esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Design and Properties of HD Multilayer Media
Figure 8b shows the shapes of magnetic fringes in the HD multilayer media recorded at 80, 120, and 180 mJ/cm\({}^{2}\). All HD multilayer media maintain the deep and clear magnetic fringes and consequently show high diffraction efficiencies. The magnetic fringe shape recorded at 180 mJ/cm\({}^{2}\) indicates that the saturation of the diffraction efficiency is caused by the magnetic fringes reaching the full thickness of the Bi:RIG, and the HDL still functions well at this thickness. Figure 8: (**a**) Calculated diffraction efficiency of HD multilayer and single-layer media. (**b**) Calculated shapes of magnetic fringes in HD multilayer media for energy densities of 80 mJ/cm\({}^{2}\), 120 mJ/cm\({}^{2}\), and 180 mJ/cm\({}^{2}\). Figure 7: (**a**) Fundamental concept of HDLs. Most of the excess heat at the heated spots diffuses to the HDLs, so the amount of heat laterally diffused in the Bi:RIG decreases and the increase in temperature of the Bi:RIG is also reduced. (**b**) Model for design of thicknesses of garnet and HDL. Based on these results, we fabricated the HD multilayer medium shown in Figure 9 in which TGG is used as the HDL material for easy fabrication. In this medium, the uppermost SiO\({}_{2}\) layer is used as an antireflective film, and the thicknesses of the TGG layers are sufficiently thicker than that required for HD. The fabricated HD multilayer medium shows a Faraday rotation angle of approximately 2.2 deg/\(\upmu\)m, which is slightly smaller than the 2.7 deg/\(\upmu\)m of the single-layer medium. Figure 9 shows the diffraction efficiency of the HD multilayer and single-layer media recorded under assist magnetic fields of 80 Oe and 20 Oe, respectively. The maximum diffraction efficiency of the HD multilayer medium is approximately 50% larger than that of the single-layer medium. This suggests that the HDLs effectively absorbed the excess heat and maintained the fringe shape, although the diffraction efficiency of the HD multilayer medium is smaller than the numerically expected efficiency that will reach approximately three times that of the single layer. This small diffraction efficiency is considered to be due to the inhomogeneity in the recording layer and the incomplete magnetization reversal, as described in Section 3.1, even if magnetic assist recording was performed. Because a diffraction efficiency larger than that of the single-layer medium was obtained, 2D data were recorded on this HD multilayer medium using the collinear interference method. The reconstructed image from the HD multilayer medium recorded at 120 \(\upmu\)J under the assist magnetic field of 120 Oe is shown in Figure 9 and is identical to the original signal pattern. This implies that error-free recording and reconstruction were achieved with the HD multilayer medium having discrete magnetic layers in the range of approximately 12 \(\upmu\)m. Therefore, the appropriately designed HD multilayer media will be applied to high-diffraction-efficiency magnetic hologram recording media.
# Changes in Spinal-Reflex Excitability during Static Stretch and/or Explosive Contraction
## 4 Discussion
### Static Stretch and Jumping Performance  
Although a large body of research [33; 34; 35] has shown decreases in athletic performance such as vertical jump, static stretch is still considered as a part of warm-up routines due to gains of tissue compliance [36], potential injury reduction [37], and performance enhancement [38]. Therefore, the practical importance of static stretch on countermovement vertical jump heights was tested as the secondary aim. Our stretch protocol volume (3 sets \(\times\) 30 s hold, alternated on each leg) did not change jumping performance, which is in line with the study in which the same stretch volume was administered [39; 40]. Overall jump heights (statistical condition effect) between the condition of contraction (48.6 cm) and stretch and contraction (49.8 cm) were different, but it is not a meaningful observation since the amount of difference was within the SEM (2.73 cm) and a small ES (0.12). Previously, a 60 s [41] or 90 s [42] hold static stretch on lower extremity led to a reduction in maximal vertical jump. Taken together, the use of intermittent static stretch may have a minimal or no detrimental effect on subsequent explosive performance, although the total duration of static stretch (90 s) exceeded the previously suggested threshold duration (> 60 s) [43; 44]. Since neither the spinal-reflex excitability nor vertical jump heights show statistical changes, we do not know how the spinal-reflex excitability influenced vertical jump height. With scientific evidence on muscle spindle activation and \(\alpha\)-motoneuron facilitation [45], our observation could be interpreted in a couple of ways: (1) Regardless of the magnitude, changed spinal-reflex excitability has a minimal effect on jumping performance; or (2) altered spinal-reflex excitability in this study was small, which was insufficient to affect explosive contraction. Examining spinal-reflex excitability along with other explanatory factors that include biomechanical and physiological variables during altered jump height after static stretch would provide better understanding of the causal relationship and mechanisms that affect jump height after static stretch.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Condition** & **Measurements** & **Mean (SD)** & **ICC** & **SEM** \\ \hline \multirow{4}{*}{Intersession} & Spinal-reflex excitability & 0.24 (0.12) & 0.85 & 0.05 \\  & Peak H-reflex latency & 17.09 (2.07) & 0.63 & 1.24 \\  & Peak M-response latency & 6.18 (0.61) & 0.77 & 0.29 \\  & Maximal vertical jump height & 47.2 (10.5) & 0.93 & 2.73 \\ \hline \hline \end{tabular} Unit: ms for latency of the H-reflex and M-response and cm for maximal vertical jump height. Note that the stretch and control condition did not perform vertical jump. SD: standard deviation; ICC: intraclass correlation coefficient; SEM: standard error of measurement.  
