esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost
## 5 Results and Discussion
### Prediction Model for Maximum Transverse Shear Stress between Paving Layer and Steel Bridge Panel  
In the training set, a combination of the grid search method and the five-fold cross-validation method was used to optimize the parameters of the machine learning model. R\({}^{2}\) was chosen as the main evaluation index to select the optimal combination of parameters, and the final evaluation result was the mean value of the five-fold cross-validation results. The optimal combination of parameters is learning_rate = 0.4; max_depth = 9; min_child_weight = 3; and n_estimators = 170. The prediction performance of the optimized model was verified on the test set, and the MAE of the XGBoost model on the test set was 0.023 and the RMSE was 0.027. The relationship between the predicted and true values of the XGBoost model on the test set is shown in Figure 16, from which it can be seen that the predicted values of most of the samples in the model are very close to the true values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MAE** & **RMSE** & **R\({}^{2}\)** \\ \hline XGBoost & 0.013 & 0.015 & 0.970 \\ KNN & 0.023 & 0.030 & 0.889 \\ SVM & 0.046 & 0.057 & 0.601 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with other traditional machine learning models.  
Figure 15: Importance of input feature variables.  
The XGBoost model was compared with other traditional machine learning models. The models were evaluated using three metrics, MAE, RMSE, and R\({}^{2}\), and a grid search combined with cross-validation was used for each model to optimize its parameters. Table 7 shows the prediction results of each traditional machine learning model on the test set, and it can be seen that the XGBoost model exhibits good performance. The XGBoost model was compared with the KNN model, and the RMSE was reduced by 28%, the MAE was reduced by 27%, and the R\({}^{2}\) was improved by 10%. The XGBoost model was compared with the SVM model, and the RMSE was reduced by 44%, the MAE was reduced by 47%, and the R\({}^{2}\) was improved by 46%.  
The importance of the input feature variables for the interpretability analysis of the developed prediction model using the SHAP architecture is shown in Figure 17. It can be seen from the figure that the most important characteristic variables are the lower pavement elastic modulus \(E2\) and the steel bridge panel thickness \(T\). The relatively more important characteristic variables are the upper pavement elastic modulus \(E1\) and the lower pavement thickness \(H2\).
# Research on Shear Behavior of Sand-Structure Interface Based on Monotonic and Cyclic Tests
###### Abstract  
In order to study the shear behavior of the interface between sand and structure, a series of shear tests were carried out using an HJ-1 ring shear apparatus (Nanjing, China). First, through the monotonic shear tests, the loose sand and dense sand were sheared at the steel interface with different roughnesses. The results showed that when the interface was relatively smooth, the shear stress-shear displacement curves of loose sand and dense sand both exhibit strain hardening characteristics. When the interface was rough, the dense sand showed strain softening. The initial shear stiffness of the sand-steel interface increased with the increase in normal stress, interface roughness, or sand relative density. Then, considering the influence of initial shear stress, through the cyclic shear test, this work analyzed the shape of the loading and unloading curves and the development law of cumulative normal deformation, and discussed the change of loading and unloading shear stiffness under different stress level amplitudes and the residual deformation generated during the cycle. The research results showed that loose sand and dense sand generally shrunk in volume during the cycle. The initial loading process was similar to the case of static loading. In the later dynamic loading process, the shear shrinkage per cycle was relatively small and continued to develop. Additionally, it was found that the unloading stiffness of the sand-steel interface is always greater than the initial loading stiffness. As the number of cycles increases, the loading stiffness increases, and it may eventually approach the unloading stiffness.  
