esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This study presents a semantic segmentation model (PVM) that integrates multi-source remote-sensing time series and crop phenology information to address the challenge of classifying diverse non-food crops across large-scale cultivated areas. By introducing a Phenology-Aware Module (PAM) and a Multi-Task Attention Fusion (MTAF) mechanism, the model effectively captures crop growth dynamics and mitigates optical data gaps caused by cloud interference. Experiments demonstrate that PVM achieves an F1-score of 74.84% and IoU of 61.38% on the Hunan dataset, significantly outperforming baseline and benchmark models. Cross-regional validation on the Canadian Cropland Dataset further confirms generalizability, with an F1-score of 71.93% and IoU of 55.94%. Ablation studies reveal the PAM module improves IoU by 8.3% by dynamically focusing on critical phenological stages, while MTAF enhances Recall by 8.91% through complementary modality fusion. These improvements are consistent with gains over existing methods, such as MTAF-TST and 2 DU-Net+CLSTM.  
The main contributions of this work are as follows: (1) to our knowledge, this is the first study to deeply integrate phenology calendars into time-series segmentation frameworks specifically for non-food crop mapping, enabling dynamic attention to growth-critical stages; (2) an adaptive fusion strategy of radar and optical time-series data ensures temporal continuity in cloudy southern regions; and (3) a Vision Transformer-based decoder structure tailored for remote sensing effectively enhances global spatiotemporal feature representation, improving F1-score by 3.59% over traditional U-Net designs. Overall, the proposed method provides a high-precision approach for monitoring non-food crop land use conversion, with implications for land resource optimization and food-ecology balance.  
Conceptualization, X.G. and M.L.; Methodology, X.G., S.C. and J.J.; Software, X.G. and M.L.; Validation, X.G. and M.L.; Formal analysis, X.G.; Investigation, S.C., J.J. and M.L.; Data curation, X.G., S.C. and J.J.; Writing--original draft, X.G. and M.L.; Writing--review & editing, X.G., S.C., J.J. and M.L.; Visualization, X.G. and M.L.; Supervision, S.C., J.J. and M.L.; Project administration, M.L.; Funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Hunan Provincial Natural Science Foundation of China (Nos. 2024J8373 and 2024J8381), and the Research Foundation of the Department of Natural Resources of Hunan Province (No. 20240115 TD).  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Our Proposal  
### Formalizing Synthesizer Control  
Considering a dataset of audio samples \(\mathcal{D}=\left\{\mathbf{x}_{i}\right\},i\in[1,n]\) where the \(\mathbf{x}_{i}\in\mathbb{R}^{d}\) follow an unknown distribution \(p(\mathbf{x})\), we can introduce latent factors \(\mathbf{z}\in\mathbb{R}^{z}\) to model the joint distribution \(p(\mathbf{x},\mathbf{z})=p(\mathbf{x}\mid\mathbf{z})p(\mathbf{x})\) as detailed in Section 2.1. In our case, some \(\bar{\mathbf{x}}\in\mathcal{D}_{s}\subset\mathcal{D}\) inside this set have been generated by a given synthesizer. This synthesizer defines a generative function \(f_{s}(\mathbf{v};p,i)=\bar{\mathbf{x}}\) where \(\mathbf{v}\in\mathbb{R}^{s}\) is a set of parameters that produce \(\bar{\mathbf{x}}\) at a given pitch \(p\) and intensity \(i\). However, in the general case, we know that if \(\mathbf{x}_{j}\
ot\in\mathcal{D}_{s}\), then \(\mathbf{x}_{j}=f_{s}(\mathbf{v})+\epsilon\) where \(\epsilon\) models the error made when trying to reproduce an arbitrary audio sample \(\mathbf{x}_{j}\) with a given synthesizer. Finally, we consider that some audio examples are annotated with a set of _categorical semantic tags_\(\mathbf{t}_{i}=\left\{0,1\right\}\), which define high-level perceptual properties that separate _unknown_ latent factors \(\mathbf{z}\) and _target_ factors \(\mathbf{t}\). Hence, the complete generative story of a synthesizer can be defined as  
