esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Comparison with Related Work  
Previous studies, such as [PERSON] et al. [5], have reported classification accuracies of up to 90% using Support Vector Machines (SVMs) applied to curated real-world datasets. While our model exhibits slightly lower overall accuracy, it is trained on a combination of real and synthetic data, making it particularly suitable for scenarios where real faulty samples are scarce or unavailable.  
More recent deep learning approaches have explored hybrid architectures--such as convolutional neural networks (CNNs) combined with bidirectional gated recurrent units (Bi-GRU)--which are effective in capturing temporal dependencies in PV system data [33]. Ensemble learning strategies, including deep stacked architectures, have also demonstrated strong generalization performance in complex classification tasks [34].  
In contrast, our approach emphasizes a physically informed and modular simulation-classification framework. The use of a Genetic Algorithm for neural architecture optimization, along with Relieff for feature selection, ensures that the model is well-tuned for both performance and interpretability. This design enables better control over input-output relationships and supports transparent validation across a range of simulated degradation scenarios.
## 3 Adaptive Subinterval Finite Element Method Based on Dynamic Sensitivity Analysis
### An Adaptive Convergence Mechanism and Computational Procedure  
Clearly, in the proposed subinterval finite element method, the accuracy increases with the number of subintervals. However, the computational cost of this method also increases. It is desirable to use as few subintervals as possible while still satisfying the accuracy. Therefore, an adaptive convergence mechanism is proposed to determine the appropriate number of subintervals.  
We propose a method to evaluate the relative error of vectors. For the vector to be compared \(\mathbf{y}=[y_{1},y_{2},\cdots,y_{n}]^{T}\) and the known vector \(\mathbf{x}=[x_{1},x_{2},\cdots,x_{n}]^{T}\), the relative error can be defined as follows:  
\[\varepsilon(\mathbf{x},\mathbf{y})=\frac{D(\mathbf{x},\mathbf{y})}{\left\| \mathbf{x}\right\|^{2}}=\frac{\sqrt{\sum\limits_{i=1}^{n}{(x_{i}-y_{i})^{2}}}} {\sqrt{\sum\limits_{i=1}^{n}{x_{i}}^{2}}} \tag{56}\]  
where \(\left\|\mathbf{x}\right\|^{2}\) is the 2-norm of vectors \(\mathbf{x}\) and \(D(\mathbf{x},\mathbf{y})\) is the Euclidean distance of \(\mathbf{y}\) from \(\mathbf{x}\).  
The relative error between two 2D vectors can be expressed as shown in Figure 3.  
Therefore, the computational process of the dynamic sensitivity-based subinterval finite element method includes the following steps:  
1. Set \(k=0\) and \(m=2^{k}\).
2. Solve the upper and lower bounds of the displacement in the original interval, noted as \(\underline{\mathbf{u}}_{(0)}\) and \(\overline{\mathbf{u}}_{(0)}\).
3. Set \(k=k+1\) and \(m=2^{k}\), and then, divide each interval into \(m\) subintervals.
4. Find the upper bound \(\overline{u}_{r}\) and lower bound \(\underline{u}_{r}(r=1,2,...,d)\) using the subinterval finite element method based on dynamic sensitivity analysis and assemble them as \(\overline{\mathbf{u}}_{(k)}=[\overline{u}_{1},\overline{u}_{2},...,\overline {u}_{d}]\) and \(\underline{\mathbf{u}}_{(k)}=[\underline{u}_{1},\underline{u}_{2},..., \underline{u}_{d}]\).
5. Calculate the iteration error \(\varepsilon_{k}=\max\Bigl{\{}\varepsilon\Bigl{(}\underline{\mathbf{u}}_{(k-1 )},\underline{\mathbf{u}}_{(k)}\Bigr{)},\varepsilon\Bigl{(}\overline{\mathbf{ u}}_{(k-1)},\overline{\mathbf{u}}_{(k)}\Bigr{)}\Bigr{\}}\).
6. If \(\varepsilon_{k}\) is not larger than the permissible error \([\varepsilon]\), output \(\overline{\mathbf{u}}_{(k)}\) and \(\underline{\mathbf{u}}_{(k)}\) as the upper and lower bounds of the structural displacement response. Otherwise, return to the third step.  
The calculation process of the dynamic-sensitivity-based subinterval finite element method is shown in Figure 4. Through this process, it is possible to obtain the upper and lower limits of the structural displacement response with relatively few iterations.  
Figure 4: Flowchart of adaptive subinterval finite element method based on dynamic sensitivity analysis.  
Figure 3: Definition of the relative error between vectors (2D vectors for example).
