esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The optimization of LSSVM is necessary to improve the forecasting accuracy. The bacterial foraging optimization algorithm (BFOA) has achieved better results in many fields and using BFOA to find the optimal parameters of LSSVM helps to enhance the accuracy of the model. The hybridization of BFOA with back propagation neural network [10], multi-layer bidirectional LSTM [11], and ANN [12] for STLF have been found in the literature, which shows excellent performance in improving forecasting accuracy. However, this paper presents a novel approach by introducing the first application of improved BFOA hybridized with LSSVM for short-term electricity load forecasting. Therefore, in this study, BFOA is selected to examine its suitability and performance when combined with LSSVM to forecast the load. Additionally, the bacteria's constant step size and its movement during chemotaxis are modified using a sine cosine equation to improve BFOA, and the improved BFOA (IBFOA) is proposed. Further, the IBFOA is used for LSSVM's parameters optimization. Therefore, the hybrid model for load forecasting based on LSSVM-IBFOA is established. The hybrid of LSSVM-IBFOA is applied for STLF of the electricity load in Peninsular Malaysia during the pandemic period. Consequently, the forecasting simulation illustrates the superiority of this hybrid optimization model in comparison to three models: deep neural network (DNN), LSSVM, and LSSVM-BFOA. The main contribution of the paper is as followings:  
1. Regarding Malaysia's geography and environment, the correlation between weather variables and the load consumption that influences the forecasting process is analyzed using [PERSON]'s correlation coefficient method where the findings will influence the direction of the next generation research. 2. A novel improved bacterial foraging optimization algorithm (IBFOA) is proposed to optimize the important parameters of the LSSVM model and enhance the forecasting accuracy of the actual electricity load demand in reflecting the sustainable power market in Malaysia. 3. A full support validation accuracy measures are incorporated into the proposed model to evaluate performance of the LSSVM-IBFOA while giving such accurate load profile demand for the power network in Malaysia. The remainder of this paper is organized as follows. Section 2 presents the previous related work. Section 3 describes the methodology used in this study. Section 4 discussed the analysis result. Finally, Section 5 gives the conclusions of the study and future recommendations.
## 2 Related Work
### Retrieval-Augmented Generation  
Retrieval-Augmented Generation (RAG) integrates external knowledge retrieval with generative language models, enabling dynamic access to up-to-date or domain-specific information during text generation [28]. Within the RAG framework, a given query or task input, along with its associated contextual knowledge, is first processed by a retrieval component to extract the most relevant information fragments from a predefined knowledge base or document collection. Subsequently, these retrieved fragments are integrated as supplementary contextual inputs into the prompt to guide the final response generation process. Key features of RAG include:  
1. Reduces hallucinations by grounding responses in retrieved evidence. RAG mitigates hallucinations, which mean the incorrect or fabricated outputs, by directly incorporating verified external data (e.g., documents, databases) into the generation process, ensuring responses align with factual sources.
2. Supports knowledge updates without retraining. Unlike traditional LLMs that require costly retraining to integrate new information, RAG dynamically accesses updated knowledge bases (e.g., latest research papers or news archives), enabling real-timeadaptation without modifying core model parameters. This mechanism allows users to seamlessly incorporate their private data or regulatory updates, ensuring compliance and relevance [29].
3. Enhances multi-domain adaptability via modular retrieval systems. RAG's architecture decouples retrieval and generation modules, allowing domain-specific retrievers (e.g., legal databases, engineering standards) to be swapped based on task requirements, improving cross-domain versatility [30].  
RAG's output quality is heavily dependent on the performance of its retrieval module, and some irrelevant or noisy retrieved documents (e.g., due to semantic mismatches) may lead to inaccurate or misleading responses generated from erroneous contexts. Moreover, to maintain the timeliness and relevance of information, it is necessary to continuously update the external knowledge base, which also incurs additional maintenance costs. These factors collectively pose certain challenges for the practical application of RAG technology [31; 32].
# An Efficient Ground Moving Target Imaging Method for Airborne Circular Stripmap SAR
## Appendix A  
This appendix drives the encoding accuracies for \(v_{e}\) and \(\alpha\).  
