esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
In this study, a parallel-polarized light source and several NIR bandpass filters were combined to predict the vitamin C content of Indian jujube with a combination of wavelengths. The probing wavelengths were designed to be incident on the fruit at \(\theta_{\mathrm{B}}\), to increase \(r_{\mathrm{P}}^{2}\) and reduce RMSEP. The \(r_{\mathrm{P}}^{2}\) and RMSEP values of our detection system were 0.84 and 1.65 mg/100 g, respectively, for the vitamin C contents of Indian jujube fruits in the prediction set. Furthermore, our system is significantly cheaper than currently available portable vitamin C measurement systems, as it simply uses a halogen light source with NIR bandpass filters and an NIR photodetector instead of a costly Fourier transform spectrometer.  
The predictive accuracy of our system is equal to or better than that of other NDT systems. For example, \(r_{\mathrm{P}}^{2}\) values of 0.81 and 0.87 were obtained by an FT-NIR-based detection system for the vitamin C content of oranges and kiwifruit, respectively, with RMSEP values of 5.9 mg/100 g and 6.4 mg/100 g, respectively [18; 29]. The \(r_{\mathrm{P}}^{2}\) values of a system based on visible NIR spectroscopy in measuring the vitamin C content of carrots and tangerines were 0.72 and 0.82, respectively and the corresponding RMSEP values were 4.3 mg/100 g and 2.3 mg/100 g, respectively [30; 31]. Another system that is based on NIR spectroscopy obtained \(r_{p}^{2}\) values of 0.80 and 0.70 and RMSEP values of 4.0 mg/100 g and 16.0 mg/100 g when measuring the vitamin C contents of apples and infant cereals, respectively [32; 33].  
In the future, we plan to improve the performance of our system using bandpass filters with special specifications and different full-width half-maxima to improve the quality of the reflectance signals. Furthermore, we will develop a chipset that will perform the necessary numerical analyses and other algorithmic operations such that our system can display the predicted vitamin C value of a fruit without being connected to another computer.  
Figure 6: Correlation between the predicted and measured values of the prediction set.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Prediction Set**} \\ \cline{3-5}  & **Wavelengths Combination** & \
\begin{tabular}{c} **Coefficient of** \\ **Prediction** (\(r_{p}^{2}\)) \\ \end{tabular} &
\begin{tabular}{c} **Root-Mean-Square Error of** \\ **Prediction** (\(RMSEP\)) **(mg/100 g)** \\ \end{tabular} \\ \hline D & N = 5 & 1200, 1400, 1450, 1500, 1550 nm & 0.84 & 1.65 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Coefficient of Prediction (\(r_{\mathrm{P}}^{2}\)) and Root-Mean-Square Error of Prediction (RMSEP) of vitamin C.
# Multi-Object Tracking with Grayscale Spatial-Temporal Features
## 2 Related Work
[PERSON] et al. [24] proposed trajectory masks for tracking. The trajectory mask network is used to extract multidimensional information, including the estimate, approximate location, and shape of the target. [PERSON] et al. [25] proposed a probabilistic autoregressive motion model, which learns and generates multimodal distributions of motion trajectories, matching based on trajectories and detection frames. In these paradigms, there are not many ways to use traditional methods to extract features and optimize ID matching. In the matching stage, as the number of detection targets increases, the matching times will increase significantly, and in order to increase the real-time performance of the program, it is necessary to reduce the use of neural networks to extract features. The grayscale spatial-temporal feature we proposed can reduce a large number of calculations, and it has better robustness during occlusion.
