esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Sizing of Battery Energy Storage System for Implicit Flexibility in Multi-Energy Microgrids
## 1 Introduction  
In recent years, regulatory initiatives have increasingly focused on promoting distributed renewable energy generation, enhancing energy efficiency, and electriying sectors such as transportation and space heating [1, 2]. Within this framework, multi-energy systems (MESs) have emerged as a key enabler of an efficient and sustainable energy transition. MESs are defined as integrated systems that simultaneously coordinate thegeneration, conversion, transmission, distribution, storage, and consumption of multiple energy carriers--such as electricity, thermal energy, natural gas, and hydrogen--across both planning and operational timeframes [3]. This systemic integration enhances the flexibility and efficiency of energy infrastructures, facilitating the effective utilization of renewable energy sources (RESs), strengthening energy supply security, and reducing greenhouse gas (GHG) emissions.  
Among the various implementations of MESs, district multi-energy systems (D-MESs) and multi-energy microgrids (MEMGs) have attracted significant attention due to their ability to harness local synergies at the community or district scale--an especially relevant aspect given that urban areas account for approximately 75% of global energy consumption [4]. D-MESs typically serve in mixed-use urban or semi-urban zones by coordinating diverse energy demands--residential, commercial, and industrial--through integrated multi-vector infrastructures [5]. MEMGs, on the other hand, are distributed energy systems that combine local renewable generation, energy storage, and flexible loads to efficiently meet both electrical and thermal requirements. They offer high operational flexibility and resilience, with the optional added capability of functioning autonomously from the main grid if needed [6].  
By implementing smart scheduling algorithms and advanced control systems, MESs enable the coordinated and dynamic management of multiple energy vectors, delivering different benefits [7]:  
* Efficient resource utilization, achieved through cogeneration, energy storage integration, and waste heat recovery.
* Decarbonization of transportation, supported by sustainable charging infrastructures for electric vehicles (EVs) powered by renewable distributed energy resources (DERs).
* Economic advantages, derived from economies of scale, improved operational efficiency, and reduced energy costs through demand-side management.
* Environmental and air quality improvements, facilitated by the substitution of fossil fuels with clean energy sources and enhanced system-wide energy efficiency.  
The optimization of MES planning, design, and operations has been widely studied, with numerous works analyzing different configurations, technologies, and control strategies to maximize system performance and support energy transition objectives.
## 3 Experimental Procedure
### Experimental Result
#### 3.5.2 Comparative Experimental Results  
The confusion matrix generated by the comparison experiment is shown in Table 3, and its heatmap is shown in Figure 3, illustrating the performance of the proposed chip grading method based on failure rates within a dynamically effective space across various categories. The specific analysis is as follows:  
1. Accuracy: 0.899, indicating that 89.9% of the samples in all test samples were correctly classified by this method.
2. Each piece of data of the confusion matrix represents the classification between different categories, which is explained as follows:  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Id** & **W\_Score** & **T\_Score** & **F\_Score** & **Grade** \\ \hline
139 & 0.994552 & 0.595353 & 0.994552 & B \\
140 & 0.986764 & 0.597397 & 0.986764 & C \\
141 & 0.966017 & 0.596029 & 0.966017 & C \\
142 & 0  
First row (samples with actual category A): No actual category A samples were correctly or incorrectly classified (all values are 0).
* Second row (samples with actual category B): 2679 actual category B samples were correctly classified as B. A total of 184 actual category B samples were incorrectly classified as C. A total of 14 actual category B samples were incorrectly classified as D.
* Third row (samples with actual category C): 82 actual category C samples were correctly classified as B. One actual category C sample was incorrectly classified as D.
* Fourth row (samples with actual category D): 23 actual category D samples were correctly classified as B. Two actual category D samples were incorrectly classified as C.  