\end{table}
Table 4: _Cont._
## 4 Experiments
### Quantitative Results
#### 4.2.3 Fine-Tuning on Dense Annotations  
Table 3 reports the results of fine-tuning on dense annotations. This fine-tuning on relevance scores for each candidate answer significantly boosts the performance of NDCG. However, existing works [17; 18; 21; 22; 24] show that the performance of non-NDCG metrics deteriorates in contrast to the case of NDCG. To alleviate the inconsistency between NDCG and non-NDCG metrics, we fine-tune the model with the joint loss (see Equation (15)). Figure 3 shows the performance change of MRR and NDCG by varying \(\alpha\), which is the weight of the discriminative loss, from 0 to 2. As \(\alpha\) increases from 0 to 1, MRR is rapidly improved while NDCG remained high. On the contrary, NDCG sharply decreases with small improvement in MRR when \(\alpha\) increases from 1 to 2. Based on this observation, we set \(\alpha\) to 1 and obtain significant improvements across two metrics. In the baseline comparisons, our MVAN model is ranked first in MRR regardless of whether it is a single or ensemble model (single: 56.06, ensemble: 65.16). In addition, MVAN is ranked first and second in AVG for single and ensemble model comparison, respectively, despite a little lower NDCG.
# StereoVO: Learning Stereo Visual Odometry Approach Based on Optical Flow and Depth Information
## 3 Approach
### SSIM Cost Volume  
A cost volume stores the matching costs for pairing a pixel from feature map \(f_{1}\) with its corresponding pixels in feature map \(f_{2}\). In stereo matching and optical flow estimation,  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Block** & **Layer** & \(\mathbf{k}\) & \(\mathbf{s}\) & \(\mathbf{p}\) & **chns** \\ \hline \multirow{6}{*}{Conv Block} & Conv1\_1 & 7 & 2 & 3 & 3/16 \\  & Conv1\_2 & 3 & 1 & 1 & 48/16 \\  & Conv Redir1 & 7 & 2 & 3 & 3/2 \\  & Conv2\_1 & 5 & 3 & 2 & 18/32 \\  & Conv2\_2 & 3 & 1 & 1 & 96/32 \\  & Conv Redir2 & 5 & 2 & 2 & 18/2 \\  & Conv3\_1 & 5 & 2 & 2 & 34/64 \\  & Conv3\_2 & 3 & 1 & 1 & 192/64 \\  & Conv Redir3 & 5 & 2 & 2 & 34/2 \\ \hline \multirow{2}{*}{Conv Block4} & Conv4\_1 & 3 & 2 & 1 & 157/256 \\  & Conv4\_2 & 3 & 1 & 1 & 471/256 \\ \hline \multirow{2}{*}{Conv Block5} & Conv5\_1 & 3 & 2 & 1 & 256/512 \\  & Conv5\_2 & 3 & 1 & 1 & 768/512 \\  & Conv Redir5 & 3 & 2 & 1 & 256/2 \\ \hline \multirow{2}{*}{Conv Block6} & Conv6\_1 & 3 & 2 & 1 & 514/1024 \\  & Conv6\_2 & 3 & 1 & 1 & 1542/1024 \\  & Conv Redir6 & 3 & 2 & 1 & 514/2 \\ \hline \multirow{2}{*}{FC} & FC1 & - & - & - & 4104/1024 \\  & FC2 & - & - & - & 1024/6 \\ \hline \end{tabular}
\end{table}
Table 1: Configuration of the convolution layers with learnable parameters. Larger kernel size is used for the beginning three Conv Blocks to capture more information. The table provides information on kernel size (\(k\)), stride (\(s\)), padding (\(p\)), and the number of input and output channels (chns) for each layer. As the depth of the network increases, the size of the feature maps decreases.  
