esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Spherulitic Morphology and Crystal Growth of P3 HBHHx Samples
#### 3.4.4 Determination of Equilibrium Melting Temperature (\(T_{\text{m}}^{\circ}\))  
Figure 18 presents the equilibrium melting temperatures (\(T_{\text{m}}^{\circ}\)) of neat P3 HBHHx and its nucleated samples, determined from the first melting peak and using the Hoffman-Weeks method. This approach involves plotting the melting temperature (\(T_{\text{m}}\)) as a function of the corresponding crystallization temperature (\(T_{\text{c}}\)) and extrapolating the linear fit to the point where \(T_{\text{m}}\) equals \(T_{\text{c}}\). The intercept represents the equilibrium melting temperature (\(T_{\text{m}}^{\circ}\)), which defines the temperature at which the crystalline and melt phases can coexist.  
For neat P3 HBHHx, the extrapolation process may introduce intrinsic error due to the significant difference between the highest accessible melting temperatures and the actual  
Figure 18: Equilibrium melting temperature was determined from the first melting peak temperature obtained from the isothermal crystallization of the different samples (black dashed line). The extrapolation of the data from only the neat polymer (blue dashed line) yielded a significantly lower value.  
\(T_{\rm m}^{\circ}\). This discrepancy can lead to an underestimation of \(T_{\rm m}^{\circ}\) for neat P3 HBHHs (143 \({}^{\circ}\)C), as the limited range of higher temperature data may bias the linear fit.  
In contrast, the presence of nucleating agents enables crystallization at higher temperatures, making it possible to obtain data at higher temperatures (i.e., closer to the real equilibrium temperature) and, therefore, decreasing the inherent error associated with the extrapolation.
## 4 Conclusions  
This study compared the penetration performance between high-frequency pulsation grouting and steady grouting. The slurry penetration shape was investigated and the flow fields of the two grouting methods were analyzed. The effects of pulsation parameters, soil properties, slurry viscosity, grout pipe length, and back pressure on the slurry penetration distance were conducted.  
1. The results show that the penetration distance of pulsation grouting is significantly greater than stable pressure grouting. The reason is the periodic changes of the flow field induced with pressure pulsations. It was also found that higher frequency results in a longer slurry penetration distance. However, as the pulsation amplitude increases, the slurry penetration distance first increases and then decreases. This is because part of the pulsation pressure values become smaller than the back pressure.
2. As the porosity and particle size decrease, and the viscosity and back pressure increase, the percentage increase in lateral penetration distance becomes larger. This is because the pulsation effect can achieve favorable results under conditions of high flow resistance. The percentage increase in vertical penetration distance initially increases and then decreases. The reason is that when the flow resistance is too high, the slurry penetrates faster under the pulsating pressure, leading more slurry to flow into the annular channel with lower flow resistance. As the grouting pipe length increases, the vertical diffusion performance remains almost unchanged, while the percentage increase in lateral penetration distance decreases, which is caused by the weakening of the pressure pulsation effect.
3. Compared to steady pressure grouting, pulsating grouting can reduce cost and construction time, and enhance engineering quality. In underground engineering projects like tunnels, underground chambers, and shafts, pulsating grouting can be employed to strengthen underground structures, improving their stability and reducing the risk of structural wear and damage. In the future, there is a need for the development of grout materials suitable for pulsatile grouting. Additionally, it may be worthwhile to consider the application of a multi-objective optimization approach to select pulsation parameters that are most suitable for specific operating conditions.  
Figure 18: Effect of length of grouting pipe on vertical penetration distance.  
**Author Contributions:** Conceptualization, L.T.; Methodology, Y.L. and X.C.; Software, F.L.; Validation, J.L.; Investigation, X.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China under grant number 2020 YFC1807200 and the National Natural Science Foundation of China (grant number: 51978674).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Research on Joint Resource Allocation for Multibeam Satellite Based on Metaheuristic Algorithms
## 2 Problem Formulation  
### Joint Resource Allocation Problem Model  
In this paper, we consider a GEO satellite in the Ka band with \(M\) fixed point beams and a multibeam technique in the four-color frequency reuse pattern [1], as depicted in Figure 1. When bandwidth allocation is performed, beams with different polarizations are independent of each other, whereas there is a bandwidth overlap constraint between adjacent beams using the same polarization.  