# Review of Smart-Home Security Using the Internet of Things
## 4 Analysis and Discussions of the IoT Setting
### Threats Faced by a Smart Home and Related Countermeasures
#### 4.2.2 Perception Layer Analysis
Therefore, by using secure equipment from certified manufacturers, either at the hardware level or at the software level, the chances of successful attacks are minimized. Nevertheless, in the presence of an attacker, several methods are suggested to avoid eavesdropping and sniffing. [PERSON] et al. [146] propose a hybrid energy-efficient privacy-preserving scheme designed to enhance privacy in smart-home environments by effectively countering wireless traffic snooping. This scheme utilizes a probability-based decision-making algorithm to control the injection of dummy packets and adjust transmission times, thereby maximizing the confusion for attackers trying to decipher the network traffic patterns. By blending energy efficiency with robust privacy protection, this approach aims to secure smart-home communications against eavesdropping without causing significant delays or excessive energy consumption. Mechanisms that leverage AI and neural networks contribute significantly to defense against side-channel and noise attacks. [PERSON] and [PERSON] [147] utilize a neural network, which through continuous measurements can learn what is the desired, \"smooth\" operation of the system, easily identifying cases where noise has entered the system (anomalies). Also, in order not to leak the sensitive information of the system, [PERSON] et al. [148] proposed a Public Key Infrastructure (PKI) solution where information is encrypted with a public key algorithm, such as the leakage resilient algorithm, in order to ensure confidentiality even in the case of loss of some bits from transmitted packets. Moreover, to counter booting attacks, [PERSON] et al. [149] suggest a secure boot process of encrypting the FPGA-bitstream to protect the IoT device from bitstream decoding, encrypting the system boot image to enhance system security, and ensuring that the device operates correctly, as intended, via authentication.
## 2 Linear Modes
For example, if only \(q_{1}(0)\
eq 0\) and all other ICs are zero, then Equation (2) gives: \(\mathbf{q}(t)=[q_{1}(0)\sum_{i=1}^{n}v_{1}e^{\lambda_{i}t}\ 0\ \cdots\ 0]^{T}\). If the eigenvalues are not distinct, the solution is more complex but still multiexponential, and each repeated eigenvalue has its own mode, e.g., \(c_{1}e^{\lambda t}\), \(c_{2}e^{\lambda t}\) are two different modes for repeated eigenvalue \(\lambda\). Multicompartmental (MC) models can have repeated and/or complex eigenvalues in their responses. We can generalize this for nonhomogeneous system inputs \(\mathbf{u}(t)\
eq\mathbf{0}\) in Equation (2). Since the exponential mode response to ICs is the same as the response to impulse inputs, i.e., \(Bu(t)=q(0)\delta(t)\) in Equation (2), the system response to step, ramp, exponential and other (different) inputs--by linearity--also will be a sum of exponential modes and additional terms inherited from \(u(t)\). A most important feature of linear modes is that they are a **minimal set of invariants** of the dynamical system, and, for any set of ICs or input(s), the modes accurately capture essential system dynamics. Among other things, this means that linear ODEs of orders higher than \(n\) can be accurately reduced (simplified) to order \(n\) if the ODE has only \(n\) modes, and this simplified model can be represented by a set of \(n\) modes, each represented geometrically as a 2-dimensional function of the eigenvalue and eigenvector associated with each model. Whether all these modes are _visible_ or _hidden_ in data is the question we address next--the key to unraveling structure from data and designing new input-output (I-O) experiments that reveal more.
###### Abstract  
Recent years have witnessed the huge popularity of Information-Centric Networking (ICN) and its realization as Named Data Networking (NDN) in the context of wireless sensor networks (WSNs). The participating nodes in WSNs are usually equipped with a single radio interface. The existing solutions lack in providing the efficient next forwarder selection in NDN-based single radio WSNs. In this work, we propose a collaborative Interest and Data Forwarding (CIDF-WSN) Strategy for Named Data Wireless Sensor Networks. CIDF-WSN develop a Neighbor Information Base (NFIB) which enables the node to select the optimal next-hop relay in Interest packet forwarding. An efficient Interest packet processing mechanism assisted by the Interest Cache Table (ICT) is provided to avoid Interest packets loss and frequent re-transmissions. In addition, CIDF-WSN also provides a robust Data packet transfer mechanism accompanied by the Temp Cache Table (TCT) to avoid Data packet losses and to ensure well-timed content delivery. Simulation results reveal that CIDF-WSN outperforms the recently published works in terms of Interest satisfaction rate, total energy consumption, Data retrieval delays, and communication overhead.  
named data networking; wireless sensor networks; internet of things; information-centric networks; single radio WSNs; layered based WSNs; environmental monitoring; agriculture monitoring; crops monitoring 2021215174.
