esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Biodiesel Properties Obtained from Y. lipolytica W29 Lipids  
Based on the fatty acid composition of _Y. lipolytica_ W29 lipids, synthesized in two-stage batch cultures with a pulse of 18 g\(\cdot\)L\({}^{-1}\) VFAs, several properties that assess the quality of biodiesel were estimated using the BiodieselAnalyzer\(\copyright\) software. The main parameters predicted--density, kinematic viscosity, cetane number, oxidative stability, and iodine value--are within the ranges of the EU biodiesel standard EN 14214 (Table 2). Moreover, some of the parameters, namely density, kinematic viscosity, cetane number, higher heating value, oxidative stability, and iodine value are comparable with those of biodiesel produced from lipids of other _Y. lipolytica_ strains growing in VFAs [9] and non-detoxified liquid wheat straw hydrolysate [16].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & _Y. lipolytica_ W29** & _Y. lipolytica_ **NCYC** & **EU Biodiesel Standard** \\  & **(This Study)** & **2904 [9]** & **_Y. lipolytica_[17]** & **EN 14214** \\ \hline Density (kg\(\cdot\)m\({}^{-3}\)) & 862 & 874 & 860 & 860–900 \\ Kinematic viscosity (mm\({}^{2}\)\(\cdot\)s\({}^{-1}\)) & 3.7 & 3.9 & 4.5 & 3.5–5.0 \\ Cetane number & 56 & 55 & 58 & 47 min \\ Higher heating value (MJ\(\cdot\)kg\({}^{-1}\)) & 38 & 39 & 42 & NR \\ Cloud point (\({}^{\circ}\)C) & 1.3 & \(-\)0.84 & NR & NR \\ Oxidative Stability (h) & 10.4 & 9.8 & 8.5 & 6 min \\ Iodine value (mg I\({}_{2}\)/100 g) & 81.2 & 82.1 & 80 & 120 max \\ Degree of unsaturation & 89.5 & 90.3 & NR & NR \\ Cold filter plugging point (\({}^{\circ}\)C) & \(-\)0.6 & \(-\)6.6 & \(-\)10 & NR * \\ Pour Point (\({}^{\circ}\)C) & \(-\)5.4 & \(-\)7.7 & NR & NR \\ Saponification value (mg\(\cdot\)g\({}^{-1}\)) & 194.6 & 202.4 & 160 & NR \\ \hline \hline \end{tabular}
\end{table}
Table 2: Biodiesel properties obtained from _Y. lipolytica_ W29 lipids estimated by the BiodieselAnalyzer\(\copyright\) software, and comparison with other _Y. lipolytica_ strains and the EU biodiesel standard EN 14214.  
Figure 3: Time course of cellular growth (\(\copyright\)), VFAs (\(\blacksquare\), acetate; \(\blacktriangle\), propionate; \(\blacklozenge\), butyrate), and glucose (\(\bullet\)) concentration obtained in two-stage batch cultures of _Y. lipolytica_ W29 (**a**). After 24 h of growth in glucose, one single pulse of a mixture of 18 g\(\cdot\)L\({}^{-1}\) VFAs was added to the culture medium. Lipids content of _Y. lipolytica_ cells obtained at 48 h, 72 h, and 96 h of cultivation (**b**). The error bars represent the standard deviation of two independent replicates. Bars with the same letter do not present statistically significant differences (_p_ \(\geq\) 0.05).
