esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this study, it was observed that a flat sheet iMBR set-up could successfully be used for stable production and in situ recovery of high concentrations of VFAs from excess sludge and food waste slurry in the long-term (3 months) semi-continuous process. To boost the digestibility of ESS and FWS, the effects of different mild thermal and thermochemical pretreatment conditions were investigated prior to iMBR fermentation. For both ESS and FWS, pretreatment with NaOH resulted in the highest increase in substrate sCOD and the roles of temperature and exposure time were minimal. The pretreatments did not affect the VFA concentration and C/N ratio considerably in the case of using ESS as substrate, however, thermochemically pretreated FWS yielded higher VFAs concentrations compared to non-pretreated FWS. VFAs yields of 0.38 gVFA/g VS\({}_{\text{added}}\) and 0.34 gVFA/g VS\({}_{\text{added}}\) were obtained, respectively, from ESS and FWS by applying anaerobic iMBR. The average flux for the excess sewage sludge reactor was 5.53 L/m\({}^{2}\)/h while it was 16.18 L/m\({}^{2}\)/h for the food waste slurry reactor. Considering different scenarios for the integration of the iMBR into the wastewater treatment process, it is estimated that using the proposed iMBRs working on food waste and/or sludge in a typical wastewater treatment plant can be a promising alternative to the provision of an external carbon source.  
Conceptualization, M.J.T., D.I., A.M. and S.W.; methodology, A.M., S.W. and M.P.; formal analysis, M.P., S.W. and A.M.; investigation, M.P. and S.W.; data curation, M.P.; writing original draft preparation, M.P., S.W., A.M.; writing review and editing, M.P., S.W., A.M., M.J.T. and D.I.; supervision, M.J.T. and D.I.; funding acquisition, M.J.T., A.M. All authors have read and agree to the published version of the manuscript.  
This research was funded by Sweden's Innovation Agency, Vinnova (Sweden), grant number 2018-03732 and the Swedish Agency for Economic and Regional Growth through a European Regional Development Fund grant number 20201656, Gryaab AB (Sweden) and Renova AB (Sweden).  
The authors would like to express their sincere gratitude to [PERSON] (Gryaab AB, Gothenburg, Sweden), [PERSON] (Renova AB, Gothenburg, Sweden) and [PERSON] (Chalmers University of Technology, Gothenburg, Sweden) for their constant scientific and technical support of the project.  
The authors declare no conflict of interest.
# Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System
## 2 Thermal Model
### Pulley Internal Thermal Analysis
#### 2.4.7 Input/Output Setting  
The input value of the thermal flux \(\Phi_{pn}\) flowing into the pulley model cannot be defined directly. In one full cycle of pulley rotation, the highlighted red surfaces at the crest of the grooved surface engage with the belt surfaces, frictional heat is generated, and a portion of this heat flows into the pulley. The flow direction is toward the pulley. Then, the surface disengages with the belt surfaces when the pulley rotates by a certain degree, and heat is dissipated through this surface. The flow direction at this instant is toward the outside of the pulley. It is impossible to change the flow direction based on the definition of the boundary conditions during the pulley high-speed revolutions. Hence, small solid sections (blue area in Figure 10) close to the surface are created to simulate the frictional heat generation. This section is close to the surface, and its influence on the temperature distribution at the pulley can be neglected. The highlighted red surfaces in Figure 10, together with the rest of the pulley surfaces, are set to dissipate the heat into the fluid domain.  
The thermal flux of the pulley \(\Phi_{pn}\) is dissipated into the environment and the end of the shaft. The boundary of the fluid domain is an open space, and hence it is defined as a pressure outlet with zero-gauge pressure. The backflow turbulent intensity is 5%, and the backflow turbulent viscosity ratio is 10. The boundary for the end of the shaft is set as a constant temperature, which is the ambient temperature.  
