esa-sceva/satcom-chunk-collection huggingface dataset:
# An Integrated Thermal-Electrical Model for Simulations of Battery Behavior in CubeSats
## 4 Results
### Simulation Results
#### 4.1.4 Mppt  
Another possibility brought by the integrated simulation method is the addition of an MPPT algorithm in the user-defined control phase. The MPPT algorithm is usually employed to search for the optimal point of the photovoltaic panel's maximum power delivery. Figures 20 and 21 illustrate one of the forms of MPPT known as \"perturb and observe\" in the integrated simulation model [37]. In this method, small disturbances in  
Figure 19: Energy levels (\(L\)) for \(T_{set}=273\) K and \(T_{set}=\) variant without eclipse energy-saving mode.  
Figure 18: State of Charge (\(SoC\)) for \(T_{set}=273\) K and \(T_{set}=\) variant without eclipse energy-saving mode.  
power consumed by the target device are used, and whether the power delivered by the panel has increased or decreased is analyzed. For more information on this method, please refer to [24].  
In Figure 20, the total input power of the satellite is compared with the ideal power that could be delivered for a given time. The control algorithm used selects the relevant tasks (\(P_{\text{LOAD}}\)) in order to try to reach the maximum theoretical power. Note that over the period shown, the battery voltage increases, making use of the difference in excess power  
Figure 21: Total input power (under MPPT algorithm) compared with the ideal power that could be delivered in a given orbit. In this scenario, the heater is also considered as a task to be executed under the MPPT algorithm perturbations.  
Figure 20: The total input power of satellite (under the MPPT algorithm) compared with the ideal power that could be delivered in a given orbit.  
(\(P_{\text{TOTAL}}-P_{\text{LOAD}}\)). It is important to note that \(P_{\text{IDEAL}}\) is based on the ideal power that would be achieved by the best possible combinations of battery voltage and current--not necessarily using the instant battery voltage. This phenomenon causes the difference between \(P_{\text{TOTAL}}\) and \(P_{\text{IDEAL}}\).  
Another interesting possibility in the use of MPPT is to take the surplus power of the execution of the tasks \(P_{\text{TOTAL}}-P_{\text{LOAD}}\) to supply the heater, as illustrated in Figure 21 for a slice of an orbit. Notice that while trying to follow the maximum power, the system balances between the heater and the execution of tasks. In the illustrated scenario, when the sum of \(P_{H}\) and \(P_{\text{LOAD}}\) exceeds the \(P_{\text{IDEAL}}\) line, it means that the battery is being used.
## 4 Results and Discussion
### Simulation Conditions
It is observed that such reference signals are indeed of impulsive nature, and impulsiveness decreases as the value of \(\alpha\) increases. Figure 4: Frequency response characteristics of FIR models for acoustic paths used in computer simulations: (**a**) Magnitude response characteristics, and (**b**) phase response characteristics. Various case studies have been carefully designed to mimic practical scenarios involving application of ANC for impulsive sources. The performance metric to carry out the performance comparison between various algorithms is the mean noise reduction (MNR)  
\[\text{MNR}(n)=\mathbb{E}\bigg{\{}\frac{\sigma_{e}(n)}{\sigma_{d}(n)}\bigg{\}}, \tag{48}\]  
where \(\sigma_{e}(n)\) and \(\sigma_{d}(n)\) denote an estimate of absolute values of \(e(n)\) and \(d(n)\), respectively. Such estimates can be obtained via the low pass estimator as in (30). The results presented below have been ensemble averaged for 100 realizations. Considering the nature of a noise source, theoretical analysis to find values for various parameters is very challenging and laborious, if not impossible. Therefore, extensive simulations have been performed to tune  
Figure 5: Typical alpha stable signals: (**a**) severely impulsive with \(\alpha=1.1\) and variance \(2.6313\times 10^{3}\); (**b**) strongly impulsive with \(\alpha=1.4\) and variance \(0.2991\times 10^{3}\); (**c**) mildly impulsive with \(\alpha=1.6\) and variance \(0.0168\times 10^{3}\). the parameters for various algorithms, as each algorithm adopts a different strategy and control mechanism for impulsive ANC systems.
