esa-sceva/satcom-chunk-collection huggingface dataset:
# Wastewater Treatment with Bacterial Representatives of the _Thiothrix_ Morphotype
## 3 Methods for Identification of _Thiothrix_ in Wastewater Treatment Plants
### Enhanced Biological Removal of Phosphorus
nov. A52, obtained from other geographical locations in Russia--a coal mine, the White Sea littoral and a sulfur spring in the Volgograd region [94]. According to a summarized pangonomic analysis, the core genome of 12 _Thiothrix_ species (_T. winogradskyi_ CT3T, _T. lacustris_ BLT, _T. litoralis_ AST, _T. subterranea_ Ku-5T, _T. calafiontis_ G1T, _T. uuzii_ A1T, _T. nivea_ JP2T, _T. fructsoivorans_ QT, _'Ca._ Thiothrix moscovensis' RT, _'Ca._ Thiothrix anitrata', _'Ca._ Thiothrix sulfatifiontis' KT, MAG of _Thiothrix_ sp. 207) contains genes for the main systems of phosphate metabolism, with the exception of only one of the three genes of the cellular phosphate transport system, _pstB_, which may be absent in some species [4]. As can be seen from the recent publications, almost all representatives of the genus _Thiothrix_, with a few exceptions, have genes for the signal regulation of Pi import into the cell, namely _phoUR_ or _phoURB_, encoding a system of two or three corresponding proteins PhoU, PhoR and PhoB, as well as genes of Pi transporters, _pstDCABS_, _pit_ and _phmcDE_; polyphosphate degradation and synthesis, _ppk1_ and _ppk2_; exopolyphosphatases, _epp_ and _ppx_; and poly-AMP phosphotransferase, _pap_ (Figure 4).
## 4 Conclusions  
The problem of orbital debris is, indeed, a multi-faceted problem with technical, political, social, and legal implications. Of the 36,000+ trackable objects in low Earth orbit, just a few thousand are working spacecraft. The rest are Earth-orbiting objects which are no longer operational and are considered to be space junk. Because this junk can no longer receive maneuvering commands from their Earth-based owners, the survivability of other spacecraft traveling through or operating in Earth orbit can be jeopardized by the impacts of any number of pieces of this space junk whose origins can be traced back to these defunct satellites. As a result, a major design parameter for Earth-orbiting spacecraft is the possibility of such high-speed impacts and the damage they can cause. Furthermore, several private companies are now launching several thousand spacecraft into Earth orbit, many of which are satellites built for communication purposes. Other satellites have been launched to expand the reach of the World Wide Web, provide better tools for disaster management, and to increase the availability of opportunities for additional collaborations among astronomers. Several topics related to space debris have been discussed in this paper, including issues in space law that concern the growing problem of orbital debris. Several key areas of concern have been noted, involving key legal questions in areas related to space traffic management that have been created by the increased congestion of Earth's orbital environments.  
This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The author declares no conflicts of interest.
# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 5 Results and Discussion  
### Overall Performance Comparison  
Our comprehensive evaluation demonstrates that UC-CMAF achieves superior performance across multiple dimensions while maintaining high interpretability standards. Table 2 and Figure 2 present comprehensive performance comparisons between our proposed UC-CMAF and various baseline methods on both the ReCOVery and MMCoVaR datasets, with evaluations using precision, recall, and F1 Score metrics across 15 representative multimodal fake news detection methods. UC-CMAF consistently achieves the best performance across all evaluation metrics, demonstrating its superiority in multimodal fake news detection. Specifically, on the ReCOVery dataset, UC-CMAF achieves an accuracy of 0.927, precision of 0.916, recall of 0.872, and an F1 Score of 0.894, outperforming all other models and surpassing the strongest baseline DGExplain (F1: 0.873) by 2.1%. On the more challenging MMCoVaR dataset, UC-CMAF further improves its performance with the highest precision of 0.939, recall of 0.881, and F1 Score of 0.909, exceeding DGExplain (F1: 0.881) by a margin of 2.8%. In contrast, traditional machine learning methods such as SVM-Based and Random Forest perform significantly worse with F1 Scores of only 0.679/0.700 and 0.723/0.736, respectively, while single-modal methods like BERT-only show relatively stable results (F1: 0.803 and 0.817) but still fall short of UC-CMAF, andunimodal baselines such as ResNet-only achieve even lower F1 Scores (0.661 on ReCOVery; 0.674 on MMCoVaR), underscoring the limitations of relying solely on either text or image modalities. Some multimodal methods such as dEFEND and EANN (F1: 0.823/0.838 and 0.824/0.814, respectively) also underperform in comparison, and