esa-sceva/satcom-chunk-collection huggingface dataset:
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 6 Classifier Maintenance
### Update Mechanisms
#### 6.3.1 Updating Mode  
Generally, the learning of data stream models can be differentiated between the incremental adaptation of the old, or the complete retraining of new models [4]. In the latter case, new training instances must be collected over time. Like the typical batch learning approach, the old model is discarded and succeeded by a new one, which is similar to the landmark time window approach. Complete retraining is often conducted by models adapted from the traditional batch setting.  
In the incremental case, the labeled data points are fed into training one by one. Well-known examples are the VFDT, where new examples are added and updated leaf statistics, and the STAGGER algorithm [53; 127].  
The update itself can affect the complete model (called global replacement), or just a part of the model (called local replacement) [4]. Whereas decision trees or decision rules can be updated partially, other algorithms, like Naive Bayes, must be retrained as a whole.
# Effect of Extraction Method and Thermosonication on Apple Juice Quality
## 1 Introduction
The primary purpose of the study was to assess the influence of juice extraction and ultrasound treatment (thermosonication) methods on the content of microorganisms in juices stored under refrigeration and in ambient temperature. An additional purpose was to evaluate the influence of ultrasonic treatment on selected parameters of apple juice quality: cloud value, antioxidant activity, total phenolic content, flavonoid content, and anthocyanin content.
# Hybrid Fuzzy-DDPG Approach for Efficient MPPT in Partially Shaded Photovoltaic Panels
## 2 Materials and Methods
### Reinforcement Learning Fuzzy-DDPG
The actor network processes the fuzzy feature vector \(\phi(s)\) obtained from the state \(s\). It comprises two fully connected hidden layers with ReLU activations, and an output layer with a bounded activation function (e.g., tanh) to produce the action:  
\[a=\mu(\phi(s);\theta_{\mu}) \tag{13}\]  
The critic network receives the concatenated fuzzy feature vector and the action, and processes this input through two hidden layers with ReLU activations to output the scalar Q-value:  
\[Q(\phi(s),a;\theta_{Q}) \tag{14}\]  
The fuzzy feature vector \(\phi(s)\) captures the essential characteristics of the state space, facilitating learning in continuous domains. By integrating fuzzy basis functions into the actor and critic networks, we aim to approximate both the policy and value function using fuzzy features, enabling efficient navigation of the complex power landscape and global Maximum Power Point tracking under partial shading conditions. The training procedure follows the standard DDPG algorithm, with the additional incorporation of fuzzy basis functions, according to Algorithms 1 and 2. ```
1:repeat
2: Compute Fuzzy Features: \(\phi(s_{t})\) from current state \(s_{t}\). 3: Select Action: \(a_{t}=\mu(\phi(s_{t})|\theta^{\mu})+\mathcal{N}_{t}\), where \(\mathcal{N}_{t}\) is exploration noise. 4: Execute Action: Receive reward \(r_{t}\) and next state \(s_{t+1}\). 5: Store Transition: Store \((s_{t},a_{t},r_{t},s_{t+1})\) in \(\mathcal{D}\). 6:until Stopping Criterion is Met
```  
**Algorithm 1** At each learning step  
```
1:repeat
2: Sample Mini-Batch: Randomly sample \(N\) transitions from \(\mathcal{D}\). 3: Compute Targets: \[y_{i}=r_{i}+\gamma Q^{\prime}(\phi(s_{i+1}),\mu^{\prime}(\phi(s_{i+1})|\theta^{ \mu^{\prime}})|\theta^{Q^{\prime}})\]
4: Update Critic: Minimize loss \(L\): \[L=\frac{1}{N}\sum_{i=1}^{N}\Bigl{(}y_{i}-Q(\phi(s_{i}),a_{i}|\theta^{Q}) \Bigr{)}^{2}\]
5: Update Actor: Update policy parameters using gradient ascent: \[\
abla_{\theta^{\mu}}J\approx\frac{1}{N}\sum_{i=1}^{N}\
abla_{\theta^{\mu}}\mu (\phi(s_{i})|\theta^{\mu})\
abla_{a}Q(\phi(s_{i}),a|\theta^{Q})|_{a=\mu(\phi( s_{i})|\theta^{\mu})}\]
6: Update Target Networks: \[\theta^{Q^{\prime}} \leftarrow \tau\theta^{Q}+(1-\tau)\theta^{Q^{\prime}}\] \[\theta^{\mu^{\prime}} \leftarrow \tau\theta^{\mu}+(1-\tau)\theta^{\mu^{\prime}}\]
7:until Stopping Criterion is Met
```  
**Algorithm 2** At each learning step  
```
1:repeat
2: Sample Mini-Batch: Randomly sample \(N\) transitions from \(\mathcal{D}\).