sand-structure interface; shear behavior; monotonic and cyclic tests; interface roughness 2021 11 11837. [[https://doi.org/10.3390/app112411837](https://doi.org/10.3390/app112411837)]([https://doi.org/10.3390/app112411837](https://doi.org/10.3390/app112411837))
# A Systematic Review of Cybersecurity Risks in Higher Education
## 8 Vulnerabilities in HE
### Technical Vulnerabilities in HE
#### 8.2.4 Vulnerability Caused by Technical and Network Complexity  
[PERSON] [42] points to the security challenges in having a complex and distributed IT architecture, and having to secure the different environments at a common university. UNIT found that several Norwegian universities had experienced an increase in technical complexity [6], which made working with infosec and privacy more challenging. The networks in the surveyed HE organizations had developed organically over time and were developed to support research and development activities, not for security purposes. The organic approach has led to large and complex networks that are hard to defend from a security perspective [5]. NCSC writes that _\"many university networks contain a collection of smaller, private networks, providing close-knit services for faculties, laboratories and other functions. The freedom this offers is balanced by the challenge it presents to protecting the data and information within\"_[10]. FireEye also points to _decentralized and poorly documented networks_ as a major challenge in universities [5], and that issue leads to poorly documented networks merged into a single, unsegmented network. Patching an organic network is a challenge, and the statistics show _Vulnerable asset_ make up 682 and _Compromised assets_ 95 of the handledincidents in UNIT [6]. Additionally, in Wangen [9], _Vulnerable asset_ was the cause of 71 of 550 incidents and _Compromised Asset_ as the cause of 107 incidents.  
Most of the reviewed sources discuss the occurrence of- and vulnerability to malware and hacking events, Table 11. These threats are typically associated with either technical vulnerabilities or social engineering. For example, [PERSON] [9] examines the causes of incidents and shows that vulnerable systems are exploited in DoS and amplification attacks. This issue is typically a sign of unpatched and exploitable systems. Similarly, both Verizon reports [63] and [PERSON] [8] provide statistics that prove DoS attacks as prevalent in HE. Organic networks, combined with unmanaged devices, and poor network segmentation, leads to a large attack surface for the University networks [5]. NCSC writes that _\"when [networks] are maintained with minimal central oversight or adherence to security policy, private networks are likely more vulnerable to persistent infection or unauthorized access\"_[10]. However, NCSC also points to that there is also opportunity in managing the networks in this manner as the separate \"private\" networks can be secured differently according to the information they handle. However, having a \"loose\" security policy regarding network devices creates challenges within configuration management and patching for the central \(\Pi\) services [5].
## 2 Related Work
[8] describes the use of mobile unmanned ground vehicles (UGVs) to perform the tasks of the UAVs in a post-disaster rescue application. Since unmanned ground vehicles are expensive, they cannot be applied to post-disaster rescue scenarios on a large scale. Moreover, there are many vehicular applications based on parked vehicles. In [26], owning to the high contact opportunities with parked cars, the authors let outside parking distribute multimedia data via Internet of Vehicles (IoV). In [27], the authors discuss how to make use of computational resources from parked cars to execute mobile applications for moving vehicles. In [28], the authors arrange parked vehicles to perform real-time AI tasks, with the aim of meeting the service quality of vehicle users. The authors in [29] propose that both parked vehicles and their neighboring mobile cars on the road are able to form vehicular social groups in the city. Efficient content transmission is achieved based on the formed groups. In this paper, undamaged vehicles that park outside are rationally arranged to take part in data computation. Furthermore, based on the superiority of DRL, we construct a DRL model to tackle data offloading from UAVs, with the aim of minimizing the desired optimization goals.
###### Abstract  
After demonstrating, in previous works, the proof of concept of adaptive rectifiers with active load modulation to operate simultaneously for short/long range RF Wireless Power Transfer (WPT) while maintaining a high Power Conversion Efficiency (PCE), the authors introduced in this paper a power link budget of the proposed adaptive rectifier with a compromise between distance and efficiency. Then, to further exhibit its capabilities and enhance its performance, this paper first introduced a discussion about the parameters preventing the rectifier from operating over a wide range of input powers was performed. Furthermore, active load modulation was implemented and its co-simulation results presented. Finally, an adaptive rectifier was fabricated and its results successfully compared to measured data. It exhibits 40% of PCE over a wide dynamic input range of incident RF power levels from \(-6\) to 25 dBm at the 900 MHz in the Industrial Scientific Medical band (ISM band), with a maximum PCE of 66% for an input power of 15 dBm. The proposed devices are therefore suitable for WPT applications to harvest energy from a controlled source.  
