esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### The Operating Environment and Running Times  
We used tensorflow2 framework to build our algorithm. The operating system is Ubuntu and we run this algorithm on a GTX TITAN Xp GPU. The code for calculating optical flow is from LiteFlowNet2 [13]. The overall detection starts from the second frame  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Video Name** & **Mean Recall** & **Mean Precision** & **Mean IoU** \\ \hline \multirow{2}{*}{Training set} & video1 & 92.76\% & 85.81\% & 80.04\% \\  & video2 & 90.71\% & 84.96\% & 77.91\% \\ \hline Validation set & video3 & 84.62\% & 83.66\% & 72.08\% \\ \hline \multirow{2}{*}{Test set} & video4 & 89.84\% & 74.28\% & 67.94\% \\  & video5 & 92.73\% & 75.79\% & 71.32\% \\ \cline{1-1}  & video6 & 89.44\% & 78.26\% & 71.51\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the rough detection network.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Video Name** & **Mean Recall** & **Mean Precision** & **Mean IoU** \\ \hline \multirow{2}{*}{Training set} & video1 & 89.35\% & 89.35\% & 80.19\% & \multirow{2}{*}{78.14\%} \\  & video2 & 90.38\% & 84.14\% & 76.09\% & \\ \hline Validation set & video3 & 84.20\% & 85.33\% & 73.12\% & 73.12\% \\ \hline \multirow{2}{*}{Test set} & video4 & 89.65\% & 78.86\% & 70.13\% & \multirow{2}{*}{72.09\%} \\  & video5 & 92.06\% & 78.59\% & 73.34\% & \\ \cline{1-1}  & video6 & 86.45\% & 82.30\% & 72.80\% & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the fine detection network.  
Figure 7: Comparison between the rough detection result and the fine detection result. For convenience of display, we have used red to mark the TV regions on the original image. (**a**) Ground truth of TV; (**b**) rough detection result; (**c**) fine detection result.  
of the video. One calculation process includes image reading and scaling (1920 \(\times\) 1200 \(\times\) 3 -> 1536 \(\times\) 1024 \(\times\) 3), optical flow calculation, slicing (15 \(\times\) 512 \(\times\) 512 \(\times\) 13 or 15 \(\times\) 512 \(\times\) 512 \(\times\) 4), rough detection and fine detection. In the process of inference, the batch size of the rough and fine detection network is 8 each time. The average running times for optical flow calculation, slicing, rough and fine detection are shown in Table 4.
###### Abstract  
We introduce an approach for the direct measurement of the gravitational potential (GP) along the trajectory of a satellite, with a specific focus on Low-Earth Orbit (LEO) satellites. A LEO satellite communicates with several Geosynchronous Equatorial Orbit (GEO) satellites via frequency signal links. The GP difference can be measured in real-time using the gravitational frequency shift approach by equipping both LEO and GEO satellites with precise atomic clocks. Since the GP at the high orbits of the GEO satellites can be precisely determined by the present gravitational field model EGM2008, the GP along the LEO satellite's trajectory can be determined. In this study, simulation experiments were conducted, featuring a GRACE-type satellite as the LEO satellite in communication with three equidistant GEO satellites. The results indicated that the accuracy of the GP measurements along the LEO satellite's trajectory primarily depends on the precision of the onboard atomic clocks. Supposing optical atomic clocks attain an instability level of \(1\times 10^{-17}\tau^{-1/2}\) (r in seconds), we determined the GP distribution covered by the LEO satellite's trajectories with 30-day observations. Then, we determined a gravitational field at the centimeter level based on the GP distribution. The GP data derived from the trajectory of a LEO satellite can be utilized to establish temporal gravitational fields, which have broad applications in different disciplines.  
relativity; satellite; gravitational potential; frequency link +
Footnote †: journal: remote sensing  
Article  
A Method for Measuring Gravitational Potential of Satellite's Orbit Using Frequency Signal Transfer Technique
# Exploring Convective Drying Behavior of Hydroxide Sludges Through Micro-Drying Systems
## 4 Practical Applicability  
The findings of this study hold significant potential for application in drinking water treatment plants (DWTPs) where sludge management remains a critical challenge due to the high energy demand of drying operations [24]. By identifying the key factors influencing the drying efficiency, such as temperature, air velocity, and shrinkage behavior, our study provides a framework for optimizing operational parameters to minimize energy consumption while ensuring effective sludge dehydration [25]. The discovery of a characteristic drying curve specific to hydroxide sludge offers a predictive tool that can be integrated into process modeling and real-time drying control systems, enabling more efficient resource utilization [3]. Additionally, insights into crack formation mechanisms can help improve the structural integrity of dried sludge, facilitating its handling, transport, and further vaporization in industries such as construction or soil amendment [3].  
