esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Integrated System Testing
### Scenario 3  
#### 5.3.1 Test Settings  
In this scenario, we wanted to verify whether the smart gateway was capable of automatically activating GSM/SMS communication as a back-up mechanism in the event that the Ethernet failed.  
The test timeline (21:00-22:00) is shown in Table 2. The monitoring system was set to transmit data every ten minutes on the temperature and humidity of the warehouse using Ethernet communication. To verify whether the back-up mechanism of the smart gateway was operational, we set the Ethernet to fail at 21:15. When the smart gateway detects that the Ethernet connection has been broken, the system should automatically switch to transmitting data via the GSM/SMS module. The Ethernet connection was reestablished at 21:45. The smart gateway should detect this and automatically switch the system back to transmitting data via the Ethernet. The test ended at 22:00.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Timing** & **21:00** & **21:15** & **21:45** & **22:00** \\ \hline \multirow{3}{*}{System State} & \multirow{3}{*}{System on} & Ethernet connection fails (system automatically switches to transmitting data via GSM) & Ethernet connection recovers (system automatically switches back to transmitting data via the Ethernet) & \\  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Testing the functionality of the smart gateway.  
Figure 17: Temperature, humidity, pressure, and battery power of Sensor 1 in Scenario 2.
# Sensorless Air Flow Control in an HVAC System through Deep Learning
## 3 Predicting Static Pressure Using Deep Learning  
In this section, we introduce a static pressure sensor prediction model for HVAC systems using Deep Neural Networks (DNNs). Since the static pressure and the input data used for the static pressure prediction have time-series characteristics, the Recurrent Neural Network (RNN) model is expected to be the most effective for learning multivariate time-series data. Among the RNN variants, we utilized the LSTM method, which avoids the vanishing gradient issue of the Vanilla RNN. This is discussed in Sections 3.1 and 3.2 in more detail.  
For the LSTM model, we identified various factors that affect the static pressure in the HVAC operation when it is adjusted according to the difference between the set-point static pressure and the current static pressure. Then, we analyzed the data characteristics of these selected input values. We used part of the accumulated data sets, which consist of multiple variables in chronological order, to train and validate the LSTM. Of the data sets used for the purpose, we used 20% for validation and 80% for training. Then, we used the LSTM to predict the current static pressure using input that was not used for training or validation. Figure 3 shows the overall flow of the training, validation, and test processes.  
Our proposed prediction model achieved a mean absolute percentage error (MAPE) of 2.45%. Below, we discuss the details of the LSTM model used for our prediction model, as well as the input data used to train it.
# Research on Performance Evaluation and Optimization Theory for Thermal Microscope Imaging Systems
## 3 Results
### MDTD Model  
Since MDTD also has many models, we derive the MDTD model of the TMISs based on the points and results of the FLIR 92 model [28]. Assuming that the target is a square with bandwidth W and the average value of the target pattern displayed on the display is \(\overline{I(x,y)}\Delta T\), where \(I(x,y)\) is the amplitude-normalized square target image, the display signal-to-noise ratio \(SNR_{d}\) for each image frame is  
