esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### RE and Maximal Incremental Running Tests  
After completing the F-V test, participants were allowed to rest for 20 min before the RE evaluation. Subsequently, they completed a 5 min warm-up running on the treadmill at 8 km\(\cdot\)h\({}^{-1}\) for women and 10 km\(\cdot\)h\({}^{-1}\) for men. Then, 2 \(\times\) 5 min bouts at 11 km\(\cdot\)h\({}^{-1}\) and 13 km\(\cdot\)h\({}^{-1}\) (for women and men, respectively) with 5 min rest were performed to measure RE. Then, participants started a graded exercise test (GXT) on the treadmill (HP Cosmos Pulsar, HP Cosmos Sports & Medical GMBH, Nussdorf-Traunstein, Germany). The test commenced at a speed of 8 km\(\cdot\)h\({}^{-1}\) and 10 km\(\cdot\)h\({}^{-1}\) (for women and men, respectively) and the speed was then increased by 1 km\(\cdot\)h\({}^{-1}\) every minute until volitional exhaustion. The treadmill slope was 1% to imitate external wind conditions [28]. Both during the RE trials and during the incremental test, respiratory variables were continuously measured using a gas analysis system (CPX Ultima Series MedGraphics, St. Paul, MN, USA), with gas calibration before each test session performed automatically by the system using both ambient and reference gases (CO\({}_{2}\) 4.10%; O\({}_{2}\) 15.92%). For the RE assessment, VO\({}_{2}\) values from the last 120 s of each bout were used to determine RE as the oxygen cost of running. The VO\({}_{2\max}\) was determined as the average of oxygen uptake values recorded during the final 30 s of the incremental test. This approach was chosen due to the significant breath-by-breath variability in pulmonary gas exchange (PGE), making the use of 30 s averaged PGE values highly effective for incremental exercise tests [29].  
In both tests, the spatiotemporal parameters of the gait cycle (contact time [CT], stride frequency [SF], stride length [SL], and flight time [FT]) were recorded using the Stryd\({}^{\circledR}\) Power Meter device (Stryd Power Meter, Stryd Inc. Boulder, CO, USA) with a sampling frequency of 1000 Hz. Then, the information was analyzed through the Stryd Power Center program available on the website. The 120 s period used for the RE assessment and 20 central seconds from each stage of the incremental test provided the 32 steps recommended for running biomechanics measurements to identify technique differences between participants [30]. To analyze relations between the F-V profile and kinematic variables at higher intensities, we used the stage of 16 km\(\cdot\)h\({}^{-1}\) for women and 18 km\(\cdot\)h\({}^{-1}\) for men. These intensities were those below the value 1 in the respiratory exchange ratio.
# A Supervised Speech Enhancement Approach with Residual Noise Control for Voice Communication
## 4 Experimental Setup
### Experimental Settings  
We sample all the utterances at 16 kHz, which are subsequently enframed by a 20-ms Hamming window and 10-ms overlap between adjacent frames. A 320-point short-time Fourier transform (STFT) is applied to transform the frames into the T-F domain, leading to 161-point spectral feature vectors. The magnitude of the spectrum is deployed as the input feature. The models are trained with stochastic gradient descent (SGD) optimized by [PERSON] [34]. The learning rate is initialized at 0.0005. We halve the learning rate only when three consecutive validation loss increment arises and the training process is early-stopped unless ten consecutive validation loss increment happens. Totally 100 epochs are trained to guarantee the network convergence. Within each epoch, the minibatch is set to 16 at the utterance level, where all the utterances are zero-padded to have the same timestep with the longest utterance.
