esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Evolution of Techniques  
Techniques that evolved in the agricultural commodity price prediction problem follow a progressive change from simple and conventional to more sophisticated and integrated methods. Traditional statistical models like ARIMA and foundational ML tools such as PLS regression and neural networks have been used in earlier studies to identify linear and nonlinear relationships in the data, but they continue to be extensively used for their interpretability, simplicity, and performance. In time, ensemble learning models that utilized the combined strengths of multiple algorithms such as Random Forest and XGBoost increased predictive accuracy [29]. These models perform better than previous approaches, especially in cases of complex data sets with many interacting features. Deep learning techniques have provided robust techniques for handling temporal dependencies and complex patterns inherent to agricultural data. Methods like LSTM and hybrid models like SARIMA-LSTM [55; 70] have been used to model sequential data. Frameworks that combine time series methods with deep learning, such as AttLSTM-ARIMA-BP [41], merge domain-specific insights with computational advances. The growing use of large datasets,  
Figure 9: Percentage of each studied crop in the literature.  
advances in computational power, and the need for improved predictions to tackle market volatility and global food security are driving the evolution of forecasting.  
The adoption of techniques for agricultural commodity price forecasting has progressed over time in distinct phases (Figure 10). Early research (prior to 2017) relied on traditional methods such as ARIMA and foundational ML models, valued for their simplicity and interpretability. From 2017-2018, deep learning approaches began to appear, although their use was limited. There was no noticeable increase in novel techniques during 2019-2020. However, starting in 2021, there was a substantial surge in the application of deep learning and time series methods, particularly LSTM-based architectures and hybrid models. Ensemble learning also gained traction in this period. In 2023-2024, deep learning and time series remained dominant, with ensemble and standard machine learning techniques also contributing to the methodological landscape.
## 2 Methods
### Back Propagation Neural Network
#### 2.1.2 BP Neural Network Algorithm  
To realize the minimum mean square error between each sample's target and final output, the BP neural network's connection weights are required to adjust. When applying BP neural network evaluation, the self-learning process is finally completed by literately modifying the network parameter settings. Each layer's connection weights are adjusted until the minimum mean square error of the final network output reaches an acceptable level. The learning rule of BP neural network requires adjusting weights along the function's negative gradient direction, which will diminish the function value fastest to explore suitable weights and acquire results more quickly.  
Assume \(p\) is the number of training samples, and the input and output mode of the \(k\)th group of samples are used to train the network. The convergence error bound value is set for \(\varepsilon_{min}\), and maximum number of learning for \(N\). According to the error function, the sample error after iterative computation can be represented by \(E_{k}=\frac{1}{2}\sum_{i=1}^{n}(h_{oi}(k)-y_{oi}(k))\) and training error \(E=\frac{1}{2}\sum_{K=1}^{P}E_{K}\) could be obtained. If the error between the network output value and the desired output value does not accomplish the error accuracy prerequisite, it will shift to error reverse propagation. Meanwhile, connection weightsand threshold values are continuously amended by the end that the error meets accuracy necessity. Connection weights and threshold correction formula are as follows:  
\[\Delta_{w}(t+1)=\eta\delta_{jk}\omega_{ik}+\alpha\Delta_{k}w_{ij}(t) \tag{4}\]  
\[\theta_{j}(t+1)=\theta_{j}+\frac{1}{p}\sum_{k=1}^{p}\Delta_{k}\theta_{j}\]  
In this formula, \(\eta\) is the learning rate and \(\alpha\) is the momentum factor, which are somewhere in the range 0 and 1; \(\alpha\Delta_{k}w_{ij}(t)\) is momentum term; \(\delta_{jk}\) is output node calculation error; and \(t\) is training times.  
Initializing the input values will be taken note of in developing the BP neural network. BP network is one of the most generally utilized neural networks for the present, and it is delicate in selecting parameters. Therefore, choosing appropriate training values becomes especially significant.
