esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Comparison of Lp_HOGS with Lp_LOGS
#### 3.1.2 The Box Blur
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Noise Level**} & \multicolumn{3}{c}{**Lp\_LOGS**} & \multicolumn{3}{c}{**Lp\_HOGS**} \\ \cline{3-8}  & & **PSNR (dB)** & **SSIM** & **GMSD** & **PSNR (dB)** & **SSIM** & **GMSD** \\ \hline \multirow{3}{*}{Passerby} & 30 & 40.8217 & 0.9457 & 0.0180 & **41.0760** & 0.9535 & 0.0124 \\  & 40 & 40.1691 & 0.9402 & 0.0194 & **40.7809** & 0.9518 & 0.0144 \\  & 50 & 38.7155 & 0.9303 & 0.0281 & **40.2154** & 0.9497 & 0.0159 \\ \hline \multirow{3}{*}{Station} & 30 & 39.8743 & 0.9474 & 0.0094 & **40.1122** & 0.9532 & 0.0080 \\  & 40 & 39.1676 & 0.9415 & 0.0114 & **39.7945** & 0.9521 & 0.0085 \\  & 50 & 37.9245 & 0.9348 & 0.0268 & **39.1034** & 0.9487 & 0.0147 \\ \hline \multirow{3}{*}{Truck} & 30 & 39.9195 & 0.9480 & 0.0091 & **40.1125** & 0.9534 & 0.0070 \\  & 40 & 39.1053 & 0.9415 & 0.0108 & **39.7635** & 0.9520 & 0.0080 \\  & 50 & 37.4036 & 0.9304 & 0.0252 & **38.7291** & 0.9481 & 0.0108 \\ \hline \multirow{3}{*}{Garden} & 30 & 41.1108 & 0.9821 & 0.0107 & **41.3335** & 0.9872 & 0.0100 \\  & 40 & 40.3230 & 0.9766 & 0.0167 & **40.8003** & 0.9863 & 0.0114 \\  & 50 & 39.0822 & 0.9658 & 0.0279 & **39.8915** & 0.9853 & 0.0126 \\ \hline \multirow{3}{*}{Stairs} & 30 & 41.0179 & 0.9832 & 0.0126 & **41.5490** & 0.9895 & 0.0102 \\  & 40 & 40.0870 & 0.9786 & 0.0173 & **41.1270** & 0.9887 & 0.0117 \\  & 50 & 38.6422 & 0.9701 & 0.0255 & **40.0578** & 0.9872 & 0.0133 \\ \hline \multirow{3}{*}{Corridor} & 30 & 40.4221 & 0.9818 & 0.0162 & **41.0429** & 0.9905 & 0.0093 \\  & 40 & 39.3766 & 0.9758 & 0.0247 & **40.5461** & 0.9897 & 0.0106 \\  & 50 & 37.7766 & 0.9578 & 0.0424 & **39.4056** & 0.9882 & 0.0123 \\ \hline \multirow{3}{*}{Car} & 30 & 39.6822 & 0.9454 & 0.0106 & **40.0593** & 0.9528 & 0.0090 \\  & 40 & 38.8954 & 0.9362 & 0.0136 & **39.8088** & 0.9511 & 0.0106 \\  & 50 & 37.5309 & 0.9199 & 0.0340 & **38.9581** & 0.9466 & 0.0136 \\ \hline \multirow{3}{*}{Buildings} & 30 & 39.6155 & 0.9534 & 0.0129 & **40.2602** & 0.9642 & 0.0099 \\  & 40 & 38.9742 & 0.9496 & 0.0154 & **39.7577** & 0.9618 & 0.0114 \\  & 50 & 37.5042 & 0.9338 & 0.0258 & **39.3106** & 0.9594 & 0.0125 \\ \hline \multirow{3}{*}{Zebra} & 30 & 40.0891 & 0.9851 & 0.0127 & **40.2653** & 0.9904 & 0.0104 \\  & 40 & 38.9207 & 0.9816 & 0.0209 & **39.8872** & 0.9896 & 0.0118 \\ \cline{1-1}  & 50 & 37.6985 & 0.9721 & 0.0308 & **38.8089** & **0.9884** & **0.0136** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The Lp_HOGS and Lp_LOGS deblurring effects on images with a 7 \(\times\) 7 box blur. The optimal indicators for each condition are denoted in bold.