The HTS system can flexibly allocate the bandwidth and power resources of each beam based on the traffic demand of each beam, thereby optimizing QoS and spectrum utilization. The resource allocation (RA) problem, a subset of the JRA problem, has been demonstrated to be an NP hard problem in the literature [10], implying that the JRA problem under consideration in this paper is also an NP hard problem. The objective function of the JRA problem involves minimizing the unmet system capacity (USC), which has been commonly used in the literature [10; 20; 21]. \(D_{i}\) and \(R_{i}\) are the traffic demand of the beam and the data rate provided by the system, respectively; \(BW_{i}\) and \(P_{i}\) are the bandwidth and power resources, respectively, allocated to beam \(i\).  
The mathematical description of the JRA problem is as follows:  
\[Obj:\text{minimize}\sum_{i=1}^{M_{b}}\max(D_{i}-R_{i},0), \tag{1}\]  
\[\text{s.t.}\mathcal{C}_{1}:\sum_{i=1}^{M_{b}}P_{i}\leq P_{tot},\forall i\in\{1,2,\cdots,M_{b}\} \tag{2}\]  
\[\mathcal{C}_{2}:P_{i}\leq Pb_{\max},\forall i\in\{1,2,\cdots,M_{b}\} \tag{3}\]  
\[\mathcal{C}_{3}:BW_{i}\leq B_{tot},\forall i\in\{1,2,\cdots,M_{b}\} \tag{4}\]  
\[\mathcal{C}_{4}:BW_{a}+BW_{b}\leq B_{tot},\forall(a,b)_{adj,pol} \tag{5}\]  
where \(\mathcal{C}_{1}\) and \(\mathcal{C}_{2}\) are the power constraints of the model, typically handled by the proportional reduction method [20]; \(\mathcal{C}_{3}\) is the bandwidth maximum constraint; and \(\mathcal{C}_{4}\) indicates that the bandwidths of adjacent beams with the same polarization mode cannot overlap.
## 3 Methodology
### ATtiny85 Microcontroller  
The optimization of electronic circuits in an energy generation system is crucial for improving energy efficiency. A common system involves combining a piezoelectric generator with an extraction circuit composed of an AC-DC rectifier and a DC-DC voltage regulator. Subsequently, the generated energy is stored before being utilized. Figure 3 illustrates the general schematic of this system [20].  
In this work, the ATtiny85 microcontroller was used due to its easy programming, low cost, and low current consumption in sleep mode. It features a 10-bit analog-to-digital converter with 4 channels and operates safely within a voltage range of 1.8 V to 3.3 V, compatible with the battery and the DC-DC buck circuit. The microcontroller was programmed to operate at a frequency of 1 MHz without the need for an external clock signal. These characteristics make it ideal for a compact device that can operate for extended periods at low voltages while connected to a battery.  
The LM35 temperature sensor was utilized to monitor temperature. This sensor has low power consumption, an operating range of 4 V to 30 V, and a current consumption of 60 \(\upmu\)A. The sensor has a sensitivity of approximately 10 mV/\({}^{\circ}\)C and can measure temperatures between \(-55\)\({}^{\circ}\)C and 150 \({}^{\circ}\)C.  
The microcontroller performs real-time temperature readings through LM35. When it detects that the temperature has exceeded 40 \({}^{\circ}\)C, it exits the sleep mode and activates its output, indicating the setpoint crossover. When the temperature returns below the setpoint, it waits for about 3 s before entering sleep mode again. Both the microcontroller and the LM35 are powered by a rechargeable Li-ion battery (model LIR3032H, 3.7 V), charged by the piezoelectric transducer, with a capacity of 70 mA/h.  
It is important to highlight that the circuit monitoring system will be evaluated through data acquisition in the Matlab Simulink environment, using the serial port. A heat gun was used to heat the sensor, allowing the evaluation of the temperature response, while the microcontroller's sleep mode input and output were assessed.
