esa-sceva/satcom-chunk-collection huggingface dataset:
# Extraction of Periodic Terms in Satellite Clock Bias Based on Fourier Basis Pursuit Bandpass Filter
## 4 Experimental Analysis
### Clock Prediction Analysis of the BDS Satellite Clocks  
To validate the capability of the FBPBPF method in extracting periodic terms through simulation experiments, this study employs the QP prediction method, the SAM prediction method based on LSM and the FBP prediction method based on FBPBPF to predict the clock bias data of BDS satellites listed in Table 2. The specific steps are as follows: First, since extracting periodic terms with period of 24 h usually requires at least 48 h of data, in order to balance the accuracy and efficiency of clock bias prediction, 72 h of clock bias data is used to fit the prediction model, and the model is used to predict the clock bias for the next 24 h. Then, the prediction is continued for 25 days, and each time the predicted value is compared with the measured clock bias data and the root mean square error (RMSE) of the prediction error from 1 to 24 h per day is calculated. The prediction error on February 8, 2023, is shown in Figure 7. The average RMSE of the 1 to 24 h over the 25 predictions is shown in Figure 8. By comparing the prediction results of QP, SAM and FBP, the improvement in the prediction accuracy of the FBP prediction method at different time intervals (6 h, 12 h, 18 h and 24 h) is evaluated, as shown in Table 3.  
The experimental results show that the FBP prediction method effectively suppresses the periodic fluctuations in prediction errors for satellites with significant periodic variations (such as C07 and C40), as shown in Figure 7 and significantly reduces the overall prediction errors, as shown in Figure 8. In contrast, due to the shorter model fitting time, the SAM prediction method exhibits relatively weaker modeling accuracy for periodic components, resulting in residual periodic fluctuations in its prediction errors, and even lower accuracy than QP in some time periods (such as C07 and C12). This further demonstrates that the FBPBPF method achieves higher accuracy in extracting periodic terms at the signal boundaries, especially under conditions of strong noise interference.  
As shown in Table 3, compared to the QP and the SAM, the FBP prediction method significantly improves prediction accuracy across all time intervals and the improvement is more significant in shorter time intervals (6 h). For predictions over 6, 12, 18, and 24 h, the average RMSE of the FBP prediction method is reduced by 28.46%, 16.44%, 13.01%, and 9.74%, respectively, relative to QP. Among these, the satellite with significant periodic variations (such as C40) achieved a remarkable reduction of 73.41% in RMSE for 6 h predictions, demonstrating outstanding performance. Compared with SAM, the average RMSE of the FBP prediction method is reduced by 15.85%, 11.04%, 6.45% and 4.01%, respectively.
## 3 Performance Analysis  
### OP of Perfect SIC  
The MU-MISO system with our proposed combination of precoding and NOMA approaches has modeled in the previous section, in this section, we start analyzing the performance of system with deriving the outage probability of \((m,n)\)th user. The data rate from the BS to every user which is denoted by \(r\), is assumed to be the same in the sense of fairness of all users. Therefore, the outage event occurs when the instantaneous capacity between the BS and the user which is calculated by \(\log_{2}(1+\gamma_{m,n})\), is below \(r\). The OP is described as follows.  
\[\text{OP}_{m,n}=\text{Pr}\left(\gamma_{m,n}\leq\gamma_{\text{th}}\right). \tag{9}\]  
where \(\gamma_{\text{th}}=2^{r}-1\) denotes the outage threshold.  
From Equation (7), we can rewrite Equation (9) by Equation (10):  
\[\text{OP}_{m,n}=\text{Pr}\left(\frac{P_{\text{S}}a_{m,n}|\mathbf{h}_{m,n} \mathbf{w}_{m}|^{2}}{\sum\limits_{i=n+1}^{N}P_{\text{S}}a_{m,i}|\mathbf{h}_{m, n}\mathbf{w}_{m}|^{2}+\sigma_{m,n}^{2}}\leq\gamma_{\text{th}}\right). \tag{10}\]  
Let  
\[b_{m,n} = \sum\limits_{i=n+1}^{N}a_{m,i},\] \[X_{m,n} = |\mathbf{w}_{m}\mathbf{h}_{m,n}|^{2},\]  
and then Equation (10) is rewritten as Equation (11):  
\[\text{OP}_{m,n}=\text{Pr}\left(\begin{matrix}X_{m,n}\\ n:1+N\end{matrix}\leq\frac{\gamma_{\text{th}}}{P_{\text{S}}(a_{m,n}-\gamma_{ \text{th}}b_{m,n})}=\theta_{i}\right). \tag{11}\]  
From Equation (11), we can recognize that the outage event always happens in case of \(a_{m,n}\leq\gamma_{\text{th}}b_{m,n}\), thus, more transmit power should be allocated to users whose channel gains are weak.  