Figure 3: Architecture of Convs and FC.  
three common types of cost volumes are used: Difference, Concatenation, and Correlation cost volume. The Difference cost volume computes the disparity between two patches, the Concatenation cost volume simply concatenates features \(f_{1}\) and \(f_{2}\), whereas FlowNet2 uses a window size of one to compute the Correlation cost volume between two patches. However, each of these cost volumes can be easily affected by outliers.  
To assist the network to match left and right features accurately, we introduce the SSIM cost volume, which employs a sliding window patch comparison between two feature maps, \(f_{1}\) and \(f_{2}\), each with width, height, and number of channels (w, h, and c). Now, we consider a single comparison of two patches for sample, using a sliding window of size \(k\) and a maximum displacement of \(d_{max}\). The SSIM cost volume is defined as follows:  
\[\mathbf{cost}(x_{0},y_{0})=SSIM(f_{1}(x_{0},y_{0}),f_{2}(x_{0}+d,y_{0})), \tag{4}\]  
where \(x_{0}\in[0,w],y_{0}\in[0,h],d\in[0,d_{\max}],f(x,y)\) denotes the feature window whose center is \((x_{0},y_{0})\). Then, the cost volume is averaged over channel dimension, and finally, a 3D matrix is achieved.
# Context-Aware Gossip-Based Protocol for Internet of Things Applications
## 6 Effects on Application Requirements
### Energy-Efficient IoT Applications  
The lifetime of an IoT network or WSN depends on the energy-saving techniques that can be adopted to ensure that the constrained sensor nodes are not drained of their energy too quickly. In the case of our motivating scenario (see Section 3), financing the smart domiciliary care system may prove draining to the city, as human life expectancy is on the rise. Therefore, energy-saving strategies to ensure the longevity of the nodes is an attractive prospect for the municipality to ensure the continuity of the IoT system. To support energy-efficiency applications, the following parameters need to be controlled (see Table 4):  
* Residual energy (\(f1\)): this parameter is activated to ensure that the source node is aware of the next node's remaining energy to ensure that the message is forwarded to the closest neighbouring node with the highest energy level. While the results in Figure 5 leaves much to be desired for this parameter, to expand the lifetime of the neighbouring nodes their residual energy was considered. This, in turn, will ensure the continuity of the underlying sensor network and the overall energy-efficient application.
* Next-node distance (\(f2\)): to reserve a node's energy, the message is forwarded to the closest neighbouring node. This was validated in Figure 5 as the parameter was observed positively impacting the network lifetime metric as the number of nodes was increased and producing the best longevity results.  
Therefore, the MFWF for energy-efficient IoT applications is adjusted based on Equation (11):  
\[w=f_{1}\cdot p_{1}+f_{2}\cdot p_{2} \tag{11}\]  
where \(p_{y1}\) is defined in Equation (2) and \(p_{y2}\) is defined in Equation (3).
## 2 Materials and Methods
### Spectral Indices  
A remote sensing index is a mathematical technique that transforms spectral data from two or more bands to visually highlight pixels with similar spectral values within a specific range, hence emphasizing specific geographical features. Every type of land cover's pattern as well as the spectral response of characteristic features serve as the foundation for establishing an index [62].  
Certain points emphasize the importance of individual bands as features. They are as follows: Water is highly reflective in visible wavelengths and lowly reflective in SWIR and NIR wavelengths. The NIR band is where the water reflects less light than the red band; except for murky water pixels, water absorbs NIR and SWIR radiation, giving the NIR and green bands, and SWIR and green bands a strong contrast [41]. These reflectance variations are used by spectral indices, including NDWI, MNDWI, and AWEI, to distinguish between pixels that contain water and those that do not.  
