esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Validation of the ATF Compressor System
### Experimental Verification of Steady-State Conditions  
The real-time simulation and experimental verification are carried out for the typical gas supply conditions of the ATF system. Figure 6 shows the digital twin data of the valve opening in physical space under steady gas supply conditions. Under the actual gas supply condition, the inlet valve 1# is fully open, the exhaust valve 2# is about 9% open, the air supply valve 3# is about 91% open, and the anti-surge valve 4# is closed throughout. The actual opening information is taken as the real-time input condition of virtual space to carry out real-time simulation. By reading the pressure and flow at the inlet and outlet of the compressor in the physical system. The actual physical parameters are compared with the simulation results.  
Figure 5: Structure diagram of BPNN-MDSG digital twin modeling for ATF system.  
Figure 6: Valve operation data in steady state condition.  
Compare the results of virtual space with the actual physical device parameters. Figure 7 shows the simulation and actual results of compressor flow, inlet, and outlet pressure. It can be found in the figure that the variation trend of the simulated value is consistent with that of the actual value, and the error is small. The average relative error of compressor flow rate is 2.57%. The average relative error of compressor inlet pressure is 0.93% and outlet pressure is 0.87%. In this steady state condition, there is a slow change in the compressor outlet pressure rather than a certain equilibrium value. This is because the compressor outlet pressure is affected by the pressure of the downstream gas supply equipment, and the downstream equipment fluctuates, which leads to its not completely steady-state condition. The real-time simulation of virtual space can better describe the dynamic fluctuation results of the actual equipment.  
In the virtual space, BPNN-MDSG modeling can be used to predict and monitor the location where no physical sensors are installed. Figure 8 shows the mass flow of each valve under the steady-state gas supply condition. As digital twin data assets, the monitoring results are used in subsequent control method research and fault diagnosis research.  
Figure 8: The predicted flow value of each valve under steady gas supply conditions.  
Figure 7: Simulation and actual results of the BPNN-MDSG model in steady-state gas supply conditions: (**a**) The result of the mass flow comparison. (**b**) The result of the pressure comparison.  
In summary, for the stable gas supply condition of the ATF compressor system, the BPNN-MDSG digital twin model can realize the real-time interaction of the actual valve action and sensor boundary parameters between the physical system and the virtual space. According to the comparative analysis of key parameters, the error between the real-time simulation results and the actual values is small. BPNN-MDSG is considered reliable in the digital twin simulation of steady-state conditions.
## 4 Results and Discussion
### The Comparison of the Number of Epochs Needed to Reach the Saturation  
The comparison of epochs required to reach the saturation during the training process has demonstrated the correlation to the training time. The relation of accuracy versus epochs for all models presented in Figure 11 for the sequence of 1000 system calls gives the best accuracy.  
Figure 11: Number of epochs vs. accuracy for the 1000 system call sequence.  
The AWSCTD-CNN-S, AWSCTD-CNN-D, and AWSCTD-CNN-LSTM models reached the saturation in maximum of 25 epochs, and they demonstrate better stability. The AWSCTD-CNN-GRU and FCN models required 30 epochs. The maximum number of epochs required by the LSTM-FCN and GRU-FCN models was 30 or more epochs.
## 3 Results and Discussion
### Analysis of the Relationship between the Grid Voltage and the Power Generated by the Inverters

Table 5: PV power values allowed in each phase at the grid feed-in point of each PV installation, obtained from the maximum values of slope and \(Y\)-axis intercept obtained in the weekly regressions. the Battery Energy Storage System (BESS) to recharge during high power generation is a strategy that is proposed as a promising solution against the overvoltages limits [7; 47; 48; 49]. More costly solutions in order to get an increment in grid capacity were also proposed as well, such as the use of Synchronous Condensers (SynCons) [50], the up gradation in distribution transformers to an increased power rating, replacing old conductors with new with higher capacity or reinforcement of LV feeders by additional parallel conductors [43]. Another option used to mitigate the voltage increase would be through the extraction of reactive power at the PCC, a strategy that is not yet exploited in LV network, which would require the development of more sophisticated and low cost power electronic interfaces in order to distributed generators can generate and control reactive power [46].
# Thermal Control of CubeSat Electronics Using Thermoelectrics
## 1 Introduction
Their solid-state nature--and lack of moving parts--makes them attractive for space applications, due to increased reliability, lifetime and reduced vibration. Unlike other active systems, they do not require fluid flow, such as in vapor-compression cycle refrigeration [8]. On the other hand, thermoelectric devices have a much lower coefficient of performance (COP) than competing active cooling technologies, which often stifles their uptake in applications [2], although they are used extensively for precise cooling of laser diodes [9]. Thermoelectric devices already have space heritage, having, for example, been used to cool CCD sensors on the Mars Curiosity Rover [10], and for thermal stabilization on the International Space Station and larger satellites [11]. They have not--to our knowledge--been previously used on CubeSats. Therefore this study is the first to assess the viability of using thermoelectric cooling for thermal management of on-board electronics on CubeSats.
