esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Home Assistive System for Severely Disabled People
## 2 Materials and Methods
### The Hardware of MCT  
The entity of MCT (shown in Figure 5) incorporates eight parts placed on the front side (Figure 5a) and back side (Figure 5b) panel respectively, including (1) two kinds of input interface, (2) microprocessor, (3) eight light-emitting diode (LED) lights, (4) initial typing speed adjustment encoder, (5) power, (6) single/double key input, (7) Human Interface Device (HID) Bluetooth module, and (8) a 3.7 V lithium battery. The microprocessor processes the real-time recognition of Morse codes and converts them to the ASCII codes, and it sends them to the screen of the computer or portable device (tablet PC and smartphone) through the Bluetooth interface. The eight LED lights appearing on the front panel indicate ACAT input (AI) mode, keyboard mode, mouse mode, single/double key input, Caps Lock, Shift, Ctrl, and Alt, respectively. This configuration provides the user with a simple way to confirm the current state of MCT. The initial typing speed adjustment encoder allows modifying the operating speed of the assistive input accessories (i.e., the initial threshold value of the fuzzy algorithm) for users with different degrees of disability; therefore, the MCT can real-time track the typing speed of the user. The power connector is a USB 2.0 micro-B port, it can connect to a PC or mobile power source directly when the lithium battery powers down. The single/double switch input allows the user to choose one or two keys to input Morse codes. The HID Bluetooth module [26] supports the transmission of the command to the computer, tablet PC, and smartphone from the microprocessor.  
The lithium battery supports the power of MCT when the user goes outside. The box of MCT (Figure 5c) is designed by a 3D printer and can be adjusted freely according to the user's preferences.
###### Abstract  
The new generation of wireless communication systems has adopted different waveforms. The universal filtered multicarrier is one of the adopted candidates that has symmetry with various numerology designs. However, the high peak to average power ratio is one of the major limitations faced by universal filter multicarrier (UFMC) designers. Moreover, recent studies utilize cubic metric along with the peak to average power ratio (PAPR) to show the power back-off effect of the signal in which the PAPR metric identifies the maximum peak and the cubic metric (CM) identifies the Out of Band emission and In-Band distortion. Most of the current solutions, such as amplitude clipping, tone reservation, and active constellation extension, decrease the PAPR but cause degradation to the bit error rate. Selected mapping is one of the promising techniques that is recently used to solve the PAPR and CM problems without causing bit error rate (BER) degradation. In this paper, the selected mapping (SLM) is integrated with UFMC to reduce the PAPR and CM without affecting the BER of 5G networks. The SLM-UFMC solution model is simulated by MATLAB and the results show that the SLM-UFMC model presents better PAPR and CM performance without BER degradation. The PAPR has been decreased to 1.5 dB with respect to eight-phase rotation vectors and the CM decreased to 1.25 dB compared to the conventional UFMC.  
universal filter multicarrier (UFMC); selected mapping (SLM); peak to average power ratio (PAPR); cubic metric (CM); bit error rate (BER); cumulative distributive function (CDF) +
Footnote †: journal: Journal of Applied Mechanics and its Applications
## 6 Application of Cybersecurity Optimizer in Training Multi-Layer Perceptions for Attack Detection  
In today's digital age, cybersecurity has become a critical priority as the prevalence and complexity of cyberattacks continue to rise. Safeguarding sensitive data and essential infrastructure requires the implementation of sophisticated defense strategies capable of detecting and neutralizing emerging threats. A promising avenue for improving cybersecurity involves leveraging artificial intelligence (AI) methods, with neural networks, particularly  
Figure 14: Histogram analysis over CEC2022 (F7–F12).  
Multi-Layer Perceptrons (MLPs), showing great potential in attack detection. These neural networks can be trained to identify patterns associated with malicious behavior, offering an automated and efficient solution for protecting digital systems.  
Training MLPs for attack detection, however, presents significant challenges. The process requires optimizing numerous parameters to achieve high accuracy and robustness against diverse attack vectors. Traditional optimization techniques often fall short in navigating the complex, high-dimensional landscapes typical of neural network training. Consequently, there is a critical need for innovative optimization strategies that can efficiently and effectively train MLPs to detect cyber threats.  