###### Abstract  
The spraying process affects the microstructure and service life of thermal barrier coatings. In this work, by changing the plasma-spraying process, the second-phase micro-agglomerated particles were introduced to prepare embedded micron-agglomerated particle thermal barrier coatings. Conventional thermal barrier coatings and embedded micron-agglomerated particle thermal barrier coatings were prepared by setting spraying process parameters with different powder feeding rates and distances between powder feeders. Sintering experiments and thermal cycling experiments were carried out on conventional thermal barrier coatings and embedded micron-agglomerated particle thermal barrier coatings. The effects of spraying process parameters on the microstructure and thermal cycle life of embedded micron-agglomerated particle thermal barrier coatings were systematically studied. Three machine learning models of BP, SVM, and GA-SVM were established to evaluate the relationship between spraying parameters and coating microstructure and thermal cycle life. The results show that the sintering resistance of the new thermal barrier coating is 500% higher than that of the conventional thermal barrier coating. When the distance between the two powder feeders is 35 mm and the powder feeder 1 rate is 12 g/min, the thermal cycle life of the coating is the best. When using machine learning model evaluation, the evaluation results of the three machine learning models have certain accuracy. Among them, the GA-SVM machine learning model has the best prediction effect and the smallest prediction error. The results of this paper provide a new paradigm for further preparation of thermal barrier coatings with high performance and long life.  
Keywords:thermal barrier coatings; thermal cycle life; embedded micron particle; machine learning
## 3 Proposed Method
### Feature Extraction Layer  
To obtain effective facial features, we employ a convolutional neural network (CNN) as the feature extractor, consisting primarily of convolutional layers, max pooling layers, and batch normalization (BN) [33] layers. The convolutional layers play a crucial role in extracting local features from the input data. The parameters of the convolutional layers, including kernel size, padding, and stride, determine the size of the extracted feature maps. By convolving the local pixel matrix of the input layer with the corresponding kernel and accumulating the multiplications, we obtain a new pixel value in the feature map. Each convolutional kernel slides over the input feature maps with a specified stride and padding is applied to the input feature maps to increase their size during the convolution process.  
The shallow convolutional layers are primarily used to extract low-level features from images, such as edge and spatial contour information. Regardless of the model used, similar information is learned at this stage. Therefore, in this study, we first employed a convolutional layer to extract shallow features from the images. The input channels of the first convolutional layer were set to the 3 channels of the original image. We used 64 3 \(\times\) 3 convolutional kernels for the convolution operation in this layer. A nonlinear  
Figure 1: Framework for the proposed BGA-Net.  
activation function was applied after the convolutional layer to enhance the network's learning and fitting capabilities. In this study, we utilized the rectified linear unit (ReLU) activation function, as shown in Equation (1).  
\[f(x)=max(0,x) \tag{1}\]  
The selection of the ReLU activation function offers several advantages. Compared to traditional activation functions like tanh and sigmoid, ReLU computation is simpler and more computationally efficient, as it avoids exponential calculations. This helps reduce the network's floating-point operations (FLOPs), enabling the utilization of hardware acceleration and successful training of deep, multi-layer networks with nonlinear activation functions through backpropagation.  
To further reduce model complexity, we applied max pooling to the output feature maps of the convolutional layers. The pooling operation downsamples the image by a factor of two while maintaining the same number of channels. Additionally, we introduced batch normalization layers at the end of the feature extraction layer. The purpose of BN is to address the issue of numerical instability in deep neural networks by ensuring that the features of each batch have similar distributions, making the network easier to train.  
The specific architecture is depicted in Figure 2, where the blue blocks represent the convolutional layers and the yellow blocks represent the pooling layers.
## 3 Data Compression Technology-Based Intelligent Data Acquisition (IDAQ) System
### Multi-Input and Multi-Output System (MIMO)  
For an accurate SHM of civil structures, generally quite diverse structural response analytics are required, such as for acceleration, speed, displacement, load/strain, temperature, humidity, slope, and deflection. Furthermore, for each structural response, effectively synchronized data is essential, together with stable operation in real-time. From this point of view, using independent data loggers by existing data type (static or dynamic) is no longer feasible. Therefore, in this study, a multiple input and output system (MIMO) was built, considering the multiple measurements required and the effective data synchronization required in the measuring environment to guarantee real-time operation, as shown in Figures 6d and 7b. The MIMOs of the IDAQ system used the NI 9233 module, which can accommodate a piezo-type sensor group, and the NI 9237 module, which can accommodate a strain-type sensor group. Together, these two modules can acquire diverse static and dynamic data by effectively synchronizing them using the existing wire sensor group. The multiple output system of the IDAQ system used the NI 9263 module to generate an analog output signal (AO) in the range \(\pm\)10 V. Furthermore, to recollect and evaluate the output signal of the NI 9263 module, the analog input-dedicated NI 9215 module was included. This MIMO configuration can be used not only for structural monitoring, but also as the integrated system for feedback control when necessary.
