esa-sceva/satcom-chunk-collection huggingface dataset:
# Corrosion and Scaling in Geothermal Heat Exchangers
## 2 Heat Exchangers in Geothermal Power Plants  
### Types of Geothermal Plants  
Geothermal power plants use subsurface heat sources to produce electricity. Each plant is different and adapts to the geothermal source available. Relevant parameters include the type of geothermal fluid (liquid, vapor), temperature, and pressure. These parameters define the plant design that will convert heat into electricity most efficiently. Four main types of geothermal plants can be distinguished.  
* Dry Steam Power Plants: These plants use high-pressure, high-temperature steam directly from underground reservoirs to turn turbines and generate electricity. The steam is separated from any liquid water or brine that may be present in the reservoir. Dry steam plants are the oldest and most straightforward type of geothermal power plant. Vapor-dominated fields with such plants include Larderello and Monte Amiata (Italy), The Geysers (California), and Kamojang (Indonesia) [14].
* Flash Steam Power Plants: In these plants, high-pressure, high-temperature water from the geothermal reservoir is released into a lower-pressure environment, causing it to \"flash\" into steam. The steam is then used to drive a turbine. The remaining water is usually reinjected into the reservoir. Flash steam plants are the most common type of geothermal power plant [15].
* Binary Cycle Power Plants: Binary cycle plants use lower-temperature geothermal fluids, typically in the range of 93-166 \({}^{\circ}\)C. Instead of directly using the hot fluid to drive a turbine, they transfer the heat to a secondary fluid with a lower boiling point (such as isobutane, isopentane, or water and ammonia). This secondary fluid vaporizes and drives a turbine, which generates electricity. Binary cycle plants are more flexible in terms of the heat source they can use [16].
* Hybrid Systems: Some geothermal power plants combine different technologies, such as binary cycle systems with flash steam systems. These hybrid systems can optimize power generation by efficiently using both high-temperature and lower-temperature resources. These plants are generally designed to optimize the exploitation of the geothermal source at hand accounting for parameters such as pressure, temperature, composition, and nature of the fluid.  
In Dry Steam Power Plants and Flash Steam Power plants, geothermal fluid is directly used to activate the turbine hence no heat exchangers are required. Heat exchangers are mainly used for Binary Cycle Power Plants, which require the heat to be transferred from the low-temperature geothermal fluid to the organic working fluid, as illustrated in Figure 1. In these plants, the heat exchanger is a critical component for efficiently transferring heat and maximizing the conversion of geothermal energy into electricity.
## 3 Design and Sizing of the Hydrogen-Battery Hybrid Power System
### Hydrogen Storage Sizing  
Based on the platform dimensions, shown in Figure 2, and the design requirements, shown in Table 1, the authors selected a metal hydride tank [17], model MyH2-2000, sourced from H2 Planet (technical data are shown in Figure 9 and Table 4). Its design and technical characteristics made it suitable for integration into the Hygrobot platform for operational testing.  
Figure 8: PEM fuel cell performance characterization before and after the rejuvenation procedure.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Efficiency & 40\% @ max power & Stack size & \begin{tabular}{l} 303 mm \(\times\) 350 mm \\ \(\times\) 183 mm \\ \end{tabular} \\ \hline UE legislation & 2023/1542 & \
\begin{tabular}{l} Impact and \\ vibration tolerance \\ \end{tabular} &
\begin{tabular}{l} EU harmonised test \\ JRC99115 \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._  
Figure 9: Detail of the hydrogen tank MyH2-2000: (**left**) in the laboratory; (**right**) outdoors, on the Hygrobot platform.  
To estimate the continuous operating time range, we should take into account the energy stored in the hydrogen tank (1), \(E_{H2}\). For this calculation, it is necessary to know the hydrogen density, \(\delta_{H2}\), at normal conditions of temperature and pressure (20 \({}^{\circ}\)C and 0.1 Mpa) [20] and the lower heating value LHV\({}_{H2}\)[21]:  
\[E_{H2}\ =\ V_{\text{bot}}.\delta_{H2}.\text{LHV}_{H2} \tag{1}\]  
where:  
\(E_{H2}\) is the energy stored in the form of hydrogen (J).  
