esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### PV Module Technologies Description
The installed power of each module varies from around 400 W to over 700 W, depending on the application requirements. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **HJT** & **N-Type** & **PERC** & **TOPCon** & **Bifacial TOPCon** \\ \hline Module installed power [W] & 430–710 & 415–600 & 550–670 & 435–615 & 430–700 \\ \hline Efficiency [\%] & 21.6–25.6 & 21–23.10 & 21.2–21.7 & 21.1–22.8 & 22–23 \\ \hline Module area [m\({}^{2}\)] & 1.95–3.11 & 1.95–2.63 & 2.51–3.11 & 1.95–2.70 & 1.95–3.11 \\ \hline Number of PV & 3 kW & 5–7 & 5–8 & 5–6 & 5–7 & 5–7 \\ modules [pcs] & 5 kW & 8 -12 & 9–13 & 8–10 & 8–12 & 9–12 \\  & 7 kW & 14–17 & 12–17 & 11–13 & 10–17 & 12–17 \\ \hline Calendar life [years] & 30 & 25–30 & 25 & 25–30 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Technical parameters of the analyzed PV module technologies. Besides their technical specifications, the economic parameters are important in the decision-making process. The initial investment cost, operation and maintenance costs and other factors can influence the benefits of selecting a PV module technology.
## 3 Result
### Electrochemical Performance of Gel p(OPal-MMA) Electrolyte  
In order to explore the practical application performance of GPEs in LIBs, the galvanostatic charge and discharge tests of Li/GPEs/LiFePO\({}_{4}\) cells were investigated and the initial profiles at 0.1 C rate were shown in Figure 6a. A typical charge-discharge potential plateau of LiFePO\({}_{4}\) appeared clearly at around 3.4-3.5 V, which was consistent with its typical electrochemical behavior previously reported [39; 40; 41], indicated a reversible cycling process. Remarkably, the GPE prepared from S2-4 membrane delivered an initial charge capacity of 167 mAh/g (Figure 7a); it was close to the theoretical specific capacity (170 mAh/g). The excellent electrochemical performance may be due to the high porosity and its consequence of outstanding ionic conductivity and considerable \(t_{Li+}\) value. The cycling behavior experiment was also carried out with a constant current rate of 0.2 C from 2.8-4.0 V and the results are presented in Figure 7b. The GPE from S2-4 revealed the most excellent cycling performance: the cyclic discharge capacity was about 166\(\sim\)168 mAh g\({}^{-1}\) and there was almost no decline after 50 cycles. This superior cyclic performance could be attributed to the outstanding ionic conductivity, considerable \(t_{Li+}\), number and good electrochemical interface stability of membranes. In addition, the coulombic efficiency of the GPE from S2-4 ranged from 96.74% to 99.98% at the whole cycle test, which reflected a good interfacial compatibility between the electrodes and the electrolyte [38]. The membrane could entrap more liquid electrolyte when it had more pores, which enabled membrane swelling and reduced interfacial resistance, and improved the interfacial stability between the GPE and electrode. Based on the above results, the GPE prepared with the DMF and acetone mixed solvent could have a good application prospect in LIBs because of its excellent reversible capacity and cyclic performance.  
Figure 6: LSV curves of GPEs prepared by different mixed solvents.
## 3 Methodology of Numerical Tests of the Platform
### Analytical Determination of Limit Loads for Model Tests  
In order to determine the limit forces for hook lifting, a simplified calculation scheme, presented in Figure 5, was used.  
Figure 4: Ten-foot intermodal platform with the load of logs with the weight of Pmax: (**a**) during the loading and unloading operation using a forklift, (**b**) during the loading and unloading operation using a hook device.  
Figure 3: Ten-foot intermodal platform: (**a**) with a load of logs with the weight of Pmax, (**b**) load diagram for rope lifting of a container. Red arrows and descriptions indicate unknown parameters determined analytically and numerically for various methods of loading/unloading the platform.  
The mass of the complete platform module was read from the 3D graphic documentation of the platform: m\({}_{\rm m}\) = 2823 kg. The load mass is m\({}_{\rm o}\) = 10,000 kg.  
Equations (4)-(6) were formulated assuming the state of equilibrium at the moment of detachment of point D of the platform with the load from the ground during hook lifting (Figure 4b).  