\[SNR_{d}=\int_{\lambda_{1}}^{\lambda_{2}}\frac{\partial M_{0}(\lambda,T_{B})}{ \partial T}\overline{I(x,y)}\Delta T\times D^{*}(\lambda)d\lambda\times\frac{ \sqrt{A_{d}}\tau_{0}{D_{NA}}^{2}}{\sqrt{\Delta f_{n}}}\bigg{[}\frac{\Delta f_{ n}}{\int_{0}^{\infty}s(f)MT{r_{m}}^{2}(f)df}\bigg{]}^{1/2} \tag{21}\]  
When the observer looks at the target image, the visual signal-to-noise ratio \(SNR_{v}\) improvement manifests itself as:  
1. Time integration enables signal-to-noise ratio improvement \((t_{ef}p_{\tau})^{1/2}\);
2. Set the size of the line expansion function of the system in the vertical direction is \(\left|r_{y}\right|\), then the improvement of the spatial integration on the signal-to-noise ratio is \(\left[\left(W+\left|r_{y}\right|\Big{/}\beta\right)\right]^{1/2}\);
3. Human-eye frequency domain filtering effect, using f instead of the system bandwidth \(\Delta f_{eye}\). Taking all these factors into account, the visual signal-to-noise ratio can be rewritten as  
\[SNR_{v}=\int_{\lambda_{1}}^{\lambda_{2}}\frac{\partial M_{0}(\lambda,T_{B})}{ \partial T}\overline{I(x,y)}\Delta T\times D^{*}(\lambda)d\lambda\times\frac {\sqrt{A_{d}}\tau_{0}{D_{NA}}^{2}}{\sqrt{\Delta f_{n}}}\bigg{(}\frac{t_{ef}p \Delta f_{n}}{\Delta f_{eye}}\bigg{)}^{1/2}\bigg{(}\frac{W+\left|r_{y}\right| }{\beta}\bigg{)}^{1/2} \tag{22}\]  
set the visual threshold \(SNR_{TH}\) when the observer becomes able to detect, then \(\Delta T\) at this point is MDTD, If the r term is ignored, then the expression for MDTD is  
\[MDTD=\frac{SNR_{TH}\sqrt{2\Delta f_{n}}}{\overline{I(x,y)}\sqrt{A_{d}}\tau_{0} \int_{\lambda_{1}}^{\lambda_{2}}\frac{\partial M_{0}(\lambda,T_{B})}{\partial T }D^{*}(\lambda)d\lambda}(\frac{f\beta\Delta f_{eye}}{t_{ef}p\Delta f_{n}})^{1/ 2}\times\frac{1}{{D_{NA}}^{2}} \tag{23}\]  
where \(\Delta f_{eye}\) indicates the system bandwidth considering human eye filtering; \(t_{e}\) is the human eye integration time, which is generally taken between \(0.1-0.25\).  
Because the detectors of the TMISs are divided into IPD (ideal photon detector) and ITD (ideal thermal detector), we will derive and analyze these two types.
## 1 Introduction
A previous InSAR study of land subsidence based on Envisat data acquired during the years 2002-2010 revealed the occurrence of land subsidence over wide areas at a maximum rate of 15 mm/yr [35]. Sinkhole studies in the Karapinar area were based on ground observations, such as the study of [36], which detected a total of 102 sinkholes. More recently, Reference [37] reported that 332 sinkholes were formed in the region. The increased sinkhole formation reflects population growth, rapid economic development, and the sharp increase in agricultural groundwater usage. In this study, we expand the InSAR investigation of land subsidence in Karapinar region [35] using different data types that were acquired more recently. We analyze both Sentinel-1 (2014-2018) and COSMO SkyMed (2016-2017) SAR data, which were processed using the SBAS technique. The study is also based on Global Navigation Satellite System (GNSS) observations and monthly groundwater level data acquired in the study area. All three data types allow us to detect spatial and temporal patterns of land subsidence and obtain a better understanding of the relations between groundwater extraction, land subsidence, and sinkhole formation in the Karapinar basin.
