esa-sceva/satcom-chunk-collection huggingface dataset:
# Physical Layer Security Based on Non-Orthogonal Communication Technique with Coded FTN Signaling
## 5 Experimental Design and Performance Evaluation
### Performance Evaluation
The performance tendency of the QPSK modulation is similar to that of the 16-QAM, shown in Figure 9. However, for the 16-QAM, the performance of the symbol-wise coded FTN system is about 1.6 dB worse than the typical 16-QAM performance, and for the QPSK, the performance of the symbol-wise coded FTN is only about 0.8 dB worse than the typical QPSK performance. Therefore, the performance degradation values of the QPSK with coded FTN signaling are lower than that of the 16-QAM. Figure 8: BER performance of variable-length block-wise coded FTN signaling in DVB-S2X system with 16-QAM. Figure 7: BER performances of symbol-wise coded FTN signaling and block-wise coded FTN signaling in DVB-S2X system with QPSK. In addition to the BER vs. SNR analysis, the Peak-to-Average Power Ratio (PAPR) was evaluated for different \(\tau\) values in the FTN system with the 16-QAM modulation. Figure 10 illustrates the PAPR performance across various \(\tau\) values (\(\tau\) = 1, 0.875, 0.75, 0.625, 0.5). The results indicate that as the \(\tau\) value decreases, the PAPR increases due to enhanced ISI. For \(\tau\) = 1, which corresponds to the Nyquist signaling condition, the PAPR is minimal. However, when \(\tau\) is reduced to 0.5, a significant increase in PAPR is observed, highlighting the trade-off between spectral efficiency and power efficiency in FTN signaling. The state-of-the-art method described in the referenced paper [21] evaluates performance primarily through the secrecy outage probability (SOP) metric, while the proposed technique in our manuscript is evaluated using the BER. This difference in performance metrics makes a direct comparison challenging. However, it is noteworthy that the BER  
Figure 10: PAPR performance of DVB-S2 system with 16 QAM according to \(\tau\) values. Figure 9: BER performance of variable-length block-wise coded FTN signaling in DVB-S2X system with QPSK. performance of our proposed technique remains highly stable, achieving exceptionally low values as the SNR increases. In contrast, the secrecy outage probability of the state-of-the-art method does not decrease significantly below 0.1, even with an increase in SNR. These results indicate that our proposed technique demonstrates stable performance in terms of reliability and robustness.
# A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion
## 3 Materials and Methods
### Structure of System
#### 3.2.5 Dropout Layer  
Deep learning models perform well when massive data are available for network training. As a result, memorization can take place as the network is being trained. However, memorization of the network creates the problem of network overfitting, which is undesirable. The memorization problem occurs when there are deeper hidden layers in the DNN. To avoid network memorization, some nodes must be disabled. When some nodes are disabled, the memorization process is halted. Hence, it improves the network performance by applying a dropout layer in the network [51]. An example of a dropout operation in a network is shown in Figure 4.
## 4 Numerical Modeling of Connection
### Material Properties  
Steel components were assumed to be made of SM355. The material properties of steel were defined using MAT_PIECEWISE_LINEAR_PLASTICITY [19]. The modulus of elasticity and the Poisson's ratio were 210,000 MPa and 0.3, respectively. For the plastic properties, the expected strengths, i.e., the nominal strengths multiplied by the strength increase factors per Ministry of Land, Infrastructure and Transport [26] were used. The nominal yield strength for the SM355 steel varied with thickness, i.e., 355 MPa for thicknesses less than 16 mm, and 345 MPa for thicknesses greater than 40 mm. The nominal tensile strength was 410 MPa regardless of thickness. The strength increase factors for both yield and tensile strengths were set to be 1.2 so that the expected yield strengths were estimated to be 425 and 413 MPa, respectively; the expected tensile strengths were 588 MPa.  
Concrete was assumed to have the compressive strength of 49 MPa based on the work of [PERSON] et al. [6], who experimentally studied CFD connections reinforced with a vertical diaphragm, noting that the beam size was also selected referring to their study, as described previously, and was modeled using MAT_CSCM_CONCRETE, developed for the United States Federal Highway Administration to model the crashworthiness of structural components of concrete [27,28]. This concrete model is based on a continuous surface cap model (CSCM) and involves the yield surface, strain-rate effects, damage-based material erosion, and strain softening for compression and tension.