First, the derivation of the encoding accuracy for \(v_{e}\) is presented. Since the bound for the Doppler chirp rate error is set to be \(1/(3T_{a}^{2})\), according to the relationship between the Doppler chirp rate and \(v_{e}\) shown in (30), the encoding accuracy for \(v_{e}\) (i.e., \(\Delta v_{e}\)) should satisfy the following equation  
\[\frac{1}{6T_{a}^{2}}=\frac{4}{\lambda}\cdot\frac{(v_{e}+\Delta v_{e})^{2}-v_{e }^{2}}{2R_{c}} \tag{10}\]  
By solving the above quadratic equation of one unknown, the solution for \(\Delta v_{e}\) can be expressed as  
\[\Delta v_{e}=-v_{e}+\sqrt{v_{e}^{2}+\frac{\lambda R_{c}}{12T_{a}^{2}}} \tag{11}\]  
Since \(v_{e}\) ranges from \(v_{e,\,\min}\) to \(v_{e,\,\max}\), the encoding accuracy for \(v_{e}\) can be calculated by  
\[\Delta v_{e}=\min_{v_{e}}\left\{-v_{e}+\sqrt{v_{e}^{2}+\frac{\lambda R_{c}}{1 2T_{a}^{2}}}\right\} \tag{12}\]  
Now, we present the derivation of the encoding accuracy for \(\alpha\). Similar to the derivation of \(\Delta v_{e}\), according to (30), the encoding accuracy for \(\alpha\) (i.e., \(\Delta\alpha\)) should satisfy the following equation  
\[\frac{1}{6T_{a}^{2}}=\frac{4}{\lambda}\cdot\frac{(\alpha+\Delta\alpha)^{2}- \alpha^{2}}{8R_{c}^{3}} \tag{13}\]  
The solution for the above quadratic equation of one unknown is given by  
\[\Delta\alpha=-\alpha+\sqrt{\alpha^{2}+\frac{\lambda R_{c}^{3}}{3T_{a}^{2}}} \tag{14}\]  
Since \(\alpha\) ranges from \(\alpha_{\min}\) to \(\alpha_{\max}\), the encoding accuracy for \(\alpha\) can be calculated by  
\[\Delta\alpha=\min_{\alpha}\left\{-\alpha+\sqrt{\alpha^{2}+\frac{\lambda R_{c} ^{3}}{3T_{a}^{2}}}\right\} \tag{15}\]
###### Abstract  
Whole crop rice (WCR) is used as an important feed for livestock ruminants. In this study, \"Yeongwoo\" variety WCR (cultivated on the Korean peninsula) was harvested at three different maturity stages (booting, heading, and milk) and their phenolic acid and flavonoid profiles in the lactic acid bacteria (LAB)-inoculated and noninoculated silage, hay, and fresh freeze-dried WCR extract were quantified. The alterations in the phenolic and flavonoid contents of the selected WCR during maturation in different samples were analyzed by the High-Performance Liquid Chromatography- Diode Array Detector (HPLC-DAD) technique. The six phenolics (caffeic acid, ferulic acid, p-comamic acid, chlorogenic acid, dihydroxy benzoic acid, and prophyl gallate) and six flavonoids (rutin hydroxide, luteolin, kaempfer, vitexin, myrietin, and quercetin) were noted to have slight differences between the LAB-inoculated and noninoculated silage samples; however, the phenolics and flavonoids were higher in hay WCR at the milk stage compared to the silage and fresh freeze-dried samples. The results indicate that WCR harvest times have different phenolic compounds in the WCR silage, hay, and fresh samples. The phenolic and flavonoid compounds were higher (\(p<0.05\)) with the increase in maturity (Stage 1-3). The stage of WCR maturity was positively related (\(p<0.05\)) to the amount of phenolic acid and flavonoid contents (\(\mu\)g/g) in hay, silage, and fresh freeze-dried extract. We observed the WCR had high amounts of phenolic acid and flavonoid concentrations at milk stage (Stage 3) hay (quercetin, kaempfer, luteolin, ferulic acid, caffeic acid, and coumaric acid were 1.28, 1.29, 0.54, 1.54, 1.92, and 1.81 \(\mu\)g/g, respectively) compared with the booting and heading stages (Stages 1 and 2), with acceptable accuracy on a pilot scale. Based on these results, it could be concluded that LAB (_Lactobacillus plantarum_)-inoculated whole crop rice silage (WCRS) did not affect the phenolics and flavonoids of secondary plant metabolites in fermented silage. However, phenolics and flavonoids were of higher rations in WCR at the milk stage. Furthermore, this phenolic acid and flavonoid effect needs to be confirmed using large-scale in vivo analysis.  