Performance metrics by category are shown in Figure 4, demonstrating the performance of the classification model in four categories (A, B, C, D). The blue line represents precision, showing the proportion of samples predicted as the category by the model that are actually in that category; the green line represents recall, showing the proportion of all samples actually in that category that are correctly predicted by the model; the red line represents the F1 score, which is the harmonic mean of precision and recall, used to measure the overall performance of the model.  
The comparison experiment shows that the proposed grading method achieves an accuracy of 89.9%, indicating that it can correctly classify chips in most cases with high accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Actual\_Predicted** & **Grade A** & **Grade B** & **Grade C** & **Grade D** \\ \hline Grade A & 0 & 0 & 0 & 0 \\ Grade B & 0 & 2679 & 184 & 14 \\ Grade C & 0 & 82 & 27 & 1 \\ Grade D & 0 & 23 & 2 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix.  
Figure 3: Heatmap of across classes.
# Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy
## 3 Results and Discussion
### Emerging of Plants and Sowing Periods
[58] considers April as the sowing month for maize in Northern Italy, and the second week of May as the reference emergence period of plants. Although global sowing calendars can only be used as general reference information and not as actual data for validation, the results obtained in this study fit well with the large-scale SP for irrigated maize provided by the crop calendars from FAO [19] and MIRCA2000 [18]. The FAO calendar indicates April as main sowing month for irrigated maize, with high probability that a small part of irrigated croplands may be sowed later. The U.S. Department of Agriculture provides monthly information for crop-specific sowing dates worldwide [59]: this calendar states that April is the main sowing period for maize in Italy. The main limitation of this work is the lack of information about actual sowing dates at the field scale, to be used for the validation of results validation: right now, results can only be compared with local declarations from farmers and large-scale calendars. As previously mentioned, local farmers were asked to answer a survey to get an overview of the maize cultivation practices in the pilot area, in order to partially fill this gap. The results of the survey are available in Appendix A, within the Supplementary Material of this study. Although this source is too scattered to be used for an actual validation, the information collected from farmers was a valuable source in giving confirmation of the results.
## 2 Engineering Background  
The project is situated in the central urban area of Shenzhen, where the terrain is relatively flat. Geological drilling at the site has revealed the main geological layers, which from top to bottom, include unconsolidated soil, unconsolidated soil (gravel), sand and gravel, gravelly cohesive soil and weathered granite. The tunnel structure's bottom plate is primarily located within gravelly cohesive soil. The site's groundwater is classified into two types, which are loose rock pore water, predominantly found in the fluvial and alluvial sand layers and residual gravelly cohesive soil layers, and bedrock fissure water, which is primarily found in blocky, strongly and moderately weathered fracture zones. This water type exhibits slight compressibility, is chiefly replenished by pore water, and is expelled through underground runoff. An existing subway tunnel is adjacent to the site, with the right-side tunnel situated 3.9 m from the north side of the foundation pit. The spatial relationship between the foundation pit and the existing tunnel is illustrated in Figure 1.  
During operations, the subway tunnel strictly controls structural deformations, such as settlement, horizontal displacement, and differential deformation. Considering factors such as engineering geology, hydrogeology, and environmental impacts, the foundation pit utilizes a support system that includes a 1000/1200 mm-thick diaphragm wall with internal bracing. Concrete supports are placed at both the north and south ends of the pit, with six concrete supports situated at each end. In the standard section, the first and fifth supports are concrete, with the second, third, fourth, and sixth supports comprising steel pipes with a diameter of 800 mm and a thickness of 20 mm.  
Automated monitoring sections are positioned at 5 and 10 m intervals along the alignment of the tunnel section, with intensified monitoring at Area 1. Each section contains five monitoring points to track deformations of the tunnel vault, haunch, and bottom plate. Figure 2 shows the foundation pit profile and the layout of monitoring points. The deformation monitoring control indicators for this project are detailed in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Monitoring Projects** & **Preliminary Alert Threshold** & **Alert Threshold** & **Control Limit** \\ \hline Absolute structural & & & \\ deformation & 6.0 mm & 8.0 mm & 10.0 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Deformation monitoring control indicators.