# Analysis of Vertical Earth Pressure Acting on Box Culverts through Centrifuge Model Test
###### Abstract  
Due to the lack of surface space, most structures are heading underground. The box culvert is underground infrastructure and serves to protect the buried structure from the underground environments, but it has a different characteristic from other structures in that the inner space is empty. Therefore, in this study, the vertical earth pressure which is the most significant effective stress acting on a box culvert was measured by conducting a geotechnical centrifuge model test. A box culvert was installed following the embankment installation method, and the vertical earth pressure acting on it was measured considering the cover depth, gravitational acceleration, and loading and unloading conditions. The soil pressure measured was greater than the existing theoretical value under high cover depth and the unloading condition, which is considered as the variability of many soils or the residual stress acting under the loading condition. Finally, a goodness-of-fit test was conducted as a part of variability analysis. The measured earth pressure was found to be considerably larger than the existing theoretical value, and the variability was large as well. This means the existing theoretical equation is under-designed, which should be reflected in future designs.  
vertical earth pressure; box culvert; geotechnical centrifuge model test; variability analysis; goodness-of-fit test +
Footnote †: journal: Applied sciences
# Systemic Evaluation of PSS in the Early Concept Phase Using Graph-Based Reasoning
## 4 Solution Approach
### Overview of the Solution Approach and Solution Elements  
The proposed approach aims to transparently predict the probability that a product service system (PSS) concept, along with its variations, will meet key evaluation criteria (EVCs) such as installability and performance with respect to certain metrics (like time and CO\({}_{2}\) emissions). Additionally, it seeks to assess the influence of knowledge gaps on this evaluation. It introduces a semantic abstraction framework to formalize the uncertain a priori knowledge and reasoning of PSS designers regarding the concept's uncertain behavior and its contribution to EVCs, formalizing it as a probabilistic graph-based reasoning model. This framework enables PSS designers to define life-cycle factors and critical properties of the PSS concept as scenarios at different levels of detail and certainty, which collectively define the way a PSS concept is intended to fulfill the EVCs accounting for uncertain conditions. By causally mapping factors and formalizing them as a graph, the reasoning of PSS designers underlying the evaluation is reflected and made executable. Systematically altering scenarios within the structure allows modifications to propagate through the graph in accordance with defined uncertainties, enabling analysis of the PSS concept's vague yet distinct behavior regarding achievements of specific EVCs. By systematically organizing uncertain prior knowledge and assessing it through reasoning analysis, the most promising PSS concept variations and influential knowledge gaps can be identified, transparently predicting EVC attainment of PSS concept variations and supporting improvement efforts. To realize this approach, two solution elements are introduced:  
1. _PSS abstraction framework_, which defines the perspective on a PSS concept and its variations, shaped by knowledge gaps, and how it is mapped into a graph-based form for the evaluation of EVCs.
2. _PSS Graph-Elicitation and Analytic Reasoning (GEAR) method_, which outlines the application of the PSS abstraction framework and execution of the steps for reasoning analysis.  
Figure 2 schematically illustrates the proposed approach and positions the solution components within. The following sections first explain the PSS abstraction framework, followed by a detailed discussion of the GEAR method.
# Metachrylate-Based Polymeric Sorbents for Recovery of Metals from Aqueous Solutions
## 4 Polymeric Sorbents
Up to this moment, various CPLs such as linear, branched, crosslinked, grafted polymers, dendrimers, star-shaped, and hyperbranched polymers with almost endless possibilities of CPLs design, rich coordination chemistry, their high affinity for various metals depending on the type of the ligand, combined with chemical stability, regenerability, and reusability have been used for metal sorption [15]. The synthesis of CPLs polymers includes three basic approaches: (co)polymerization of monomers with chelating fragments; monomers that already have ligand in their structure, polycondensation, and post-polymerization (PPM), in order to introduce ligand groups in previously synthesized (co)polymer. (Co)polymerization of monomers with chelating fragments is considered to be the simplest method that can be performed by free-radical (co)polymerization, living/controlled radical polymerization, metathesis polymerization, grafted polymerization, etc. The major polymerization techniques for the preparation of CPLs include bulk, precipitation, suspension, emulsion, and dispersion polymerization. More details could be found in the literature [15; 67]. PPM, or polymer-analogous modification, is an approach that enables the polymerization of monomers with functional groups inert towards the polymerization conditions, which can be converted through an additional reaction step into a variety of different functional groups [68]. Thus, the obtained functional polymers have identical average chain lengths and chain-length distributions and diverse functional groups.
## 2 Energy Optimization Challenges in UIoT Networks
### Technical Challenges  
* Node mobility: the UIoT networks are deployed with static and dynamic nodes. Most UIoT nodes move from place to place to transmit information. Due to auto-mobility settings or autonomic operation, the UIoT nodes consume high amounts of energy. This causes the easy draining of energy in UIoT nodes [62].
* Improper medium selection: in general, the UIoT devices can transmit information via different communication mediums such as acoustic, infrared (IR), visible light, radiofrequency (RF), and magnet induction (MI). Even though the UIoT networks can use a different medium for communication, the unsuitable medium selection in UIoT networks can consume more energy. This can reduce the battery lifetime of UIoT devices [35].
* Unaware routing: due to internal waves, mammals' activity and other objects' behaviors lead to high mobility, path loss, and routing errors, etc. [60]. The frequent changes in the position of nodes can cause rerouting. In effect, it consumes high amounts of energy for routing in UIoT networks.
* Automaticity: in UIoT networks, the sensor nodes and other devices, such as UW-SNodes, UUVs, and ROVs, etc., are programmed to perform their operations by themselves. This includes automatic behavior such as sensing, transmitting, moving, and rerouting, etc., which can affect the battery life of UIoT devices.
* Real-time monitoring: the UIoT applications, such as diver networks monitoring, early warning system, and object tracking, etc., are the real-time applications developed for preventing the disasters that occur in UIoT networks. Due to real-time sensing and transmission, energy consumption is very high, which reduces the battery life of UIoT devices.
* High transmitting (Tx)/receiving (Rx) energy: in UIoT networks, the transmission and receiving of the energy of acoustic and optical mediums are high. In addition, the optical medium consumes a high amount of energy even for short-range data transmission [63].
* Security attacks: the UIoT networks consist of numerous attacks, such as black-hole attacks, routing attacks, battery attacks, and Sybil attacks, etc., among which battery-oriented attacks can directly attack the battery of UIoT devices [58]. This causes energy down in UIoT nodes and reduces network lifetime.
## 5 Results and Discussion
### SACGES-CMMD vs. FEM Comparison Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Scaling** & \begin{tabular}{c} **Mainization** \\ **Task Size** \\ **(******mm**)** \\ \end{tabular} & \begin{tabular}{c} **Mainization** \\ **Task Size** \\ **(****mm**)** \\ \end{tabular} & \begin{tabular}{c} **Mesh Size** \\ **(****mm**)** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Gradient** \\ **(****mm**)** \\ \end{tabular} & \begin{tabular}{c} **Preventage** \\ **Randomization (****\%**)** \\ \end{tabular} & \
\begin{tabular}{c} **Mainization Shear** \\ **Sensor (****MMP)** \\ \end{tabular} &
\begin{tabular}{c} **Generation** \\ **Task Size** \\ **(****mm**)** \\ \end{tabular} \\ \hline \hline FEM & 0.002 & 0.002 & 0.002 & 0.002 & 96,302 & 7.34 & 10.01 & 0.108 & 18.95 & \(-\)0.11 \\ SACGES-CMMD & 0.0008 & 1.320 & 1.008 & 55,996 & 7.34 & 10.07 & 0.127 & 18.97 & \(-\)0.29 \\ FEM & 0.0008 & 1.5 & 1.1 & 69,020 & 12.31 & 10.09 & 0.179 & 17.25 & \(-\)0.29 \\ FEM & 0.0000 & 1.8 & 0.000 & 46,783 & 10.72 & 0.161 & 17.03 & \(-\)0.25 \\ FEM & 0.0000 & 2.02 & 1.1 & 96,510 & 15.63 & 10.12 & 0.168 & 16.22 & 4.75 \\ FEM & 2.722 & 2.000 & 30,64 & 30,644 & 10.08 & 0.188 & 16.46 & \(-\)0.35 \\ SACGES-CMMD & 0.0004 & 30 & 1.1 & 33,178 & 19.34 & 0.47 & 0.215 & 15.99 & 3.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of FEM and SACGES-CMMD results. Figure 10: Comparison of shear stress analysis results. (**a**) FEM shear stress distribution (scaling factor of 0.0892); (**b**) SACGES-CMMD shear stress distribution (scaling factor of 0.0892). stress analysis method provides a thought process for model equivalence transformation to solve the issue of computational infeasibility of the solid model before and after the cycloidal wheel modification across large-scale grid divisions. This is due to the fact that the amount of modification is at the micron level, while the tooth profile shape is at the millimeter level. It offers a solution for large-scale grid divisions in computational models.
## 6 Conclusions  
To address challenges related to data diversity and stragglers in synchronous Federated Learning (FL) while also minimizing the risk of model divergence found in asynchronous FL, we introduce LESSON. This novel approach blends semi-synchronous client selection with model aggregation, ensuring the participation of all clients in the FL process, albeit at differing frequencies.  
The simulation results show that LESSON and FedAvg have comparable model test accuracy, both of which outperform FedCS at least 10% under different non-IID scenarios. In addition, LESSON reduces the test accuracy by around 5% but accelerates convergence rate at least \(2\times\) faster as compared to FedAvg. The adaptability of LESSON is further highlighted through its deadline parameter \(\tau\), which allows for adjusting the trade-off between model accuracy and convergence rate, that higher \(\tau\) can improve model accuracy. Due to the high convergence rate, LESSON can be applied to applications that require quickly deriving a reasonable model adaptive to dynamic environments, such as autonomous drone swarm control [40; 41], where actor-critic networks that can be adaptive to be the current wind perturbation should be quickly trained and derived to, for example, avoid collisions.  
Future work is poised to delve into the adaptive modification of the \(\tau\) parameter to augment LESSON's performance. This paper has demonstrated that a lower \(\tau\) accelerates the model convergence rate, whereas a higher \(\tau\) achieves a better model accuracy, especially in non-IID data scenarios. As such, dynamically calibrating \(\tau\) to achieve a resilient and practical FL algorithm is critical but unexplored. In addition, the computing and uploading latency of a client may change over time. If a client moves towards the edge of a base station's coverage area, the uploading latency of this client will be significantly increased. As a result, this client will finally be clustered into a higher tier to train and upload its local model at a lower pace but with a higher learning rate. Yet, how LESSON dynamically adjusts the client clustering based on the updated computing and uploading latency and how the client clustering adjustment affects the performance of LESSON is still unveiled and will be part of our future work.  
Conceptualization, L.Y.; Methodology, L.Y. and X.S.; Software, L.Y.; Investigation, C.Y.; Resources, L.Y.; Writing--original draft, L.Y. and R.A.; Writing--review & editing, X.S., R.A. and C.Y.; Supervision, X.S. and C.Y.; Project administration, X.S.; Funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Science Foundation under Award under grant no. CNS-2323050 and CNS-2148178, where CNS-2148178 is supported in part by funds from federal agency and industry partners as specified in the Resilient & Intelligent NextG Systems (RINGS) program.  
Not Applicable, the study does not report any data.  
The authors declare no conflict of interest.
## 1 Introduction  
Spinal cord injury (SCI) currently impacts 50,000 people in the UK with the addition of 2500 people every year [1]. Despite these rising figures, there is still no permanent cure for this injury [2]. Not only does it massively influence the quality of life of the individual, but it also massively drains limited healthcare budgets with increasing surgeries and multiple failed treatments, as these patients need to be monitored much more frequently. SCI increases societal demands by requiring additional resources to support the incapacitated individual. Since the Egyptian times, SCI has been neglected due to a lack of facilities and advances in medical engineering. In pharmacology and medical engineering, SCI has been labelled a terminal illness and surgery has been accepted as the sole option. In recent times, there has been a revelation that if the right intervention is applied, SCI is treatable and can significantly reduce the mortality rate as well as offer an increased quality of life for the individuals. Though SCI remains one of the most complex neurological conditions, device-based treatments offer promising avenues for functional restoration. While numerous technologies, from functional electrical stimulation (FES) to exoskeletons, have demonstrated significant efficacy, the field lacks a cohesive framework to evaluate these treatments across dimensions of invasiveness, longevity, and patient-centred outcomes.  
This review introduces a comparative view of recent breakthrough methods in spinal cord injury treatments, including implantable and external devices, emphasising trade-offs in biocompatibility, power source limitations, and user accessibility. Implantable devices, such as electrical epidural stimulators and smart sensors, offer precision and long-term integration, yet face challenges in surgical risk and regulatory hurdles. External devices, including robotic exoskeletons and surface FES systems, provide non-invasive alternatives; however, they often suffer from the challenge of bulkiness and limited personalisation. These factors will be mentioned in more detail in the following review, as well as emerging directions, including closed-loop systems and AI-driven neuroprostheses, reshaping SCI treatments. These innovations show promise in bridging the gap between device functionality and neural adaptability, offering dynamic, responsive interventions, personalised to everyone's neurophysiological profiles.
## 5 Conclusions  
In this research, we primarily investigate the prediction of traffic accidents in long tunnels in cities. We also design a deep learning-based framework, namely, GCN-LSTM, to predict and fill in the missing values of the input variables, in addition to analyzing the traffic accidents themselves. This method can be applied to traffic research in other scenarios in the future, in particular missing data on traffic flows in large road networks. Random forest and BP neural networks are used in studies of traffic accidents and accident durations for two reasons. First, a large of prior research has confirmed that both models perform well in crash analysis and forecasting in other scenarios. Second, both of these models have better landing prospects than other more complex deep learning frameworks, and they have better accuracy than traditional models based on statistics. Our work may provide guidance to transportation operators in formulating more rational policies during special hours and provide a relevant benchmark for the implementation of emergency relief in long-distance urban tunnels. However, it should be noted that the study only used data from 200 vehicular accidents in a tunnel in Nanjing, which could limit the representativeness of the findings. Given these limitations, we will thus focus on the following directions for future work, one of which is to continue collecting more data on traffic accidents, not only from the inside of the tunnel but also from the road network surrounding the tunnel. By improving the data, we can improve the feature extraction of the model, which will make a significant contribution to the model's credibility and robustness. Second, we view deficits in data quality and quantity as inevitable in some scenarios, and that small-sample learning may be the key to addressing this issue.  
**Author Contributions:** Conceptualization, Y.S. and F.W.; methodology, Y.S.; software, Y.S.; validation, Y.S. and F.W.; formal analysis, F.W.; investigation, Y.S.; resources, Y.S.; data curation, F.W.; writing--original draft preparation, F.W.; writing--review and editing, Y.S.; visualization, F.W.; supervision, C.Z.; project administration, C.Z.; funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the Jiangsu Transportation Science and Technology Project (2021G09).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** On behalf of all the authors, the corresponding author states that our data are available upon reasonable request.  
**Acknowledgments:** We acknowledge the funding support from the Jiangsu Transportation Science and Technology Project Fund and the contribution of Nanjing Communication Group.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Numerical Results
### Probabilistic Analysis of the Reference Case
#### 3.3.3 Sensitivity Analysis  
The Sobol GSA, as presented in Section 2.2.2, is carried out in this part to quantify the sensitivity of the QoI in each liner with respect to each parameter of the vector \(\mathbf{X}=[G_{M},\,G_{K},\,\eta_{M},\,\eta_{K},\,\lambda,\,P_{0}]\). For the sake of clarity, only the critical positions of the liners are considered: flexible liner at the crown and concrete liner at the springline. As observed in Figure 7, among the four parameters of the Burger model that characterize the creep behavior of the viscoelastic rock, the [PERSON]' spring element \(G_{K}\) affects the most the stability of both liners. The influence of the Maxwell spring element \(G_{M}\) is higher than the one of the two dashpots. These results are consistent with the observations provided from the parametric studies in [17]. However, to complete the previous contribution, the resulted obtained in this work highlighted that the initial stress \(P_{0}\) with a highest value of Sobol index is the most affecting factor on the failure probability in each support element. With respect to the absolute value of initial stresses, the sensitivity analysis exhibits a moderate effect of anisotropic ratio \(\lambda\) on the stability of tunnel supports.  
Figure 6: Probability density functions (PDF) of the volumetric deformation in the flexible liner (**a**) and of the equivalent stress in the concrete liner (**b**) at the springline, crown, and inclined angle 45\({}^{\circ}\) with respect to the springline.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Angle**} & \multicolumn{4}{c}{**Volumetric Strain (\%)**} & \multicolumn{4}{c}{**Equivalent Stress (MPa)**} \\ \cline{2-9}  & **Mean** & **Std (\({}^{\text{1}}\))** & **Low (\({}^{\text{2}}\))** & **Up (\({}^{\text{3}}\))** & **Mean** & **Std** & **Low** & **Up** \\ \hline
0 & 22.69 & 5.19 & 14.00 & 34.31 & 37.96 & 7.89 & 24.14 & 55.22 \\
45 & 27.19 & 5.57 & 17.23 & 39.28 & 27.39 & 5.61 & 17.71 & 39.61 \\
90 & 31.88 & 6.58 & 20.35 & 46.50 & 16.70 & 5.62 & 7.71 & 29.47 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of the volumetric strain and equivalent stress.