esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural Network-Based Analysis and Its Application to Spectroscopy for Mango
## 2 Equipment and Method
### Results and Discussion
#### 2.3.1 Data Processing
The preprocessed spectral curve, depicted in Figure 6b, was acquired, setting the stage for the model's accuracy through meticulous data preprocessing. As illustrated in Figure 6b, the absorption within the 1300-1500 nm range is prominent, with diminished absorption observed within the 1600-2200 nm range. This distinctive peak is attributed to the yellow hue of the mango peel and the absorption properties of pigments like carrots present in the mango. Additionally, a subtle characteristic peak is noted around 1970 nm, signifying the absorption band associated with O-H and C-H components [10]. The observed outcomes can be attributed to the light absorption by substances present in the mango [28]. * Calibration Data Acquisition  
The mango sample undergoes processing involving peeling, crushing, and pressing, while the sugar content is calibrated through visible spectrophotometry employing a DHR 95 refractometer [29]. Each point undergoes three measurements, with the average value serving as the standard brix content for that specific point. Based on the aforementioned analysis, points 5-1, 5-2, and 5-3 are identified as calibration data for mango brix content. Corresponding boxplots are generated for the collected data, depicted in Figure 7a-c. Notably, point 5-1 represents the mango's root section, point 5-2 reflects the equatorial region, and point 5-3 corresponds to the mango's top portion. Importantly, all brix values for these points exhibit an absence of outliers, thus necessitating no additional data preprocessing. Simultaneously, a statistical analysis was conducted on the brix content of the three points and is presented in Table 1.
# An Adaptive Hybrid Automatic Repeat Request (A-HARQ) Scheme Based on Reinforcement Learning
## 4 The Proposed Adaptive HARQ Q-Learning Model  
### Model Description  
The major components of the proposed Adaptive HARQ Q-learning model include the agent, state, action, and reward calculator. The agent serves as a representation of the 5G NR system, which performs an action according to the Q-Table obtained by reinforcement learning. The action here refers to when and how many times to transmit or retransmit the packet. The transmission results will affect the system state. The feedback from the system will determine the rewards obtained using the reward calculator and the optimization of the Q-Table. The objective of the proposed Q-learning method is to identify a set that will optimize the overall cumulative reward. The following is a detailed description of the model sections.
# Competing Reverse Channels' Performance with Sustainable Recycle Innovation Input
## 5 Comparison and Insights
### Comparison of Equilibrium Outcomes
Analysis shows that innovation investment not only forces manufacturing to lower retail prices, which leads to higher demand, but also creates more profits for manufacturers by having mixed channels. However, it is counterintuitive that it can be best among integrated manufacturers in the hybrid system. Such an edge increases when the amount of recyclers in the reverse channel increases, because the scale operated by the integrated manufacturer in model \(H\) is higher than that in model \(BE\), and the cost savings of recycling and remanufacturing is increased due to collectors competition. The collector's profits are related as follows, \(\Pi_{\mathbb{C}_{1}}^{H*}>\Pi_{\mathbb{C}}^{BE*}\), \(\Pi_{\mathbb{C}_{2}}^{H*}>\Pi_{\mathbb{C}}^{B*}\). However, the relationship among \(\Pi_{\mathbb{C}}^{BE*}\), \(\Pi_{\mathbb{C}_{2}}^{H*}\) and \(\Pi_{\mathbb{C}}^{B*}\) is not clear. As mentioned in the assumption of [1], when \(c_{m}-c_{r}>A\) and \(b-A>0\) are satisfied, it is economically viable for recyclers. Reference [13] pointed out that for retailers, even \(b_{1}^{H*}<A\), can compensate for losses by expanding demand. However, for the individual collectors in this article, only through cutting the price \(A\) and inflating the transfer price \(b\) can they obtain the residual value of the waste products, and expand profits by expanding the demand for recycling. The higher the \(\Delta=c_{m}-c_{r}\), the higher the return rate \(\tau\), the more profitable the recycler will be. In the market competition environment, in order to gain more market share, some recyclers will improve their recycling efficiency by introducing technological innovations, increasing recycling efforts, and expanding recycling scale and other business strategies to gain competitive advantage. **Theorem 8**. : _The profits of the supply chains are related as follows: \(\Pi_{T}^{B*}<\Pi_{T}^{BE*}<\Pi_{T}^{H*}\)._  
Interestingly, the supply chain profit is the highest in the hybrid system with two collectors in comparison to the basic centralized channel vertical Stackelberg equilibrium. Thus, competition of collectors through the investment of innovation input is advantageous for the supply chain.