## 2 Methodology
### Measurement Update  
The EKF measurement update equation incorporates a dynamic weighting factor \(\omega\), which is calculated based on the current accuracy of RTK and INS measurements, as follows:  
\[K=PH^{T}\Big{(}HPH^{T}+R\Big{)}^{-1} \tag{22}\]  
\[x=x+K(z-Hx) \tag{23}\]  
\[P=(I-KH)P \tag{24}\]  
In the above equations, \(K\) is the Kalman Gain, \(H\) is the measurement matrix, \(R\) is the measurement noise covariance matrix, \(z\) is the measurement vector, and \(\omega\) is the dynamic weighting factor, calculated as follows:  
\[\omega=\frac{\sigma_{INS}}{\sigma_{RTK}+\sigma_{INS}} \tag{25}\]  
where \(\sigma_{RTK}\) and \(\sigma_{INS}\) are the current standard deviations of the RTK and INS measurements, respectively.  
In addition to the details above, it is vital to highlight the essential elements encompassed within the tightly coupled (TC) RTK/INS fusion framework. This fusion approach involves critical components, including the mechanization and initial alignment of the INS system. To integrate the INS system effectively, it must convert its readings into comprehensive position, velocity, and attitude values. The position and velocity data from the navigation frame are then translated into INS measurements. Within the tightly coupled RTK/INS fusion process, the EKF calculations are based on the outcomes of the INS mechanization. Notably, this fusion scheme operates in a feedback loop, employing the state error estimations derived from the INS mechanization to counteract long-term drift. This mechanism is executed at the same frequency as the prediction step, where updates are performed less frequently than predictions. The rotation matrix for both frames is determined during the initial alignment phase, which encompasses the establishment of the body and navigation frame attitudes. After completing this alignment within the TC RTK/INS fusion, it is recommended to activate the fusion process. The TC RTK/INS fusion can yield three distinct outcomes based on its outputs. When valid GNSS measurements are inaccessible, the INS mechanization outcomes will be directly utilized. In such cases, the EKF estimates the errors associated with the states and feeds these errors back into the INS mechanization process, ultimately generating a float solution. Alternatively, a fixed INS solution is obtained by resolving carrier-phase ambiguities using the LAMBDA method, which identifies the optimal integer ambiguity set. This process involves initial float solution ambiguity estimation followed by validation through a ratio test to ensure the correctness of the fixed ambiguities. The detailed process of the ambiguity resolution is implemented within the smartphone's RTK positioning algorithm. The entire methodology flowchart is visually depicted in Figure 1.
## 4 Proposed Method
A client with a high number of forgettable examples means the local model did not contribute much to the global model. Therefore, rebalancing clients inverse to the number of forgettable examples would assure that less influential clients would have more influence, and vice versa. In our case, we used simple normalization, dividing numbers of forgettable examples by the sum of them as in Equation (1). \[norm(X)=\frac{X}{\sum X}, \tag{1}\]  
However, applying normalization directly is not recommended because the weights, made by normalizing the numbers of forgettable examples, could drastically change local model parameters. To alleviate radical changes, we added updated ratio hyperparameter \(\alpha\). The ratio \(\alpha\) decides how much change the weights will have. If \(\alpha\) is small, then there will be less change. \[(1-\alpha)+\alpha(norm(X)\times N)\quad\text{where}\;\;0\leq\alpha<1, \tag{2}\]  
The \(N\) in Equation (2) is the number of clients. Multiplying the weights with the local model parameters provides a rebalanced set of local models. This could be applied to any averaging algorithm-based federated learning methods. In case of FedAvg, the recalculated local model parameters will be averaged; therefore, executing weighted averaging. The complete process is explained in Algorithm 1. ```
1:Input: Training data \(\{X,Y\}\); neural network \(f\); neural network parameter \(\theta\); global server \(g\); number of clients \(N\); local epoch \(E\); learning rate \(\eta\); event period \(t\); update ratio \(\alpha\)
2:Output: Global model parameter \(\theta_{g}\)
3:procedurefederated weighted averaging
4:initialize\(\theta_{g}\); \(F\leftarrow\) list of \(N\) ones; \(W\leftarrow\) list of \(N\) ones
5:while not training once do
6:for\(n=1\) to \(N\) in parallel do\(\triangleright\) Clients execute in parallel
7:\(\hat{Y}\gets f_{\theta}(X);\hat{Y}_{g}\gets f_{\theta_{g}}(X)\)
8:\(\theta\leftarrow\theta_{g}\)
9:for\(e=1\) to \(E\)do
10:\(\theta\leftarrow\theta-\eta\
abla L(\theta;X)\)\(\triangleright\) Update local model
11:endfor
12:if current round \(\text{mod}\,t==0\)then
13:\(F[n]\gets count(\hat{Y}:=Y\&\&\hat{Y}_{g}\
eq Y)\)\(\triangleright\) Upload forgettable example count
14:endif
15:endfor
16:\(W\leftarrow(1-\alpha)+\alpha(norm(F)\times N)\)\(\triangleright\) Calculate averaging weights (Equation (2))
17:\(\theta_{g}\leftarrow\frac{1}{N}\sum_{n=1}^{N}\theta_{n}\times W[n]\)\(\triangleright\) Update global model
18:endwhile
19:return\(\theta_{g}\)
20:endprocedure
```  
**Algorithm 1** Federated weighted averaging by example forgetting
# Optical Channel Selection Avoiding DIPP in DSB-RFoF Fronthaul Interface
## 2 System, Methods, and Network Components
### Fiber-Optic Fronthaul Path
In the part of the optical path, on the DWDM link side, an optical amplifier may be used. Such a system is not taken into account during calculations and simulations due to the significant band limitations introduced in the wavelength domain. If there is an amplifier in the optical path, the choice of the optical channel is significantly limited. The attenuation of the individual components in the optical path varies with the wavelength of the optical signal carrier. However, the proportions of wavelength-dependent changes are greatest in the case of attenuation of the optical fiber itself. With a short optical path with a length not exceeding 20 km, the number of connections and node devices is not too large. Constant average values provided by the system or device manufacturer may be used in the calculations. In order to visualize the changes taking place in the averaged chromatic dispersion coefficient, sample calculations were made for five different variants (Table 3) based on the network diagram in Figure 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Path Component**} & **Fiber** & **Splice** & **Connector** & **ROADM** & **ROADM** & **Splitter** \\  & **(a)** & **(a\({}_{s}\))** & **APC/UPC** & **Add-Drop** & **Passthrough** & **(a\({}_{s}\))** \\  & & & **(a\({}_{c}\))** & **(a\({}_{ad}\))** & **(a\({}_{pt}\))** & **(a\({}_{st}\))** \\ \hline Loss (dB) & - & 0.05–0.1 & 0.1–0.3 & 1.5–3 & 1–4 & 3.5–20 \\ Attenuation (dB/km) & 0.2–0.4 1 & - & - & - & - & - \\ \hline \hline \end{tabular}  
* Changing with wavelength according to Figure 2.  
\end{table} 
Table 2: Example loss/attenuation of passive components in the optical fronthaul path. Figure 4: Example of the optical fronthaul path assembled with use of different standards of the single-mode optical fibers. In all variants, it was assumed that link B is equipped with an optical fiber adapted to work in the DWDM environment, while external access cables are based on standard fibers, which have a higher chromatic dispersion coefficient. The calculation results presented in Figure 5 show that despite the relatively short optical path, the variability of the averaged chromatic dispersion coefficient, with a large variety of the fibers used, may be significant. The nodal devices are not taken into account in the calculations, as they do not introduce dispersive phenomena large enough to be of significant importance in creating the averaged dispersion characteristic.
# OrientedDiffDet: Diffusion Model for Oriented Object Detection in Aerial Images
## 4 Experiments  
### Datasets  
To evaluate our proposed method, we conducted extensive experiments on two widely used object detection datasets: DOTA [35] and HRSC2016 [11].  
**DOTA.** DOTA is currently the largest dataset for object detection of aerial images with bounding box annotations. It consists of 2806 large-size images across 15 categories, including baseball field (BD), ground track (GTF), small vehicle (SV), large vehicle (LV), tennis court (TC), basketball court (BC), storage tank (ST), football field (SBF), roundabout (RA), swimming pool (SP), and helicopter (HC). The fully annotated DOTA dataset contains 188,282 instances. Instances in this dataset exhibit variations in scale, orientation, and aspect ratio. The DOTA dataset provides an evaluation server. We used the training sets for training and the test set for testing. We performed limited data augmentation. After image remapping, we cropped a series of 1024 \(\times\) 1024 patches from the original image at a stride length of 800.  
**HRSC2016.** HRSC2016 is a challenging dataset for ship detection in aerial images, collected from Google Earth. It comprises 1061 images and includes more than 20 categories of ships with various appearances. The image sizes range from 300 \(\times\) 300 to 1500 \(\times\) 900. The training, validation, and test sets consist of 436, 181, and 444 images, respectively. For data augmentation, we only employed horizontal flipping. The images were adjusted to a size of (512, 800), where 512 represents the length of the shorter edge, and 800 represents the maximum length of the image.
