esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 4 Artificial Intelligence in Production, the Fermentation Process, and Bottling Quality Control
### Monitoring Fermentation Variables
#### 4.1.1 Temperature Control  
Temperature is one of the most critical factors in the fermentation process. Maintaining an ideal temperature range is essential for yeast metabolism, preventing the formation of unwanted by-products, and ensuring consistent flavor profiles in the wine [75]. AI systems equipped with sensors and machine learning algorithms can monitor and adjust the temperature in real-time to maintain optimal conditions.  
Advanced AI systems integrate data from multiple sources, including ambient temperature, fermentation vessel temperatures, and even heat generated during the fermentation process. The comprehensive data analysis by AI can predict temperature fluctuations and make proactive adjustments to prevent deviations that could affect the fermentation process. This enables precise control over the fermentation environment, thereby reducing the risk of thermal shock or prolonged exposure to suboptimal temperatures [76].  
A study by [PERSON] et al. demonstrated the effectiveness of AI system that predicted temperature fluctuations and automatically adjusted cooling mechanisms to maintain the optimal fermentation temperature, resulting in improved wine quality and consistency [75].  
[PERSON] et al. also highlighted how integrated AI systems that incorporate data from multiple sources provide precise control of the fermentation environment, reducing the risk of thermal shocks and optimizing outcomes [76].  
Moreover, AI systems can also learn from historical fermentation data, enabling them to identify patterns and trends that may affect temperature stability. This learning capability allows the AI to continuously improve its predictive accuracy and control strategies, adapting to the specific needs of different grape varieties and fermentation conditions.
# A Study on Detection of Malicious Behavior Based on Host Process Data Using Machine Learning
## 2 Related Works
### Machine Learning Algorithms
#### 2.2.1 K-Nearest Neighbors (KNN)  
To detect anomalies in a dataset, it is necessary to measure the degree to which an anomaly is far from \"normal\" data points. KNN anomaly detection performs this task based on distance. Specifically, it finds the K-nearest neighbors of a given data point and measures the distance to them. If the distance between a given data point and its K-nearest neighbors is far, then the data point is considered an anomaly. Here, K is a hyperparameter that can be specified by the user. Generally, a smaller K value detects anomalies more sensitively, and a larger K value detects anomalies more conservatively. During anomaly detection, KNN calculates the distance as shown in Equation (1).  
\[d\big{(}x_{i},x_{j}\big{)}=\sqrt{\sum\
olimits_{k=1}^{n}\big{(}x_{i,k}-x_{j,k }\big{)}^{2}} \tag{1}\]  
To detect anomalies in the dataset, the distance from the \"normal\" data points of the anomalies must first be measured. KNN anomaly detection performs this based on distance. That is, it finds the K nearest neighbors to a given data point and measures the distance to them. If the distance between the given data point and its K neighbors is far, then the data point is determined to be an anomaly. Here, K is a hyperparameter that the user can specify. Generally, the smaller the value of K, the more sensitive it is to detect anomalies, and the larger the value of K, the more conservative it is to detect anomalies. When performing anomaly detection, KNN calculates distance using Equation (1), where \(x_{i}\) and \(x_{j}\) are two data points, and n is the number of dimensions of the data points. There are various ways to perform anomaly detection, but one of the representative methods is to use KNN distance. KNN distance uses the largest value among the distances between K neighbors. That is, it uses the distance to the K-th nearest neighbor. This can be used as an outlier score. The higher the anomaly score, the more likely the data point is an anomaly.