The output results of the numerical method provide abundant information, for instance, the temperature distribution of the pulley \(\mathbf{T}_{pn}^{\prime}\), area-weight average temperature on the pulley contact surfaces \(T_{pcn}^{\prime}\), and heat dissipation flux on the pulley exposed surfaces \(\Phi_{pn}^{\prime}\). The primes on these parameters denote that this information is based on the baseline conditions and not the predicted temperatures and information under the designed operating conditions.
# Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting
## 3 Simulation Study and Discussion of Results
### Simulation Results and Discussion
#### 3.4.2 Case Study 2--Fixed Load Curve  
The second case study considers a fixed daily load demand curve for the 15 days of the simulation, as given in Table 4. Following the same approach as in the case study 1, the MAPE value of wind power forecasts remains the same at over 26.62% in the reference scenario (a), while a clear improvement of 12.82% is observed in scenario (b), and even better results in scenario (c) with a MAPE of 12.02%. Regarding the distribution of forecasting accuracy on a daily basis, it is noted that 75% of forecasted values have an absolute error less than 45.36% in scenario (a), when compared with 16.81% in (b) and 15.46% in (c). Figure 5 shows the error distribution for the scenarios in case study 2. These results confirm the benefit from combining the ANN model based on statistical feature parameters with the output adjustment of the proposed PLM method in order to reduce the wind power forecasting errors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hour** & **Daily Load (kWh)** & **Hour** & **Daily Load (kWh)** & **Hour** & **Daily Load (kWh)** \\ \hline
1 & 6 & 9 & 6 & 17 & 6 \\
2 & 3 & 10 & 7 & 18 & 10 \\
3 & 2 & 11 & 8 & 19 & 12 \\
4 & 2 & 12 & 9 & 20 & 17 \\
5 & 2 & 13 & 15 & 21 & 13 \\
6 & 2 & 14 & 17 & 22 & 12 \\
7 & 2 & 15 & 12 & 23 & 11 \\
8 & 5 & 16 & 8 & 24 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Case study 2: Fixed daily load demand.  
Figure 4: Case Study 1: Electricity purchased vs. electricity sold in scenarios (**a,b**). The diagram on the right shows the increase in selling events with respect to the scenario (**a**).
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 4 Proposed Method
### Location Estimation
#### 4.3.2 Sampling  
For location estimation, each node generates new samples from the current sample. The node will choose a random point within the radius \(v_{max}+\ \alpha\) from the current sample [16] as shown in the given equation:  
\[p(S_{t}|S_{t-1})=\ \left\{\begin{array}{ll}\frac{1}{\pi(v_{max}+\ \alpha)^{2}}&\ \ \ \textit{if}\ d(S_{t},S_{t-1})\leq\ v_{max}\\ 0&\ \ \ \ \textit{if}\ d(S_{t},S_{t-1})>\ v_{max}\end{array}\right. \tag{4}\]  
\(d(S_{t},S_{t-1})\) represents the distance between the locations of a sample at time \(t\) and \(t-1\) while \(v_{max}\) is the maximum speed of a node. \(\alpha\) is the variability for choosing new samples and \(\alpha\) must not be too low so that it can provide variability in sampling even when the node's speed is slow. In this paper, we set \(\alpha=0.1r\) that is determined empirically as in MSL\({}^{*}\)[16], where \(r\) is the radio range radius of the sensor nodes. The sample also must be within the \(v_{max}\) radius for mobile nodes.  
The weight of each sample is determined by the product of partial weights, \(w_{s}^{\prime}(q)\)[16] as described in the following equation:  
\[w_{s}(p)=\ \prod\
olimits_{q=1}^{k}w_{s}^{\prime}(q) \tag{5}\]  
\(k\) represents the number of neighbors in the first hop and second hop. \(q\) is a neighbor of node \(p\).  
