation-based dynamic traffic assignment algorithm was proposed to obtain traffic flow in [76] and achieved a high examination accuracy (approximately 5% MAPE). The only public source of toll ticket data used for traffic flow prediction that the authors know is the Amap data for KDD CUP 2017 ([[https://tianchi.aliyun.com/dataset/dataDetail?dataId=60](https://tianchi.aliyun.com/dataset/dataDetail?dataId=60)]([https://tianchi.aliyun.com/dataset/dataDetail?dataId=60](https://tianchi.aliyun.com/dataset/dataDetail?dataId=60))). The pros of tool ticket data include their high coverage and low cost. However, the cons are clear. Toll ticket data can only be collected in places where ETC systems are applicable.
## 3 Software Design of Frequency Stabilization System
### Obtaining Reference Voltage and Error Fragment (S2)
#### 3.1.2 Differentiation to Obtain the Error Signal  
It can be seen in Figure 4c that one scan cycle contains a scanning signal of 20 k data points, corresponding to two cycles of SAS, and the data points from 5 k to 15 k contain a complete set of 6 saturation absorption peaks. Figure A2 of the Appendix A shows the differential to obtain the error signal program, setting the starting value of the intercept to 5 k, the length of the intercept to 10,001 points, and solving for primary differential of the signal. Because of the discontinuity of the collected data, the differential results of the two data points before and after are invalid [27]. Thus, they are split, and 9997 points are saved in each While loop. In the differential processing, the noise will be amplified, and the DAQ card also has periodic random noise, so the signal needs to be smoothed. Figure 6a,b show the original error signal and the smoothed 40 points of the error signal, respectively. It can be seen that the SNR of the original error signal is poor, and the peak cannot be detected. After smoothing, the noise is effectively suppressed, and the peak threshold can be visualized. After the signal has been processed by differentiation, splitting, and smoothing, the scanning signal with the corresponding SAS signal and error signal can be obtained, as shown in Figure 6c.
## Appendix A Simulation Setup for the IEEE 802.11 Family
### Physical Layer for IEEE 802.11  
A detailed system analysis is required to implement WLAN systems. The IEEE 802.11 WLAN standard provides several PHYsical layers (PHY) options specified for each IEEE 802.11 standard in channel bandwidth, modulation, and spreading spectrum techniques. The IEEE 802.11a PHY adopts OFDM, IEEE 802.11n is based on high-throughput (HT)-OFDM. Very high throughput (VHT)-OFDM is the physical layer for IEEE 802.11 ac, and high-efficiency (HE)-OFDM is the PHY layer for IEEE 802.11 ax. Table 1 summarizes the simulation parameters for the PYH layer of IEEE protocols which were selected for our analysis: IEEE 802.11a, IEEE 802.11n, IEEE 802.11n, IEEE 802.11 ac (_Wi-Fi5_), and IEEE 802.11 ax (_Wi-Fi6_).
# IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial
## 2 Several Promising Techniques: IRS, LIS, and Radio Stripes
### Categorizing Recent Studies on Intelligent Reflecting Surfaces
#### 2.1.2 Power/Spectral Optimizations in IRS-Aided Communications  
Energy efficiency, measured by the ratio of spectral efficiency to power consumption, has become a key performance indicator for making wireless networks that are good for the environment and last a long time [20]. Reconfigurable intelligent surface (RIS)-assisted wireless communication has recently been presented as a promising approach to improving the energy efficiency of wireless networks. A RIS is a low-cost passive metasurface that can be used to turn a wireless channel into a region with some predictability [23]. In order to enhance user communication quality and optimize the characteristics of wave propagation, a BS transmits control signals to a RIS controller in the RIS communication structure. The RIS only serves as a reflector and accomplishes no digitization. Therefore, when used correctly, a RIS uses significantly minimal energy than standard amplify-and-forward (AF) relays [15,24].
## 4 Results
### Comparison with Deep Learning Models  
'PA_B-J48' is also compared with six state-of-the-art deep learning models, including ResNet-50, ResNet-101, VGG-19 [83], Inception-V3, MobileNet, and Xception using the datasets, including DrivFace dataset(DF), Boston University dataset (BU), and FT-UMT dataset (FT-UMT), as shown in Table 7. It can be observed that our proposed approach outperforms the deep learning models. The deep learning models require a large dataset to achieve better results and hence could not perform well on DF, BU, and FT-UMT.