In this context, the CYO emerges as a groundbreaking solution. This novel optimizer is inspired by the adaptive and resilient behaviors observed in advanced cybersecurity systems. It leverages evolutionary computation techniques, particularly adaptive differential evolution, to optimize the training of MLPs for attack detection. By dynamically adjusting key parameters such as scaling factors and crossover rates, the CYO mimics the continuous adaptation processes characteristic of robust cybersecurity frameworks. This adaptability ensures that the optimizer remains effective across a diverse range of attack scenarios, thereby enhancing the MLP's ability to generalize from training data and accurately identify new threats.  
Moreover, the CYO incorporates an archival mechanism, enabling it to retain and reintegrate successful solutions from previous generations. This approach is analogous to cybersecurity practices that utilize historical attack data to inform current defense strategies. By maintaining a diverse genetic pool, the optimizer avoids premature convergence, thus enhancing its robustness and ensuring a thorough exploration of the solution space.  
Additionally, the optimizer employs a hybrid mutation strategy, which combines random mutations with best-solution mutations. This dual approach mirrors the layered defense strategies often used in cybersecurity, balancing randomness to reduce predictability with targeted adaptations to address identified vulnerabilities. The result is an optimizer that not only improves the training efficiency of MLPs but also aligns closely with the dynamic and adaptive nature of effective cybersecurity defenses.
## 3 Case Study  
Taking the vibration and noise level test in a subway tunnel as an example, INV3062V network distributed acquisition instrumentation and LC0161 BG piezoelectric acceleration sensors are used for the data acquisition, with a frequency response of 0.1-1000 Hz, a measuring range of 5 g, and a testing accuracy of 0.0002 g. The arrangement of measuring points is shown in Figure 3. The measuring points of the tunnel section are arranged and include the rail, the track slab, and the tunnel wall. Eight channel data are selected, and each channel has 12,800 sampling points for analysis to ensure the validity of the network model.  
In order to verify the accuracy of this prediction model, two different data processing methods will be used to predict the missing channel data. The first method is to directly train and verify the neural network with the raw data (time-domain data) collected by sensors. The second method is to perform a Fourier transform on the original data (frequency domain data) and then use the transformed data to train and verify the neural network.
## 2 Materials and Methods
### Stent Placement under Endoscopic and Fluoroscopic Guidance  
Anesthesia was induced by intramuscular injection of 50 mg of ketamine under the supervision of a veterinarian. An endotracheal tube was placed and anesthesia was maintained by inhalation (0.5-2% isoflurane (Ifran; Hana Pharm. Co., Seoul, Korea) with oxygen (510 mL/kg per min) at 1:1). The pigs were then positioned in the prone position and baseline endoscopic images of the nasopharyngeal orifice of the ET were obtained. The metallic guiding sheath (arrowheads, Figure 1a) was then advanced through the nostril to the nasopharyngeal orifice (arrow, Figure 1a) of the ET under endoscopic guidance (Figure 1). A micro-guidewire with a balloon catheter (Genoss Co., Ltd., Suwon, Korea), which had been crimped with a stent, was then advanced through the metallic guiding sheath (arrowheads, Figure 1c) into the ET until its tip met resistance near the bony-cartilaginous isthmus of the ET. This procedure was conducted under combined endoscopic (VISERA 4K UHD Rhinolaryngoscope; Olympus, Tokyo, Japan; arrows, Figure 1e) and fluoroscopic (Ziehm Vision RFD Hybrid Edition; Ziehm Imaging GmbH, Nuremberg, Germany) guidance. Cobalt-chrome alloy stents of two different diameters (18 mm length and 85 um strut thickness, Genoss BMS, Genoss Co., Ltd., Suwon, Korea) were then placed into the left and right ETs (right-side ET, 3.5 mm; left-side ET, 2.5 mm). The balloon catheter was fully inflated with a contrast medium to 12 atmospheres, as determined by a pressure gauge monitor. After maintaining the fully expanded balloon for 30 seconds, the balloon catheter was deflated under continuous fluoroscopic monitoring. The sheath, balloon catheter, and guidewire were removed after stent placement and the ET orifice was examined by endoscopy to confirm the location of the proximal end of the stent and detect any mucosal injuries. Technical success was defined as successful stent placement in the cartilaginous portion of the porcine ET. All pigs were euthanized using an overdose of xylazine hydrochloride (Rompun; Bayer, Seoul, Korea) at 4 weeks after stent placement. Before sacrifice, follow-up endoscopic examinations were performed to evaluate the proximal end of the stent.  