several approaches including BTIC, SAFE, and ExFaux exhibit noticeable fluctuations in performance across datasets, suggesting limited generalizability. UC-CMAF maintains strong and consistent performance on both datasets, validating its robustness and ability to generalize across different scenarios with different semantic and stylistic characteristics. These findings confirm that UC-CMAF's unified cross-modal attention mechanism enables it to effectively model fine-grained interactions between textual and visual modalities, leading to superior detection performance in complex multimodal fake news detection tasks, with the visual annotations clearly demonstrating the consistent superiority of the UC-CMAF approach over all baselines. Figure 3 presents the accuracy and F1 Score trends of 15 representative models on the ReCOVery and MMCoVaR datasets. Overall, models that leverage multimodal fusion strategies consistently outperform unimodal baselines such as BERT-only and ResNet
# Random Frequency Division Multiplexing
## 2 Preliminaries  
### OFDM Modulation  
A symbol vector \(\mathbf{s}\in\mathbb{C}^{N\times 1}\) in the frequency domain is modulated by the inverse fast fourier transform (IFFT) to generate a time-domain signal \(\mathbf{x}\),  
\[\mathbf{x}=\mathbf{F}_{\mathbf{N}}^{\mathbf{H}}\mathbf{s}, \tag{1}\]  
where \(\mathbf{F}_{\mathbf{N}}\) denotes the \(N\)-point normalized FFT matrix and \(\mathbf{F}_{\mathbf{N}}^{\mathbf{H}}\) is its conjugate transpose matrix. Then, a cyclic prefix (CP) is added to \(\mathbf{x}\) and transmitted over mobile time-varying channels. After OFDM demodulation, the received signal is  
\[\mathbf{y}=\mathbf{F}_{\mathbf{N}}(\mathbf{H}\mathbf{x}+\mathbf{v}), \tag{2}\]  
where \(\mathbf{H}\) is a circulant channel matrix and \(\mathbf{v}\sim\mathcal{CN}(\mathbf{0},\sigma^{2}\mathbf{I})\) is additive white Gaussian noise (AWGN).
## 2 Materials and Methods
### Input Data  
This section outlines the key input parameters that underpin the energy analysis and simulation framework. This approach combines comprehensive meteorological data with detailed load demand records. The meteorological datasets, obtained from NASA's Prediction of Worldwide Energy Resource database as monthly averages, are interpolated within HOMER to produce hourly estimates for precise simulations. Meanwhile, the load demand data were gathered from local distribution meters throughout Aras de los Olmos, subsequently quality-controlled, and aggregated into hourly, daily, and monthly profiles to ensure a realistic representation of consumption behavior.  
Additionally, specific inputs, including the biomass feedstock derived from regional livestock waste, are included to assess the biogas reactor's performance and model its contribution to renewable energy generation. All datasets undergo careful preprocessing and synchronization to support reliable energy generation, storage, and system efficiency simulations. The following sections delve deeper into each input parameter and illustrate how these data collectively inform the system's design and optimization.
## 3 Results and Discussion
### Study of the Diatomic Influence from Zhalpak Deposit on the Properties of Products  
Table 1 presents some results from the determination of the properties of materials including pure loam and diatomic from the Zhalpak deposit.  
After burning at 1000 \({}^{\circ}\)C, the pure loam sample had a density of 1601 kg/m\({}^{3}\) and a compressive strength of 10 MPa. After burning at 1100 \({}^{\circ}\)C, the pure loam sample had a density of 1647 kg/m\({}^{3}\) and a compressive strength of 17.46 MPa.  
The study of the influence of diatomic additives from the Zhalpak diatomic deposits showed that with an increase in the amount of diatomic in the sample compound, regardless of the burning temperature, in general, a reduction in the compressive strength and average density of the samples was observed. So, starting with the additive of 15% to 50% diatomite, the average density of the samples decreased from 1560 to 1283 kg/m\({}^{3}\). For the ceramic masses containing 20-30% diatomite, conditionally effective wall materials with an average density of 1410 to 1550 kg/m\({}^{3}\) and a compressive strength of 10 to 14 MPa were obtained.  
When studying the effect of diatomic from the Zhalpak deposit on the properties of loam, it was found that the 15-20% addition increased the strength and a decreased the density of the products. Similar results were obtained in [11], where the addition of 10, 15, and 20% diatomite to loam allowed products corresponding to the density of the conditionally efficient and strength grades of M 75 and M 100 to be obtained. The average density of the products was reduced after the addition of diatomite due to its low bulkdensity, which is, on average, 800 kg/m\({}^{3}\), and, in this case, the burning temperature at which there was incomplete sintering of the ceramic tiles.