# Comparative Impact Analysis of Cyclone Ana in the Mozambique Channel Using Satellite Data
## 4 Discussion
### Spatial and Regional Trends  
As the subject of this study is relatively recent and occurred only six months before the time of writing and is in an area underrepresented in scientific literature, there are very few studies or reports that may attempt to validate this study. However, it was found that this study's predicted impact and flooding model followed the storm track (Figure 4). Specifically, it was found that the majority of the flooding occurred in the areas where the depression and wind speeds were the highest, namely in Zambezia, Mozambique on 24 January with pressures as low as 994 hPa and wind speeds of 95-100 km/h [22]. Impacts were nonetheless felt in all four regions of the study, including the Boeny region, Madagascar which accounted for the majority of the relative flooded area, despite the storm being only categorized as a tropical depression [32] when it occurred in the region on 23 January. This could be because the Boeny region has a higher population of 931,171, three times the size of the Melaky region, and so has a higher number of urban areas [33]. As such, the increase of urban areas prevents rain from infiltrating into the soil underneath, causing urban impervious areas to display faster and larger hydrological responses than natural pervious areas [34].  
In conjunction with a higher number of urban areas, vulnerability to flooding in Madagascar is emphasized by the lack of sustainable urban planning, insufficient capacity, and poor functioning of the drainage network because of clogging with solid waste and deterioration throughout the country [35].  
In Mozambique, the flooding resulting from Ana mainly occurred in croplands, with urban areas being negligeable. This is particularly because croplands were found mainly around the Zambezi River, where the majority of production consists of easily flooded rice fields, as well as maize, sorghum, millet and cassava [24]. Long term, this may have impacts on food security and flooding recovery as lowland crops such as cassava and beans tend to be planted during the rainy season between January-March [24].  
Within each country, the highest impact on population was felt in areas near rivers, namely the Zambezi River and Betsiboka River in Mozambique and Madagascar, respectively. Flooding was also caused in the Zambezia province following the Licungo River's overflow resulting in damaged bridges, transportation, and communication lines following moderate to high floods in the mainland [7].  
Another key factor that also determines differences in flooded areas is topography. In the regions of Sofala and Zambezia, most of the elevation is classified as low and ranges from 0-150 m above sea-level on average [36]. In a study of cyclone Idai, it was found that areas with low elevation and close to the coast were more likely to be affected by floods [37]. As such, proximity to the Zambezi River and areas of low elevation in both Sofala and Zambezia regions may explain the spatial differences in flooding within each region and will be more likely to be flooded [37].
## 5 Results and Discussion
### Motor Periodic Excitation  
All of the data obtained from piezoelectric sensors passed through an offline fifth-order Butterworth bandpass filter. The frequency range was set at 0.01-50 Hz, and the purpose was to filter out the disturbances from the DC and 60 Hz power source for clearer result analysis. We used a PI controller to perform the closed loop tracking control of the motor around the yaw axis, causing the motor to perform periodic motions at 20 s with amplitude 0.05 rad and frequency 1 Hz so that the thin plate swung back and forth. From the perspective of the thin flexible plate, this motion can be considered a periodic disturbance. Figure 10 displays the tracking error and control input of the motor around the yaw axis, which produced periodic steady-state error of approximately 4%. Figure 11 lists the measurement results obtained from the piezoelectric sensors without vibration suppression. As can be seen, the periodic motions of the motor induce clear periodic vibration responses at 1 Hz in the thin plate output. FFT results (Figure 11b,c) revealed that the first mode also produced vibrations at 1.4 Hz, which is in agreement with the results obtained from finite element simulations. The peak values in the FFT plots revealed that the vibration responses in the flexible structure are influenced primarily by periodic disturbances. To suppress the vibrations in the thin plate during dynamic tracking motions, we placed a piezoelectric actuator and sensor on the thin flexible plate and reduced the amount of vibration in the structure using active vibration-suppression control.  
Figure 10: Periodic tracking results of the Yaw axis motor: tracking error (**top**); and control input (**bottom**).