# Post-Traumatic Segmental Tibial Defects Management:
## 2 Materials and Methods
### Inclusion and Exclusion Criteria  
Inclusion criteria (Table 1) were defined prior to the literature search and were formulated according to the \"Population, Intervention, Comparison, Outcome, Study Design (PICOS)\" format. Studies were considered eligible if they met the following inclusion criteria. Population: cohorts with patients \(\geq\) 16 years old, suffering from post-traumatic segmental tibial defects; intervention: any type of distraction osteogenesis or Masquelet technique for the treatment of post-traumatic segmental tibial defects; comparison: patient cohorts treated with D.O. or Masquelet for post-traumatic segmental tibial defects; outcome: the primary outcome of interest was bone union rate. The secondary outcomes of interest were infection rate, the type and rate of other complications, and clinical and radiological outcomes. A mean follow-up of at least 1 year was required for inclusion. Study design: both experimental (randomized control trials--RCTs) and observational studies (case series or prospective or retrospective cohort studies) in English were included. Exclusion criteria were the following: (1) studies on segmental bone defects with an etiology other than trauma (e.g., infection, bone tumor, or pseudoarthrosis); (2) studies on post-traumatic segmental defects in bones other than the tibia; (3) studies that did not adequately report on at least the primary outcome of interest; (4) studies with an inadequate follow-up; (5) studies on patients \(<\) 16 years old; (6) case reports and studies with cohort sizes of 5 patients or less; (7) animal or biomechanical studies.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Inclusion Criteria:** & **Exclusion Criteria:** \\ \hline Post-traumatic segmental tibial defects & Defects following infection \\ \hline Age \(\geq\) 16 years & Defects following bone tumor \\ \hline More than 5 cases & Defects following pseudoarthrosis \\ \hline Mean follow-up \(\geq\) 1 year & Defects in bones other than the tibia \\ \hline Distraction osteogenesis and/or Masquelet technique & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inclusion and exclusion criteria for this study.
## 3 Calculation Methodology of the PVCS Carbon Impact
### GHG Assessment Methodology  
In order to assess the carbon impact of the PVCS, a method for calculating the GHG emission has been implemented. Based on the Bilan Carbone Association and a Massive Open Online Course (MOOC) organized by Avenir Climatique [27], a calculation method for assessing the carbon impact of PVCS has been established. These databases are used to collect the carbon emission coefficients associated with each emission element of the initially considered PVCS.  
The first step of this method is to define a study perimeter that sets the carbon emission limits that will be considered. Once this perimeter has been defined, the second step is to fill a data collection matrix, which contains the carbon emission coefficients associated with each emission element. These carbon emission coefficients are based on several references, including the \"Ecoinvent\" database [5], the study of national renewable energy laboratory in the LCA harmonization project [28], and the study presented in [29], in order to reduce the total carbon impact of the PVCS. The quantification of these carbon emission coefficients is studied in the carbon impact of a product defined in ISO 14067, and studied as part of this project.  
Based on these carbon emission coefficients, it is then possible to assess the carbon impacts of the different sub-systems that made up the PVCS, such as, the PV system, the stationary storage, the charging terminals, the infrastructure related to the structure of the PVCS, and finally the electricity from the power grid, supplied in addition to that produced by the PV system. The sum of these carbon impacts constitutes the global carbon impact of the PVCS, based on the LCA approach over 30 years.
## 5 Comparative Analysis  
This section summarizes the most relevant research in the area of recognizing and classifying a guitarist's finger motion and pattern and how it compares to our approach. The authors in [31] used chord classifiers through deep learning, reaching around 90 percent for 3 chords and going down to 70 percent. On the other hand, ref. [14] used CNNs to obtain the accuracy of left hand positions on the reftooard for single note picking, reaching an accuracy of 95, 96, 94, and 96 percent for the index, middle, ring, and pinky fingers consecutively. Other studies, such as [32], focused on using wrist-worn devices to recognize how much a guitarist moves their hands, which helped with analyzing whether the guitarist would make unnecessary movements. However, we believe that using any hardware device or wrist-worn gloves could impede the guitarist's movement. The difference between our study and the different studies using CNNs is that these studies only classified finger positions, treating their dataset as images. Inspired by the motivation in these studies and the methodology of research using the mediapipe framework [28], we extracted the finger positions as coordinates from our images, treating our dataset as a set of \(x\), \(y\), and \(z\) coordinates in space. From this point on, we were not only able to recognize the finger positions of the guitarist, but we were also able to classify whether the guitarist played correctly. The accuracies provided in our experimental results in Section 4 resulted in accuracies of 71, 97, 70, 98, and 90 percent on SVM (before tuning), KNN, naive Bayes, random forest, and XGBoosting, labeling the position as either correct or incorrect. To further enforce the accuracy of our system, we used signal detection to detect the note played whenever a guitarist plays a note. This opens other possibilities for training our ML models so that they are not only limited to one exercise but can also classify a guitarist's overall technique, which will be further mentioned in Section 8.