Partial weights, \(w_{s}^{\prime}(q)\) that corresponds to neighboring seed nodes are set to 1 if \(d(s,q)\leq r\) for first hop seed nodes and \(r\leq d(s,q)\leq\ 2r\) for second hop seed nodes. For neighboring normal nodes, \(q\) in the first and second hop, the partial weights \(w_{s}^{\prime}(q)\) can be calculated according to (6) and (7) respectively as follows:  
\[w_{s}^{\prime}(q)=\ \sum_{q_{i}}w(q_{i}),\ \ \textit{where}\ d(s,q_{i})\leq r+\ v _{max} \tag{6}\]  
\[w_{s}^{\prime}(q)=\ \sum_{q_{i}}w(q_{i}),\ \textit{where}\ \ r-v_{max}\leq d(s,q_{i})\leq 2r+\ v _{max} \tag{7}\]Sample \(S\) is kept if \(w_{s}(p)\) is larger than the threshold value \(\beta\) in which, \(\beta\) depends on the total number of neighbors in first hop and second hop (\(t\)) of node \(p\). The value of \(\beta=\left(0.1\right)^{t}\) represents that the uncertainty will decrease when the number of neighbors increase, which is unique for each node. After the number of samples obtained is sufficient or at maximum, the weights are normalized to ensure their total is equal to one. The weight normalization for the \(i\)-th sample is given by:  
\[\frac{w_{i}(p)}{\sum_{j=1}^{N}w_{j}(p)} \tag{8}\]  
where \(N\) denotes the total number of the sample used for node \(p\).
# Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery
## 2 Materials and Methods
### Data Sources
#### 2.2.2 Ground Truth Data  
Field investigations were conducted in late April, 2015 to coincide with the acquisition time of Landsat TM-8 OLI remote sensing images. The date of the field work was selected at the early spring to avoid the impact of frozen and the vegetation on the spectral characteristics of the saline-alkali land. In this case, 42 sampling sites were evenly distributed at the study area and were located on both sides of the roads considered its representatives  
\begin{table}
\begin{tabular}{c c c} \hline
**Band Name** & **Spectral Range/\(\mu\)m** & **Spatial Resolution/m** \\ \hline Band1 Coastal & 0.43–0.45 & 30 \\ Band2 Blue & 0.45–0.51 & 30 \\ Band3 Green & 0.53–0.59 & 30 \\ Band4 Red & 0.64–0.67 & 30 \\ Band5 NIR & 0.85–0.88 & 30 \\ Band6 SWIR1 & 1.57–1.65 & 30 \\ Band7 SWIR2 & 2.11–2.29 & 30 \\ Band8 Pan & 0.50–0.68 & 15 \\ Band9 Cirrus & 1.36–1.38 & 30 \\ Band10 TIRS1 & 10.60–11.19 & 100 \\ Band11 TIRS2 & 11.50–12.51 & 100 \\ \hline \end{tabular}
\end{table}
Table 1: The 11 bands and their resolutions of the Landsat-8 OLI data.  
Figure 1: The study area and sampling plots in the Songnen Plain.  
and accessibility (Figure 1). The geographic coordinates of the sampling sites were obtained using a handheld Trimble Geo Explorer GPS device (Trimble Navigation Ltd., Inc., Sunnyvale, CA, USA). The land cover type and vegetation communities were recorded at the sample sites. Surface soil samples (0-30 cm) were collected, and the soil salinity (electrical conductivity (EC)) was measured with a conductivity meter (REX DDS-307). The water content of the surface soil samples was obtained in the laboratory using the drying weighing method. It was converted to relative humidity (%) using the following formula [26]:  
\[RH=\frac{W_{M}}{\left(\frac{\mathrm{d_{s}-r_{s}}}{\mathrm{d_{s}}}\right)} \times 100\% \tag{1}\]  
where \(RH\) is the relative soil moisture, \(W_{M}\) is the mass water content of the soil, \(\mathrm{d_{s}}\) is the density of soil particles. A constant of 2.65 g/cm\({}^{3}\) was used according to the empirical calculation. \(\mathrm{r_{s}}\) is the soil bulk density.