The equilibrium equations referring to the scheme (Figure 5) were formulated in the form of the following dependences:  
\[\sum\!\!P_{\rm x}=0=>-S_{\rm x}+T=0 \tag{4}\]  
In order to calculate the horizontal component of the \(\rm{S_{x}}\) pulling force from Formula (4), the following relationship was used:  
\[\rm{S_{x}=T=\mu\cdot N} \tag{8}\]  
where  
T--friction [N];  
N--pressure [N];  
\(\rm{\mu}\)--friction coefficient.  
Friction forces were determined for two different values of friction coefficients. The friction force and the \(\rm{S_{x}}\) force component for a metal-concrete friction coefficient of \(\rm{\mu_{min}=0.45}\) is as follows:  
\[\rm{T_{min}=\mu_{min}\cdot N=28,303.6\ [N]}\]  
From Formula (4):  
\[\rm{S_{x}=S_{x\cdot min}=T_{min}=28,303.6\ [N]}\]  
Friction and \(S_{x}\) force for a metal-rubber coefficient of \(\rm{\mu_{max}=0.6}\) is as follows:  
\[\rm{T_{max}=\mu_{max}\cdot N=37,740\ [N]}\]  
From Formula (4):  
\[\rm{S_{x}=S_{x\cdot max}=T_{max}=37,740\ [N]}\]  
The calculated values of the component forces identified in the hook lifting process using different friction conditions between the ground and the platform are as follows:  
For the minimum friction coefficient--\(\rm{\mu_{min}=0.45}\):  
\[\rm{S_{y}=62,896.815\ [N]}\]  
\[\rm{S_{x}=<0;28,303.6\ [N]>}\]  
For the maximum friction coefficient--\(\rm{\mu_{max}=0.6}\):  
\[\rm{S_{y}=62,896.815\ [N]}\]  
\[\rm{S_{x}=<0;37,740\ [N]>}\]
## 3 Approximate DFT
### Theoretical Background  
The discrete Fourier transform is a linear operator that relates an \(N\)-point input signal \(\mathbf{x}=\begin{bmatrix}x[0]&x[1]&\ldots&x[N-1]\end{bmatrix}^{\top}\) to the output signal \(\mathbf{X}=\begin{bmatrix}X[0]&X[1]&\ldots&X[N-1]\end{bmatrix}^{\top}\) according to the following expression:  
\[X[k]=\sum_{n=0}^{N-1}x[n]\cdot\omega_{N}^{-nk},\quad k=0,1,\ldots,N-1, \tag{1}\]  
where \(\omega_{N}=\exp(2\pi\
icefrac{{j}}{{N}})\) is the \(N\)th root of the unity, and \(j\triangleq\sqrt{-1}\). Computed by definition, the DFT presents a computational complexity in \(\mathrm{O}(N^{2})\), which is prohibitively high for real-time applications.  
In practice, the DFT is computed by means of one of many efficient numerical routines--fast algorithms, which are collectively referred to as \"FFTs\". Particular popular choices of FFTs are the radix-2-based algorithms, such the Cooley-Tukey FFT, which can recursively decompose the \(N\)-point DFT block in two \(N/2\)-point DFT sub-blocks [85].  
In general, FFTs are capable of reducing the computational complexity of the DFT computation from \(\mathrm{O}(N^{2})\) to \(\mathrm{O}(N\log N)\)[86]. In many cases [87; 88; 89; 90; 91; 92], such a computational cost reduction achieves the theoretical minimum multiplicative complexity of the DFT [93]. As a consequence, it is impossible to further reduce the multiplicative complexity. This being a quite mature area of research, the proposition of new FFTs capable of offering significant reductions in computation cost is an unlikely event.  
However, noticeable reductions can be accomplished under a fault-tolerant paradigm. If the DFT computation is permitted a given error level, then the computational effort can be re-adjusted to meet the lower, but acceptable, precision level. As a consequence, fewer arithmetical operations are needed for the DFT estimation [94].
# Dynamic Weighted CNN-LSTM with Sliding Window Fusion for RFFE Final Test Yield Prediction
## 2 Materials and Methods  
In the methods section of this paper, we have constructed an integrated architecture with multistrategy dynamic weighted fusion aimed at achieving precise prediction of RFFE chip final test yield. A multistrategy fusion model was constructed, extracting the impact of test data on yield from diverse perspectives. Additionally, a dynamic incremental prediction approach was employed, enabling the model to continuously learn and adapt to changes introduced by updated data. The overall architecture is illustrated in Figure 1.  