## 3 Results and Discussion  
### Effect of Pomelo Cultivars on Juice Yield, Total Soluble Solid, pH of Juices  
In the food industry, fruits with a high juice yield could reduce the cost of producing NFC juices to a certain extent [5]. Hence, the impact of pomelo cultivars on juice yield was studied and the results were presented in Table 1. The juice yields of six pomelo cultivars were significantly different. Among these six pomelo cultivars, grapefruit had the highest juice yield (73.22%), which was 28.04%, 11.73%, 22.18%, 26.36% and 38.06% higher than that of _cv._ [PERSON], _cv._ Wendanyu, _cv._ Liangpinyu, _cv._ Pingshanyu and _cv._ Shatanyu, respectively. The lowest juice yield was from _cv._ Wendanyu. There was little difference in the juice yield between _cv._ [PERSON] and _cv._ Pingshanyu. [PERSON] et al. [15] reported a juice yield of 70.12% for grapefruit, which was similar to the result of this study. In addition, the juice yield of _cv._ Wendanyu was lower than the conclusion reached by [PERSON] et al. [29] in their analysis of lemon juice. The results showed that pomelo cultivars significantly affect the juice yield.  
It had been reported that the TSS content in juices in general did depend on the cultivars [34]. The TSS content for these juices ranged from 10.55 to 13.30 'Brix', which was much higher than for the white pomelo juices reported by [PERSON] et al. [19]. Moreover, in addition to HP Liangpinyu juice (HPLU), the TSS contents of most HP juices were always higher than those of LP pomelo juices. The taste of the juices was great affected by pH value, and a significant difference was found in the pH value among different pomelo juices. The pH value of the LP juice of the same pomelo cultivar was lower than that of HP ones. Graprefruit juice had the lowest pH value (3.24), while the pH of HP Liangpinyu juices (HPLU) and HP Shatianyu juices (HPSU) were the highest (4.75). These results indicated that the TSS content, pH value, glucose and sucrose concentration of the HP pomelo juice of most pomelo cultivars were higher than those of LP pomelo juices. Additionally, there was wide diversity between cultivars.
## 4 Experimental Results  
### Elimination of Undesired Beat from Common-Mode Noise for the Three One-Dot Vertical Lines  
The proposed signal processing was applied to eliminate the undesired beat from the common-mode noise for the three one-dot vertical lines described in Section 3.2.1. The same image for the three one-dot vertical lines as is shown in the inset of Figure 4 was displayed on the monitor. The measurement setup is presented in Section 2. Figure 7 shows the changes in the common-mode noise waveforms by the above  
Figure 6: Sequential frame of the information writing on the monitor.  
signal processing. Figure 7a shows an original noise waveform before the processing. The waveform was reformed (as shown in Figure 7b) after processing for the first down-step. Then, the waveform shown in Figure 7b was reformed to the waveform shown in Figure 7c after processing for the first up-step. Afterwards, the waveforms continued to be processed similarly. Finally, the beats were eliminated almost completely, and only three sharp pulses could be derived, as shown in Figure 7g.  
Figure 7: Common-mode noise waveforms of the signal processing: (**a**) before processing, (**b**) after processing for first down-step, (**c**) after processing for first up-step, (**d**) after processing for second down-step, (**e**) after processing for second up-step, (**f**) after processing for third down-step, (**g**) after processing for third up-step.