## 5 Comparative Analysis Results and Discussion
### Hyperparameter Finetuning Results
#### 5.3.1 Convolution Layer
Using the LrFinder function [53], the best learning rate was determined for each architecture. The training process initiates with 100 epochs and uses the early-stopping method to avoid the overfitting problem, as described earlier. The best results were achieved when we utilized two convolutions, with filter sizes of 256 and 128, respectively. We found that a kernel size of two performed best for both convolutions. Figure 6 compares the highest accuracy based on the number of convolutions and filter sizes. Figure 6 illustrates the effect of filter size on training time and accuracy for different BERT + CNN models. According to the results, if the filter size is small, the model takes longer to train. This can be attributed to the fact that the model takes more epochs to converge. Figure 6 illustrates how the accuracy increases as the filter size increases. Input parameters are more informative for models, which start building their feature map with a filter size of 256. As a result, the model will start with significantly better features and therefore can converge more quickly. The two plots on the right side shows the distribution of training time and accuracy for models with same filter size but different convolution and dense layers. In the bottom plot, for instance, we see that the accuracy distribution for models with a filter size of 128 is between 91 and 96, whereas that of models with a filter size of 32 is between 89 and 93. However, the same model training time with filter size 32 is twice as long as a model with filter size 128.
## 5 Conclusions and Discussion
However, when the ratio of training is 50% (i.e., the training set number is 318), the F1-Score of bounding box is only 0.421. However, well annotated datasets can be time-consuming, laborious and not reproducible, especially for classification tasks with large numbers of classes or with fine-grained classes. Even for the labelling of mature green tomatoes in this manuscript, which does not require expert knowledge, a skilled person can only label about 200 images per day. One approach to overcome this drawback is to apply a semi-supervised-based deep neural network, which is used to transfer knowledge from the labeled training data to unlabeled ones, which proved to be beneficial [37; 38; 39]. Images acquired in this study are only obtained from one greenhouse. Additional images from different kinds of greenhouse will be acquired to train a more robust model, which will require a more laborious labelling work; the semi-supervised methods could be referenced. Tomatoes are commercially picked at the mature green stage due to the market requirement on fruit freshness, firmness and transportation-related injury [4; 5; 6]. Considering the color similarity between mature green tomatoes and environment, overlap among fruits, cover by leafs and branches, this paper proposes to use Mask R-CNN for the detection and segmentation of mature green tomatoes. Compared to laboratory experiment conditions, the greenhouse environment can be more challenging for image analysis, which can limit the placement of location of a camera and its field of view. Can we accurately detect and segment mature green tomato fruits in real harvesting conditions? The specific work is summarized as follows:  
In order to make sure the diversity of a mature green tomato dataset, and simulate the automatic harvesting condition, this study proposes an automatic image acquisition method. The greenhouse mobile robot is designed to traverse along the navigation path by the electromagnetic tracking method and shoot the video of tomatoes in a greenhouse, the image dataset is obtained by intercepting the video frames. The detection and segmentation of mature green tomatoes is realized based on the Mask R-CNN model. A total of 2240 tomato images are selected as a training set to train the Mask R-CNN network model, 620 images as a verification set and 320 images as a test set. Simulation results prove the effectiveness of this method, especially for those images with fruit overlapping, leaf occlusion, branch occlusion and other complex states. When IoU is 0.5, the model performance is the best, the F1-Score of bounding box and mask region for test set both reach 92.0%. This method can significantly improve the detection and segmentation performance of mature green tomatoes in the complex greenhouse environment, which will lay a foundation for the accurate identification of mature green tomatoes for harvesting robots
# Feasibility Study of Using Carbide Slag as In-Bed Desulfurizer in Circulating Fluidized Bed Boiler
## 4 Conclusions  
The desulfurization experiments were carried out on TGA, a 1 MWth pilot CFB boiler, and a 690 t-h-1 boiler to explore the feasibility of carbide slag as an alternative in-bed desulfurizer for the coal-fire CFB plant. The following conclusions can be drawn:  
(1) Compared with the limestone, the carbide slag had significantly finer particle size and a lower calcination temperature. Its calcined product had a better pore structure and desulfurization  
Figure 11: Bed temperature on original SO2 emissions in 690 t-h-1 CFB boiler (Ca/S = 0).  
