esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Results and Discussion
### The Prediction Model of the Daily Maximum Thermal Gradient Variation  
The experimental thermal loads shown in Figure 4 were used to verify the experimental steel I girder model as a first step in conducting the thermal analysis. The verified thermal FE model was then used to analyze the selected standard steel I girders under similar conditions (thermal boundary conditions, solar radiation components, initial temperature conditions, air temperatures, material properties and other similar conditions). Using the data collected from the parametric studies and applying a regression analysis, a reliable prediction model was developed to predict the daily maximum thermal gradient variation (DTGmax). A regression analysis was conducted for seven climate regions and eight standard steel I girders. The parameters used to develop the prediction model are half the girder's depth (d/2), half the flange width (b/2), the flange thickness (t), the total solar radiation (I) and the daily maximum air temperature (DTa). A proposed equation, a function of the parameters mentioned earlier, was developed to predict the daily maximum thermal gradient variation in \({}^{\circ}\)C. The proposed equation is expressed in Equation (14). The prediction model accuracy was evaluated based on the coefficient of determination (R\({}^{2}\)). An impressive R\({}^{2}\) of 0.98 was obtained, which is a strong indication of the excellent correlation between the proposed daily maximum thermal gradient variation and the FE daily maximum thermal gradient variation, as illustrated in Figure 8. This result  
Figure 7: The positive vertical thermal gradient for (**a**) Region 1, (**b**) Region 2, (**c**) Region 3, (**d**) Region 4, (**e**) Region 5, (**f**) Region 6 and (**g**) Region 7.  
demonstrates the effectiveness of the proposed equation in predicting DTGmax with a high degree of accuracy.  
\[\text{DTGmax}=15.87(\text{b}/2)+20.80(\text{d}/2)+0.0103(\text{I})-0.0213( \text{DTa})-142.53(\text{t})-5.87 \tag{14}\]
## 3 Performance Indexes of the Electromagnetic Isolation System  
### The Kinetic Equation of an Electromagnetic Isolation System  
The model of the vibration isolation system can be simplified in Figure 2, the mass of the electromagnet is \(m\), the damping coefficient of the vibration isolation system when the coil without currents is \(d_{1}\) and the stiffness of spring is \(k_{1}\). The electromagnetic force between two electromagnets is \(f_{e}\). The plate will  
Figure 1: Structure of the electromagnetic isolation system.  
cause the vibration displacement \(x(t)\) to emerge after the plate of the system suffers an external force \(f_{d}\). The downward direction is regarded as the positive direction.  
It is assumed that the condition without currents in two electromagnets is the stable state of the system. The electromagnetic vibration isolation system will become unstable state after the system suffers an external force \(f_{d}\), which is equal to a step signal in this paper. In order to counteract the external disturbance \(f_{d}\) and get the system back to the stable state, the electromagnetic force \(f_{e}\) is aroused by the controllable currents.  
After the system suffers an external force \(f_{d}\), the kinetic equation of the electromagnetic vibration isolation system can be acquired based on the transfer law of the vibration as follows:  
\[M\ddot{x}+d_{1}\dot{x}+k_{1}x+f_{e}(y,i)=f_{d} \tag{2}\]  
where \(M=m+m_{0}\), in which \(m=530\) g is the mass of electromagnet and \(m_{0}=200\) g is the mass of an isolated object; \(g=9.8\) N/kg is the gravity of earth; \(k_{1}=589\) N/m is the stiffness of spring; \(d_{1}=10\) N \(\cdot\) s/m is the damping coefficient of the system. The damping coefficient of the vibration isolation system without electric current is determined by experiment, which can be referred to [26].  
Due to the fact that the electromagnetic force is determined by the gap \(y\) and the current \(i\). The relationship between \(x\) and \(y\) as follows:  
\[y=g(x)=L_{0}-2*h_{0}-x \tag{3}\]  
where \(L_{0}=0.083\) m is the original length of a spring, \(h_{0}=0.03\) m is the height of a electromagnet.  
