esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Applications of Infrared Spectroscopy in BSFL as Feed or Food  
### Proximate Composition  
The prediction of proximate composition is where IR spectroscopy methods have been applied most often [18; 19; 50; 51]. The ability of portable NIR instruments in the two different wavelength regions of 900-1700 nm and 1350-2562 nm, combined with PLS and SVM regression, was evaluated to predict protein and lipid content (%) in BSF larvae flour [19]. The PLS and SVM algorithms yield similar regression models for the determination of the percentage of protein, using both instruments with a residual predictive value (RPD = SD/SECV) > 2.5 and a root mean standard error of prediction (RMSEP) of 1.9%. The SVM regression model yields a better prediction for the total lipid content with an RMSEP of 3.51% and an RPD value of 4.32 [19].  
A bench laboratory NIR instrument was used to predict the chemical composition and nutritive value of BSFL (fifth and sixth instars) as well as frass sampled from soy waste and customized bread and vegetable-based diets [50; 51]. PLS regression yields an R\({}^{2}\)cal of 0.90 and an RPD value of 3.6 for the acid detergent fiber (ADF) and an R\({}^{2}\)cal of 0.76 as well as an RPD value of 2.1 for total carbon (TC). Moderate accuracies were observed for the prediction of crude protein (CP) (R\({}^{2}\)cal of 0.63; RPD value of 1.4), crude fat (CF) (R\({}^{2}\)cal of 0.70; RPD value of 1.6), neutral detergent fiber (NDF) (R\({}^{2}\)cal of 0.60; RPD value of 1.6), starch (R\({}^{2}\)cal of 0.52; RPD value of 1.4), and sugars (R\({}^{2}\)cal of 0.52; RPD value of 1.4) [50; 51].  
The evaluation of hyperspectral imaging in the NIR range in combination with PLS and SVM to predict the proximate composition of BSFL (intact and individual samples) was also reported [18]. The authors have applied a wavelength selection step using iPLS and GA algorithms. The PLS and SVM algorithms reported an RMSEP value ranging between 1.57 and 1.66% and RPD values ranging between 2.0 and 2.5 for the prediction of CP (% protein range 25.5-43.5%) [18]. The authors also showed that iPLS yielded better regression statistics than wavelengths selected using GA, as indicated by the lower error in prediction. Chemical maps based on hyperspectral images showed the non-uniform distribution of the protein in a single larva or a batch of larvae samples [18].  
Short wavelengths in the near infrared (SWIR) combined with hyperspectral imaging were evaluated to predict the proximate composition of dried BSFL samples, including moisture, CP, crude fat (CF), crude fiber, and ash content [52] The BSFL samples were dried at various temperatures and times, and images were collected using a SWIR hyperspectral imaging camera. In this study, PLS regression was used to develop calibration models for moisture, CP, CF, crude fiber, and ash content with R\({}^{2}\) values > 0.89 and RMSEP values within 2% based on the images (data points) [52]. Table 1 summarizes the calibration statistics reported on the prediction of proximate analysis using different spectral techniques and chemometric methods.
# Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Health Features
## 3 Algorithm Principle Explanation
### Four-Vector Optimization Algorithm
The entire process aims to efficiently solve the global optimization challenges of complex problems through systematic parameter configuration and iterative optimization mechanisms while avoiding falling into local optimality. Then, the initial parameters of the CNN-LSTM-Attention model are set, including the number of hidden layer nodes, regularization coefficients, and the selection of activation functions. This allows the neural network to have sufficient learning ability, and after finding the optimal initial parameters in the optimization algorithm, the model is constructed with the optimal weights and parameters, and then the SOH is predicted and estimated. The flowchart of the overall model is shown in Figure 5.