## 2 Background
### Other Existing Methods
#### 2.1.2 Data Prediction  
Data prediction on the other hand, has received lots of attention in various application domain in WSNs, due to the ability to use this simple and yet powerful technique to suppress data in the network. Part of this method involves training models that could be used to predict future data traces, both at the base station and on the nodes. One of the areas in which this method is useful is in environmental monitoring application, where the goal is to follow both space and time evolution of the physical phenomena. Most of the prediction models often considered in the literature aim at approximating or predicting sensor measurement, which generally takes the form  
\[M_{\theta}:\Upsilon\rightarrow\mathbb{R} \tag{1}\]  
\[x\mapsto\hat{n}_{i}[t]=M_{\theta}(x) \tag{2}\]  
where \(\hat{n}_{i}[t]\) denotes the approximation of the value of a sensor node \(n_{i}[t]\) at time \(t\), and \(x\) is the sensor measurement, which could be temperature, wind speed, humidity, _etc_. The real task of this method is how to build a model \(M\) such that a bounded approximation error \(\epsilon\) is minimized _i.e._,  
\[|n_{i}[t]-\hat{n}_{i}[t]|<\epsilon,\forall i\in N,t\in T \tag{3}\]  
Various optimization techniques [32; 33; 34; 35; 36; 37; 38; 39; 40] have been employed in literature to tackle the choice of the model buildup. Arguably these methods have yielded improved resource management with reduced communication cost in WSNs. One of our focus is how to apply this data prediction approach, together with a data compression method introduced previously to achieve a further improvement in communication reduction.  
In the next section, we discuss our solution to event-detection problems, which is applicable to the _threshold method_ as mentioned earlier. Furthermore, we examine a use-case scenario where our design model is applicable.
## 5 Results and Discussion
### Effects of Rotor Speed on Sealing Performance
#### 5.2.3 Synchronous Changes in the Speed of Inner and Outer Rotors  
The speed of inner and outer rotors in engines often changes synchronously. The combined mode of the speed of inner and outer rotors shown in Table 5 presents the effects of the synchronous changes in inner and outer rotor speed on sealing performance, with an absolute speed difference between the two rotors (3000 rpm) unchanged. The results are shown in Figure 13.  
Our formulas indicate that the gas film seal's minimum thickness exceeds the critical value under each corresponding speed condition (i.e., it is in a state of full fluid lubrication). When the inner and outer rotor speeds increase synchronously, the difference in speed between the inner and outer rotors and the dynamic pressure effects remains unchanged. However, increases in the absolute speed also increase the centrifugal expansion of the  
Figure 12: Schematic diagram of the sealing ring’s centrifugal expansion.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Speed Combination Number** & **Outer Rotor Speed** & **Inner Rotor Speed** \\ \(N\) & \(n_{2}\) (rpm) & \(n_{1}\)(rpm) \\ \hline
1 & 5000 & \(-\)8000 \\
2 & 6000 & \(-\)9000 \\
3 & 7000 & \(-\)10,000 \\
4 & 8000 & \(-\)11,000 \\
5 & 9000 & \(-\)12,000 \\
6 & 10,000 & \(-\)13,000 \\ \hline \hline \end{tabular} Note: The signs indicate the same or counter-rotation direction.  
\end{table}
Table 5: Synchronous changes in outer and inner rotor speed.  
graphite sealing ring with a small elasticity modulus, slightly increasing the minimum film thickness. This exacerbates leaks and decreases friction power consumption.
# A Mathematical Model for the COVID-19 Pandemic in Tokyo through Changing Point Calculus
## 2 Materials and Methods  
### Conceptual Model  
#### 2.1.1 Model Compartments  
The SIR model is a compartmental model used in the field of epidemiology that describes the transmission process of an infectious disease through a system of differential equations. As we can see in Figure 1, the total population is divided into three mutually exclusive compartments, namely Susceptible (\(S\)), Infected (\(I\)), and Recovered (\(R\)) [13]. Individuals move from one compartment to another as time passes based on different transfer rates.  
The SEIAMD model that we propose is an extension of the SIR model and it consists of six compartments: Susceptible, Exposed, Identified, Asymptomatic, iMmunized, and Deceased (see Figure 2).  
The Susceptible compartment (\(S\)) contains the susceptible individuals of the population, i.e., those who can be infected by the virus. The Exposed compartment (\(E\)) contains the individuals who have been exposed to the virus by being in contact with infected individuals. The addition of this compartment to the basic SIR model allows our proposed model to take into account the virus latency period.  
Although it is difficult to quantify the incidence of asymptomatic individuals with certainty, several studies have shown their importance [14]. Therefore, we decided to split  
Figure 1: Compartments of the SIR epidemic model (Susceptible–Infected–Recovered) and associated parameters.  
Figure 2: Compartments of the SEIAMD epidemic model (Susceptible–Exposed–Identified–Asymptomatic–iMmunized–Deceased) and associated parameters.  
the original \(I\) defined in SIR into our \(I\) and \(A\) compartments. Thus, the infected individuals are in the Identified (\(I\)) and Asymptomatic (\(A\)) compartments. The \(I\) compartment contains the infected individuals that have been identified as positive for SARS-CoV-2, and the \(A\) compartment contains those individuals who are infected but do not have symptoms, i.e., they are asymptomatic, so they have not been tested and, therefore, have not been identified as positive.  
The Immunized compartment (\(M\)) contains those individuals who are immunized. An individual can be immunized after being infected (identified or asymptomatic) or after being vaccinated. We create this compartment to contemplate the immunity loss of the population and the subsequent reinfection possibility. Thus, over time, immunization declines, and individuals become susceptible again.  
Finally, as the population must be constant in time, and this virus causes the death of some infected individuals, who logically will not be reinfected or infect other individuals, such unfortunate deaths must be taken into account in the model. We, therefore, define the Deceased compartment (\(D\)) to contain those individuals who have died. Here, we assume that all people who have died have been identified as positive.  
As is shown in Figure 2, different parameters determine the number of individuals who move from one compartment to another. We define these parameters in the next section.
## 2 Materials and Methods  
### Data Acquisition  
Currently, research on semantic part segmentation mostly focuses on publicly available datasets and explores relevant phenotypes through model adjustments and improvements. There is currently a lack of publicly available data samples specifically for tilapia part segmentation. Tilapia has a spindle-shaped body, a flat head, and more than ten dorsal fin spines. It also exhibits black stripes and spots on its body, which are important features for tilapia part segmentation and are among the challenging aspects in the field of image segmentation. Moreover, research on the semantic segmentation of fish mainly focuses on segmenting the entire body, lacking specific annotations for individual parts. To address this gap, we have created a dataset specifically for tilapia part segmentation. This dataset emphasizes the annotation of tilapia parts, enhancing its suitability for fine-grained analysis. For example, it can be used to study the impact of tilapia parts on phenotype, behavior, or disease diagnosis. Furthermore, the dataset encompasses diverse angles and poses in capturing tilapia, aiding in the assessment of algorithmic robustness and generalization performance. In contrast to overall segmentation tasks prevalent in publicly available datasets, our dataset is distinctly focused on the segmentation of tilapia parts, allowing for the design and training of models tailored to this specific task, thereby furnishing data support for subsequent phenotype measurements. Therefore, this study establishes a sample set for semantic part segmentation of tilapia.  
The tilapia (African Carp) samples used in the experiments were collected from the Coastal Aquaculture Base of Shanghai Ocean University in Shanghai, China, as shown in Figure 1. To quickly obtain tilapia samples, the tilapias were placed in batches in an aquaculture tank during image acquisition. A stereo camera was horizontally positioned at the bottom of the tank. The camera captured images of individual fish, multiple fish, and occluded fish by moving the camera accordingly. The image resolution was set to 1920 \(\times\) 960. To address issues such as overfitting during model training and the redundancy of left and right stereo images, we utilized Matlab tools to segment and remove duplicate samples from the left and right images. This resulted in a total of 855 unique sample images. Figure 2 shows a part of the image of the tilapia part segmentation dataset.
## 4 Techniques Used in Academia to Improve Performance Metrics
### Frequency Diversity
#### 4.1.2 Error Control Mechanisms  
The three main error control schemes employed by WSNs to combat the unreliability of the wireless channel are Forward Error Correction (FEC), Automatic Repeat Request (ARQ) and Hybrid ARQ (HARQ) [32].  
FEC is accomplished by adding redundancy to the transmitted information so that the receiver can retrieve the information if a limited number of bits are corrupted. FEC schemes improve error resiliency at the cost of communication overhead by exchanging more information as well as decoding packet energy. Decoding energy is one of the main concerns when applying the FEC scheme in WSNs, where low clock-rate CPUs are used.  
ARQ schemes depend on the retransmission of failed packets. In case of errors, ARQ incurs significant communication overhead as well as additional latency. ARQ is, therefore, infeasible for applications with real-time requirements. However, in the case of good channel quality, ARQ overhead is low compared to FEC. ARQ can be considered as a temporal diversity technique.  