# Enhancement of Metabolite Production in High-Altitude Microalgal Strains by Optimized C/N/P Ratio
## 4 Discussion
Examples such as the optimization of _Dunaliella para_ via response surface methodology prove that a high content of NaNO\({}_{3}\) (0.63 g/L), NaCl (1.61 M), and low KH\({}_{2}\)PO\({}_{4}\) (0.02 g/L) will increase the final content of lipids up to 1.4 times [128]. In the specific context of this research, the concentration of nitrate employed in the optimization was 0.075 g/L of NaNO\({}_{3}\), while the concentrations of KH\({}_{2}\)PO\({}_{4}\) and K\({}_{2}\)HPO\({}_{4}\) were 0.056, and 0.131 g/L, respectively. These results are similar to those reported using _Selenastrum_ sp. GA66 (0.06 g/L of NaNO\({}_{3}\) and 2-fold increase in total lipids) [140]. However, the evaluation of the C-N-P ratio is a rare approach that has not been studied in-depth. Since CO\({}_{2}\) is the most common carbon source, the analysis of other inorganic sources and even some organic carbon sources has been limited over the years. Our results prove that the proper adjustment of not only N and P, but also the specific addition of an organic source, will effectively increase the final content of total lipids in this _Chlorella_ sp. UFPS019 strain. However, in the long run, this method (especially when nitrogen is reduced) will substantially reduce the final content of proteins and the overall biomass produced in the selected strains. Regarding carotenoids, the results demonstrated that all the different experiments were successful in maximizing carotenogenesis. The latter may be due to the lack of other stressful conditions such as light and salinity [141; 142]. Finally, to produce proteins, both algae and cyanobacteria require adequate concentrations of carbon, nitrogen, and phosphate sources, since proteins and amino acids are the building blocks for the synthesis of multiple enzymes and different cellular processes [143; 144]. Therefore, excesses of N and P will allow the fast synthesis of different proteins, which is reflected in the optimization conditions obtained in this research (0.25 g/L of NaNO\({}_{3}\) and 1.875 g/L of KH\({}_{2}\)PO\({}_{4}\) + K\({}_{2}\)HPO\({}_{4}\)).
The increasing demand for rapid, accurate, and non-invasive analytical techniques has led to the exploration of alternative methods and various spectroscopy techniques, including Fourier transform infrared (FTIR), near-infrared (NIR), Raman, and nuclear magnetic resonance (NMR) spectroscopy [8; 9; 10; 11], as well as other methods like UV spectro-photometry [12], differential scanning calorimetry (DSC) [13], and high-performance liquid chromatography (HPLC) [14]. Low-resolution magnetic resonance relaxometry offers a promising solution, providing a fast and automated way to determine the relaxation times (T1 and T2) of hydrogen nuclei in oil samples. This technique eliminates the need for complex chemical processes and enables the rapid analysis of large sample quantities [15]. From a rheological perspective, the IV, and thus the degree of unsaturation, is a factor that significantly influences the molecular structure and mobility of oils. A higher IV indicates more double bonds, contributing to increased molecular degrees of freedom and reduced viscosity due to the less rigid structure of unsaturated vs. saturated fatty acids [16]. Similarly, in NMR, the relaxation times (T1 and T2) are influenced by the molecular dynamics of the sample. oils with greater molecular freedom and lower viscosity tend to exhibit longer relaxation times. Given that both the IV and relaxation times reflect molecular mobility and viscosity, it is expected that the IV would be related to the degrees of freedom within the sample, providing insights into the oil's unsaturation level and overall fluidity [17; 18]. In this study, we present a novel, non-invasive approach to determining the IV in commercial edible oils using TD-NMR and MRI relaxometry. By correlating the relaxation times with the IV, we aim to demonstrate a quick and reliable method for assessing the unsaturation levels of oils without the need for destructive testing or extensive sample preparation. This method has significant potential for applications in quality control, especially for oils highly prone to oxidative degradation during cooking or industrial processing.
# An Efficient Steganographic Protocol for WebP Files
###### Abstract  
In this paper, several ideas of data hiding in WebP images are presented. WebP is a long-known, but not very popular file format that provides lossy or lossless compression of data, in the form of a still image or an animation. A great number of WebP features are optional, so the structure of the image offers great opportunities for data hiding. The article describes distinct approaches to steganography divided into two categories: format-based and data-based. Among format-based methods, we name simple injection, multi-secret steganography that uses thumbnails, hiding a message in metadata or in a specific data chunk. Data-based methods achieve secret concealment with the use of a transparent, WebP-specific algorithm that embeds bits by choosing proper prediction modes and alteration of the color indexing transform. The capacity of presented techniques varies. It may be unlimited for injection, up to a few hundred megabytes for other format-based algorithms, or be content-dependent in data-based techniques. These methods fit into the container modification branch of steganography. We also present a container selection technique which benefits from available WebP compression parameters. Images generated with the described methods were tested with three applications, including the Firefox web browser, GNU Image Manipulation Program, and ImageMagick. Some of the presented techniques can be combined in order to conceal more than one message in a single carrier.