\[p(\mathbf{x},\mathbf{v},\mathbf{t},\mathbf{z})=p(\mathbf{x}|\mathbf{v}, \mathbf{t},\mathbf{z})p(\mathbf{v}|\mathbf{t},\mathbf{z})p(\mathbf{t}| \mathbf{z})p(\mathbf{z}). \tag{8}\]  
This very general formulation entails our original idea that we should uncover the relationship between the latent audio \(\mathbf{z}\) and parameters \(\mathbf{v}\) spaces by modeling \(p(\mathbf{v},\mathbf{z})\). The advantage of this formulation is that the reduced dimensionality \(\mathbb{R}^{z}\ll\mathbb{R}^{x}\) of the latent \(\mathbf{z}\) simplifies the problem of parameters inference, by relying on a more adequate and smaller input space. Furthermore, this formulation also provides a natural way of learning _macro-controls_ by inferring \(p(\mathbf{v}|\mathbf{z})\), where separate dimensions of \(\mathbf{z}\) are expected to produce smooth auditory transforms. Interestingly, this can be seen as a way to learn the principal dimensions of audio variations in the synthesizer
## 7 Real-Time Experiment
### Control System for Charge-Amplifier Drift Compensation
The process closed-loop responses for the proposed PID\({}_{\text{b}}\), PID\({}_{\text{f2}}\), and PID\({}_{\text{fh}}\) controllers with a reference step change (\(r=0.5\)) and an artificially added process input step-disturbance (\(d=1\)) are shown in Figure 41. As can be seen, the proposed method results in the efficient control of drift compensation. The comparison illustrates the superior tracking performance of the PID\({}_{\text{f2}}\) and PID\({}_{\text{fh}}\) controllers. As expected, the disturbance-rejection performance of the PID\({}_{\text{f2}}\) and PID\({}_{\text{fh}}\) controllers is equal to that of the PID\({}_{\text{b}}\) controller with \(b=0\). This is confirmed by the criteria values presented in Figure 42. The IAT\({}^{2}\)E measure is, in general, not recommended for noisy measured signals (real plants). However, the process output noise was negligible in our case. Figure 41: The control system for the charge-amplifier drift compensation closed-loop response.
# Antioxidant Activity and Polyphenolic Content of North Macedonian Wines
## Materials and Methods
### Spectrophotometric Analyses
#### 2.3.7 Evaluation of the Antioxidant Activity by DPPH (2,2-Diphenyl-1-Picrylhydrazyl) Assay  
The original DPPH assay [19] slightly modified for a final volume of 1 mL was used [20]. Red wines were diluted 1:10 (v/v) with dH\({}_{2}\)O while white wines were used without dilution. The DPPH solution (100 \(\upmu\)M) was prepared by dissolving 3.60 mg of DPPH in 100 mL of EtOH. 25.0 \(\upmu\)L of the sample was mixed with 975 \(\upmu\)L of DPPH solution and left for 30 min in the dark before measuring the absorbance at 515 nm. A blank sample was also prepared by using EtOH instead of wine. Standard solutions of trolox (0.20-1.4 mmol/L) were used for the calibration and the %radical scavenging activity (%RSA) was calculated by:  
\[\%\text{RSA}=\frac{\text{Abs}_{\text{blank}}-\text{Abs}_{\text{sample}}}{ \text{Abs}_{\text{blank}}}\times 100\]  
and expressed as mmol/L trox equivalent (mmol TE/L).
## 3 Methodology
### Latency Evaluation over a Mobile Hot-Spot and Edge VPN  
Telerobotic operations may not always have strong network connections. In the case of rural or disaster-stricken areas, wireless access may be limited. Thus, the third test scenario we evaluated was the client connecting through a mobile hotspot over a cellular network, shown in Figure 5. This test also included the same Edge VPN, but further increased the network distance and restricted network resources compared to the previous test scenarios. The cellular network utilized in this test was a commercial 4G network, which added additional network routing through the cellular provider's backhaul network before being routed through the University's VPN. This also added complexity to session establishment compared to the previous Wi-Fi network test.