## 3 Results and Discussion
### Weighted Average Ensemble Models
Similar values were obtained for the training and test datasets, indicating that no overfitting was detected. Next, we analysed the discrete ranking. In this case, higher overall ranks were obtained for models (6), (2) and (4). Since model (2) obtained the worst discrete rank in the training process, we concluded that the settings of models (4) and (6) are preferable. As can be seen from Table 14, both models performed well on the test dataset, with similar values of goodness-of-fit. Model (4) had a mean determination coefficient of 0.8201, a mean RMSE of 3.757, a mean MAE of 2.8768 and a mean MAPE of 15.845%, for the test dataset, while for model (6), the values were 0.822, 3.7562, 2.8755 and 15.8056%, respectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**WAE Model** & **Dataset** & \(\mathbf{R}^{2}/\mathbf{1}-\mathbf{R}^{2}\) & **MAE** & **MAPE** & **RMSE** & **Total Rank** & **Overall Rank** \\  & & **DIC** & **DIC** & **DIC** & **DIC** & **DIC** & **DIC** \\ \hline (1) & Training & 2/0.9925 & 3/0.9882 & 6/0.9779 & 2/0.9962 & 13/3.9548 & 21/7.9505 \\ \(\alpha_{1}=0,\,\alpha_{2}=0.4,\,\alpha_{3}=0.60\) & Test & 1/1 & 1/1 & 5/0.9957 & 1/1 & 8/3.9957 & 21/7.9505 \\ \hline (2) & Training & 3/0.9878 & 2/0.9923 & 2/0.9904 & 3/0.9940 & 10/3.9645 & 32/7.9222 \\ \(\alpha_{1}=0.15,\,\alpha_{2}=0.2,\,\alpha_{3}=0.65\) & Test & 5/0.9796 & 6/0.9928 & 6/0.9953 & 5/0.9900 & 22/3.9577 & 32/7.9222 \\ \hline (3) & Training & 1/1 & 1/1 & 1/1 & 1/1 & 4/4 & \\ \(\alpha_{1}=0.25,\,\alpha_{2}=0.15,\,\alpha_{3}=0.60\) & Test & 6/0.9790 & 5/0.9934 & 4/0.9927 & 6/0.9898 & 21/3.9594 & 25/7.9594 \\ \hline (4) & Training & 6/0.9691 & 5/0.9847 & 4/0.9858 & 6/0.9844 & 21/3.9240 & \\ \(\alpha_{1}=0,\,\alpha_{2}=0.15,\,\alpha_{3}=0.85\) & Test & 3/0.9829 & 2/0.9945 & 1/1 & 2/0.9916 & 8/3.9690 & 29/7.8930 \\ \hline (5) & Training & 4/0.9700 & 4/0.9852 & 3/0.9863 & 5/0.9849 & 16/3.9264 & 26/7.8949 \\ \(\alpha_{1}=0.01,\,\alpha_{2}=0.15,\,\alpha_{3}=0.84\) & Test & 2/0.9829 & 3/0.9944 & 2/0.9998 & 3/0.9914 & 10/3.9685 & \\ \hline (6) & Training & 5/0.9700 & 6/0.9835 & 5/0.9823 & 4/0.9850 & 20/3.9208 & 35/7.8861 \\ \(\alpha_{1}=0,\,\alpha_{2}=0.2,\,\alpha_{3}=0.80\) & Test & 4/0.9823 & 4/0.9941 & 3/0.9975 & 4/0.9914 & 15/3.9653 & 35/7.8861 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Ranking analysis of WAE models; D: discrete rank, C: continuous rank, learning rate \(=0.05\), number of estimators \(=130\), maximum depth \(=3\), \(p=0.75\), \(\epsilon=0.04\), \(\lambda=0.001\), \(C=6\).
## 3 Results and Discussion
In the case of polyethylene, material model 24 gives differences of 4.78% for the F\({}_{\rm z}\) force, 4.68% for the F\({}_{\rm x}\) force and 4.30% for the F\({}_{\rm y}\) force, while material model 89 gives differences of 2.43% for the F\({}_{\rm z}\) force, 2.71% for the F\({}_{\rm x}\) force and 2.60% for the F\({}_{\rm y}\) force. According to the analysis of the data presented above, a very good agreement can be observed in this case as well, especially when using material model 89, but also when using material model 24, with a maximum difference between simulation and experiments of 4.78%. Material 89 outperforms material 24 in predicting forces because it provides a continuous, analytical stress response (no tabular interpolation), which avoids artificial stiffness or \"steps\". The decrease in accuracy in predicting specific strain at PA 6.6 is due to the existence of a yield plateau and material 89 uses a simple equation which oversimplifies this curve, especially in the post-yield region. Also, strain prediction accuracy depends on how well the material model can capture strain localization (necking). Material 24's tabulated form allows it to follow strain softening or local necking onset better than material 89.
