esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Intervertebral Disc Stress  
At the implanted L3-L4 level, the disc stress in all models decreased when compared with INT (Table 5). The disc stress decreased by 31-33% in flexion, where the maximum occurred in the anterior superior edge; 42-43% in extension, where the maximum occurred in the posterior inferior edge; 3-19% in torsion, where the maximum occurred in the left inferior edge; and by 45-55% in lateral bending, where the maximum occurred in the left superior edge.  
In contrast, the disc stress of the cephalic L2-L3 adjacent levels all increased. This parameter increased by 24-27% in flexion, where the maximum occurred in the anterior superior edge (Figure 3); by 12-14% in extension, where the maximum occurred in the posterior inferior edge; by 0-7% in torsion, where the maximum occurred in the left inferior edge; and by 21-24% in lateral bending, where the maximum occurred in the left superior edge.  
Figure 2: Changes in the ROM normalized and compared with the INT at the adjacent L2–L3 level (**top**) and the implanted L3–L4 level (**bottom**). The ROMs were all increased at L2–L3 and all decreased at L3–L4. There was a downward trend in ROM changes at all levels as the NS diameter became smaller.  
Figure 3: The von Mises stress distribution of the adjacent L2–L3 disc in flexion. The arrow indicates the maximum at the anterior superior edge. There was no obvious trend in disc stress at the adjacent level as the NS diameter changed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Level** & **Flexion** & **Extension** & **Torsion** & **Bending** \\ \hline INT & L2–L3 & 893 & 488 & 678 & 1160 \\  & L3–L4 & 810 & 414 & 751 & 1130 \\ \hline Nt45 & L2–L3 & 1130 & 556 & 722 & 1440 \\  & L3–L4 & 543 & 236 & 611 & 512 \\ \hline Nt35 & L2–L3 & 1130 & 556 & 722 & 1430 \\  & L3–L4 & 547 & 236 & 611 & 544 \\ \hline Nt25 & L2–L3 & 1110 & 556 & 687 & 1430 \\  & L3–L4 & 548 & 239 & 696 & 585 \\ \hline Nt15 & L2–L3 & 1110 & 548 & 675 & 1400 \\  & L3–L4 & 559 & 240 & 731 & 621 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of stress of intervertebral disc (KPa) in finite element models. The von Mises stress on the intervertebral disc. The NS diameter was inversely proportional to the disc stress at the implanted L3–L4 level; it did not markedly influence the disc stress at the adjacent L2–L3 level.  
Overall, the differences between these models were within 10%, except for torsion at the implant level. The NS diameter was inversely proportional to the disc stress at the implant level; it did not markedly influence the disc stress at the adjacent level.
## 2 Technical Description of the Two Types of Diffusers
### The Quadratic Residue Diffusers (QRD)  
The spatially dependent reflectivity for the QRD follows the numerical sequences with a uniform spatial Fourier transform of their reflection coefficient.  
The phenomenon of diffusion started to be studied extensively in 1975 by [PERSON] [8], who provided a way of designing highly diffusing surfaces based on binary maximum-length sequences showing that these periodic sequences have equal harmonic amplitudes.  
Later on, he extended his method and proposed surface structures that give excellent sound diffusion over larger bandwidths. Specifically, this kind of diffuser is composed of a periodic sequence of grooves having the same length and different widths. The maximum width is given by the maximum wavelength to be diffused, while the width of every groove is determined by the dimension of half-wavelength to be diffused. The depth of the sequence is determined by Equation (1).  
\[d=n^{2}\cdot mod(p) \tag{1}\]  
In this case, \(n\) is an integer number, \(p\) is an odd prime number and \(mod()\) is the operator modulus. By the input of these two variables, it is possible to determine the depth of the diffusing panel by the relationship of the maximum depth (\(d_{max}\)) and the maximum number of the sequence (\(n_{max}\)), as summarized in Equation (2).  
\[d=\frac{d_{max}}{n_{max}} \tag{2}\]  
Scientific studies [8] confirm that the limits for the effectiveness of the quadratic residue diffusers are in function of the wavelength and, thus, of the frequency. Below the lowest effective frequency, the diffuser works as a flat panel because the sequences are too small to be influencing the incident sound wave, having no scattering effect. In fact, the typical thickness of the QRD is half of the wavelength, corresponding to the low cut-off frequency.  