# Adaptive Learned Belief Propagation for Decoding Error-Correcting Codes
## 1 Introduction
In this work, we improve the generalization of WBP to enhance its practical applicability. The WBP is a static NN, trained offline based on a dataset. The main contribution of this paper is the proposal of adaptive learned message-passing algorithms, where the weights assigned to messages are determined for each received word. In this case, the decoder is dynamic, changing its parameters for each transmission in real time. Two variants of this decoder are proposed. In the parallel decoder architecture, the weights take values in a discrete set. A number of WMS decoders are run in parallel to find the best sequence of weights based on the Hamming weight of the syndrome of the received word. In the two-stage decoder, a secondary NN is trained to compute the weights to be used in the primary NN decoder. The secondary NN is a CNN that takes the LLRs of the received word and is optimized offline. The performance and computational complexity of the static and adaptive decoders are compared in two applications. In the first application, a number of regular and irregular quasi-cyclic low-density parity-check (QC-LDPC) codes, along with a BCH and a polar code, are evaluated over an additive white Gaussian noise (AWGN) channel in both low- and high-rate regimes. The results indicate that the adaptive WMS decoders achieve decoding BERs up to an order of magnitude less than the BERs of the static WMS decoders, at about the same decoding complexity, depending on the code, its rate, and the SNR. The coding gain is 0.32 dB at a bit error rate of \(10^{-4}\) in one example. The second application is coding over a nonlinear optical fiber link with wavelength division multiplexing (WDM). The data rates in today's optical fiber communication system approach terabits/s per wavelength. Here, the complexity, power consumption, and latency of the decoder are important considerations. We apply concatenated coding by combining a low-complexity short-block-length soft-decision inner code with a long-block-length hard-decision outer code. This approach allows the component codes to have much shorter block lengths and higher BERs than the combined code. As a result, it becomes feasible to train the WBP for decoding the inner code, addressing the curse of dimensionality and sample complexity issues. For PB QC-LDPC inner codes and a spatially coupled (SC) QC-LDPC outer code, the results indicate that the adaptive WBP outperforms the static WBP by 0.8 dB at about the same complexity and decoding latency in a 16-QAM 8 \(\times\) 80 km 32 Gbaud WDM system with five channels. The remainder of this paper is organized as follows. Section 2 introduces the notation, followed by the channel models in Section 3. In Section 4, we introduce the WBP, and in Section 5, two adaptive learned message-passing algorithms. In Section 6, we compare the performance and complexity of the static and adaptive decoders, and in Section 7, we conclude the paper. Appendices A and B provide supplementary information, and Appendix C presents the parameters of the codes.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 3 Measurement Techniques
### Range Free Technique
#### 3.2.4 Mobile Anchor Node  
In the mobile anchor node-based technique, a mobile anchor node with GPS ability keeps moving into a sensing region and intermittently broadcasts its present location. The remaining sensor nodes gather the position coordinates of the mobile anchor node.  
Figure 7: Hop count scenario.  
Afterward, the sensor nodes select three non-collinear coordinates of the mobile anchor node and apply different mechanisms to estimate position. A geometric conjecture-based localization approach with a mobile anchor node is proposed in [39]. The adjacent sensor nodes follow up of coordinates incoming and leaving anchor nodes to build an arc within its communication range. This process continues until the sensor node identifies at least three coordinate mobile anchor nodes, and then two chords are built among the identified points. Finally, the perpendicular bisector of these two chords generates the location approximations of the sensor node. This approach is further enhanced by which the intercept of any two identified coordinates regulates the limit region of the sensor node [40]. A similar process is continued for the other two pairs to reduce the area of the limit region of the sensor node. Then the mean value of all the intercept points gives the location estimate. Finally, a restricted area-based localization approach that uses a moving anchor node is proposed in [41]. Trajectories of moving nodes generate a particular type of restricted region for the sensor node. To recognize the probable position of the sensor node inside distinct limit regions, multiple intercepts are generated till the ultimate position is attained. However, an arbitrary waypoint motion model for the mobile anchor node generates higher localization error, and its computational complexity is also high. Geometric curve constraint approximation for the localization algorithm [42] uses the approximated constraints to create the chord on the virtual circle. The perpendicular bisector of the chords and approximated radius estimate the location of the sensor node. This algorithm also enhances the accuracy for borderline nodes. Though several algorithms have been developed using this technique, the major drawback is that they are not well suited for extensive periodic intervals and the unbalanced radio broadcast framework.
## 4 Discussion
### Uncertainty in Field-Measured CI Data  
The field-measured CI data used in this study are collected from different sources, and uncertainties arising from different observational instruments and methods should be considered an error source in validating the MODIS CI data. The field measurement of CI data is usually based on observations of transmitted light of the canopy by using instruments, such as TRAC [77; 112] and DHP [113; 114]. Various methods have been developed to estimate the CI data from field measurements, such as the method based on gap size distribution [77] and its corrected method [112], the logarithmic average method [115], and the combined gap size and logarithm method [90]. While previous studies reported a high correlation among different methods, observations with different view zenith angles, the assumption of the leaf projection function and the segment size may influence the assessment of field CI values [76; 116]. Moreover, the measurement conditions, such as sunlight, terrain, and observation height, may also influence the correct estimation of the field CI [76; 117].  