## 2 Materials and Methods
### Experimental
#### 2.1.3 Ultrasound-Assisted Extraction  
Because the extraction results may be influenced by the small variations in the experimental conditions, a 250 mL flat bottom flask with 7 cm diameter was selected due to a optimum extraction result according to the previous report [29]. Moreover, the bottom flask was kept at 2.5 cm above the bottom of the bath and the liquid height in the flask was kept at 6.4 cm. As the extraction solvent, the ethanol aqueous solution was added into the flask. Then, it was heated to the desired temperature. Five grams of dried sample powders were added into the flask. Agitate the mixture and then immerse the flask into the axis center position of the bath, which contains 3.5 L of water. The ethanol aqueous solution surface in the flask was maintained at the same height level of water. After that, ultrasound power and ultrasound frequency were adjusted to the proper values (according to the experimental planning). After irradiating under ultrasound for a desired time, the quercetin and rutin were induced into the liquid phase. When extraction finished, the flask was taken out and cooled to room temperature. The extract solution was filtered through a 0.45 \(\upmu\)m filter before HPLC analysis. The extraction yield of quercetin and rutin can be calculated using Equation (1) [30].  
\[\text{Extraction yield }\left(\text{mg/g}\right)=\frac{\text{amount of target analyte }\left(\text{mg}\right)}{\text{sample weight }\left(\text{g}\right)} \tag{1}\]where amount of target analyte represents the average mass of three subsequent sample determinations, and sample weight represents the average weight from 3 samples before extraction.
# Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review
## 4 Practical Implications
### Psychological and Affective States  
The integration of machine learning and wearable technologies into tennis offers several advanced methodologies for enhancing player performance through the monitoring of psychological states. Based on the studies by [PERSON] et al. [55] and [PERSON] et al. [43], several practical implications for coaches emerged.  
Firstly, the use of wearable technologies, such as PsychWear, which leverage inertial measurement unit (IMU) data, can predict a player's psychological state, specifically 'the zone', with an accuracy exceeding 85%. This requires initial training sessions where coaches provide subjective labels for each player, resulting in highly personalized models. These personalized assessments underscore the importance of the coach's role, as the system is designed to complement, rather than replace, their expertise. The subjective nature of labeling 'the zone' is not a limitation but a strength, considering the highly individual nature of this state. Each player must be labeled by their own coach, who understands their unique behaviors and cues. Consistency in labeling practices is essential, as the coach's expert insights are crucial for accurate model training. This personalized approach ensures that assessments are context-specific and tailored to individual needs.  
The principles of using wearable technology for psychological state monitoring in tennis can also be applied to other sports and physical activities where mental state significantly impacts performance. Moreover, utilizing video footage from real tennis matches allows for the training of machine learning models in naturalistic environments. This approach enhances the ecological validity of the models and their applicability to real-world scenarios. Advanced convolutional neural networks (CNNs) used in these models analyze players' bodily expressions to recognize affective states with up to 68.9% accuracy, providing coaches with valuable insights into players' emotional states during competition. As the field of wearable technology and machine learning evolves, coaches should stay informed about new developments and integrate the latest research findings into their training practices.