# GPS/BDS RTK Positioning Based on Equivalence Principle Using Multiple Reference Stations
## 2 Methodology
### SD Observation Equations
Thus, the weight matrix of Equation (2) is block-diagonal and can be derived as follows  
\[P=diag(P^{j}_{L},P^{j}_{P},P^{k}_{L},P^{k}_{P},\ldots,P^{n}_{L},P^{n}_{P}) \tag{4}\]  
where the sub-matrix \(P^{s}_{L}\) and \(P^{s}_{P}\) are the weight matrix of SD observations for carrier phase and code corresponding to single satellite \(s\). The specific expression of weight matrix for SD observations of single satellite \(s\) is as follows  
\[P^{s}=\left[ \begin{array}{cccc}\sigma^{s}_{a}+\sigma^{s}_{u}&\sigma^{s}_{u}& \sigma^{s}_{u}\\ \sigma^{s}_{u}&\sigma^{s}_{b}+\sigma^{s}_{u}&\sigma^{s}_{u}\\ \sigma^{s}_{u}&\sigma^{s}_{u}&\sigma^{s}_{c}+\sigma^{s}_{u}\end{array} \right]^ {-1} \tag{5}\]  
where \(\sigma^{s}=a_{0}+a_{1}e^{-\theta^{s}/10}\), \(\sigma^{s}\) is the variance of undifferenced observation, and the symbol \(\theta^{s}\) is the satellite elevation angle with \(a_{0}=2\) mm, \(a_{1}=4\) mm for carrier phase and \(a_{0}=20\) cm, \(a_{1}=40\) cm for code, the cut-off elevation angle is set to \(10^{\circ}\).
# The Online Reservation Problem
## 2 A Lower Bound on CRs  
We use [PERSON]'s principle [27] to derive a lower bound on the CR of any algorithm for the ORP. We start by stating the [PERSON]'s principle, as applicable to the ORP, in Theorem 1 below.  
Theorem 1: _[PERSON]'s Principle for the ORP: Let \(\mathcal{A}\) be the set of all algorithms for the ORP, as defined in Section 1.1, and let \(\mathcal{A}_{D}\) be the subset of deterministic algorithms in \(\mathcal{A}\). If \(\mathcal{K}\subseteq\mathcal{L}(\mathcal{D})\) is a set of instances and \(\mathcal{Q}\) is a probability distribution with \(\mathcal{K}\) as its support, then \(\inf\limits_{A\in\mathcal{A}_{D}}\left(E(OPT(L))/E(v_{A}(L))\right)\) is a lower bound on the CR of any algorithm in \(\mathcal{A}\). The expectations are over the probability distribution \(\mathcal{Q}\)._  
Next, in Theorem 2, we derive a lower bound on CR of any algorithm for the ORP.  
Theorem 2: _The CR of any algorithm for the ORP is bounded below by \(\underline{r}\), where \(\underline{r}(n,\Delta)\doteq\ln\Delta+2\)._  
**Sketch of the Proof.** In the detailed proof that is presented in Appendix A, we present a set of instances \(\mathcal{K}\), a probability distribution \(\mathcal{Q}\) over \(\mathcal{K}\), find a lower bound on \(\inf\limits_{A\in\mathcal{A}_{D}}\left(E(OPT(L))/E(v_{A}(L))\right)\), and apply Theorem 1. The key challenge in this exercise is to come up with \(\mathcal{K}\) and \(\mathcal{Q}\), such that a closed-form expression for the lower bound on \(\inf\limits_{A\in\mathcal{A}_{D}}\left(E(OPT(L))/E(v_{A}(L))\right)\) can be derived. We also want to construct \(\mathcal{Q}\) and \(\mathcal{K}\) such that the lower bound is as large as possible.  
The lower bound is increasing in \(\Delta\) because the CR of every algorithm must be increasing in \(\Delta\) by definition. This lower bound is tight for some parameter values, as we will show later. It is also tight in the sense that we present an algorithm later (Algorithm \(R\)) that is \(\mathcal{O}(\ln\Delta)\)-competitive.