# Current Status and Economic Prospects of Alternative Protein Sources for the Food Industry
## 2 Alternative Protein Sources
### Plant-Based
Both intrinsic (protein content, amino acid profile, overall net charge, isoelectric point) and extrinsic (temperature, pressure, pH) factors govern the interaction between proteins and other molecules in food models ([PERSON] et al., 2022) [32]. Off-flavors, such as beany or grassy tastes, are usually perceived in PB proteins derived from pulses and green leaves, even after hydrolysis and concentration ([PERSON] et al., 2018; [PERSON] & [PERSON], 2019) [25; 33]. While studies have shown that off-flavors might be related either with the binding of proteins to flavor-inducing molecules under processing conditions (usually pH and temperature) during isolation/concentration, or with the oxidation of polyunsaturated fatty-acids from the plant sources ([PERSON] et al., 2022) [32], product development strategies are usually necessary to either reduce or mask inherited flavors. Texture, on the other hand, is dependent not only on the protein technological attributes, but also on the overall food model characteristics. Wettability, water/oil adsorption, foaming, gelation and emulsification capacities, as well as the ability of the protein to maintain these attributes during storage, shape the textural properties of the proteins in food model systems. Studies have shown that process conditions such as temperature and pH have major impact on plant proteins solubility and, consequently, on their ability to interact with oil droplets, air bubbles and create stable films, for instance ([PERSON] et al., 2022) [30]. The life cycle of plant-based proteins; however, is highly geographically dependent and influenced by national agricultural and transportation policies ([PERSON] et al., 2019) [5]. The technological development of high yielding crops, such as soybean and lentils, for example, is crucial for more efficient land use and water consumption ([PERSON] et al., 2023) [3]. Furthermore, technological development of crops and enhanced agricultural practices are expected to drive a significant change in the PB protein supply chain ([PERSON], 2022; [PERSON] et al., 2023; [PERSON] et al., 2020) [34; 35].
## 2 Materials and Methods
### Analytical Methods  
Measurement of immobilized dry algal biomass: After ten days of immobilized cultivation, the algal and substrate layers were harvested and dried at 105 \({}^{\circ}\)C for 2 h [15]. The dried product was cooled in a desicctor for 30 min and weighed. The drying process was repeated until an unchanged total weight of m\({}_{\rm b}\) was obtained. The dry biomass (per m\({}^{2}\) of biofilm) was calculated as M\({}_{\rm a}\) = (m\({}_{\rm a}\) - m\({}_{\rm b}\))/0.05 (g\(\cdot\)m\({}^{-2}\)), where m\({}_{\rm b}\) is the  
Figure 1: A chamber of a small-scale angled porous substrate photobioreactor (PSBR) was used in the experiments.  
weight of dry substrate layer without algae and 0.05 is the square of a biofilm area in the small-scale PSBR.  
Dry algal biomass productivity (g\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\)) was calculated based on equation M\({}_{\rm p}\) = (M\({}_{\rm a}\) - M\({}_{\rm i}\))/10 (where M\({}_{\rm a}\) is the dry biomass after ten days and M\({}_{\rm i}\) is the initial immobilized dry biomass).  
Measurement of carotenoid/chlorophyll ratio in the dry biomass: The carotenoid/chlorophyll ratio is an essential indicator of the physiological state of _H. pluvialis_, whether it is the green stage or the red stage [29]. The ratio of carotenoid to chlorophyll is about 0.2 in the green stage and increases in the red stage by order of magnitude to reach about 2-9 [30; 31]. The cells transforming green vegetative motile cells to green vegetative palmella cells have carotenoid/chlorophyll ratios of about 0.2-1.0 [32]. This ratio in the dry biomass was determined based on the method of [PERSON] et al., (1972). To 0.005 g of dry biomass, 2 mL of acetone 90% were added, and then the mixture was ground with a glass pestle and centrifuged at 800\(\times\)\(g\) for 5 min, and then the supernatant was collected. We continued adding acetone 90% to the residue, grinding, and repeated the process until the resulting solution was colorless. The volume was made up to precisely 10 mL with 90% acetone, kept in a 15 mL centrifuge tube, and covered tightly to limit the evaporation of acetone. The pigment extract was spectrophotometrically measured at 665, 645, 630, and 480 nm [33].  