# Valley-Selective Response of Nanostructures Coupled to 2D Transition-Metal Dichalcogenides
## 2 Optical Properties of 1L-TMDCs
In 1L-TMDCs, the coherence time is usually less than the exciton lifetime (at room temperature), and it is key for valleytronics and quantum-optics applications [86]. The dynamics of photoexcited electron\(-\)hole pairs in 1L-WSe\({}_{2}\) at room temperature and ambient conditions was directly traced in Reference [87]. It was shown that, after highly nonresonant interband excitation by a femtosecond laser pulse, the concentration of free carriers increased, reaching its maximum after 0.5 fs. This excitation was followed by a rapid carrier relaxation toward the respective band minima. More than half of the carriers are already bound into excitons 0.4 ps after the excitation. The ratio between excitons and unbound electron-hole pairs increases up to 0.5 ps. Interestingly, the exciton concentration grows even after 0.5-0.6 ps, when the free-carrier concentration starts decaying, and it continues up to 1 ps. Then, both concentrations decay on a time scale of a few picoseconds, while a significant fraction of free carriers is still observed after 5 ps. We note that the results for cryogenic temperatures are similar because of strong binding energies (\(\sim\)0.5 eV) of excitons, which are well above the thermal energy (\(\sim\)50 meV). The valley-polarization degree dynamics of 1L-WSe\({}_{2}\) was experimentally studied in Reference [79] using the pump-probe Kerr rotation technique. It was demonstrated that the exciton valley depolarization time decreased significantly from \(\sim\)6 ps to \(\sim\)1.5 ps when the lattice temperature increased from 4 K to 125 K, which is in good agreement with theoretical estimations [88]. In Reference [81], these results were obtained from ab initio calculations, which took into account all possible mechanisms of valley depolarization. Similar results were demonstrated in Reference [41]. At room temperature, the valley depolarization times are considerably shorter than 1 ps, which is much shorter than the exciton lifetime. Thus, all previously reported carrier and exciton valley Hall effects relying on nonzero valley polarization were observed at low temperatures. At room temperature, the valley-polarization lifetime is too short to be utilized in real applications. However, since the binding energy of excitons is very large in 1L-TMDCs, it is, therefore, both feasible and highly desirable to design a photonic device to manipulate valley excitons at room temperature, even without valley polarization. These structures are reviewed in the next section.
# LSTGINet: Local Attention Spatio-Temporal Graph Inference Network for Age Prediction
## 4 Experimental Results and Discussion
### Discussion  
To further intuitively demonstrate the efficiency of the proposed LSTGINet age prediction framework and the effectiveness of the designed weighted loss function during model operation, Figure 5 provides a visual demonstration.  
Based on Figure 5, we can see that the FLOPs and parameters (Param) of the prediction framework we proposed are within the controllable range under the condition of achieving the best performance. BC-GCN-SE has the best prediction efficiency, but the prediction performance on these two datasets is poor. The shallow features obtained are insufficient to describe the brain information. Although the GLTransformer method has good prediction performance, it is inefficient and has too many parameters, making it difficult to adapt to large-scale data applications.  
It can be seen from the upper part of Figure 6 that the median and mean of the absolute value of the difference between the predicted brain age and the actual brain age of our proposed LSTGINet on the CamCAN and NKI datasets are both the lowest, that is, the predicted age is closer to the actual age, which is sufficient to prove that the proposed model has good brain age prediction performance. The main reason is that, unlike other methods that only use a single feature to represent brain information, we model from the perspective of space and time and form dependencies and interactions between them. At the same time, we use the constructed topological graph to explore hidden associations, further optimize the representation of global contextual semantics, and ultimately improve the prediction performance.  
In the lower part of Figure 6, it can also be found that compared with the single-module SPM and \(\mathcal{L}_{m}\) method, with the \(\mathcal{L}_{m}+\mathcal{L}_{\zeta}\) method, the median and mean of the absolute value of the difference between the predicted brain age and the actual brain age of the SPM + TEM + LAM method and the SPM + TEM + GIM method are lower, which further shows that each module plays a positive role in the model. In addition, it has been proved that the cooperation between different modules can improve the model's performance in predicting the brain information age better.  
Figure 5: The time efficiency and model parameter count of different prediction methods. FLOPs represent floating point operations per second, with the unit of calculation being \(G\). Param represents the number of parameters involved in the model calculation, and the unit is M.