In a similar way, above the highest effective frequency, the wave propagation becomes irregular and it cannot be considered a plane wave. The lower and upper limits are defined by Equations (3) and (4).  
\[\lambda_{max}=2p\frac{d_{max}}{n} \tag{3}\]  
\[\lambda_{min}=2w \tag{4}\]  
The input data for the measurements presented in this paper are as follows:  
* \(p=17\) for the horizontal sequences;
* \(p=13\) for the vertical sequences.  
The dimensions of the panels tested are 900 \(\times\) 600 mm [L \(\times\) H], as shown in Figure 1. The same dimensions are valid for both MDF and plywood materials.  
The choice of the \(p\) numbers falls on the maximum efficiency of the panel to be comprised between 250 Hz and 2 kHz.
# EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
## 2 Materials and Methods
### Ratio Features from PPG Signals
#### 2.5.2 R1, R2, and R3 as Features  
R1, R2, and R3 represent ratios of different AC/DC values of the PPG signal. It can also be defined as ratio of ratios [21]. For all three wavelengths used in this study, R1 was considered as the ratio of the green AC/DC to red AC/DC, R2 was considered as a ratio of the blue AC/DC to the red AC/DC and R3 was considered as a ratio of green AC/DC to the blue AC/DC. These are expressed in the following equations.  
\[\text{R1}=\frac{\left[\frac{\text{AC}}{\text{DC}}\right]_{\text{green}}}{ \left[\frac{\text{AC}}{\text{DC}}\right]_{\text{red}}} \tag{1}\]  
Figure 3: AC/DC values vs. Glucose levels.  
\[\text{R2}=\frac{\left[\frac{\text{AC}}{\text{DC}}\right]_{\text{blue}}}{\left[ \frac{\text{AC}}{\text{DC}}\right]_{\text{red}}} \tag{2}\]  
\[\text{R3}=\frac{\left[\frac{\text{AC}}{\text{DC}}\right]_{\text{green}}}{\left[ \frac{\text{AC}}{\text{DC}}\right]_{\text{blue}}} \tag{3}\]  
In PPG, the absorbance of light by blood is directly related to the amount of blood flowing through the tissue. This is because the light absorption changes with the pulsation of blood flow, which corresponds to the heartbeats. The PPG signal consists of both an AC component (a pulsatile component related to cardiac cycle) and a DC component (a non-pulsatile component related to tissue structure). The absorbance for each wavelength can be determined by the ratio of the AC component to the DC component of the PPG signal. This ratio reflects how much light is being absorbed by pulsating blood compared to the baseline blood volume. The term \"ratio of ratios\" comes from how SpO\({}_{2}\) is calculated in the study [21]. It involves taking the ratio of the AC/DC value for red light and dividing it by the AC/DC value for infrared light. In that formula, each AC/DC value is a measure of absorbance at a specific wavelength, and thus the term \"ratio of absorbances\" is used. The division of these two AC to DC ratios (red and infrared) yields the \"ratio of ratios\". These ratios are derived from the PPG signal and calculated on the basis of three different wavelengths and their combination in this study.  
R1, R2, and R3 are important metrics in assessing the strength of a PPG signal as they provide a normalized comparison of the AC and DC components of the signal and can provide important information about cardiac output and peripheral perfusion. These calculated ratios, derived from multi-wavelength PPG data, have been utilized as features in this study to improve blood glucose level predictions. Figure 4 shows the trends of R1, R2, and R3 versus glucose levels for 34 subjects. As shown in Figure 4, the R3 value is the smallest compared to R1 and R2. As the red signal intensity is smaller, R1 and R2 are relatively larger than R3.
## 9 Real-World Applications, Case Studies, and Tools
### Multimodal Data Labeling  
AI annotation agents have transformed multimodal data labeling through the sophisticated orchestration of specialized models. In computer vision, platforms like Label Studio and CVAT now integrate agent-based assistance for automatic object detection, segmentation propagation, and complex annotation workflows. Overexy IRIS exemplifies this advancement, combining multiple AI models under LLM guidance to automate image annotation for complex datasets like traffic camera footage. This system achieves remarkable 100x speed improvements while effectively handling challenging visual conditions including rain, night scenes, and motion blur [132; 133]. The system processed 5 million surveillance frames, annotating license plates and vehicle types in days rather than months by employing intelligent prompting techniques. In audio annotation, agents now efficiently label speech transcripts with speaker characteristics and sentiment indicators, while also categorizing audio clips for specific events. VisionAgent further demonstrates the evolution of annotation agents by auto-generating code and models from high-level vision task prompts [132; 133], illustrating how agents can orchestrate entire annotation pipelines for specialized use cases like manufacturing defect detection. Some locality-aware query operators achieve similar gains for geo-tagged frames and map overlays [134; 135].