The different spatial scales of field-measured CI data and MODIS-derived CI data may introduce scaling errors when validating the MODIS CI data [25; 78; 89]. Generally, field-measured data are optimally integrated with high-resolution data to validate moderate-resolution products [118; 119]. As there are no adequate high-resolution CI data to conduct this validation strategy, the homogeneity and representativeness of field sites are usually used to assess the field-measured CI data and reduce spatial scaling errors [25; 26; 29; 49]. While these assessments of sites are limited by the use of high-resolution single-angular reflectance data, further studies with high-resolution multiangle data and retrieved CI data are urgently needed to perform comprehensive validations of moderate-resolution CI data.
# Research on a Dynamic Calibration Method for Photogrammetry Based on Rotary Motion
###### Abstract  
Photogrammetry as an engineering measurement technology for the rapid and non-contact acquisition of geometric parameters, such as the attitude, position, and shape of a measured object, is widely used in the development and production processes of the aerospace and automobile industries. The calibration of a corresponding photogrammetry system is the basis for ensuring the accuracy of photogrammetry. The dynamic and static calibration of existing photogrammetry systems has not yet established a system calibration specification or standard, and such calibration has mainly focused on the internal and external parameters of the camera used in a photogrammetry system. The calibration of static parameters cannot fully guarantee the dynamic performance of a photogrammetry system in the process of measuring dynamic targets or micro-deformations. Aiming at the problem of dynamic parameter calibration of photogrammetry systems, this paper proposes a dynamic calibration method based on a circular trajectory standard device, which can realize the calibration of parameters, such as dynamic length measurement error, and ensure an accurate evaluation of the dynamic measurement performance of photogrammetry systems.  
dynamic parameter calibration; circle track; photography system; rotary motion +
Footnote †: journal: Applied sciences
## 3 Mathematical Formulation
### Digital Twins
#### 3.1.1 Dt-Ev
The degradation model considers four principal phenomena: solid electrolyte interface (SEI) growth, lithium plating, particle cracking, and loss of active material, as described in [34]. By integrating these algorithms, the PBM provides insights into the current state of the LIB and forecasts its behavior, considering operating conditions (such as temperature, usage pattern, c-rate, etc.) and their impact on coupled degradation mechanisms. The SOH is then calculated as a function of the lithium lost estimated by the PBM. The arbitrary evolution of degradation phenomena [33], the simplifications of the thermal model, and the assumptions made by [34] may lead to possible deviations from the real EV degradation state. To mitigate the risk of inaccurate state estimations, a DDM has also been implemented to forecast both SOC and SOH more reliably. In this case, a model based on the Random Forest (RF) technique is used, a supervised learning technique that employs ensemble learning for classification and regression, suitable for working with battery data and predicting state variables [35]. It is build on the \"wisdom of crowds\", where multiple independent models collectively outperform the response of each one working individually. It is an effective method for complex nonlinear problems and time series data, and requires the definition of the number of trees. In an RF regression, the final prediction for an input \(x\) is the average of predictions from all trees. Each tree \(i\) applies its decision function \(f_{i}(x)\), and the overall prediction is given by  
\[y_{pred}(x)=\frac{1}{N}\sum_{i=1}^{N}f_{i}(x) \tag{1}\]  
The data used for validating both models and training the DDM has been generated at the laboratory under different temperatures (20-40 \({}^{\circ}\)C) and with different vehicle usage patterns. Using different types of algorithms to analyze changes in variables such as the SOC or SOH enhances the accuracy of the provided assessments. On the one hand, the PBM is a robust algorithm and provides consistent results with a physical interpretation. However, it is a bit rigid, has low adaptability, and the assumptions considered can lead to deviations in future LIB performance. In contrast, the developed DDM can learn from the processed data and adapt to the battery's state. Nevertheless, if the input values fall outside the training range, the model could produce incorrect results falling outside operational limits. Both algorithms have been implemented in Python using PyBamm library [36] for the PBM and Scikit-Learn for the DDM [37].