# Molecular Regulatory Mechanisms Affecting Fruit Aroma
## 4 Regulation of Key Genes Involved in Aroma Formation
### Noncoding RNAs  
In addition to the widely known coding RNAs involved in transcriptional regulation, small RNA (smRNA) sequences, such as microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), which do not have protein coding potential, are also key regulators of gene expression, RNA processing, and translation at the transcriptional level [51; 52]. miRNAs are endogenous ncRNAs that regulate gene expression at the post-transcriptional level by binding to target mRNAs [53; 54]. miRNAs regulate fruit softening and aroma biosynthesis during banana ripening [55]. [PERSON] et al. [56] identified differentially expressed miRNAs, including mdm-miR8T2a-h, mdm-miR8159a/b/c, and mdm-miR8160a-e, in response to low temperature during the formation of the 'Nanguo' pear aroma. These miRNAs bind to their respective target genes, playing an important regulatory role in the process of flavor weakening in the 'Nanguo' pear stored in cold conditions. lncRNAs represent a class of RNA transcripts longer than 200 nucleotides (nt) with no identifiable protein-coding potential. lncRNAs participate in the fruit development and ripening process of tomatoes, strawberries, and peaches. Compared with those in coding RNAs, lncRNAs in peach fruits exhibit lower expression levels, lower complexity of variable splicing, shorter isomers, and fewer exons [57]. Expression analysis has shown that lncRNAs in peach fruit have lower expression levels and fewer exons [57]. The 575 differentially expressed lncRNAs were divided into six clusters, which may be related to physiological and metabolic changes associated with the fruit ripening, such as the flavonoid biosynthesis, fruit texture softening, chlorophyll decomposition, and the accumulation of aroma compounds [57].
## 1 Introduction
To the best of our knowledge, no study has evaluated the potential of _Chromochloris zofingiensis_ in the context of a full biorefinery approach involving simultaneous nutrient removal, biomass production, lipid accumulation, and astaxanthin biosynthesis in whey wastewater. Additionally, ref. [16] used filtration to remove solids and autoclaving as pretreatment techniques of dairy wastewater, resulting in a significant biomass of 3.8 g L\({}^{-1}\)\(\pm\) 0.0 g L\({}^{-1}\) and high lipid productivity of 132.1 mg L\({}^{-1}\) d\({}^{-1}\)\(\pm\) 1.9 mg L\({}^{-1}\) d\({}^{-1}\). By contrast, ref. [24] used centrifugation to remove suspended particles before autoclaving and diluted the effluent prior to microalgal cultivation. Recovering waste nutrients from wastewater using microalgae without a high-cost pretreatment process and producing valuable bioproducts like lipid and astaxanthin would enhance the wastewater treatment process and align with the principles of a sustainable circular bioeconomy [31]. In this work, we intentionally avoided solid removal pretreatments to simplify the process and reduce energy costs. The only applied treatment was autoclaving to prevent bacterial contamination. We hypothesize that _Chromochloris zofingiensis_ can effectively grow in autoclaved, non-filtered whey wastewater, allowing for simultaneous pollutant removal and production of valuable biocompounds. Therefore, the main objectives of this study are to:  
(1) Assess the growth and biomass productivity of _C. zofingiensis_ in different dilutions of whey wastewater;  
(2) Evaluate nutrient removal efficiency (total nitrogen (TN), nitrate (NO3-), ammonia (NH4+), phosphate (PO4-), Chemical oxygen demand (COD), total carbon (TC), total organic carbon (TOC), lactose, lactate, acetate);  
(3) Quantify the accumulation of lipids, fatty acids, pigments, and astaxanthin;  
(4) Analyze the feasibility of this process as an integrated, low-cost biorefinery strategy.