whole crop rice; maturity; lactic acid bacteria; phenolics; flavonoids; HPLC-DAD
# Application of Machine Learning to Express Measurement Uncertainty
## 1 Introduction
* It uses the procedure for expressing the type B measurement uncertainty [29] and the maximal value of the empirical absolute loss function of the ML model. Since expressing type B measurement uncertainty is based on the experience and judgment of the measurer, the approach is referred to as \"judgmental [21]\". * The considered ML model is related to the testing and monitoring of power equipment, i.e., determining the partial discharge (PD) location by the non-iterative, all-acoustic method [30; 31]. * The example uses the dataset representing the correlation of the mean distance of partial discharge and acoustic emission (AE) sensors and the temperature coefficient of sensitivity of the non-iterative algorithm [30]. * Based on the highest coefficient of determination value, the dropout additive regression trees algorithm (DART) achieved the best performance. * The proposed estimation procedure is not limited to the presented example but can also be used in all similar regression problems, including big data analysis. * The proposed procedure is applicable even when the probability distribution of deviation of predicted values from correct values is unknown. This paper consists of the following sections: Introduction; Methods; Experimental data, comparative analysis, and discussion; and Conclusion. The Methods section introduces measurement uncertainty, empirical loss function, a description of the DART algorithm, and the measurement uncertainty of the ML model. The following section explains the reasons for conducting the experiments and presents experimental results with appropriate analysis. Finally, the Conclusion provides deductions drawn from the results achieved in this study.
# Simplification of Complex Structural Dynamic Models:
## 2 Model Descriptions
### Finite Element Models  
The grid for the associated finite element model, as shown in Figure 1b, is a structured mesh using 20-node quadratic hexahedral elements, C3D20 (ABAQUS v6, [1]) and applying meshing guidelines according to [PERSON] et al. (2017, 2018). A total of 54,000 elements were used, which corresponds to 236,541 nodes. This reference model (modelling approach **1**) has no simplifications and ideal free-free boundary conditions. In the next step, three simplified finite element models are investigated (see Figure 1c-f).  
For modelling approach **2**, c.f. Figure 1c, the effect of the tip-mass was modelled as an ideal rigid clamping condition, which defines zero motion at this point. This is possibly the most common implementation in ABAQUS/CAE, which removes the complexity of modelling a tip-mass. This compares to an approximately infinite mass and infinite rotary inertia at the end-point. For more realistic models, two additional finite element modelswere investigated. Modelling approach **3**, c.f. Figure 1d, approximates the tip-mass by an ideal free-tip mass, coupling a point mass to the beam's end.  
Extending this approach, modelling approach **4** additionally takes the rotary inertia into account. For both cases, the point mass is located at the centre of gravity of the 3D tip mass and is connected to the front surface of the beam via kinematic coupling. The rotary inertia \(I_{\text{t}}\) for the tip-mass is calculated by  
\[I_{\text{t}}=\frac{m_{\text{t}}(L^{2}+H^{2})}{12}+\frac{1}{4}L^{2}m_{\text{t}}. \tag{7}\]  
The modelling approaches **2**, **3** and **4** include simplifications to reduce the degrees of freedom and, thus, the complexity of the numerical modal analysis. In addition to the simplifications, the finite element mesh for the beam was left unchanged and, hence, matched the reference model. In addition to the finite element models, Figure 1f shows an analytical Euler-Bernoulli beam model considering an ideal free-tip mass and the rotary inertia. This corresponds to modelling approach **5**. The theoretical background is explained, and can be found in Section 2.4 for reference.  
Figure 1: Investigated structure and finite element models. (**a**) Geometry of the specimens. Coordinate axes follow: length of beam (\(l\)) x-axis, beam thickness (\(h\)) z-axis, and beam width (\(w\)) y-axis. (**b**) Finite element model **1** of the real structure with ideal free–free boundary condition—reference model. (**c**) Simplified finite element model **2** with ideal rigid clamping. (**d**) Simplified finite element model **3** with ideal free-tip mass modelling. (**e**) Simplified finite element model **4** with ideal free-tip mass and rotary inertia modelling. (**f**) Euler–Bernoulli beam model 5 with ideal free-tip mass and rotary inertia modelling.