# Assessing Drivers' Physiological Responses Using Consumer Grade Devices
## 4 Discussion and Conclusions
### Photoplethysmography (PPG) Limitations  
E4 uses PPG, which is according to the literature less accurate than ECG, used by Faros [79]. PPG can provide somewhat different (delayed, malformed, etc.) results than ECG since the blood vessels are elastic and the pulse transit times may vary. Furthermore, it is considered that PPG can provide a valid representation of IBI during rest, but not during activity [83]. This suggests additional caution when assessing ECG data with wearable devices that use PPG and not ECG sensors.  
When examining the raw data from E4, multiple inter-beat interval (IBI) samples were incomplete for most drivers in both of the driving phases; furthermore, the collected data were completely missing for two drivers. However, the IBI samples recordings were complete for the baseline phase, suggesting the set-up was performed correctly.  
There are two potential reasons for incomplete IBI samples when recording with E4. As the missing samples occurred mainly in the dynamic driving environment, we can assume motion artefacts caused by steering negatively influence the E4. Furthermore, using the accelerometer and consequentially its internal algorithm for obtaining IBI from blood volume pulse (BVP), the device might detect intensive motion activity, which may have invalidated some of the IBI samples. Since there is an entire sequence of missing samples recorded, it is impossible to determine the differences between successive samples. Therefore, analysis of variables based on successive differences of such samples (i.e., RMSSD and pNN50) cannot be used for making reliable conclusions.
## 2 Fatigue Strength Assessment of Real Components
### Fatigue Assessment of the Compressor Mounting Bracket
#### 2.1.3 Experimental Study  
The experimental setup shown in Figure 3 consisted of a compressor suspension system mounted on an electromagnetic shaker. Two acceleration sensors were installed on the test rig, the first close to the shaker table to control the input PSD signal, and the second sensor mounted on the vibrating mass in one of the collision regions to control the system PSD response required for further FEM simulation.  
The system was excited in a vertical direction using a PSD profile with RMS acceleration of 0.9 g (Table 1). Figure 2 displays the graphical representation of the PSD input. The shape and measurement frequency range of the defined PSD input is based on automotive standard IEC 60068-2-64 Environmental testing--Part 2-64: Tests--Test Fh: Vibration, broadband random and guidance.  
Registered input and output acceleration signals and their histograms used to verify statistical properties are presented in Figure 4. Finally, the measurements results are summarized in Table 2.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Point** & **Frequency [Hz]** & **PSD [g2/Hz]** \\ \hline
1 & 5 & 0.0196 \\
2 & 10 & 0.0235 \\
3 & 20 & 0.0235 \\
4 & 200 & 0.0003 \\ \hline RMS acceleration & 0.90 g \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vibration test profile used in the test.  
Figure 3: Test rig for compressor dynamic response measurement in collision region.  
Parameters for the input signal with a mean value around 0 g, kurtosis close to 3 and a negligible skewness, indicating the expected distribution shape, which closely follows a normal distribution. In contrast, the histogram for the output signal illustrating the collision condition totally deviated from normal distribution. Similarly to the input signal, it shows a central peak at around 0 g; however, it has a more pronounced peak and a steeper drop-off, evidenced by a higher kurtosis (Ku = 8.15). This indicates a more leptokurtic distribution, with values more tightly clustered around the mean and a significant tail reflecting more extreme acceleration events resulting from collision conditions. Here, the skewness is negative (S = \(-\)0.69), suggesting a tail with more frequent negative acceleration values.  
To obtain the dynamic response in the frequency domain needed for further FEM simulation, the acceleration time history data for the vibrating mass (Figure 5a) was subjected to analysis through the Welch method [17; 18; 19], resulting in the PSD function presented in Figure 6.  
Figure 6 illustrates the power distribution of the signals over the tested frequency range. The system response under the testing conditions shows two distinct peaks. The first peak, at around 13 Hz, corresponds to the natural frequency of the structure, while the second peak, at around 45 Hz, is due to collision conditions. Such a response using a linear FEM solver is not possible to obtain in a direct way, as non-linearity cannot be defined from a definition.