\(V_{bot}\) is the maximum hydrogen volume stored in the bottle (2000 dm\({}^{3}\)).  
\(\delta_{H2}\) is the hydrogen density at 20 \({}^{\circ}\)C and 1 atm (0.089 g/dm\({}^{3}\)).  
LHV\({}_{H2}\) is the hydrogen's lower heating value (120 MJ/kg).  
This results in \(E_{H2}\) = 21.36 MJ. The electrical energy that the PEM fuel cell can supply with this tank, \(E_{FC}\), is determined using the efficiency values shown in Table 3 with Equation (2):  
\[E_{FC}\ =\ E_{H2}.\eta_{FC} \tag{2}\]  
where:  
\(E_{FC}\) is the electrical energy delivered by the fuel cell (J).  
\(\eta_{FC}\) is the fuel cell efficiency (40%).  
This yields \(E_{FC}\) = 8.54 MJ. Then, the continuous operating time range of the fuel cell, \(T_{FC}\), is calculated with Equation (3):  
\[T_{FC}\ =\ \frac{E_{FC}}{P_{FC}} \tag{3}\]  
where:  
\(T_{FC}\) is the continuous operating time of the PEM fuel cell (h).  
\(P_{FC}\) is the fuel cell power when working at \(V_{FC}=V_{DCbus}\ =\ 36\) V (170 W, see Figure 8).  
This results in \(T_{FC}\) = 13.96 h.
# Research on Vehicle Trajectory Prediction and Warning Based on Mixed Neural Networks
## 2 Related Works
### Long Short-Term Memory  
Long short-term memory (LSTM) [15] is a kind of recurrent neural network (RNN) [16], which is the optimal choice for addressing the problem of time sequences, for instance predicting the stock price for the next week. The share price must be changed every weekday. Thus, the stock price will change as time passes by every day, in which each time sequence can represent one specific value of the stock price. LSTM and RNN are the most suitable for conducting the problem of time sequences.  
LSTM is the improved version of RNN. The structure of LSTM is shown in Figure 5 [17]. There are three specific features in the LSTM structure, including input gate, output gate, and forget gate (Figure 6). The major tasks of the gates are controlled by the LSTM, namely whether to abandon the history data, whether to let the data of new time sequences enter, and what data to output.  
Figure 4: Deciding the class of the bounding box. (**a**) Prediction bounding box; (**b**) the class probability map.  
The process of LSTM consists of three stages. In the first stage, the forget gate operates, making the decision to either keep or discard the last time sequence data. The forget gate compares the history data with the new input data and eventually makes a choice. Next, the input gate controls what new data is allowed to pass through. Finally, the output gate determines the content of the output data. All these three gates are sigmoid activation layers. The sigmoid value is either zero or one. If the sigmoid value is zero, the gate will close. When the sigmoid value is one, the gate will open.  
The above is the process of LSTM, and we can find that LSTM is very appropriate to handle the problem of time sequences. In addition, the model of LSTM can learn rapidly by controlling three gates. Due to the particular structure of the LSTM model, the LSTM model is easier to train than the RNN model.
## 2 Numerical Mean Echo Power Model for SAR Altimetry
### Altimetric Scenario  
The altimetric scenario and coordinate system are shown in Figure 1, where the radar altimeter moves in the \(y\)-\(z\) plane at constant velocity \(v\) and incident angle \(\mu\). Here, the \(x\)-\(y\) plane corresponds to the flat surface illuminated by radar antenna, which can be considered flat for airborne applications, while the \(z\) axis is provided by the line from the nadir point on the ground to the radar antenna. Thus, the location of the radar at time \(t\) is provided by  
\[x_{r}(t)=0,\;y_{r}(t)=vt\cos\mu,\;z_{r}(t)=h-vt\sin\mu, \tag{1}\]  
where \(h\) is the altitude when \(t=0\).  