## 1 Introduction
### Formal Definition of Approximate Frequent Item Set  
For any time interval \(I\) and any data item \(a\), let \(f_{a}(I)\) denote the frequency of item \(a\) in interval \(I\), _i.e._, the number of arrived items named \(a\) with timestamps falling into \(I\). Define \(f_{*}(I)=\sum_{a\in U}f_{a}(I)\) to be the total number of all arrived items with timestamps within \(I\).  
Given a user-specified error bound \(\epsilon\) and a window size \(W\), we want to maintain a data structure to answer any \(\epsilon\)-approximate frequent item set query for any sub-window (specified at query time), which is in the form \((\phi,W^{\prime})\) where \(\phi\in[\epsilon,1]\) is the required threshold and \(W^{\prime}\leq W\) is the sub-window size. Suppose that \(\tau_{\mathtt{cur}}\) is the current time. The answer to such a query is a set \(S\) of item names satisfying the following two conditions:  
1. \(S\) contains every item \(a\) whose frequency in interval \(I=[\tau_{\mathtt{cur}}-W^{\prime}+1,\tau_{\mathtt{cur}}]\) is at least \(\phi f_{*}(I)\), _i.e._, \(f_{a}(I)\geq\phi f_{*}(I)\).
2. For any item \(a\) in \(S\), its frequency in interval \(I\) is at least \((\phi-\epsilon)f_{*}(I)\), _i.e._, \(f_{a}(I)\geq(\phi-\epsilon)f_{*}(I)\).  
The set \(S\) is also called an \(\epsilon\)-approximate \(\phi\)-frequent item set. For example, assume \(\epsilon=1\%\), then the query \((10\%,10,000)\) would return all items whose frequencies in the last \(10,000\) time units are each at least \(10\%\) of the total item count, plus possibly some other items with frequency at least 9% of the total count.
# Single-Handed Gesture Recognition with RGB Camera for Drone Motion Control
## 4 Experiments and Results
### Posture-Based Gesture Recognition  
#### 4.2.1 Dataset Acquisition  
Before implementing the proposed MLP model-based HGR, it is essential to collect data to learn the posture-based hand gestures that need to be recognized. By default, in our system, an RGB camera is mounted on the forehead, so all scenes capturing hand gestures are considered from an egocentric perspective. As discussed in Section 3.2, the hand gestures associated with controlling the pitch and yaw can align directly with the drone's flight direction. These gestures can be estimated using only the coordinates of the hand model, eliminating the need for training. Hence, the data acquisition focused solely on ten posture-based gestures, specifically those indicating the roll, throttle, stop, and neutral states. Four subjects participated, with each subject performing each gesture an average of 25 times, resulting in a total of 1000 scenes. Of these, 75% were used for training and the remaining 25% for testing. To capture natural hand motions, the subjects were instructed to transition from the neutral state (with only the index finger extended) to other gestures during the experiment. To minimize storage usage, the gesture data were collected and stored as the 2D coordinates of 21 keypoints extracted from the MediaPipe hand tracker.  
Figure 6: Examples of motion-based hand gesture recognition. (**a–c**) Pitch-related gestures: forward, neutral, and backward. (**d–f**) Yaw-related gestures: yaw_left, neutral, and yaw_right.  
Figure 7: The confusion matrices of motion-based hand gesture recognition. (**a**) Pitch-related gestures. (**b**) Yaw-related gestures.