Figure 1: (**a**) Photograph showing the metallic guiding sheath developed for the porcine Eustachian tube (ET) model. (**b**–**h**) The technical steps of stent placement in a porcine ET. (**b**) Baseline endoscopic image showing the nasopharyngeal orifice (arrow) of the right ET. (**c**) Endoscopic image showing the location of the metallic guiding sheath (arrowheads) at the ET orifice. (**d**) The proximal end of the stent (arrow) protrudes from the nasopharyngeal orifice of the ET. (**e**) Radiograph obtained during stent placement showing the metallic guiding sheath (arrowheads) and flexible endoscopy (arrows). (**f**) A balloon catheter with a crimped stent (arrows) was inserted through the sheath (arrowheads) into the ET. (**g**) The balloon catheter (arrows) was fully inflated. (**h**) Radiograph obtained immediately after stent placement showing a fully expanded stent (arrows).
## 2 Related Work
[26] fully utilized geographic, social, and interest features to identify optimal relay nodes for efficient data propagation. The proposed Geo-Social-Interest protocol outperforms others by selecting relays based on spatial contact patterns, social characteristics, and user interests. Ref. [27] proposed an activity-based message-forwarding algorithm that selects relay nodes and optimal paths to improve delivery ratios and reduce delays and overhead compared to opportunistic routing algorithms. Ref. [28] focused on protecting location privacy when accessing location-based services in opportunistic mobile social networks. Two obfuscation protocols that utilize social ties to anonymize user identities and locations were proposed for that purpose. Despite the proposed protocols' efficiency with higher query success ratios than existing methods by facilitating multi-hop routing through social contacts, ref. [29] tackled the problem of privacy leaks, enabling external adversaries to infer user demographics from exposed location profiles and match shared locations to real mobility traces and points of interest. The authors proposed a context-based, system-level privacy protection solution that automatically learns user preferences and provides transparent control over location sharing to address this. The two last works are limited only to the location-based service's privacy, limiting their use. Although these works contributed greatly to the OMSN routing field, most focus on ensuring a high delivery rate with low latency and acceptable network overhead without considering security and privacy. It is also worth noting that research works explicitly tackling the privacy issues in OMSN at the routing level are rare. Therefore, privacy preservation in OMSNs constitutes a challenging issue. This paper presents lightweight privacy-aware routing schemes for OMSN by adding a privacy layer to the state-of-the-art OMSN routing decision models. This layer approximates real-world scenarios and provides user-centric message delivery according to user incentives and requirements. The proposed privacy-aware forwarding modes minimize privacy misuse while keeping the message-delivery process effective and efficient.
