esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Results of the Proof of Concept
#### 5.1.3. Cloud Removal by Means of a Multi-Spectral Reference Image  
On the way towards the rating of the potential of SAR data to fill images obscured by clouds, the image restoration by means of a multi-spectral data source is mandatory to serve as a reference value of expectancy. Figure 12 shows the results of the reconstruction of missing values within the SPOT 4 scene. Figure 12(b,c) compares the original with the filled image. Furthermore, Figure 12(d) displays a visual example of the enhanced image quality due to further spatial averaging (3 \(\times\) 3 Median filter). The findings of the image restoration by means of a multi-spectral image can be outlined as follows:  
1. The integration of a real existing dataset in the algorithm enables the removal of cloud cover from multi-spectral images, resulting in a meaningful image content of the restored image.
2. The visual appearance of the restored areas is affected by clutter noise. The origin of this noise needs further investigation. It appears to be helpful to compensate for this effect by means of spatial averaging.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **CC 10\%** & **CC 20\%** & **CC 30\%** & **CC 40\%** & **CC 50\%** \\ \hline SAM & 0.4535 & 0.4539 & 0.4545 & 0.4551 & 0.4567 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Spectral Angle Mapper.  
Figure 12: Visual presentation of the SPOT image restoration by means of a multi-spectral image (Landsat). (**a**) Cloud Cover 20%; (**b**) Original Image; (**c**) Filled Image; (**d**) 3 \(\times\) 3 Median Filter of the filled image.
## 2 Experiment System  
Figure 1 is a schematic diagram of the experimental system and microchannel used to study the droplet breakup behavior in this paper. The experimental system is divided into four parts: two-phase liquid input system, microchannel reaction system, data acquisition system, and waste liquid collection system. In order to clearly photograph the flow of droplets, an acrylic glass (PMMA) plate with a transmittance of 90% was used as the microfluidic reactor and a 100 W light source was used at the bottom to provide the required brightness. The microchannel is designed as a symmetrical Y-junction entrance and a symmetrical Y-junction bifurcation structure with an angle of 90 degrees and is carved on a PMMA board by a precision engraving machine. The channel consists of two inlets and one outlet, with a square cross-section (400 \(\upmu\)m wide \(\times\) 400 \(\upmu\)m high). Two immiscible liquids from two high-precision syringe pumps (LSP01-1A, Baoding Lange, Baoding, China) were respectively introduced into the two inlets. Silicone oil was used as the dispersed phase, and distilled water containing 0.5 wt % sodium dodecyl sulfate (SDS) was used as the continuous phase. The physical parameters of the fluid are shown in Table 1. The channel outlet leads to a waste bottle, which acts as a waste collection system. A high-speed camera (Photron Nova S6, Tokyo, Japan) was placed right above the microfluidic reactor, saving 2000 frames per second, and the captured data was transmitted to a computer in real time for data acquisition and recording.  
For ease of discussion and understanding, a schematic diagram of the relevant parameters of the droplet breakup process in this work is given in Figure 2. Among them, \(W\) is the main channel width, \(W_{\text{m}}\) is the neck width during the droplet breakup process, and \(L_{\text{d}}\) is the sub-droplet length after the droplet breakup.
## 4 Experimental Results
### Comparison  
**Quantitative results.** Table 3 presents the evaluation metrics for CHASE, as well as the un-scrambling version CHASE_WO, alongside other steganography methods on the DIV2K, COCO, and ImageNet test sets. Notably, CHASE_WO outperformed existing state-of-the-art high-capacity image steganography methods and even low-capacity alternatives for cover-stego image pairs on various datasets at equivalent capacities. While CHASE introduced some degradation in the quality of the stego image, it achieved higher quality for the reconstructed secret image compared to low-capacity steganography methods. Specifically, CHASE_WO improved PSNR by 1.85 dB for cover-stego image pairs, 2.55 dB for secret-reconstructed secret image pairs, and 0.02 in SSIM compared to DeepMIH [34].  
In our CHASE, both cover-stego image pairs and secret-reconstructed secret image pairs exhibited PSNR values surpassing 30 dB, indicating excellent visual quality. The PSNR and SSIM of secret-reconstructed secret image pairs were improved by up to 1.43 dB and 0.02, respectively, on the COCO dataset compared to low-capacity steganography methods, showcasing competitive image quality advantages.  
**Qualitative results.** Figure 6 displays images generated by different steganography methods, including 4 bit-LSB, [PERSON] et al. [22], DeepMIH [34], our CHASE, and CHASE_WO. Visually, 4 bit-LSB exhibited noticeable artifacts, [PERSON] et al. produced a yellowish tint, while CHASE, after image scrambling, closely resembled the corresponding cover image. The secret image obtained from the reduction process was nearly identical to the original secret image, showcasing high-fidelity results even with large capacity and complex hidden information.
## 2 LPWAN Technologies
### Ng-IoT  
Narrowband Internet of Things (NB-IoT) technology was introduced by 3 GPP to provide LPWAN services [14]. The NB-IoT standard utilizes mobile network features (e.g., user identity confidentiality, authentication, and integrity). It is designed to achieve excellent coexistence performance between Global System for Mobile Communication (GSM) and Long-Term Evolution (LTE) technologies [16]. NB-IoT requires 180 kHz minimum system bandwidth for both downlink and uplink. Therefore, a GSM operator can replace one GSM carrier of 200 kHz to NB-IoT. An LTE operator can deploy NB-IoT inside an LTE carrier by reallocating one of the Physical Resource Blocks (PRBs) of 180 kHz to NB-IoT.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Operating** & **Bandwidth** & **Range** & **Data Rate** \\  & **(MHz)** & **(kHz)** & **(km)** & **(kbps)** \\ \hline LoRaWAN & 433/868 (EU) & 125/250/500 & 5 (urban) & 0.3–50 \\ Sub-1 GHz & 915 (US) & & 20 (rural) & \\ \hline LoRaWAN & 2400 & 203/406 & 0.5 (urban) & 0.595–253.91 \\
2.4 GHz & & 812/1625 & 10 (rural) & \\ \hline Sigfox & 868 (EU) & 0.1 (UL) & 10 (urban) & 0.1 (UL) \\  & 915 (US) & 0.1 (DL) & 40 (rural) & 0.6 (DL) \\ \hline NB-IoT & Licensed & 180 & 1 (urban) & 150 (UL) \\  & LTE bands & & 10 (rural) & 127 (DL) \\ \hline D7 AP & 433/868 (EU) & 25/200 & 1–2 (urban) & 9.6/55.6 \\  & 915 (US) & & 5 (rural) & 166.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: An overview of the physical layer characteristics of LPWAN technologies.  
NB-IoT utilizes BPSK and Quadrature Phase Shift-Keying (QPSK) modulations at a frequency band of 700 MHz, 800 MHz, and 900 MHz. Consequently, NB-IoT can provide a communication link in rural areas up to 10 km [10].
# A Stability Analysis of the Ancient Site of Liye Based on the Strength Reduction Method
## 6 Conclusions  
This paper analyzes, evaluates and summarizes the factors leading to the degradation of the Liye ancient moat site. Through field investigation, sampling, testing and numerical simulation, the safety and stability of the most dangerous road sections were evaluated by using a combination of macro and micro analysis methods. Our main conclusions are as follows:  
1. The main factors leading to soil degradation include rainfall erosion, freeze-thaw cycle, man-made destruction and salt migration. According to the field evaluation and test results, it was found that the change of water content in the earth site caused by rainfall is an important factor affecting the stability of the site. Under the influence of water migration in the soil, the internal and surface cracks of the site are serious.
2. The two-dimensional simulation analysis conducted using the ABAQUS software indicates a safety factor of 1.5 when the soil moisture content is 20%. When the moisture content rises to 26%, the wall is in a wet state and the safety factor declines to 1.25. This indicates that rainwater has an impact on the strength of the site wall.  
Figure 12: Diagram of the simulation results of longitudinal stress of dangerous sections under condition 2. (**a**) section B\(-\)B; (**b**) section E\(-\)E; (**c**) section H\(-\)H; (**d**) section J\(-\)J (the unit in the legend is N).  
Figure 13: Relationship between safety factor and displacement of dangerous sections under different conditions. (**a**) Condition 1; (**b**) condition 2.  
Compared with the displacement in condition 1, the overall trench produced a large amount of displacement under condition 2, with the maximum displacement being concentrated on the top of the city wall.
3. The chemical composition and microstructure of the trench soil samples were analyzed by an X-ray diffractometer and a scanning electron microscope. It was found that there is no difference in the mineral composition between wall soil samples and trench soil samples, but the content of salt and oxide is inconsistent, with the salt content of trench soil being higher than that of wall soil. Wall soil has larger pores and more initial cracks than trench soil, which means it is easy to peel off.  
Methodology, J.Y.; software, X.H.; formal analysis, X.H.; resources, J.Y., Z.W. and L.Z.; data curation, X.H.; writing--original draft preparation, J.Y. and X.H.; writing--review and editing, J.Y. and X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation of China (the funding number: 51978634) and the Henan Science and Technology research project (the funding number: 212102310287 and 212102310271).  
Not applicable.  
The data presented in this study are openly available within the document.  
The authors declare no conflict of interest.
## 3 Dynamical Model for Motion Planning
### Validation of Single-Coupled Model  
To verify the accuracy of SDM, we have compared it with CDM, the uncoupled single-track model [25], the kinematic model [32], and CarSim [33] by open-loop simulations. In order to reflect the coupling effects, the simulation conditions, containing both acceleration/deceleration, and steering operations, were selected from the FOT data in Section 2. The comparison results were given in Figure 6a-c, in which the single-track model and the kinematic model are denoted by \"Single-track\" and \"Kinematic\", respectively.  
Since the lateral coupling force was large in the range of 20-30 s and 80-90 s, as shown in Figure 6e, the single-track model has a large error because it does not consider the dynamical coupling. The kinematic model cannot describe the cornering characteristics of tires, so it also has a large error when the slip angle becomes large at 20-30 s and 80-90 s, as shown in Figure 6d. The proposed SDM can overcome the above shortcomings of the single-track and kinematic models, meanwhile, compared with CDM, SDM has a more concise expression, which is beneficial to the numerical optimization of the motion planning.
# Development of Patient Specific Conformal 3D-Printed Devices for Dose Verification in Radiotherapy
## 2 Materials and Methods
### Dosimetric Evaluation  
Treatment plans were used to evaluate the influence of individualized 3D-printed boluses (0.5 and 1 cm thickness) on dose distribution. Three single field treatment plans were created for the 3D-printed patient-specific phantom using the Eclipse Treatment Planning System (developed by Varian Medical System, Palo Alto, CA, USA). One plan was created for the patient-specific phantom without a bolus, and two other plans were created for the cases when boluses of different thicknesses (0.5 cm and 1.0 cm) were placed on the surface of the patient-specific phantom. The target was delineated to cover the nose and cheeks while selecting a target volume of 3.8 cm3. The three plans were set with a single field 6 MV photon beam. The other parameters of the treatment plan were: field size of 5.6 cm \(\times\) 3.6 cm and gantry angle 20\({}^{\circ}\). The prescribed dose to the target was set to 2 Gy/fr. In order to assess the clinical feasibility of 3D boluses in the case of more complex radiotherapy modalities, three additional plans were created using five-fields IMRT, and setting the photon energy to 6 MeV FFF with gantry angles of 0\({}^{\circ}\), 21\({}^{\circ}\), 45\({}^{\circ}\), 120\({}^{\circ}\) and 300\({}^{\circ}\)  
Figure 3: 3D-printed bolus: (**A**) frontal view; (**B**) lateral view; (**C**) boluses with different thicknesses (0.5 cm and 1.0 cm).  
Other treatment parameters, including prescribed dose and treatment target, were kept identical to the case of single field plan. The treatment plans with and without 3D-printed boluses were normalized for the maximum prescribed dose to cover 95%, 98% and 100% of the target volume, respectively. All plans were analyzed and compared in terms of the dose volume histogram (DVH) of the target volume. The following dosimetric parameters were estimated and compared for all the treatments: D95%, D98% and D100% (dose that covers at least 95%, 98% and 100% of the target volume, respectively).
# A Low-Profile Wideband Monocone Antenna Using Bent Shorting Strips
###### Abstract  
A low-profile wideband monocone antenna with bent shorting strips, and parasitic and circular sleeves is proposed. By loading the bent shorting strips, parasitic sleeves, and circular sleeves, miniaturization of the antenna is achieved. Along with bent shorting strips from the monocone hat to the ground plane, parasitic sleeves, and circular sleeves are mounted to enhance the impedance bandwidth. From the experimental results, the -10 dB reflection coefficient bandwidth of the proposed antenna ranges from 810 MHz to 5340 MHz. In addition to the wide bandwidth characteristics, the proposed antenna has highly desirable omnidirectional radiation properties for wireless communication systems.  