## 3 Results
### Verification of On-Site Falling Rock Tests  
An on-site rockfall collision test was carried out on a highway slope in Linwei District, Weinan City. This test aimed to evaluate the reliability of the multi-factor interaction regression model, as shown in Figure 18. The areas are typical for frequent falling rock occurrences and are influenced by gravity and geological conditions year-round. The slope height is about 8 m, with a slope angle of approximately 45\({}^{\circ}\). The natural soil slope consists of sand material. The falling rock is released under the influence of gravity without initial velocity, and its collision process is recorded using the high-speed camera.  
To validate the established multi-factor interaction model, the on-site test results for different falling rock shapes were compared with the estimated values of \(R_{\text{n}}\) from an empirical model developed by previous scholars, as shown in Table 5.  
The results show that the estimated values of the multi-factor interaction model under different falling rock shapes exhibit small errors and high reliability. In contrast, the errors of the non-interaction model and the other three empirical models are large [15; 24; 25], especially the Giacomini empirical model, where errors are significant and deviate considerably from the on-site test results. The multi-factor regression interaction model is relatively reliable and improves accuracy by approximately 68.52% compared to the Giacomini empirical model, making it suitable for widespread application in engineering.
## Appendix A Detailed CNN Model Description  
The proposed CNN-based power allocation model is implemented in PyTorch (version 1.10.0) and follows a dual-output architecture that simultaneously predicts both CPU-to-AP and AP-to-UE power distributions.  
Each sample is represented as a four-channel input tensor of size \(M\times K\), where \(M\) and \(K\) denote the number of APs and UEs, respectively. The four input channels include:  
* Channel gain magnitudes (from APs to UEs)* AP-UE distances
* Interference estimates
* Initial SINR values.
* **Architecture**:
* **Conv1:**\(3\to 16\) channels, \(3\times 3\) kernel, ReLU activation, batch normalization
* **Conv2:**\(16\to 32\) channels, \(3\times 3\) kernel, ReLU activation, batch normalization
* **Flatten:** Converts the 3D feature map to 1D vector of size \(32\times M\times K\)
* **FC1:** Fully connected layer with 1024 neurons, ReLU
* **Dual output heads:*
* **Head 1:**\(1024\to M\) \(\rightarrow\) CPU-to-AP power vector
* **Head 2:**\(1024\to M\times K\) \(\rightarrow\) AP-to-UE power matrix.  
**Training Setup:**  
* Optimizer: [PERSON]
* Learning rate: \(10^{-3}\)
* Batch size: 32
* Epochs: 50
* Loss function: Dual MSE loss with regularization (see Equation (7))
* Supervision: Optimal power values derived from max-min optimization.  
**Output:** The first output head predicts the CPU-to-AP power vector (\(M\)-dimensional), while the second predicts the AP-to-UE power matrix (\(M\times K\)). These outputs are constrained during postprocessing to meet per-AP power limits.