Figure 12: The SO2 emission for each condition of field tests.  
activity. The sulfation of carbide slag was always in the kinetics-controlled stage during the whole sulfation process.  
(2) The carbide slag exhibited higher desulfurization efficiency in the pilot tests, which is conducive to saving the amount of desulfurizer. At the same time, it had better sintering-resistance properties, which makes it possible to effectively control SO\({}_{2}\) emissions even at high combustion temperatures (>910 \({}^{\circ}\)C).  
(3) The mean size of carbide slag was only 52.5 \(\upmu\)m. Therefore, different results appeared in the field test. When the separation efficiency of the cyclone was low, the desulfurization efficiency of carbide slag was inferior than limestone. When the separation efficiency of the cyclone was high, the desulfurization efficiency of the carbide slag was better than limestone.  
(4) The desulfurization efficiency of carbide slag with the same Ca/S mole ratio was higher than that of limestone after cyclone vortex finder modification. When Ca/S = 2, the average SO\({}_{2}\) emission concentration using carbide slag was 78.3% of limestone, and for Ca/S = 3, the result was 58.4%.  
Writing--Review & Editing, Z.H., J.L. and L.D.; Project administration, D.C.  
This study was supported by the Beijing Nova Program (XX2018033).  
[PERSON] and [PERSON] provided valuable advice on the study idea and write-up.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
###### Abstract  
Biodiesel is a renewable fuel that has been widely used in recent years. There are various resources used as biodiesel feedstocks, including animal fats, waste oils, and vegetable oils. In the present study, _Chrozophora_ _tinctoria_ seed oil is introduced as a new biodiesel feedstock. _C. tinctoria_ is a weed and non-edible plant. So, the primary cost of this resource is very low, and hence it can be considered as a biodiesel source. This plant can also grow in most weather conditions. In the present study, the research team tried to produce biodiesel from _C. tinctoria_ seeds through a transesterification reaction. To intensify the transesterification reaction, an ultrasonic device was used. In order to perform the transesterification reaction, potassium hydroxide was used as a catalyst. Important parameters, such as the reaction temperature, reaction time, molar ratio of methanol, and concentration of the catalyst, were adjusted. Based on the adjusted conditions, a biodiesel yield of 84% was attained. The properties of the _C. tinctoria_ biodiesel was compared with the American Society for Testing and Materials (ASTM) standard. The results show the properties of a biodiesel: the density, kinematic viscosity, pour point, flash point, cloud point, and acid number are 0.868 g/cm\({}^{3}\), 3.74 mPa, -7 \({}^{\circ}\)C, 169 \({}^{\circ}\)C, 4 \({}^{\circ}\)C, and 0.43 mg, respectively. The specification properties of _C. tinctoria_ biodiesel can thus pass the requirement of the ASTM standard. So, _C. tinctoria_ seed oil can be used as a suitable fuel source instead of petroleum-derived fuels.  