# A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks
## 3 The Improved Clustering Scheme
### Cluster Formation
#### 3.2.3 Selecting SCHs
On the other hand, if the cluster member \(x\) finds that its own _ID_ is not in the power acknowledge list message received by it, it retransmits the PAREP packet at the transmission power increased by \(\varepsilon\) since the PAREP packet is not successfully transmitted at the original transmission power (see lines 15-20). ```
Run at a FCH node (e.g., \(w\))  Input: the hello messages of all neighbors  Output: NULL
1. Broadcast a PAREQ packet at its maximum transmission power to the cluster members
2. if receive the first PAREP packet from any cluster member (e.g., \(x\)) then
3. Add \(ID_{x}\) to the power acknowledge list message
4. Set the timer \(t_{\phi}\) as \(\phi\)
5. while the timer \(t\) does not expire do if receive the PAREP packet from any cluster member (e.g., \(y\)) then add \(ID_{y}\) to the power acknowledge list message
7. endwhile
8. endif
9. Broadcast the power acknowledge list message at its maximum transmission power
10. if the PAREP packet of any cluster member (e.g., \(x\)) arrives after the time \(\phi\) then go to step 3 Run at any cluster member (e.g., \(x\))  Input: the hello messages of all neighbors and the very small real number \(\varepsilon\)  Output: the adjusted transmission power from cluster member to FCH
11. if receive the PAREQ packet from the FCH (e.g., \(w\)) then
12. Adjust the transmission power \(p^{t}x\),\(w\) according to the Formula (11)
13. Send a PAREP packet at \(p^{t}x\),\(w\) to the FCH
14. endif
15. if receive the \"power acknowledge list message\" from the FCH (e.g., \(w\)) then
16. if the \(ID_{x}\) is not in the \"power acknowledge list message\" then
17. \(p^{t}x\),\(w=p^{t}x\),\(w+\varepsilon\)
18. Send a PAREP packet at \(p^{t}x\),\(w\) to the FCH
19. else return endif
20. endif
```  
**Algorithm 6**Power Adjustment for Cluster Members (_PA_-_CM_)
## 2 Materials and Methods
### Material Characterisation  
The morphologies, particle size distribution, energy dispersive x-ray spectroscopy (EDS) analysis and elemental mapping were performed on a Philips CM200 operated at 200 kV. The materials were dispersed in cyclohexance followed by ultrasonication for a few seconds before being dropped onto a carbon-coated copper grid and dried in an argon filled glovebox. The prepared copper grid was transferred to the TEM in a quick manner to minimize air exposure. To verify that the observed particles were the stabilized-alanate nanoparticles, elemental mapping was carried out on the representative LiAlH\({}_{4}\)-DDT-1 and NaAlH\({}_{4}\)-DDT-1 nanoparticles (Figure S1).  
The crystalline nature of the material was determined by X-ray Diffraction (XRD) using a Philips X'pert Multipurpose XRD system operated at 40 mA and 45 kV with a monochromated Cu K\(\alpha\) radiation (\(\lambda\) = 1.541 A) from 20\({}^{\circ}\) to 70\({}^{\circ}\). The materials were placed on the stainless-steel holder and protected against oxidation from air by a Kapton foil.  
Infrared analysis was carried out on a Bruker Vertex 70V equipped with an MCT-detector and a Harrick-Scientific Praying Mantis Diffuse Reflectance Infrared Fourier Transform Spectroscopy accessory. The materials were mixed with KBR in the glove box and then loaded in an air-tight chamber fitted on the Praying Mantis. The spectra were recorded at room temperature from 3500 to 400 cm\({}^{-1}\) with 124 scans and a resolution of 1 cm\({}^{-1}\).  
Hydrogen desorption profiles were acquired by Thermogravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC) in conjunction with Mass Spectrometry (MS)-OmniStar instrument (Pfeiffer). The measurements were conducted under argon flow at 20 mL min\({}^{-1}\) from 20 to 500 \({}^{\circ}\)C with a heating rate of 10 \({}^{\circ}\)C min\({}^{-1}\). Masses between \(m/z\) = 2 and 100 were followed by MS.
## 3 Simulation and Experiment
### Real Well Test  
The experimental data collected in Xinjiang, China, in October 2017 was used to further validate the present technique. Figure 8 shows the abridged general view of the MPT experimental site and Figure 9 is the actual experimental site [30]. In the real well experiment, the drilling rig was equipped with two mud pumps and worked at depths exceeding 3000 m.  
The input signal is modulated by the BFSK with 1000 Hz sample rate and 10 bit/s symbol rate. The main steps of demodulation include filtering, denoising, synchronization, channel estimation, and equalization. Following the initial processing steps, Figure 10 displays the received signal after denoising, corresponding to a well depth of 2799 m, and then LFM signals are utilized to estimate the mud pulse channel. The channel estimates obtained using the fractional Fourier transform method are shown in Figure 11, demonstrating that there are three or four obvious multipath paths within a frequency of 10 Hz and the ISI is quite severe. Therefore, the following channel equalization is essential for addressing ISI during demodulation.  
Figure 8: General view of MPT experimental site.  
Figure 7: Comparison of BER with different SNRs.  