The ordered variable \(X_{m,n}\) of PDF function is represented by Equation (6.58) [21] in Equation (12):  
\[f_{X_{m,n}}(x)=\frac{N!}{(N-n)!(n-1)!}\sum\limits_{j=0}^{N-n}(-1)^{j}\binom{N -n}{j}f_{X_{k}}(x_{k})\Big{[}F_{X_{k}}(x_{k})\Big{]}^{n+j-1}, \tag{12}\]  
where Equations (13) and (14):  
\[f_{X_{k}}(x_{k})=\frac{1}{\Omega_{k}}\exp\Big{(}-\frac{x_{k}}{\Omega_{k}}\Big{)}, \tag{13}\]  
\[F_{X_{k}}(x_{k})=1-\exp\Big{(}-\frac{x_{k}}{\Omega_{k}}\Big{)}. \tag{14}\]  
Replacing Equations (13) and (14) into Equation (12), and then using the approximation of binomial expansion, the PDF function for the SINR of \((m,n)\)th user is obtained. Owing to the definition of OP, e.g., \(\text{OP}=\int_{0}^{\theta^{*}}f_{X_{m,n}}(x)dx\), the approximation of OP of the \((m,n)\)th user is depicted by Equation (15):  
\[\text{OP}_{m,n}= \frac{N!}{(N-n)!(n-1)!}\sum\limits_{k=0}^{N-n}\frac{(-1)^{k}}{n+ k}\binom{N-n}{k}\left[1-\exp\Big{(}-\frac{\theta^{*}}{P_{\text{S}}\Omega_{m,k}} \Big{)}\right]^{n+k}, \tag{15}\] \[\text{with }k\leq n\leq N,\,\theta^{*}=\max\limits_{i=1:N}\{ \theta_{i}=\frac{\gamma_{\text{th}}}{(a_{m,n}-\gamma_{\text{th}}b_{m,n})}\}.\]
## 5 Discussions
### Future Edge Flying Servers  
Edge computing is an optimization method that consists of processing data at the edge of the network, close to the data sources, i.e., near the sensors (at the border of the IoT perception layer). Basics of Mobile Edge computing are provided in [94]. Drones with more computing capacity than sensors can thus play the role of Mobile Edge Servers. Therefore, it would be possible to minimize the bandwidth requirements between the sensors and the data processing centers by undertaking the analyses as close as possible to the On-ground data sources. There are several advantages to placing computation at the drone level. Indeed, this makes possible the placement of functions to reduce the volumes of data to be transmitted, perform complex tasks in particular for reconfigurable sensor architectures, correlate data from heterogeneous sources, etc. Furthermore, UAV-enabled Edge computing in IoT helps to avoid the transmission of irrelevant data to data centers or the cloud, thus bringing fluidity and speed of analysis and decision-making.  
UAV-enabled edge server approaches require mobilizing resources that may not be permanently available. Consequently, the same networking challenges highlighted above are raised: the intermittency of the links, the Air-Ground contact duration, and the energy consumption, but also computing ones such as responsiveness and prioritization of task execution. Several issues can be considered in UAV-enabled edge computing for IoT, particularly those of data and task offloading.
# DGSS-YOLOv8s: A Real-Time Model for Small and Complex Object Detection in Autonomous Vehicles
## 3 Methodology
### Architecture Overview
#### 3.1.2 Optimized-GFPN Module  
Feature pyramid networks (FPNs) [28] and their variants (e.g., PANet [31], BiFPN [32], GFPN [53; 54], etc.), although successful in multi-scale feature fusion, still have obvious limitations in complex traffic scenarios. The unidirectional information flow of FPNs restricts the adequate fusion of high-level semantic and low-level spatial information; especially when dealing with both long-range and close-range targets, the detection accuracy of the model tends to decrease. For example, PANet solves the problem of unidirectional information flow by introducing bottom-up paths, but the complex structure increases the computational overhead and reduces the real-time performance of the model. BiFPN improves the information flow, but the computational cost is high, which makes it difficult to satisfy the demand for real-time detection. Although GFPN performs well in multi-scale feature processing, especially in small object detection and contextual information integration, it is accompanied by increased computational complexity and design complexity challenges. To prioritize high FPS while maintaining strong multi-scale fusion, we designed Optimized-GFPN. Inspired by efficiency-focused designs like BiFPN [32], our OptimizedGFPN modifies the standard YOLOv8s neck structure by removing specific upsampling layers identified as potentially redundant through experimentation, directly reducing the computational graph depth and latency. It is shown in Figure 3. Meanwhile, CSPModule (Cross-Stage Partial module) is introduced in Optimized-GFPN to replace traditional 3 \(\times\) 3 convolutional feature fusion operations. Following the Cross-Stage Partial Network (CSPNet) principle [14], this splits feature channels, processing only a portion and then merging, aiming to reduce computation while enriching feature representation diversity. Combined with the reparameterization mechanism (Rep convolution) [55] and Efficient Layer Aggregation Network (ELAN) [56], this streamlined architecture enhances fusion efficiency and detection accuracy while contributing positively to the overall model FPS.  
