esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficiency Analysis of Fixed-Speed Pump Drives with Various Types of Motors
## 1 Introduction
The analysis shows that the steady-state efficiency and power factor of the LSPMSM are significantly higher than that of the IM. However, the analysis was performed at the rated load. The operation of the LSPMSM as part of a pump unit is given in [PERSON] et al. [15]. The design of the 2.2 kW IM and LSPMSM and their characteristics in steady-state and transient modes in the case of the rated load and without load are provided in  
Figure 1: Energy consumption of various applications [5]. [PERSON] et al. [16]. At the rated power mode, a factor of annual cost savings in the case of replacing the IM by the LSPMSM is determined. One of the main conclusions drawn in References [14; 15; 16] is the apparent advantage of LSPMSMs over IMs based on parameters such as efficiency and power factor. However, it should be noted that in said publications, the comparison between IMs and LSPMSMs was performed mainly for the rated modes. The IE class of an electric motor is determined by its efficiency in the rated mode, in accordance with the IEC 60034-30-1 standard [6]. On the other hand, in heating, ventilation, and air conditioning (HVAC) applications, electric motors run in the rated mode for only a small fraction of the overall operation time. The present paper discusses operating modes of IMs and LSPMSMs as part of a pump unit with a variable load depending on instantaneous water consumption, for example, in an HVAC application. The paper includes the energy consumption calculations for electric drives at loads different from the rated load of the electric motor, and a comparison of the obtained data in order to assess the energy-saving potential of electric motors of the IE3 and IE4 energy efficiency classes. The main purpose of the present paper is to determine the criterion for selecting electric motors based on the condition of minimum energy consumption by considering a duty cycle of centrifugal pump units.
## 2 Materials and Methods
### Mathematical Model
\right.,\quad 2n \tag{7}\]  
where  
\(C(L_{i})=exp\left(-\frac{K_{g}\cdot\Delta y\cdot i}{L_{f}}\right)\)--the regularity of changes in the relative concentration of fuel depending on the length of the torch;  
\(K_{g}\)--parameter of the fuel burnout model, which depends on the completeness of fuel combustion in the flare and the type of burner;  
\(L_{f}\)--the length of the combustion zone, (m). Output parts of the heat balance of the \(i\)th zone are calculated according to (8)-(10). \[Q_{Me\;i}=C_{Me}\cdot\left[\left(t_{i}+273\right)^{4}-\left(t_{Me}+273\right)^ {4}\right]\cdot\Delta F_{Me_{i}} \tag{8}\]  
\[Q_{lost\;i}=k_{lost}\cdot t_{i}\cdot\Delta F_{Me_{i}}\cdot\xi_{gr} \tag{9}\]  
\[Q_{exg\;i} =\left(1-\sum\limits_{k=1}^{i}m_{k}\right)\cdot\left(B_{f}\cdot t _{i}\cdot c_{f}+L_{n}\cdot B_{f}\cdot t_{i}\cdot c_{air}\right)+ \tag{10}\] \[+\sum\limits_{k=1}^{i}m_{k}\cdot v_{exg}\cdot B_{f}\cdot t_{i} \cdot c_{exg}+\sum\limits_{k=1}^{i}\left(\Delta G_{2n-k+1}-\Delta G_{k}\right) \cdot t_{i}\cdot c_{exg}\]  
where  
\(C_{Me}\)--equivalent coefficient of radiation in the zone with respect to the conduction, (W/(m\({}^{2}\) K\({}^{4}\)))  
\(t_{Me}\)--temperature of the material surface, (\({}^{\circ}\)C);  
\(\Delta F_{Me_{i}}=\frac{F_{Me}}{2\cdot n}\)--metal surface area in the \(i\)th zone, (m\({}^{2}\));  
\(F_{Me}\)--total heat exchange area of the material, (m\({}^{2}\));  
\(k_{lost}\)--coefficient of heat transfer to the environment, (W/(m\({}^{2}\cdot\)K));  
\(\xi_{gr}=F_{lin}/F_{Me}\)--gradation of furnace laying;  
\(F_{lin}\)--total area of the furnace laying, (m\({}^{2}\));  
To determine the temperature change of the gases in the working chamber of the furnace, a system of heat balance equations was compiled for each \(i\)-th calculation zone. The total number of equations was \(2n\). Next, by solving the system, the temperature distribution of the gases along the trajectory of their movement was relatively determined. The value \(\Delta t\) = \(t_{max}-t_{min}\) was calculated as a parameter of the uniformity of gas temperature distribution in the working chamber of the furnace, where \(t_{max}\) and \(t_{min}\) are the maximum and minimum temperatures of gases in the furnace). Article [9] presents a comparison of the temperature distribution in the furnace chamber with a U-shaped trajectory of flue gas movement obtained by using a mathematical model with the results of temperature distribution measurements in a physical model- laboratory furnace. It was shown that the accuracy of the calculated results was approximately 1.4-2.45%. The mathematical model of the processes of gas dynamics and heat exchange in the heating furnace is quite complex, and does not allow establishing the analytical dependence of the uniformity of the gas temperature distribution \(\Delta t\) on the parameters that affect it. The use of the method of successive approximation [3] allows establishing dependence and eliminating the need to solve a system of \(2n\) nonlinear equations each time to determine the value of \(\Delta t\).