# Approaches for Dealing with Seasonality in Clinical Prediction Models for Infections
## 6 Limitations and Future Work  
Although we used interpretable models and techniques, the complexity of a final model can complicate its interpretation. For example, the best logistic regression models obtained for the _S. pneumoniae_ dataset had a mean of 35.31 features in our experiments, which might be overwhelming for an expert to understand and apply. Moreover, as discussed in Section 4.5, the use of multiple models in windows and ensembles impacts the interpretability of the overall model. However, we consider this trade-off acceptable due to the reduced number of models and their interpretation being clear in terms of how they are applied, i.e., we use a different model each month/season.  
Our discussion regarding the differences between each approach relies on the the graphical representations of the results and rankings of the combinations with best performance. While performing statistical tests among all possible combinations can provide additional insights and detect statistical relevance, it is challenging due to the large number of combinations and the complexity of the data being compared. In light of these limitations, we opted for a clearer and more manageable approach to analyzing and discussing the results.  
In our experiments, logistic regression models with LASSO usually obtained better results than those based on C5.0. However, it is important to note that decision trees offer a wide range of tuning possibilities, such as pruning heuristics and boosting, which were not extensively explored in this study. While further research should be carried out in order to determine the best interpretable model for a particular clinical problem, we believe that our experiments can provide valuable insights into the effects of seasonality techniques in both logistic regression and decision tree models.  
In our future work, we intend to study further variations of the approaches presented here. One straightforward extension would be to use different sizes of sliding windows depending on the month for which the model is being created. This would make it possible to use wider windows in months with a low number of samples, allowing the creation of more robust models, and narrower ones in months with abundant data, allowing the creation of more precise models.  
Furthermore, we intend to experiment with the adaptation of clinical datasets similar to those considered here for use with new algorithms developed for datastream mining, rather than adapting the algorithms to the datasets, which was the approach followed in this work.
# The Fungicidal Effect of Essential Oils of Fennel and Hops against Fusarium Disease of Pea
## 3 Results and discussion
### Determination of Antifungal Activity Ex Vitro
#### 3.2.1 Results of the Pot Experiment
The gDNA of _Fusarium culmorum_ was amplified in the qPCR assay by quantification of the _TRI5_ gene (Figure 2), which encodes the trichodia synthase that has been shown to catalyse the first step in the trichothecene pathway of _Fusarium_[81]. The use of the qPCR assay based on the quantification of the _TRI5_ gene can be used both as a tool to assess the level of infection of plant material, as shown by other studies [8; 9], and according to our results in this study, to indicate that the application of FEO and HEO reduces the concentration of DON, a group B trichothecene. The degradation of DON _in vitro_, with essential oils from other plants was presented in a study by [PERSON] et al. [82]. The severity of Fusarium wilt was highest in control plants, where the amount of _F. culmorum_ DNA reached 645 pg. The amount of _F. culmorum_ DNA in pea plants grown at a lower concentration (500 ppm) of FEO and HEO was determined to be 48.16 and 108.74 pg, respectively. Both oils had a stronger antifungal effect when applied at a concentration of 2000 ppm, and relatively low levels of _F. culmorum_ DNA were detected in the treated plants. The average amount of _F. culmorum_ DNA was determined to be 13.5 pg in the plants sprayed with FEO, while it was higher at 34.27 pg in the plants treated with HEO (Figure 2). Figure 1: Reduction in Fusarium wilt (_Fusarium culmorum_) by use different concentrations of essential oils from fennel and hops—disease index (%) (A,B,C,D,E—significant at _p_-0.01 by [PERSON]’s HSD test). Linear regression analysis showed that the results of the qPCR test were consistent with the disease index of Fusarium wilt. In the greenhouse experiment, a positive correlation was found between the symptoms of Fusarium wilt and the results of the qPCR quantification of _F. culmorum_ (_TRI5_ gene), which was confirmed by the linear relationship between the variables (y = 0.76x+ 25.5) and the value of Pearson's R (0.76) (Figure 3).