## 4 Experiments
### Ablation Study
#### 4.5.4 Discussion  
The ablation studies conducted on the NYUv2 dataset validate three critical observations:  
1. Self-training: iterative pseudo-label generation mitigates overfitting in limited-data regimes.
2. Noise augmentation: simulated input perturbations boost the robustness to real-world data artifacts.
3. Pixel-wise knowledge distillation: the teacher-student knowledge transfer preserves structural patterns, proving essential for edge-detail reconstruction in dense prediction tasks.  
While these experiments conclusively demonstrate the module efficacy in indoor scenarios, their generalizability across diverse environments remains to be verified. Future work will extend the ablation analysis to outdoor benchmarks like KITTI DC, investigating two critical dimensions:  
1. Domain-specific adaptation: outdoor environments exhibit fundamentally different noise characteristics--sensor ranges exceed 80 m in KITTI versus 10 m in NYUv2, and the LiDAR sparsity patterns differ from those of Kinect depth sensors. We plan to develop adaptive noise scheduling that dynamically adjusts the perturbation intensity based on depth distribution statistics and scene semantics.
2. Cross-domain knowledge transfer: the pixel-wise distillation mechanism currently operates within single domains. We propose cross-domain knowledge bridging where teacher models pretrained on NYUv2 guide student training on KITTI pseudo-labels, potentially reducing annotation dependency. This would require addressing domain gaps through attention-guided feature alignment between indoor structural priors and outdoor geometric regularities.
## 5 Numerical Examples  
In this section, we report our investigation of the performance of the RFlex generation iterative process using two numerical examples. In this study, a quadruple pendulum problem and a slider-crank mechanism are considered. The FMBD simulation results were obtained using RecurDyn [29], and the external subroutine program for the iterative algorithm was developed for an RFlex generation. The generalized-\(\alpha\) method [31] is used for a time integrator, where the initial step size and the maximum time step were \(10^{-6}\) and \(10^{-2}\), respectively. The error tolerance of the Newton-Raphson method is \(5\times 10^{-3}\). The damping is neglected in this study.
# End-to-End Multi-Modal Speaker Change Detection with Pre-Trained Models
## 4 Experiments
### Dataset Statistics  
Experiments were conducted using the AMI meeting corpus [1], a well-established audio dataset commonly utilized in speech and audio processing research. The AMI corpus provides a diverse collection of meeting recordings with noisy data, interrupted data, etc., allowing for the evaluation of the model under a variety of conditions and scenarios. The dataset used for training and evaluating the multi-modal speaker change detection model is detailed in two tables.  
Table 1 presents the overall statistics of the training dataset, which includes 137 meetings with an average of 790.99 change points per meeting. The dataset exhibits an average of 306.61 overlap points, indicating instances of simultaneous speech, and an average of 3.99 speakers per meeting. The average length of each meeting is 34.11 min, with a total duration of 4673.69 min. The class distribution shows that 30.85% of the data are positive instances (indicating speaker changes), while 69.15% are negative instances (no speaker changes).  
Table 2 provides statistics for the test set, covering eight meetings identified by specific IDs (e.g., EN2002a, EN2002b, etc.). It reports the total number of change points, overlap points, and speakers per meeting, as well as the audio length in minutes. For example, the EN2002a meeting has 1366 change points, 678 overlaps, 4 speakers, and a duration of 43.90 min. The test set averages 821.31 change points, 328.19 overlaps, 3.94 speakers, and 33.38 min per meeting. Additionally, the class distribution for each meeting is provided, with percentages for positive (P) and negative (N) classes. On average, 31.40% of the test set instances are positive, and 68.60% are negative, reflecting a similar imbalance to the  
training set. These statistics highlight the complexity and variability of the dataset, which are particularly due to overlapping speech and the distribution of speaker changes.
## 6 Sensitivity Analysis of Kinematic Reliability  
Formulate the Limit State Function of the Position Errors in the Combined Direction Based on Response Surface Method  
According to Equation (18), the calculation of the output position errors of the mechanism in the \(x,y,z\) directions are based on the approximate solution of the linear addition of the slight errors. From Equations (12) and (14), the calculation of the output position errors of the mechanism are completed by using the first-order Taylor approximation at the theoretical value of the random variable of each input error when the dimensional errors and driving errors are taken into account. The above factors result in a lack of precision in the calculation. Moreover, the limit state function is unknown. Therefore, the explicit limit state function of the mechanism output position error is established by applying the response surface method, and the sensitivity of kinematic reliability to the randomness of each input error is analyzed based on this function.  