## 4 Experimental Design  
The proposed framework aims to automatically detect and quantify road damage using high-resolution satellite image pairs captured before and after a disaster. This section describes the experimental design of the study including datasets, labeling methodology, model implementation, training parameters, and computing environment. To ensure a fair and rigorous comparison despite architectural differences among the three proposed models, a strictly controlled experimental environment was established. For example, all models were trained, validated, and tested on identical data splits. Key hyperparameterssuch as the optimizer (Adam), learning rate (0.0001), and batch size were held constant. For each model, training proceeded for up to 100 epochs, but the final version used for evaluation was selected based on the checkpoint that achieved the lowest validation loss rather than the final epoch. This approach ensures that each model is evaluated at its optimal generalization state. All experiments were conducted on the same hardware and software environment. Finally, a unified evaluation framework (F1b, F1d, F1s) was employed to enable direct performance comparison across all models. These controls ensure that any observed differences in performance are attributable to the models' intrinsic architectures rather than experimental variation.
# Glucose Injections at Birth, Warmth and Placing at a Nurse Sow Improve the Growth of IUGR Piglets
###### Abstract  
Intrauterine growth-restricted piglets (IUGR) have a lower rectal temperature, whole-blood glucose, and lower glycogen storages at birth than normal piglets, giving them less energy to maintain body temperature and compete at the udder. The present paper investigated the effects of giving an energy supplementation three times after birth on rectal temperature, glucose levels, and growth until weaning in an on-farm trial. Eighty-eight newborn piglets were classified as IUGR (based on head morphology), placed under a heating lamp for one hour and allocated to one of four treatments--warmed water (WATER), glucose injection (GLUC), colostrum bolus (COLOS; porcine colostrum), and colostrum bolus and glucose injection (GLUC + COLOS)--before being placed at a nursing sow. Weight differences were found at day 21, with GLUC and GLUC + COLOS groups being the heaviest. Piglets in GLUC + COLOS had higher glucose levels at \(t=3\), 6, and 9 h compared to the other treatments (\(p=0.027\)), but from \(t=24\) h and onwards, no difference was observed. For rectal temperature, no differences were observed. Collectively, these findings suggest that glucose injections at birth (i.e., as an energy source), one hour's exposure to warmth and the placement of piglets with a nurse sow to reduce competition, enhance the growth of IUGR piglets.  
Keywords:colostrum supplementation; glucose injection; Intrauterine Growth Restriction; piglets; rectal temperature +
Footnote †: journal:
# Research on Backstepping Linear Active Disturbance Rejection Control of Hypersonic Vehicle
## 6 Conclusions  
Based on backstepping control and active disturbance rejection theory, the velocity and altitude tracking problem of hypersonic vehicles under the influence of internal parameter uncertainties and external disturbances is studied. The main results are as follows:  
1. The LADRC controller is designed based on the ADRC theory. The LESO can effectively compensate the total disturbance suffered by the hypersonic vehicle.
2. By designing a backstepping control law instead of LSEF in LADRC, it is realized on-line compensation for disturbance.
3. The stability is demonstrated using Lyapunov stability theory. Compared with the classical PID control, the control scheme proposed in this paper can overcome the adverse effects brought by external uncertainties and track the control instructions stably and accurately without overshoot; whether from the point of view of control accuracy or error, the control scheme proposed in this paper can ensure the accurate tracking of the given and can ensure almost no overshoot, can ensure its control accuracy and improve the robustness of the control system compared with the classical PID control.  