rectifier; schottky diode; efficiency; wireless sensor network; WPT +
Footnote †: journal: Electronics
###### Abstract  
The frequency estimation of complex exponential carrier signals in noise is a critical problem in signal processing. To solve this problem, a new iterative frequency estimator is presented in this paper. By iteratively computing the interpolation of DTFT samples, the proposed algorithm obtains a fine frequency estimate. In addition, its mean square error (MSE) analysis is presented in this paper. By analyzing influences of the selectable parameters on the estimation accuracy of the model, a method for choosing appropriate parameters is discussed, helping to reduce the estimation error of the proposed estimator. Simulation results show that compared with other algorithms with a comparable estimation accuracy, the proposed iterative estimator can obtain a root mean square error (RMSE) that is closer to Cramer-Rao lower bound (CRLB).  
frequency estimation; spectral analysis; parameter estimation; DTFT interpolation
## 2 Study Area, Lake Model, and Data  
### Study Area  
Lake Namco, a brackish lake, is situated in the central region of the Tibetan Plateau, spanning approximately 30.5-30.95\({}^{\circ}\) N, 90.2-91.05\({}^{\circ}\) E. It is the largest and highest lake in the central plateau. Characterized by a semi-arid cold climate, Namco has an elevation of around 4718 m and covered an area of approximately 1998 km\({}^{2}\) in 2019. The lake's maximum and mean depths are 90 and 40 m, respectively [41]. The primary water sources for Namco are precipitation and glacier melting, with evaporation being the primary mode of water loss [42]. Typically, the lake begins freezing in late November and thaws in late March or early April, with the ice cover lasting for approximately 5 to 6 months [43]. Thermal and dynamic processes within Namco are predominantly influenced by the local climate and environmental factors. With a high altitude and cold climate, Lake Namco is often regarded as an oligotrophic lake. With minimal human activities, Namco serves as an ideal and representative alpine lake for exploring natural lake processes [44; 45; 46].
## 4 Results and Discussion
### Energy Management Results  
#### 4.3.1 Power Allocation of the Proposed Learning-Based MPC Energy Management Strategy  
Figure 8 shows the power allocation results for the legged robot using MPC with (Figure 8a) and without (Figure 8b) the penalty term for battery power fluctuation, respectively. It can be observed that the proposed MPC with the battery power fluctuation penalty term can significantly suppress the battery power fluctuation, which better protects the battery. It is also observed that the power fluctuation of SC is more intense, making full use of its high power density. The results show that energy management strategies during the motion of legged robots, including a battery power smoothing term, are feasible, and good performance can be achieved.  
As the weighting factor changes in Equation (21), the distribution of the battery and supercapacitor currents also changes. Therefore, suitable weights must be determined to achieve the objectives under different operation conditions. The sum of the three weighting factors is set to 1 so that the learning-based MPC converges in line with the conventional MPC. \(w_{1}\) directly affects the battery current rate, and a larger \(w_{1}\) will reduce the discharge current of the battery under the same circumstances. \(w_{2}\) affects the battery current fluctuations, and a large value of \(w_{2}\) will result in a smoother battery power. \(w_{3}\) determines the supercapacitor SoC and will affect the rate at which the battery charges the supercapacitor. The larger the \(w_{3}\), the more rapidly the supercapacitor SoC tracks, but it will increase the battery current. A higher supercapacitor SoC will provide greater instantaneous power. As  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **MAE** & **RMSE** & \(\mathbf{R^{2}}\) \\ \hline Dual-layer LSTM & 0.8591 & 1.4911 & 0.9343 \\ DNN & 1.0348 & 1.6149 & 0.9235 \\ Single-layer LSTM & 1.3716 & 2.3953 & 0.8358 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical indices of the compared prediction methods.  
Figure 7: Comparison of the power prediction with three deep learning prediction methods.  
shown in Figure 9, it can be observed that the weighting factors are changing during the operation of the legged robot, and large \(w_{1}\) and \(w_{2}\) are set under general power demand cases, while a large \(w_{3}\) is used in high power demand cases.
## 1 Introduction
### The TWA SPME Approach  
Solid-phase microextraction (SPME) combines passive air sampling and sampling preparation. SPME uses a compact-size sampler that consists of a polymeric fiber that is kept inside a hollow metallic needle. During air sampling, VOCs are collected on an SPME fiber. SPME was shown to provide low detection limits reaching parts-per-trillion levels. After VOCs are transferred to an analytical instrument (e.g., via hot gas chromatography (GC) injector), extracted VOCs are thermally desorbed from the fiber, which can be reused. Thus, SPME eliminates the need for solvents and works with existing analytical technologies.  
SPME is applicable for assessment of TWA concentrations in continuous sampling mode where the SPME fiber is retracted into the needle at a known distance during the desired sampling time. In contrast to the exposed fiber where an analyte reaches an equilibrium with the SPME, extraction of VOCs via the retracted fiber is controlled by diffusion. Since the fiber is kept inside the needle and extraction of VOCs is controlled by diffusion, the extraction rates are lower. Thus, the fiber in the protecting needle can be used for longer periods before reaching an equilibrium with the environment [22]. Analytes accumulated on the SPME fiber enable the measurement of the average gas (e.g., a VOC or total VOCs) concentration to which the fiber was exposed [23].  
Quantification of the TWA concentrations with a retracted SPME fiber follows [PERSON]'s first law of diffusion (Equation (1)): the mass extracted on the fiber is proportional to (1) the diffusion coefficient of the analyte (\(D_{S}\)), (2) the concentration of the analyte in the gas phase (\(C_{gas}\)), (3) sampling time (\(t\)), and (4) cross-sectional area of the SPME needle opening (\(A\)); it is inversely proportional to the diffusion path length (\(Z\), i.e., the distance from the needle opening to the tip of retracted fiber).  
\[n=D_{S}\frac{A}{Z}\int C_{gas}(t)dt \tag{1}\]
# AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
## 2 Introduction to Related Technologies
### Blockchain
#### 2.1.3 Smart Contract
(3) Security: Smart contracts rely on blockchain encryption algorithms and a chain-based structure, making data extremely difficult to alter or forge. This ensures the integrity and reliability of transactions. (4) Cost Reduction: By minimizing dependence on traditional intermediaries, smart contracts streamline transaction processes, lower management and operational costs, and optimize contract execution efficiency, ultimately improving economic benefits. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Advantage** & **Description** & **Applicable Scenarios** & **Potential Challenges** & **Typical Application** \\ \hline Speed, Efficiency, and Accuracy & Automatically executes without human intervention, reducing errors and improving transaction efficiency. & Supply chain, payment settlement & High computational costs, slow execution for complex contracts & Supply chain settlement, insurance claims \\ \hline Trust and Transparency & Public and transparent data reduces information asymmetry and enhances trust. & DeFi, government record-keeping technologies & Potential privacy leakage, requires privacy protection technologies & Transparent charity, decentralized exchanges \\ \hline Security & Encryption and consensus mechanisms ensure transaction security and immutability. & Identity verification, medical data storage & Code vulnerabilities may be exploited by hackers & Digital identity, electronic health records \\ \hline Cost Savings & Reduces reliance on intermediaries, lowering transaction and operational costs. & Finance, real estate and operational costs. & High development and computational costs & Securities clearing, P2P lending \\ \hline Automation and Autonomy & Automatically executes when predefined conditions are met, minimizing human intervention. & Logistics, enterprise automation & Immutable code requires rigorous testing & Supply chain payments, smart insurance \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Advantages of smart contracts. Figure 4: Blockchain smart contract schematic.
## 1 Introduction
However, the clustering results of the NNC algorithm are influenced by the initial representative point selection, and the structural simplification process is very cumbersome. [PERSON] et al. [16] proposed a method for three-dimensional fuzzy modeling that utilizes KL decomposition to derive space base functions for the resulting sets of the three-dimensional fuzzy rule. The resulting sets consist of a restricted amount of space base functions with higher weights. This method pertains to the scope of dimension reduction and results in dynamic characteristics that have not been modeled. Based on the above discussion, this paper introduces an innovative three-dimensional fuzzy modeling method for DPS. Utilizing the Affinity Propagation (AP) clustering algorithm, the preceding components of the fuzzy rule are derived within the three-dimensional fuzzy modeling framework. By considering every data point as a potential class representative point, the AP algorithm circumvents the issue of clustering outcomes being constrained by the choice of initial class representative points. Unlike existing methods such as KL decomposition or support vector regression to learn space base functions, this paper sets the Fourier space base function as the resulting component of three-dimensional fuzzy rules. The construction of Fourier space base functions is simple and can better approximate the actual spatial distribution characteristics. Furthermore, the parameters of the Fourier space base function are fine-tuned using the simultaneous perturbation stochastic approximation (SPSA) algorithm. The key innovations of this study are outlined below:  
1. The AP clustering approach is utilized to determine the most suitable quantity of fuzzy rules and construct preceding sets for the three-dimensional fuzzy model. 2. The Fourier space base functions are initially introduced as the resulting sets of the three-dimensional fuzzy model. 3. The SPSA technique is employed to dynamically update the coefficients of Fourier space base functions for the three-dimensional fuzzy model. This paper is outlined as follows. Section 2 offers problem description.Section 3 offers an in-depth explanation of the three-dimensional fuzzy modeling method. Section 4 applies the proposed modeling method to a rapid thermal chemical vapor deposition system. In Section 5, the conclusion is given.
# A Wideband Eight-Port MIMO Antenna with Reduced Mutual Coupling for Future 5G mm-Wave Applications
## 2 Antenna Design  
An eight-port MIMO antenna with improved bandwidth was designed for future 5G communication. The printed antenna features a unique design printed on Roger RT/duroid 5880 substrate in Shenzhen Bomin Electronic Technology Co., Ltd., Shenzhen, China. The design antenna operates from 28 to 46 GHz with 30 dB enhanced isolation. This section is divided into four parts, discussing single-, dual-, quad- and eight-port MIMO antennas.
## 5 Conclusions
### Scientific and Practical Implications  
From a scientific perspective, the outcomes of this study contribute to the literature in two main ways. The systematic literature review offers a comprehensive analysis of the COVID-19 topic in SC and logistics, together with a view on additional topics which appear to be related to the COVID-19 pandemic. Sustainability, in particular, emerged as a relevant interrelated aspect, followed by 14.0; the relevance of these topics was also confirmed by the case study outcomes. The case study, indeed, provides evidence of the response of a real logistic company to the COVID-19 pandemic, by examining its activities from the pre-COVID-19 to the post-COVID-19 period; as such, it also sheds some light on the role of transport and logistics companies and the medium/long term after the pandemic. The specific outcomes are partly aligned with evidence in the literature, but they also go beyond the available knowledge. In particular, the impact of COVID-19 on logistics and SC processes in the medium-term has been investigated by [PERSON] and [PERSON] [16]. These authors have highlighted the fact that the transport and logistics field was somehow unique compared to other sectors, as it did not need to adopt specific countermeasures against COVID-19, and was one of the few sectors experiencing an increase in the volume of activity. As far as the post-COVID-19 period is concerned, however, the literature which is available provides less evidence, even if some studies have indicated that in the medium term the activity of the transport and logistics sector was still greater than in the pre-COVID-19 period in terms of volumes [16]. This study adds knowledge in this respect, as the case study company was analyzed also in the post-COVID-19 period (up to the end of 2022), providing further evidence.  
Looking forward, some practical recommendations could be elaborated. For sure, the transport and logistics sector should continue leveraging the usage of digital/14.0 technologies, which make logistics activities more efficient, allowing them to respond to the consumer's demand and, hopefully, even to reduce cost, which is a prerequisite for remaining competitive on the market. Enhancing sustainability is, for sure, another leverage for competitiveness, due to the increasing push towards the adoption of environmentally -friendly logistics solutions.