Pigments were calculated according to formula:  
carotenoid/(chlorophyll(a + b)) = 4.0 \(\times\) E480/((11.6 \(\times\) E665 \(-\) 1.31 \(\times\) E645 \(-\) 0.14 \(\times\) E630) + (20.7 \(\times\) E645 \(-\) 3.4 \(\times\) E665 \(-\) 4.42 \(\times\) E630)), where E\({}_{\rm i}\) is the extinction value obtained at the respective wavelength i [33].  
Cell morphology determination: Cell samples were collected from the biofilm after ten days of culturing, suspended in an isotonic solution, fixed with 2% formaldehyde, and observed morphologically with a Nikon DS-Fi1 camera microscope (Nikon Corporation, Japan) to analyze different phases of the life history of _H. pluvialis_.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 2 M2D2 System Framework
### Multi-Modal Data Collection and Preprocessing  
M2D2 begins by rigorously consolidating numeric, textual, and categorical data, followed by a series of preprocessing and feature engineering tasks to align these variables in a single analytical space. Practically, this involves streaming pipelines or data buses that continually ingest sensor readings and maintenance logs and applying mechanisms to rectify noise and missing values in the raw records. For instance, when temperature or pressure data are absent, linear interpolation or autoencoder-based inpainting can be employed. Textual inputs, including maintenance logs or error messages, may be transformed into embeddings (e.g., via BERT or Word2 Vec), supplemented as needed by domain-specific dictionaries or keyword annotation. As for batch IDs or machine category fields, one-hot encoding or learned embeddings can preserve their discriminatory power while averting dimensional explosion. Finally, normalization methods like Z-score or min-max scaling standardize these diverse inputs, yielding multi-modal feature vectors for subsequent baseline modeling.
## 2 Study Area and Data  
### Study Area  
Xilinhot (Figure 1) is located in the hinterland of Xilingol, and covers an area of about 15,000 km\({}^{2}\) (43\({}^{\circ}\)02\({}^{\prime}\)-44\({}^{\circ}\)52\({}^{\prime}\)N, 115\({}^{\circ}\)13\({}^{\prime}\)-117\({}^{\circ}\)06\({}^{\prime}\)E). The elevation in this area shows a decreasing gradient from the southeast to the northwest. This area is characterized by a typical temperate continental semi-arid climate. Winters are cold and dry, where conditions are influenced by the air flow from the Mongolian plateau, whereas summers are wet and warm, where conditions are influenced by monsoons. This area has an average annual temperature of 0.5-1.0 \({}^{\circ}\)C and an annual mean precipitation of 350 mm. Precipitation is often distributed unevenly in the region, and droughts occur frequently [46]. In addition, 90% of this area is dominated by temperate steppe, and the typical growing season is from May to September. Grassland biomass shows a decreasing trend from southeast to northwest that follows the elevation transition in the study area [35; 47].