Based on the aforementioned equations, Equation (1) can be changed to keep the unity of the variables as follows:  
\[\begin{split} f_{e}(x,i)&=8259.7863*x^{2}+2.4055*i^{ 2}-317.9956*x\\ &-4.0467*i+311.2811*x*i+2.4351\end{split} \tag{4}\]
# Data-Driven Predictive Modeling of Neuronal Dynamics Using Long Short-Term Memory
## 3 Simulation Results
### Regular Bursting
(**a**) Prediction using 1 timestep predictive LSTM network (\(N_{p}=1\)); (**b**) Prediction using 50 timesteps predictive LSTM network (\(N_{p}=50\)); (**c**) Prediction using 100 timesteps predictive LSTM network (\(N_{p}=100\)); (**d**) Prediction using 200 timesteps predictive LSTM network (\(N_{p}=200\)). Figure 12b shows the LSTM networks performances, represented in terms of the root mean squared error vs time over 5000 realizations, for \(N_{p}=1\), 100, and 200 timestep prediction horizon LSTM network. As shown in this figure, the root mean squared error decreased with the increased predictive horizon of the LSTM network for all time, which is consistent with the result shown in Figure 12a. Note that we have excluded the simulation result for \(N_{p}=50\) as we found out in our detailed analysis that the trained LSTM network for \(N_{p}=50\) led to instability in predicting spiking responses for some of the initial condition values in this regime. The reason for this may be that the network may not have seen these initial conditions during the training.
## 2 Experiment and Data Preprocessing
### Raman Spectroscopy Detection  
Spectral detection was performed on 175 insulation oil samples using a Raman spectroscopy detection platform, as shown in Figure 2. The laser was focused on the surface of the insulation oil using an upright microscope for Raman scattering, and the Raman scattered light was collected using an SR-500i dispersive Raman spectrometer and a CCD. Prior to the Raman scattering experiment, the operating temperature of the CCD was set to \(-75\)\({}^{\circ}\)C. The spectral scanning range was set to 500-3050 cm\({}^{-1}\), and the laser source power was kept constant at 300 mW. The integration was performed 10 times. To avoid interference from natural light during Raman spectroscopy detection, the experiment was conducted in a dark environment [21; 22].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Four Categories of** & **Ten Categories of** & **Degree of Aggregation** & **Sample Size/(N)** \\
**Aging State** & **Aging State** & **Range/(DP)** & **Sample Size/(N)** \\ \hline Unaged & Phase 1 & \(>\)1000 & 25 \\ \hline \multirow{4}{*}{Pre-aging} & Phase 2 & 900–1000 & 20 \\  & Phase 3 & 800–900 & 19 \\  & Phase 4 & 700–800 & 17 \\  & Phase 5 & 600–700 & 19 \\  & Phase 6 & 500–600 & 18 \\ \hline \multirow{2}{*}{Middle aging} & Phase 7 & 350–500 & 19 \\  & Phase 8 & 250–350 & 15 \\ \hline \multirow{2}{*}{End of aging} & Phase 9 & 150–250 & 12 \\  & Phase 10 & 0–150 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of aging states.  
Figure 1: Changes in the degree of polymerization.  
The Raman spectroscopy data on samples with different aging durations are depicted in Figure 3. All samples in each stage exhibited distinct characteristic bands around 1500 cm\({}^{-1}\) and 2950 cm\({}^{-1}\). The characteristic band at 1500 cm\({}^{-1}\) was attributed to bending vibrations of C-H bonds and stretching vibrations of C=O bonds in organic molecular groups, while that at 2950 cm\({}^{-1}\) was ascribed to stretching vibrations of C-H bonds. During the aging process of oil-paper insulation, abundant C-H and C=O bonds were detected in ketone compounds owing to oil oxidation. There was also the presence of alcohol andfuran compounds derived from paper degradation, as well as acid compounds generated by the co-aging of oil and paper. Therefore, the differences between Raman band intensities contained certain information regarding the aging of oil-paper insulation [23]. Additionally, as the aging duration increased, the baseline of the Raman spectrum from insulation oil gradually rose. This was attributed to the deepening color of the insulation oil samples with increasing aging, resulting in a stronger fluorescence background in the Raman spectrum [24]. Although the color deepening of the sample will have a weak effect on the Raman spectrum peak, the change in the color of the oil sample also contains a small amount of information about the aging of the oil paper insulation, so this part of the effect is meaningful and does not need to be corrected.
## 3 VR Conferencing  
### What a Virtual Reality Conferencing Application Needs to Succeed  
As mentioned above, a virtual reality conference room is proposed with the aim of obtaining a feeling of immersion for users that is lacking in traditional video conference rooms. The design of the system focuses on this.  