###### Abstract  
This paper developed a Soil Moisture Forecasting (SMF) model and a Constant-moisture Automatic Irrigation System (CAIS). The SMF model used the soil moisture data at different depths in an experimental plot inside a greenhouse to infer the soil moisture level after a specific interval. CAIS integrated the SMF data with dynamic watering interval adaption to maintain soil moisture at a constant level. Most intelligent irrigation products incur high installation costs that farmers cannot afford. CAIS used a low-cost component to achieve the same functionality that is found in intelligent irrigation products. Most low-cost irrigation systems water the plants from a single point that may lead to variable soil moisture if the terrain or the soil density is uneven. CAIS divided the experimental plot into multiple virtual planting areas (VPAs) and dynamically adapted the watering interval of each VPA to balance the soil moisture of the whole experimental plot. Results showed that the forecasting error of the SMF model was less than 12 moisture levels over a scale of 1024 levels. Furthermore, CAIS maintained the soil moisture of the whole experimental plot at a constant level within \(\pm 5\) error points with multiple watering points.  
Soil Moisture Forecasting (SMF) model; segmented regression function; Virtual Planting Areas; wireless sensor networks +
Footnote †: journal: Applied Soft. 2020, 10, 6352; doi:10.3390/app10186352
# Technology-Economy-Policy: Biochar in the Low-Carbon Energy Transition--A Review
## 7 Economic Assessment  
The economic feasibility of biochar as a versatile material in agriculture, energy production, and environmental protection has been widely discussed in the recent literature. In agricultural contexts, biochar has shown promise for improving soil health, increasing crop yields, and reducing dependency on chemical fertilizers. Research conducted on Mediterranean crops has indicated that biochar significantly boosts plant growth and grape production, with the cost-benefit ratio yielding returns within 1 to 4 years, depending on the biochar cost and the type of crops used. Similarly, studies on maize cultivation suggest that biochar can lower the costs associated with fertilizers and irrigation, with a payback period ranging from 2.8 to 5.5 years, contingent upon the price of biochar and the reductions in CO\({}_{2}\) emissions. However, not all studies confirm the economic advantages of biochar. For instance, in soybean production in Poland, its application was found to be less cost-effective than traditional fertilization methods unless carbon credit incentives were factored in. These results highlight the need for localized economic assessments that consider specific agricultural practices and regional market conditions when evaluating biochar's profitability. Additionally, the production costs of biochar vary widely based on the type of biomass used and the methods of production, with estimates in a wide range. This underscores the importance of developing cost-efficient production techniques to make biochar economically viable. Ultimately, while biochar has the potential to offer significant economic benefits, its widespread implementation requires the careful consideration of both local economic circumstances and environmental factors [209; 216; 217; 218; 219].
## 5 Experiments and Results
### Methodology
#### 5.2.3 Finding the Best Model Topology  
We compare several variations of the topology of Model A: replacing the last fully connected layer with a CRF layer, or changing the number of biLSTM layers (see the part \"Topology modifications\" in Table 11). Eventually, a combination of dictionary features, emotion markers, 3-layer LSTM and CRF achieves the highest accuracy for ADR and disease entities. For medication, the combination of ELMo and 3-layer LSTM shows slightly better results. This is therefore the accuracy level of Model A (see \"Model A--Best combination\" in Table 11).  
Then, in order to evaluate the effectiveness of XLM-RoBERTa-large, we run it without additional input features, as described in Section 4.3.2 (see the last row in Table 11). Overall, XLM-RoBERTa-large outperforms all the experiments with Model A, and so we use it as the basis for Model B (described in Section 4.3.3).
# Design, Synthesis and Antifungal Activity of Novel Benzoylcarbamates Bearing a Pyridine Moiety
###### Abstract  
Many natural and synthetic pyridine derivatives have good biological activity, and are widely used in the fields of pesticides and medicines. On the other hand, carbamate fungicides possess some unique properties, such as high efficiency, strong selectivity, low toxicity, and environmental friendliness, and are often used to control many plant diseases. Therefore, discovering novel pyridine-based carbamates is of great significance. In this paper, we chose the excellent fungicides tolprocarb and picarbutrazox as lead compounds, integrating benzoyl, carbamate, and pyridinyl moieties into a molecule. Thus, we designed and synthesized a series of substituted benzoyl carbamates containing a pyridine ring, and evaluated the in vitro antifungal activity. The target compounds exhibited moderate to strong bioactivity against _Botrytis cinerea_, among which the compounds **4d**, **4f**, **4g**, and **4h** exhibited significant activity with EC\({}_{50}\) values (the concentration resulting in a 50% inhibition) of 6.45-6.98 \(\upmu\)g/mL, and their activities were near or superior to that of chlorothalonil. Additionally, **4h** exhibited moderate activity against _Sclerotinia sclerotium_ with an EC\({}_{50}\) value of 10.85 \(\upmu\)g/mL.  
