esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Federated System
### Simulation Results  
To evaluate the effectiveness of the FDRL system, a comprehensive simulation was conducted that consisted of three main tests. Each test was designed to analyze different aspects of the federated learning process in a multi-client O-RAN environment, ensuring that the proposed solution would be robust, scalable, and adaptable to various real-world constraints.  
The goal of these simulations is to validate the ability of the federated learning-based slice admission control to accomplish the following:  
1. Ensure convergence of the global model across different training conditions.
2. Demonstrate how clients benefit from collaboration in the federated setting.
3. Evaluate the system's response to asynchronous participation of clients, which reflects real-world scenarios where different O-RAN instances may have variable availability.  
Each simulation test represents a unique network condition, focusing on federation dynamics, client participation variability, and system resilience against synchronism loss. The training process involves multiple clients acting as local learners under their respective environments, while a federated aggregation server updates a global model to optimize decision making across all participants.  
In these tests, the reward function was designed to maximize the number of correctly processed slice admission requests within the simulated network, where an ideal outcome would be a reward equivalent to the number of slice requests in an episode, that is, a value of 100. The DQN agents, deployed as xApps in near-RT RICs, continuously improve their policies through reinforcement learning, learning from past decisions and optimizing their slice selection strategies in a collaborative manner via federated learning.  
The simulation framework was structured as follows:  
* Each client undergoes local training rounds where it updates its DQN-based slice admission control policy based on locally collected data.
* After a defined number of local training steps, the clients transmit their learned models to the aggregation server, which aggregates the model parameters and distributes a new global model to all participating clients.
* The training process is iterative, with the global model progressively improving through multiple federation rounds.
* The simulations examine the effects of late client arrivals and synchronization loss, assessing the system's adaptability in real-world conditions.
## 2 Materials and Methods
### MODIS Data  
MODIS is a sensor onboard Terra and Aqua satellites [38]. In this study, we used the MYD17A2H dataset from 2014 to 2017. MYD17A2H is a Level-4 standard product, which includes Gpp_500 m, PsnNet_500 m, and Psn_QC_500 m. We used the Gpp_500 m dataset, which has a spatial resolution of 500 m and temporal resolution of eight days. 'Gpp_500m' data is an eight-day summation GPP. To translate the numerical value of MODIS GPP into the sequestered carbon value, the appropriate scale factor (=0.0001) for the MYD17A2H data was used as recommended by MYD17 users guide [39]. We applied a scale factor of 0.0001 to each MYD17A2H pixel to obtain GPP products in units of kgCm\({}^{-2}\)day\({}^{-1}\). We extracted the MYD17A2H data within 5 and 25 km of each EC flux tower and averaged all valid data as the daily MODIS GPP. The daily GPP was interpolated from the eight-day GPP dataset using a linear-weighted method. Table 2 shows the spatial and temporal resolution and coverage of the MODIS, OCO-2 SIF, and GOSIF data.
## 4 Numerical and Anechoic Chamber Experiments
To further highlight the recognition superiority of the proposed method for parabolic antenna components, the next section will focus on analyzing the performance indicators of different algorithms for parabolic antenna detection. Moreover, the relevant parameters are \(epochs=300\) and \(patience=50\), which means the program will stop when the number of epochs reaches 300, or when there is no significant improvement over a continuous 50 iterations. So, we can see that different algorithms vary the epochs in Figure 11.  
Taking into account the visual proximity among neighboring observation apertures, a selection of 620 pristine images is compiled by spacing them at intervals of 0.58 degrees within a full 360-degree range, as suggested in [26]. The dataset is segmented into training, validation, and testing subsets, with allocations of 70%, 10%, and 20% each for these respective phases. To enrich the variety of the training images, conventional data augmentation techniques such as horizontal mirroring, brightness adjustment, and the introduction of random noise are employed. To improve the model's generalization ability, mosaic enhancement is applied to each image; to reduce computational load, the image size is reduced to 0.5 times the original size; the image brightness will be adjusted to about 0.4 of the original brightness; there is a 0.5 probability of horizontally flipping the input image during each random training sample generation. All the experiments were  
Figure 11: The mAP50 (left) and mAP50-95 (right) of different networks in the training set. conducted on a system featuring an Intel(R) Core(TM) i9-13900 HX processor running at 2.20 GHz, complemented by an NVIDIA GeForce RTX 4060 graphics card. The PyTorch deep learning library, specifically the GPU-enabled version, was utilized as the primary framework for these experiments. CUDA 11.8 was employed for the parallel processing capabilities of the GPU. The SGD optimizer was chosen for updating and optimizing the network parameters. The initial training learning rate was initialized to \(1\times 10^{-3}\). The mini-batch size was specified as 32, and the total number of training epochs was set to 300. The input image size was 640, the number of worker threads for data loading was 8.
# MoCoformer: Quantifying Temporal Irregularities in Solar Wind for Long-Term Sequence Prediction
###### Abstract
To address the challenge of significant temporal irregularities in solar wind sequences, this study proposes an innovative Transformer-based approach, focusing on two pivotal components: Multi-Mode Decomp Block and Mode Independence Attention. In the first step, the Multi-Mode Decomp Block adapts to the dynamic features of solar wind data, effectively extracting inherent patterns for adaptive decomposition and modeling. The subsequent application of Mode Independence Attention further fortifies predictions by capturing relationships between time series. This novel approach establishes a resilient long-term forecasting model, mitigating the impact of irregular features and providing a precise solution for solar wind prediction. This study introduces novel methods to tackle the challenges of irregular features, particularly in solar wind forecasting, with considerable academic and practical value. In summary, this study presents the following contributions:  
* **Multi-Mode Decomp Block:** Introducing a block named Multi-Mode Decomp for Multi-Mode decomposition of time series. This innovative method enhances the extraction of in-depth correlated information within the sequence, revealing latent patterns and structures in the solar wind data. * **Mode Independence Attention:** A self-attention module is proposed, the Mode Independence Block, which computes attention independently for each subsequence. This module is designed to focus on capturing the correlations in time series, enhancing the influence of valid features on prediction, and reducing the adverse effects of irregular features on overall forecasting accuracy. * **Experimental Evaluations:** Conducting extensive experiments on the solar wind dataset, the results showcase MoCoformer's state-of-the-art performance in the realm of time series solar wind prediction.
## 6 Discussion
### Future Directions  
To further enhance the methodology, several future research directions can be explored:  
1. Online Learning and Adaptive Models: Implementing incremental learning techniques can allow the model to adapt to evolving sensor data patterns in real time, improving its applicability in dynamic industrial environments.
2. Transfer Learning for Generalization: Applying the trained model to diverse types of engines with minimal re-training could enhance its usability across various industrial applications.
3. Hybrid Clustering Approaches: Combining a GMM with other clustering techniques, such as spectral clustering or deep embedding clustering, could improve the classification accuracy for complex datasets.
4. Integration with Digital Twin Technologies: Embedding the predictive maintenance framework into digital twin systems could enable real-time monitoring and optimization of engine performance.
# Waterproof Performance of Sealing Gasket in Shield Tunnel: A Review
## 3 Rubber Material Characteristics  
The waterproof performance of shield tunnel gaskets is mainly realized by the contact stress between the gasket surfaces [32]. The commonly used materials can be divided into two categories: elastic material, such as ethylene propylene diene monomer (EPDM), chloroprene rubber (CR), etc., and water-swelling rubber material, such as water swelling rubber (WSR). The in-use materials are different, while the overall performance during service is similar. [PERSON] et al. made a detailed review of the material properties, material selection, and chemical mechanism of aging of gasket rubber materials [33]. Test data of water swelling polymer (WSP) and water-swelling rubber polymer (WSRP), under different serving environments, were given by [PERSON] et al. [34], and the performance of the tested materials is compared [35].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Project** & **Material 1** & **Specimen** & **Purpose** & **Reference** \\ \hline Chicago Subway Tunnels & NP & Steel clamp & Effects of joint opening & [8] \\ Singapore Subway Tunnel & EPDM & Steel clamp & Effects of joint opening/offset & [36] \\ Shanghai Qingcaosha & EPDM & Steel clamp & Influence of joint & [9] \\ water tunnel & & & opening/offset/rotation & \\ Los Angeles Subway & & & Influence of seismic load on & \\ Extension & & & waterproofing performance & [22] \\ Beijing Subway Tunnel & COMPOUND & Steel clamp & Effects of high temperatures in fire & [15; 16] \\ Los Angeles Subway & & & conditions & \\ Extension & & & the sealing gasket & \\ Nanjing Weisan Road & & & & \\ Tunnel & EPDM & Concrete specimen & Summary of test technology & [30] \\ A Subway Tunnel & EPDM & Steel clamp & Effects of joint opening/offset & [18] \\ Suri Tunnel & EPDM & Steel clamp & Design selection under earthquake & [10] \\ Nanjing Subway Tunnel & EPDM & Steel clamp & Double-layer waterproof & [17] \\ Changsha Subway Tunnel & EPDM & Steel clamp & Effects of joint opening/offset & [12] \\ A Subway Tunnel & EPDM & Steel clamp & Effects of soil erosion & [20; 21] \\ Shashai deep water & & & Double-layer waterproof & [11] \\ storage tunnel & & & & \\ Su’ai Tunnel & EPDM & Steel clamp & Comparison of static and dynamic & [24] \\ \hline \hline \end{tabular}  
* EPDM: ethylene propylene diene monomer; NP: neoprene; COMPOUND: EPDM and water-swelling rubber compound.  
\end{table}
Table 2: Summary of joint waterproof performance indoor tests of representative shield tunnel.
## 3 Dataset and Performance Index
### Performance Index  
To evaluate the performance of the DNFS algorithm comprehensively, four performance indices (e.g., mean absolute error (MAE), root mean squared error (RMSE), Akaike information criterion (AIC) [36], and symmetric mean absolute percentage error (SMAPE)) were introduced. MAE, RMSE, and SMAPE mainly measure the precision, while AIC considers the precision and simplicity simultaneously [37]. They are defined as:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert\hat{y}_{i}-y_{i}\rvert \tag{7}\]  
\[RMSE=\sqrt{\left(\frac{1}{n}\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}\right)} \tag{8}\]  
\[AIC=n\log(RMSE)^{2}+2k \tag{9}\]  
\[SMAPE=\frac{100\%}{n}\sum_{i=1}^{n}\frac{2\lvert\hat{y}_{i}-y_{i}\rvert}{( \lvert\hat{y}_{i}\rvert+\lvert y_{i}\rvert)} \tag{10}\]  
where \(n\) is the number of samples, \(\hat{y}\) and \(y\) are the predicted value and the true value, respectively, and \(k\) is the number of parameters that can be optimized. The parameters of a submodule are equal to the number of antecedent parameters (\(A*T*S\)) plus the number of consequent parameters (\(R*(S+1)\)). \(A\) and \(T\) are the number of MF parameters and the MF in each input domain, respectively. \(S\) is the input dimensions. \(R\) is the number of fuzzy rules (\(T^{S}\)). The parameters of the DNFS are equal to the sum of the parameters of each submodule.  
In order to reflect the comprehensive performance of the model, this paper also defined an evaluation method to rank each index. The final score was the sum of the scores of each index. The experimental platform of this paper was a desktop computer running MATLAB 2020a and Windows 10 Education 64x, with Intel Core i7-9700 CPU @ 3.00 GHz, 16 GB memory, and a 512 GB solid state drive.
# The Role of Correlation in the Performance of Massive MIMO Systems
## 7 Concluding Remarks and Future Research Directions  
This study evaluated the m-MIMO performance using different levels of correlations at the base station and considering different topologies with ULA and URA configurations. To this end, these different topologies and their resolution capabilities for estimating the channels were examined. The results showed that increasing the correlations minimizes the MSE of channel estimates. This MSE enhancement comes from reducing the uncertainty in the channel, which needs to be estimated. However, this enhancement is not necessary for improving the SE performance. This is because increasing the correlation would increase the interference in the channels and, thus, reduce the overall SE. As such, there is a requirement for developing a robust interference management technique in order to avoid SE degradation. In addition, this study showed that there is a trade-off between the MSE minimization and the SE maximization. This trade-off should be carefully considered when making a decision on which antenna configurations are used. Although this study considers frequency-flat channels, frequency-selective channels with orthogonal frequency-division multiplexing (OFDM) can be considered in future. Furthermore, considering m-MIMO systems with a terahertz (THz) frequency is also worth investigating in future. Finally, evaluating the m-MIMO using a hybrid automatic repeat request for spatial multiplexing transmission systems, as in [53], may also be considered in future.  
Figure 11: Spectral efficiency vs. SNR in dB for the URA topology, \(U=10\) UEs, \(M=128\), with different values of angular spread \(\alpha_{\text{A}}(AS)\).  
**Author Contributions:** Conceptualization, M.A.; Data curation, M.A.N.; Formal analysis, M.A.N. and M.I.S.; Funding acquisition, M.A.N. and M.I.S.; Investigation, M.A.N.; Project administration, M.A.N.; Resources, M.A.; Software, M.A.; Validation, M.I.S.; Visualization, M.A.N.; Writing--original draft, M.A.N.; Writing--review and editing, M.I.S. and M.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to acknowledge University of Baghdad for general support.  
**Conflicts of Interest:** The authors would like to declare that there is no conflict of interest.
## 4 Results and Discussion
### Classic vs. Integrated Printing Framework for Working with Non-Standard Recycled Filaments  
The strategy of using non-standard filament facilitates better valorization, as discussed above. However, this alternative involves more expert and advanced operation when carrying out printing jobs with a classic standard 3D printing environment. The following experiment shows the differences between using a classic printing environment and the one proposed in this article, with both printing the same part with no standard recycled filaments.  
* Standard/Classic 3D printing environment. The steps are as follows: select part file; create a new profile configuration; set--at least--temperature, cooling, deposition rate, retraction, layer height, infill density, brim and print speed configurations; slice; save file to flash memory, insert it in 3D printer, search the machine code file; and print. More than 38 variables have to be set, which often requires expert knowledge (first row in Table 6).
* Integrated 3D printing environment. The material and quality configurations are carried out by the service provider through the Cloud Service, externalizing the most technical steps for the user. The user steps consist of filing part, material and qualities selection, and printing (Figure 7c), consisting of about four steps (second row in Table 6).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Steps** & **Variables** & **Time to Print (min)** &
\begin{tabular}{c} **User 3D Technology** \\ **Knowledge** \\ \end{tabular} \\ \hline
**Standard** & 15 & 38 & 15 & High \\ \hline
**Integrated** & 4 & 3 & 2 & Low \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental evaluation of standard and integrated 3D printing environments with non-standard filament.  
Figure 7: Strapping tape recycling experiment: (**a**) Shredded strapping tape from the waste; (**b**) result of a second shred; (**c**) end-user application; (**d**) warping effect of the strapping-tape-based material during printing trials.  
The number of operations and time required to perform a print job are much lower in the case of the integrated 3D printing environment, as the test shows.  
Finally, there is no single company in the surveys that identified enough possible products for printing that would have justified the effort of putting the system into practice. However, taking the port area as a whole (and conditioned by the size of the port area), the total number of parts to be printed reaches more feasible values. Therefore, the potential use depends on a \"broad system\" approach, with many plastics and many different single parts being printed by different companies with very different business activities.
## 3 Loads Caused by Dewatering  
### Draudown Curve  
The dewatering of a foundation pit inevitably causes changes in the surrounding groundwater level. After prolonged dewatering in an infinite confined aquifer, a stable drawdown curve of the groundwater level forms around the dewatering well [27], as shown in Figure 4. In this figure, \(r\) is the horizontal distance from a specific point to the dewatering well, \(h\) is the water level at that location, \(H_{0}\) is the initial water level of the confined aquifer, \(H_{\mathrm{t}}\) is the water level within the well after dewatering, and \(R_{0}\) is the radius of the dewatering well.  