## 5 Conclusions  
Based on empirical measurements and model simulations, the effects of the leaf angle and chlorophyll content on LAI-sensitive narrow-band indices were examined. Kendall's correlation coefficients between LAI and the vegetation indices were between 0.34 and 0.64 for all the tested indices. The accuracy of the indices in estimating LAI was restricted by the variation in MTA and Cab. The relationship was stronger within specific canopy architectures (defined by a constant MTA), making it difficult to estimate LAI using VIs for areas covered by different vegetation types. Of the studied indices, we found BNDVI to be the least affected by the leaf tilt angle and chlorophyll content, and thus the most suitable one for retrieving LAI using remote sensing (\(\tau_{k}\) = 0.64 for empirical data). Nevertheless, the performance of all studied VIs in LAI estimation, including BNDVI, was affected by the leaf tilt angle, especially at LAI < 3. Most of the studied indices were suitable for monitoring the LAI of crops with a constant leaf angle distribution ([PERSON]'s tau \(\tau_{k}\) > 0.7 in the simulated dataset), with SR outperforming others in linearity and applicability to both measured and simulated data. In the future, more crop species with different leaf angle distributions, leaf pigment contents, contrasting canopy architectures, and different growth stages should be used to empirically validate the effects of leaf angle and Cab on LAI-sensitive indices, so that the results can be applied to a wider geographic region.  
**Author Contributions:** [PERSON] designed the paper and experiments, and carried out the fieldwork and image processing; [PERSON] designed and supervised the model simulations; [PERSON] carried out the analysis with [PERSON] and [PERSON]; [PERSON] supervised and managed the setup of the agricultural sites; [PERSON] designed the airborne flight campaign and wrote the manuscript with [PERSON], [PERSON] and [PERSON], which was commented on by [PERSON].  
**Funding:** This work was funded by Natural Science Foundation of Jiangsu Province, China (grant number BK20170950), the Innovation and Entrepreneurship Program of Jiangsu Province (grant number R2017 SCB02), Scientific Research Start-Up Funds of NUIST (grant number 2016R27), the Graduate school in airborne imaging spectroscopy application and research in Earth sciences of the University of Helsinki (AISARES), and the Academy of Finland (grant number 266152).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Wideband Termination Based on Laser-Scribed Lossy Microstrip Line Structures
## 2 Structural Design  
### Device Structure  
The termination of the signal is realized by introducing a resistive rGO film on the strip conductor of a conventional MS line, which forms a lossy transmission line structure to effectively absorb the signal, as shown in Figure 1.  
Figure 1: Illustration diagrams of the laser-scribed termination.  
On the other hand, adding resistive film typically enlarges the imaginary part of the impendence in several magnitudes and leads to a decrease in the real part. This naturally enlarges the impendence mismatch and therefore enlarges the reflections. Therefore, the resistive film is regulated to a trapezoid shape with its width which is consistent with that (\(w_{1}\)) of the strip conductor at the input end and gradually widens to \(w_{2}\) at the other end. This provides small gradual changes in impedance, as required in the small reflection theory, and is possible to shorten the length of the whole device. Table 1 lists the parameters of the trapezoid-shaped termination.