Due to platform's movement, the radar antenna may not look vertically downward towards the nadir point, and its beam directions are defined by angle \(\xi\) with respect to the \(z\) axis and angle \(\phi\) with respect to the \(x\) axis. For simplification, we consider a Gaussian approximation of the radar antenna gain in the form  
\[G(\theta)=G_{0}e^{-(2/\gamma)\sin^{2}\theta}, \tag{2}\]  
where \(\theta\) is the angle relative to the antenna's boreside direction and \(\gamma\) determines the antenna's beamwidth, which is provided by \(\theta_{\rm 3 dB}=2\sin^{-1}\sqrt{(\gamma/2)\log 2}\).
## 4 Result and Discussion  
### The Impacts of Ethanol Addition to Fuel  
Figure 3 indicates the impacts of adding ethanol to the fuel at various fraction in different engine speeds. As can be seen in this figure, the engine power slightly increased by adding ethanol. The effect of employing ethanol as a blended secondary fuel on engine equivalent BSFC is shown in Figure 4. By increasing the ethanol fraction in the fuel mixture, the equivalent BSFC decreased, confirming the benefit of using biofuel in reducing fuel consumption and carbon dioxide. However, the increase in the ethanol fraction in the fuel mixture increased nitrogen oxide emissions, as shown in Figure 5. The exhaust nitrogen oxides levels increased by 18%, 46% and 110% at ethanol fractions of 5%, 10% and 20%, respectively, compared to the gasoline only operation. As indicated, the highest nitrogen oxide increase belongs to the E20 fuel; however, it had a significant BSFC reduction effect, as shown in Figure 3. E20 is selected to evaluate the effect of ammonia injection on nitrogen oxide emissions, and also other engine emission and performance parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Engine Speed** \\ **(RFM)** \\ \end{tabular} } & \multicolumn{3}{c}{**Engine Torque (Nm)**} & \multicolumn{3}{c}{**Engine BSFC (g/kWh)**} & \multicolumn{3}{c}{**Nitrogen Oxides Production Rate**} \\ \cline{2-10}  & **AVL Model** & **Experiment** & **Error (\%)** & **AVL Model** & **Experiment** & **Error (\%)** & **AVL Model** & **Experiment** & **Error (\%)** \\ \hline
1000 & 137.1 & 148.1 & 7.434 & 299 & 306.5 & 2.44 & 478 & 444 & 7.65 \\
2000 & 152.8 & 161.9 & 5.627 & 275.1 & 294.9 & 6.721 & 804 & 843 & 4.62 \\
4000 & 185.4 & 191.7 & 3.312 & 284.7 & 273.3 & 4.157 & 672 & 650 & 3.38 \\
6000 & 151.9 & 153.1 & 0.7838 & 319.9 & 319.8 & 0.024 & 558 & 540 & 3.3 \\
6500 & 136.9 & 140.2 & 2.375 & 337.3 & 342 & 1.387 & 439 & 456 & 3.72 \\
7000 & 123.9 & 129.8 & 4.569 & 352.2 & 358 & 1.611 & 413 & 402 & 2.73 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of engine torque, BSFC and nitrogen oxide production rate of AVL model with experimental tests at different engine speeds [3].  
Figure 2: The KIA Cerato engine installed in engine test room.
## 3 Results and Discussion
### Dependence of Extraction on Nitrate Concentration  
In order to investigate the type of complex formed, either ionic complex by ionization of phenolic group or the ion-pair complex by co-extraction of nitrate ion, the effect of nitrate concentration on the extraction was investigated. SC4A can hardly extract Na(I), thus sodium nitrate was added to aqueous phase to obtain desired nitrate concentration. A plot of log \(D\) versus log Na[NO\({}_{3}\)\({}^{-}\)] was constructed and shown in Figure 5. It can be seen that there is no significant effect of nitrate ion on extraction of Cs(I) and Sr(II). This result confirmed that the electroneutrality of the metal-SC4A complex was achieved by ionization of phenolic group(s) rather than the involvement of nitrate ion at the extraction conditions.  
Figure 4: Relationship between log \(D\) and log [SC4A]. Conditions: \(T=298\) K, pH = 6, [Metal]\({}_{\text{(a)}}\) = 1 \(\times\) 10\({}^{-4}\) M, contact time = 60 min, shaking speed: 150 rpm.