According to the Box-Behnken sampling, when the number of variables is greater than 3, the required samples are obtained by the combination of two-level factorial design and incomplete block design [31]. Similarly, the permutations and combinations of 12 variables \((x_{1},\ x_{2},\cdots,\ x_{12})\) are obtained as shown in Equation (25) by Box-Behnken sampling. Meanwhile, 192 edge midpoints and 12 center points are obtained shown in Appendix B.  
\[\begin{array}{cccccccccccc}x_{1}&x_{2}&x_{3}&x_{4}&x_{5}&x_{6}&x_{7}&x_{8}&x _{9}&x_{10}&x_{11}&x_{12}\\ \pm 1&\pm 1&0&0&\pm 1&0&\pm 1&0&0&0&0&0\\ 0&\pm 1&\pm 1&0&0&\pm 1&0&\pm 1&0&0&0&0\\ 0&0&\pm 1&\pm 1&0&0&\pm 1&0&\pm 1&0&0&0\\ 0&0&0&\pm 1&\pm 1&0&0&\pm 1&0&\pm 1&0&0\\ 0&0&0&0&\pm 1&\pm 1&0&0&\pm 1&0&\pm 1&0\\ 0&0&0&0&0&\pm 1&\pm 1&0&0&\pm 1&0&\pm 1\\ \pm 1&0&0&0&0&0&\pm 1&\pm 1&0&0&\pm 1&0\\ 0&\pm 1&0&\pm 1&0&0&0&0&\pm 1&\pm 1&0&0\\ 0&0&\pm 1&0&\pm 1&0&0&0&0&\pm 1&\pm 1&0\\ 0&0&\pm 1&0&\pm 1&0&0&0&0&\pm 1&\pm 1\\ \pm 1&0&0&\pm 1&0&\pm 1&0&0&0&0&\pm 1\\ 0&0&0&0&0&0&0&0&0&0&0\end{array} \tag{25}\]  
According to the above sample points, the unknown coefficients of the response surface function are determined by the least square method in regression analysis, thus the response surface function is determined (Equation (1)) and used to replace the real response expression of the structural system in the following work.
# A New One-Dimensional Compound Chaotic System and Its Application in High-Speed Image Encryption
## 3 The Proposed Image Encryption Scheme
### The Encryption Process
#### 3.1.3 Column-by-Column Encryption Processing
### Decryption Process

The decryption process is the reverse operation of the encryption process. Each round of decryption also includes three stages of operation, but the order of processing the threestages is: (1) generate the transformed chaotic sequence; (2) column-by-column decryption processing; (3) decrypt row by row. #### 3.2.1 Generate a Modified Chaotic Sequence

In this stage, four modified chaotic sequences **X**, **Y**, **I** and **J** are generated by chaotic system (4). The detailed steps of the algorithm are the same as Algorithm 2, except that the number of rows \(M\) and columns \(N\) of the image should be obtained according to the image **C** to be decrypted. #### 3.2.2 Column-by-Column Decryption Processing

The detailed operation steps of the column decryption phase are described by the following Algorithm 5. ```
Input: Image matrix to be decrypted \(\textbf{C}=\{C(i,j)\,|\,i=1,\,2,\,\ldots,\,M;j=1,\,2,\,\ldots,\,N\}\), \(x_{0},\,y_{0},\,a,\,b,\,C_{0}\) and \(N_{0}\). Output: The decrypted image matrix \(\textbf{P}=\{P(i,j)\,|\,i=1,\,2,\,\ldots,\,M;j=1,\,2,\,\ldots,\,N\}\). ```
**Algorithm 5. Column-by-column decryption algorithm. **
``` Input: Image matrix to be decrypted \(\textbf{C}=\{C(i,j)\,|\,i=1,\,2,\,\ldots,\,M;j=1,\,2,\,\ldots,\,N\}\), \(x_{0},\,y_{0},\,a,\,b,\,C_{0}\) and \(N_{0}\). Output: The decrypted image matrix \(\textbf{P}=\{P(i,j)\,|\,i=  
**Step 4:**\(i\)\(\leftarrow\)1, For the first row, use Equations (29), (14) and (27) to decrypt:  
\[\mathbf{c}=[C_{0},C_{0},\dots\ ]^{\mathrm{T}}. \tag{29}\]
# Research on a Support System for Automatic Ship Navigation in Fairway
## 5 Numerical Simulation and Result Discussion
### Automatic Ship Navigation in a Fairway without the Heading Stabilizing Controller  
As illustrated in Figure 13, the simulation was also carried out without the heading stabilizing function active. The performance of controlled ship motion from WP\({}_{1}\) to WP\({}_{9}\) is acceptable. However, the track cross error between (WP\({}_{9}\) and WP\({}_{10}\)) is large when compared with Figure 10, and the ship exhibits an increased risk of collision with the wharf. In addition, the ship's heading cannot track toward the wharf in this situation. In this case, it would be necessary to employ tugboats for ship berthing. Additionally, if the propeller revolution is not reduced when navigating in the fairway, the navigation process also exhibits an inability to approach the wharf area.