pyridine; benzoyl; carbamate; synthesis; bioactivity; antifungal activity +
Footnote †: journal: Applied Sciences
# Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach
## 2 Methods
### Data Processing and Classification
#### 2.1.3 Knn-Ls-Svm  
While global SVMs consider the same weight for all training instances in the optimization process, local learning approaches allow for training samples near a test point to be more influential than others. Localized approaches of SVMs [31] are based on weighting functions \(\lambda(x_{s},\ x_{i})\) that express the similarity between the features vectors of the \(i^{\text{th}}\) data point \(x_{i}\) and the test instance \(x_{s}\). For an LS-SVM, this leads to the following cost function:  
\[\min_{w,\ b;\ e}\ \frac{1}{2}\ \mathbf{w}^{\text{T}}\mathbf{w}+\ \gamma\sum_{i=1}^{N} \lambda(x_{s},\ x_{i})e_{i}^{2} \tag{6}\]  
such that  
\[y_{i}\big{(}\mathbf{w}^{\text{T}}\varphi(x_{i})\ +\ b\big{)}\geq\ 1\ -\ e_{i}\ \text{and}\ e_{i}\ \geq\ 0,\ i\ =\ 1,\ 2,\...,\ N. \tag{7}\]  
For KNN-LS-SVM a binary valued similarity criterion:  
\[\lambda(x_{s},\ x_{i})\ =\ \left\{\begin{array}{c}1\ \text{if}\ \big{\|} \varphi(x_{s})-\ \varphi(x_{i})\big{\|}^{2}\ \leq\ r_{s}\\ 0\ \text{otherwise}\end{array}\right. \tag{8}\]  
where \(r_{s}\) is the \(K^{\text{th}}\) smallest distance among \(\{\|\ \varphi(x_{s})-\ \varphi(x_{i})\|;1\leq j\leq N\}\). This formulation leads to the hybrid KNN-LS-SVM method [31]. In practice, implementing the hybrid classifier of KNN-LS-SVM, as shown in Figure 1, starts with receiving an unlabelled new test point \(x_{s}\) and finding the nearest \(K\) points from the training set in the feature space. Based on the nearest \(K\) points, an LS-SVM model is trained only with the new subset, hence, for each test point a dedicated model is trained. The advantage of this localized approach is that it can enhance the classification performance in case of class imbalance, in addition to the computational and temporal efficiency especially for online modelling and streaming analytics. For more detail concerning localized learning, reference [31] includes a detailed explanation of the algorithms.  
Figure 1: A flow chart illustrating the algorithm of K-Nearest Neighbours Least Squares Support Vector Machines (KNN-LS-SVM) classifier.
## 4 Results and Discussion
### Audograms' Population Clusters Evaluation  
The clustering algorithm is applied to the data set generated as described in Section 3.4.2. The Original data size was generated (25,307 audiograms) to represent the selected 55 audiograms with the corresponding associated percentages of the total population (28,244 audiograms). Then, the authors applied a spectral clustering algorithm with the same input arguments used earlier to these numbers of audiograms. Still, the analysis failed to generate a similarity matrix to cluster this large data, reflected by the high negative silhouette coefficient of value \(-0.6012\). This result matches the findings from the literature, and the spectral clustering technique is not practical for large data [41; 42; 43; 44]. Next, the authors annotated the generated 25,307 audiograms with the produced 8 and 10 clusters. The wrongly assigned ones with negative Silhouette coefficient were removed in stage 2. Stage 3, has only audiograms with positive Silhouette coefficients in their clusters. Hence, 20,956 audiograms were annotated using 8 clusters in stage 3, and 22,002 audiograms were annotated using 10 clusters. Figure 5 shows Silhouette plots of stage 1 and 3 for 8 clusters, while Figure 6 shows the results of stage 1 and stage 3 for the 10 clusters. Table 5 summarizes criteria evaluation values for both numbers of cluster. as it can be seen from the results that the SI values are higher than 0.5 which indicates good performance of the algorithm in both cases 8 clusters and 10 clusters. At stage 3, the data is cleaned and the SI, CHI, and DBI have the best values. These criteria values are better for eight clusters.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Criterion** & **Stage 1** & **Stage 2** & **Stage 3** \\ \hline No. of audiograms & 55 & 51 & 49 \\ Silhouette Criterion Values & 0.3814 & 0.4239 & 0.4461 \\ Calinski-Harabasz Criterion Values & 34.612 & 37.3905 & 37.3974 \\ Davies-Bouldin Criterion Values & 1.0424 & 1.0329 & 1.0234 \\  & 0 & 0 & 0 \\  & 0.0503 & 0.0284 & 0.0305 \\  & 0.1312 & 0.1010 & 0.0991 \\  & 0.2078 & 0.1735 & 0.1403 \\ Eigenvalues & 0.2750 & 0.2665 & 0.2790 \\  & 0.3693 & 0.3155 & 0.3239 \\  & 0.4153 & 0.3589 & 0.3620 \\  & 0.5060 & 0.4045 & 0.4607 \\  & 0.5686 & 0.5086 & 0.5045 \\  & 0.6261 & 0.5624 & 0.5625 \\ \hline \end{tabular}
\end{table}
Table 4: Different clustering evaluation criteria for 10 clusters.  