Hybrid ARQ schemes combine the advantages of both FEC and ARQ by incrementally using more powerful FEC codes in the transmitted packets when the packet is received in error.  
In [32], it is shown that the FEC schemes can be exploited to improve network error resiliency by reducing the transmit power, which improves the network capacity by reducing the communication interference, or by constructing longer hops compared to ARQ by using the same transmit power. Reference [32] reveals that an increase in the hop-length can decrease the end-to-end delay. FEC is, therefore, more suitable to delay sensitive applications in WSNs when compared to ARQ. Similarly, in [33], a cross-layer analysis framework, which considers the impact of routing and MAC protocol, is presented. Additionally, in [33], Reed-Solomon codes have been included to exploit the benefit of FEC codes in WSNs. In [34], [PERSON] _et al_. use ARQ schemes, which provide reliability by applying both implicit and explicit acknowledgments. Reference [34] exploits an adaptive retransmission scheme, in which maximum retransmission attempts are adjusted, based on the packet error rate at each node.
# A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
## 4 Results and Analyses
### Analysis of the Deployment Test Results of the Model on Jetson TX2  
To evaluate the model's detection performance for cotton boll diseases on embedded devices, the YOLOv11n-ECS and the original YOLOv11n models were deployed on the Jetson TX2 development board for comparative testing. At the same time, TensorRT was used to optimize and accelerate the models with high-performance operators, and Int8 quantization was applied to further reduce the model size and computational complexity. This approach helps prevent a significant drop in detection speed when migrating the model from a computer to a resource-constrained embedded device. The same cotton boll disease dataset was used for testing. The detection results and performance comparison are shown in Table 6 and Figure 11.  
As shown in Table 6, the [EMAIL_ADDRESS] of both models decreased after deployment on the Jetson TX2. Specifically, the [EMAIL_ADDRESS] of YOLOv11n and the improved model dropped by 1.9 and 1.4 percentage points, respectively. As illustrated in Figure 11, due to the small size of cotton bolls and the complexity of the background environment, the YOLOv11n model exhibited issues such as false positives and duplicate detections when identifying diseased bolls. In contrast, the improved model demonstrated better detection performance, with more accurate target localization and fewer false positives. The detection speeds of the improved model and the original YOLOv11n model on the Jetson TX2 development board were 56 FPS and 52 FPS, respectively. The improved model achieved an [EMAIL_ADDRESS] of 84.2%, demonstrating good detection performance on embedded devices. Compared to the original model, it achieved a 7.7% increase in detection speed, indicating its capability to meet the real-time detection requirements for diseased cotton bolls in actual field conditions.
## 3 Results
### Signal Processing
The main objective of reducing noise in higher frequencies was to generate a real-time visual signal during the experiments, allowing continuous observation and providing an additional analytical tool for specialists. Next, a Butterworth bandpass filter with 8 to 13 Hz cutoff frequencies is applied, resulting in the signal shown in Figure 10.  
This signal only includes frequencies related to alpha waves, which are the main focus of this study. In this study, a Butterworth filter is utilized to analyze specific frequency ranges that highlight the relevant features for detection. A filter between 8 and 13 Hz is employed to identify relaxation, emphasizing variations in alpha waves. A filter between 4 and 20 Hz is applied for blink detection, revealing a clear signal increase during blinking events. By filtering the signal within these designated ranges, most unwanted signals are eliminated, allowing for a focused analysis of the signals of interest. After filtering, the signal was divided into one-second time windows, and the FFT was used to obtain the frequency response for each segment, as shown in Figure 11.  
As observed, the frequency spectrum from 8 to 13 Hz is obtained and generated for each 1-s segment with a 100 ms step in the signal. This study applies the FFT to obtain the signal's spectrum, highlighting relevant features and facilitating the identification of the signal of interest. The FFT provides precise information about the amplitude at specific frequencies, which, when combined with techniques such as numerical integration using the trapezoidal rule, enhances the identification of amplitude variations, thereby improving the accuracy of detecting the desired signal. The area under the curve was approximated using the trapezoidal method for these frequency representations, and the resulting data were used to define the thresholds during training. Subsequently, the data were categorized into three levels in the classification phase: 0, 1, and 2. Level 0 indicates that the user is in an active state with their eyes open; level 1 reflects an increase in alpha waves associated with inactivity and closed eyes; and  
Figure 10: Signal filtered in the 8 to 13 Hz range using a Butterworth filter. level 2 indicates an additional increase in alpha waves related to a higher level of relaxation. On the other hand, in the case of blinking, the same process was carried out, obtaining the thresholds and classifying only two states: an active state and a null state.