# Background-Filtering Feature-Enhanced Graph Neural Networks for Few-Shot Learning
## 3 Method
### Feature Extraction  
In order to create feature vectors, which serve as a strong basis for subsequently made classification choices, the feature extraction network must first process the original image data in the classification task. In particular, a background-filtering module is introduced by the BCNN (background-filtering convolutional neural network) used in this article. This novel extension of the CNN architecture is intended to improve the CNN's performance during the feature extraction process and successfully weaken the complex background in the original image against the target.  
The architecture of the BFCNN's feature extraction model illustrates its novel design concepts, as seen in Figure 2. The background dilution module in this procedure is the initial step in processing the input image. It cleverly employs algorithm strategies to dilute the image background, enhancing the visibility of the target object's features and establishing ideal conditions for the feature extraction that follows. The CNN feature extraction layer next receives the optimized picture input. After a sequence of convolution, pooling, and other processes, a highly summarized and highly distinct feature vector is produced, providing a strong basis for further classification tasks.  
\[v_{i}^{l=0}=f_{BFCNN}(x_{i};\theta_{BFCNN}) \tag{1}\]  
The above formula can be split into the following two modules:  
\[\widetilde{x}_{i}=f_{BF}(x_{i},\theta_{BF}) \tag{2}\]  
\[v_{i}^{l=0}=f_{cm}(\widetilde{x}_{i};\theta_{cm}) \tag{3}\]  
Figure 2 is a preview of the image effect obtained by passing the background-filtering module alone.  
The background in the original image on the left was obviously complex, with a variety of colors and textures. This presented obvious hurdles for the feature extraction and classification processes that followed (see the comparative image above, Figure 2). Following processing by the well-thought-out background-filtering module, the image on the right demonstrates a noteworthy optimization effect: the various colors in the background are deftly unified, minimizing the interference of redundant information, while the primary object that needs to be distinguished is still fairly visible, thus providing exceptional clarity and identification with little interference. The image content is significantly simplified by this processing effect, which also highlights the major object's features and produces more precise and lucid input data for the feature extraction processes that follow. This allows the feature extraction network to produce more precise and useful feature vectorsby concentrating more on obtaining the salient characteristics of the primary object. In the end, this modification not only makes the classification process more efficient overall but also greatly increases the classification's accuracy and resilience, greatly improving the image classification tasks' performance.