At present, this paper is limited to theoretical analysis. Although it is widely used and has difficult to adjust parameters in classical PID control, the control accuracy will be greatly reduced in the face of nonlinear complex system similar to a hypersonic vehicle, and it is not suitable for the precise control of this nonlinear complex system. For the control scheme proposed in this paper, although it solves the nonlinear modeling of the model and improves the anti-interference ability of the system, this modeling depends on the accuracy of the model. Because the control rate is established based on the mathematical model of the system itself, if the mathematical model of the system is inaccurate, it is similar to the approximate or simplified mathematical model. This scenario will not be applicable to this type of model. In addition, the actual flight of aircraft is much more complicated than the simulation, so this paper only carries out an approximate simulation. Subsequent work will be carried out to study more complex simulation to further enhance the control performance of the proposed seven-point control system.  
Conceptualization, C.B.; Software, T.Z.; Validation, C.B.; Resources, T.Z.; Data curation, C.B. and G.Z.; Writing--original draft, C.B.; Writing--review & editing, T.Z.; Visualization, G.Z.; Supervision, G.Z.; Project administration, T.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 13: Velocity tracking error curve with interference.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare that they have no competing interests.
# A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography
## 5 Discussion
Therefore, in the optimization process, methods used in the literature, such as [118], which can manage broad and local changes in the optical properties simultaneously, are needed. To obtain a reliable image, prior structural information from other imaging modalities such as MRI, X-ray CT, and ultrasound imaging is indispensable. Therefore, methods introduced in the literature [120; 123; 126; 130], which incorporate prior structural information and recover the heterogeneity inside the segmented organs, should be employed. Because of the difficulty in selecting the regularization parameter, two-stage image reconstruction [120; 123] incorporates hard prior information as an initial estimate. In the second stage, to reconstruct heterogeneities in the segmented regions, combination use of the sparsity regularization methods using \(p\)-norm (\(0\leq p\leq 1\)) [104; 106; 109; 110; 111; 113] can be useful to achieve the high-resolution image. The studies indicated that appropriate regularization improved not only the spatial resolution but also the quantification. Other biomarkers, such as water, lipids, collagen, and the scattering coefficient are useful for diagnosis, as shown in [136]. The spectral prior method improved the quantification performance of the DOT. The combined use of DOT and other optical imaging methods is one approach for obtaining a higher spatial resolution. Some studies combining QPAT/FDOT and DOT have been reported [160; 161; 162; 163; 164; 165; 166]. QPAT and FDOT are related to the DOT. Photoacoustic tomography (PAT) achieved a higher spatial resolution than DOT by exploiting the low scattering characteristic of ultrasound excited by NIR light [167]. FDOT can enhance the contrast of malignant lesions using fluorescent molecules [47]. Both PAT and FDOT highlighted tissue abnormalities. However, the background optical properties, which are not observed in the PA and FDOT images, can be imaged by DOT. Moreover, the background optical properties are useful for quantifying PAT/FDOT images because the image intensity of PAT/FDOT depends on the fluence rate of the excitation light, which in turn depends on the optical properties of the heterogeneous background. The reconstruction of the optical properties of the PA sources [50] and the fluence compensation modification of the PA image [168; 169; 170; 171] by QPAT will be improved using background optical properties quantified by DOT, which will play a very important role in guaranteeing quantification by PAT/FDOT. In this context, the aforementioned two-stage DOT image reconstruction method is useful. A quantitative comparison between abnormalities imaged by PAT/FDOT and normal tissues imaged by DOT may provide useful information for diagnosis.
# Low-Cost RSAC and Adsorption Characteristics in the Removal of Copper Ions from Wastewater
## 3 Results and Discussion
### Adsorption Experiments
#### 3.1.3 Effect of Initial Concentration  
The relationship between the initial concentration and the residual concentration of copper ions is shown in Figure 6. The removal rate of copper ions and the adsorption amount per unit mass of RSAC particles in each sample after adsorption for 72 h is presented in Table 9. As shown in Figure 6, a high initial copper ion concentration led to a corresponding steep adsorption curve and a fast adsorption rate compared with a low initial concentration sample in the pre-sorption stage. This could be attributed to high initial copper ion concentration providing a sufficient driving force for mass transport, which could make ions occupy the active site on adsorbents rapidly, facilitating the adsorption of copper ions by RSAC particles [27; 28].  
