esa-sceva/satcom-chunk-collection huggingface dataset:
# IIoT System for Intelligent Detection of Bottleneck in Manufacturing Lines
## 4 Experimentation  
The case study involved a proof-of-concept to assess the performance of the proposed IIoT architecture and Big Data Machine Learning component. Two manufacturing machines controlled by PLCs were simulated, extracting key variables, such as temperature and motor turn counting, for bottleneck detection. Simulated changes within a measured range indicate bottleneck risk. The study began with data acquisition from a temperature sensor and lap counting at workstations.  
The distinct tiers of the architecture will proceed with their workflow from bottleneck detection in the PLC devices to the enterprise platform layer, where the subsequent actions related to bottleneck identification will be undertaken:  
* First, the Andon system [49; 50; 57] warns when a real risk of bottleneck occurs with the response provided by the Big Data system algorithm with the data trained through the input dataset provided.
* Second, the real-time graphical observation system captures the information and displays it on the timeline to analyze moments of risk. We also measured the historical trend of the variables considering when they impacted during the bottleneck risk. This allowed us to see if there was oscillation, focus the problem on the variable in question, and make decisions.
* Third, this experimentation environment also allowed us to cross between sensor and actuator measurements. In this way, we can compare their behavior, for example, whether key or risky moments aligned or not. Similarly, we measured the response times to make contrasts and comparisons of the system against latencies.  
The experimentation offered insights into the interaction of technologies and communication protocols within the proposed architecture, focusing on layer integration and latency times tracked by timestamps in the traceability component.  
The diagram depicted in Figure 9 shows the architecture at the system level with the interrelationship between its components; in the yellow legend, we can see the tier to which they belong.  
Figure 9: Systems diagram of the implemented experimentation.
# Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain
## 2 Related Works
### Robust Watermark  
Robust watermarking algorithms based on deep learning have become a popular research topic due to their excellent image reconstruction quality and robustness. [PERSON] et al. [13] first proposed a framework for robust watermarking algorithms based on deep learning, comprising four components: watermark embedding network, attack network, watermark extraction network, and generative adversarial network (GAN). Later, [PERSON] et al. [14] proposed a fully convolutional neural network with residual structure, improving the imperceptibility and robustness of watermarking. [PERSON] et al. [15] used a multilayer convolutional neural network to model the geometric or noise attacks that may be encountered in digital images and trained the convolutional neural network using adversarial training to improve the robustness of the model to unknown distortions and indistinguishable distortions. [PERSON] et al. [16] proposed a watermark embedding framework  
Figure 1: The data structure of DWI images.  
with staged training, achieving a robustness to signal processing attacks and geometric attacks while ensuring the visual quality of the image.  
The above algorithms all focus on the study of robust watermarking algorithms for natural images. [PERSON] et al. [17] proposed a robust watermarking scheme for DWI images in medical images by combining global and local features for image reconstruction and embedding watermark redundancy into the features of multi-scale reconstruction. [PERSON] et al. [18] proposed a combination of a discrete cosine transform and a deep learning convolutional neural network. The DCT-transformed cover image and the IF coefficients of the watermark were used as inputs to a DLCNN for watermark embedding. [PERSON] et al. [19] proposed a lightweight convolutional neural network using pre-trained network parameters to reduce the time to reconstruct the watermark image and extract the watermark, achieving high robustness to geometric attacks. [PERSON] et al. [20] proposed a copyright watermark recognition model based on deep learning techniques to replace the traditional copyright authentication algorithm based on error rate and normalized equivalence similarity estimation.
# Effect of Nitrogen Atoms in the CNT Structure on the Gas Sensing Properties of PANI/CNT Composite
###### Abstract
% nitrogen were synthesized, which were used as templates for the PANI formation during in-situ oxidative polymerization of aniline. We carried out a comparative analysis of the morphology, structural-chemical state, and electronic properties of nanotubes with different nitrogen content and PANI-nanotube composites. For this, we used transmission electron microscopy (TEM), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), near-edge X-ray absorption fine structure (NEXAFS) spectroscopy, 4-probe method, and Kelvin probe force microscopy. Then the sensor response to ammonia (at 2-10 ppm), when individual PANI/CNT nanostructures were deposited on the multi-electrode array, was measured. The use of individual nanostructures made it possible to eliminate the influence of the free polymer, which is not directly bonded to nanotubes, on the gas response. As a result, we found that morphological features of the polymer layer on nanotubes with different nitrogen content have a greater effect on the response to gas than changes in the electronic properties of the polymer at the PANI-CNT interface.
# GazeGuide: An Eye-Gaze-Guided Active Immersive UAV Camera
## 8 Challenges Faced During Implementation of GazeGuide  
Eye trackers, although in their infancy, are very sensitive instruments. The Pupil Labs eye tracker used for this study has some limitations. Although it had good accuracy, calibration was required every time the tracker was used, making it difficult to test. The eye cameras often heated up, causing jitters in the gaze data. We did not attempt to measure this error; rather, we just re-calibrated the Pupil every time this occurred. We could achieve acceptable results (to the participants) with the Pupil eye tracker, which was the economical and best solution.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Questions**} & \multicolumn{4}{c}{**Control Type**} & \multicolumn{3}{c}{**Paired \(t\)-Test**} \\ \cline{2-7}  & \multicolumn{2}{c}{**GazeGuide**} & \multicolumn{2}{c}{**RC**} & \\ \cline{2-7}  & **Mean** & **SD** & **Mean** & **SD** & **T(14)** & **P** \\ \hline Q1 & 4.16 & 0.73 & 2.74 & 1.01 & 4.59 & 0.00041 \\ Q2 & 3.75 & 0.88 & 2.63 & 1.07 & 3.54 & 0.00324 \\ Q3 & 4.80 & 0.28 & 2.80 & 0.52 & 18.3 & \(1.73\times 10^{-11}\) \\ Q4 & 4.73 & 0.40 & 3.03 & 0.60 & 8.50 & \(6.7\times 10^{-7}\) \\ Q5 & 4.06 & 0.49 & 3.00 & 0.71 & 4.30 & 0.00073 \\ Q6 & 3.23 & 0.41 & NA & NA & NA & NA \\ \hline \hline \end{tabular}
\end{table}
Table 5: Subjective questionnaire results (five-point scale: 1—strongly disagree, 2—disagree, 3—neutral, 4—agree, 5—strongly agree).
# Pullout Behavior of Nail Reinforcement in Nailed Soil Slope
###### Abstract  
The pullout resistance and displacement performance of reinforcement have significant effects on the safe and economic design of a reinforced-soil system. In this study, the nail pullout tests are conducted to assess the pullout behavior of soil nail reinforcement at different levels in the soil slope of granular materials. The similitude laboratory models of a reinforced soil system with a scale of 1:10 are prepared. The construction sequence used in a full scale slope was precisely followed in the laboratory model. The models consist of a Perspex wall box filled with sand and steel bars as a reinforcement. The models of sand beds are formed using an automatic sand raining system. Devices and instruments are installed to record the nails pullout resistance and displacement. The tests are carried out at variable footing pressures to get the pullout force of the nails based on a strain control technique. The finite element models of nailed soil slope are also analyzed to validate the laboratory model results. It infers from the numerical model results that the laboratory models underestimate the pullout behavior of nail reinforcement in nailed soil slope. The pull-out force in nail reinforcement increases as the displacement increases and then decreases slightly and becomes constant with an increase in displacement in the case of deeper placed nails, but it becomes constant immediately for upper nails.  