## 2 Results and Discussion
### Concentrations of the Target Analytes
Detection frequency of E1, E2, and E3 in raw wastewater samples at the five WWTPs was 80%, 60%, and 100%, respectively. E3, E1, and E2 were detected in raw wastewater at all WWTPs with concentrations ranging from \(21.4\pm 1.5\) ng/L to \(273.2\pm 16.2\) ng/L, \(<\)LOQ to \(156.0\pm 1.6\) ng/L, and \(<\)LOQ to \(99.1\pm 1.1\) ng/L, respectively. At some WWTPs, the concentration of E1 was higher than E3 in raw wastewater, while at other WWTPs, the concentration of E3 was higher than E1. The concentration of E3 is expected to be a function of temperature, pH and photolysis rate and faster conversion of E1 to E3 may have occurred at some WWTPs. E3 is a degradation product of both E1 and E2, and the frequency of detection of E2 was lower than E1. E3 was detected in treated wastewater at three WWTPs from \(12.0\pm 1.3\) ng/L to \(30.1\pm 0.8\) ng/L. In treated wastewater, E1 was detected at one WWTP at \(12.5\pm 1.5\) ng/L, while E2 was detected at two WWTPs from (\(30.5\pm 0.1\) ng/L and \(107.0\pm 8.9\) ng/L), which was within the range reported at other WWTPs [4; 11; 12; 13; 14; 15; 16]. Concentrations of E2 were significantly higher in treated wastewater than raw wastewater at one WWTP (\(107.0\pm 8.9\) ng/L and \(42.4\pm 4.7\) ng/L, respectively), while other WWTPs in Saskatchewan observed lower concentrations of E2 in treated wastewater. Most WWTPs outside of Saskatchewan observed lower concentrations of E2 in treated wastewater than raw wastewater [4; 11; 12; 13; 14; 15; 16]. The higher concentrations of E2 in treated wastewater may have been due to the de-glucuronidation and de-sulfation of the conjugated E2 as suggested by a previous Canadian study [15]. The maximum concentration of E2 in treated wastewater in this study was higher than previously reported in the literature [13]. DES was not detected. Prednisone (PRDN) is an analyte that is not commonly monitored in water or wastewater and was detected in raw wastewater collected from three of the five WWTPs (\(198.3\pm 14.2\), \(296.2\pm 56.7\) L, \(350.2\pm 14.3\) ng/L), while prednisolone (PRNL), which is the degradation product of PRDN was only detected at one WWTP (PRNL in raw wastewater at \(18.3\pm 0.2\) ng and in treated wastewater at \(57.8\pm 2.4\) ng/L). PRDN was also detected in raw wastewater at this wastewater treatment plant, indicating that degradation can occur within the wastewater treatment process. Both PRDN and PRNL were not detected downstream, indicating that they were removed by the wastewater treatment process and PRNL has been shown to degrade by UV/chlorination process [48]. Concentrations reported for PRDN, and PRNL herein were lower than in wastewater influent of the WWTP in Switzerland [5].
# Opportunistic Sensor Data Collection with Bluetooth Low Energy
## 3 Using Bluetooth Low Energy for Opportunistic Sensor Data Collection
### Bluetooth Low Energy Overview
#### 3.1.1 Physical Layer  
At the Physical Layer, BLE defines 40 Radio Frequency (RF) channels in the Industrial Scientific Medical (ISM) 2.4 GHz band. Such channels are divided into three advertising channels, which are used for broadcasting purposes, and 37 data channels, which allow bidirectional message exchange between two connected devices. The physical data rate is 1 Mbit/s.
# Modernization of Heavy Loaded Tram Radial Effect on Noise and Vibration
## 5 Findings from Experimental Noise Measurements  
The main objective of experimental noise measurements in localities **B1**, **B2**, and **B3** was to determine noise levels generated by standard operation in the modernised sections. In the location on Stur Street, in the densely built-up zone of the city, noise levels were measured in the vicinity of building no. 20 (building of the Police of the Slovak Republic) during standard operation on the tram line and local road in the given section, at point **B1H** (approx. 1.4 m from the building). As a relevant comparative noise parameter, the values of the equivalent continuous noise level \(L_{Aeq}\) in (dB) at weighted function A were applied. In measurements in the location on Stur Street, it was possible to measure noise levels in various acoustic and traffic conditions (diffuse noise field at maximum tram speeds, acceleration, and retardation of trans from distant stops).  