# A Channel Allocation Mechanism for Cellular Networks
## 5 Simulation Analyses  
Trace-driven experiments were designed and performed to analyze relationship of the traffic information and communication behaviors in cellular networks. For vehicle movement trace generation, this study simulated the highway scenario by VISSIM (V.5.20; Planung Transport Verkehr AG, Karlsruhe, Germany) and assigned the position of each cell and the position of the handover. The vehicle movement and MS communication traces are generated by a traffic simulation program VISSIM. This study considered a highway scenario that is characterized by the Wiedemann \"psycho-physical\" car-following model and lane changing model [34; 35; 36]. The conceptual development and limited available data can be considered by the Wiedemann model to generate traffic steam data for highways and freeways [27; 28]. For MS communication trace generation, this study assumed that the distribution of call holding time is exponential with the mean \(1/\mu\) and the distribution of call inter-arrival time is exponential with the mean \(1/\lambda\)[26; 27; 28; 29; 30; 31; 32; 33]. The communications of each MS were simulated according to the assumptions. Finally, this study assumed that an MS is carried in a car, so the vehicle movement traces and MS communication traces were combined in accordance with car IDs (shown in Figure 17).
# Outlier Reconstruction of NDVI for Vegetation-Cover Dynamic Analyses
## 2 Materials and Methods  
### Datasets  
Experimental analysis was performed on datasets for the estuary of Salween River (Figure 0(a)). Salween River is one of only a few long free-flowing rivers in Southeast Asia, originating in the Tibet Plateau and emptying into the Andaman Sea in the Indian Ocean [34]. On the basis of Shuttle Radar Topography Mission (SRTM) data, Figure 0(a),0(b) show the location and digital elevation model (DEM) of the study area, an area of about \(3.6\times 10^{3}\) km\({}^{2}\). We used optical remote-sensing images acquired by Landsat series satellite from the summer of 1973 to the summer of 2020. Unfortunately, the majority of summer images included a large number of outliers in cloud-covered pixels. Clear-sky images that could be used for deriving NDVI are from winter. Table 1 shows the temporal and spatial extent of the experimental dataset. Data were divided into training and testing sets; the former was used to determine the values of algorithm parameters, while the latter was used to evaluate the performance of the proposed algorithm. In this article, we reconstruct outliers of summer NDVI on the basis of the above datasets.  
The atmosphere correction method [35; 36] was used for remote-sensing images downloaded from the USGS Earth explorer system ([[https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)]([https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)), accessed on 19 September 2020). The spatial resolution of all optical remote-sensing images was resampled at 30 \(\times\) 30 m, and the study area was divided into 2160 \(\times\)1866 pixels (4,030,560 pixels). NDVI image series (Figure 1) were computed on ENVI 5.3 software. We stored all NDVI data in multidimensional arrays to construct the tensor.  
Figure 1: Study area location (**a**) DEM (**b**) and NDVI image series (Terrain data based on SRTM downloaded from USGS).
# Deterioration Process of Cementitious Material Properties under Internal Sulphate Attack
## 3 Results and Discussion
### Strength Measurements
#### 3.4.2 Effect of Gypsum Sand Particle Size on Strength  
Figure 16a,b show the compressive strength and flexural strength of mortar specimens with different particle sizes of gypsum aggregates at 4.8% SO\({}_{3}\) content in the aggregates under 20 \({}^{\circ}\)C curing, respectively. The results showed that the smaller the particle size of gypsum sand, the lower the compressive and flexural strengths of mortar specimens. The strengths of mortar specimens mixed with <0.3 mm, 0.3-1.18 mm and 1.18-2.36 mm gypsum sand grew first and then decreased; the 28-d compressive strengths of specimens with particle sizes <0.3 mm, 0.3-1.18 mm and 1.18-2.36 mm were 36.9 MPa, 35.7 MPa and 41.5 MPa; and flexural strengths were 6.3 MPa, 6.4 MPa and 6.8 MPa, respectively. At 360 d, compressive strengths were 32.9 MPa, 39.3 MPa and 43.7 MPa and flexural strengths were 4.6 MPa, 5.1 MPa and 6.4 MPa, respectively. From Figure 16, it can be seen that gypsum with particle sizes <0.3 mm, 0.3-1.18 mm and 1.18-2.36 mm mortar specimens swelling rate is 0.55%, 0.36% and 0.24%, respectively; <0.3 mm mortar expansion is the largest, causing greater losses. Compressive strength and flexural strength at 360 d are lower than at 28 d, because the smaller the particle size, gypsum and mortar slurry reaction contact area is  
Figure 15: Strength of mortars added with 4.8% SO\({}_{3}\) at different curing temperatures.  
large, accelerating the rate of calcium alumina formation and accelerating the attack on the specimen.