Figure 10: Arrhenius relationship between effective diffusivity and temperature.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Air Velocity (m/s)** & **Temperature (\({}^{\circ}\)C)** & **D\({}_{\text{eff}}\) (m\({}^{2}\)s\({}^{-1}\))** & **Ea (J/mol)** & **r** \\ \hline \multirow{3}{*}{1} & 110 & \(1.75\times 10^{-8}\) & 32,547.04 & 0.996 \\ \cline{2-5}  & 90 & \(7.33\times 10^{-9}\) & 32,213.81 & 0.985 \\ \cline{2-5}  & 70 & \(3.84\times 10^{-9}\) & 31,879.23 & 0.897 \\ \hline \multirow{3}{*}{2} & 110 & \(1.21\times 10^{-8}\) & 33,785.47 & 0.994 \\ \cline{2-5}  & 90 & \(6.22\times 10^{-9}\) & 33,698.51 & 0.974 \\ \cline{2-5}  & 70 & \(3.73\times 10^{-9}\) & 32,987.31 & 0.989 \\ \hline \multirow{3}{*}{3} & 110 & \(1.04\times 10^{-8}\) & 33,457.21 & 0.975 \\ \cline{2-5}  & 90 & \(8.25\times 10^{-9}\) & 32,496.93 & 0.996 \\ \cline{1-1} \cline{2-5}  & 70 & \(4.36\times 10^{-9}\) & 31,548.63 & 0.938 \\ \hline \end{tabular}
\end{table}
Table 3: Effective diffusivity and activation energy at different drying conditions for hydroxide sludge.
### Work Aims  
This work aims to create and test a model that can accurately determine an individual's stress level using PPG signals--data extracted from a national project. The model is compared with studies using machine-learning techniques to detect stress through differentbiological signals. Typically, the biological markers used to diagnose stress rely on the shape of biological signals. These signals can be easily affected by temporary changes in an individual's physical activity or mindset and can also vary depending on the timing of the measurement. As a result, these markers may not accurately reflect the level of stress a person is experiencing.  
This paper is focused on analyzing the geometric distribution of the dynamics or diffusive behavior of the PPG signal. We will use the \((p,q)\)-plane suggested by the 0-1 test [39; 40; 41] to achieve this. Based on the two-dimensional structure of this distribution, equivalent to an image, we propose a model that utilizes DL to automate stress detection in a subject. In the last decade, CNNs have experienced a notable expansion, with magnificent results in the field of computer vision, in particular, in image recognition. Its proven reliability in image classification tipped the balance in its favor when choosing the neural network (NN) model we could implement. The promising results produced by this work further confirm its leading role.  
It is essential to mention that before this work, no method had been proposed to identify stress based on the PPG signal considering the diffusion dynamics of the unique signal of each individual. The uniqueness of the dynamics is because the structure of each person's vascular bed affects the diffusion constant of blood flow.  
The proposed formulation will allow a highly portable system sensor crucial in certain risky professions, such as truck drivers, pilots and factory workers, among others, with a neural network of low weight in terms of memory consumption and efficiency above 90% in detecting acute stress.  
In addition, the proposed method can be generalized in the state-of-the-art artificial intelligence classifiers--our proposal uses a conventional CNN--as a method of classifying time series via prior conversion to the \((p,q)\)-plane.  
The rest of the paper is organized as follows. Section 2 describes the proposed convolutional neural network architecture, supported by an analysis of the PPG signal-diffusive behavior, as well as the metrics to evaluate its performance. Section 3 shows the obtained results, both graphically and numerically, regarding the metrics that allow to judge the capacity of the neural network to distinguish a stressed state in a subject. In Section 4, we analyze and interpret the obtained results and provide a comparative analysis of these findings with other relevant studies. Finally, in Section 5, we shortly outline the conclusions drawn from this study, which serve as the basis for future work.
# Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX
## 3 Hyperspectral Anomaly Detection Based on the Channel Attention Mechanism and LRX
### LRX-Based Anomaly Detection  
In this paper, the LRX algorithm is employed for anomaly detection. The LRX algorithm calculates the Mahalanobis distance between the pixel to be detected and the mean value of the background on the obtained reconstructed image \(\hat{F}\) in order to detect anomalies. Specifically, the Local RX algorithm compares the statistics of the inner window  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Output Size** &
\begin{tabular}{c} **Activation** \\ **Function** \\ \end{tabular} \\ \hline Input \(X\) & \(N\times D\) & \\ Dense & \(N\times 128\) & \(\mathrm{RELU}\) \\ Dense & \(N\times 64\) & \(\mathrm{RELU}\) \\ Dense & \(N\times 32\) & \(\mathrm{RELU}\) \\ Dense \(Z\) & \(N\times 16\) & \(\mathrm{RELU}\) \\ Dense & \(N\times 32\) & \(\mathrm{RELU}\) \\ Dense & \(N\times 64\) & \(\mathrm{RELU}\) \\ Dense & \(N\times 128\) & \(\mathrm{RELU}\) \\ Output \(X^{\prime}\) & \(N\times D\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network structure.  
to those of the outer window in order to identify anomalies. The inner window captures the local spatial context, while the outer window provides a background reference. A higher RX anomaly score indicates a higher likelihood of an anomaly. This paper employs the HYDICE urban dataset, AVIRIS aircraft dataset, and Salinas Valley dataset for LRX-based hyperspectral anomaly detection. The outer window \(W_{out}\) and inner window \(W_{in}\) sizes are set to \((5,3)\), \((15,9)\) and \((7,5)\), respectively.
_Article_  
**Implications of NVM Based Storage on Memory Subsystem Management**  
**[PERSON] 1,*(r) and [PERSON] 2**  
1 Department of Computer Engineering, Ewha University, Seoul 03760, Korea  
2 Embedded Software Research Center, Ewha University, Seoul 03760, Korea; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +82-2-3277-4247 check for updates  
Received: 22 November 2019; Accepted: 30 January 2020; Published: 3 February 2020  
**Featured Application: The authors anticipate that memory and storage configurations explored in this article will be helpful in the design of system software for future computer systems with ever-growing memory demands and the limited density of DRAM.**  
**Abstract:** Recently, non-volatile memory (NVM) has advanced as a fast storage medium, and legacy memory subsystems optimized for DRAM (dynamic random access memory) and HDD (hard disk drive) hierarchies need to be revisited. In this article, we explore the memory subsystems that use NVM as an underlying storage device and discuss the challenges and implications of such systems. As storage performance becomes close to DRAM performance, existing memory configurations and I/O (input/output) mechanisms should be reassessed. This article explores the performance of systems with NVM based storage emulated by the RAMDisk under various configurations. Through our measurement study, we make the following findings. (1) We can decrease the main memory size without performance penalties when NVM storage is adopted instead of HDD. (2) For buffer caching to be effective, judicious management techniques like admission control are necessary. (3) Prefetching is not effective in NVM storage. (4) The effect of synchronous I/O and direct I/O in NVM storage is less significant than that in HDD storage. (5) Performance degradation due to the contention of multi-threads is less severe in NVM based storage than in HDD. Based on these observations, we discuss a new PC configuration consisting of small memory and fast storage in comparison with a traditional PC consisting of large memory and slow storage. We show that this new memory-storage configuration can be an alternative solution for ever-growing memory demands and the limited density of DRAM memory. We anticipate that our results will provide directions in system software development in the presence of ever-faster storage devices.  
**Keywords:** NVM; storage performance; buffer cache; prefetching; PCM; STT-MRAM
## 3 Case Study
### Results and Analysis of Abnormal Stations
#### 3.2.3 Analysis of Representative Transfer Stations  
Here, several representative transfer stations were selected to analyze the fluctuation in tap-in passenger flow when the disruption occurred. Military Museum station is a transfer station on line 1 and line 9 that was close to the disrupted station. Another transfer station is Guomao station on line 1 and line 10, which was far away from the disruption station. The fluctuation curve of tap-in passenger flow from 7:00 a.m. to 10:00 a.m. at Military Museum station and Guomao station are shown in Figure 5.  
Figure 4: The fluctuation curve of tap–in passenger flow at ordinary stations. (**a**) The fluctuation curve of tap–in passenger flow at Babaoshan station. (**b**) The fluctuation curve of tap-in passenger flow at Dawanglu station.  