On the basis of the findings, the following can be stated:  
1. The results of experimental noise measurements at the measuring points on Stur Street were achieved for trans with speeds from 20 to 50 km/h in the given sections. To define the noise conditions for a given number of performed measurements, it was possible to select the passages of trans on the K1 and K2 tracks with extreme values of noise levels. Extreme values of noise levels \(L_{Aeq}\) in location **B1H** were achieved during tram passages on track K1 (approximately 3 m from point **B1H**), where in cases ofolder types of trans, the value max \(L_{Aeq}\) = 75.56 dB was reached (no. 12). In the case of new types of trans, the value max \(L_{Aeq}\) = 74.15 dB (no. 1). For the tram passage on track K2 (approximately 5.2 m from **B1H**), the value max \(L_{Aeq}\) = 72.24 dB (no. 8) was reached for older tram types and the value max \(L_{Aeq}\) = 72.90 dB (no. 10) for new tram types.
2. Extreme values of noise levels \(L_{Aeq}\) in **B2H** location (bridge approximately 4.0 m from track K1) were achieved during the passages of a new type of trans on track K1, where the value of max \(L_{Aeq}\) = 76.39 dB (No. 7), and during the passage of these trans on track K2 (approximately 6.0 m from **B2H**), the value max \(L_{Aeq}\) = 70.77 dB was reached (no. 6). Extreme values of noise levels \(L_{Aeq}\) in **B3H** location (bridge--approximately 4.0 m from track K1) were achieved for passage of a new type of trans on track K1 in the value max \(L_{Aeq}\) = 74.56 dB (no. 1) and during the passage of these trans on track K2 (approximately 6.0 m from B2H) value max \(L_{Aeq}\) = 68.41 dB (no. 4). Vibration and noise events are strongly correlated and influence each other. When the track oscillates while the tram is running on a geometrically and structurally poor-quality track, the noise from rolling the wheel on the track becomes the dominant source of the noise. Therefore, the correct and modern design of tram lines with anti-vibration elements in the subsoil significantly reduces the emission of noise into their surroundings.
# Structure-Aware Scheduling Methods for Scientific Workflows in Cloud
## 4 Algorithmic Solutions
### Level-Based Approach
#### 4.1.2 Task Assignment  
In task assignment, each \(vm_{i}\in R\) is expected to profit from executing a task. Accordingly, each task is assigned to the VM that obtains the maximum profit (utility). The profit is formulated to contribute towards minimizing the execution cost and time. Task assignment begins by organizing the tasks according to their EST (Algorithm 2, line 2). Beginning with the task with the earliest EST, the VM that achieves the highest utility from executing the task is identified (lines 4-10). For \(vm_{j}inR\), the execution utility of task \(v_{i}\) is determined using the following equation:  
\[u(vm_{j},v_{i})=\begin{cases}1&:AST(vm_{j},v_{i})=EST_{i}\\ \frac{1}{|AST(vm_{j},v_{i})-EST_{i}|}&:\text{Otherwise}\end{cases} \tag{2}\]  
where \(AST(vm_{j},v_{i})\) is the actual starting time (AST) of task \(v_{i}\) on \(vm_{j}\). The AST and EST are indirectly related to the obtained schedule efficiency. When the AST and EST values are equal, the ensuing schedule can be presumed to attain the best execution time results. Nevertheless, such a condition cannot be certain given the limited number of VMs available. Thus, a compromise is required to address both the execution time and the cost. Accordingly, the presented utility function aims to allocate a task to the VM, which reduces the gap between the EST and AST for the considered task. Nonetheless, a VM will not execute a task that will increase the VM run time to exceed the execution time upper bound, which is calculated by dividing the total running time of the group tasks by the total number of available VMs. Such constraints enable the establishment of an inter-VM load-balancing mechanism.  
```
Data:\(G\): set of groups; \(R\): available VMs Result:\(S\): schedule
1\(G\gets sortBasedonLevel(G)\) for\(g_{i}\in G\)do
2 for\(v_{i}\in g_{i}\)do
3 for\(vm_{i}\in R\)do
4 calculate \(u(vm_{i},v_{i})\)
5 end for
6\(temp\gets highestUtilityVM(v_{i})\) \(Add(v_{i},temp)\) to \(S\)
7 end for
8
9 end for return\(S\)
```  
**Algorithm 2**Task Assignment