soil nailing system; nails characteristics; soil slope; footing pressure; nails orientation; pullout force
## 2 Theory
### Formalism of the HHG Calculation  
The interaction between the laser field and the graphene is depicted as  
\[H_{I}=-e\vec{E}\cdot\sum_{ij}\vec{r}_{ij}c_{j}^{\dagger}c_{i} \tag{1}\]  
where \(\vec{E}=\vec{E}(t)=\vec{A}\sin^{2}(\pi t/\tau)\sin(\omega t)\) is the time-dependent electric field of the laser pulse. \(-e\), \(|\vec{A}|\), \(\tau\) and \(t\) are the electron charge, laser field amplitude, pulse duration, and time, respectively. \(c^{\dagger}\) and \(c\) are the electron creation and annihilation operators. \(e\vec{r}\) is the dipole operator, whose matrix elements are computed from the single-electron momentum operator through [22]  
\[\vec{r}_{ij}=\frac{\hbar\vec{p}_{ij}}{im(E_{i}-E_{j})} \tag{2}\]  
where \(p_{ij}\) is the electron momentum matrix element between states \(i\) and \(j\), \(m\) is the electron mass, \(E_{i}\) and \(E_{j}\) are the eigen-energies of states \(i\) and \(j\). The momentum matrix elements are calculated with the Furthmuller's method [23].  
In this work we consider a laser pulse with 32 laser field cycles, and the electric field is polarized in the \(z\)-direction. When a laser impinges on the graphene, the time evolution of the density matrix obeys the Liouville Equation:  
\[i\hbar\frac{\partial\langle\hat{\rho}_{ij}\rangle}{\partial t}=\langle[H,\hat{ \rho}_{ij}]\rangle \tag{3}\]  
where \(\hat{\rho}\) is the density operator. \(H=H_{0}+H_{I}\), and \(H_{0}\) is the system's Hamiltonian, \(H_{0}=\sum\limits_{i}E_{i}n_{i}\). By solving the Liouville equation, we find the density matrix. The time evolution of the electron dipole is obtained from \(\vec{d}(t)=\mathrm{Tr}(\rho(t)\vec{r})\). Then it is Fourier transformed to the frequency domain, \(\mathrm{FT}[d(t)]\Rightarrow D(\omega)\).
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## Appendix A Appendix A Appendix A Appendix A Appendix A.1
### Appendix A.2  
```
Input: Environment dimensions, ESP32 modules, TurtleBot3 robot, Wi-Fi modules, signal strength (RSSI) data Output: Optimized node placement and network coverage
1 Initialize TurtleBot3 for autonomous navigation
2 Initialize the Wi-Fi module on TurtleBot3 for RSSI measurement
3 Initialize ESP32 modules as relay nodes in initial positions
4 Set up the environment workspace map
5 for each workspace (i.e., room or section of the industrial site) do: 1. Deploy TurtleBot3 to navigate the workspace 2. Collect RSSI data at different positions using Wi-Fi 3. Record RSSI data and corresponding coordinates (X, Y) end for
6 Synchronize RSSI data with TurtleBot3's coordinates
7 Process collected data: 1. Apply Extra Tree Regressor to analyze signal strength and interference zones 2. Generate REM based on RSSI and spatial data 3. Identify signal strength peaks and weak areas (interference zones)
8 Optimize sensor and relay node placement: 3. Identify optimal positions for ESP32 relay nodes using the REM 4. Place ESP32 nodes at locations that ensure optimal signal coverage and minimal interference 5. Update the network configuration to reflect new node placements
9 Verify optimized node placements by re-measuring RSSI levels across the workspace 10 If (network coverage is not optimal): 1. Adjust node placement and re-evaluate the network else: 2. Confirm final node placement and terminate end if
```  
**Algorithm A2** REM construction for optimizing sensor placement
## 4 Experimental Evaluation
### IoT Device Power Consumption  
The IoT device we developed was powered by a power bank with a capacity of 30,000 mAh. To check the power consumption of the device, we placed it in the front of a beehive to capture the video stream constantly. Since the varroosis rarely occurs in well-performed bekeeping, measuring power consumption in such a state would not give a good view of the possible requirements for energy (the IoT device would not transmit anything to the Cloud). For this reason, we simulated the occurrence of _Varroa destructor_ on some video frames by swapping every 10 th frame in the video stream by a randomly chosen frame from the test set containing 20 frames with some infected bees. This approach led to sending an MQTT message with images of affected bees to the data center in the Cloud after analyzing every ten frames of the video stream.  