Figure 5: The fluctuation curve of tap–in passenger flow at the same transfer station on different lines. (**a**) The fluctuation curve of tap-in passenger flow at Military Museum station on line 9. (**b**) The fluctuation curve of tap-in passenger flow at Military Museum station on line 1. (**c**) The fluctuation curve of tap-in passenger flow at Guomao station on line 10. (**d**) The fluctuation curve of tap-in passenger flow at Guomao station on line 1.  
As shown in Figure 5, the period for which transfer stations were affected by the disruption was from 7:45 a.m. to 9:15 a.m., which was significantly longer than that for ordinary stations (Section 3.2.2). The trends of the fluctuation curve of tap-in passenger flow at Military Museum station and Guomao Station were the same. Specifically, the tap-in passenger flow at transfer stations on line 1 decreased significantly after the disruption occurred, while the tap-in passenger flow at transfer stations on line 9 and line 10 increased significantly after the disruption occurred. This indicated that many travelers changed their travel routes from origins to destinations.
# Chemical and Sensorial Characterization of Scented and Non-Scented Alstroemeria Hybrids
## 2 Materials and Methods
### Evaluation of Scent Composition of Scented Alstroemeria Hybrids through GC-MS  
Flowers of alstroemeria hybrids confirmed to be scented by the sensorial analysis (13M07 and 14E07), plus a confirmed non-scented hybrid (13B01) used as a control, were analysed through GC-MS. This analysis was performed using three replicates and each replicate consisted of one single flower at stage 4 taken from different infloresances of the same plant, between 1:00 p.m. and 4:00 p.m. (average stage and time of maximum floral scent output) with the following protocol.  
The extraction of the volatile compounds was done through Solid Phase Microextraction technique (SPME) with a triple fibre of Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CAR/PDMS) (StableFlex fiber, Sigma-Aldrich, Gillingham, UK) which was exposed for 15 min to the headspace of 20 mL flasks containing the flower with a penetration of 21 mm. The incubator was set at 45 \({}^{\circ}\)C and afterwards the fibre was desorbed for 180 s at 280 \({}^{\circ}\)C. Gas chromatography analysis was carried out using a 7890B Agilent GC system coupled to a quadrupole mass spectrometer Agilent 5977 inert (Agilent Technologies, Palo Alto, CA, USA) and employing a DB Wax capillary column (60 m \(\times\) 0.25 mm, and 0.25 \(\upmu\)m film thickness) (J&W Scientific, Folsom, CA, USA) using helium as the carrier gas at a flow rate of 1 mL/min. The oven temperature program started at 35 \({}^{\circ}\)C for 1 min increasing to 130 \({}^{\circ}\)C at 12 \({}^{\circ}\)C/min and held for 1 min, then to 160 \({}^{\circ}\)C at 1 \({}^{\circ}\)C/min, and then to 220 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min (held for 10 min). Electron ionization mass spectra in the scan mode were recorded at 70 eV with the electron energy in the range of 35 to 300 amu.  
A solution of 10 \(\upmu\)L of 4-methyl-2-pentanol (0.75 mg/L) Merck (Darmstadt, Germany) in ethanol was used as an internal standard to check the correct operation of the equipment and the use of the fibre. For calculation of retention (Kovats) indices, a solution of alkanes (C\({}_{7}\)-C\({}_{30}\)) was injected under the same conditions as described for the samples.  
The peaks displayed in the chromatograms were analysed with an MS ChemStation (Agilent Technologies, Palo Alto, CA, USA). Identification was performed by comparing mass spectra of each compound with the NIST Library (ver. 1.2.) and with the Kovats index found in the literature, Flavornet, Pherobase and NIST Mass Spectrometry Data Center. A total of three biological replicates were performed. Semi-quantification was performed in terms of absolute areas and the standard deviation for each replicate was also calculated.
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 2 Preliminaries
### Lstm  
This paper focuses on one of the popular deep learning algorithms called long short-term memory (LSTM), which is known to be able to learn and memorize long-term dependencies [11]. LSTMs consist of one cell state and various gates. The cell state is the \"memory\" part of the LSTM, which can be altered by various gates, each comprising of a sigmoid neural network layer and a pointwise multiplication operation [12]. While different variants of LSTMs exist, such as peephole connections [13] and gated recurrent unit (GRU) [14], this paper uses the standard LSTM as it is ideal for classifications of sequence data. A detailed explanation of the LSTM process can be found in [11].