## 3 Experiments
### Further Comparison and Results Visualization  
In this work, the training and testing speeds of each method were evaluated under the environment as mentioned in Section 3.5 and shown in Table 5. From Table 5, we can see that training the SVM model took much less time than that of the 1D-CNN model because training 1D-CNN requires more parameters and epochs. The time required for testing the SVM model and testing 1D-CNN model were both small, but the SVM model was still 33% faster than 1D-CNN. The training time required for the two-stage model 1D-CNN and SVM was 38.5 s, which was 10% faster than that of 1D-CNN and 1D-CNN which needed 43.2 s, and the testing time required for both methods was 1.2 s and 1.5 s, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **1D-CNN\&1D-CNN** & **1D-CNN\&SVM** \\ \hline  & **Location** & **Severity** & **Location** & **Severity** \\ \hline Fold 1 & 0.9989 & 0.9743 & 0.9984 & 1.0 \\ Fold 2 & 1.0 & 0.9734 & 1.0 & 1.0 \\ Fold 3 & 0.9968 & 0.9876 & 0.9991 & 1.0 \\ Fold 4 & 0.9937 & 0.9804 & 1.0 & 1.0 \\ Fold 5 & 1.0 & 0.9856 & 0.9994 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of 1D-CNN and 1D-CNN with 1D-CNN and SVM in two-stage classification performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **SVM** & **1D-CNN** & **1D-CNN\&1D-CNN** & **1D-CNN\&SVM** \\ \hline Fold 1 & 0.75 & 0.9718 & 0.9833 & 0.9966 \\ Fold 2 & 0.6964 & 0.9364 & 0.9921 & 1.0 \\ Fold 3 & 0.7143 & 0.9833 & 0.9845 & 0.9983 \\ Fold 4 & 0.8036 & 0.9718 & 0.9743 & 1.0 \\ Fold 5 & 0.8214 & 0.9645 & 0.9874 & 0.9989 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The 5-fold cross-validation results of the 4 methods on the test set.  
We further adopted t-distributed stochastic neighbor embedding (T-SNE) to visualize the classification results with the proposed model. T-SNE is a nonlinear dimensionality reduction method that can reduce high-dimensional data to two or three dimensions for visualization [31]. The T-SNE results of the test samples before and after classification are shown in Figure 6(a,b, respectively. Different colors represent different types of damage, and it can be found that the sample distribution was chaotic and nearly indistinguishable before classification; however, after classification, samples of different damage types were clustered together separately, indicating that the method proposed in this paper has a strong classification ability for structural vibration response samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **SVM** & **1D-CNN** & **1D-CNN\&1D-CNN** & **1D-CNN\&SVM** \\ \hline Train & 9.1 s & 31.5 s & 43.2 s & 38.5 s \\ Test & 0.6 s & 0.9 s & 1.5 s & 1.2 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: The time required for training and testing of the 4 methods.  
Figure 6: Confusion matrixes of 1D-CNN and 1D-CNN and SVM methods.  
Figure 7: _Cont_.
###### Abstract
The specific goals were as follows: (i) assessing energy consumption: analyzing the energy demands of different asphalt mixture production technologies (such as hot mix and warm mix) to identify high-energy consumption stages; (ii) analyzing environmental impact: reviewing the resource utilization, emissions, and potential environmental impacts associated with the production of asphalt mixtures, and emphasizing the importance of effective management and technological innovation in achieving energy conservation and sustainable development; (iii) proposing improvement measures: investigating how to reduce carbon emissions through the enhancement of raw materials, heating technologies, and the use of clean energy sources (such as substituting natural gas for fuel oil), with the aim of identifying construction materials that minimize their impact on the human environment, thereby promoting human health and contributing to the green development of asphalt concrete. This research holds significant importance in the field of road engineering. The following outlines the structure of this paper. Section 2 introduces the research approach and methods employed in this study. Section 3 provides foundational knowledge on road types, emphasizing the importance of assessing the energy consumption and greenhouse gas emissions associated with asphalt road surfaces. Next, Section 4 clarifies the impact of temperature on the energy consumption and gas emissions of asphalt mixtures. In Section 5, a detailed overview of the energy requirements and gas emissions during the different production processes of asphalt mixtures is presented. Section 6 reviews the energy consumption of asphalt and highlights the necessity of implementing measures to reduce the carbon footprint for sustainable development. Subsequently, Section 7 discusses low-carbon green development of road asphalt from the perspectives of recycled materials (recycled asphalt, recycled shredded rubber, and biochar) and clean energy. Finally, Section 8 summarizes the main conclusions of this investigation and proposes recommendations for future research directions.