## 2 The Learning Algorithm
### Annealing Algorithm for Data Prediction  
The classical SVM algorithm constructs \(\vec{w}\) and \(b\) from the training data and then predicts each test data by computing \(\vec{w}\cdot\vec{x}+b\). We propose an algorithm such that it is possible to predict multiple test data points with a single run of the prediction algorithm. The prediction is made by finding the ground state of the Ising model:  
\[H=-\sum_{i<j}J_{i,j}s_{i}s_{j}-\sum_{i}h_{i}s_{i}. \tag{11}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(x_{i}\) & \(y_{i}\) & \((1+y_{i})/2\) \\ \hline
00 & \(-1\) & 0 \\
01 & \(1\) & 1 \\
10 & \(1\) & 1 \\
11 & \(-1\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: An example of four training data’s look-up table for function \(f(x)\).  
Figure 4: The quantum circuit implementation for the DJ query. The circuit consists of one \(n\)-Toffoli gate and one \(n\)-Toffoli gate with negative control.  
Figure 3: The quantum circuit for the 4-data point example in Table 1. (**a**) The oracle circuit \(U_{f}\). (**b**) The circuit for the DJ algorithm.  
The number of spins is \(N=n_{sv}+n_{t}\), where \(n_{sv}\) is the number of support vectors and \(n_{t}\) is the number of test data. The coupling constants are functions of the data positions \(J_{ij}(\vec{x}_{i},\vec{x}_{j})\) defined in Equation (4). We use the notation  
\[J_{ij}=\begin{cases}(J_{sv})_{ij},&\text{if }\vec{x}_{i},\vec{x}_{j}\in\{ \text{support vectors}\}\\ (J_{t})_{ij},&\text{if }\vec{x}_{i},\vec{x}_{j}\in\{\text{testing data}\}\\ (J_{int})_{ij},&\text{if }\vec{x}_{i}\in\{\text{testing data}\},\vec{x}_{j}\in\{ \text{support vectors}\}\end{cases}. \tag{12}\]  
\(J_{sv}\) is an \(n_{sv}\times n_{sv}\) matrix, \(J_{t}\) is an \(n_{t}\times n_{t}\) matrix and \(J_{int}\) is an \(n_{sv}\times n_{t}\) matrix. Notice that the \(J_{sv}\) matrix is given by the training algorithm and does not need to be computed again. The magnetic field is defined as  
\[h_{i}=\begin{cases}y_{i},&\text{if }\vec{x}_{i}\in\{\text{support vectors}\}\\ 0,&\text{if }\vec{x}_{i}\in\{\text{testing data}\}\end{cases}. \tag{13}\]  
The prediction result is given by the ground state of the Ising model:  
\[y_{i}=s_{i}\text{, for }\vec{x}_{i}\in\{\text{testing data}\}. \tag{14}\]  
The configurations \(s_{i}\) of the support vectors are dummy variables and not used for prediction. Intuitively, the model favors parallel spins, since \(J_{ij}>0\). Hence, we expect the prediction to have the same spin direction as nearby training data. In principle, the Ising ground state can be calculated by any Ising solver. However, the problem of finding the general Ising model ground state is equivalent to quadratic unconstrained binary optimization (QUBO), which is NP-hard. In this work, we use adiabatic quantum computing implemented on a quantum circuit simulator to find the ground state [33]. The prediction labels are determined by  
\[s_{i}=\begin{cases}-1,&\text{if }p_{-1}(\vec{x}_{i})>p_{+1}(\vec{x}_{i})\\ +1,&\text{if }p_{-1}(\vec{x}_{i})<p_{+1}(\vec{x}_{i})\end{cases}, \tag{15}\]  
where \(p_{\pm}\) is the probability that the point label is predicted to be \(\pm 1\).