Spectral index equations of AWEI [27], MNDWI [24], and NDWI [23] calculated in this manuscript are listed as Equations (1)-(3).  
\[\text{AWEI}=4(\text{Green}-\text{SWIR1})-(0.25\times\text{NIR}+2.75\times \text{SWIR2}) \tag{1}\]  
\[\text{NDWI}=\frac{\text{Green}-\text{NIR}}{\text{Green}+\text{NIR}} \tag{2}\]  
\[\text{MNDWI}=\frac{\text{Green}-\text{SWIR}}{\text{Green}+\text{SWIR}} \tag{3}\]  
The NDWI endeavors to maximize the reflectance of water, minimize the low NIR reflectance of water features, and take advantage of the high NIR reflectance of plant and soil characteristics by employing green wavelengths. Water characteristics are consequently increased because they have positive values, but vegetation and soil typically have zero or negative values, which means they are repressed [23]. Given that NDWI is unable to effectively suppress the signal coming from structured surfaces, and using the NDWI threshold of 0 cannot accurately distinguish structured surfaces from water pixels, a new band, the MNDWI band, has been suggested by substituting the SWIR band for the NIR band in the NDWI [24]. Optimizing the separability of water and non-water pixels through band differencing, addition, and using different coefficients was the primary objective in developing the AWEI [27]. The best water and land segmentation threshold for all water indices under various sensor settings must be established using Canny edge detection in the water-land border extraction process [63].
## 4 Vibration Signal Denoising
### IVY-CYCBD Algorithm  
For a raw vibration signal, the Cyclostationary Blind Deconvolution (CYCBD) algorithm can effectively perform denoising. However, the choice of two key parameters, the cyclic frequency and filter length, has a significant impact on the denoising performance. If the cyclic frequency is set too low, the filter may not sufficiently suppress the noise; while if the cyclic frequency is set too high, it may suppress the useful components of the signal, thereby affecting the denoising effect. Similarly, the selection of filter length is also crucial. A filter that is too short may not effectively capture the signal features, while a filter that is too long may introduce computational complexity, affecting the algorithm's efficiency.  
Therefore, optimizing these two parameters is essential for improving the denoising performance of the CYCBD algorithm. In this section, we focus on optimizing both the cyclic frequency and filter length, and propose a novel IVY-CYCBD algorithm. This algorithm adaptively adjusts these two parameters, achieving efficient denoising of vibration signals. The specific process of the algorithm is shown as follow in Figure 2:
## 3 Results and Discussion
### Selection of the Design Parameters
Regarding the absorber's effectiveness, it is found that a larger PZT patch size results in more effective absorption, as it is evident from the lower saturation force threshold and the reduced vibration amplitude as the patch size increases. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Patch.** & **Dimensions (\(\mathbf{L_{p}}\times\mathbf{b_{p}}\))** & **Surface Area** & **EMFC (\(\mathbf{k_{1}}\))** &
\begin{tabular}{c} **PZT** \\ **Capacitance** \\ \end{tabular} \\ \hline A & 0.09 m \(\times\) 0.014 m & 0.00126 m\({}^{2}\) & 2.71\% & 81.13 nF \\ \hline B & 0.12 m \(\times\) 0.0117 m & 0.00140 m\({}^{2}\) & 2.90\% & 90.17 nF \\ \hline C & 0.10 m \(\times\) 0.015 m & 0.00150 m\({}^{2}\) & 2.97\% & 96.58 nF \\ \hline D & 0.11 m \(\times\) 0.016 m & 0.00176 m\({}^{2}\) & 3.23\% & 113.3 nF \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Size variation and properties for five different piezoelectric patches. Figure 8: Force response plot of the beam’s vibration for three PZT-patch beam’s locations: (a) root, (b) middle, and (c) tip. Finally, we consider the effect of the thickness of the patch on the absorber's performance. The thickness of the piezoelectric patch was varied from 0.3 mm to 0.7 mm, and the coupling coefficient and the PZT capacitance were calculated, as shown in Table 3. It can be noticed that as the patch thickness increases, the coupling coefficient increases, and the capacitance decreases. This is similar to the effect of the patch surface area, and hence, a similar trend is expected in terms of vibration suppression effectiveness, as shown in Figure 10.  
Figure 10: Force-response plots of the beam’s vibration for three piezoelectric patches with different thicknesses, as per Table 3.  
Figure 9: Force-response plots of the beam’s vibration for five piezoelectric patches with different surface areas.
###### Abstract  
The present research proposes the implementation of an architecture for industrial process monitoring and control for a manufacturing execution system (MES) using an immersive digital twin (DT). For the design of the proposal, cyber-physical systems (CPS), MES, robotics, the Internet of Things, augmented reality, virtual reality, and open platform communication-unified architecture (OPC UA) communication protocols were used to integrate these technologies and enhance the functionalities of the DT by providing greater performance. The proposed work is implemented in an Industry 4.0 laboratory that is composed of Festo Cyber-Physical Factory and CP-Lab stations. The implementation of the architecture is based on ISO 23247, where the following requirements were considered for the design of DTs: (1) observable attributes and 3D design and visualization of all physical production lines in all of their stages, (2) a communication entity through the OPC UA protocol for the collection of state changes of manufacturing elements, (3) a DT entity where digital models are modeled and updated based on the collected data, and (4) user entities through the use of AR and VR to make manufacturing more efficient. The experimental results showed that the architecture enables interoperability between different platforms and control subsystems. It allows for the detection and diagnosis of problems during the execution of the production line; in addition, the high-fidelity simulation and AR and VR environments provided by the DT with data obtained in real time can improve the accuracy and efficiency of manufacturing through a more detailed analysis of the process, providing advantages such as interactive creation for customized products and continuous innovation.  
