esa-sceva/satcom-chunk-collection huggingface dataset:
# Hypoplastic Modeling of Soil-Structure Contact Surface Considering Initial Anisotropy and Roughness
## 2. Fundamental of the Hypoplastic Constitutive Model for the Soil-Structure Contact Surface
### Contact Surface Model Considering Initial Anisotropy and Roughness
#### 2.4.1 Quantitative Description of Initial Anisotropy  
Based on the simulation results of direct shear test of sand-structure contact band in Li [37] using DEM (discrete element method), the peak shear stress of the initial anisotropic sand-structure contact band under various simulation conditions is listed in Table 1.  
The peak stresses presented in Table 1 are normalized against the peak stress under the horizontal deposition direction, which serves as the isotropic reference state, to analyze the relationship with initial anisotropy. This normalized peak stress is referred to as the peak stress coefficient. Figure 3 illustrates the variation in the peak stress coefficient with respect to the deposition direction. A fitting approach is utilized to delineate this relationship, which can be expressed as follows:  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Deposition** & **Peak Stress/kPa** & **Peak Stress/kPa** & **Peak Stress/kPa** & **Peak Stress/kPa** & **Peak Stress/kPa** \\
**Angle/\({}^{\circ}\)** & **(100 kPa)** & **(200 kPa)** & **(400 kPa)** & **(800 kPa)** \\ \hline
0 & 80 & \(150\) & 230 & 435 \\
30 & 62 & \(110\) & 215 & 379 \\
60 & 75 & \(145\) & 235 & 420 \\
90 & 87 & \(145\) & 260 & 481 \\ \hline \end{tabular}
\end{table}
Table 1: Peak shear stress of the contact bands with different initial deposition angles under different normal stresses.  
\[\chi=b_{1}+b_{2}\exp\left[-\frac{(\delta-\delta_{c})^{2}}{b_{3}}\right] \tag{26}\]  
where \(\chi\) is the deposition direction peak stress coefficient characterizing the influence of deposition direction on the peak stress; \(\delta\) is the deposition direction; \(\delta_{c}\) is approximated by taking the angle between the failure surface and the horizontal direction when the deposition direction is horizontal, i.e., \(45^{\circ}-q_{c}/2\); and parameters \(b_{1}\), \(b_{2}\), and \(b_{3}\) are constant.  
Then, the deposition direction peak stress coefficient is introduced into the constitutive model proposed in Equation (24). By considering the influence of initial anisotropy, the index \(a_{1}\) in Equation (6) is modified as follows:  
\[a_{1i}=\chi^{m}[c_{1}+c_{2}\|\phi^{*}\|(1+\cos\theta)]^{-1} \tag{27}\]  
where \(a_{1i}\) denotes the radius of the critical stress surface considering the effect of initial anisotropy, and the exponent \(m\) is a dimensionless positive constant.
###### Abstract  
In central receiver systems, there are local controls that modify the position of the heliostats, where the trend is to increase the intelligence of the local controls in order to give them greater autonomy from the central control. This document describes the design and construction of a SCADA (Supervisory Control and Data Acquisition)-based heliostat control system (HCS) with a fuzzy logic controller (FLC) for the orientation control. The HCS includes a supervisory unit with a graphical user interface, a wireless communication network, and a stand-alone remote terminal unit (RTU) implemented on a low-cost microcontroller (MCU). The MCU uses a solar position algorithm with a maximal error of 0.0027\({}^{\circ}\) in order to compute the position of the sun and the desired angles of the heliostat, according to a control command sent by the supervisory unit. Afterwards, the FLC orients the heliostat to the desired position. The results show that the RTU can perform all the tasks and calculations for the orientation control by using only one low-cost microcontroller with a mean squared error less than 0.1\({}^{\circ}\). Besides, the FLC orients the heliostat by using the same controller parameters in both axes. Therefore, it is not necessary to tune the controller parameters, as in the traditional PID (Proportional-Integral-Derivative) controllers. The system can be adapted in order to control other two-axis solar-tracking systems.  