# InMemQK: A Product Quantization Based MatMul Module for Compute-in-Memory Attention Macro
## 3 Optimized Product Quantization and InMemQK Based on Time-Domain MAC
### ADC-Free InMemQK Module Design  
Drawing from the time-domain method presented in the literature [24], we developed a time-domain vector-matrix multiplication (VMM) module. As illustrated in Figure 4a, inputs are encoded using pulse width, while weights are represented by programmable current sources constructed from NVM cells. Assuming the i-th current source is activated with current \(I_{i}\) for an input duration of \(\Delta_{i}\), the current integral over a unit period is the product of \(I_{i}\) and \(\Delta_{i}\). The dot product of N elements can be computed by summing the charges in a single column of the NVM array. NVM-CS cannot represent negative inputs or negative weights, whereas the query and key in the self-attention module exhibit both positive and negative representations.  
To realize signed MatMul, we employ a differential structure to implement VMM, using \(VMM^{+}\) and \(VMM^{-}\) to handle the positive and negative paths separately. The expression for the positive path is given by  
\[VMM^{+}=Q_{i}^{+}\cdot K_{i}^{+}+Q_{i}^{-}\cdot K_{i}^{-} \tag{3}\]  
Specifically, for \(K_{i}>0\), we have \(K_{i}^{+}\) = \(K_{i}\) and \(K_{i}^{-}\) = 0, while for \(K_{i}<0\), we have \(K_{i}^{+}\) = 0 and \(K_{i}^{-}=K_{i}\), and the same for \(Q\). Similarly, the expression for the negative path is  
\[VMM^{-}=Q_{i}^{-}\cdot K_{i}^{+}+Q_{i}^{+}\cdot K_{i}^{-} \tag{4}\]  
As depicted in Figure 4b, our proposed InMemQK computation module implements an approach where keys are quantized into codebooks. During stage 1, the distances between \(key_{i}\) and the centroids in the key codebook are computed. Given that the distance from the key to all centroids remains constant, the minimum L2 distance between the subvector and centroids can be equivalently represented as the maximum inner product between the key subvector and \(centroid_{i}\). In stage 2, utilizing the maximum inner product index identified in stage 1, the approximate inner product between the input tensor \(query_{i}\) and \(key_{i}\) is computed. The inner product calculation of \(query_{i}\) and \(key_{i}\) is accomplished  
Figure 4: (a) Time-domain multiplication–accumulation macro. (b) Two-stage MatMul calculation pipeline.  
by utilizing the crossbar array structure, with the activated centroid word lines serving as weights. Lastly, the inner product of \(query_{i}\) and \(key_{i}\) is calculated, and the inner products of C subvectors are aggregated to yield the approximate MatMul result of \(Q\cdot K^{T}\).
# Cell Disruption and Hydrolysis of _Microchloropysis salina_ Biomass as a Feedback for Fermentation
## 2 Materials and Methods
### Determination of Cell Disruption Degree  
For quantitative evaluation of the mechanical disruption of _M. salina_ cells, each sample was analyzed using a flow cytometer (CytoFLEX V0-B4-R2, Beckman Coulter GmbH, Krefeld, Germany). The proportion of intact cells and cell debris as a frequency in total incidence count was based on shifts in the measured forward-scattered light (FSC) and the fluorescence in the wavelength range of 640-780 nm (F640-780). The use of F640-780 for the analysis was based on an empirical approach making use of the fluorophore content of microalgae, such as allophyocyanin with an emission peak at 660 nm, which would presumably be released upon cell disruption [25]. In order to ensure an accurate measurement, each sample was diluted with distilled water so that its optical density measured at a wavelength of 750 nm was around 0.150, and the flow rate was adjusted to a value corresponding to less than 3000 incidences per second. Per sample, 100,000 incidences were recorded and used to plot a log-log graph of F640-780 values with respect to FSC. The areas occupied by distilled water, intact _M. salina_ cells, cell debris, and an unknown fraction were marked on this dot plot to sort the incidences accordingly. The degree of cell disruption (DCD) was then calculated as:  
\[\text{DCD}=\text{i}_{\text{cells}}\cdot(\text{i}_{\text{cells}}+\text{i}_{ \text{debris}})^{-1}, \tag{2}\]  
where icells and idebris are the incidence counts of intact cells and cell debris, respectively.