Keywords:digital twin; CPS; MES; Industry 4.0; virtual reality; augmented reality +
Footnote †: journal: Applied
## 2 Background
### Quantization Index Modulation  
Quantization index modulation (QIM) schemes represent a set of non-linear data hiding. Most previous research has focused on applying QIM in image, audio, and video. However, few watermarking methods based on QIM have been proposed for 3D data. With the aim of inserting a binary message composed of 0 and 1 in the 3D mesh, two quantifiers are needed. Moreover, QIM methods are simple to implement and have a small complexity. In addition, they ensure a high tradeoff between capacity and robustness.  
Let \(b\in(0,1)\) represent the watermark bit and \(x\) the host signal to quantize. It is worth noticing that the QIM operates independently on the above-mentioned elements. Two quantizers \(Q_{0}\) and \(Q_{1}\) are needed to insert a watermark bit \(b\)[16]. These quantizers are calculated as follows:  
\[Q_{b}(x)=\Delta[\frac{1}{\Delta}(x-(-1)^{b}\frac{\Delta}{4})+(-1)^{b}\frac{ \Delta}{4}] \tag{4}\]  
where \([]\) refers to the rounding operation and \(\Delta\) refers to the quantization step. In order to extract the watermark bits, the two quantizers are recalculated as in an embedding process. The extracted bits are calculated as follows:  
\[\hat{b}=argmin\left\|y-Q_{b}(y)\right\| \tag{5}\]  
where \(y=Q_{b}(x)+n\) and \(n\) refers to the noise caused by the channel.
# _Applied Sciences_
###### Abstract  
This research presents _Gradient Boosted Tree High Importance Path Snippets_ (gbt-HIPS), a novel, heuristic method for explaining gradient boosted tree (GBT) classification models by extracting a single classification rule (CR) from the ensemble of decision trees that make up the GBT model. This CR contains the most statistically important boundary values of the input space as antecedent terms. The CR represents a hyper-rectangle of the input space inside which the GBT model is, very reliably, classifying all instances with the same class label as the explananum instance. In a benchmark test using nine data sets and five competing state-of-the-art methods, gbt-HIPS offered the best trade-off between coverage (0.16-0.75) and precision (0.85-0.98). Unlike competing methods, gbt-HIPS is also demonstrably guarded against under- and over-fitting. A further distinguishing feature of our method is that, unlike much prior work, our explanations also provide counterfactual detail in accordance with widely accepted recommendations for what makes a good explanation.  
Keywords:explainable artificial intelligence; human-understandable AI systems; gradient boosting; black box problem; machine learning interpretability +
Footnote †: journal: Applied Sciences
# Numerical Analysis on Self-Propulsion of a Waterjet-Propelled Ship with Different Propulsion Models
## 3 Results and Discussions
### Computations of Self-Propulsion  
Three different approaches are applied to simulate the waterjet-propelled ship model. The impeller rotation is considered in the steady MRF model, and the thrust is computed by integrating the forces acting on the waterjet. The self-propulsion point is obtained by modifying the impeller rotational speed until the horizontal force is balanced. To simplify the geometry as much as possible, the impeller and guide vane blades are removed from the body force model, as shown in Figure 6. The initial value of the axial body-force is set equal to the axial force acting on the impeller and the guide vanes computed from the MRF model. Then, for a self-propelled ship, this value has to be adjusted repeatedly to achieve resistance/thrust balance. The self-propulsion is calculated at \(Fr\) = 0.3, 0.4, 0.5, 0.542. When the net force of waterjet-propelled ship converges to zero (resistance/thrust balance) in the MRF model, the sliding mesh model is triggered. The time step in the sliding mesh method corresponds to 1\({}^{\circ}\) rotation of the impeller. The three methods have the same mesh number in hull region, about 7.22 million. As for the waterjet system, the mesh number in body-force model is about 1.49 million, far smaller than 5.28 million in the MRF and sliding mesh models.