# A Fabric Defect Segmentation Model Based on Improved Swin-Unet with Gabor Filter
## 3 Proposed Method
### Swin-Unet  
In recent years, transformer blocks have shown superior performance in visual tasks as compared to equivalent CNN modules. Microsoft utilized CNN design principles to design the Swin Transformer [37] block, which divides images into smaller patches based on a shifting window. Computing only the self-attention within each window is much less complex than calculating the self-attention between all pixels. Moreover, using the design principle of shifting windows, attention information can be exchanged between adjacent windows, effectively utilizing the relative position information. Unlike the conventional multi-head self attention (MSA) module used in ViT, the Swin Transformer block can be thought as a series of two modules: a regular window-based MSA (W-MSA) module, and a shifted window-based MSA(SW-MSA) module.In model design, W-MSA and SW-MSA are usually applied consecutively to ensure that attention between windows is not limited to a single area. Figure 1 illustrates a common configuration of two consecutive Swin Transformer blocks.  
UNet [27] is a classic segmentation network that has profoundly impacted subsequent network design due to its symmetric U-shaped structure with skip-connections. It utilizes multi-layer convolution and upsampling in the decoder, forming a U-shaped structure symmetrical to the encoder. The skip connections help to fuse high-resolution features at different scales from the encoder, reducing spatial information loss caused by downsampling and significantly improving segmentation performance. This influential U-shaped design has inspired many researchers to propose improved models based on it. The Swin-Unet [34] architecture used in this paper is obtained by turning the U-Net backbone into a Swin Transformer block. Swin-Unet adopts a transformer-based U-shaped Encoder-Decoder architecture with skip connections.  
Regarding image downsampling, Swin Transformer uses patch merging to compress the width and height of image features instead of average pooling or maximum pooling, thereby retaining feature information to a greater extent. Based on this method, Swin-Unet uses a patch-expanding upsampling method, enabling the U-shaped network to utilize more information (including uncompressed image features at the same level) during upward image reconstruction. However, Swin-Unet cannot avoid the problem, common in transformer-based models, of the transformer block being unable to quickly capture the texture features of the fabric image in the early stages of training, which causes the model output to quickly fit to a local optimal solution.
## 2 Materials and Methods
### Marginal and Internal Fit Measurements  
All frameworks were tried on the tooth replica abutments to ensure proper fitting. The intaglio surface of zirconia FDPs was grit etched with aluminum oxide. Subsequently, the zirconia FDPs were cemented to the abutment replicas with Panavia F2.0 resin cement (Kuraray, Okayama, Japan) according to the manufacturer's recommendations. Prior to cyclic loading test, specimens were stored in water at 37 \({}^{\circ}\)C for 24 h. The specimens were subjected to compressive cyclic loading prior to marginal and internal fit measurements. The cyclic loading test was performed in water at 37 \({}^{\circ}\)C using a universal testing machine (Instron, Canton, MA, USA), with a load range of 50-600 N for up to 500,000 cycles. All specimens were examined under light microscope. Minor and moderate chipping within the veneering porcelain was recorded for both ZL and ZP groups, whereas ZM and CAD-on specimens survived the fatigue test with no failure. However, no specimen was excluded from the study, neither due to debonding nor due to fracture. After completion of the compressive cyclic loading test, the specimens were sectioned mesiodistally to determine the internal fit tolerance by measuring the cement film thickness and margin gap under a scanning electron microscope (SEM) at 50\(\times\) magnification. Measurements were performed at ten pre-assigned points via a single examiner for each abutment (Premolar and Molar). The measurement points were defined as follows: 1: marginal gap (MG), 2: cervical (C), 3: axial (A), 4: axio-oclusal (AO), and 5: occlusal (O) (Figure 2). Image analysis software (Image), National Institute of Health) was used to measure the marginal and internal fit from SEM images (Figure 3).  
Previously, normality test was conducted, and the data were not normally distributed. Therefore, the marginal and internal discrepancies were statistically analyzed using Kruskal-Wallis test followed by Mann-Whitney U test at (\(\alpha=0.05\)).
# Privacy Framework for the Development of IoT-Based Systems
## 6 Proposal of Privacy Guidelines
### Key Findings and Reflections
#### 6.2.2 Analysis Part 2: Detailed Analysis of Participant Recommendations  
This section presents a detailed analysis of the recommendations provided by participants during the survey. Their feedback offers valuable insights into practical concerns, implementation challenges, and potential improvements for IoT privacy guidelines. These recommendations have been carefully examined to understand their implications and their impact on the overall context of the study. The objective is to provide in-depth insights that can contribute to enhancing future practices based on the participants' opinions and expertise.  