Figure 3: Structural diagram of Optimized-GFPN.  
Figure 2: Structural diagram of DCNv3_LKA_C2f.  
The structure of CSPmodule is shown in Figure 4. The CSPmodule reduces redundant computations and improves the diversity of feature representations by splitting the input feature map into two parts, one subjected to the convolution operation and the other kept unchanged, and then merging the two parts of the feature map. Meanwhile, the Rep convolution in this module can make the model different at training time and inference time to improve efficiency.
# Heterogeneous Network Switching Strategy Based on Communication Blind Area Dwell Time
## 4 Simulation Results
### Communication Stability Performance Evaluation
#### 4.1.1 Performance Analysis for Handover Failure Probability  
From Figure 7, the I-VHO scheme has the highest handover failure probability of 0.018, which is caused by frequent handovers. The D-VHO\({}_{\mathrm{t=0.5}}\) scheme and D-VHO\({}_{\mathrm{t=1}}\) scheme have a handover failure probability of 0.016 and 0.013, respectively, which is because the scheme does not perform handover during partial dwell time, and the CBDHVHO scheme has a handover failure probability of 0.009 due to its lower handover cost and seamless connection. The handover failure probability is 0.009 because the handover failure probability is closely related to the number of handovers; more handovers will result in more delay time and more dwell packets, so the handover failure probability will increase.
## 1 Introduction
[PERSON] et al. [33] used a neural network (ANN) with three FBG sensors and three photodetectors to expand the temperature measurement range (from 25 \({}^{\circ}\)C to 250 \({}^{\circ}\)C). The accuracy obtained was \(\pm\)0.4 \({}^{\circ}\)C (2\(\sigma\), where \(\sigma\) is the standard deviation in normal distribution) or \(\pm\)4 pm up to 150 \({}^{\circ}\)C and \(\pm\)0.9 \({}^{\circ}\)C (2\(\sigma\)) or \(\pm\)9 pm above 150 \({}^{\circ}\)C. [PERSON] et al. [34] reported on the use of a network of radial basis functions for approximating the FBG reflectance spectrum. The neural network was trained according to the algorithm described by [PERSON] [35]. Using the approximation of a spectrum consisting of 1000 points, an accuracy of 2 pm (mean error) and 25 pm (\(\sigma\)) was achieved on an interval of 4 nm. In this work, in contrast to [31; 32; 33; 34], we proposed to use a neural network of a simpler configuration to increase the processing speed of FBG sensors in the interval \(\Delta\) under study, the spectra of which were taken at a low resolution of the spectrum analyzer (\(\sim\)167 pm/pixel), and the aim was the accuracy of the FBG central wavelength shift deter \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Wavelength Estimation Error** & **Temperature Estimation Error** & **Data Resolution** \\ \hline
[31] & 1.45 pm (RMSE) & 0.145 \({}^{\circ}\)C & 1 pm \\
[32] & 2.081 pm (RMSE) & 0.2081 \({}^{\circ}\)C & - \\
[33] & 2 pm (2\(\cdot\)\(\sigma\)) & 0.2 \({}^{\circ}\)C & - \\
[34] & 2 pm (mean), 25 pm (\(\sigma\)) & 0.2 \({}^{\circ}\)C & 4 pm \\ Current work & 0.411 pm (MAE), 0.533 pm (\(\sigma\)) & 0.05 \({}^{\circ}\)C & 167 pm \\ \hline \end{tabular}
\end{table} 
Table 1: Accuracy of neural network methods for FBG interrogation. mination with an error not exceeding 1 pm. The obtained accuracy corresponded to 0.04 \({}^{\circ}\)C (mean absolute error, MAE) and 0.05 \({}^{\circ}\)C (\(\sigma\)). Looking ahead, we include in Table 1 the data obtained from our study to compare them with those of other researchers. As the processing power of computers has increased over the past few decades, artificial neural networks and other machine-learning techniques have become widespread in all areas of science and technology. However, while many articles (e.g., [31; 36; 37; 38]) demonstrate results when similar approaches are used, this does not exhaust the potential of artificial neural networks.