# A Reversible Data-Hiding Method with Prediction-Error Expansion in Compressible Encrypted Images
## 2 Related Work
### Encryption-then-Compression Images  
EtC systems have been proposed for secure image transmission. Most studies on EtC systems are based on the premise that a proprietary scheme is used for compression. Meanwhile, block-scrambling-based encryption methods [34,35], which are compatible with image coding standards, have been proposed for EtC systems. [PERSON] et al. [39] extended these methods to enhance security. Their method transforms an original image from RGB into YCbCr and combines each component to derive a grayscale-based image. Block-scrambling-based encryption is conducted on the grayscale image. Since the encrypted image is three times as large as the original image, the key space of the encrypted image is thereby larger than that of conventional methods [34,35]. This contributes to enhancing robustness against brute-force (BF) attacks. Further, this method is more resistant to jigsaw-puzzle solver (JPS) attacks because it uses grayscale images with less color information. We refer to this method as G-EtC. G-EtC is used for encryption in our proposed method. Figure 3 shows the procedure of the G-EtC method.
# Physiological and Biochemical Variations in Celery by Imidacloprid and Fenpyroximate
## 3 Results
### Superoxide dismutase Activity  
The superoxide dismutase (SOD) activity (Figure 4) was thoroughly analyzed in stem and leaf of celery plants exposed to diverse pesticide treatments at the concentrations of 1 F and 10 F. The variation of SOD activity in celery after spraying IMI at the levels of 0, 1 and 10 F under 2 h, 1, 3, 5, 8, 10, 14, 21 and 28 d application was exhibited in Figure 4A. For SOD activity in tissue stem (histogram), a remarkable shift (reduced) was first observed after one day at 1 F and continued until 3 d. The apparent promotion was found after 5 d and 8 d, then a significant decline occurred at 10 d and 14 d, and finally the increased markedly after 21 and 28 d. In the case of severe stress (10 F), the detected result of activity of SOD was increased by 47.4% (highest) after 10 d compared to control. Broadly speaking, the results indicated that the obvious positive variation was caused by mild stress compared to CK according to the average level of SOD activity. For SOD activity in the leaf stem (line chart), the visible increase in SOD activity occurred only after 14 days of mild and severe treatment, and then returned to the initial level. In general, the SOD activity in leaf was much higher than that in the stem.  
As depicted in Figure 4B, in view of the results that were detected by a microplate reader at 450 nm, the SOD activity in stem and leaf of celery under different stresses (0, 1  
Figure 4: The activities of superoxide dismutase (SOD) in stem (histogram) and foil (line chart) after spraying IMI (**A**) and FEN (**B**) at the 1 F and 10 F, respectively.  
and 10 F) of FEN was discussed. The histogram and line chart represent the SOD activity in stem and leaf, respectively. For tissue stem, a marked reduction of SOD activity in T\({}_{3}\) after 5 d compared to CK, while T\({}_{4}\) had no statistically significant change. The SOD activity quantum of boost was highest at 49.01% at 10 F level compared to control as well as T\({}_{4}\) increased to 13.79% after 10 d. SOD activity was all boosted, obviously, while T\({}_{4}\) increased the most, at 43.03 % after 28 d. The result demonstrated that tissue stem of celery had a higher tolerance to FEN compared to T\({}_{3}\). In the tissue leaf of celery, initial stage (2 h) caused significant physiological changes. In other words, the leaf of celery was more sensitive to FEN compared to IMI. The most sharp reduction in SOD activity occurred on 5 d, decreased by 53.76% (T\({}_{3}\)) and 58.57% (T\({}_{4}\)), respectively. The regular increase appeared on 14 d, increasing by 19.19% (T\({}_{3}\)) and 41.55% (T\({}_{4}\)), respectively. There was an extreme drop at various levels on 28 d compared to the SOD activity in 21 d and had a prominent reduction compared to CK.
# Investigation of Visual Stimulus Signals Using Hue Change for SSVEP
## 2 Materials and Methods
### Measurement of Visual Fatigue  
Visual fatigue was assessed by measuring and evaluating subjective evaluation responses (12 symptoms considering general discomfort and eye fatigue), signal flickering response after each set of colors, and critical fusion frequency (CFF) values and ophthalmic parameters before and after the experiment. Twelve symptoms [10] of common discomfort and eye fatigue were scored on five-point scales in a subjective questionnaire (1 = none, 2 = slight, 3 = moderate, 4 = clear, 5 = severe). The subjective evaluation of the signal flicker when observing the flashing signal was called flicker rating and was rated on three levels by a subjective questionnaire (1 = none, 2 = slight, 3 = clear). The frequency threshold value at which a blinking sensation is generated is called the CFF value [11; 12]. The CFF value has been reported to decrease with the accumulation of fatigue [13]. Therefore, it is widely used in the field of ergonomics and occupational health as a test for mental fatigue. In our experiment, the CFF value was measured by decreasing the frequency threshold by 1 Hz every 5 s from 45 Hz using a red LED and acquiring the frequency threshold at which a blinking sensation was generated. Measurements were performed on both eyes. Measured CFF values were evaluated by comparing changes before and after the experiment.