# Rapid Construction of Aerocapture Attainability Sets Using Sequential Convex Programming
## 4 Solving with Sequential Convex Programming
### Procedure of Iteration  
The successive procedure to solve _Problem 1_ is summarized as follows:  
_Step 1:_ Set \(z=0\) and the state vectors \(\{\mathbf{x}_{i}^{0}\}\) to an initial profile. In accordance with the trajectory characteristic of a normal aerocapture flight, the initial profile of radial distances is given as \(\{r^{01}\,r^{02}\,r^{03}\}\), as shown in Figure 5(a). \(r^{01}\) is a straight line from \(r_{atm}\) to \(r_{m}=1+h_{atm}/2\) divided evenly as \([N/10]\) points, \(r^{02}\) is a straight line from \(r_{m}\) to \(r_{m}\) having \(N+1-2[N/10]\) points, and \(r^{03}\) is a straight line from \(r_{m}\) to \(r_{atm}\) having \([N/10]\) points. The symbol \([\;]\) is the floor function mapping a real number onto the largest integer that is less than or equal to the real number. The initial flight-path angle profile is chosen as a constant value \(\gamma_{0}\) as shown in Figure 5(b). Note that choosing the initial guess is not necessary because the solution of the convex optimization problem is unique. However, a good initial profile can accelerate the convergence of the numerical procedure.  
_Step 2:_ The solution of the \(z\)-th iteration \(\mathbf{x}^{z}\) is chosen to be the initial profile in the \((z+1)\)-th iteration to solve \(\mathbf{x}^{z+1}\) and \(u^{z+1}\)[49; 50].  
_Step 3:_ Check the convergence criterion  
\[\max_{0\leq i\leq N}\left|\mathbf{x}_{i}^{z+1}(e)-\mathbf{x}_{i}^{z}(e)\right|\leq\epsilon \tag{29}\]  
If it is satisfied, then proceed to step (4); otherwise, go to step (2) for the next iteration.  
_Step 4:_ The solution of _Problem 1_ is found to be \(\mathbf{x}^{*}=\mathbf{x}^{z+1}\) and \(u^{*}=u^{z+1}\).  
It is worth to note that approximating by a sequence of convex optimizations as in _Problem 3_, although the convergence of sequential approximation to the original _Problem 1_ remains to be proven, a considerable number of preceding studies have provided sufficient confidence in the convergence [37; 38; 39; 40; 41; 42; 43]. In this paper, the numerical simulations of solving the aerocapture attainability sets given in Section 5 showed good performance and reliability of the developed convex method.  
In general, aiming at the convergence of the method proposed in this paper, it can mainly be improved in three aspects. First, the dynamic model reduces the state quantity by one dimension through the substitution of independent variables, thereby reducing the complexity of the problem. Secondly, the groove-shaped initial trajectory is given for the solution of boundary points for the first time calculation, which increases the feasibility of the problem. Finally, when solving other boundary points, the initial value is given by the optimal solution of the previous solution. This adjacency idea greatly improves the sensitivity of the initial value.
# Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring
## 4 Experimentation and Results  
### Objectives  
The main objective of a system field test, as with any application, is to validate its complete functionality and identify possible improvements. Yet, given the complexity and amount of device interactions in the SIMMA system, several tests were required prior to its validation as a whole. The objectives of these tests were to:  
* Validate the LoRa wireless communications amongst the control nodes and sensor nodes;
* Verify the proper stability and functionality of the control nodes and sensor nodes;
* Analyze the LoRa communications behavior in a similar environment to the final field test, and its interaction with the UAV's transceivers.
###### Abstract  
Background: A change to new tramways in Graz (Austria) led to severe complaints in residential areas. To understand the underlying reasons for these complaints, a systematic measurement campaign was designed. Methods: Six locations in Graz and two locations in a comparably sized city were selected. Parallel indoor recordings of sound and vibrations were conducted from 8:00 p.m. to 8:00 a.m. (due to sleep problems) at all locations. Results: Vibration levels remained below the limits of the Austrian standard (Wm-weighting) although variability was observed among sites, tram types and pass-bys. A complex characteristic of the acoustic feature space was found with A-weighting (differences between A- and C-weighting of more than 15 dB were observed). C-weighted background to peak noise ratios clearly distinguished \"old\" from \"new\" trans. Psychoacoustic indices indicated a high variability between locations and tram types. Roughness and loudness was higher in \"new\" versus \"old\" trans at most locations. \"New\" trans exhibited high sharpness values and variability, especially at higher speeds--when compared with trans from a control city. Conclusions: Standard indicators of sound and vibration were not sensitive enough to uncover the reasons for the complaints. Only the integrated analysis of the ambient soundscape (high signal-to-noise-ratio), the more noticeable sound (in psychoacoustic terms) and the observed high variance of the immissions provided guidance to implement appropriate technical solutions.  