## 2 Background and Theory
### Preprocessing Methods
#### 2.1.2 High-Pass Filtering  
The subsequent step in EEG data preprocessing includes utilizing high-pass Butterworth filters (HPFs) [59], designed to allow EEG components with higher frequencies to pass while suppressing lower-frequency signals. HPFs are commonly employed to reduce low-frequency noise and retain essential high-frequency neural activities, making them particularly suitable for artifact removal and correcting electrode drift in scalp-based BCI systems. A high-pass Butterworth filter with order \(N\) and cutoff frequency \(\omega c\) can generally be represented as shown in Equation (2):  
\[C(z)=\frac{B(z)}{A(z)} \tag{2}\]  
In this equation, \(B(z)\) and \(A(z)\) denote the \(Z\)-transforms of the filter coefficients \(b\) and \(a\), respectively. When employing a fourth-order high-pass Butterworth filter, the transfer function takes the form indicated by Equation (3):  
\[C(z)=\frac{b_{0}+b_{1}z^{-1}+b_{2}z^{-2}+b_{3}z^{-3}+b_{4}z^{-4}}{1+a_{1}z^{-1 }+a_{2}z^{-2}+a_{3}z^{-3}+a_{4}z^{-4}} \tag{3}\]  
Configuring this filter involves determining the coefficients \(b\) and \(a\) based on a fourth-order Butterworth filter with a defined normalized cutoff frequency (\(\omega c\)). Applying this filter to EEG signals is executed via multiplication in the \(Z\)-domain, as presented in Equation (4):  
\[Filtered\ Signal(z)=C(z)\cdot EEG\ Signal(z) \tag{4}\]
## 3 Results and Discussion
### Estimation of Soil Nutrient Content for Satellite PRISMA Hyperspectral Region  
PRISMA (Precursors IperSpettrale Della Mission Applicative) is an Italian hyperspectral imaging spacecraft that was launched in 2023 [31]. This space-borne mission collects data from three different spectral regions: the very near infrared region (VNIR), the short wave infrared region (SWIR), and the panchromatic region (PR). The VNIR spectrum spans 400 nm to 1010 nm, with a spectral resolution of 12 nm, 66 continuous spectrum bands, and  
Figure 7: Spectral reflectance for different AOC, AN, AP, and AK levels. (**a**) Spectral reflectance curves for three AOCs. (**b**) Spectral reflectance curves for two ANs. (**c**) Spectral reflectance curves for three APs. (**d**) Spectral reflectance curves for two AKs.  
a spatial resolution of 30 m [32; 33]. In SWIR, the spectrum ranges from 920 nm to 2505 nm with 12 nm spectral resolution, 171 endless spectrum bands, and 30 m spatial resolution. The spectrum ranges from 400 nm to 700 nm in PR with a one-micrometer spectrum band and 5 m spatial resolution, as shown in Table 7.
## 3 Methodology for Verification and Validation
### System Functionality  
The evaluation consisted of (a) analyzing the interaction between the Gateway and the Database through the Graphical Interface Design; (b) examining the Installation process; and (c) evaluating the quality and significance of the data. The assessment did not cover user interface experience and specific functional tests, as the main goal was to guarantee data integrity throughout the data fusion pipeline when accessed by one or more users at the same time. Figure 6 summarizes the user scenarios that were tested in this assessment divided into collection, file, and model levels.
###### Abstract  
The paper presents the first results of Hg determination in the hair of prehistorical animals (woolly mammoth, steppe bison, and woolly rhino). Hair of prehistorical mammals can be used as an archive that preserves changes of environmental pollution at the paleoscale. The aim of our study was to assess the levels of Hg exposure of ancient animals and to understand whether Hg concentration in hair could be used as a proxy indicating changes of mercury levels in the environment following global climate changes. We assessed changes of Hg exposure recorded in hairs of seven specimens of mammoth fauna mammals that inhabited the Yakuttia region in the period from 45 to 10 ka yr BP. Hg concentrations in hair varied from 0.017 to 0.177 \(\mu\)g/g; the lowest Hg concentration were determined in older specimens (45-33 kyr yr BP). The two highest concentrations belonged sample from the Last Glacial Maximum and the Karginian interstadial (57-24 kyr BP) periods. Our hypothesis is the increase of Hg concentrations in hair reflecting environmental Hg level might be forced by high dust load in cold periods and thawing permafrost in warm climatic periods. Long-term variations of Hg level recovered from Ice Age animals' hair correlate with Hg profiles of concentration and deposition reconstructed from the Antarctica ice core.  