###### Abstract  
Acetylene (C\({}_{2}\)H\({}_{2}\)) gas sensors were developed by synthesizing a reduced graphene oxide (rGO)-loaded SnO\({}_{2}\) hybrid nanocomposite via a facile two-step hydrothermal method. Morphological characterizations showed the formation of well-dispersed SnO\({}_{2}\) nanoparticles loaded on the rGO sheets with excellent transparency and obvious fold boundary. Structural analysis revealed good agreement with the standard crystalline phases of SnO\({}_{2}\) and rGO. Gas sensing characteristics of the synthesized materials were carried out in a temperature range of 100-300 \({}^{\circ}\)C with various concentrations of C\({}_{2}\)H\({}_{2}\) gas. At 180 \({}^{\circ}\)C, the SnO\({}_{2}\)-rGO hybrid showed preferable detection of C\({}_{2}\)H\({}_{2}\) with high sensor response (12.4 toward 50 ppm), fast response-recovery time (54 s and 23 s), limit of detection (LOD) of 1.3 ppm and good linearity, with good selectivity and long-term stability. Furthermore, the possible gas sensing mechanism of the SnO\({}_{2}\)-rGO nanocomposites for C\({}_{2}\)H\({}_{2}\) gas were summarized and discussed in detail. Our work indicates that the addition of rGO would be effective in enhancing the sensing properties of metal oxide-based gas sensors for C\({}_{2}\)H\({}_{2}\) and may make a contribution to the development of an excellent ppm-level gas sensor for on-line monitoring of dissolved C\({}_{2}\)H\({}_{2}\) gas in transformer oil.  
SnO\({}_{2}\) nanoparticles; reduced graphene oxide; nanocomposite; dissolved gases in transformer oil; acetylene gas sensor; gas sensing mechanism +
Footnote †: journal: Applied Sciences
# Measuring the Performance of Wastewater Treatment in China
## 3 Empirical Analysis and Results
### Analysis of Inputs Efficiency Results from Year 2011 to 2015  
Observing Table 3, we see seven regions are located on the frontier of efficiency, with a result score of 1: Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, Shandong, and Guangdong. Those regions have better economic growth, and their expense on water treatment is larger. The other four regions, Inner Mongolia, Tibet, Hainan, and Ningxia, are also efficient, characterized by small populations with less sewage, no large-scale factories, and low industrial wastewater pollution. Qinghai has a population of 57 million people, and its wastewater treatment expense is only 1/5 of the previous year at 100 million RMB. Qinghai's discharged wastewater rose just a little by 0.1%, improving all efficiency results to 1 and allowing it to advance to the frontier in 2012. Comparing 2012 with 2011, the overall efficiency of China regressed. Actually, the average expense on wastewater treatment was 1.11 billion RMB less than 2011. However, expense efficiencies still showed the worst results. The nine regions, which are very inefficient on wastewater pollution treatment, performed under 0.6. Gansu was the worst at 0.164, having only 1.092 billion RMB on wastewater discharge expenses. Shaanxi improved its expense efficiency to 0.680. Guangxi wastewater treatment expense was 943 (100 million), less than 1444 (100 million) in 2011. In 2013, there were 13 regions having expense efficiencies under 0.6, making it the worst year under the 12 th Five-Year Plan. These 13 regions were very inefficient on wastewater pollution treatment expenses. The drop in wastewater treatment expenses in 2012 negatively impacted on 2013's expense efficiency. Although most regions saw better economic growth, they still neglected problems with the environment. In 2014, Liaoning spent 242 million RMB less on wastewater treatment than in 2013. The spending in the previous year brought excellent results this year. Inner Mongolia spent 2.777 billion RMB on wastewater treatment, which is its highest yearly expense during 2011-2015, bringing it an efficiency score of 0.9906. However, its expense efficiency was only 0.0602, which is the worst over the same full period. In 2015, there were 13 regions with expense efficiencies under 0.6. Guangxi wastewater treatment expense was 1.237 billion in 2014, more than before, resulting in an expense efficiency of 0.6251, or better than 13 regions. Six regions had expense efficiency under 0.6, which are the best results during 2011-2015. This implies the expense on wastewater discharge treatment saw lots of improvement. It was working in many regions in year 2015.
## 2 Models and Definitions
### Coverage Redundancy Rate  
The coverage redundancy rate (\(CRR\)) of node \(s_{i}\) is defined as the ratio of the sensing overlapping area of itself with other nodes within its one-hop communication radius and its sensing area [37]. \(\gamma(s_{i})\) is formulated as follows:  
\[\gamma(s_{i})=\frac{volume\left(\left(\underset{s_{i}\in neighbor(s_{i})}{ \cup}area(s_{j})\right)\cap area(s_{i})\right)}{volume\left(area(s_{i}) \right)} \tag{4}\]  
where \(area(s_{i})\) represents the sensing area of node \(s_{i}\).  