monocone antenna; wideband antenna; shorting strip; circular sleeve
## 3 Distributed Estimation of Laplacian Spectrum
### Multiplicities Estimation  
Now, turning to the last stage, which is the corresponding Laplacian eigenvalues multiplicities estimation. In [16], the authors have proposed a linear integer programming optimization problem to figure out the multiplicities:  
**Proposition 1** ([16]).: _Consider a connected undirected graph of \(N\) vertices with degree sequence \(\{d_{i}\}\) and Laplacian matrix \(\mathbf{L}\), having \(D=|\mathcal{S}_{2}|\) distinct non-zero Laplacian eigenvalues \(\mathcal{S}_{2}\). Let \(\mathbf{m}\in\mathbb{Z}^{+D\times 1}\) be the vector of the corresponding multiplicities and be obtained by solving the integer programming below:_  
\[\min_{\mathbf{m}\in\mathbb{Z}^{+D\times 1}} \mathcal{S}_{2}^{T}\mathbf{m}\] (24) _subject to_ \[\mathcal{S}_{2}^{T}\mathbf{m}=\sum_{i=1}^{D}d_{i}\] \[\mathbf{T}^{T}\mathbf{m}=N-1\] \[\mathbf{m}\in\mathbb{Z}^{+D\times 1}\]  
The proof was given in [16]. Since all the multiplicities \(\mathbf{m}\) are positive integers, then Brand-and-Bound method has been deployed to derive \(\mathbf{m}\). Therefore, the problem (24) can be rewritten equivalently in linear integer programming form as follows:  
\[\min_{\mathbf{m}\in\mathbb{Z}^{+D\times 1}} \mathcal{S}_{2}^{T}\mathbf{m}-\sum_{i=1}^{D}d_{i} \tag{25}\] \[\text{subject to} \mathbf{1}^{T}\mathbf{m}=N-1\] \[\mathbf{m}\in\mathbb{Z}^{+D\times 1}\]  
The Algorithm for this problem has been described clearly in [16]. At this step, the whole Laplacian spectrum \(sp(\mathbf{L})\) has been obtained.  
In fact, the estimation problem can be converted into a convex form and can be solved effectively by using ADMM-based method proposed in [16]. However, the purpose of this study is extremely appropriate for non-convex optimization problem through deploying the promising ALADIN-based method.
## 4 Results and Discussion: Finite Element Validation
### Evaluation of Torque Ripple Reduction by Means of Four-Step Discrete Skew
For all cases, \(N=2\), \(N=3\) and \(N=4\), the average torque remains relatively constant. Therefore, when assessing close-to-purely-sinusoidal electromagnetic torque waveforms in a SynRM, two-step skew may then be sufficient as a single ripple component. Conversely, if there are several preponderant \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Harmonic Order** & \multicolumn{2}{c}{**Four-Pole SynRM**} & \multicolumn{2}{c}{**Six-Pole SynRM**} \\ \hline  & \(k_{\text{rs,$p$}}\) & \(k_{\text{rs,$p$}}\) & \(k_{\text{rs,$p$}}\) & \(k_{\text{rs,$p$}}\) \\  & **(Analytical)** & **(FEA)** & **(Analytical)** & **(FEA)** \\ \(v=6\) & 0.84 & 1.01 & 0.65 & 0.74 \\ \(v=12\) & 0.43 & 0.35 & 0.00 & 0.06 \\ \(v=18\) & 0.00 & 0.08 & - & - \\ \(v=24\) & - & - & 0.00 & 0.12 \\ \(v=30\) & - & - & - & - \\ \(v=36\) & 0.00 & 0.05 & 0.00 & 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Torque ripple harmonic component reduction as a result of three-step skewing. The 3D results are considered, and relevant harmonic components analyzed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Skewless** & **Skewless** & **Four-Step Skew** & **Four-Step Skew** \\  & & **2D** & **3D** & **2D** & **3D** \\ \hline \multirow{2}{*}{Four-pole SynRM} & \(T_{\text{avg}}\) & 51.0 Nm & 50.2 Nm & 49.8 Nm & 48.7 Nm \\  & \(T_{\text{rp}}\) & 73.9\% & 72.1\% & 15.0\% & 18.3\% \\ \multirow{2}{*}{Six-pole SynRM} & \(T_{\text{avg}}\) & 54.7 Nm & 53.8 Nm & 51.4 Nm & 51.0 Nm \\  & \(T_{\text{rp}}\) & 49.2\% & 45.9\% & 11.2\% & 12.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average torque and torque ripple comparison when applying four-step skewing, by means of 2D and 3D FEA simulations. harmonic components, then it is worth taking a multi-step skewing approach, to mitigate multiple harmonics at once. A study case is addressed in Section 5 covering this issue. Consequently, it is necessary to correctly analyze the harmonic distribution of the electromagnetic torque to properly choose N, since increasing the number of steps does not always guarantee a significant reduction in torque ripple and could lead to other different manufacturing costs.
# Dynamic Weighted CNN-LSTM with Sliding Window Fusion for RFFE Final Test Yield Prediction
## 3 Results
### Optimization of Network Structure Parameters  
In constructing the CNN-LSTM model, the selection of network parameters impacts prediction outcomes. Following the determination of the activation function, multiple simulation experiments were designed to optimize parameters, including CNN kernel size, pooling method, batch size, LSTM units, iteration count, and Dropout strategy, aiming to establish a CNN-LSTM model for accurate yield prediction. A grid search algorithm was employed to select model parameters and quantify their effects on yield.  