Figure 4: The dewatering curve diagram.  
Based on the Dupuit assumption, the distribution of \(h(r)\), i.e., the groundwater level around a dewatering well (the foundation pit can be considered to be a large well), is as follows [27]:  
\[h(r)=\sqrt{H_{0}^{2}-\big{(}H_{0}^{2}-H_{t}^{2}\big{)}\frac{\ln\frac{R}{r}}{\ln \frac{R}{R_{0}}}} \tag{19}\]  
where \(R\) represents the radius of the dewatering influence zone, commonly referred to as the dewatering radius. According to the Sakukin formula [27], \(R\) can be obtained as follows:  
\[R=2s_{w}\sqrt{k_{t}H_{0}} \tag{20}\]  
in which \(k_{t}\) represents the soil mass permeability coefficient, while \(s_{w}\) signifies the height of the water level drop in the dewatering well, and it is defined as \(s_{w}=H_{0}-H_{t}\). As illustrated in Figure 5, when there is an existing tunnel within the influence zone of dewatering, the reduction in water level results in an increase in effective stress in the soil above the tunnel, thereby generating additional loads on the tunnel structure. In Figure 5, the horizontal distance between the dewatering well and the existing tunnel is denoted as \(d\). From the nearest point on the tunnel axis towards the dewatering well, which is denoted as Point O, we established the \(x\)-axis along the tunnel's direction. Points A and A\({}^{\prime}\) are the intersections of the influence zone and the axis.
## 3 Sample, Experimental Devices, and Protocols  
### Samples  
Experiments were conducted to confirm the performance of reflection-mode USCT using the pulse-mode and WCE-mode methods on a newborn arm phantom (True Phantom Solutions, Windsor, CA, USA) and a complete ex vivo chicken drumstick. The phantom, initially used for ultrasound-guided training for nurses and physicians, was composed of a soft body, artificial blood vessels, and bones. The study was conducted at H1 = 6 cm and H2 = 14 cm from the tip of the hand (Figure 1). The phantom was made of durable and long-lasting materials. The blood vessels were prefilled with an ultrasoft rubberlike material containing blood-mimicking fluid. The bony part was a rubbery urethane-based composite material reinforced with ceramic powder. The bony part had a density similar to that of human bones, but a much lower velocity and a higher attenuation. Some properties of the materials constituting the phantom are presented in Table 1.  
The chicken drumstick contained skin, fat, muscle, tibia, and fibula, and the study was conducted in the proximal area 25 mm from the upper tibio-fibular joint. The interest in this sample was that it presented well-identified adjacent structures such as muscles and skin, with nerves and fat, and had two bones, tibia and fibula, joined together. The fibula was much smaller than the tibia in the area studied in the chicken drumstick. It was  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Type of Tissue**} & **Ultrasonic Velocity** & **Mass Density** & **Impedance** & **Attenuation** \\  & **(m/s)** & **(g/cm3)** & **(MRays)** & **(dB/cm)** \\ \hline Soft body & 1423 \(\pm\) 10 & 1.0 & \(\approx\)1.423 & 1.1 \(\pm\) 0.2 \\ Blood vessel & 1400 \(\pm\) 10 & 1.02 & \(\approx\)1.428 & 1.7 \(\pm\) 0.2 \\ Bone & 1129 \(\pm\) 5 & 2.16 & \(\approx\)0.0024 & 21 \(\pm\) 2.0 \\ \hline \end{tabular}
\end{table}
Table 1: Mean properties of materials constituting the newborn arm phantom (True Phantom Solutions, Windsor, CA, USA) measured at 2.25 MHz and 1 MHz.  
Figure 1: The newborn arm phantom (True Phantom Solutions, Windsor, CA, USA).  
thus possible to study the limit in resolution of USCT, and the power of detection of small bones, when low ultrasonic intensity was transmitted.