The architecture is primarily divided into three modules:  
1. Data Preprocessing Module First, testing metrics are screened and grouped through RF correlation analysis (such as Spearman's correlation coefficient and VIF evaluation). Principal Component Analysis (PCA) is then employed to reduce the dimensionality of high-dimensional data, extracting key features with high contribution from the original dataset to form an optimized dataset.
2. Submodel Construction and Fusion Prediction Module Based on the preprocessed data, three complementary submodels are developed: * Submodel A utilizes a CNN-LSTM structure, where convolutional layers extract local features from historical yield data and LSTM captures long-termdependencies, reflecting both short-term fluctuations and long-term trends in process parameters;
* Submodel B employs multichannel inputs to integrate multidimensional electrical and RF test data with historical yield data, thereby revealing the intrinsic relationships among various testing metrics;
* Submodel C adopts an adaptive sliding window averaging strategy that dynamically weights and smooths short-term noise, providing a robust baseline prediction.  
Finally, a learnable dynamic weighted fusion mechanism integrates the predictions from all submodels, and a cross-constraint loss is incorporated to ensure consistency in feature representation across submodels, thereby enhancing overall prediction accuracy and robustness.  
(3) Dynamic Incremental Training and Prediction Mechanism  
To accommodate the continuously changing process conditions during production, a dynamic prediction mechanism is introduced. By quantifying prediction uncertainty through techniques such as Monte Carlo dropout, the system can trigger localized retraining or adjust fusion weights in real time. Additionally, by employing adaptive learning rates and time-decay sample weighting strategies, incremental training is implemented, enabling the model to update continuously, respond rapidly to data fluctuations, and maintain stability and efficiency in real-time predictions.  
Overall, this architecture seamlessly integrates dimensionality reduction, temporal feature extraction, multidimensional data fusion, and dynamic adjustment strategies, forming a robust, flexible, and highly accurate yield prediction system for RFFE final testing.
# Key-Recycling Strategies in Quantum-Key-Distribution Networks
## 5 QKR Mechanism and Strategy
### All Recycling  
In order to improve the security of the recovery key and recover as many keys as possible, the quantum key all-recycling algorithm is designed as shown in Algorithm 2. In order to guarantee the security of the recycling key, the original key needs to be mixed into the recycling key. So, the first step of this algorithm is to set the mix ratio denoted by K\({}_{0/\text{r}}\) and look up the capacity of the current original key. Then, according to the relationship:  
\[N_{key\_original}=K_{0/r}\star\text{N}_{key\_reycled} \tag{4}\]  
where the maximum number of keys that can be recycled can be calculated. Finally, the algorithm recycles the key and performs post processing.  
The post-processing phase disorders the recycled keys by linear adjustment or non-linear mapping to improve the security of the recycling key. Therefore, the security of the post-processing algorithm determines the security of the quantum key recycling algorithm and the post-processing algorithm brings the cost of the recovery algorithm to the use of the original key. The S-box has the following three characteristics: Each row of the table can traverse all possible values. Changing an input bit, changes at least two output bits, and the input and output are statistically independent. Therefore, in this paper, the design method of the S-box in the data encryption standard (DES) algorithm is used as a post-processing method for recycling the keys [29,30]. It is important that the two key pools that manage a pair of keys must perform the same post-processing strategy.  
Post-processing is divided into three steps as shown in Figure 8. Step 1: The key is mixed, and a certain proportion of the original key is inserted into the recycling key sequence according to the pre-defined insertion rules. Step 2: Extend the key mix sequence according to the rule of extended permutation table (2). Each 4-bit key is divided into a group (dark yellow area in the dotted line), and then a bit is added at the beginning and the end of the key group (light yellow area outside the dotted line). Step 3: The extended key sequence is nonlinearly compressed according to the rules of the S-box (3).  
The advantage of the algorithm is that all keys are recycled as much as possible and the security of the recycled keys is high. The disadvantage of the algorithm is that the security of the recycled keys is mainly dependent on the design of the post-processing algorithm. And the post-processing algorithm may introduce the consumption of the original key, which may result in the original high-security-level service being unable to be encrypted.  
The complexity of the all-recycling algorithm is mainly due to the call of [PERSON]'s algorithm and a post processing algorithm. The former can be achieved at a complexity of O (V\({}^{2}\)), and the latter has a complexity of O (N\({}_{\text{key}}\)), where N\({}_{\text{key}}\) is the number of keys being recycled. Thus, all-recycling algorithm has a complexity of max {O (V\({}^{2}\)), O (N\({}_{\text{key}}\))}.