# An Investigation into Audio-Visual Speech Recognition under a Realistic Home-TV Scenario
###### Abstract
On this basis, we also explored other parts of the AVSR system, including model architectures, audio and video fusion methods, system fusion methods, and so on, which may further improve the performance of the AVSR system. We evaluated the use of different methods on the released MISP 2021 dataset [35], and the experimental results show that the pre-training methods can help the AVSR system achieve significant improvement. Specifically, after combining effective fusion strategies, the performance of our AVSR system is better than that which achieved first place in the MISP 2021 challenge [37]. The contributions of this paper can be summarized as follows:  
1. We explore different pre-training strategies in the AVSR tasks, and this can provide some useful experience for deploying AVSR systems in real scenarios where there is only a limited size of audio-visual data;
2. Based on (1), we explore the impacts of different model architectures for audio-visual embedding extractors on the AVSR performance;
3. We explore the performance of different audio-visual fusion methods. Our final system achieved 23.98% CER on the first MISP 2021 AVSR corpus, which is better than the champion system [37] of the first MISP challenge (CER = 25.07%). The rest of the paper is organized as below: In Section 2, the employed end-to-end AVSR framework is introduced. In Section 3, the explored methods are explained in detail. In Section 4, we present and analyze the experimental results. Finally, we conclude in Section 5.
# Optimal Base Station Density of Dense Network:
## 4 Throughput of MAC Layer in SmBS Tier
### The Fairness of the Tier Association  
Fairness of resource allocation is an important problem in practical networks. Because ensuring user-level fairness would involve specific scheduling strategies and make the analysis intractable, we discuss a simple tier-level fairness strategy in this paper. The network in this paper is a two-tier network, so the fairness of the connection to different tiers is also an interesting question. In the HetNets, the rates of MaBS and SmBS tiers may differ and the user may have different user experience when connecting to different tiers. Thus, the fairness problem is to let the users have the same average rate no matter which tier they connect to. This is equivalent to requiring both tiers to have the same effective areal throughput (total throughput divided by effective cell area). A technology called Cell Range Expansion (CRE) [30; 31] has been proposed to control the fairness of the association. CREmakes the user association biased by adding an association bias. To be more specific, within the two available tiers for a user to associate, the index of the chosen tier is  
\[\text{index}=\text{arg}\,\max_{\text{M,m}}\left(P_{\text{M}}R_{\text{M}}^{- \alpha},BP_{\text{m}}R_{\text{m}}^{-\alpha}\right), \tag{17}\]  
where \(B\) is the bias of the SmBS tier. With the association bias, it effectively expands (\(B>1\)) the effective coverage area of SmBSs when they have a larger rate than MaBSs, or decreases (\(B<1\)) the effective coverage area of SmBSs when they have less rate than MaBSs.  
For the fairness of the users associated with both tiers, their average rates should be same. Thus, we have  
\[\frac{S_{\text{M}}Q_{\text{area}}\lambda_{\text{M}}}{\left(1-A_{\text{m}} \right)Q_{\text{area}}}{\color[rgb]{0,0,0}=}\frac{S_{\text{m}}Q_{\text{area}} \lambda_{\text{m}}}{A_{m}Q_{\text{area}}}, \tag{18}\]  
where \(Q_{\text{area}}\) is the total area of the HetNets and \(S_{\text{M}}\) is the throughput of MaBS. The left side of the equation can be deduced using the method in Theorem 2 of [24]. Based on Equation (18), we can obtain an equation of \(B\). By solving it using search algorithms, we can get the association bias \(B\). Since the fairness of allocation is not the main topic of this paper, the detailed research of the fairness and its algorithms will be presented in our future work.  
With the bias \(B\), the users will have the same rate no matter to which tier they associate. Hence, fairness between the two tiers is guaranteed. However, CRE technology is only a suboptimum solution for network throughput [31]. Deployers of the HetNets need to choose CRE or the optimal solution by deciding what is most suitable with their demand.
## 3 The KLSVM Model
### Gram Matrix  
The dual form can be important when there are many features than data points, that is, \(N>>m\), a key thing to notice about dual form of the SVM is that quadratic program involves the features \(x\) only through their dot products. In other words, the coefficient of \(i\) and \(j\) interaction of the term between \(\lambda_{i}\) and \(\lambda_{j}\) is the inner product of the data point of \(x^{(i)}\cdot x^{(j)}\).  
Let's call this inner product \(K_{ij}\), that is, \(i^{th}j^{th}\) entry in the matrix \(K\) sometimes called the Gram Matrix. We can think of the quantity as measuring the similarity of two data points \(x^{(i)}\) and \(x^{(j)}\) through their dot product if and only if the vectors are in the same direction. It takes its maximum value and its orthogonal at zero; if they are on opposite directions, it has negative values.