# An Efficient Ground Moving Target Imaging Method for Airborne Circular Stripmap SAR
## 5 Experimental Results  
### Validation of the Proposed Range Model  
In Section 2.2, we have demonstrated the accuracy superiority of the proposed range model over the second-order and third-order Taylor approximated range models. In this subsection, to further validate the proposed range model, the focusing results based on the original range equation, the proposed range model, the second-order and third-order Taylor-approximated range models are analyzed and compared. The parameters of CSSAR used in the experiments are given in Table 1.  
Figure 5 shows the focusing results, with the expanded range and azimuth profiles of the focused target being plotted. In addition, the focusing quality parameters are measured and the results are shown in Table 2. The measured quality parameters include the azimuth impulse response width (IRW) broadening, the peak sidelobe ratio (PSLR), and the integrated sidelobe ratio (ISLR) [16]. From Figure 5 and Table 2 it can be seen that the focusing quality of these four range models in range are almost the same. This indicates that the accuracies of these range models are sufficient for range compression and RCM correction. However, it can also be seen that the azimuth profile obtained based on the second-order [PERSON] approximated range model suffers significant sidelobe asymmetry, IRW broadening, PSLR and ISLR losses. Besides, the focusing result obtained based on the third-order Taylor-approximated range model also suffers azimuth IRW broadening, PSLR and ISLR losses. Nevertheless, quality parameters obtained based on the proposed range model are very close to those obtained based on the original range equation, which indicates that the target is accurately focused.
# Logistics Distribution Vehicle Routing Problem with Time Window under Pallet 3D Loading Constraint
## 5 Example Analysis
### Results and Analysis  
An example is based on an improved hybrid simulated annealing-genetic algorithm, where the initial temperature is \(T_{0}=100\), and the end temperature is \(T_{end}=1\). The population size is 150. In this study, the goods are loaded onto a pallet of 1 m in length and 1.2 m in width, and the maximum height of the pallet load is restricted to 2 m, with the pallet bottom bracket being 0.1 m high. Additional pallets can be added when the goods are  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cargo** & **Weight** & **Long** & **Wide** & **High** & **Volume** \\ \hline
1 & 10 & 0.55 & 0.25 & 0.25 & 0.034375 \\
2 & 5 & 0.4 & 0.33 & 0.25 & 0.033 \\
3 & 7 & 0.6 & 0.25 & 0.2 & 0.03 \\
4 & 4 & 0.33 & 0.2 & 0.2 & 0.0132 \\
5 & 2 & 0.2 & 0.2 & 0.15 & 0.006 \\
6 & 1 & 0.2 & 0.1 & 0.15 & 0.003 \\
7 & 1 & 0.1 & 0.1 & 0.1 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Goods specifications.  
Figure 9: Map of each logistics and distribution point.  
loaded beyond the 2 m limit. This algorithm runs on MATLAB R2018a, Windows 10 Intel(R) Core(TM) i7-7660U CPU @ 2.50 GHz.
# Solar Filament Detection Based on an Improved Deep Learning Model
## 5 Results and Discussion
### Data  
Using the solar filament dataset built as described earlier in this article for training, we divided the 150 images into training, validation, and testing sets in the ratio of 6:2:2, resulting in 90, 30, and 30 images in each set, respectively. However, due to the challenging nature of annotating solar filaments and the high cost of manual labeling, the number of images in the dataset was limited. Therefore, data augmentation techniques were employed to expand the training set.  
Before training, various augmentation methods were applied, including rotation, vertical flipping, brightness adjustment, and contrast adjustment. These operations effectively increased the diversity of the training data, generating a total of 2700 training samples from the original 90 training images. Rotation and vertical flipping enabled the network to capture image features from various angles and directions, while brightness and contrast adjustments simulated diverse lighting and exposure conditions.  
Using these data augmentation techniques, we not only enriched the diversity of the training data but also significantly improved the generalization and robustness of the model.