* Virtual space: Immersion means the users' experience of the scene. A virtual scene is the main part of the visual information and is an important source of immersion. The layout of the scene should conform to the appearance of the conference room, with tables and chairs for virtual avatars to move and interact. So far, there are many modeling tools as well as models made by others that can be obtained, which can be easily imported into the game engine for use.
* Avatar: Facial presentation is the most important and complicated factor in this section. One way to achieve this is to update the avatar's facial animation in real time by capturing the participants' facial information in real time, such as the eye rotation, lip movement, etc. When a user wears a helmet-mounted display, it becomes difficult to capture the face data, and the action of the face needs to be controlled by the content of the participants' speech [23]. As multiple participants join the conference room, each participant can see the virtual avatar of all other participants. When a user's position status or body movements change, the new status should be synchronized with other users, for which a network module that synchronizes the status of all players is required.
* Audio sense of space: Vision and hearing are presently critical aspects of bringing immersion to virtual reality devices. In order to obtain the same real experience as in the real conference room, auditory perception is as important as vision. To realize realistic auditory perception, spatial audio technology should be applied to virtual reality conferences. When in the same virtual conference room, each participant should be able to correctly sense the orientation of the other speakers as well as the reverberation of sound after multiple reflections from the room walls. While novel interactions are designed in the conference scenario, users can perceive the sound effect generated by the interaction when some interactions occur.
* Interaction: Communication in the meeting room should be supplemented by content presentation, such as slides, pictures, or videos. A virtual screen can be set up as the display area in the scene. In addition to basic interactions, there are many possibilities for interaction. In [23], VR painting and model import were mentioned. There are also new attempts to interact among participants.
# An Improved Lightweight Model for Defect Detection on Paths in Images
## 3 Results and Analysis
### Attention Mechanism Module Experiment  
In the YOLOv8s model, we replaced the original module with the DyHead module, which is based on a multi-attention mechanism, and compared its performance with the original network. The DyHead module effectively employs an integrated attention mechanism, resulting in clearer and more focused feature maps; each application of attention optimizes the feature representation, enhancing detection performance and efficiency. Table 7 displays the experimental data, which indicate a 0.6% rise in [EMAIL_ADDRESS] and a 1.7% increase in [EMAIL_ADDRESS]:0.95.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Params** & **Size** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]-0.95** & \multirow{2}{*}{**F1**} & **FPS** \\  & **(M)** & **(m)** & **(\%)** & **(\%)** & **(\%)** & \\ \hline YOLOv8s & 11.1 & 22.5 & 86.5 & 56.5 & 0.82 & 357 \\ v8s + & 9.3 & 18.9 & 85.5 & 56.7 & 0.80 & 344.8 \\ v8s + ADown & 9.5 & 19.2 & 86.3 & 57.0 & 0.82 & 312.5 \\ \hline \hline \end{tabular} Note: Table 6 evaluates the efficacy of the proposed ADown module against the SCDown module for lightweight down-sampling.  
\end{table}
Table 6: ADown module experiment verification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Params** & **Size** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]-0.95** & \multirow{2}{*}{**F1**} & **FPS** \\  & **(M)** & **(m)** & **(\%)** & **(\%)** & **(\%)** & \\ \hline YOLOv8s & 11.1 & 22.5 & 86.5 & 56.5 & 0.820 & 357 \\ v8s + BiFPN & 10.2 & 20.8 & 85.7 & 56.6 & 0.816 & 333 \\ v8s + CCFM & 7.3 & 14.8 & 85.0 & 56.3 & 0.812 & 357 \\ \hline \hline \end{tabular} Note: The results in Table 5 validate the effectiveness of the proposed CCFM module for lightweighting the feature fusion network.  
\end{table}
Table 5: Feature fusion network Lightweight experiment verification.  
Furthermore, the model's parameters, computational complexity, and weights have all been reduced. The outcomes demonstrate that the DyHead module enhances the original model's feature extraction potential, which boosts the algorithm's detection power even more, as shown in Table 7.
# Fall Detection Based on Continuous Wave Radar Sensor Using Binarized Neural Networks
## 5 Conclusions  
This study proposes a lightweight fall detection method that uses CW radar and a BNN. We preprocessed the radar-acquired data to generate a spectrogram representing the micro-Doppler signature of human motion and used a deep-learning algorithm to classify it for fall detection. However, the many parameters of deep-learning algorithms and the resulting high memory requirements pose significant limitations for fall detection systems in home environments. To resolve this issue, a BNN was used to implement a lightweight fall detection method. The proposed method has a lower or similar overall performance compared with previous studies; however, it has a larger number of distinguishable motions and significantly reduced memory usage, making it more suitable for home-based fall detection systems. The proposed method achieved an accuracy of 93.1%, which is up to 2% lower than that in previous studies. However, it can distinguish up to 2.75 times more actions and uses up to 99.9% less memory (11.9 KB), confirming its suitability for home fall detection systems where light weight is essential.  