## 3 Results and Discussion
### Influence of Impact Velocity  
The third studied parameter was the effects of impact velocity on the specimen responses. Here as well, two sets of experiments were designed. First, by maintaining the impact energy at 550 J, the impact velocity was increased from 3.95 to 4.69 m/s and then to 5.65 m/s by decreasing the impact mass from 70 to 50 kg and then to 34 kg. In the second case, the impactor mass was kept 70 kg and impact velocity was increased from 3.96 to 4.69 m/s and then to 5.34 m/s. Figure 15 illustrates the representative force-displacement curves obtained for glass-fiber-reinforced specimens.  
While the curves of Figure 15a show a trend similar to the one already noted in Figure 13a, the curves of Figure 15b are nearly superimposed and have good trend stability. This means that, for the particular considered material, the impact energy does not affect the crushing mechanism, while the use of a 70 kg falling mass leads to a more stable progressive trend in the failure evolution. Figure 16 shows the companion results obtained from testing GG630T-37 carbon fiber flat laminates at different velocities. In one set of tests, the impact mass was 50 kg; in the other set, it was 70 kg. Here as well, the obtained force data are similar for different velocities, and only the crushed length increases due to the higher impact energy.  
Figures 15 and 16 illustratively and Table 6 quantitatively demonstrate that, in the range of impact velocities and masses that were examined, impact velocity, similar to impact mass, does not affect the SEA capabilities of the composite material. This was reported in the literature by other researchers as well [34], which supports the reliability of the results obtained from the novel fixture.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Impact Mass (kg)** & **SEA (kJ/kg)** & **Standard Deviation (kJ/kg)** \\ \hline \multirow{3}{*}{NEMA FR4 glass fiber composite [32]} & 34 & 50.892 & 4.02 \\  & 50 & 48.905 & 2.61 \\ \cline{1-1}  & 70 & 50.663 & 1.85 \\ \cline{1-1}  & 34 & 45.537 & 1.30 \\ \cline{1-1} GG630T-37 carbon fiber laminate [33] & 50 & 45.706 & 2.18 \\ \cline{1-1}  & 70 & 45.945 & 2.96 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Specific energy absorption calculated for tests with different impact masses.  
Figure 15: Effect of impact velocity on glass-fiber-reinforced epoxies: (**a**) force–displacement curves for tests at 550 J of impact energy; (**b**) force–displacement curves for tests with impact mass of 70 kg.
## 2 Experimental Section
### Analytical Methods  
Soil samples were stored in the plastic collection bags prior to laboratory analysis. They were analyzed at the UNLV Environmental Health Laboratory and Department of Chemistry. The composite sample (five soil samples from each site) was thoroughly mixed, air-dried, sieved (2.29 mm), and placed in polyethylene, open-ended XRF samples cups (Chemplex(r) Industries, Inc., PalmCity, FL, USA). Three analytical samples were prepared from each composite sample. The sample cups were capped on one end with Diamond(r) plastic wrap (Reynolds consumer products, Richmond, VA, USA), filled with 16-18 g of soil, and capped on the other end. The chemical composition at each site of triplicate samples was determined with a Niton XLp 703A portable X-ray spectrometer, equipped with a 40 mCi 100 Cd radioisotope source and a silicon PIN Peltier-cooled detector (Thermo-Scientific Niton, Tewksbury, MA, USA). This instrument was designed to measure 15 elements. They are, in order of increasing atomic number (with detection limits in mg/kg in parenthesis): Cr (115), Mn (60), Fe (100), Co (50), Ni (75), Cu (50), Zn (30), As (10), Se (7), Rb (5), Sr (10), Zr (5), Mo (5), Hg (15), and Pb (12). The soil samples were measured for 60 s in bulk sample mode. Each triplicate sample was analyzed six times, three runs from the top of the sample cup and three runs from the bottom. Thus, each composite sample concentration value is the average of 18 measurements. The XRF's readout is in ppm (mg/kg) with \(\pm\) one standard deviation.
# Real-Time Contrail Monitoring and Mitigation Using CubeSat Constellations
## 5 Future Roadmap and Implementation
### Integration with Flight Management Systems (FMSs)
With advancements in airspace management, such as reduced navigation performance (RNP) and unified airspace systems, flight paths are no longer constrained to fixed navigational beacons, enabling smoother and more flexible routing. This flexibility supports the practical implementation of contrail mitigation strategies while maintaining economic viability. Figure 11: Integration of CubeSat data into flight management system (FMS) for contrail-aware flight planning. Figure 12: Workflow of data processing and contrail detection in the CubeSat-based monitoring system.