## 2 Theory and Derivation
### Numerical Modeling of the Coupled-MTF
According to Equation (14), for the coupled discrete sampling system, the coupled-MTF of the system is related to the number n of fibers in the imaging bundle, the spatial frequency of the input signal \(f\), the difference between the cosine peak and the initial position of the fiber center \(\delta\), and the coupling error \(\Delta j\) along the two directions between the hexagonal fiber imaging bundle and the area array \(\Delta i\) CCD image elements. These were further observed by mathematical simulation.
## 4 Experiments
### GPS Evaluation  
Having obtained all data at an equal rate, with the same coordinate system (PL-2000) and corrected the coordinates using antenna offsets, the navigation GPS accuracy may be calculated. Initially a Euclidean distance between the navigation GPS interpolated position (\(\hat{p}_{GPS}\)) and the RTK referenced position was calculated, in accordance with following equation:  
\[d\left(\text{P}_{\text{GPS}},\text{P}_{\text{RTK}}\right)=\sum_{n_{R}=1}^{n_{R }}\sqrt{\left(\hat{\text{P}}_{\text{GPS}_{n_{R}}}-p_{RT\text{K}_{n_{R}}} \right)^{2}} \tag{14}\]  
Consequently, the dynamic navigation GPS accuracy was calculated in accordance with formula in [44].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **dy** & **dx** & **d\({}_{\text{H}}\)** & **\(\left|\text{COGA}_{\text{1}}\right|\)** & **\(\left|\text{COGS}_{\text{1}}\right|\)** & **Remarks** \\ \hline GPS & \(-1.22\) & \(0.16\) & \(0.89\) & \(16.402\) & & For Equations (12) and (13) \\ Origin & \(0\) & \(0\) & \(0\) & \(0\) & \(15.186\) & In hydrographic system \\ Antenna RTK 1 & \(-1.22\) & \(-0.64\) & \(0.89\) & & In hydrographic system \\ Antenna RTK 2 & \(-1.22\) & \(0.96\) & \(0.89\) & & In hydrographic system \\ \hline \hline \end{tabular}
\end{table}
Table 4: Navigation equipment offsets.  
Figure 9: Example of corrected position for GPS at profile no. 10 (black dots).
## 7 Challenges, Opportunities, and Future Trends
### Future Trends in Energy Harvesting and Security
#### 7.3.2 Innovations in Energy Management  
The recent innovations in energy management include the following:  
* Enhanced Energy Storage Solutions: Advanced supercapacitors and solid-state batteries are two examples of energy storage innovations that will offer more dependable and effective energy storage options for EH-WSNs [130; 131].
* Adaptive Energy Management Systems: Future energy management systems are predicted to be more flexible and intelligent, leveraging AI and machine learning [132; 133] to optimize energy consumption in real time based on network circumstances and energy availability.
* Blockchain for Energy Transactions: Energy transactions in EH-WSNs might be managed and verified using blockchain technology [113; 134], guaranteeing security and openness in the distribution and consumption of energy.
## 3 Constellation Station Maintenance Method
### Calculation of Phase-Holding Limit Loop Control Parameters  
Due to the influence of atmospheric drag, the actual phase of the satellite will deviate from the theoretical nominal value in one orbital period. According to Formula (1), which provides the relationship between the amount of satellite phase angle drift and the amount of semi-major axis attenuation, it can be seen that the change in the semi-major axis after satellite control is approximately a straight line, and the trend of the phase difference  
Figure 1: Approximate relationship between satellite phase drift and semi-major axis change.  