In future work, we plan to implement the BNN on a VLSI to reduce power consumption and shorten the inference time, thereby developing a more advanced lightweight fall detection system.  
[PERSON] designed and implemented the proposed fall detection system, collected radar data, performed the experiment and evaluation, and wrote the paper. S.K. evaluated the proposed fall detection system and revised this manuscript. [PERSON] evaluated the proposed fall detection system and revised this manuscript. [PERSON] conceived of and led the research, analyzed the experimental results, and wrote the paper. All authors read and agreed to the published version of the manuscript.  
This work was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (RS-2024-00438007) and the MOTIE (Ministry of Trade, Industry, and Energy), Korea, under the Technology Innovation Program (RS-2024-00433615); the CAD tools were supported by IDEC.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Effects and Mechanisms of Microbial Remediation of Heavy Metals in Soil: A Critical Review
## 4 Microbial Remediation of Heavy Metal-Contaminated Soil
### Comparison of Microbial Removal Ability
#### 4.2.2 Adsorption Equilibria  
Equilibrium sorption isotherms are used to describe the capacity of an adsorbent, where their values express the affinity and surface properties of the adsorbent. Recently, the most widely applied adsorption isotherm is the Langmuir isotherm, which assumes that adsorption occurs at specific homogenous sites within the adsorbent [61]. The saturated monolayer isotherm can be represented as:  
\[Q\text{eq}=(Q\text{max}\times bC\text{eq})/(1+bC\text{eq}),\]  
where \(Q\text{eq}\) (mg/g biomass) indicates the quantity of metal ions adsorbed by the bacteria, \(b\) (L/mg) is the adsorption constant, which is related to the affinity of the binding sites, and \(C\text{eq}\) (mg/L) is the concentration of metal ions remaining in the equilibrium solution [34, 47].  
Usually, the Langmuir isotherm is valid for the following simple cases: The adsorbing site surface is a perfectly flat, homogenous plane with no corrugations, and all sites are equivalent [62]. The shortcoming of this isotherm is that it fails to account for the surface roughness of the adsorbent, and, thus, deviates significantly in many cases. In addition, the model overlooks the interaction with adsorbate, which affects the adsorption of other adsorbate molecules [63].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Microbe** & **Metals Which Can Be Easily Removed** \\ \hline _Escherichia coli_ K-12 & Hg, Cd, Pb, Cu, Ni, Zn etc. \\ _Rhizopus arrhizus_ & Zn, Cu, Cd, Th \\ _Aspergillus niger_ & Zn, Cu, Cr, Pb, Th, Co, Mn, Ni \\ _Sachcharomyces cerevisiae_ & Cu, Cd, Pb, Ag \\ _Thiobacillus thioxidans_ & Cu, Pb, Zn, Cd \\ \hline \hline \end{tabular}
\end{table}
Table 3: Metals that can be removed by different microbes.  
Compared with Langmuir isotherm, the Freundlich isotherm is a multisite adsorption isotherm for rough surfaces [61] that can be represented as:  
\[Q\text{eq}=K_{\text{F}}C\text{eq1}/N,\]  
where the two parameters \(K_{\text{F}}\) and \(N\) are the Freundlich constants related to the adsorption capacity and adsorption intensity of the adsorbent, respectively. Because the Freundlich isotherm has two parameters while the Langmuir isotherm only has one, it is more flexible and is able to fit data on rough surfaces better than [PERSON]'s equations [64].
## 2 Material and Methods
### Case Studies Description
#### 2.1.1 The Livorno Event  
The first case study (type II) is the Livorno extreme weather event that took place in the night between 9 and 10 September 2017. Starting from the morning of 9 September a large trough deepened on the western Mediterranean, generating an intense flow of mild and extremely humid air from the South, over all the Tyrrhenian sectors and the eastern part of the Ligurian Sea. From the evening of 9 September, the cold airflow associated with vorticity at 500 hPa was supportive of instability conditions on Tuscany region (not shown).  