mercury; mammoth fauna mammals; hair; environmental changes; paleoclimate; Pleistocene; Yakuttia 2020, 10, 8664; doi:10.3390/app10238664
# Enhancing Tactile Internet Reliability: AI-Driven Resilience in NG-EPON Networks
## 4 Proposed Architecture
### Bandwidth Waste  
Figure 13 showcases the trend of decreasing bandwidth waste as the traffic load increases with various scenarios including normal conditions and faults. At lower traffic loads, the bandwidth waste tends to be a surplus of allocated but unused bandwidth, leading to higher waste. As the traffic load increases, the demand for bandwidth rises, making the RDBA allocate nearly all available bandwidth to meet this demand, thereby minimizing waste. Thus, the RDBA demonstrates a robust capability to optimize resource management, particularly crucial when the network load reaches full capacity.  
The results from the comparison show that the RDBA mechanism outperforms the baseline DBA, particularly under fault conditions. While the baseline DBA experiences significant delays in high-priority traffic (EF and TI) during fault scenarios, the RDBA mechanism mitigates these delays using backup nodes, ensuring that critical traffic maintains low-latency performance even when multiple faults are present. While the RDBA performs better in handling faults and maintaining service continuity, it introduces some complexity in terms of system management and leads to higher  
Figure 12: Packet drop rate.  
Figure 13: Bandwidth waste.  
traffic (AF and BE), particularly under high-load conditions. This trade-off highlights the need for balancing fault tolerance and resource management in heavily loaded networks.
## 3 Results
### Determination of Optimal Density and FBS Concentrations for Caco-2 Cells Growth
Representative photomicrograph of Caco-2 cells stained on an E-16 plate for occludin (red, Alexa Fluor 564), Leica DMi8 (20\(\times\)) (**D**). The Assessment of TNF-\(\alpha\) Effect on Caco-2 Epithelial Barrier Function by xCELLigence RTCA S16  
The loss of integrity of the monolayer as well as barrier functions of the human colorectal adenocarcinoma cell line Caco-2 are usually examined by the addition of a proinflammatory cytokine, TNF-\(\alpha\), in the apical compartments of the Transwell(r) plate. The cells require at least 18 days post-seeding on the Transwell(r) membrane before treatment with TNF-\(\alpha\) to be fully confluent and to acquire the ability to fully express TJ proteins such as occludin or zonulin-1 (ZO-1) and to form TJs [12,21]. The TEER is measured by a voltohm meter every 24-48 h to ensure that the monolayer of cells acts as a barrier between the apical and the basolateral compartments. The TEER values are directly proportional to barrier integrity, suggesting that the highest values are reached as the cells proliferate and become confluent, indicating the tightness and intactness of the monolayer of Caco-2 cells. In our in-house experiments the TNF-\(\alpha\) was added to apical compartment of Transwell(r) insert at 10, 50 and 100 ng/mL concentrations when the membrane resistance of cultivated Caco-2 cells reached around 1000 \(\Omega\). A dose-dependent decline of the resistance was observed next at different time points, such as 0, 4, 8, 12, 24, and 48 h, when compared to the control (Figure 6A). For the highest concentration of TNF-\(\alpha\), 100 ng/mL, a marked decrease in TEER value up to 500 \(\Omega\) was measured at 48 h. Thus, we selected the highest used concentration of TNF-\(\alpha\) (100 ng/mL) for the real-time assay on E-plate 16 in order to determine if the similar decline of impedance as was shown for TEER can also be recorded by xCELLigence RTCA S16. In this experiment Caco-2 cells (2000/well) were seeded in complete media on E-plate 16. The media change from 20% to 1% FBS was performed at 260 h of incubation. The cells reached confluence with a maximum CI at 385 h post-seeding. The Caco-2 cells were treated next with TNF-\(\alpha\) (100 ng/mL) at 450 h of incubation (cyan curves). However, no significant decline in CI was recorded in real-time for 48 h after treatment with TNF-\(\alpha\). As was shown in Figure 6B, the real-time CI lines were comparable between control (red curves) and tested wells.