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 3 Review of DC-DC Converters for Satellites
### Isolated Topologies
#### 3.2.8 Active-Clamp Forward Converter  
Ref. [110] proposed an improved active-clamp forward (ACF) converter to develop a reliable, lightweight, and EMI-compatible EPS for satellites. Although the ACF converter has a simple structure, fewer components, and clamping properties, its oscillating input current, high \(di/dt\) value, and voltage stress across the switches demand a larger filter size and make it a poor choice, especially for high-input-voltage applications. These problems were addressed with the improved ACF proposed in [110], which uses two series-connected switches to decrease semiconductor stresses. Moreover, during turn-off, a delay is provided to avoid the imbalance in switch voltage stress.  
The experimental results also revealed a lower ripple in the current on the input side. Thus, the improved ACF topology is a strong contender for satellites that require high power density. Figure 16b shows the improved ACF topology proposed in [110]. A comparison of isolated DC-DC converters in terms of the switching frequency, component count, and power is shown in Table 2.  
Figure 16: (**a**) Active-bridge active-clamp converter. (**b**) Improved active-clamp forward converter.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Converter** & **References** & \begin{tabular}{c} **Switching** \\ **Frequency (kHz)** \\ \end{tabular} & \begin{tabular}{c} **Component Count** \\ **S** \\ \end{tabular} & \
\begin{tabular}{c} **Power (W)** \\ \end{tabular} &
\begin{tabular}{c} **Main Features** \\ \end{tabular} \\ \hline Full-bridge converter [Figure 13a] & [96] & 50 & 4 & 4 & 1 & 2 & 1000 & Wide output voltage, good efficiency, shoot-through problems. \\ Current-fed Weinberg converter & [96] & 50 & 2 & 5 & 0 & 2 & 500 & Shoot-through tolerant, high efficiency, wide output voltage, suitable for LV applications, HV stress across switches. \\ Current-fed cascaded buck and full-bridge converter [Figure 14a] & 150 & 2 & 3 & 1 & 1 & 1500 & Fast dynamic response. \\ Resonant LLC converter [Figure 14b] & [96] & 50 & 5 & 6 & 1 & 1 & 1000 & Blind formation in operating frequency, lower efficiency \\ Conventional DAB [Figure 15a] & [104] & - & 8 & 0 & 1 & 2 & - & Soft switching, galvanic isolation, simple structure. \\ ISOP-DAB [Figure 15b] & 100 & 8 & 0 & 1 & 2 & 8400 & Low EMI noise, low efficiency. \\ INPC-DAB [Figure 15c] & [104] & - & 12 & 4 & 1 & 3 & - & Low volume and weight, low EMI noise, low efficiency. \\ ANPC-DAB [Figure 15d] & [104] & - & 16 & 0 & 1 & 3 & - & High efficiency, good heat management, high power density. \\ Parallel interleaved DAB [Figure 15e] & [104] & 50 & 8 & 0 & 5 & 4 & 250 & High efficiency, high power density. \\ ABAC converter [Figure 16a] & [67,98] & 100 & 8 & 0 & 3 & 4 & 8400 & High efficiency, high power density. \\ ACF [Figure 16b] & [110] & 300 & 2 & 2 & 3 & 3 & 100 & Reliable, lightweight, low EMI noise, high power density. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of various isolated DC-DC converters.
## 3 The Radiative Transfer Equation
### RTE Analytical Solutions with Simplification  
Simplified analytical solutions to the RTE for a water medium are often sought after for various oceanography, remote sensing, and underwater imaging applications. These solutions aim to provide tractable mathematical expressions that describe the behaviour of light propagation in water under certain assumptions or simplifications.  
It is important to note that while simplified analytical solutions offer computational efficiency and insight into the behaviour of light in water, they may not capture all the complexities of real-world scenarios. Hence, they are often used with numerical simulations, laboratory experiments, and field observations to validate and refine their predictions.  
The scaler RTE can be deduced from various approximate analytical solutions after simplifying the RTE in various ways. The SSA is an example of such an approach. This solution is developed by assuming that the water is homogeneous, the sea surface is level, the sun is a point source in a black sky, no internal sources are considered, and only a single scattering of photons is considered. This approximation will be discussed more below in Section 3.3.2.  
These approximate solutions of the RTE help isolate the main factors influencing underwater radiances. However, the solutions depend on various assumptions, and the predicted radiances are typically accurate to a few tens of percent at best. They can be off by an order of magnitude [5; 6; 7].  
Some commonly used simplified analytical solutions for the RTE in a water medium are discussed below.