## 5 HAWT Blade Design
### Configuration  
A favourable reduction in rotor nacelle weight and manufacturing costs occur with the use of fewer blades [16]. However, dynamic structural and balancing difficulties of the polar asymmetrical rotor are apparent [16]. Increased wear, inferior aesthetic qualities and bird conservation problems are also associated with one and two bladed rotors [17,18]. The three blade turbine (Figure 5) has been widely adopted (Table 4) as the most efficient design to meet environmental, commercial and economic constraints and therefore dominates today's large scale wind turbine industry. Modern commercially available wind turbines include complex control and safety systems, remote monitoring and maintenance with provision for the survival of lightning strike (Table 5).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Turbine Name**} & **Pitch or** & **Rotor Dia** & **No. of** & **Nacelle and Rotor** & **Weight per Swept** \\  & **Stall** & **(m)** & **Blades** & **Weight (kg)** & **Area (kg/m\({}^{2}\))** \\ \hline
**Mitsubishi MWT-1000 (1 MW)** & P & 57 & 3 & unspecified & \\
**Nordex N90 (2.3 MW)** & P & 90 & 3 & 84,500 & 13.3 \\
**Nordex N80 (2.5 MW)** & P & 80 & 3 & 80,500 & 16 \\
**Repower 5M (5 MW)** & P & 126 & 3 & Unspecified & \\
**Siemens** & P & 107 & 3 & 220,000 & 24.5 \\
**SWT-3.6-107 (3.6 MW)** & & & & \\
**Siemens** & P & 93 & 3 & 142,000 & 20.9 \\
**SWT-2.3-93 (2.3 MW)** & & & & \\
**Gamesa G90-2 MW (2 MW)** & P & 90 & 3 & 106,000 & 16.7 \\
**Gamesa G58-850 (850 kW)** & P & 58 & 3 & 35,000 & 13.3 \\
**Enercon E82 (2 MW)** & P & 82 & 3 & Unspecified & \\
**GE wind 3.6 sl (3.6 MW)** & P & 111 & 3 & Unspecified & \\
**Vestas V164 (7.0 MW)** & P & 164 & 3 & Unspecified & \\
**Vestas V90 (2 MW)** & P & 90 & 3 & 106,000 & 16.7 \\
**Vestas V82 (1.65 MW)** & P & 82 & 3 & 95,000 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 4: A selection of turbine size and weight configurations.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Rotor** \\ \hline _Diameter_ & 90 m \\ _Swept Area_ & 6362 m\({}^{2}\) \\ _Rotational Speed_ & 9–19 rpm \\ _Direction of Rotation_ & Clockwise from front \\ _Weight (including hub)_ & 36 T \\ _Top Head Weight_ & 106 T \\ \hline
**Blades** \\ \hline _Quantity_ & 3 \\ _Length_ & 44 m \\ _Aerofoils_ & Delft University and FFA-W3 \\ _Material_ & Pre impregnated epoxy glass fibre + carbon fibre \\ _Mass_ & 5800 kg \\ \hline
**Tower** \\ \hline _Tubular modular design_ & _Height_ & _Weight_ \\ _3 Section_ & 67 m & 153 T \\ _4 Section_ & 78 m & 203 T \\ _5 Section_ & 100 m & 255 T \\ \hline
**Gearbox** \\ \hline _Type_ & 1 planetary stage, 2 helical stages \\ _Ratio_ & 1:100 \\ _Cooling_ & Oil pump with oil cooler \\ _Oil heater_ & 2.2 kW \\ \hline \hline \end{tabular}
\end{table}
Table 5: A Typical modern 2 MW wind turbine specification.