## 3 Results and Discussion
### Sensitivity of the Electrochemical Process to the Environment
The experiment was executed with different potentials applied during the adsorption phase (see legend); the elution was repeated each time with a potential switch to \(-800\) mV (horizontal line): (**a**) injection of 5 g/L maleic acid in DI-water; (**b**) injection of 5 g/L maleic acid in 10 mM phosphate buffer. The experiment was performed in column B with a potential switch at 30 mL. Figure 7 contains a plot of the percentages of the adsorbed maleic acid in DI-water and 10 mM phosphate buffer respectively, at different potentials. The use of an ionic mobile phase decreases the retention capacity for maleic acid. Thus, 92% of maleic acid is adsorbed with DI-water at +800 mV, while the hindrance due to the buffer ions reduces the value to just 43% maleic acid when a buffer is used. Furthermore, an exponential correlation between applied potential and column capacity is given within each mobile phase: \(0.062\times\mathrm{e}^{0.4\mathrm{x}}\) for DI-water and \(0.005\times\mathrm{e}^{0.6\mathrm{x}}\) for phosphate buffer. Additional potential-step experiments in three independent PCC columns using DI-water and 5 g/L maleic acid demonstrate the reproducibility of the results (Figure S16). All experiments show that if more than the minimal saturation amount of maleic acid is injected (the column is overloaded), over 80% of the molecules are adsorbed at +800 mV and are eluted when the potential switch to \(-\)800 mV takes place. Reducing the injection concentration of maleic acid from 5 g/L to only 2 g/L (column C, Figures S17 and S18), 100% of the maleic acid can be bound at +800 mV in DI-water. These results confirm that PCC works when using CNT-K as a stationary phase and working electrode. They also highlight the differences between water and buffer as mobile phase and that water offers advantages for higher adsorption capacity.
# A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images
## 3 Results and Discussion
### Dataset Requirement  
Referring to Table 1, the ranges used for the dataset in the training process with the ultrasound muscle images are 300 to 120,423 images. The minimum dataset without an image data solution was shown by [PERSON] et al. (2019) [37] with 713 images for measuring the area in urogenital hiatus and puborectalis muscle with CNN segmentation. The largest dataset without an image solution featured 1000 images for 3D image assessment with the U-Net architecture approach for segmentation [39]. The datasets used in the ten studies were derived from capturing B-mode ultrasound images. In addition, the use of segmentation in thyroid nodules can be used in real-time [64]. This allows the possible application of real-time deep learning with muscle ultrasound images.  
Difficulties with the number of images were common challenges among the two studies which did not use augmentation or transferred learning techniques with the muscle ultrasound images. In comparison to other artificial intelligence techniques, deep learning requires a higher amount of data, therefore the data quantity required for training needs to be known. Furthermore, data augmentation would not support geometries such as 3D images [65], except for automatically segmenting 3D volumes of biomedical images, where multidimensional gated recurrent units are used as the main layers of the neural network model. The method proposed involved on-the-fly data augmentation [66, 67] or manual augmentation of data in robust 3D models with different orientations and sizes [68]; however, gated recurrent units and automatic on-the-fly data augmentation requires further investigation in the context of muscle ultrasound images.
## 4 Simulation and Experiment
### Experimental Results
The experiment results almost follow the simulation results. Figure 19: Power and information transmission under different coil distance. (**a**) System efficiency with and without communication. (**b**) Measured bit error rate (BER) under different data rates. Figure 20: The fluctuations of secondary and primary currents with simulation and experiment under different coupling coefficients. (**a**) Forward communication. (**b**) Backward communication.
# Utilizing Graphite Waste from the Acheson Furnace as Anode Material in Lithium-Ion Batteries
## 3 Results and Discussion
### Characterization of SGW9, SGW9@RCT, and CAM as Anode Materials for LIBs  
To confirm the electrochemical performance of SGW9, SGW9@RCT, and CAM, we prepared coin-type lithium-ion battery (LIB) cells using SGW9, SGW9@RCT, or CAM as anode materials (Figure 7). For objective comparison, the mass loading and electrode density were controlled (Table S6). The mass loading of the anode materials was controlled within the range of 10.2 to 10.6 mg/cm\({}^{2}\), and the electrode density was maintained between 1.53 and 1.55 g/cc.  
As shown in Figure 8 and Table 4, the prepared LIB cells exhibited typical charge-discharge behavior during the half-cell tests. Specifically, the open-circuit voltage (OCV) of all cells exceeded 3.0 V, which is higher than the standard reduction potential of lithium. This confirms the successful fabrication of all LIB cells. We then calculated the ICE and capacity of the LIB cells. For ICE, SGW9, SGW9@RCT, and CAM exhibited values of 82.1%, 85.1%, and 93.9%, respectively. These results demonstrate that the applied carbon coating process improved the ICE of GW, although it remained lower than that of CAM. Given the inverse relationship between ICE and SSA, the higher SSA of SGW9 and SGW9@RCT resulted in lower ICE compared to CAM. However, with respect to capacity, the samples derived from GW outperformed CAM. The capacities of SGW9, SGW9@RCT, and CAM were 352.1 mAh/g, 348.9 mAh/g, and 348.3 mAh/g, respectively. This superior capacity can be attributed to the inherent high crystallinity of the starting material, GW. In other words, the developed SWG9@RCT demonstrates a specific capacity comparable to that of CAM, a high-end grade commercial anode material, while exhibiting an ICE of 85.1%. These findings highlight the practical applicability and mid-to-high-quality performance of SWG9@RCT as a sustainable and cost-effective alternative for LIB anodes. Consequently, while the anode samples derived from GW may face challenges in achieving high efficiency, they exhibit significant potential as high-capacity anode materials. It was concluded that GW from the Acheson furnace holds promise for conversion into commercially viable high-capacity anode materials.  
Figure 7: Preparation of LIBs using SGW9, SGW9@RCT, or CAM as anode materials.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **D50 1 (\(\mu\)m)** & **Span (-)** & **SSA 2 (m2/g)** & **TD 3 (g/cc)** \\ \hline SGW9@RCT & 9.6 & 1.00 & 2.4 & 0.83 \\ \hline \hline \end{tabular}  
* Average particle size; 2 specific surface area calculated using the Brunauer–Emmett–Teller (BET) equation; 3 tap density.  
\end{table}
Table 3: Average particle size, span, specific surface area, and tap density of JMGW and SGWXs.
# Carcass Yield and Meat Quality of Broiler Chicks Supplemented with Yeasts Bioproducts
## 3 Results
### Texture Profile Analysis
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline
**Group** & **SC** & **RM** & **Hard** & **Adhe** & **Cohe** & **Spri** & **Gumm** & **Chew** & **Resi** \\ \hline
**1** & **0** & **No** & 4124.82 & 0.458 & 0.335 & 2.563 & 1732.19 & 30.36 & 0.209 \\
**2** & **0.6** & **No** & 4189.36 & 0.444 & 0.307 & 2.611 & 1620.02 & 28.40 & 0.196 \\
**3** & **1** & **No** & 3953.39 & 0.434 & 0.343 & 2.739 & 1713.82 & 30.04 & 0.207 \\
**4** & **1.3** & **No** & 3915.09 & 0.492 & 0.338 & 2.537 & 1668.12 & 29.24 & 0.202 \\
**5** & **0** & **Yes** & 4022.42 & 0.463 & 0.310 & 2.737 & 1573.22 & 27.58 & 0.190 \\
**6** & **0.6** & **Yes** & 4264.88 & 0.414 & 0.330 & 2.512 & 1775.92 & 31.13 & 0.215 \\
**7** & **1** & **Yes** & 4108.10 & 0.438 & 0.325 & 2.597 & 1677.89 & 29.41 & 0.203 \\
**8** & **1.3** & **Yes** & 4119.53 & 0.468 & 0.304 & 2.652 & 1565.94 & 27.45 & 0.189 \\
**SEM** & & & 246.213 & 0.038 & 0.015 & 0.084 & 99.064 & 1.736 & 0.012 \\ \hline \multicolumn{2}{l}{\(p\) value} & \multicolumn{3}{c}{0.929} & \multicolumn{1}{l}{0.945} & \multicolumn{1}{l}{0.279} & \multicolumn{1}{l}{0.186} & \multicolumn{1}{l}{0.425} & \multicolumn{1}{l}{0.425} & \multicolumn{1}{l}{0.425} \\ \hline \end{tabular}
\end{table} 
Table 6: Yeasts supplemented broilers’ breast meat texture profile. No considerable interaction (SC \(\times\) RM) was observed on the broiler breast or thigh meat texture profile.