change is approximately a parabola. It is assumed that the variation trend of the satellite phase difference over time after satellite control is as follows:  
\[\Delta\lambda(t)=\lambda(0)-\frac{3}{2}\sqrt{\mu}a^{-\frac{5}{2}}\bigg{(}a_{0}t+ \frac{1}{2}\dot{a}_{d}t^{2}\bigg{)}. \tag{6}\]  
According to the implementation idea of the phase-holding ring method, when the relative drift of the semi-major axis is 0,  
\[a_{0}=-\dot{a}_{d}t. \tag{7}\]  
Thus, the time of half a drift cycle of the phase difference is  
\[t=-\frac{a_{0}}{\dot{a}_{d}}. \tag{8}\]  
This formula is combined with Formula (5) to obtain  
\[\lambda(0)=\frac{3}{4}\sqrt{\mu}a^{-\frac{5}{2}}\frac{a_{0}^{2}}{\dot{a}_{d}}. \tag{9}\]  
The following is defined:  
\[\lambda(0)=2\lambda_{0}. \tag{10}\]  
Then, the attenuation of the semi-major axis in a phase-holding period is  
\[a_{0}=\bigg{(}\frac{8\dot{a}_{d}a^{\frac{5}{2}}\Delta\lambda_{0}}{3\sqrt{\mu}} \bigg{)}^{\frac{1}{2}}. \tag{11}\]  
The target value of the semi-major axis deviation of the phase-keeping orbit maneuver is  
\[\Delta a{=4}\bigg{(}\frac{2\dot{a}_{d}a^{\frac{5}{2}}\Delta\lambda_{0}}{3\sqrt {\mu}}\bigg{)}^{\frac{1}{2}}. \tag{12}\]  
The time interval required for two semi-major axis controls is  
\[\Delta t{=4}\bigg{(}\frac{2\frac{5}{2}\Delta\lambda_{0}}{3\dot{a}_{d}\sqrt{\mu }}\bigg{)}^{\frac{1}{2}}. \tag{13}\]  
For a quasi-circular orbit, according to the simplified satellite continuous small-thrust perturbation equation, the tangential velocity increment required by the change in the semi-major axis of the phase-maintaining orbit maneuver can be written as  
\[\Delta V=\frac{n}{2}\Delta a. \tag{14}\]  
Based on the influence of the space atmospheric density and solar activity, the change rate of the satellite's average value of the semi-major axis is also different due to the different orbital altitudes and satellite windward areas (or surface-to-mass ratios) of different satellites in the constellation. Therefore, the phase-holding control period and semi-major axis adjustment amounts of different satellites in the constellation should be calculated correspondingly.
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 5 Energy-Latency-Reliability Trade-Off
### Packet Success Probability
#### 5.2.2 Blind Retransmissions (BR)  
In this case, node A blindly transmits \(R\) times the packet with a packet successful reception probability \(P\) over each transmission. The number of successful packet transmissions \(h\) after \(R\) independent transmission trials is a random variable \(S\) that follows the binomial distribution with parameters \(R\in\mathbb{N}\) and \(P\in[0,1]\) and is given by the probability mass function  
\[f(h,R,P)=pr(S=h)=\left(\begin{array}{c}R\\ h\end{array}\right)\!\!P^{h}(1-P)^{R-h}. \tag{23}\]  
In accordance with [64; 65], the probability that at least one packet is received successfully within \(R\) independent attempts can be calculated using  
\[P_{R}=1-pr(S=0)=1-\left(1-P\right)^{R}=1-\left(1-\left(1-P_{e}\right)^{N} \right)^{R}. \tag{24}\]  
Figure 16: Impact of the free distance \(d_{m}\) on the packet success probability.  
Figure 17: Impact of the \(\beta_{free}\) parameter on the packet success probability.  
Figure 18 shows the impact of \(R\) on the packet success probability. When using BR, the required time to send a packet is given by  
\[t_{TRX}^{R}=R\frac{l}{D_{R}}+t_{id}(R-1), \tag{25}\]  
where \(t_{id}\) is the time spent in idle mode between two packet transmissions. Therefore, the energy consumption of the transceiver can be expressed as follows  
\[E_{TRX}^{R}=V\bigg{[}R\frac{l}{D_{R}}(I_{TX}+I_{RX})+2I_{id}t_{id}(R-1)\bigg{]}. \tag{26}\]
## 5 Simulation Results
### Oscillation Suppression Strategy  
Figure 22 gives the simulation results when applying the oscillation suppression strategy at 1.5 s. As it can be seen, when without the compensation resistor, the system oscillates and gradually diverges. Furthermore, when the compensation resistor is applied at 1.5 s, the oscillation gradually decreases and eventually returns to a stable operating state, which verifies the effectiveness of the proposed suppression strategy.