# A Blockchain Protocol for Authenticating Space Communications between Satellites Constellations
## 5 Simulation Results  
To assess the efficiency of the proposed Blockchain protocol, PoST, firstly the PoST was implemented, a dataset was created and then two simulation experiments were conducted. The dataset consists of three swarms of satellites, A, B, and C. each swarm consists of 10 satellites. Table 1 shows the satellite transactions within swarm A, where the size of transactional data is 1049 bytes. Table 2 shows the satellite transactions that have been exchanged between swarm A and B where the size of transactional data is 2049 bytes. Table 3 shows the satellite transactions that have been exchanged between swarm B, and C where the size of transactional data is 3049 bytes. For each transactional data size (i.e., 1049, 2049, and 3049), the proposed protocol has been simulated on six samples of transactions each having 50, 100, 150, 200, 250, and 300 transactions. As there is no benchmark data suitable to evaluate the proposed blockchain protocol, the data set has been created using python random library as depicted in Tables 1-3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**A/A** & **S2** & **S10** & **S4** & **S5** & **S1** & **S7** & **S3** & **S8** & **SUM** \\ \hline
**S1** & 7 & 10 & 8 & 6 & & 6 & 4 & 9 & **50** \\ \hline
**S2** & & 13 & 19 & 15 & 17 & 10 & 14 & 12 & **100** \\ \hline
**S7** & 20 & 24 & 18 & 19 & 25 & & 21 & 23 & **150** \\ \hline
**S9** & 21 & 25 & 25 & 29 & 27 & 24 & 26 & 24 & **200** \\ \hline
**S5** & 38 & 32 & 30 & & 33 & 39 & 36 & 42 & **250** \\ \hline
**S10** & 45 & & 38 & 44 & 36 & 50 & 47 & 40 & **300** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Satellites transactions within Swarm A (size of a transaction is1049 bytes).  
Two main simulation experiments were designed to evaluate the performance of the PoST protocol.  
The purpose of the first experiment was to evaluate the blockchain performance in managing satellite transactions either exchanged within the same swarm (i.e., a constellation as specified in Algorithm 1) or between two different swarms as specified in Algorithm 2. While the purpose of the second experiment was to evaluate the reliability and authenticity of the proposed PoST.
# Enhanced Pet Behavior Prediction via S2 GAN-Based Heterogeneous Data Synthesis
## 2 Related Works
### Comparison with Previous Works  
Table 1 summarizes and compares existing studies based on data utilized, the algorithm of behavior prediction models, and the number of predicted behavior patterns. Research targeting pets is particularly challenging due to various constraints on data collection, such as pet stress, data collection spaces, and efforts to capture diverse behavior patterns. Especially, behaviors that occur with a low frequency are difficult to detect due to data imbalance, leading to a limited number of behaviors predicted in most studies. Collecting heterogeneous data is even more challenging because of the need for temporal synchronization and integration of data from different sources.  
Among the existing research on pets that use heterogeneous data, studies that incorporate both image data and sensor data include [PERSON] et al. (2021) [25] and [PERSON] et al. (2022) [9]. The study in [25] integrates sound data alongside image and sensor data, with a focus on predicting the behavior of rescue dogs. This research has limitations in that the video data annotated with sensor data are also limited.  
The study in [9] focused on behavior prediction using heterogeneous data, which integrates areas detected from video footage with accelerometer and gyroscope data collected from wearable devices, demonstrating the potential to enhance prediction accuracy by integrating various data sources. However, installing cameras in every location where pet data are collected, such as homes, can raise cost issues and privacy concerns for pet owners. This often leads to missing video data, highlighting the need for innovative solutions to address these challenges.  
This paper proposes an enhanced behavior prediction method using heterogeneous data, building on insights from previous research. The biggest issue with utilizing heterogeneous data is the problem of missing data during the collection process, which occurs more frequently in video data with a limited collection radius. Therefore, by synthesizing the features of the skeleton based on sensor data, we aim to address this issue and improve the performance of pet behavior prediction models.