Figure 4: Silhouette plots for 10 clusters.
## 1 Introduction
### Paper's Organization  
The structure of the paper is outlined as follows: Section 2 presents the proposed ISAC system model, exploring the core concept with introductory insights into direct sequence spread spectrum technology. Section 3.1 evaluates the ISAC system performance through BER for the communication system and peak-to-sidelobe (PSL) ratio of the radar correlator output. Section 3.2 includes the system model using the photonics technology and presents results obtained using specialized photonics-based software and lab experiments. Section 4 concludes with final remarks.
# Laser Intensity Noise Suppression for Preparing Audio-Frequency Squeezed Vacuum State of Light
###### Abstract  
Laser intensity noise suppression has essential effects on preparation and characterization of the audio-frequency squeezed vacuum state of light based on a sub-threshold optical parametric oscillator (OPO). We have implemented two feedback loops by using relevant acousto-optical modulators (AOM) to stabilize the intensity of 795-nm near infrared (NIR) fundamental laser and 397.5-nm ultraviolet (UV) laser generated by cavity-enhanced frequency doubling. Typical peak-to-peak laser intensity fluctuation with a bandwidth of \(\sim\)10 kHz in a half hour has been improved from \(\pm\)7.45% to \(\pm\)0.06% for 795-nm NIR laser beam, and from \(\pm\)9.04% to \(\pm\)0.05% for 397.5-nm UV laser beam, respectively. The squeezing level of the squeezed vacuum state at 795 nm prepared by the sub-threshold OPO with a PPKTP crystal has been improved from \(-\)3.3 to \(-\)4.0 dB around 3-9 kHz of analysis frequency range.  
laser intensity noise; feedback loop; squeezed vacuum state of light; acousto-optical modulator (AOM); audio frequency +
Footnote †: journal: Applied
## 3 Results and Discussion  
### Characterization of the Adsorbents  
#### 3.1.1 Functional Groups on MOP and AC-MOP  
The surface functional groups of the MOP and AC-MOP adsorbents were obtained using FTIR spectroscopy; the results can be viewed in Figure 2. The MOP showed more peaks than the AC-MOP. The intense and broad peak at 3286 cm\({}^{-1}\) resembles the -OH stretching vibrations of cellulose, hemicellulose, lignin and the absorbed water [19]. The two bands at 2920 and 2851 cm\({}^{-1}\) were due to -CH\({}_{3}\) and -CH\({}_{2}\), respectively. The ketonic group -C=O was detected at 1759 cm\({}^{-1}\)[28]. The 1640 and 1600 cm\({}^{-1}\) doublet peaks indicated -NH\({}_{2}\)[29]. The band at 1001 cm\({}^{-1}\) is attributed to the C-O stretching vibrationof ether and ester [30]. After carbonization of the MOP, most of the peaks disappeared whilst others blue-shifted, and others had less intensity. The peak at 3301 cm\({}^{-1}\) is assigned to the -OH group. The peak detected at 2915 cm\({}^{-1}\) is attributed to the aliphatic -C-H stretching vibrations in lignin, cellulose and hemicelluloseoses. The -C=O group was observed at 1709 cm\({}^{-1}\). The 1577 cm\({}^{-1}\) peak represented the presence of COOH vibrations [30]. The absorption peak at 1046 cm\({}^{-1}\) was due to the C-O stretch. The disappearance and shifting of the OH, C=O, C-O and NH\({}_{2}\) functional groups' peaks suggest an interaction between AC-MOP and the pollutants [31].