In order to quantitatively evaluate the suggested equalization method, further processing was performed on the equalization output in the demodulation process. The same noise signal still exists, with variations in symbol rate and utilization of BFSK modulation. Table 1 shows the BER results after demodulation with two techniques (only DFE and DFE + FrFT) at different depths. From the table, it can be seen that both schemes can successfully demodulate in cases 1 and 3. However, for cases 2 and 4-8, the method employing only uses time-domain DFEs fails to achieve correct demodulation, resulting in a BER significantly higher than the standard requirements. When using channel estimation based on FrFt plus  
Figure 10: Received signal after denoising in experiment.  
Figure 9: The actual experimental site.  
DFE, BER is decreased by 90%, which is shown in Table 1. In general, integration of FrFT equalization reduces the BER to below 0.5%, which meets the engineering requirements. The results demonstrate the effectiveness of the presented technique, indicating enhanced performance and usability in real well tests.
# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 4 Challenges and Discussion
### Performances of Visual SLAM and VOE Systems
Research has also been conducted on large databases of RGB-D images. However, these studies are often only applied and tested on one or a few databases. Each database is often collected under certain environmental conditions. For VOE and Visual SLAM methods to meet the real and production environment, many external factors need to be considered. For example, the KITTI database was collected in outdoor environments and large spaces, without taking into account small indoor spaces in rooms. In this paper, we will introduce a benchmark dataset that we collected using Intel Real Scene D435 in the next section. At the same time, to move from research to the application of Visual SLAM in practice, it is necessary to solve many challenges such as sensor technology, cost, and processing environment. For example, in the application of building autonomous vehicle control software using Visual SLAM technology, RGB-D data need to be combined with Radar [215] or LiDAR [216] signals.
## 3 Results
### Aerial Triangulation (AT)
#### 3.1.2 Bundle Adjustment  
The image coordinates of 44 GNSS-based points (31 GCPs and 13 CPs) were measured in Inpho and used for the AT also in Metashape to avoid further measurement bias in the image observations. Considering that the camera shows limited radial distortions, the Metashape _frame_ camera model was used, which corresponds to a central projection camera with [PERSON]'s distortion model. With the optical center at the original position, the [PERSON]'s distortion model divides the total distortions into two components in polar coordinates along the radius and in tangential directions with the center on the imaging plane.  
Table 3 reports interior and additional parameters, including estimated errors (standard deviation), computed in Metashape: focal length (F), principal point coordinates (Cx, Cy), affinity and skew-non-orthogonality transformation coefficients (B1 and B2), radial distortion coefficient (K1, K2, K3), and tangential distortion coefficients (P1 and P2).  
The focal length was not estimated, as the value reported in the camera calibration certificate was considered reliable. This is a common approach in the case of working with historical aerial images [22; 48]. The principal point values (Cx, Cy) in the photogrammetric dataset are very close to the values that are recorded in the camera's calibration report (Cx = Cy = \(-\)6 \(\upmu\)m). In the consumer-grade dataset the principal point coordinates were moved more than 0.5 px. B1 and B2 values for a consumer-grade scanner are 25-30 times higher than values for the professional-grade scanner; this means that the consumer-grade scanner images have much higher affine distortions caused by the lower quality scanning mechanism. It is worth noting that errors for every parameter (Cx, Cy, B1, B2, K1, K2, K3) are 2-3 times higher for the consumer-grade scanner compared to the professional scanner.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scanner** & **Consumer-Grade** & **Photogrammetric** \\ \hline RMSE & & \\ Min/Average/Max values [\(\upmu\)m] & 17.5/24.3/38.7 & 1.5/4.4/7.4 \\ \hline Sigma0 & & \\ Min/Average/Max values [\(\upmu\)m] & 35.0/48.6/77.3 & 3.1/8.8/14.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of the affine transformation for consumer-grade and photogrammetric scanner datasets performed in Inpho to determine the interior orientation of each scanned image.  
AT results have been evaluated in terms of root mean square error (RMSE) of CPs (Table 4). The photogrammetric dataset outperforms the regular outputs with both software (with more marked differences in the Inpho results), underling the importance of high-quality scanners to maximize the accuracy in the 3D object space. Comparing AT results for a single dataset (photogrammetric or consumer-grade), the use of self-calibration in Metashape reduces the impact of the scanning technology and improves the achieved results.