## 3 Results and Discussion  
### Validation of Analytical Method  
The value of LOD and LOQ were in the range of 0.114-0.185 ug/kg and 0.363-0.590 ug/kg for heavy metals, 0.064-0.108 ug/kg and 0.213-0.359 ug/kg for As species, and the values of correction coefficient (R\({}^{2}\)) calculated for all calibration curves were in the range of 0.9992-0.9997 for heavy metals, and 0.9996-0.9997 for As species (Table 2). The LC-ICP/MS chromatogram for standard solution of As species is presented in Figure 2. In this analytical method for As species, chromatographic peak separation was investigated. In Table 3, for CRMs analyzed using the sample matrix, the %bias values and %C.V were below \(\pm\)15% and below 10% deviation from the certified values. In addition, the recovery percentages were in the range of 85.4-105.4% for heavy metals and 95.6% for inorganic As, which is within the acceptable criteria [26; 28]. Moreover, extraction efficiencies (%mass balance; sum of As species/total As \(\times\) 100) were in the range of 72.7-95.4% for seaweeds, 107.8-110.6% for crustaceans, 93.6-116.7% for cephalopods, 63.6-121.7% for fish, and 61.8-86.3% for shellfish, respectively (Tables 4 and 5). Similarly, [PERSON] [24] reported that extraction efficiencies were ranged from 52.9 to 112.3% in marine samples. Based on these results for quality assurance, the analytical methods followed were found to be very efficient for application in the determination of heavy metal and As species in seaweeds and marine animals. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**Heavy Metals**} & \multicolumn{6}{c}{**As Species**} \\ \cline{2-10}  & **Cd** & **Pb** & **As** & **As(V)** & **As(III)** & **MMA** & **DMA** & **AsB** & **AsC** \\ \hline Linearity (R\({}^{2}\)) & 0.9992 & 0.9996 & 0.9997 & 0.9998 & 0.9999 & 0.9998 & 0.9996 & 0.9998 \\ LOD (ug/kg) \({}^{a}\) & 0.114 & 0.126 & 0.185 & 0.067 & 0.098 & 0.108 & 0.080 & 0.064 & 0.080 \\ LOQ (ug/kg) \({}^{b}\) & 0.363 & 0.402 & 0.590 & 0.223 & 0.328 & 0.359 & 0.268 & 0.213 & 0.268 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quality parameters of analytical methods for determination of heavy metals and As species.
## 4 Survey of Existing Works
### PSO in WSNs
#### 4.1.2 PSO in Node Localization
In this paper, a comparatively inexpensive localization scheme is presented. It is based on the measurements from a pedometer and communication ranging between neighbouring nodes. For ease of testing, a person equipped with a pedometer and an electronic compass is considered as the deployment agent. The pedometer provides the distance and the electronic compass gives the angle of heading with respect to the magnetic north. The proposed system works well in a sparse network. The localization information is obtained through a probability based algorithm that requires the solving of a nonlinear optimization problem. To obtain the optimum location of the sensor nodes, the PSO scheme that can be realized with a microcontroller for real time application is investigated in this paper. Experimental results show that the localization results of PSO and GNA are only slightly different. For the similar given measurements, both schemes are able to find similar maxima. The slight variations are due to the different stopping criteria. From the simulations, the run time is also found to be comparable. Nevertheless, it is to be noted that the PSO is more robust as it constantly yields a distinctive result whilst the GNA involves matrix inversion during its iteration. In another work, [PERSON] _et al._ have proposed a localization scheme for unknown emitter nodes in a WSN [57]. This system assumes that there are four beacon nodes with known locations. One or more unknown nodes transmit RF signals that can be received by the anchor nodes. A node at location \(O\) in the sensor field can estimate its distance from a beacon as where \(P\) is the power transmitted by the beacon and \(P_{0}\) is the power at unit distance \(d_{0}\). The only available information to the system is the received signal strength indicator which is in general not very accurate. To obtain better estimated sensor node locations, the PSO scheme that can be realized in real time is investigated in this paper. It is observed from the experimental results that the calculated loss exponent \(\alpha\) (a particle member) is between 3 to 5, which is a reasonable value as compared to other published research works. In general, the experimental errors are reasonable and are consistent with the simulation results. The results also validate that whenever the emitter node is near the centre of the rectangle, the error tends to be smaller. However, if the emitter node is moved closer to the area of the rectangle, the error increases significantly.