The formulation in practical use is defined as follows:  
\[\gamma(s_{i})=1-\prod_{j=1}^{n}\left(1-\frac{2\left[\frac{2}{3}\pi\left(R_{s} ^{3}-\frac{d^{3}(s_{i},s_{j})}{8}\right)-\frac{\pi d(s_{i},s_{j})}{2}\left(R_{ s}^{2}-\frac{d^{2}(s_{i},s_{j})}{4}\right)\right]}{\frac{4}{3}\pi R_{s}^{3}}\right) \tag{5}\]  
where \(n\) is the number of neighbor nodes of node \(s_{i}\) and \(d(s_{i},s_{j})\) is the distance between node \(s_{i}\) and its neighbor node \(s_{j}\).
## 2 Methodology  
### Dataset Description  
#### 2.1.1 Data Acquisition  
There are several natural image datasets available for the testing and evaluation of image classification models, such as ImageNet [15]. However, there is currently no publicly available dataset for radar images of highways, making it difficult to evaluate the performance of classification models for highway distress. From 2017 to 2022, this study successfully collected data from several highways in East China, with a total highway mileage of nearly 1500 km, mainly including the Ning-Yang (Nanjing-Yangzhou) Highway, Hu-Ning (Shanghai-Nanjing) Highway, Yanjiang Highway, and Zhendan Highway in Jiangsu Province, and the Taijin Highway, Yong-jin (Ningbo-Jinhua) Highway, and Hangxinjing (Hangzhou-Xin'anjiang-Jingde Town) Highway in Zhejiang Province.  
Highways in the region typically have a three-layer structure consisting of a surface layer, a water-stabilized base layer (including an upper and lower base layer), and a subbase layer, as shown in Figure 1.  
For the field data collection, the fourth-generation high-dynamic ground-penetrating radar MALA GX750 series manufactured by the Swedish company GuidelineGEO, Stockholm, Sweden, was used, with the following technical specifications: antenna frequency: 750 MHz; scanning speed: 1290 (traces/s); detection depth: 0\(\sim\)1.5 m; antenna coupling mode: air coupling; weight: 3.6 kg; detection vehicle speed: 80 (km/h).  
The distress information in the collected raw GPR data was difficult to recognize due to a large amount of background noise. To highlight the characteristics of each distress, a series of processing steps were taken, including dewow, move start time, compensation of energy decay, background removal, deconvolution, and bandpass filtering.
# Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video
###### Abstract  
Video clients employ HTTP-based adaptive bitrate (ABR) algorithms to optimize users' quality of experience (QoE). ABR algorithms adopt video quality based on the network conditions during playback. The existing state-of-the-art ABR algorithms ignore the fact that video streaming services deploy segment durations differently in different services, and HTTP clients offer distinct buffer sizes. The existing ABR algorithms use fixed control laws and are designed with predefined client/server settings. As a result, adaptation algorithms fail to achieve optimal performance across a variety of video client settings and QoE objectives. We propose a buffer- and segment-aware fuzzy-based ABR algorithm that selects video rates for future video segments based on segment duration and the client's buffer size in addition to throughput and playback buffer level. We demonstrate that the proposed algorithm guarantees high QoE across various video player settings and video content characteristics. The proposed algorithm efficiently utilizes bandwidth in order to download high-quality video segments and to guarantee high QoE. The results from our experiments reveal that the proposed adaptation algorithm outperforms state-of-the-art algorithms, providing improvements in average video rate, QoE, and bandwidth utilization, respectively, of 5% to 18%, about 13% to 30%, and up to 45%.  
fuzzy logic; adaptive bitrate; DASH; video streaming; QoE +
Footnote †: journal: Applied  
1
Footnote 1: email: {[EMAIL_ADDRESS]} (W.u.R.); [EMAIL_ADDRESS] (M.D.H.)