Under the above simulation conditions, the IoT device could work for 10 h and 35 min, which means the whole day of monitoring the beehive without a need of changing or recharging the power supply. Taking into account the fact that in natural conditions, the device would not send MQTT messages so often, the obtained results are satisfactory but lead to the conclusion that the best choice would be to power the device with an additional photo-voltaic panel.
## 1 Introduction
The key findings of this paper are set out below:  
* Incorporating Levy flights makes it easier for this algorithm to bypass local optima by increasing its exploration potential. * The integration of the golden sine technique greatly advances the algorithm's local search accuracy. * To increase the global search capabilities and diversity of the algorithm, Archimedean spiral mutation is suggested. * The algorithm is validated on benchmark functions, engineering optimization problems, and QoS-CSC problems. The following is an outline of this paper's structure: Work in this area is reviewed in Section 2. Section 3 describes the QoS-CSC problem and its mathematical model in detail. Section 4 briefly describes the standard ARO. Section 5 details the multi-strategy improved ARO algorithm. Section 6 offers an evaluation and discussion of the comparative experimental findings of MIARO on benchmark functions. Section 7 is concerned with discussing and analyzing the experimental results of applying MIARO to the engineering design problem and the QoS-CSC problem. A brief summary of the article and suggestions for further research are provided in the final section.
###### Abstract  
Leveraging space-based optical platforms for space debris and defunct spacecraft detection presents several advantages, including a wide detection range, immunity to cloud cover, and the ability to maintain continuous surveillance on space targets. As a result, it has become an essential approach for accomplishing tasks related to space situational awareness. However, the prediction of the orbits of space objects is crucial for the success of such missions, and current technologies face challenges related to accuracy, reliability, and practical efficiency. These challenges limit the performance of space-based optical space situational awareness systems. To drive progress in this field and establish a more effective and reliable space situational awareness system based on space optical platforms, this paper conducts a retrospective overview of research advancements in this area. It explores the research landscape of orbit determination methods, encompassing orbit association methods, initial orbit determination methods, and precise orbit determination methods, providing insights from international perspectives. The article concludes by highlighting key research areas, challenges, and future trends in current space situational awareness systems and orbit determination methods.  
space situational awareness; orbit association methods; initial orbit determination; precise orbit determination; angle-only measurement +
Footnote †: journal: _Photochics_  
**Clication:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON].  
Overview on Space-Based Optical Orbit Determination Method Employed for Space Situational Awareness: From Theory to  
Application. **Plotonics** **2024**, _11_, 610. [[https://doi.org/10.3390/photonics1107610](https://doi.org/10.3390/photonics1107610)]([https://doi.org/10.3390/photonics1107610](https://doi.org/10.3390/photonics1107610))
## 3 Experimental Procedure
### Processing of AE Data  
Collected AE signals with their recorded times were extracted, and their waveforms, frequency spectra, and signal parameters (amount of hit, amplitude, energy, and frequency characteristics) were obtained to correlate them with mechanical responses and PoD results from AEWin for SAMOS E4.75 software. The number of AE hits represents the number of AE hits recorded by all sensors, amplitude (dB) is the maximum magnitude the signal reaches, and energy (\(\upmu\)sV) is the area under the rectified signal envelope. The frequency centroid and peak frequency are taken from the Fast Fourier Transform (FFT) spectrum. The frequency centroid represents the mass centroid of the FFT spectrum, whereas peak frequency is the frequency value of the peak magnitude encountered in the FFT spectrum. In addition, the Swansong II filter [53] was applied to AE data before analysis, which eliminates the signals based on duration/amplitude relations to enhance the quality of the dataset and ensure that only damage-related AE signals are provided.  
To jointly evaluate the signal response and concrete behavior, the stress vs. strain behavior of the concrete specimen under compression was used, as described in the Section 4. Moreover, regression analysis, a statistical method that examines a relationship between two or more variables, was also utilized to comprehend the relationship between the PoD results and mechanical response and to generalize the behaviors. In the following figures, rate trend lines and regression curves were created using regression analysis.
# Parameter Optimization for Ship Antiroll Gyros
## 4 Mathematical Model for Parameter Optimization of Antiroll Gyro  
According to the study in Section 3, there was a complicated nonlinear relationship between the roll reduction rate and the rotor's size. In addition, since the gyro is restricted by the ship, the parameter optimization is needed to obtain the optimal parameters. In this Section, the ship roll reduction rate and the rotor mass were modeled as objective functions. Then, the ship space, power drive, and material strength were considered as constraints to form the optimization model.
## 5 Discussion
### The Role of Voice Stimuli in Enhancing Alertness Under Fatigue  
This study, through a simulated driving experiment, confirmed the significant inhibitory effect of fatigue on driver alertness, as evidenced by prolonged reaction times, decreased subjective arousal, and increased the eyelid closure rate. These findings are consistent with a large body of previous research [39; 61; 62], further underscoring the negative impact of fatigue on driving safety. As a non-intrusive intervention method, voice interaction--particularly high-stimulation voice stimuli--significantly improved alertness under conditions of severe fatigue. A real-world driving study also found that engaging drivers in conversation noticeably enhanced their alertness [63].  
Moreover, this study revealed a significant interaction between the fatigue level and voice stimulus intensity, indicating that the effectiveness of voice stimuli depends on the driver's current fatigue state. This finding aligns with the view that the effectiveness of fatigue mitigation interventions varies with fatigue severity, highlighting the importance of dynamically adjusting the intervention's intensity [64; 65]. Differences in voice stimulus responsiveness among professional driver groups suggest that future intelligent driver assistance systems should incorporate drivers' occupational backgrounds and personal preferences to achieve more targeted and effective interventions. For instance, private car drivers showed weaker responses to high-stimulation voice stimuli under mild fatigue but expressed a stronger preference for personalized interactions, suggesting that the content and style of voice stimuli should be customized--a key direction for future research.
# Damage Identification of Stay Cables Based on a Small Amount of Deflection Monitoring Data
## 4 Analysis of Influencing Factors of Cable Damage Identification
### Influence of Bending Stiffness of Beam Body
#### 4.3.2 Damage Degree Identification  
The deflection difference of the beam at the anchorage point of cable M7 and M16 after the stiffness of the main beam decreases is used as the basis to identify the damage degree of cable. The damage index is re-identified according to the damage degree identification method, and the damage identification results of double-cable can be obtained. The results show that the difference between the recognized damage index and the actual damage index (the preset damage index) is very small (the maximum error is 1.5%). It can be seen that the when the stiffness of the main beam decreases, the accuracy of damage degree identification is improved.
# Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems +
## 4 Dynamic Scheduling and Resource Allocation
### Time Complexity Analysis  
The time complexity of Algorithm 1 is \(O(N_{MS}\times N_{all})\), where \(N_{all}\) is the total number of RBs in a wireless communication frame at the BS. In general, when the number of items to consider is \(n\) and the total capacity of the knapsack is \(W\), the time complexity of the dynamic programming algorithm for the 0/1 knapsack problem is \(O(nW)\)[81]. \(O(nW)\) is pseudo-polynomial complexity, since there is no guarantee that \(W\) is a polynomial function of \(n\), but it could be arbitrarily large (e.g., exponential with respect to \(n\)). In practice, however, \(N_{all}\) during a wireless communication frame is a fixed constant known a priori. For example, in LTE, one frame is 10 ms, and \(N_{all}\) is six and 100 when the channel bandwidth is 1.4 MHz and 200 MHz, respectively. Each RB consists of 84 resource elements when each RB consists of seven symbols (time slots) in the time axis and 12 subcarriers (15 kHz each) in the frequency axis [82]. As long as \(N_{all}\) remains a constant or is a polynomial function of \(N_{MS}\) in practical implementations of the 5G standard, the time complexity of our algorithm remains polynomial.