# Numerical and Experimental Investigation of Guided Wave Propagation in a Multi-Wire Cable
## 2 UGW Propagation in Multi-Wire Waveguides
### Contact Acoustic Nonlinearity of Coupled Double Rods  
Higher harmonic ultrasonic waves are generated in a structure with imperfect interfaces, in which excited waves with a large amplitude are incident. This phenomenon is called the contact acoustic nonlinearity (CAN) effect [24]. This phenomenon is caused by the repeated reflection of incident waves between two surfaces. The same behavior occurs when a longitudinal UGW propagates to an imperfect interface formed by two surfaces of the contact structures. The compressional part of the wave can penetrate through the interface and propagate into another structure, but the tensile part cannot. Therefore, it is equivalent to a half-wave rectification of the transmitted wave, which is a nonlinear effect. This nonlinearity is embodied in the form of higher harmonics. In nondestructive inspection using a guided wave, when the characteristic length of the rough contact surfaces or a micro-crack is much smaller than the incident wavelength, a nonlinear phenomenon occurs.  
Figure 2: The vibration displacement distribution of (**a**) longitudinal, (**b**) torsional, and (**c**) flexural waves in a 7-mm-diameter steel wire. Wave structure of the (**d**) L(0,1) and (**e**) F(1,1) modes at different frequencies.  
In a pioneering work [30], the second harmonics generated by the nonlinear nature of the contact stiffness with the frequency of 2f was theoretically observed in both transmitted and reflected waves, where f is the frequency of incident waves. A pair of coupled waveguides were created with an active wire and a passive wire closely in contact with each other. An ultrasonic guided wave was excited on the left end of the active wire. The radial displacements of the active wire were partially coupled to the passive wire through contact stress in the contact interface. Additional waves were generated in the active and passive wires as a result of the local load provided by the contact stress, as shown in Figure 3.
## 3 Experimental Results  
### Convolutional Neural Network (CNN) Results  
Figure 7 depicts the loss function during training and Figure 8 shows three planes of how a representative resulting pseudo-CT evolves during training.  
the high quality of the resulting pseudo-CT and the robustness of the Franken-CT method, which is able to capture the details of the bone contours and spikes in non-smooth areas such as the sinuses and the cervical vertebrae. The shape of the skull was estimated correctly despite synthesizing the resulting image from knowledge obtained from images acquired with a completely different FOV. Neck areas show limited detail in resolution compared to upper brain areas, probably due to the difference in the number of atlases for those specific areas. Moreover, certain differences can be noticed in nasal sinus cavity, mastoid cells and in air cavity delineation, possibly due to the complexity of these anatomies. Overall, the image texture is a bit smoother in the pseudo-CT compared to ground-truth CT.  
The experimental ZNCC was 0.9220 \(\pm\) 0.0255 and 95% confidence interval (0.9010, 0.9430); the experimental Mean Absolute Error (MAE) was 73.9149 \(\pm\) 9.2101 HU and 95% confidence interval (66.3383, 81.4915); the Structural Similarity Index Measure (SSIM) was 0.9943 \(\pm\) 0.0009 and 95% confidence interval (0.9935, 0.9951); and the experimental Dice coefficient for bone tissue was 0.7051 \(\pm\) 0.1126 and 95% confidence interval (0.6125, 0.7977). Moreover, the voxel-by-voxel correlation plot as well as the Bland-Altman plot between  
Figure 8: Examples of how the pseudo-CT synthetized by the network changes over the training process.  
Figure 7: Loss function during training. The loss function used was the Mean Absolute Error (MAE) and the training was performed during 25 epochs (\(\sim\)500 k iterations).  
pseudo-CT and CT were computed for all test participants (Figure 10). The correlation plot showed an excellent correlation between pseudo-CT Hounsfield Units and ground truth CT Hounsfield Units (m = 0.87; adjusted R\({}^{2}\) = 0.91; \(p\) < 0.001). The Bland-Altman plot showed that the average of the differences was low (\(-\)38.6471 \(\pm\) 199.6100; 95% CI (\(-\)429.8827, 352.5884)); the difference between methods tended to decrease as the average increased, accumulating the error in voxels around 0 HU.  