# Optical Cable Lifespan Prediction Method Based on Autoformer
## 2 Factors Impacting OPGW Lifespan
### Effect of Residual Length on OPGW Lifespan  
Throughout the installation and operational phases, faults in optical fiber length significantly impact the cable's lifespan and can be categorized into three stages [13]. During the initial 1-3 years of deployment, wind-induced vibrations and thermal stresses arising from temperature fluctuations can induce minor variations in the residual length of OPGW. This effect tends to balance the residual fiber length, line pitch, and cable characteristics. Hence, if during this phase, the OPGW cable experiences increased optical fiber loss or experiences a sudden surge, it is likely attributed to insufficient fiber optic residual length. Between 3 and 15 years of the cable's lifespan, it undergoes expansion due to the creep characteristics of aluminum-clad steel raw materials. This expansion diminishes the available margin for the optical fiber, which remains relatively constant in length compared to the cable's growth. The decrease in the residual length of the optical fiber results in increased optical fiber loss, particularly when the remaining length of the optical fiber decreases significantly. At the end of the 15-25 year period, an inspection will reveal that the fiber optic paste will gradually denature over time. This begins with the formation of small particles, followed by evaporation, decomposition, drying and solidification. Notably, significant denaturation usually starts around 18 years. The direct interaction of the optical fiber with the optical fiber paste initiates a notable elevation in its acid value, triggering an augmented precipitation of hydrogen. In addition, paste oxidation affects the stability of the optical unit, reducing the fiber's ability to adapt to various stresses or strains, weakening the buffering effect, and ultimately shortening the life of the cable.
# Weight Effects on Vertical Transverse Vibration of a Beam with a Nonlinear Energy Sink
## 5 The Effects of Weight on the Beam's Vibration
### The Forced Vibration of Second-Order Primary Frequency, Simulated by the Runge-Kutta Method  
The influence of weight on the vibration of second-order primary frequency is investigated in this section. The excitation amplitude is set as \(F=30\) N/m and the excitation frequency as \(\Omega=59.07\) rad/s. The initial values of the beam and the nonlinear energy sink are all given as 0.  
The Runge-Kutta method was utilized to obtain the time history curves. Based on Equation (23), in the case that the weights were assumed to be balanced by the static deflection, the Runge-Kutta method was used to investigate the time responses.  
The time history curves of the forced vibration with the second-order excitation frequency are shown in Figure 12. For the definition of the error in Figure 12, one can refer to Section 5.1. Figure 13 shows the amplitude-frequency diagrams under second-order excitation frequency obtained by the fast Fourier transform.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Frequency** & **Amplitude** \\ \hline One-fourth point, neglecting weight & 21.44 Hz & \(3.84\times 10^{-3}\) m \\ One-fourth point, considering weight & 10.69 Hz, 21.46 Hz & 0.98 \(\times\) 10\({}^{-3}\) m, 1.89 \(\times\) 10\({}^{-3}\) m \\ One-eighth point, neglecting weight & 21.42 Hz & \(1.18\times 10^{-3}\) m \\ One-eighth point, considering weight & 10.71 Hz, 21.42 Hz & 0.34 \(\times\) 10\({}^{-3}\) m, 0.59 \(\times\) 10\({}^{-3}\) m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the forced vibration with the 1 st order excitation frequency when the nonlinear energy sink is placed at 1/2 point of the beam.  
Figure 12: Time history curve of forced vibration with second\(-\)order excitation frequency: (**a**) response of 1/8 point of beam when the nonlinear energy sink is placed at 1/2 point of beam; (**b**) response of 1/4 point of beam when nonlinear energy sink is placed at 1/2 point of beam.  
Comparisons of time histories when considering and neglecting weight were investigated. After the fast Fourier transform, the frequency components and corresponding amplitudes are depicted as Table 5. It can be seen that the displacements of the one-fourth point and one-eighth point of the beam are nearly coincident, whether the weight is considered or not. The comparisons indicate that there exists no new frequency component when the beam is under the excitation frequency of second resonance frequency, nor when the excitation frequency changes to the third or fourth resonance frequency.
# Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems
## 2 Explicative Head-Mounted Fault Display System Design
### Fault Diagnoser Model Computation
#### 2.3.3 Selecting Reliable Sensor Data
In this relation, \(\mathcal{P}(u^{m}(t),t)\) represents the Pearson cross-correlation between a set of sensor \(m\) to describe the behavior of another component to which it does not directly belong. To maximize the cross-correlation, we adopt the following updating rule:  
\[\mathcal{P}(x_{t}^{m},t+1)=\lambda\mathcal{P}(x_{t}^{m},t)+\sum_{j=1}^{2}c_{j}r _{j}(\gamma_{t}^{m}). \tag{7}\]  
This formulation enables a nuanced analysis of the statistical measurements concerning the components they represent that could simplify a targeted maintenance strategy. In Equation (7), \(r_{j}\) is a random number between 0 and 1, while the constants \(\lambda\) and \(c_{j}\) are specific to the PSO algorithm. The term \(\gamma^{m}(t)\) denotes the position that has yielded the best \(h(x)\) value for each feature so far. These positions are updated with each iteration to reflect the optimal feature settings \((h^{*}(x)=\mathbf{d})\) discovered up to that point. Lastly, since multiple features are evaluated to identify the optimal solution, each feature can be updated concurrently. It is sufficient to collect the updated values for each iteration. To ensure the convergence of the optimal cross-correlation, one verifies whether the global cross-correlation between each attribute and feature meets the required criteria. Then,  
\[\left\{ \begin{array}{ll}\mathcal{P}_{q}(x_{t}^{m},t)\geq\Lambda&\mathbf{ Break},\\ \text{\emph{Otherwise}}&\mathbf{Continue}\end{array} \right. \tag{8}\]  
where \(\Lambda\) represents the performance desired and defined for the experiment and \(q=1,\ldots,n_{q}\) is the iteration number allowing us to verify condition (8). Once persistent sensors are determined, one can follow the dynamic behavior of the system by the following metric. Let us define \(\Delta\mathcal{S}_{t}^{m}\), for a given sensor indexed with \(m\) at timestamp \(t\), the deviation estimating how unusual or unexpected the observed data are for the learned ordinary turbine functioning manifold:  
\[\Delta\mathcal{S}_{t}^{m}=\sqrt{\sum_{i=0}^{p}(\mathbf{d}_{i,t}^{m}-\mathbf{ \hat{d}}_{i,t}^{m})^{2}}. \tag{9}\]  
Then, anomalies are defined using the following rule:  
\[\epsilon_{t}^{m}= \begin{cases}1,&\text{if }\Delta\mathcal{S}_{t}^{m}\geq\tau_{m}: \text{abnormal situation}\\ 0,&\text{otherwise}\end{cases}  \tag{10}\]  
In these equations, \(p\) is the number of features in the sub-vector state, \(i\) defines the \(i^{th}\) element of the sub-vector. \(\tau_{m}\) is a threshold determined empirically and depends on the model prediction accuracy. In other terms, if the diagnoser model accurately represents the system's behavior and the most relevant features well describe the abnormal situation, the threshold can be adjusted empirically to reduce false alarms.
# Real-Time, Model-Agnostic and User-Driven Counterfactual Explanations Using Autoencoders
## 4 Experimental Framework
### Employed Models
#### 4.2.1 Mnist  
**Black-box model**.  
The black-box model \(f(\cdot)\) used has the same architecture as the one used in [10]. That is, the model consists of two convolutional layers with 32 and 64 2 \(\times\) 2 filters, respectively, with ReLU activation function. In addition, a 2 \(\times\) 2 MaxPooling layer is applied after each convolutional layer to reduce dimensionality and avoid overfitting. Dropout with a fraction of 30% is applied during training. After the second MaxPooling layer, the output is flattened to apply a fully connected layer with 256 units, ReLU activation function, and 50% dropout. Finally, another fully connected layer with 10 units and a softmax activation function is used for classification. For more details, see Figure 4.  
**Autoencoder**.  
The encoder is composed of two convolutional layers with 32 and 64 2 \(\times\) 2 filters, respectively, with ReLU activation function. The output of the second convolutional layer is flattened to feed a fully connected layer with 16 units, from which a 16-dimensional latent vector \(\mathbf{z}\) is obtained. Then, this vector is fed to a fully connected layer that transforms the \(\mathbf{z}\) vector into a vector of the same dimensionality as the flattened output of the second convolutional layer. This vector is reshaped into a 7 \(\times\) 7 \(\times\) 64 tensor to initialize the transposed convolutions. The decoder has three transpose convolution layers, with 64, 32, and one 2 \(\times\) 2 filters, respectively. All the transpose convolutions are followed by a sigmoid function. For more details, see Figure 5.  
Figure 4: Black-box model \(f(\cdot)\) used in MNIST.  
Figure 5: AE used for ensuring data-manifold closeness.  
Generator.  
The architecture of the generator closely resembles that of the AE (see Figure 5), except that in this case the latent vector \(\mathbf{z}\) is concatenated with the one-hot representation of the class to which the input has to be changed, i.e., \(\mathbf{y}_{cf}\).
## 7 Conclusions  
In post-disaster scenarios, infrastructures are damaged, but some parked vehicles are not. Because parked vehicles have computational resources, this paper places emphasis on reasonably organizing undamaged ground parked vehicles in each UAV's coverage area to complete various types of uploading tasks from the UAVs. To tackle the task scheduling issue, a deep reinforcement learning model is trained periodically, in order to reduce the task execution cost and the task completion time. Simulation was performed to investigate the performance of the proposed scheme from multiple aspects. A large number of experimental results prove that the scheme in this paper achieves both higher successful rate and lower task execution cost than baselines policies. In addition, the time of task assignment is short and the convergence of the results is guaranteed. In the future, task  
Figure 10: Simulation results with diverse value of C\({}_{th}\). (**a**) Task successful rate under C\({}_{th}\), and (**b**) execution cost under C\({}_{th}\).  
Figure 9: Task assignment duration using our proposed model.  
offloading performance will be further improved and new applications based on parked vehicles will be studied.  
J.Z.: original draft writing, and review and editing; H.Z.: supervision; Y.W.: simulation; C.M. and Q.L.: review. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Natural Science Foundation of China under grants (Nos. 61902282, 62002263, 61872083, 61872081) and in part by the Scientific Research Project of Tianjin Education Commission under Grant (No. 2021 KJ186), and in part by the Science and technology project of Guangdong (No. 2020 ZDZX3054).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Method  
### Single-Shot Off-Axis Digital Holographic Interferometer  
Our proposed interferometer and ensemble deep learning (I-EDL) method consisted of a single-shot off-axis digital holographic interferometer and an ensemble deep learning system for raw hologram capturing and complex object wavefront recognition. The interferometer provides off-axis holograms with a shifted spectrum of the object's real image that can be easily extracted and processed by the fast Fourier transform method, which is explained in detail by [10; 11]. The optical setup is based on the principle of spatial coherence and is installed on a curtain-enclosed optical table [15]. The coherent light source is a red He-Ne laser with a wavelength of 632.8 nm [14], and the laser beam is approximately 2 mm in diameter. The object light formed by a laser beam passes through the tissue specimen and records the object information related to it. Change the specimen positions in x-y directions and record hundreds of experimental data images. The holograms are captured by a CMOS camera equipped with a Nikon Plan microscope objective [16]. The following Figure 1 shows a schematic diagram of the optical setup in detail, where MO is a microscopic objective, and NDF is a neutral density filter. The reference light is separated by several beam splitters (BS2, BS3) into three light beams and polarized by three linear polarizers (LP0\({}^{\circ}\), LP45\({}^{\circ}\), and LP90\({}^{\circ}\)) under 0\({}^{\circ}\), 45\({}^{\circ}\), and 90\({}^{\circ}\), respectively. Then, they are combined by BS4 and BS5 and interfere with the object light to form the fringe image. The system employs two neutral density filters in the object light beam (Lo) and references light beam (Lr) to adjust the light intensity from overexposure. The raw hologram of the object is recorded by the camera. Compared with a standard off-axis system, the setup can get raw holograms with different polarization states in a single shot, reduces image degradation due to light scatterings, and has higher imaging efficiency. The extra polarization information is capable of image denoising and more details can be found in [17].