SCADA system; sun tracking; heliostat; orientation control; fuzzy logic controller; solar energy
## 8 A Look-Ahead, Missing Functionality and Research Challenges
### Research Challenges
#### 8.3.3 Elastic SLO Management  
With respect to auto-scaling concepts, Kubernetes provides besides the Horizontal Pod Autoscaler [163] (HPA) also the Vertical Pod Autoscaler [353] (VPA).  
With the increasing focus of recent Kubernetes releases to improve QoS management, the question arises if these auto-scaling concepts can also be configured to meet service-level objectives (SLOs). However, we have demonstrated in previous research that the Horizontal Pod Autoscaler is too simplistic for meeting service level objectives (SLOs) of database clusters. We handled this problem by developing a tailored auto-scaler component that is customized to the type of database cluster [49]. Unless the HPA for StatefulSets can be tailored via Kubernetes' _annotations_ or _modular interceptors_, the HPA for StatefulSets will need to be redeveloped by relying on a framework or library by means of which custom auto-scaling policies and complex event monitoring policies can be specified and enforced.  
The Kubernetes' VPA concept is promising but there is one big disadvantage with respect to SLO compliance: adjusting resource allocation policies of Pods requires killing these Pods and waiting till the scheduler assigns a new Pod with the adjusted allocation policies. Obviously, this operation needs to be performed at run-time without restarting containers in order to avoid temporary performance degradation with SLO violations. Ironically, although run-time adjustment of container resource allocation policies is by default supported in Docker engine, they are not supported by any CO framework except Docker stand-alone. Indeed recent research presents a middleware for vertical scaling of containers that is implemented on top of Docker engine exactly because the presented middleware requires adjusting resource allocation policies without restarting containers [401].  
Finally, cost-effective auto-scaling to meet SLOs has been shown a sub-linear resource scaling problem [402], especially when co-locating tenants that are being offered different types of SLOs [403]. However existing HPAs of Kubernetes only support linear scaling, therefore leading to either SLO violations or allocating too many resources. A work-around that simulates sub-linear scaling is to combine HPA and VPA, but it would be much better to support managing container replicas with heterogeneous resource allocation policies.  
In summary, existing auto-scalers of Kubernetes are not ready for managing performance SLOs. This lack is also the main reason why we have not grouped these auto-scaling features under the \"Container QoS Management\" aspect.
## 4 Conclusions  
In order to investigate the dynamic mechanical properties of FRC with PVA on strain rates corresponding to seismic loads (i.e., \(10^{-5}\), \(10^{-4}\), \(10^{-3}\), and \(10^{-2}\) s\({}^{-1}\)), the dynamic compression and splitting tensile mechanics experiment for two kinds of matrix strengths (i.e., C30 and C40) with four kinds of fiber volume contents (i.e., 0, 0.2, 0.4, and 0.6%) were carried out by using the RMT-201 rock and concrete mechanics experiment system. The physical and mechanical properties were obtained, and the following conclusions can be drawn.  
1. PVA has some enhancement and improvement effects on the concrete, mainly regarding the improvement of the compressive strength, splitting tensile strength, the toughness index, and the post-peak mechanical properties of the stress and strain curves at the descending stage. The addition of PVA can also significantly improve the failure behavior of the concrete, which changes from the fall-block and caving of plain concrete to a relatively complete form of FRC with a residual strength of 3-5 MPa. The enhancing effect of PVA on the concrete differs for two matrix strengths. The lower the matrix strength, the more obvious the reinforcing effect of the fiber is on the concrete.
2. PVA FRC is a rate-sensitive material similar to plain concrete. The uniaxial compressive strength, splitting tensile strength, and elastic modulus of concrete increase with increasing strain rate, while the peak strain of concrete decreases, indicating that the FRC under a high strain rate is more brittle than that under a low strain rate.
3. The PVA FRC with a 0.2% volume content has greater advantages than the other two kinds of fiber concrete in improving concrete's mechanical properties. Considering cost factors and construction convenience, concrete with a 0.2% PVA content is recommended in engineering applications.  
Concrete workability should be guaranteed. Moreover, the durability issue should be deeply analyzed. In this work, to highlight the influence of strain rates on dynamic mechanical properties, these two parts were ignored. However, at a wider level, further research is also required.  
**Author Contributions:** W.Y.: experimental tests and writing-original draft preparation; S.W.: experimental tests and discussion of the results; Z.W.: elaboration data and discussion of the results; T.X.: discussion of the results and writing-review & editing.  
**Acknowledgments:** The financial support from National Basic Research Program of China (973 Program) (No: 2015 CB057906), the natural science foundation of China (No: 51679172, 41130742, 51574180, 51579238), Hubei Provincial Natural Science Foundation of China (2018 CFA012), and Youth Innovation Promotion Association CAS.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Harnessing Biotechnology for the Remediation of Organic Pollutants in Coastal Marine Ecosystems
## 5 Current Advances in Biotechnological Remediation  
Specific biotechnological techniques for the remediation of organic pollutants in coastal marine ecosystems have been extensively explored and documented in the literature. These approaches have increasingly benefited from advances in related fields such as genetic engineering, synthetic biology, nanotechnology, and environmental genomics, which have significantly improved remediation efficiency and adaptability to marine environments [15; 18; 98]. One of the most well-studied techniques is bioremediation, which uses microbial systems to break down or transform organic pollutants into less harmful compounds. This process may involve complete mineralization, partial degradation, or immobilization of the contaminants.  
Effective bioremediation depends on several factors: (a) the characterization and proliferation of microbial communities; (b) contact between microbes and pollutants; (c) adequate microbial biomass; and (d) optimal environmental conditions such as nutrient availability, oxygen, salinity, and temperature [11]. Mechanisms such as biosorption, bioaccumulation, precipitation, and enzymatic conversion (e.g., via dioxygenenses, hydrolases, dehalogenases) are commonly employed by microbial consortia. These systems are valued for their precision, low ecological impact, and compatibility with environmentally sensitive marine zones [13; 98; 99].
# A Multi-Level Optimization Method for Elastic Constants Identification of Composite Laminates
## 3 Numerical Examples of Global Minimum Determination
However, for larger bounds, e.g., the Bukin function (II), only the present MLOM could produce the exact solution, while the prediction using PS had errors as large as 5%. Furthermore, it is noted that the total number of starting points generated in the proposed method was much less than those of the selected global minimization methods. This revealed the fact that the proposed method was efficient and capable of producing accurate results. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Function**} & \multirow{2}{*}{**Level**} & \multicolumn{2}{c}{**Variable**} \\ \cline{3-4}  & & \(x\) & \(y\) \\ \hline \multirow{4}{*}{\begin{tabular}{c} Schaffer function \\ (\(-100\leq\text{x}\leq 100\), \(-100\leq\text{y}\leq 100\)) \\ \end{tabular} } & \multicolumn{2}{c}{Actual} & 0.00 & 0.00 \\ \cline{2-4}  & \multirow{2}{*}{1 st} & Mean (\(\mu\)) & \(-6.89\times 10^{-5}\) & \(1.50\times 10^{-5}\) \\  & & Standard deviation (\(\sigma\)) & \(3.50\times 10^{-4}\) & \(2.65\times 10^{-5}\) \\ \cline{2-4}  & \multirow{2}{*}{2 nd} & Mean (\(\mu\)) & \(1.53\times 10^{-7}\) & \(1.73\times 10^{-7}\) \\  & & Standard deviation (\(\sigma\)) & \(6.27\times 10^{-7}\) & \(5.73\times 10^{-7}\) \\ \cline{2-4}  & \multirow{2}{*}{3 rd} & Identified & \(-7.95\times 10^{-8}\) & \(3.29\times 10^{-7}\) \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Bukin function (I) \\ (\(-15\leq\text{x}\leq-5\), \(-3\leq\text{y}\leq 3\)) \\ \end{tabular} } & Actual & \(-10.00\) & 1.00 \\ \cline{2-4}  & \multirow{2}{*}{1 st} & Mean (\(\mu\)) & \(-8.88\) & 0.83 \\  & & Standard deviation (\(\sigma\)) & \(2.13\) & 0.360 \\ \cline{2-4}  & \multirow{2}{*}{2 nd} & Mean (\(\mu\)) & \(-9.98\) & 1.00 \\  & & Standard deviation (\(\sigma\)) & \(0.19\) & 0.04 \\ \cline{2-4}  & \multirow{2}{*}{3 rd} & Identified & \(-10.0\) & 1.00 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Bukin function (II) \\ (\(-100\leq\text{x}\leq 100\), \(-100\leq\text{y}\leq 100\)) \\ \end{tabular} } & Actual & \(-10.0\) & 1.00 \\ \cline{2-4}  & \multirow{2}{*}{1 st} & Mean (\(\mu\)) & \(-4.27\) & 1.10 \\ \cline{2-4}  & Standard deviation (\(\sigma\)) & \(9.58\) & 1.64 \\ \cline{2-4}  & \multirow{2}{*}{2 nd} & Mean (\(\mu\)) & \(-9.98\) & 1.00 \\ \cline{2-4}  & Standard deviation (\(\sigma\)) & \(0.54\) & 0.11 \\ \cline{2-4}  & \multirow{2}{*}{3 rd} & Identified & \(-10.0\) & 1.00 \\ \cline{2-4}  & & Actual & \(-10.0\) & 1.00 \\ \cline{2-4}  & \multirow{2}{*}{1 st} & Mean (\(\mu\)) & \(-4.27\) & 1.10 \\ \cline{2-4}  & & Standard deviation (\(\sigma\)) & \(9.58\) & 1.64 \\ \cline{2-4}  & & Mean (\(\mu\)) & \(-9.98\) & 1.00 \\ \cline{2-4}  & Standard deviation (\(\sigma\)) & \(0.54\) & 0.11 \\ \cline{2-4}  & \multirow{2}{*}{3 rd} & \multirow{2}{*}{Identified} & \(-10.0\) & 1.00 \\ \cline{2-4}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Global minimum of function predicted using the proposed multi-level optimization method (MLOM).
# An ERNIE-Based Joint Model for Chinese Named Entity Recognition
###### Abstract  
Named Entity Recognition (NER) is the fundamental task for Natural Language Processing (NLP) and the initial step in building a Knowledge Graph (KG). Recently, BERT (Bidirectional Encoder Representations from Transformers), which is a pre-training model, has achieved state-of-the-art (SOTA) results in various NLP tasks, including the NER. However, Chinese NER is still a more challenging task for BERT because there are no physical separations between Chinese words, and BERT can only obtain the representations of Chinese characters. Nevertheless, the Chinese NER cannot be well handled with character-level representations, because the meaning of a Chinese word is quite different from that of the characters, which make up the word. ERNIE (Enhanced Representation through nNowledge IntfGration), which is an improved pre-training model of BERT, is more suitable for Chinese NER because it is designed to learn language representations enhanced by the knowledge masking strategy. However, the potential of ERNIE has not been fully explored. ERNIE only utilizes the token-level features and ignores the sentence-level feature when performing the NER task. In this paper, we propose the ERNIE-Joint, which is a joint model based on ERNIE. The ERNIE-Joint can utilize both the sentence-level and token-level features by joint training the NER and text classification tasks. In order to use the raw NER datasets for joint training and avoid additional annotations, we perform the text classification task according to the number of entities in the sentences. The experiments are conducted on two datasets: MSRA-NER and Weibo. These datasets contain Chinese news data and Chinese social media data, respectively. The results demonstrate that the ERNIE-Joint not only outperforms BERT and ERNIE but also achieves the SOTA results on both datasets.  