biomass; biodiesel; _Chrozophora_ _tinctoria_; seed oil
# The Application of Structural Reliability and Sensitivity Analysis in Engineering Practice
## 1 Introduction
Standard safety assessments of civil engineering systems are carried out using safety factors. An alternative method to this approach is the assessment of an engineering system using reliability analysis of the structure. In the reliability analysis of the structure, both the uncertainty of the load and the properties of the materials or geometry are taken into account explicitly. The uncertainties are described in a probabilistic manner. Once we have defined the ultimate and serviceability limit state functions, we can calculate the failure probability for each state. Additionally, we can check what impact individual random variables have on the probability of failure. We will use both local and global sensitivity measures for this purpose. Classical, local measures are vectors, whose coordinates are the first partial derivatives of reliability indices evaluated in the design point. These values are obtained as a by-product of the First-Order Reliability Method. In this paper, global sensitivity measures are Sobol indices. Computations of the Sobol indices are carried out using the classic Monte Carlo method. Of course, the calculation of Sobolevindices can be implemented in various ways, for example, using the polynomial chaos expansion method or advanced variance reduction methods. The key problem of using the polynomial chaos expansion method is the choice of regression points needed to calculate the coefficients. Widely used methods are Pure Random Sampling or Latin Hypercube Sampling. Unfortunately, these are computationally expensive methods. The Monte Carlo method is the optimal choice. It is not computationally expensive nor too demanding. The aim of this article is not to define new sensitivity measures, but to show the advantages of using structural reliability and sensitivity analysis in everyday design practice. Using a simple cantilever beam as an example, we will present the calculation of probability failure, additionally with local and global sensitivity measures. If the influence of a given random variable on the probability of failure is small, then, in further calculations, we can treat it as a deterministic variable. The calculations will be performed using COMREL modules of the STRUREL computing environment. The STRUREL computing environment was developed at the Technical University of Munich and has been developed and tested for over 30 years. STRUREL 2022 (V.13) is a modular software package. The main STRUREL modules are the following: COMREL (for reliability analysis of components), SYSREL (for reliability analysis of systems), COMREL-TV (for reliability analysis of time-dependent variables), COSTREL (for reliability optimization), and STATREL (for statistical data analysis). The software offers state-of-the-art numerical methods for structural reliability analysis.
## 3 Numerical Calculation Application and Experimental Setup
### Rotordynamics Model Setup
#### 3.4.4 Constraint and Load Condition  
Nodes on the bearings which are far away from the main shaft (representing the outer rings of the bearings) were fixed. Loading conditions of the rotordynamics model include two parts: (1) forces changing with time was applied on the mass point of impeller, including hydraulic radial forces on the impeller along the X and Y directions (coming from the calculated results of unsteady CFD model) and the gravity of impeller along the Y direction. (2) Rotational angular velocity was applied onto the main shaft and impeller to calculate the Coriolis force. The Coriolis force comes from the rotating inertia of the rotor system, and it is used to describe the migration effect of rectilinear motion mass in the rotating system. The calculation formula of the Coriolis force is shown as  
\[F_{k}=-2n\overset{\rightarrow}{\alpha\
u}\times\overset{\rightarrow}{\upsilon} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Density (kg m\({}^{-3}\))** & **Elasticity Modulus (Pa)** & **Poisson’s Ratio** \\ \hline
7890 & \(2.09\times 10^{11}\) & 0.269 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material properties of the main shaft.  
Figure 7: Finite element mesh of the rotordynamics model.  
where \(F_{k}\) is the geostrophic force (N), m denotes the mass (kg), \(\overrightarrow{\omega}_{r}\) is the angular velocity vector of rotor (rad/s), and \(\overrightarrow{v^{\prime}}\) is the velocity vector relative to the reference rotating frame (m/s).
# Route Optimization for Open Vehicle Routing Problem (OVRP): A Mathematical and Solution Approach
## 3 Materials and Methods
### Experiment Design
#### 3.1.1 Experimentation  
The purpose of the proposed computational experimentation is to compare the AMPL, LKH-3 and heuristic tools implemented in Python. Their performance is evaluated in terms of solution quality and execution time. The mathematical formulation with AMPL, allows for the solving of variants of the OVRPs [26]. Consequently, a comparison with LKH-3 allows us to relate the performance of the generated instances; this algorithm stands out for its efficiency in solving the Traveler of Trade and VRP [27]. The development of heuristic algorithms in Python 3.12 allow us to implement and evaluate heuristic algorithms, such as Greedy, Nearest Neighbor and 2-opt. These algorithms are designed to provide solutions in a reasonable computational time.