## 3 Results and Discussion
### Equilibrium Adsorption Experiments  
The Sb(V) concentrations in the aqueous and solid phases at equilibrium are plotted in Figure 4, and the parameters of the equilibrium adsorption models are provided in Table 3. The adsorption amount increased with increasing initial concentrations, and Sb(V)  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **Model Parameter** & & \(R^{2}\) & \(SSE\) \\ \hline Pseudo-first-order & \(q_{e}\) (mg/g) & & \(k_{1}\) (1/min) & 0.896 & 2.494 \\ Pseudo-first-order & 12.7 & & 5.512 & & \\ Pseudo-second-order & \(q_{e}\) (mg/g) & & \(k_{2}\) (g/mg/min) & 0.977 & 0.562 \\ Pseudo-second-order & 13.1 & & 0.919 & & \\ Pseudo-nth-order & \(q_{e}\) (mg/g) & & \(k_{n}\) ((mg/g)\({}^{1-n}\)/h) & \(n\) & 0.978 & 0.527 \\  & 13.3 & & 0.592 & 2.245 & & \\ \hline \end{tabular}
\end{table}
Table 2: Model parameters and model suitability for kinetic adsorption data obtained from pseudo-first-order, pseudo-second-order, and pseudo-nth-order models.  
Figure 3: Kinetic adsorption results of Sb(V) adsorption by Fe-CMB and model fits using pseudo-first-order, pseudo-second-order, and pseudo-nth-order models.  
adsorbed onto Fe-CMB reached a plateau at an initial concentration of 150 mg/L. The driving force for Sb(V) adsorption was minimal at low initial concentrations owing to small Sb(V) concentration gradient between the aqueous and solid phases [29].  
A higher \(R^{2}\) and lower \(SSE\) values of the Langmuir model than those of the Freundlich model indicate that Sb(V) is adsorbed by Fe-CMB through monolayer adsorption rather than multilayer adsorption. The Temkin model, derived using molecular statistical theory [25], fit the least with \(R^{2}\) = 0.877. The heat of Sb(V) adsorption calculated from the Temkin model was 0.217 kJ/mol. The \(1/n\) value of the Freundlich model was 0.575, which is slightly higher than 0.5, indicating that the intensity of Sb(V) adsorption is slightly weak [31]. The maximum adsorption capacity of Fe-CMB obtained from the Langmuir model was 58.3 mg/g, which was higher than that of the other adsorbents such as Ce-doped magnetic biochar: 25.0 mg/g [9]; \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\): 11.0 mg/g [32]; manganese iron oxide: 10.0 mg/g [33]; goethine nanopowder: 7.5 mg/g [34], as reported in literatures. This result indicates that Fe-CMB can be used as a promising adsorbent for Sb(V) removal from aqueous solutions.
## 3 Results
### Performance Metrics and Evaluation  
Given that both B-scan and C-scan data were available for the murine (rat) samples acquired in the database, the clinical discrimination capability of the model on the differentiation of benign versus malignant polyps was calculated for both types of data. To evaluate each C-scan, the mean of the individual predictions for the B-scan images that form the volume was calculated. The performance of the model was measured using the conditions provided by the confusion matrix (see Table 1).  
In the clinical context being analyzed in this work, these conditions can be seen as:  
* True positive (TP): Malignant lesion correctly identified as malignant.
* False positive (FP): Benign lesion incorrectly identified as malignant.
* True negative (TN): Benign lesion correctly identified as benign.
* False negative (FN): Malignant lesion incorrectly identified as benign.  
The metrics that were employed to measure the model performance based on the previous conditions are described below.  
* Sensitivity. Also known as the true positive rate (TPR). Number of true/all positive assessments. TPR = TP/(TP + FN) = number of malign lesions with positive test/total number of malign lesions.
* Specificity. Also known as the true negative rate (TNR). Number of true/all negative assessments. \(\text{TNR}=\text{TN}/(\text{FP + TN})\) = number of benign lesions with negative test/total number of benign lesions.