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 6 Discussion of Results
### Concerning the Location and Installation of Measuring Devices  
The development of proprietary networks enables the creation of dense and spatially uniform monitoring systems that align with LCZ classifications, as recommended by the WMO. In these networks, the geographic coordinates and relevant environmental metadata for each AWS are carefully documented, ensuring spatial accuracy and consistency.  
In contrast, CWONs often exhibit spatial data gaps (particularly in urban areas with lower socioeconomic status), resulting in \"data shadows\". Furthermore, the recorded coordinates of CWON AWSs are not always reliable. As discussed in Section 4.3, inaccuracies may arise from several factors, including data privacy concerns, changes in residential addresses without corresponding updates, and limited cooperation from property owners. These issues can lead to a disconnect between the recorded data and the actual measurement location, undermining the spatial validity of the observations.  
Another critical limitation of CWONs is the lack of vertical consistency in AWS placement. Devices may be installed at varying heights (on low or high floors) without regard to urban canopy height or surface roughness, both of which are essential for accurate UHI characterization. Moreover, CWONs typically do not record metadata such as installation height or other micro-environmental factors. As a result, the data may reflect hyper-local microclimates that are not representative of the broader LCZ. These discrepancies cannot be identified or corrected through standard data quality control procedures.
## 3 Results and Discussion
### Drying Time  
Figure 1 shows the effect of drying air temperature, thickness and ultrasonic pretreatment time on the drying time of kiwifruit slices. As shown in Figure 1, the maximum drying  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & \(RMSE\) & \(\chi^{2}\) & \(\mathbf{R}^{2}\) \\ \hline Two-term & 0.0501 & 0.0167 & 0.9979 \\ Two-term exponential & 0.0648 & 0.0221 & 0.9968 \\ [PERSON] & 0.0402 & 0.0092 & 0.9985 \\ [PERSON] et al. & 0.0367 & 0.0074 & 0.9988 \\ [PERSON] et al. & 0.0211 & 0.0017 & 0.9995 \\ [PERSON] ([PERSON]) & 0.0761 & 0.0272 & 0.9958 \\ [PERSON] & 0.0285 & 0.0037 & 0.9991 \\ [PERSON] and [PERSON] & 0.0473 & 0.0132 & 0.9981 \\ [PERSON] and [PERSON] & 0.0533 & 0.0192 & 0.9977 \\ Logistic & 0.0177 & 0.0007 & 0.9997 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical analysis of mathematical modeling of kiwifruit slices drying kinetics.  
time was obtained at 50 \({}^{\circ}\)C with a thickness of 8 mm (control sample) for 420 min. Also, the minimum drying time was obtained at 70 \({}^{\circ}\)C, 4 mm thickness and ultrasonic radiation time of 30 min for 70 min. As expected, the drying time is significantly reduced (\(p<0.05\)) by raising the temperature due to the increased thermal gradient (as a result of higher mass and heat transfer) and greater and quicker removal of moisture from the product [36].  
The results obtained in Figure 1 also show that the highest effect of using ultrasonic pretreatment on the drying time of kiwifruit is seen for 30 min of ultrasonic application and the lowest one is seen for the control sample. By applying the cavitation process (asymmetric bursting of bubbles) near the foodsurface, ultrasonic pretreatment causes the rapid and eruptive flow of sound waves to the surface and forms microscopic channels in the samples by creating successive contractions and expansions. In addition, by increasing the duration of applying this energy (ultrasound), the channels expands and the product has a spongy texture, facilitating the flow of water out of the product during the drying through the created channels [37]. Similar results were observed for carrot drying [24], banana drying [31], and blackberry drying [29]. They showed that, by increasing the ultrasonic radiation time, the drying time decreases significantly (\(p<0.05\)). As shown in Figure 1, the drying time was longer as the thickness of the samples increased. The reason for this issue is the hardening mechanism of the cutting surface, increased amount of water, and also a longer path of vaporremoval, which made it difficult to transfer moisture through the texture and ultimately increased the drying time [38]. [PERSON] and [PERSON] for drying tomatoes and [PERSON] et al. for drying potatoes showed that the drying time increases by enlarging the thickness of the samples [28; 36].
# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
## 3 System Model and Utility Function
### QoE-Based Utility
\(Q(r_{i})\)=\(\delta\cdot r_{i}\) and the constant \(\delta\) is the energy factor. The higher the rate, the more the energy consumption of transmission. Therefore, \(COST_{i}=P(r_{i})+Q(r_{i})\) and the utility for each UAV can be formulated as  
\[ \begin{split} u_{i}&=QoE_{MOS}-COST_{i}\\ &=Const_{fps}\cdot\{A\cdot[a+b\cdot\sqrt{\frac{r_{i}}{c}}(1- \frac{c}{r_{i}})]+B\}-\theta\cdot\frac{r_{i}}{Chand}-\delta\cdot r_{i}\\ &=\Omega\cdot[a+b\cdot\sqrt{\frac{r_{i}}{c}}(1-\frac{c}{r_{i}})] -\eta\cdot r_{i}+\Lambda,\end{split}  \tag{5}\]  
where \(\Omega\) and \(\Lambda\) are the constant terms after the combination of the above QoE factors, and \(\eta\) is the constant term after the combination of rental fee of the network and energy factors.
## 2 Proposed Cone Cell Identification Method
### Bias Field Correction  
To mitigate intensity differences across the cone cells in an AO-SLO image and improve the identification accuracy, we apply bias field correction [39] to denoised AO-SLO images.  
Figure 3: AO-SLO image (**a**) before and (**b**) after denoising using proposed method based on TV-L1 optical flow registration.  
Figure 2: Flowchart of adaptive optics scanning laser ophthalmoscope (AO-SLO) image denoising.  
First, an intensity bias field image is created by applying a Gaussian filter to the denoised image:  
\[\text{Bias field image}\ =\ \text{Gaussion filter}(\text{Denoised image}) \tag{1}\]  
The denoised image is corrected by extracting the bias field image [39]:  
\[\text{Bias field corrected image}(\text{x},\text{y})\ =\ \text{Mean}(\text{Bias field image})\times\frac{\text{ Denoised image}(\text{x},\text{y})}{\text{Bias field image}(\text{x},\text{y})} \tag{2}\]  
Figure 4 illustrates bias field correction performed on a representative AO-SLO image. The intensity of the cone cells is more uniform after bias field correction. To accurately identify the cone cells, we also isotropically magnify the corrected image four times via bicubic interpolation after bias field correction and before identification.
# Adaptive Learning in AI Agents for the Metaverse:
## 5 Theoretical Application Analysis
### Epic Games Virtual Events
#### 5.1.2 Analysis of AI Adaptive Learning  
The adaptive learning capabilities of AI agents used in Epic Games' virtual events reveal significant advancements in how these technologies are tailored to enhance user experience and effectively manage complex, multi-user environments. Through detailed analysis, it becomes evident how adaptive learning models, specifically those developed under the ALMA framework, are critical in driving the success of such large-scale interactive platforms.  
1. Adaptive Content Delivery  
One of the standout features of AI agents during these virtual events is their ability to deliver content adaptively. Based on real-time analysis of user interactions and preferences, the AI dynamically alters the event's visual and auditory aspects to suit the audience's tastes and responses. This level of adaptation ensures that each participant's experience is personalized and engaging, which is crucial for retaining interest in an environment where user attention can easily wane.
2. Behavioral Modeling and Prediction  
AI agents in Epic Games' virtual events utilize sophisticated behavioral models to predict and respond to user actions. This involves recognizing patterns in user behavior and anticipating future actions, allowing the AI to proactively adjust the environment, such as changing the difficulty level of interactive challenges or initiating new game narratives that align with user interest trends. This predictive capability enhances the fluidity and relevance of the virtual experience, making it more immersive and satisfying for users.
3. Real-time Interaction Management  
Another critical aspect of adaptive learning in these AI agents is their ability to manage thousands of interactions simultaneously. This includes moderating discussions, ensuring compliance with community guidelines, and facilitating cooperation and competition among players. The AI's learning algorithms continuously refine these processes based on ongoing user feedback, which is vital for maintaining a harmonious and engaging virtual environment.  
In conclusion, the analysis of AI adaptive learning in Epic Games' virtual events highlights the effectiveness of ALMA in real-world applications within the Metaverse. These AI agents exemplify how adaptive learning can transform user interactions in virtual spaces, making them more engaging, responsive, and personalized. This case study demonstrates the current capabilities of adaptive AI and sets a benchmark for future developments in the field.