# BSD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection
## 2 Materials and Methods
### Evaluation Metrics  
To comprehensively assess the performance of the improved model, the evaluation metrics used in this paper include precision (P), recall (R), mean average precision (mAP50), number of parameters, giga floating-point operations (GFLOPs), and frames per second (FPS).  
**Precision (P).** Precision is used to measure the model's accuracy in detecting signature marks, with higher accuracy corresponding to a lower false detection rate. The calculation formulas are as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
**Recall (R).** Recall is used to evaluate the comprehensiveness of the model, with higher recall corresponding to a lower missed detection rate, as defined by Equation (9).  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
where True Positive (TP) represents the number of signature marks correctly identified, False Positive (FP) represents the number of backgrounds falsely detected as signature marks and False Negative (FN) represents the number of signature marks that were not detected.  
**Average Precision (AP).** AP represents the area under the precision-recall curve, calculated using Equation (10).  
\[AP=\int_{0}^{1}Precision(Recall)d(Recall) \tag{10}\]  
**Mean Average Precision (mAP).** mAP represents the average AP value across all categories, indicating the model's overall detection performance across the entire dataset, as defined by Equation (11).  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i} \tag{11}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration Parameter** & **Value** \\ \hline operating system & Ubuntu 18.04 \\ CPU & 12 vCPU Intel(R) Xeon(R) Silver 4214R CPU @ 2.40 GHz \\ GPU & RTX 3080 TI (12 GB) \(\times\) 1 \\ RAM & 90 GB \\ deep learning framework & PyTorch 1.8.1 \\ programming language & Python 3.8 \\ CUDA version & CUDA (11.1) \\ training epochs & 200 \\ batch size & 16 \\ learning rate & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model training configuration parameters.  
**mAP50**. mAP50 is the average precision calculated at an IoU threshold of 0.5.  
**FPS.** FPS was calculated by validating the testing set, during which we measured the average times for preprocessing, inferencing, and postprocessing and then used these values to determine the final FPS. The formula for this computation is given below:  
\[FPS=\frac{1}{preprocess+inference+postprocess} \tag{12}\]
# Cable Temperature Prediction Based on RF-GPR for Digital Twin Applications
## 3 RF-GPR Prediction Model of Cable Temperature
### Construction of RF-GPR Cable Temperature Prediction Model
According to [PERSON] principle, prior functions are established in data set \(D\) during model training, and \(n_{*}\) data sets are set to test the prediction model, as shown in Formula (17). \[D_{*}=\{(x_{i},T_{i})\},i=n+1,n+2\cdots n+n_{*} \tag{17}\]  
If the test results meet the set model error conditions, the cable temperature prediction model can be output; otherwise, if the test results do not meet the set model error conditions, it is necessary to readjust the hyperparameters and conduct model training again until the model error conditions are met. In the test data set, the input variable is \(x_{*}\), and the output variable is \(y_{*}\). The joint Gaussian distribution between the output value \(y\) of the training set and the output value \(y_{*}\) of the test set is obtained as follows:  
\[ \begin{bmatrix}y\\ y_{*}\end{bmatrix} \sim N\bigg{(}0,\ \begin{bmatrix}K(X,X)+\delta_{n}^{2}I_{n}&K(X,x_{*}) \\ K(x_{*},X)&k(x_{*},x_{*})\end{bmatrix} \bigg{)} \tag{18}\]  
where \(K(X,X)\) is the symmetric positive definite covariance matrix of order \(n\times n\), \(\delta_{n}^{2}\) is the variance of white Gaussian noise, \(I_{n}\) is the unit matrix of order \(n\), and \(K(x_{*},X)=K(X,x_{*})^{T}\) is the covariance matrix of order \(n\times 1\) between the prediction input variable \(x_{*}\) and the training input variable \(X\). \(k(x_{*},x_{*})\) is the covariance of the input variable \(x_{*}\). The regression equation of Gaussian process can be written as follows:  
\[y_{*}|X,y,x_{*}\sim N(\overline{y}_{*},\ cov(y_{*})) \tag{19}\]  
where \(\overline{y}_{*}\) and \(cov(y_{*})\) are the mean and variance of test variable \(x_{*}\) to test result \(y_{*}\), respectively.