* Positive predictive value (PPV). In case of a malignant prediction, probability that the lesion is actually malignant. PPV = TP/(TP + FP) = Number of true positives/number of positive calls.
* Negative predictive value (NPV). In case of a benign prediction, probability that the lesion is actually benign. NPV = TN/(TN + FN) = Number of true negatives/number of negative calls.  
The desired value for these metrics was as close as possible to 1, 1 meaning a perfect test.  
Additionally, as the accuracy (measure of the number of samples that were correctly classified in the expected class) is a misleading metric in imbalanced datasets, the balanced accuracy was calculated. This metric normalizes true positive and true negative predictions by the number of positive and negative samples, and then divides the sum by two, providing an accuracy value where the class frequencies are the same.  
* Balanced accuracy (BAC). Measures the number of samples that were correctly classified in the expected class considering class frequencies. Number of correct/all assessments considering class frequencies. BAC = (TPR + TNR)/2 = (Sensitivity + Specificity)/2.
## 2 Experimental Methods  
### Cephalometric Landmarks  
There are two well-known classifications of cephalometric landmarks [3], namely, (1) based on the origin, we distinguish between (i) anatomic and (ii) derived or constructed cephalometric landmarks, and (2) based on the structures involved, we differentiate between (a) hard tissue and (b) soft tissue cephalometric landmarks. Anatomic landmarks represent the actual anatomic structures of the skull (e.g., nasion, point A, point B, ANS, PNS, etc.), while derived or constructed landmarks are obtained secondarily from anatomic structures in a lateral cephalogram (e.g., gathion, anterior point of occlusion, etc.). On the other hand, the hard tissue cephalometric landmarks represent the actual hard tissue structures of the skull, such as the nasal bone, frontal bone, maxillary bone, etc., while soft tissue landmarks, as their name suggests, are located on the soft tissues (e.g., on the forehead, nose, lips, etc.) [3]. Examples of hard tissue cephalometric landmarks are nasion, temporale, sella, menton, and gonion, while examples of soft tissues landmarks are subnasale, subspinale, stomion, soft tissue pogonion, and soft tissue gathion [3].
# Amplitude Modeling of Specular Multipath Components for Robust Indoor Localization
## 4 Analysis of the GPR-Based Channel Model  
In this section, various REM parameters, i.e., SINR, PEB, etc will be analyzed, using the measurement data. We first analyze the energy \"captured\" by the SMCs in relation to the total energy of the observed CIRs, denoted as energy capture (EC) [52] and the number of visible SMCs along the trajectory. Secondly, the GPM of the SMC amplitudes and the resulting REMs for SMC amplitudes, SINR-values, and the PEB are illustrated.
# A Critical Review of Supersonic Flow Control for High-Speed Applications
## 4 Active Control Methods
### Soft Computing Methods
#### 4.3.2 Fuzzy Logic  
[PERSON] et al. [160] discovered a fuzzy logic methodology for investigating pressure loss in a sudden expansion duct. The authors aimed to notice minor pressure loss when the L/D was 1, as evidenced by the fuzzy logic formulation. According to the authors, this can be a qualitative examination of internal fluid flow through a nozzle with a sudden expansion duct using the fuzzy logic methodology [161]. Because of all other characteristics, such as wall static pressure and loss pressure, including the base pressure, they discovered the best value of L/D. When the Mach number is changed to 1.58 or 2.06, all other parameters remain the same as in the sudden expansion duct with cavities [162]. They found that the fuzzy logic technique L/D of 1 is sufficient for smooth flow growth based on de Laval nozzle and wall static pressure changes in both circumstances. [PERSON] et al. [163] described the critical aspects of the fuzzy logic technique in turbulent supersonic flow simulations as a cost-effective methodology. The Mamdani-based fuzzy logic methodology was utilized to build connections between input and output in the CFD findings. Triangular, generalized bell shape and Gaussian membership functions were employed in this technique.