## 3 Results and Discussion
### Monitoring of Decolorization of TB and BR in RTWW: COD and FTIR Analyses
Compared with the FTIR spectrum of treated STWW-TB-SCD, TSWW-BR-SCD, RTWW-SCD, STWW-TB-SSM, and RTWW-SSM (Figure 6(c-g)), it is observed that the absorption peak of azo vibrations of wave number at 1400-1600 cm\({}^{-1}\) was significantly altered, which argued that the double bond -N=N- of TB and BR was depleted [24; 45]. Indeed, the degradation of azo dyes starts with the breaking of the azo double bond in the functional chromatic group of azo dyes. Similarly, the two peaks at 1100 and 1150 cm\({}^{-1}\) had completely disappeared, suggesting the cleavage of the \begin{table}
\begin{tabular}{l c c} \hline  & \multicolumn{2}{c}{**Inoculum**} \\ \cline{2-3}  & **SCD** & **SSM** \\ \hline Input COD (mg/L) & 654 & 950 \\ Output COD (mg/L) & 98 & 514 \\ COD output control (mg/L) & 211 & 682 \\ COD removal rate (\%) & 85 & 46 \\ COD removal rate control (\%) & 67 & 30 \\ Removal rate of TB (\%) & 88 & 72 \\ Removal rate of BR (\%) & 84 & 81 \\ \hline \end{tabular}
\end{table} 
Table 2: COD removal efficiency of TDWW from different reactors SCD and SSM used as inoculums. sulfonic groups. However, the peak around 3450 cm\({}^{-1}\) remained unchanged, which could be explained by new degradation products containing -NH\({}_{2}\) group that were produced after dye degradation.
# Handover Management for Drones in Future Mobile Networks--A Survey
###### Abstract  
Drones have attracted extensive attention for their environmental, civil, and military applications. Because of their low cost and flexibility in deployment, drones with communication capabilities are expected to play key important roles in Fifth Generation (5G), Sixth Generation (6G) mobile networks, and beyond. 6G and 5G are intended to be a full-coverage network capable of providing ubiquitous connections for space, air, ground, and underwater applications. Drones can provide airborne communication in a variety of cases, including as Aerial Base Stations (ABSs) for ground users, relays to link isolated nodes, and mobile users in wireless networks. However, variables such as the drone's free-space propagation behavior at high altitudes and its exposure to antenna sidelobes can contribute to radio environment alterations. These differences may render existing mobility models and techniques as inefficient for connected drone applications. Therefore, drone connections may experience significant issues due to limited power, packet loss, high network congestion, and/or high movement speeds. More issues, such as frequent handovers, may emerge due to erroneous transmissions from limited coverage areas in drone networks. Therefore, the deployments of drones in future mobile networks, including 5G and 6G networks, will face a critical technical issue related to mobility and handover processes due to the main differences in drones' characterizations. Therefore, drone networks require more efficient mobility and handover techniques to continuously maintain stable and reliable connection. More advanced mobility techniques and system reconfiguration are essential, in addition to an alternative framework to handle data transmission. This paper reviews numerous studies on handover management for connected drones in mobile communication networks. The work contributes to providing a more focused review of drone networks, mobility management for drones, and related works in the literature. The main challenges facing the implementation of connected drones are highlighted, especially those related to mobility management, in more detail. The analysis and discussion of this study indicates that, by adopting intelligent handover schemes that utilizing machine learning, deep learning, and automatic robust processes, the handover problems and related issues can be reduced significantly as compared to traditional techniques.  
d 20
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 3 Sensors and Actuators in the IoMT
### Role of Body Sensors in the loMT
#### 3.1.6 Temperature Sensor  
Temperature sensors [17] play a crucial role in detecting changes in body temperature, which can signify the presence of fever, infection, or inflammatory conditions. The ability to continuously monitor temperature using wearable devices and medical-grade thermometers is critical for early detection and prompt intervention, which is key to managing infectious diseases and preventing complications. Furthermore, temperature sensors have a wide range of applications in environmental monitoring, food safety, and industrial processes where precise temperature control is essential. Examples include the Withings Thermo Smart Temporal Thermometer, iHealth No-Touch Forehead Thermometer, Kinsa Smart Ear Thermometer, etc.
# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 2 Related Work
### Machine Learning Model  
YOLO stands for \"You Only Look Once\" and is a real-time object detection and classification suite based on the Darknet framework [23]. It is developed in an open source fashion and uses neural networks to process single images. Additionally, a CUDA implementation supports massive parallelization which offers great performance benefits and enables the real-time usage of this framework. YOLO provides to the system the ability to detect, classify and locate multiple objects within a single frame. YOLO was first introduced in 2016 but is under active development. Over the years, many improvements have been made by the YOLOv2/YOLO9000 and YOLOv3 releases [24; 25; 26].  