## 3 Results
### Validation on an Historically Contaminated Soil
#### 3.2.2 Cucurbita pepo  
The second set of experiments on AC2 amended soil was performed on _Cucurbita pepo_ plantations (n = 4) (Figure 4). Similarly, to environmental availability results, high reduction of NDL-PCBs transfer was observed for amended soil, except for PCB 101 for which all results, even for non-amended soil, were below LOQs. Minimal reduction levels derived from this assay were the following: (i) 94% (mean) for PCB 138, (ii) 96% for PCB 153 and (iii) 27 to 90% for PCB 180, confirming the reduction levels found on OECD soils.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & \multicolumn{2}{c}{**Mean Concentrations**} & \multicolumn{2}{c}{**Min-Max Concentrations**} \\  & & **in**\(\mathbf{\mathrm{\SIUnitSymbolAng}}\)**\(\mathbf{\mathrm{g}}^{-1}\) & **in**\(\mathbf{\mathrm{\SIUnitSymbolAng}}\)**\(\mathbf{\mathrm{g}}^{-1}\) & **(Number of** **Repetition)** & **Mean or Minimal** \\ Environmental availability & PCB101 & SS & 0.10 & all values \(<\) LOQ (n = 3) & \\  & & AC2 & 0.10 & all values \(<\) LOQ (n = 3) & 0\% a \\  & & AC3 & 0.10 & all values \(<\) LOQ (n = 3) & 0\% a \\  & PCB138 & SS & 0.96 & [0.84–1.03] (n = 3) & \\  & & AC2 & 0.16 & [0.10–0.29] (n = 3) & 83\% \\  & & AC3 & 0.10 & all values \(<\) LOQ (n = 3) & 90\% a \\  & PCB153 & SS & 1.09 & [0.96–1.29] (n = 3) & \\  & & AC2 & 0.10 & all values \(<\) LOQ (n = 3) & 91\% a \\  & & AC3 & 0.10 & all values \(<\) LOQ (n = 3) & 91\% a \\  & PCB180 & SS & 0.18 & [0.12–0.23] (n = 3) & \\  & & AC2 & 0.10 & all values \(<\) LOQ (n = 3) & 44\% a \\  & & AC3 & 0.10 & all values \(<\) LOQ (n = 3) & 44\% a \\ In vivo bioavailability & PCB138 & AC2 & 0.10 & all values \(<\) LOQ (n = 4) & 94\% a \\  & & SS & 1.73 & [0.86–2.73] (n = 5) & \\  & PCB153 & AC2 & 0.10 & all values \(<\) LOQ (n = 4) & 96\% a \\  & SS & 2.23 & [1.05–3.59] (n = 5) & \\  & PCB180 & AC2 & 0.10 & all values \(<\) LOQ (n = 4) & 90\% a \\  & & SS & 1.04 & [0.56–1.63] (n = 5) & \\ \hline \end{tabular}  
* a: Reduction factor.  
\end{table}
Table 2: Transfer reduction found on an historically contaminated soil.
## 2 Materials and Methods
### Methods
#### 2.3.2 Physical Model
Considering the complicated structure of multi-layer clouds and the limited penetration of infrared radiance to clouds, the proposed algorithm applied only to single-layer clouds in this study. The multi-layer cloud detection algorithm developed by [PERSON] et al. [13] is employed to classify the AHI pixels as single-layer clouds or multi-layer clouds. Moreover, previous studies have indicated that it was difficult to estimate CBH of deep convection clouds based on the relationship between CWP and CGT, due to the significant uncertainty in estimation of CWP for such cloud type [6; 31]. However, there has been an effective approach that uses convective condensation level and lift condensation level derived from numerical weather forecasting to infer the CBH of deep convection clouds [6]. Therefore, the cloud pixels classified as deep convection clouds (i.e., CTH \(\geq\) 6 km and CWP \(\geq\) 1.2 kg/m\({}^{2}\)) are also not considered in this study.
# Performance Comparison of ANFIS Models by Input Space Partitioning Methods
###### Abstract  
In this paper, we compare the predictive performance of the adaptive neuro-fuzzy inference system (ANFIS) models according to the input space segmentation method. The ANFIS model can be divided into four types according to the method of dividing the input space. In general, the ANFIS1 model using grid partitioning method, ANFIS2 model using subtractive clustering (SC) method, and the ANFIS3 model using fuzzy C-means (FCM) clustering method exist. In this paper, we propose the ANFIS4 model using a context-based fuzzy C-means (CFCM) clustering method. Context-based fuzzy C-means clustering is a clustering method that considers the characteristics of the output space as well as the input space. Here, the symmetric Gaussian membership functions are obtained by the clusters produced from each context in the design of the ANFIS4. In order to evaluate the performance of the ANFIS models according to the input space segmentation method, a prediction experiment was conducted using the combined cycle power plant (CCPP) data and the auto-MPG (miles per gallon) data. As a result of the prediction experiment, we confirmed that the ANFIS4 model using the proposed input space segmentation method shows better prediction performance than the ANFIS model (ANFIS1, ANFIS2, ANFIS3) using the existing input space segmentation method.  
Keywords:adaptive neuro-fuzzy inference system (ANFIS); input space partitioning method; context-based fuzzy C-means (CFCM) clustering; prediction +
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