# A Medical Image Segmentation Network with Multi-Scale and Dual-Branch Attention
## 4 Experiments and Results
### Comparison of Parameters and FLOPs  
We compared the complexity and number of parameters of multiple models and performed a detailed analysis. As shown in Figure 8, our proposed method has lower complexity compared to classical CNN methods such as U-Net. Additionally, our method is less complex than transformer-based network structures such as UCTransNet and TransUNet. Figure 9 illustrates that compared to TransU-Net, our proposed method also reduced the number of parameters. Overall, our model shows certain advantages in practical applications by maintaining low complexity and ensuring no increase in the number of parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Acc** & **Dice** & **Precision** & **IoU** & **Recall** & **Infer-T** \\ \hline U-Net [11] & 0.868 & 0.805 & 0.823 & 0.718 & 0.811 & **2.93 s** \\ U-Net++ [25] & 0.849 & 0.790 & 0.795 & 0.656 & 0.811 & 5.96 s \\ MultiResUNet [45] & 0.851 & 0.764 & **0.828** & 0.672 & 0.746 & 2.45 s \\ AttentionU-Net [12] & 0.858 & 0.776 & 0.806 & 0.692 & 0.779 & 3.84 s \\ TransU-Net [46] & 0.849 & 0.819 & 0.795 & 0.736 & **0.889** & 5.36 s \\ UCTransNet [47] & 0.850 & 0.773 & 0.799 & 0.683 & 0.783 & 4.58 s \\ Ours & **0.869** & **0.811** & 0.823 & **0.721** & 0.827 & 3.79 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative comparisons with various models on the Colon Cancer Slice dataset.  
Figure 6: Visual comparison of image segmentation results of the ISIC 2018 dataset.
## 2 Port Vessel Detection System  
### Detection Process  
Usually, a frame of an image is read from the image sequence for detection. The detection process includes most of the elements presented in Figure 1, including the satellite, antennas, network equipment, server, cameras (video of the operation), etc. These elements work together to obtain well-defined output video image results.  
This study focuses on surveillance camera systems, which are tools that monitor and record activities in a specific environment. These systems consist of cameras, recording devices (DVRs or NVRs), and display systems. These elements all work together to improve security by capturing video footage. The cameras can be analog or IP, offering several functions, including motion detection, night vision, and PTZ (pan, tilt, and zoom). For data transmission, IP systems use network cables, while analog systems use coaxial cables. The recorded videos are stored on hard drives. Digital video recorders (DVRs) handle analog data, and network video recorders (NVRs) manage digital data from IP cameras. There are several types, including wireless, digital IP, and analog cameras, which meet different requirements. Their storage times vary depending on factors such as the resolution of video, the number of cameras recording, and the storage capacity of the device [14].  
In surveillance setups, the monitoring and display systems are the two main ways to view videos, including locally and remotely. For local viewing, monitors are often part of the system, allowing users to observe live images directly on site. These can be regular TVs or even computer monitors that connect to the recorder via an HDMI cable. For remote access, modern systems allow users to monitor their monitoring streams remotely, either via the internet or through a dedicated network. Cameras and related devices should be plugged into standard electrical outlets so that they can continue to record and store videos [15].  
Video images are powerful tools for communication; they comprise graphic interfaces that allow a digital camera or camcorder to send a video signal to an external viewing or recording device via a video output cable. Original video images contain noise interference, which is a mixture of nuisance alarms and/or false alarms that must be effectively eliminated to avoid misleading results. By definition, any signal produced by an adverse event is called a nuisance alarm, while a signal produced by the system's electronics that has nothing to do with a sensor or an event is called a false alarm [16; 17; 18; 19; 20; 21; 22; 23]. To avoid false alarms, the components of the system must be not only in a good state but also well-connected to each other [24].