Most other detection and classification systems repurpose classifiers to perform the process of detection: A popular contemporary approach is the Recurrent Convolutional Neural Network (R-CNN) which operates with region proposal methods to restrict the image to potential bounding boxes. Afterwards, a selective search method generates potential bounding boxes and a convolutional network extracts the features. These models commonly run much slower than YOLO [24].  
YOLO provides a completely different approach and reframes object detection. It transforms the detection process into a single regression problem and therefore performs bounding box estimation and class probability calculation straight from image pixels in a single step, thus coining the name \"You Only Look Once\" [24]. All bounding boxes across all classes are predicted simultaneously which enables the network to reason about the image and its objects globally. Figure 2 demonstrates the bounding box estimation process coarsely, while Figure 3 depicts the YOLO CNN architecture [24].  
Figure 2: In the YOLO architecture, an input image is divided into an \(S\times S\) grid where each grid cell predicts \(B\) bounding boxes defined by position, size and confidence. The class probability map decides the final label of a high-confidence bounding box. By [PERSON] et al. [24].
# Research on Spaceborne Target Detection Based on Yolov5 and Image Compression
## 5 Simulation Results  
### Detection Performance Based on the Original Image  
In this section, we used the proposed Yolov5m network to test the detection performance on real satellite images. The detection accuracies of the small objects on the satellite images were different under different settings. First, we used the DOTA1.0 data set as the training set to obtain a good target detection network [21], and we used the original Yolov5s as the detection algorithm. Then, we used this model to detect the images taken by satellite. The recognition results were not very good as the original images were of poor quality without any preprocessing. The images in Figures 10 and 11 are the detection results of real satellite images.  
Figure 11: The detection performance for ships with the original Yolov5 in the original satellite image.  
Figure 12: The detection performance for aircraft with modified Yolov5 in the original satellite image.  
Figure 10: The detection performance for aircraft with the original Yolov5 in the original satellite image.  
Figure 14: The detection performance for aircraft in the DOTA set.  
Figure 13: The detection performance for ships with modified Yolov5 in the original satellite image.  
Figure 15: The detection performance of scene of river in the DOTA set, where yellow boxes represent detected ships, green boxes represent detected harbors, orange boxes represent detected storage tanks and blue boxes represent detected bridges.
# Effects of the Damping Parameters on the Opening and Closing Characteristics of Vent Valves
## 2 Model  
### Structure and Principle  
A two-stage pressure control vent valve is a pneumatic valve applied to an aero engine which is composed of an inlet joint, filter, welded metal bellow, middle support, center housing, reverse turning bed and piston chamber. The gas enters the valve through the intake joint, and the filter blocks the large particles in the gas entering the system. The welded metal bellow is a pressure-sensitive element in the system, which is compared to the control gas pressure and environmental pressure to produce displacement. The first pressure control is composed of a welded metal bellow and nozzle of middle support, and the second pressure control is composed of a membrane assembly and the nozzle of the center housing. The working principle of the vent valve is shown in Figure 1, where P0 is the ambient gas pressure, P1 is the intermediate gas pressure of the welded metal bellow and P2 is the control gas pressure. The control gas enters the system after passing through the filter, and the first gas enters the inner control chamber of the welded metal bellow to control the displacement of the welded metal bellow. The second gas enters the middle support through the damping hole A and is discharged to the outside world by the nozzle of the middle support. The third gas passes through the damping hole B, the stabilizer plate and the damping hole C into the front nozzle of the film and the piston chamber. In summary, the structure in a certain environmental pressure P0, through the input control pressure adjustment piston chamber pressure, adjusts the angle of the valve plate so as to control the piston movement of the piston chamber, completing the valve's opening and closing action.  
The exhaust valve is a complex pneumatic system with many components. The working characteristics of the components such as the membrane assembly, steady flow plate and welded metal bellow are not clear. It is necessary to obtain the characteristics of each valve component and model the two-stage pressure control valve as a whole.  
Figure 1: Schematic diagram of two-stage partial pressure relief valve.