The participants' feedback can be categorized into five major themes:  
1. _Need for More Precise Definitions and Examples_ A recurring concern among respondents was the ambiguity in certain guideline definitions. Participants pointed out that terms such as \"minimizing data acquisition\" and \"minimizing knowledge discovery\" are vague and context-dependent. To address this, they suggested the following: 1. Incorporating specific use-case examples for each guideline to illustrate its practical applications more clearly. 2. Aligning definitions with established privacy frameworks, such as GDPR and the National Institute of Standards and Technology (NIST), to ensure consistency with regulatory standards. 3. Providing clear thresholds for implementation, such as specifying what constitutes \"excessive data retention\".
2. _Balancing Privacy and Performance in Encrypted Data Processing_ Although encryption was among the highest-rated guidelines, several respondents raised concerns about its computational cost. Advanced encryption methods, such as homomorphic encryption and multi-party computation, can introduce latency and processing overhead, making them impractical for resource-constrained IoT devices. Participants recommended the following: 1. Adopting hybrid encryption models that combine end-to-end encryption with selective decryption to enhance efficiency. 2. Utilizing lightweight cryptographic approaches, such as differential privacy and zero-knowledge proofs, to reduce processing demands. 3. Implementing adaptive encryption policies that dynamically adjust encryption levels based on data sensitivity and device capabilities.
# Performance-Enhancing Sulfur-Doped TiO2 Photoanodes for Perovskite Solar Cells
## 4 Conclusions  
In summary, the application of S-doped TiO\({}_{2}\) photo-anode as the ETL for planar PSC had been demonstrated. The optimized sintering temperature was 450 \({}^{\circ}\)C. The optimized sulfur dopant was 10% because a higher loading induced a drop in the absorbance capacity of the photo-anode. The S as dopant was proved to be present in the TiO\({}_{2}\) photo-anode layer after the deposition of S containing the precursor solution. The S-doped TiO\({}_{2}\) photo-anode was superior to the un-doped TiO\({}_{2}\) photo-anode in terms of higher absorbance, photo-catalytic activity and photo-current density (13.75% increase). Perovskite solar devices with S-doped TiO\({}_{2}\) also showed better efficiency (18% increase) fabricated under relative humidity of 40-50%. The increase in the electron collection capacity of the carrier charge and photo-current density enhanced the PCE at 6.0%. Additional studies on the relation between hydrophobicity of ETL and stability of the PSC device should be performed. This work may facilitate the tailoring of the TiO\({}_{2}\) photo-anode to match the energy levels of an ETL with the perovskite absorber layer.  
Conceptualization, M.A.M. and N.A.L.; methodology, M.A.M.; software, M.A.M. and N.A.L.; validation, N.A.L., M.D. and M.A.I.; formal analysis, M.A.M.; investigation, M.A.M.; resources, M.F.M.N., M.A.M.T. and N.A.L.; data curation, M.A.M.; writing--original draft preparation, M.A.M. and N.A.L.; writing--review and editing, N.A.L., M.S.S., M.D. and S.S.; visualization, M.A.M., N.A.L. and M.D.; supervision, N.A.L.; project administration, N.A.L.; funding acquisition, N.A.L. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the financial support from the Universiti Kebangsaan Malaysia (UKM) under the Dana Impak Perdana (DIP-2019-025). The first author also wants to acknowledge the UKM for the PhD scholarship under the Skim Zamalah Yayasan Canselor.  
Not applicable.  
The authors also appreciate the technical and management support from the Centre for Research and Instrumentation (CRIM), UKM.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **J\({}_{\text{sc}}\) (mA/cm\({}^{2}\))** & **V\({}_{\text{OC}}\) (V)** & **FF (\%)** & **PCE (\%)** & **R\({}_{\text{ct}}\) (\Omega)** \\ \hline \multirow{2}{*}{TiO\({}_{2}\)} & Best & 13.30 & 0.98 & 39.88 & 5.10 & 18.72 \\  & Average & 13.18 \(\pm\) 0.08 & 0.97 \(\pm\) 0.01 & 38.70 \(\pm\) 0.77 & 4.98 \(\pm\) 0.13 & 20.68 \(\pm\) 1.54 \\ \hline S-doped & Best & 13.9 & 0.997 & 43.18 & 6.0 & 14.26 \\ \(\text{TiO}_{2}\) & Average & 13.65 \(\pm\) 0.17 & 0.99 \(\pm\) 0.003 & 42.19 \(\pm\) 0.83 & 5.76 \(\pm\) 0.19 & 14.68 \(\pm\) 1.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Photovoltaic parameters derived from J-V measurements for perovskite solar cells device using S-doped TiO\({}_{2}\) and undoped TiO\({}_{2}\) with a 0.07 cm\({}^{2}\) active area.