Figure 5: Effect of adsorbent amount on the adsorption of copper ions by RSAC particles.
# Evaluating V2X-Based Vehicle Control under Unreliable Network Conditions, Focusing on Safety Risk
## 4 Results and Discussion
### Performance Evaluation  
In order to evaluate our method, we proposed a simple evaluation to see how our concept is working. In our study, we evaluate our system from the perspective of safety. According to this, we have chosen the headway as our main parameter to see the performance of this solution.  
\[Headway=\frac{relative\;distance}{v_{HV}}[\text{s}] \tag{7}\]  
Headway is defined as the fraction of the relative distance and the HV velocity (Equation (7)). For our research, we calculated the headway values for the whole scenario and introduced the Scenario Aggregate Headway (SAH) (Equation (8)).  
\[SAH=\sum_{t}\frac{relative\;distance\;(t)}{v_{HV}\;(t)} \tag{8}\]  
Figure 14: Distance traveled by HV with SRI in different scenarios.  
To provide a comparable performance metric, we calculated the sum of headway values with a sampling time (\(t\)) of 0.1 s for the whole scenario. In the case of negative relative distance, when collision occurred, we calculated with a zero value for the relative distance. To be able to create a safety performance indicator for our study, we have chosen a headway reference value of 1.5 s (Equation (9)).  
\[SAH_{ref}=\sum_{t}1.5 \tag{9}\]  
Based on these equations, we could calculate the safety performance (SP) for our control by dividing SAH with the reference SAH in Equation (10).  
\[SP=\frac{SAH}{SAH_{ref}} \tag{10}\]  
In Table 4, we can see the different scenarios and different modulations' SP values. For a better evaluation of performance, we executed a couple more scenarios: one with a constant additional noise level of 10 dB and one with the same 400 m radius circle but with a lower noise peak power. Based on Table 4, we can see that values greater than 1 mean that there was no collision in that scenario. Without our proposed control concept, we can see that, in the case of high relative velocity, an accident occurred in all cases. With the introduction of our safety concept, higher values can be seen for the safety performance, which shows that our concept could be a good direction of development in the future. However, in some cases, the performance values are higher than what they should be, which shows that our concept is overcautious and needs further optimization.
# Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques
## 5 Results and Discussion
### Comparing the MLP and LSTM Models
By calculating RMSE, a widely used metric in predictive modeling, we quantitatively assessed the accuracy of these predictions. By comparing the RMSEs of models utilizing only technical variables (T) with those incorporating additional Twitter and news variables (T+), we analyzed how these supplementary factors contribute to improved prediction accuracy. In addition, we conducted several statistical tests to validate observed differences in the MLP and LSTM models' performance. Despite the LSTM's reputation for handling sequential data and its inherent complexity, the study finds that MLP consistently outperforms LSTM across various subsets. One possible reason for the superiority of MLP is the volume of data. More specifically, the memory feature is a key advantage of LSTMs that becomes more beneficial in larger test sets. One plausible explanation for MLP's superiority lies in the volume of data. Specifically, while the memory feature of LSTMs becomes more advantageous in larger test sets due to increased prediction periods [166; 167], it may not include the same benefits in smaller datasets. This result highlights the sophisticated relationship between model complexity and dataset size in predictive modeling.
# Analyzing the Nonlinear Performance of Miniature Loudspeakers in Consideration of the Creep Effect
## 2 Nonlinear Model with Creep  
### Equivalent Circuit Model  
We focused on moving-coil miniature loudspeakers because these are the most common type of miniature loudspeakers. These miniature loudspeakers can be modeled by an equivalent circuit when the wavelength is large compared to the geometric dimensions [8]. The electrical equivalent circuit diagram of the nonlinear model with creep (referred to as the NL-C model) is depicted in Figure 1. In addition to the commonly included electrical and mechanical components, the NL-C model also incorporates the SLS model from viscoelastic mechanics to characterize the creep effect [16]. In the NL-C model, the loudspeaker suspension is considered as a combination of elastic and viscous properties. Therefore, a secondary stiffness and a secondary damper were introduced and could be adjusted to increase the displacement at low frequencies, thereby simulating the creep effect.  