Figure 10: Voxel-by-voxel correlation plot between CT and pseudo-CT of each dataset evaluation image, expressed in Hounsfield Units (HU) (**a**) and Bland–Altman plot between CT and pseudo-CT for all validation subjects (**b**). Gray scale bars show density of voxels in histogram grid. Axis representation is focused on values in the range of human tissues, from lungs to cortical bone.  
Figure 9: Axial, Coronal, and Sagittal CT images from Subject fct-test-02 showed in (**a–c**), respectively, and their corresponding computed pseudo-CT showed in (**d–f**). Bone delineation purpose looks quite good except for the neck region.
## 3 Background of Study
### Working Principle of the G50150 Micro Drill Bit ARIS  
The working process of the micro drill bit ARIS is fundamentally robotic--it repeats the tasks for a single micro drill bit at a time: loading, grinding, optical inspection/scanning, and unloading [19]. Figure 1 shows the functional plan view of the G50150 micro drill bit ARIS and the key components of interest--the collet and regrinding unit.  
As shown in Figure 1, the G50150 micro drill bit ARIS has many components--each serving high-precision functions for an accurate (and well-timed) regrinding process. From the bit transfer unit, the drill bits (stacked in crates) are loaded onto the conveyor, where the index robot intelligently picks the drill bits (one at a time) with the help of the collet (highlighted in a red dotted square). Once a drill bit is picked, the index robot rotates 90 degrees in an anti-clockwise direction such that the drill bit faces the grinding unit (highlighted in a yellow dotted square). The grinding unit houses the two regrinding wheels spinning in opposite directions and are powered by high-speed gas bearings. With the help of its mobile base, it moves closer to the collet such that the spinning grinding wheels make contact with the drill bit's heel alternatively. By so doing, some of the drill bit's heel wears off, thereby sharpening its cutting edges, lips, and chisel.  
Next, the index robot rotates again by 90 degrees in an anti-clockwise direction such that the sharpened drill bit faces the inspection unit--a high-resolution camera-based inspection system that captures the drill bit's cutting surface for the operator's visual inspection via a PC's display unit. After yet another 90-degree rotation in the anti-clockwise direction, the reground (and visually inspected) drill bit is collected by the transfer unit to the unloader, which is carefully collected on the dispensing ray in crates. The cycle continues for all the drill bits in the crate. As the grinding operation continues and the drill bit comes in contact with the grinding wheels, the regrinding surface gradually wears to a point--end-of-life (EoL)--where it is no longer recommended for use. Meanwhile, high-frequency responses are generated and can be captured via high-sensitivity accelerometers. If the grinding process is repeatedly performed for a long time and the grinding wheels approach EoL, their respective cutting power is greatly reduced, and this results in poor grinding operations, which may even cause the drill bits to break.
# An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering +
## 4 Results  
### BBPSO Comparisons  
In Table 1, we compare the performance of CM-BBPSO with MOEA/D BBPSO and BBPSO evaluated on 13 UCI datasets and using 5 metrics: accuracy, F1-score (F1), Kappa ([PERSON]'s \(\kappa\) index), between sum of squares (BSS), within sum of square (WSS) and quantization error (QE). The best result for each measure is highlighted in bold. We observe that CB-MOEAD-BBPSO performed best in the CMC, Ecoli and Sonar datasets, three of which are challenging datasets containing either many imbalanced, highly overlapping and/or mixed density clusters. These datasets appear to be more challenging as shown in our previous work ([4,30]). This demonstrates that using the cluster-based selection of optimal solutions helps to choose good solutions for challenging datasets. We observed that M-BBPSO produced the best WSS values in 11 datasets, indicating that M-BBPSO tends to optimize cluster compactness. BBPSO produced the best BSS values in all the datasets, indicating that BBPSO optimizes cluster well-separatedness. CM-BBPSO had the worst (smallest) BSS values in four datasets and the worst (largest) WSS valuesin two datasets. M-BBPSO produced the worst BSS values in 9 datasets, while BBPSO produced the worst WSS values in 11 datasets. This shows that BBPSO is good for finding well-separated clusters, such as in Iris and Seeds, with a small number of clusters, while M-BBPSO is good for finding clusters that are compact, such as WDBC and WOBC. Based on the BSS and WSS measure, we observed that CM-BBPSO balances between these two properties of compactness (WSS) and well-separatedness (BSS), not doing better on one, such as M-BBPSO and BBPSO as discussed earlier. Such characteristics are useful when dealing with datasets with highly overlapping clusters and high dimensions. QE is another type of measure to indicate compactness and shares the same trend as WSS.
## 3 Results and Discussion  
### The Comparison of Sorption Capacity of PHB-Chitosan and PLA-Chitosan Compositions  
The use of the biodegradable binary polymer compositions PHB-chitosan and PLA-chitosan as sorbents of metals from wastewater is one of their promising application directions, representing a large potential interest due to possibility of utilizing these polymers after the end of their service life.  
Table 1 shows the quantitative characteristics of iron ions' sorption by PHB-chitosan and PLA-chitosan film compositions from FeCl\({}_{3}\) solutions with different concentrations of time, obtained by the method of X-ray fluorescence analysis, which is described above  
in the division Materials and Methods. The mass concentration of absorbed iron ions was determined by the position and intensity of the lines in the diffraction spectrum.  
As can be seen from Table 1, a growth in the FeCl\({}_{3}\) concentration in the solutions leads to an increase in the amount of sorbed iron ions. At the same time the sorption capacity of binary PHB-chitosan compositions is more than twice as high than in PLA-chitosan compositions. This can be explained by the fact that, although PLA and PHB have the same functional groups and repeating units in their chains, their properties slightly differ from each other and, in addition, the crystallinity of PHB is higher than that of PLA. For these reasons, the molecules of polyesters in various degrees screen the- NH\({}_{2}\). groups of chitosan, that are affected by its sorption capacity.  
In this connection, a comparative study of the thermophysical behavior of these compositions and the determination of thermophysical characteristics both in the absence and in the presence of sorbed iron ions and the evaluation of their influence on the relevant process parameters was carried out.
## 5 Conclusions  
The control strategy based on the bioinspired neural dynamics model proposed in this paper demonstrates excellent trajectory tracking performance, as verified through comparative simulation experiments. This approach effectively addresses two key issues: the velocity jump problem inherent in backstepping control methods and the control signal chattering issue common in sliding mode control. As a result, it is better suited for operating motors with existing input-output constraints. Moreover, under disturbance conditions, this strategy can effectively eliminate tracking errors, making it ideal for precise tracking tasks in complex real-world environments. However, it is crucial to note that the assumptions made in modeling the multi-rotor UAV system and the effects of actuator delays in practical applications cannot be overlooked. When implementing this strategy, it is essential to evaluate the applicability of these assumptions based on specific situations and modify or extend them as necessary.  
Figure 10: Thrust and torque commands with disturbance. (**a**) Blue, traditional sliding mode; Red, bioinspired sliding mode. (**b**) Blue, sliding mode with saturation; Red, bioinspired sliding mode.  
**Author Contributions:** Methodology, [PERSON] ([PERSON]); Software, X.L.; Formal analysis, [PERSON] ([PERSON]); Data curation, X.L.; Writing--original draft, X.L.; Writing--review & editing, [PERSON] ([PERSON]), [PERSON] ([PERSON]), B.C. and J.S.; Funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (No. 62273142), the Hunan Province Natural Science Foundation, China (No. 2023 JJ30437, 2023J50052), the science and technology innovation Program of Hunan Province (No. 2021 GK2010) and the Research Foundation of Education Bureau of Hunan Province, China (Grant no. 22A0490).  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflict of interest.