# Rheological Approaches of Wheat Flour Dough Enriched with Germinated Soybean and Lentil
###### Abstract  
Germination is a convenient technique that could be used to enhance the nutritional profile of legumes. Furthermore, consumers' increasing demand for diversification of bakery products represents an opportunity to use such germinated flours in wheat-based products. Thus, this study aimed to underline the effects of soybean germinated flour (SGF) and lentil germinated flour (LGF) on the rheological behavior of dough during different processing stages and to optimize the addition level. For this purpose, flour falling number, dough properties during mixing, extension, fermentation, and dynamic rheological characteristics were evaluated. Response surface methodology (RSM) was used for the optimization of SGF and LGF addition levels in wheat flour, optimal and control samples microstructures being also investigated through epifluorescence light microscopy (EFLM). The results revealed that increased SGF and LGF addition levels led to curve configuration ratio, visco-elastic moduli, and maximum gelatinization temperature rises, while the falling number, water absorption, dough extensibility, and baking strength decreased. The interaction between SGF and LGF significantly influenced (\(p<0.05\)) the falling number, dough consistency after 450 s, baking strength, curve configuration ratio, viscous modulus, and maximum gelatinization temperature. The optimal sample was found to contain 5.60% SGF and 3.62% LGF added in wheat flour, with a significantly lower falling number, water absorption, tolerance to kneading, dough consistency, extensibility, and initial gelatinization temperature being observed, while dough tenacity, the maximum height of gaseous production, total CO\({}_{2}\) volume production, the volume of the gas retained in the dough at the end of the test, visco-elastic moduli and maximum gelatinization temperatures were higher compared to the control. These results underlined the effects of SGF and LGF on wheat dough rheological properties and could be helpful for novel bakery products development.  
germination; lentil; soybean; wheat flour; rheology; microstructure +
Footnote †: journal: Applied Sciences
## 3 Results
### Performance Measures  
Performance was evaluated in two scenarios: far-end single-talk and utterances containing near-end speech, either double-talk or near-end single-talk. When only the far-end speaker spoke, the goal was to reduce the output signal's energy as much as possible. Optimally, the enhanced signal was silent during these periods. We utilized the echo return loss enhancement (ERLE) measure to evaluate performance during far-end-only periods. The ERLE in decibels is defined as:  
\[\text{ERLE}=10\text{log}_{10}\frac{||m||^{2}}{||\bar{d}||^{2}}. \tag{8}\]  
During double-talk periods, the goal was to maintain near-end speech quality while suppressing the residual echo. Since the performance measures used during these periods focused on speech quality rather than echo reduction, these measures were also used during near-end single-talk periods. Two different measures were employed to evaluate performance during these periods. Perceptual evaluation of speech quality (PESQ) [32] aimed to approximate a subjective assessment of an enhanced speech signal. PESQ was intrusive, i.e., the enhanced signal was compared to the clean, ground-truth signal. PESQ score was in the range \([-0.5,4,5]\). PESQ is known to only sometimes correlate well with subjective human ratings. Therefore, deep noise-suppression meant opinion score (DNSMOS) [33] was also used to evaluate performance during these periods. DNSMOS is traditionally used to assess noise suppressors, although it can be employed to estimate speech quality in any setting. DNSMOS is non-intrusive, i.e., it does not require a clean near-end signal to evaluate speech quality. DNSMOS is a neural network trained with hundreds of hours of ground-truth subjective human speech quality ratings. The model predicted a score in the \([1,5]\) range.  
Further measures used in the next section included the SER, measured in double-talk scenarios and defined in decibels as:  
\[\text{SER}=10\text{log}_{10}\frac{||d||^{2}}{||s||^{2}}, \tag{9}\]  
and the echo-to-noise ratio (ENR), measured in far-end-only scenarios and defined in decibels as:  
\[\text{ENR}=10\text{log}_{10}\frac{||s||^{2}}{||v||^{2}}. \tag{10}\]
# Effects of Diet on Mercury Bioaccumulation in Farmed Gilhead Seabream (_Sparus aurata_)
## 4 Discussion  
### Mercury Bioaccumulation Pattern  
With the exception of muscle tissue, mercury concentrations were significantly higher in exposed fish compared to unexposed controls across all the analyzed tissues. This indicates that mercury exposure induced distinct accumulation patterns in the organs of gilthead seabream, thus representing a potential risk for the health of consumers. The magnitude of bioaccumulation depends on the exposure period and on the type of tissue [17].  
Specifically, we observed that mercury accumulation in tissues of gilthead seabream in mercury-contaminated seawater decreased in the following order for the first 10 days of exposure: gills > kidney > liver > gut > muscle. These distribution patterns are consistent with previous studies that investigated mercury uptake in fish from seawater. Specifically, similar mercury distribution patterns were found in tissues of the fish species _C. carpio_ (common carp) [45], _A. melas_ (black bullhead) exposed to inorganic mercury for 10 days [46] and gilthead seabream exposed to inorganic mercury for 25 days [17].  