As shown in Figure 1, the electrical part of the system (enclosed within the red dashed line) is modeled by the voice coil inductance \(L_{e}\) and electrical voice coil resistance at direct current (DC) \(R_{e}\). The mechanical part (enclosed within the blue dashed line) is modeled by the mechanical resistance \(R_{m}\), moving mass \(M_{m}\), and mechanical stiffness \(K_{m}\). The  
Figure 1: Equivalent circuit of the NL-C model.  
electrical and mechanical parts are coupled by the magnetic force factor \(Bl\). In this model, the acoustic part is neglected because it is negligibly small when compared with the other two parts. The secondary damper \(R_{2}\) and the secondary stiffness \(K_{2}\) in the blue shaded area are connected in parallel with mechanical stiffness \(K_{m}\), representing the suspension creep of the miniature loudspeaker. It should be noted that if \(R_{2}\) and \(K_{2}\) are disregarded (i.e., considered to be infinitely large), the NL-C model degenerates into the nonlinear model without creep (referred to as the NL-NC model) that has been used for nonlinear analysis in the previous study [6].
# DRL-Based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks
## 3 System Model
### PPO Algorithm
```
0: Current network state \(G_{t}=\{N_{t},L_{t},M_{t},RC_{t},RL_{t}\}\); future network state sequence \(F_{t}=\{M_{t+k}|k=1,2,\ldots,K\}\), SPC request set \(R_{t}=\{src_{i},dst_{i},V_{i},D_{i},T_{i}\}\)
0: SFC Orchestration Policy
1: Initialize parameters of policy network and value network \(\theta\), \(\phi\)
2:for\(epoch=1,\ldots,N\)do
3: Initialize the LEO network SFC dynamic orchestration environment
4: Initialize the mini-batch of state transition trajectory
5:for timestep \(t=1,\ldots,M\)do
6:if new time sequence arrived then
7: update \(G_{t}\), \(F_{t}\), \(R_{t}\)
8: Obtain the set of SFCs that need to be readjusted \(R_{readj}\)
9:endif
10:if\(R_{readj}\
eq\emptyset\)then
11: randomly select \(r\in R_{readj}\)
12:elseif\(R_{t}\
eq\emptyset\)then
13: randomly select \(r\in R_{t}\)
14:endif
15: Obtain the set of the top \(K\) shortest paths between the input node and output node
16: Sample an action \(a_{t}\) from the policy \(\pi_{\theta}(a|s_{t})\) and interact with the environment to obtain the next state \(s_{t+1}\), immediate reward \(r_{t}\), sequence end marker \(done_{t}\)
17:if\(done_{t}\)then
18: attach cumulative reward \(R_{\tau}\)
19:endif
20: save transition tuple \((s_{t},a_{t},r_{t},s_{t+1},done_{t},\pi_{\theta}(a_{t}|s_{t}))\)
21:endfor
22:for each trajectory in mini-batch do
23: calculate GAE advantage estimation \(\hat{A}_{t}\)
24: Standardized advantage value \(\hat{A}_{t}=\frac{\hat{A}_{t-\mu_{A}}}{e_{A}}\)
25:endfor
26: calculate clip target \(L^{CLIP}(\theta)\)
27: calculated value function loss \(L^{VF}(\phi)\)
28: calculate total loss \(L^{TOTAL}(\theta,\phi)\)
29: update parameters of policy network and value network \(\theta\), \(\phi\)
30:if epoch is not endthen
31: repeat 4-29
32:endif
33:endfor
```  
**Algorithm 1** DRL-based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks