esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Performance Comparisons of Proposed Method
By the 15 th iteration, the classical DRSN reached 83%, whereas Log-DRSN achieved an accuracy of 96%. In the 30 th iteration, the classical DRSN reached 91%, and Log-DRSN achieved an impressive 98% accuracy. Similarly, Table 5 provides a more detailed comparison of learning rates. This table illustrates the accuracy rates of the two models between the 5 th iteration and the 45 th iteration. It becomes evident that Log-DRSN exhibits a faster learning pace compared to the classical model.
## 4 Experimental Process and Analysis
### Prediction of Dye Formulas and Ultrasonic Pretreatment Power
#### 4.4.2 Model Training and Testing  
The IDBO-SAT Fusion-Gated BiLSTM prediction model was constructed using the spectral reflectance of the dyed veneer surface, the spectral similarity metric parameters, and the spectral characteristic parameters selected by the MIC analysis as inputs, and the dye concentration and ultrasonic pretreatment power as outputs. The dataset was split into training (70%), validation (10%), and test (20%) sets, with all data normalized before being input into the model. The optimization parameter ranges were determined through experimental tuning, ensuring that they were well-suited to the dataset and task, and outperformed other tested ranges in preliminary trials. Optimization was performed using the Improved Dung Beetle Optimization (IDBO) algorithm, with the Mean Squared Error (MSE) as the fitness function, a population size of 50, and a maximum of 200 iterations. The parameters of the model are set as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Meaning** & **Value** \\ \hline Hidden\_dim\_size & Number of linear layer neurons in the feature extraction module & 128 \\ BiLSTM\_dim\_size & Number of neurons in the BiLSTM layer & [100, 600] \\ Lr & Learning rate & [1 \(\times\) 10\({}^{-4}\), 1 \(\times\) 10\({}^{-2}\)] \\ Dr & Dropout rate & [0.1, 0.5] \\ Best epochs & Model training rounds & [750, 1000] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training parameter settings for the model.  
Figure 11: Spectral reflectance of the dyed veneers with and without the ultrasonic pretreatment.  
Figure 10: Spectral reflectance of the veneer stained with different concentrations of dyes.
## 2 Results and Discussion
### Principal Component Analysis of Volatiles during N-Sufficient and N-deficient Treatments  
Using Principal Component Analysis (PCA) to examine the data, PCA scores plots allow visual representation of any separation of cucumber leaf volatiles when comparing the N-sufficient and N-deficient treatments. In the present study, the complete data set was selected for PCA and was performed on an average content of all compounds from N-sufficient and N-deficient treatments. Principal Component 1 (PC1) and PC2 accounted for approximately 75.30% of the total variability. More specifically, PC1 accounted for 40.49%, and PC2 was responsible for 34.81% (Figure 2).  
Figure 1: Changes of total volatile content in 9930 leaves during N-sufficient and N-deficient treatments. N+ and N\(-\) mean nitrogen sufficient and nitrogen deficient treatments, respectively. Bars present means \(\pm\) standard deviations, data were analyzed by one-way ANOVA followed by the [PERSON]’s multiple range tests to make comparisons within both treatments in same day. Asterisks indicate significant difference, **\(p<0.01\). Values are means of three replicates.  
Based on the loadings for this PCA, most volatiles of cucumber leaves grown in N-sufficient nutrition had higher concentration. When all volatiles of both N-sufficient and N-deficient treatments were combined, PCA using cucumber leaf composition parameters provided good separation (Figure 2). PCA on the profiles of volatile compounds resulted in a clustered pattern in which volatiles of the N-deficient treatment were mainly clustered in the positive axis of PC1 and volatiles of N-sufficient treatment were mainly clustered in the positive axis of PC2, with the exception of (_E_)-2-hexneal, (_E_,_E_)-2,4-hexadienal, total C6 aldehydes, and total volatiles, which displayed significant improvement in some points during N-deficient treatment. These results indicated that nitrogen supply affected the production of most cucumber leaf volatiles.
###### Abstract
These platforms also introduce relevant functionality for the treatment, such as medication reminders or online consultations with the doctor. Moreover, the literature has proposed using smartphone applications to ease the acquisition of blood pressure measurements [8]. These applications have the advantage of directly acquiring the measurements by pairing the blood pressure device, in contrast to traditional web pages where the data is introduced manually. Nevertheless, web ecosystems are complementary to these applications since web services are also needed in these approaches. Additionally, there are solutions using machine learning (ML) and deep learning (DL) techniques to predict hypertension, increasing the complexity in the development of the system. Including these approaches in the development of web platforms would be greatly beneficial to assist hypertensive patients. In this context, the development of intelligent web ecosystems able to predict hypertension presents several challenges. First, there is a lack of web platforms able to acquire patients' data in real-time, using monitoring devices able to send automatically blood pressure measurements. Furthermore, there is a lack of technological solutions considering the impact that environmental dimensions have over hypertension. Finally, there is an absence of web platforms integrating ML and DL approaches to offer real-time blood pressure predictions. To improve the previous challenges, this manuscript presents SENIOR, a web ecosystem to predict hypertensive crises in a early phase. The main contributions of this work are the following:  
* The design of SENIOR, consisting of three main elements: a wearable providing heart blood pressure measures, a smartphone application used to acquire data, and a web platform to process the data and perform predictions. This ecosystem providespredictions in real-time and it is able to identify blood pressure values within the next 8, 16, and 24 h, notifying the user about potentially dangerous values. * The identification of new environmental features to predict hypertensive crises: weather and pollution. Furthermore, the mobile application implements several surveys that patients have to complete periodically, providing information about the location, weight, physical activity, diet, medication, social support, stress, depression, and alcohol and tobacco consumption. * The implementation of a proof of concept with seven volunteers and different ML algorithms such as Bayesian Ridge, Support Vector Regression, and Random Forest. The obtained results demonstrated the feasibility of SENIOR as a real-time ecosystem, showing similar results to the offline solutions existing in the literature. The remainder of the paper is structured as follows. Section 2 introduces the related work existing in the literature. After that, Section 3 presents the methodology followed and the architecture of the solution. Section 4 presents the results obtained in a proof of concept with different volunteers and machine learning algorithms. Finally, Section 5 highlights conclusions and future work.
## 1 Introduction
A 0.3 m s\({}^{-1}\) flow velocity is the most commonly used flow velocity for biomass production [33]. The surface-to-volume ratio in raceways is always 1/pond depth. A lower depth increases the surface-to-volume ratio, improving light penetration [34]. Thus, for autotrophic growth, shallower ponds are more productive than deeper ponds [34]. However, at water depths lower than 0.15 m, a severe decrease in flow and turbulence occurs in large-scale raceways [35]. This long light path results in large areal volumes (150 L m\({}^{-2}\)) and cell concentrations of less than 0.6 g L\({}^{-1}\), which promote the occurrence of contaminants (i.e., grazers and competing microalgae) [35]. Another problem of open systems is the low productivities obtained, due to temperature fluctuations, inefficient mixing and CO\({}_{2}\) dispersion, light limitations and evaporation losses [29]. Typically, well-managed raceway ponds may achieve 20-25 g m\({}^{-2}\) d\({}^{-1}\) for short periods, while long-term productivity in large commercial raceways rarely exceeds 12-13 g m\({}^{-2}\) d\({}^{-1}\)[35]. Lopez et al. [36] point out that 20 g m\({}^{-2}\) d\({}^{-1}\) is the threshold for the average annual productivity, which is rarely exceeded in large-scale outdoor production. Several studies have been carried out in order to study the design of the raceway pond, with the aim of reducing the energy costs and increasing the growing efficiency of microalgae. Most of the energy losses occur at the hairpin bends. Liffman et al. [37] used computational fluid dynamics (CFD) to model different bend configurations. These authors showed that, when compared with the traditional constant-width/constant-depth bend configuration, some of the new bend configurations not only minimize the energy consumption, but also improve the mixing of the raceway ponds by depletion of low speed and stagnant regions [37]. Another CFD-based approach demonstrated that a raceway configuration with a minimum of three semicircular deflector baffles and an \"island\" at the end of the central divider, apart from being more energy-efficient than the standard configuration, can completely prevent the development of dead zones [32]. New design studies rely mostly on modeling and very few in actual experiments. Despite the evolution of the models and the associated technology, errors are always encountered when compared to experimental data [38; 39]. In this context, this work aimed to compare the performance of two raceway ponds with different configurations based on three criteria: areal productivity, energy consumption, and the biochemical composition of biomass produced using _Nannochloropsis oceanica_ as model microcalga.
## 3 Preliminary and Model
### Traffic Degradation Image Restoration Model
#### 3.4.2 Image Semantics Encoder Module  
When directly employing a pre-trained CLIP image encoder to extract features from a degraded image, latent features inevitably incorporate degradation information such as rain or fog noise. This contamination adversely interferes with the reverse sampling process of SDM. At the same time, if potential features are directly extracted from text prompts, there may be ambiguity in the text description, resulting in generated results that are inconsistent with the actual background semantics. To address this issue, we proposed the image semantics encoder module (ISE). SE contains features that align with the latent description of the real background while excluding degradation-related information and effectively constrains the sampling generation process. This module includes an image semantics encoding module and a text semantics encoding module, as shown in Figure 6.  
The image semantics encoding module contains two image encoders: one is a pre-trained CLIP image encoder with frozen parameters to preserve its powerful capability for extracting general visual semantic features. The other is a trainable copy of the CLIP image encoder, which gradually fuses features at each level with the frozen version through zero convolution. In the initial training phase, the output of zero convolution approximates the original features from the frozen version. As training progresses, the trainable version gradually adjusts its output features. This design prevents randomized initial parameters from corrupting pre-trained features during early training stages, thereby ensuring model stability.  
For text semantics encoding module, it is only used during the training phase. It employs a prompt generator to produce text prompts corresponding to clear traffic images, which are then fed into a pre-trained CLIP text encoder to obtain text semantics embedding that match the textual descriptions. Finally, the image semantics encoding module extracts degradation-free image semantics embedding from DI. These image embeddings are then aligned with the text semantics embeddings using cosine similarity as a loss function during training. After training, the image semantics encoder is integrated into TDIRM to extract image semantic latent features, which serve as the first condition embedding called SE.
## 2 Accelerator
### First Commissioning Experience and Outlook  
A detailed summary of the commissioning of elements of SwissFEL in the former SwissFEL test facility can be found in [49], here we will focus on our recent commissioning activities after installation in the facility, including the generation of our first FEL photons. The commissioning of the electron gun started in August 2016. Once the electron source was operating, electrons were further accelerated with two S-band RF stations to an energy of 145 MeV and transported to the injector beam dump for the first time in early September 2016. At that time only the first two S-band RF stations were available. Soon after the first injector transmission, however, the first of the linac C-band modules could be integrated into the acceleration process, thereby increasing the available beam energy to about 380 MeV. The first transmission through the entire accelerator including the undulator line was achieved at this energy in mid-November. The initial commissioning of essential accelerator instrumentation systems (charge and beam position monitors, screens) was also performed at this energy.  
For the occasion of our official inauguration event in December 2016 an attempt was made to lase with the 380 MeV beam. To reach a sufficient charge density the beam was somewhat compressed in the first bunch compressor, while the accrued energy chirp was compensated for in the one available C-band module. After some further tuning, characteristic FEL radiation from the SASE process could be observed with a photodiode located after the undulator line. The photon wavelength derived from the beam energy and the undulator parameters was in the UV range (24 nm or 50 eV).  
By mid-2017, all injector RF stations as well as two more linac RF stations had become operational, pushing the beam energy close to 1 GeV. This resulted in successful SASE soft X-ray operation at 4.1 nm (300 eV) in May of 2017 (see Figure 5). In the following months the beam energy will be further increased step-by-step in accordance with the availability of the remaining RF stations. First pilot experiments are foreseen at a beam energy of 3 GeV towards the end of 2017, with hard X-ray user operation in 2018.
## 4 GDOP and WGDOP Calculation with Matrix Multiplication  
### GDOP Calculation with Matrix Multiplication  
GDOP is a measurement which is used to select the better satellite set in a GPS system. GDOP is used to determine which measurements are suitable for carrying out the positioning process. In order to reduce the complexity and without having to invert a matrix, reference [31] proposed a simple formula for computing GDOP values. They use [PERSON]'s identities to calculate GDOP from the input parameters, including the traces, second and third powers, and the determinant of the measurement matrix.
# Antifungal Effects of Fermented _Sophora flavescens_ and _Eleutherococcus sessiliflorus_ Extract
###### Abstract  
In this study, a microbial strain was isolated from humus soil to ferment _Sophora flavescens_ and _Eleutherococcus_ sessiliflorus_ extracts. The isolated microbial was identified as the _Bacillus_ genus by 165 rRNA sequence analysis. The fermented plant extracts exhibited antifungal effects against four types plant pathogen, _P. carotorum, B. cinerea, C. fructicola_ Sau-3, and _C. glovesporoides_, according to incubation time. In particular, the fermented plant extracts showed the most activity for _Colletotrichum_ genus in inhibiting mycelium growth. Metabolite changes in fermented _S. flavescens_ and _E. sessiliflorus_ extracts were confirmed through LC-Q-TOF/MS. Flavonoid and peptide derivatives were improved in fermented _S. flavescens_ and _E. sessiliflorus_ extracts compared to their unrefmented counterparts. This study suggested that isolated _Bacillus_ microbial fermentation could be a valuable tool in improving the bioactivity of _S. flavescens_ and _E. sessiliflorus_ extracts, with the potential to form more environmentally friendly antifungal agents.  
Keywords:antifungal effects; _Sophora flavescens; Eleutherococcus sessiliflorus_; fermented plant extracts; LC-Q-TOF/MS analysis +
Footnote †: journal: Applied Sciences
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
## 3 Single-Anchor Localization Method
### Localization Protocol
(**a**) Traditional way of extended two-way ranging methods. (**b**) Improved way of extended two-way ranging methods. distance and angle measurements in Figure 2b is obtained at the anchor side, which is an anchor-based architecture. Thus, the mentioned agent and anchor-based architectures are completely different. Last but not least, with the protocol in Figure 2b, we will show that our SAL method can further enhance the measurement efficiency by receiving concurrent agent responses from a single estimated CIR.
## 4 Simulation Results
We only compare the proposed STC method with the conventional STC method in Figure 12. The total cost is illustrated in Figure 13.  
Figure 11: The regional demand \(U_{AR}\). Figure 10: The governor input of the gas turbine generator \(\Delta x_{g}\). According to the communication cost comparison in Figure 12, it is known that the communication cost of the proposed self-triggered control method is lower than that of the conventional self-triggered control method. At the end of the simulation time, it costs about 530 control actions using the proposed self-triggered control method. In comparison, it costs about 600 control actions using conventional self-triggered control method. According to the definition of communication cost, the system takes 530 control actions for sensor data acquisition to compute control output with the proposed method, and needs 600 control actions using the conventional method. Based on the total cost curve comparison, the proposed self-triggered method costs less than the conventional method. At the end of simulation, the conventional method costs about 120 control actions, and the proposed method costs about 110 control actions. The cost of the proposed method is not much lower than for the conventional method. When the communication cost is high, bandwidth is limited, or communication network is publicly shared, the triggering time of proposed method further improves. Besides, the computation cost of calculating triggering time in the proposed method is reduced by the approximation method. Above all, the proposed method is better than the conventional method in frequency control application of power system, at least under this situation. Figure 12: Communication cost comparison. Figure 4:0 Cost curve comparison.
# Simplified Energy Model and Multi-Objective Energy Consumption Optimization of a Residential House
## 2 Materials and Methods
### Mathematical Model
#### 2.1.4 Photovoltaic Subsystem  
The photovoltaic subsystem is built into the model in a similar way to the heating power of the sun. It is described by the area of the photovoltaic panels, the irradiation forecast with cloudiness levels, and the efficiency of turning the solar power into electrical power. Electrical power obtained from the photovoltaic panels is described by:  
\[\mathrm{P_{pv}=I_{cloud}\cdot A_{pv}\cdot k_{pv\_efficiency}}, \tag{9}\]  
where  
\(\mathrm{P_{pv}}\) is the electrical power generated by the photovoltaics, \(\mathrm{W}\);  
\(\mathrm{A_{pv}}\) is the area of the photovoltaic panels, \(\mathrm{m^{2}}\);  
\(\mathrm{k_{pv\_efficiency}}\) is the efficiency factor of photovoltaic panels.  
The first part of Figures 3 and 4 shows the overall predicted power consumption of the home and the predicted power generation from the photovoltaic panels. By combining both terms, the overall power consumption of the home is lowered by the power generated by the photovoltaic panels, as shown by the energy balance on the second graph. The surplus of generated energy results in the possibility of selling it back to the grid. These results were obtained for two different areas covered by photovoltaic panels-namely 10 m\({}^{2}\) and 30 m\({}^{2}\).  
Figure 4: Forecast of the energy balance for 30 m\({}^{2}\) of photovoltaic panels.  
Figure 3: Forecast of the energy balance for 10 m\({}^{2}\) of photovoltaic panels.  
Figures 5 and 6 show the amount of money that is forecasted to be paid in each step as well as a cumulated sum of the money that will be paid on the forecast horizon.  
The efficiency of the panels was considered to be constantly 20%. The pricing was considered to be 0.185 EUR per kWh consumed from the grid and 0.018 EUR per kWh sold back to the grid. The tariffs were considered to be fix-priced; however, in future studies, it will be important to consider flexible price tariffs.
# Algorithms Utilized for Creep Analysis in Torque Transducers for Wind Turbines
## 10 Signal Average Estimators of Low and High Level  
On the basis of the measurement points for the upper and lower levels, as well as the adjusted linear relationship described by the polynomial function of the third degree, a set of estimators was prepared for the measurement of the average value of the torque (presented in Table 2).  
Table 2 shows the estimators of average values determined for both levels of the measured low and high signals.  
From the above tables, it can be seen that from Table 3 (a) that the median resistant to outliers and the weighted average emphasizing the final, practically established values of the sampled signal under equilibrium conditions (mean 1.0465 mV/V) are the most similar to each other. The center of the span and the arithmetic mean stand out significantly from this level. On the other hand, Table 3 (b) shows that the mean of the integration of the most fitted third-degree curve (tp3), taking into account the uncertainty of time and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Symbol** & **Formula** \\ \hline Arithmetic mean & \(\overline{y}\) & \(\frac{1}{n}\sum\limits_{i=1}^{n}y_{i}\) \\ Median & \(y_{M}\) & \(\frac{y_{i=n}+\overline{y}_{i=n+1}}{\sum\limits_{i=1}^{n}y_{i}}\) \\ Middle of spread & \(y_{R}\) & \(\frac{y_{m}+\overline{y}_{m}}{\sum\limits_{i=1}^{n}y_{i}}\) \\ Weighted average & \(\overline{y}_{W}\) & \(\frac{1}{n}\sum\limits_{i=1}^{n}\frac{y_{i}}{i}\sum\limits_{i=1}^{n}t_{i}\) \\ Means from integration over time & \(<y>_{t}\) & \(\frac{1}{t_{i}-t_{1}}\sum\limits_{i=1}^{n}f(t)dt\) \\ Mean over time integration 1 & \(<y>_{tp1}\) & \(a\frac{t_{i}+\overline{y}_{t}}{2}+b\) \\ Mean over time integration 2 & \(<y>_{tp2}\) & \(\frac{1+t_{i}}{2}\left(\frac{b}{2}\left(\left(t_{1}^{2}+t_{n}^{2}\right)+b_{1} \right)+b_{0}\right.\) \\ Mean over time integration 3 & \(<y>_{tp3}\) & \(\frac{\frac{1+t_{i}}{2}\left(\frac{a}{2}\left(t_{1}^{2}+t_{n}^{2}\right)+a_{1} \right)+\frac{a}{3}}{\left(t_{1}^{2}+t_{1}t_{2}+t_{n}^{2}\right)+a_{0}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Signal average estimators.  
Figure 8: Fitted tertiary curves \(y=a_{3}\)\(t^{3}+a_{2}\)\(t^{2}+a_{1}\)\(t+a_{0}\) to measurement points: solid line (purple) condition (4)—dashed red line condition (5): (**a**) low level and (**b**) high level.  
signal, differs by less than 0.000 08 mV/V. Similarly close to this solution is the polynomial curve (tp2) below 0.000 01 mV/V. On the other hand, the fit of the trend line (tp1) gives the differences between the estimated means above 0.0001 mV/V.