## 2 Materials and Methods
### Characterizations  
The optical images were taken using an optical microscope (ZEISS (Oberkochen, Germany), Axio Imager A2m). The Raman spectra were collected with a micro-zone confocal Raman system (WITec alpha 300R) under 532 nm laser illumination. The thickness of the MoTe\({}_{2}\) film was measured using an atomic force microscope (Asylum Research (Abingdon, UK), Cypher S). All the electrical measurement was conducted in a probe station that was connected to a semiconductor characterization system (Keithley 4200-SCS). For the NIR photoresponse measurement, the 1064 nm laser was introduced to the probe station through a multimode optical fiber with normal incidence. The laser beam is 2.4 mm in diameter. A mechanical chopper, a fixed resistor (5 k\(\Omega\)), and a digital phosphor oscilloscope (Tektronix DPO 2024) were employed to characterize the temporal photoresponse performance. All the characterizations were performed at room temperature in ambient conditions.
# Hybrid Window Decoding for Joint Source Channel Anytime Coding System
###### Abstract  
Joint source channel anytime coding (JSCAC) is a kind of joint source channel coding (JSCC) systems based on the causal spatially coupled coding and joint expanding window decoding (JEWD) techniques. JSCAC demonstrates greatly improved error correction performance, as well as higher decoding complexity. This work proposes a joint hybrid window decoding (JHWD) algorithm for JSCAC systems, aiming to reduce the decoding complexity while maintaining comparable error correction performance with the state of the art. Unlike the traditional JEWD technique and its variants, the proposed JHWD algorithm utilizes a hybrid window structure. It achieves this by implementing adaptive local expanding window decoding in the sliding window region, guided by syndrome-check-based detection. The hybrid window decoding characteristics of the proposed JHWD algorithm can both effectively reduce the decoding complexity caused by window expanding, and also mitigate the error propagation caused by window sliding. An improved density evolution algorithm is proposed for the asymptotic performance analysis of the proposed JHWD algorithm. Both the analytical and simulation results indicate that the proposed JHWD decoding scheme is a good low-complexity implementation option for JSCAC systems, which is also suitable for other JSCC systems with the spatially coupled coding structure.  
joint source channel coding (JSCC); anytime coding; adaptive local expanding window decoding 2024 26 940 10.3390/e26110940
# Insight into the Role of Cerium (III) Addition to a MgAl-LDH Coating on AA6082
## 4 Conclusions  
In this study, we aimed to gain mechanistic insight into the role of Ce(III) as an active corrosion inhibitor embedded in the LDHs layer. For this reason, we performed the deposition of Ce doped Mg-Al LDHs layers by the in-situ method on AA6082.  
XRD and FT-IR measurements along with SEM images, collected before and after 400 h of continuous immersion in the 0.1 M NaCl solution, suggested a corrosion inhibition mechanism based on the dissolution and re-precipitation of Ce compounds. In particular:  
1. Ce-based crystalline structures such as Ce(OH)\({}_{3}\) or CeO\({}_{2}\) forms during the LDH in-situ synthesis;
2. Upon exposure to the NaCl solution, Ce-based structures dissolves (thus leading to cerium ions release), the LDH layer cracks and corrosion initiates;
3. At the cathodic sites, oxygen reduction leads to the formation of an alkaline environment, which promotes the precipitation of amorphous Ce oxides/hydroxides, reducing the cathodic reaction rate and providing mitigation of the corrosion rate of the metal.  
The EIS investigation in the mild electrolyte (0.1 M Na\({}_{2}\)SO\({}_{4}\) + 0.05 M NaCl solution) further supports the proposed mechanism: the total resistance of the protection system shows an initial decrease in the first hours of immersion, attributed to the LDH layer degradation, followed by a gradual increase for prolonged immersion time, attributed to the effect of cerium ions (in accordance with the mechanism described in (3)).  
Conceptualization, M.F. and M.Z.; methodology, M.F. and M.Z.; validation, M.F. and M.Z.; formal analysis, M.Z.; investigation, M.Z.; resources, M.F.; data curation, M.Z.; writing--original draft preparation, M.Z.; writing--review and editing, M.F.; visualization, M.Z.; supervision, M.F.; project administration, M.F.; funding acquisition, M.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 13: Ce-rich agglomerate on C10-1 surface after 226 h of immersion (**a**) and EDS analysis in correspondence of the red spot (**b**).
## 2 Methods/Experimental Approach
### Power Consumption Model  
The stochastic energy consumption model analytically developed for the NB-IoT network at an NB-IoT node can be represented as follows. Traditionally, energy consumption in NB-IoT is often analyzed in terms of its average value [16]. The energy in an NB-IoT is consumed at different levels and through different processes describing the network operation. For every communication in an NB-IoT, part of the energy is consumed by the electronic circuitry of the transmitting node while another important part is consumed in the transmission process at the sender NB-IoT node in the case of an upload operation. Finally, energy is consumed in the reception process at the receiving node in the case of a download operation.