Keywords:tramway; vibration; noise; psychoacoustics; annoyance; sleep; health risk assessment +
Footnote †: journal: Applied Sciences
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 3 Privacy-Protecting Attributes Classification for the Storage of Cloud Data
### Design Management
#### 3.1.4 External Assets  
External assets within the privacy system can be referred to as third-party tools that enhance the overall functionality of the technique structure [50]. By incorporating external assets, the privacy-preserving technique gains complexity and exceptional privacy capabilities [50]. The integration of external mechanisms has significantly enhanced the architecture of privacy systems. The integration of external assets and advanced techniques in cloud data storage enhances the overall security and privacy of the system. These assets and techniques contribute to the development of robust defense mechanisms against various security attacks, ensuring the effective implementation of encryption and key generation techniques.  
One example is the utilization of the RFC (6238) standard for one-time passwords (OTP), which adds an additional layer of security by providing defenses against access control attacks. OTPs generated through this protocol offer a dynamic and time-limited authentication method, reducing the risk of unauthorized access to cloud resources. The application of the Chinese remainder theorem (CRT) in the design of encryption and key generation techniques provides an efficient approach for secure data transmission. By leveraging the properties of the CRT, encryption algorithms can achieve faster computations and optimize the storage and retrieval of encryption keys, strengthening the overall security of cloud data storage.  
In addition, innovative techniques such as Lagrange interpolation and the Monotonic encryption algorithm offer unique solutions for encrypting mathematical representations of digital data while preserving their order. These techniques are particularly useful when dealing with sensitive data that requires mathematical operations or maintaining a specific order, such as financial records or sequential datasets. By incorporating these algorithms into the encryption process, the privacy and integrity of numerical data in the cloud can be effectively preserved.  
Overall, the integration of external assets and advanced techniques in cloud data storage adds layers of security and ensures the confidentiality, integrity, and availability of data. By leveraging these assets and techniques, cloud service providers can enhance the protection of sensitive information and maintain the trust of users in the secure storage and management of their data.
## 3 Results
### Degradation of Soil Pesticides Under Different Tillage Methods
#### 3.5.1 The Degradation of Pesticides Under the Same Tillage Treatment  
Under identical tillage regimes, substantial variations in the degradation rates (as reflected by T\({}_{1/2}\) values) and transformation pathways among the five selected herbicides were observed.  
Under the CK treatment, the order of degradation rates was nicosulfuron (T\({}_{1/2}\) = 11.55 d) \(>\) atrazine (14.14 d) \(>\) mesotrione (22.35 d) \(>\) acetochlor (31.50 d) \(>\) aachlor (40.76 d).  
The incorporation of SS treatment markedly altered the degradation patterns of the tested herbicides. The most striking change was the significant reduction in the T\({}_{1/2}\) value of mesotrione from 22.35 d under CK to 14.14 d under SS, making it the fastest-degrading pesticide in this treatment. On the other hand, the degradation of atrazine, acetochlor, alachlor, and nicosulfuron slowed down under SS compared to CK, with T\({}_{1/2}\) values increasing to 19.80 d, 43.31 d, 49.50 d, and 19.25 d, respectively.  
The impact of the ST treatment was more complex. The degradation rate of atrazine under ST (T\({}_{1/2}\) = 13.86 d) was slightly faster than under CK. Degradation dynamics indicated a significantly higher degradation rate by day 3 (30% vs. CK and SS), reaching a high degradation efficiency of 94% by day 55. In contrast, the degradation of alachlor was significantly inhibited under ST (T\({}_{1/2}\) = 53.31 d). Although its early-stage degradation rate (39% by day 7) was higher than under CK (15%) and SS (21%), the degradation rate plateaued in later stages, resulting in a lower final degradation efficiency (70 days) compared to the other pesticides.