## 3 Aircraft Elastic Deformation Estimation and Inertial Network Algorithm Design
### Distributed Data Fusion Algorithm
#### 3.2.2 Distributed State Fusion Algorithm
Taking node 1 as an example, define the quadratic cost function of node 1 as follows:  
\[F_{1}=\sum\limits_{J=1-n}\Big{(}C_{J}^{1}x_{J}-x_{1}\Big{)}^{\mathsf{T}}C_{J} ^{1}P_{J}^{-1}C_{1}^{I}\Big{(}C_{J}^{1}X_{J}-x_{1}\Big{)} \tag{36}\]  
where \(x_{1}\) is the true local similar state value of node 1, and \(F_{1}\) is a cost function to measure the displacement between the local state estimation and the true value. The state fusion filter is designed to minimize \(F_{1}\), which is called the minimum weighted mean square error criterion, and the local state update values are as follows:  
\[ \begin{array}{l}\overline{x}_{1}=\overline{P_{1}}^{-1}({P_{1}}^{-1}\hat{x}_ {1}+{C_{2}}^{1}{P_{2}}^{-1}\hat{x}_{2}+{C_{3}}^{1}{P_{3}}^{-1}\hat{x}_{3}+ \cdots+{C_{n}}^{1}{P_{n}}^{-1}\hat{x}_{n})\\ \overline{P_{1}}^{-1}=({P_{1}}^{-1}+{C_{2}}^{1}{P_{2}}^{-1}{C_{1}}^{2}+{C_{3}}^ {1}{P_{3}}^{-1}{C_{1}}^{3}+\cdots+{C_{n}}^{1}{P_{n}}^{-1}{C_{1}}^{n})^{-1}\end{array}  \tag{37}\]Similarly, the update equation at the other nodes is as follows:  
\[ \begin{array}{l}\overline{x}_{2}=\overline{P}_{2}{{}^{-1}}(C_{1}{{}^{2}}P_{1}{{}^ {-1}}\hat{x}_{1}+P_{2}{{}^{-1}}\hat{x}_{2}+C_{3}{{}^{2}}P_{3}{{}^{-1}}\hat{x}_{3 }+\cdots+C_{n}{{}^{2}}P_{n}{{}^{-1}}\hat{x}_{n})\\ \overline{P}_{2}{{}^{-1}}=(C_{1}{{}^{2}}P_{2}{{}^{-1}}C_{2}{{}^{1}}+P_{2}{{}^{-1 }}+C_{3}{{}^{2}}P_{3}{{}^{-1}}C_{2}{{}^{3}}+\cdots+C_{n}{{}^{2}}P_{n}{{}^{-1}}T _{2}{{}^{n}})^{-1}\\ \vdots\\ \overline{x}_{n}=\overline{P}{{}^{-1}}(C_{1}{{}^{n}}P_{n}{{}^{-1}}\hat{x}_{1}+ C_{2}{{}^{n}}P_{2}{{}^{-1}}\hat{x}_{2}+C_{3}{{}^{n}}P_{3}{{}^{-1}}\hat{x}_{3}+ \cdots+P_{n}{{}^{-1}}\hat{x}_{n})\\ \overline{P}_{n}{{}^{-1}}=(C_{1}{{}^{n}}P_{n}{{}^{-1}}T_{1}{{}^{2}}+C_{2}{{}^{ n}}P_{2}{{}^{-1}}T_{n}{{}^{2}}+C_{3}{{}^{n}}P_{3}{{}^{-1}}T_{n}{{}^{3}}+\cdots+P_{n}{ {}^{-1}})^{-1}\end{array}  \tag{38}\]  
**4. The Fusion of Inertial Network and FADS to Solve Flight Parameters**  
_4.1. The Flight Parameters Are Solved by the Fusion of Inertial Network_  
Many flight parameters exist, like angle of attack, side slip angle, Mach-Number, static and dynamic drag, etc. Due to the limitation of space, this paper will use the commonly used angle of attack as the flight parameter to be solved, and the solving process of the remaining parameters is similar to that of the angle of attack. According to [PERSON]'s law, the inertial navigation system passes the three-axis acceleration and angular velocity information under the inertial frame. After coordinate transformation, the attitude angle, velocity and position information of the aircraft are obtained by integral, and the angle of attack is estimated by inertial velocity. \(U_{B},V_{B},W_{B}\) are the components of inertial velocity in the body coordinate system. Without considering the wind disturbance, the calculation formula of angle of attack can be written as  
\[\hat{\alpha}=\arctan\frac{W_{B}}{U_{B}} \tag{39}\]
## 6 Simulation Results
### Reconstruction Error for Different Schemes  
Figure 14 compares the reconstruction error for DFT, diffusion wavelets based on degree, and diffusion wavelets based on distance. Here, we again extract the sensory data [42]. In Figure 14, the reconstruction error is high under DFT sparse basis, where the error is up to 0.34 when the number of CS measurements is 20. However, the recovery error is less than 0.1 when the number of CS measurements is 220. In other words, the method does not accurately recover the signal. The blue curve with triangles in Figure 14 indicates the reconstruction performance of diffusion wavelets based on degree. It is noted that the scheme can sparsity the signal, and can recover the original signal with smaller reconstruction error than the DFT at the sink node. The reconstruction error changes steadily along with the increase of the number of CS measurements. The reason is that the approach only considers some featured such as the network topology, namely the number of neighbor nodes for a given node, without capturing the geographical position features of the neighbor nodes. Accordingly, the diffusion wavelets based on distance take advantage of the favorable conditions to promote recovery, as is shown in the red star curve in Figure 14.  
Figure 14: Reconstruction error vs. number of CS measurements.  
Figure 13: Comparison between original signal and reconstruction signal (a) original signal (b) reconstruction signal.
# Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
###### Abstract  
Effective quality control is crucial in industrial manufacturing for influencing efficiency, product dependability, and customer contentment. In the constantly changing landscape of industrial production, conventional inspection methods may fall short, prompting the need for inventive approaches to enhance precision and productivity. In this study, we investigate the application of smart glasses for real-time quality inspection during assembly processes. Our key innovation involves combining smart glasses' video feed with a server-based image recognition system, utilizing the advanced YOLOv8 model for accurate object detection. This integration seamlessly merges mixed reality (MR) with cutting-edge computer vision algorithms, offering immediate visual feedback and significantly enhancing defect detection in terms of both speed and accuracy. Carried out in a controlled environment, our research provides a thorough evaluation of the system's functionality and identifies potential improvements. The findings highlight that MR significantly elevates the efficiency and reliability of traditional inspection methods. The synergy of MR and computer vision opens doors for future advancements in industrial quality control, paving the way for more streamlined and dependable manufacturing ecosystems.  
quality inspection; smart manufacturing; object detection; computer vision +
Footnote †: journal: Applied sciences
# Nanomaterials-Based Ion-Imprinted Electrochemical Sensors for Heavy Metal Ions Detection: A Review
## 3 Application of Nanomaterials in HMIIECSs
### Silica Nanomaterials
The influence of different cations and anions on the determination of target ions was studied, and the electrode was found to be highly selective for the determination of Hg\({}^{2+}\). In addition, this method has been successfully applied to the determination of Hg\({}^{2+}\) in water and some food samples. Figure 14: Schematic diagram of Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@IIP modified GCE. Reprinted with permission from Ref. [138]. Copyright 2018, Elsevier. [PERSON] et al. developed an all-solid-state polyvinylchloride (PVC) membrane cesium potentiometric microsensor based on Cs(I)-MIIP, which was synthesized by the surface-imprinting technique with functionalized magnetic Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) microspheres as the supporter, Cs(I) ion as the template and carboxymethyl CS as the functional monomers [140]. The Cs(I)-MIIP was used as an ion carrier in the construction of the PVC membrane electrode to obtain a selective potentiometric microsensor for Cs\({}^{+}\). This was the first time to report the electrochemical determination of Cs\({}^{+}\) by IIP. The prepared sensor showed a highly sensitive response in the concentration range of 1\(\upmu\)M to 0.1M. The response time was quite short (less than 3 s), and the LOD was determined to be 0.3\(\upmu\)M. The microsensor worked effectively in the pH range of 4-6 and showed ideal selectivity for many other cations including alkali, alkaline earth and heavy metals. It can also be used for the determination of Cs\({}^{+}\) content in the environmental water samples. The potentiometric results were consistent with those obtained with the ICP-MS method. [PERSON] proposed a new chemically modified CPE (IIP@SiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\)) which can be used for the accurate, simple, sensitive and selective determination of Mo\({}^{6+}\)[141]. IIP@SiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\) was prepared by the sol-gel process. The proposed sensor obtained high sensitivity and a wide linear range for determining Mo\({}^{6+}\) attributed to sensitive and selective interaction between the target ion and nano-IIP at the surface of the IIP@SiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\) electrode. The results showed that the linear dynamic range and the LOD were 0.2-500.0 ng/mL and 0.04 ng/mL, respectively. The response reproducibility and stability of the modified electrode were investigated. By investigating the influence of different cations and anions on the determination of target ions, it was found that the electrode had a high selectivity for the determination of Mo\({}^{6+}\). This method was also used for the determination of Mo\({}^{6+}\) in actual samples. The nanomaterials-based HMIIECSs for the detection of Pb\({}^{2+}\), Cd\({}^{2+}\), Cu\({}^{2+}\) and Hg\({}^{2+}\) in real samples were overviewed in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
# A Database for Tsunamis and Meteotsunamis in the Adriatic Sea
## 3 The Database of the Adriatic Meteotsunamis and Tsunamis (DAMT)
Coversely, the Croatian coast of the central Adriatic, characterized by many islands, channels and narrow bays, is the area where the most meteotsunamis occur (Figure 4), most of which are very powerful. On the Italian coasts, most of the effects were due to the 21 June 1978 event, the strongest in the Mediterranean, but some locations were also affected by the 25 June 2014 event. As for tsunamis, and also for meteotsunamis reported in the WebApp, the user can click on each point where the effects were observed. The pop-up window shows the date of the event (or several dates if there is more than one event at the same point), a short description of the observed effects, a link to the general description of the event and the intensity. Since  
Figure 4: (a) Geographical distribution of the Tsunami Posits (ATOP layer). (b) An example of the pop-up for the Manfredonia (Apulia) observation point. Figure 5: Geographical distribution of the meteotsunami effects. meteotsunamis are a very complex phenomenon involving meteorological, hydrological and bathymetric factors, for their classification, [PERSON] and [PERSON] [3] introduced a value, the QIndex, that indicates how detailed the information about the event is. The QIndex ranges from 1 (elementary description of sea-level variability) to 5 (analysis of oceanographic and meteorological data combined with both oceanographic and meteorological modeling), depending on what kind of bibliographic sources support the available data. In DAMT, the same type of classification has been used, maintaining the QIndex values assigned by [3] and assigning the QIndex to the new events inserted. Figure 6 shows an example of the pop-up for the 11 May 2020 event.