The environment was also conducive to the development of intense persistent local convective precipitation systems, not only because of the slow evolution of the depression area but also because of the wind shear (not shown).  
The resulting observed Quantitative Precipitation Estimate (QPE) for the time period 18 UTC 9 September 2017-06 UTC 10 September 2017 is shown in Figure 1. The map is obtained interpolating the raingauge data using a semivariogram directly estimated from data themselves [21] (courtesy of the Italian Civil Protection Department). The lower panel of Figure 1 highlight the very timely concentrate rainfall event (Type II) with more than 200 mm of rain felt down in three hours. It was also found that an intense lightning activity, indicative of the ongoing convective dynamics, took place (not shown).  
Figure 1: Quantitative Precipitation Estimate (QPE) using Italian raingauge data network for the time period 18 UTC 9 September 2017–06 UTC 10 September 2017 (**upper panel**) and raingauge observation near the Livorno area (**lower panel**, courtesy of the Italian Civil Protection Department).
# Region-of-Interest Based Coding Scheme for Live Videos
## 3 Proposed Approach  
Our proposed method is grounded on the R-\(\lambda_{cu}\) model tailored for HEVC. Building upon the GOP rate control strategy, our scheme introduces a joint optimization and control mechanism at both frame level and Coding Unit (CU) level, thereby enhancing the subjective quality of Regions of Interest (ROI) while maintaining overall bitrate stability. The relationship between R and \(\lambda\) described earlier is leveraged to compute the Quantization Parameter (QP) for each frame and individual CU within an image. This approach has demonstrated superior performance compared to traditional quadratic models [7; 8]. In this section, we delve into the details of our proposed methodology, with a particular focus on the two steps in the ROI rate control process: Firstly, utilizing known information coupled with a Convolutional Neural Network (CNN) model to infer the \(\lambda_{cu}\) values for both ROI and Region of Uninterest (ROU); Secondly, discussing methods for gathering training data to effectively train the CNN model.
## 2 Materials and Methods
### Methodology Workflow  
This paper primarily describes the methods and experimental steps used to predict soil salinity in the Yellow River Delta. First, 86 points were sampled, and field coordinates were recorded. Then, EC values were measured in the laboratory and converted into salinity units (g/kg) using a specific formula. Next, Landsat 8 OLI Collection 2 data for 12 months were obtained, and the months with cloud cover below 10% were selected. The Level-1 data underwent radiometric calibration and atmospheric correction to extract spectral values, while Level-2 data were directly extracted. Afterward, Pearson correlation and expert insights were used to determine suitable dates for inversion. Spectral indicators and mathematical transformations were then calculated for the sampling points. Some points were removed due to \"NONE\" values in the \"CRSI\" indicator. The samples were sorted in ascending order of salinity. Then, every fourth sample was selected to form a test set, and the remaining samples were used to form a training set. Finally, models were built in Python 3.10. The precision of the three models was evaluated, and the salinity distribution was visualized using the best-performing model. The entire workflow is illustrated in Figure 5.
# A Novel Compliant 2-DOF Ejector Pin Mechanism for the Mass Transfer of Robotic Mini-LED Chips
###### Abstract  
The continuous development of mini-LEDs has led to higher requirements for chip transfer technology, which makes it difficult for the intermittent transfer method with a mechanical ejector pin to meet these requirements. To solve this problem, a novel compliant 2-DOF ejector pin mechanism for the mass transfer of robotic mini-LED chips is proposed in this paper. The compliance matrix method and the Newton method are employed for system kinematic modeling and dynamics modeling, respectively. The static and dynamic analyses of the mechanism are carried out via ANSYS Workbench, and the results of FEA are demonstrated the effectiveness of theoretical calculation. Then, an ILC is utilized to control the device via a parameters regulation approach in the frequency domain. Finally, an open-loop test and a trajectory tracking test for the prototype are carried out verify the effectiveness of proposed device. The test results indicate that the working stroke of the mechanism reaches 120 \(\upmu\)m, the natural frequency of the device is 250.85 Hz, the coupling rate is less than \(\pm\)0.5% and the tracking errors of 10 Hz, 20 Hz and 30 Hz sinusoidal signals are all within \(\pm\)1.5%. According to the results of theoretical analyses, FEA and test, it has been proved that the designed mechanism for the mass transfer of mini-LED chips is superiority and effective.  
compliant mechanism; ejector pin; mini-LED; chip mass transfer; iterative learning control Article