joint training; named entity recognition; pre-training models; ERNIE; BERT +
Footnote †: journal: Applied Sciences
# MEMS and IoT in HAR: Effective Monitoring for the Health of Older People
## 6 Discussion
First, it helps avoid order bias, preventing the model from learning patterns based on the specific data sequence, which could hinder generalization to new, unseen data. By introducing diversity and breaking away from fixed patterns, shuffling improves the model's ability to generalize effectively. This process is especially crucial for reducing overfitting, as it prevents the model from memorizing patterns associated with the order of the data. The novelty of this work lies in its simplified recognition method, high recognition accuracy, reduced computational load through local processing, diversity of recognizable activities, and its potential for integration and communication with smart home technologies and IoT platforms for monitoring activities. The model proposed in the article provides a specific and targeted solution for monitoring the health of older people, emphasizing ease of use and sustainability. However, testimonials highlight various approaches and technologies that can deliver benefits, including increased accuracy and robustness. These alternatives may also introduce greater complexity and cost. While the proposed system is effective, it has some limitations due to the types of subjects and the testing environment utilized. The tests were conducted with healthy, young participants. Future experimentation with older individuals and those with limited mobility is necessary. Additionally, the tests were conducted in simulated living environments with limited obstacles. Extending these tests to real home settings and nursing homes with various layouts would be beneficial. Future research could also explore movements beyond those typically studied and evaluate the effectiveness of integration into a smart home system. Using a hybrid approach that combines RF with convolutional neural networks (CNNs) or Long Short-Term Memory (LSTM) networks can enhance the robustness and generalization capabilities of the classification system. While Random Forest models offer stability and interpretability, neural networks are more adept at adapting to complex variations in data.
# Flexible UHF RFID Tag for Blood Tubes Monitoring
## 3 Design of the Tag Antenna  
In this section, the design of the tag antenna is presented. The structure of the proposed tag was developed from some initial design considerations. Then, an optimization of the tag dimensions attached to a tube full of blood was carried out at the center frequency \(f_{c}\) = 867 MHz of the UHF band. Finally, the optimized tag was simulated to obtain the generalized reflection coefficient in different scenarios, namely, tag attached to a tube full of blood, tag attached to an empty tube, tag removed from the tube but maintaining its initial curvature of the tube, tag removed from the tube and unfolded, and tag unfolded and attached to a bag full of blood.
# Secure Cryptographic Key Encapsulation and Recovery Scheme in Noisy Network Conditions
## 4 CRP Mechanism  
CRPs are a fundamental component in the domain of cryptography, particularly in authentication protocols. The mechanism is based on the principle of proving one's identity or authenticity without revealing the actual secret or key involved in the process. This method aids in mitigating the risks associated with the direct transmission of sensitive information over potentially insecure channels.  
**Challenge**: The verifier (which could be a server, system, or any entity requiring authentication of another entity) issues a challenge to the requestor (the entity attempting to prove its identity, such as a user or a device). This challenge is typically a randomly generated value or a specific query designed to produce a unique response based on a shared secret. Figure 1 illustrates how the challenge is generated from the crypto table shared between the user and the server. The challenge is designed to ensure that the response will be dynamic and unique for each session, thus preventing replay attacks where an attacker might reuse a previously intercepted response to gain unauthorized access.  
**Response**: Upon receiving the challenge, the requestor computes a response using a pre-shared secret or a cryptographic key that both the requestor and verifier have agreed upon in advance. This computation often involves cryptographic algorithms that ensure the response is securely tied to the challenge. The key aspect here is that the response should be impossible (or computationally infeasible) to generate without possessing the secret, thereby ensuring that only the legitimate requestor can produce the correct response.  
CRPs can be derived from a variety of sources, including PUFs, digital files, or biometric data. During the enrollment phase, the verifier leverages these sources of randomness to generate unique responses tailored to each entity. In the subsequent verification cycles, the CRP mechanism is repeated to produce the corresponding responses, which are essential for both authentication and decryption processes.  
The overview of how these CRPs can be generated from digital files and biometric information is provided next in Example of CRP Generation for Noise-Tolerant Cryptographic Protocols.