## 2 Problem Statement and Methodology
### Single Gaussian Model-Based Uncertainty Propagation  
Generally, uncertainty propagation is used to determine the specific statistical moments of dynamics (1) or (2), with a given initial probability distribution and propagation period. The state moment, i.e., mean, and covariance matrix or probability density function (PDF) are specific quantitative measures of statistical moments. The mean and covariance matrix could be defined as follows with given random vector \(x\in\mathbb{R}^{n}\) and PDF \(p(t)\):  
\[\begin{array}{l}\mathbf{m}=E[\mathbf{x}]=\int_{\infty}\xi p(\xi)d\xi\\ \mathbf{P}=E\left[(\mathbf{x}-\mathbf{m})(\mathbf{x}-\mathbf{m})^{\mathrm{T}}\right]=\int_{\infty} (\xi-\mathbf{m})(\xi-\mathbf{m})^{\mathrm{T}}p(\xi)d\xi\end{array} \tag{4}\]  
where \(\xi\) is a random variable.  
The Gaussian weighted integral (4) can be approximated efficiently with different quadrature rules, such as the unscented transformation (UT), Conjugate Unscented Transform (CUT), and Cubature rules (CRs). In this paper, the fifth-order CR is utilized for propagating the single Gaussian distribution. The fifth-order CR is detailed in Ref. [47], which will not be repeated here.
## 4 Real-Time Threat Detection and Response
### Latency and Computational Constraints  
\(\bullet\)**Model Compression and Quantization:** Complex deep neural networks may yield high detection accuracy but suffer from large computational overhead. Techniques such as quantization, pruning, and knowledge distillation can reduce model size and inference latency without significantly compromising detection quality [44]. Some example tools are the TensorFlow Lite, which offers post-training quantization to deploy compressed models on edge devices, and the PyTorch Mobile, which supports quantized inference and can run on resource-constrained hardware.  
\(\bullet\)**Hardware Acceleration:** Utilizing specialized accelerators, such as Field-Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs), can dramatically reduce inference times and meet the strict latency requirements of 5G and 6G networks [45]. Some example tools are the Intel OpenVINO, which optimizes deep learning models for Intel CPUs, VPUs, and FPGAs, and NVIDIA TensorRT, which provides high-performance inference optimizations for GPUs.  
\(\bullet\)**Lightweight Feature Extraction:** To ensure minimal delay, feature extraction should be efficient. Approaches based on packet-level features or flow-level metrics can be processed quickly, enabling near real-time threat identification [46]. An example tool is the Elastic Stack (Elasticsearch, Logstash, Kibana), which can be integrated with lightweight data collectors (e.g., Beats) for real-time pipeline analytics.
## 3 Methodology
### System Optimization
#### 3.4.2 Entropy Minimization for Dialogue Efficiency  
In addition to variance reduction, another approach to minimize dialogue rounds involves controlling the entropy of the responses. Entropy \(H\) quantifies the uncertainty of the LLM's responses, and is defined as follows:  
\[H=-\sum_{k}p_{k}\log p_{k}, \tag{4}\]  
where \(p_{k}\) is the probability of the \(k\)-th response option. High entropy suggests that the LLM is uncertain about its responses, which could lead to prolonged dialogues. By minimizing entropy alongside variance, we ensure that the LLMs provide more confident and consistent answers, thus reducing the need for additional rounds.  
Finally, the system monitors the combined effect of these measures by evaluating the total number of tokens used across all rounds \(T_{total}\), and the total response time \(T_{response}\). The objective is to minimize the following:  
\[\min(T_{total},T_{response}). \tag{5}\]  
By integrating variance and entropy penalties into the dialogue process, the system efficiently reduces the number of dialogue rounds while maintaining response quality, leading to faster and more accurate task completion.