Tuning of CNN internal parameters yielded results shown in Figure 8. Kernel sizes of 2, 3, and 4 were tested, revealing that a kernel size of 3 maximized the coefficient of determination at 0.68. Similarly, filter counts of 64, 128, and 256, and pooling sizes of 2, 3, and 4 were evaluated, with 64 filters and a pooling size of 2 demonstrating superior performance.  
Additionally, optimization was conducted for LSTM units, batch size, epochs, and Dropout. Figure 9 visually illustrates the hyperparameter determination process for the hybrid model.  
Figure 8: CNN parameter selection performance chart.  
Figure 9: LSTM parameter selection performance chart.  
In the training of this model, the \"dropout\" technique is utilized, involving the random deactivation of a subset of neurons to facilitate localized updates within the neural network. During the simulation process, the dropout parameters are configured as 0.1, 0.15, and 0.2, respectively, with the specific results presented in Table 5 below.  
The results indicate that the coefficient of determination (R2) between the evaluated yield and the true values reaches 0.72. This suggests that discarding a portion of the hidden layer neurons with a probability of 20% during training helps reduce dependency on certain local features. Although a dropout parameter of 0.15 yields a higher correlation between the evaluated and true values, a dropout rate of 0.2 is ultimately selected to enhance the model's generalization ability and mitigate overfitting effects. Based on the above analysis, the grid parameters of the constructed CNN-LSTM fusion model are presented in Table 6.  
By configuring the batch size for updates as 5, 10, 15, 20, 25, and 30, it was observed that a batch size of 25 yields a correlation coefficient of 0.72. Additionally, the number of training epochs was set to range from 5 to 30, sampled at intervals of 5, revealing that an epoch counts of 25 also results in a coefficient of determination (R2) of 0.72. When tuning the units of the LSTM neural network, values were tested between 5 and 20 with six increments, and it was found that an LSTM layer size of 20 achieves the highest R2 value. For the activation function of the LSTM layer, Tanh was selected, while a dropout rate of 0.2 was adopted as a strategy to prevent overfitting.
## 2 Related Work  
### Usage of LLM in Solving AWPs  
In recent years, following the breakthroughs in NLP by LLMs such as ChatGPT, researchers have become interested in their reasoning capabilities for AWPs [4; 11]. COT is a prompting-based method that optimizes input prompts to augment the outputs of LLMs [6]. This method demonstrates that prompting the model to leverage the knowledge obtained during pre-training has more advantages over fine-tuning methods for mathematical reasoning tasks [22]. However, it has not achieved satisfactory accuracy in solving AWPs.  
To enhance solving performance, a series of studies have developed novel prompting methods based on COT, referred to collectively as \"COT-like\" methods in this paper [14; 23]. For instance, the Program-Of-Thought (POT) method prompts the LLM to convert input AWPs into a series of executable programming languages [23]. The Equation-Of-Thought (EOT) method directly converts AWPs into systems of equations [14]. The X-Of-Thought (XOT) method employs a majority voting strategy to select answers from the outputs of multiple COT-like methods [14]. Nonetheless, these methods perform poorly on complex AWPs that involve more reasoning steps.  
To address complex AWPs, [PERSON] et al. [24] proposed an improved COT method that enables LLMs to generate and solve simpler subproblems at each step of the reasoning chain. [PERSON] et al. [25] proposed a dual-agent model method, where one agent specializes in decomposing the problem, and the other agent specializes in solving each subproblem. Although these studies indicate that linguistic and world knowledge of LLMs can be utilized to decompose complex AWPs, their correct solving depends on repeatedly invoking a code interpreter during generation [7], with the success rate of invocation limiting the final solving accuracy.  
Based on the aforementioned studies, this paper concludes that while current LLMs can leverage external knowledge to understand and decompose complex AWPs, they are not proficient in precise reasoning and calculation. Combining the strengths of LLMs with other reliable methods may result in more effective algorithms for solving AWPs. Hence, this paper proposes a COS prompting method, which leverages the advantages of LLMs to generate scenes for AWPs rather than solving them directly.
## 1 Introduction
### Research Contribution and Novelty
The components and software utilized are open source, which promotes additional innovation and advancement in the industry. The IoT monitoring system advances the development of smart grid technologies by incorporating IoT-based monitoring into transformer maintenance procedures, thereby facilitating the shift to more resilient, intelligent, and efficient power grids.