This result demonstrates that gills are the main target for mercury at the beginning of exposure and thus the most sensitive organ for biomonitoring studies in contexts of short-term exposure. It is well documented that gills represent the main entry routes for metals directly adsorbed from the surrounding waters through respirations and passive diffusion [47]. Previous studies have shown that mercury ions (Hg\({}^{2+}\)) dissolved in seawater can bind to mucopolysaccharides (constituents of mucoproteins, glycoproteins) on the surface of the gills, which are rich in thiol groups (\(-\)SH) [48]. This might result in the formation of a protective mucous trap over the gills for Hg\({}^{2+}\) ions.  
As expected, mercury exposure leads to a significant time-dependent increase in concentration in the kidney and liver, the most involved organs in detoxifying processes due to the complexation of metals with metallothioenin (MT) [49]. Specifically, after 10 days of exposure, the kidney became the main target organ for mercury bioaccumulation, probably because the contaminant induces an increase in MT synthesis. Previous studies documented the best mercury bioaccumulation efficiency of the kidney with respect to the liver in other fish species exposed to inorganic mercury [17; 45; 46; 50; 51].  
In accordance with previous studies [11; 17; 50; 51], the lowest mercury concentrations with negligible bioaccumulation over time were observed in muscle tissues of gilthead seabream, as a consequence of the preferential storage of mercury in the other organs involved in detoxifying processes. However, [17] reported a slight mercury bioaccumulation in muscle tissue of gilthead seabream after 25 days of exposure as a consequence of an exponential decrease in mercury bioaccumulation rates in kidney and liver. This strongly suggests that bioaccumulation processes in muscle can occur only when mercury uptake tends to saturate in the liver and kidney.
# Investigating Load-Bearing Capabilities and Failure Mechanisms of Inflatable Air Ribs
## 3 The Load-Bearing Performance of Tents with Different Rise-Span Ratios
### The Analysis of the Deformation of the Tent  
The load applied on the tent consists of gravity and the wind load, which is derived from CFD simulation results. Membrane structures are typically lightweight and have low natural frequencies. They produce large deformation under wind load, and the relationship between the load and deformation is nonlinear [35; 36]. An explicit dynamics algorithm is used to simulate the structural response accurately. The bottom nodes of the tent are fixed in all translational directions when it is established on the ground. The tent is primarily modeled as a membrane due to its extreme thinness, resulting in negligible bending stiffness. As such, it cannot withstand the bending forces. Additionally, the wind ropes are modeled as truss elements, and the pre-tensioned stress of the wind ropes is 100 Pa. The model consists of 164,942 elements and 165,329 nodes, providing a detailed simulation of the tent's behavior under load conditions. The analysis is conducted over a duration of 50 s to ensure the accuracy of the computational results.  
The wind pressure is loaded on the tarpaulin, which deforms and transfers the force to the internal air rib through the bonding strip. Figure 12 illustrates the deformation of the air ribs in different models. Tent-0.5-S/R shows significant deformation at the end air rib on the windward side with a maximum displacement of 0.268 m. The maximum deformation of Tent-0.667-R and Tent-0.583-R also appears at the top of the end air rib on the windward side. For Tent-0.333-S and Tent-0.417-S, the maximum deformation occurs at the top of the end air rib on the leeward side, with values of 0.125 m and 0.134 m, respectively. The influence of the rise-span ratio on the bearing performance under pressure can be seen from the deformation. For example, the rib named 0.333-S has the best bearing performance under pressure; so, the deformation of the end rib on the windward side decreases, and the maximum deformation appears at the end rib on the leeward side.  
Figure 11: The distribution of the wind pressure coefficient on the surface of the tent.  
The deformation of the tarpaulin determines the usable interior space. Figure 13 shows the deformation of the tarpaulin in five types of tents. The maximum displacement of the five models occurs at the center of the end face. The tents sorted by the maximum displacement are Tent-0.333-S \(<\) Tent-0.417-S \(<\) Tent-0.667-R \(<\) Tent-0.583-R \(<\) Tent-0.5-S/R. For Tent-0.333-S, the maximum displacement is 0.112 m, which is reduced by 65.8% compared with that of Tent-0.5-S/R. These results indicate that the structural stiffness of the air ribs is a critical factor affecting the tent's load-bearing performance. Changing the rise and span of the end ribs effectively improves the load distribution on the tarpaulin and enhances the tent's load-bearing performance.