esa-sceva/satcom-chunk-collection huggingface dataset:
# Model-Free Identification of Nonlinear Restoring Force with Modified Observation Equation
## 1 Introduction
### Literature Survey
Increasing attention has been received in this area and much progress has been made. Early contributions to nonparametric nonlinear identification can be traced back to the 1980s when [PERSON] et al. developed their famous restoring force surface (RFS) methods by using Chebyshev polynomial [22; 23; 24; 25]. The data processing and experimental design for the investigation of RFS method were further conducted by [PERSON] [26; 27]. A two-stage general procedure was presented by [PERSON] et al. [28] for developing data-based, model-free representations of complex nonlinear systems under arbitrary dynamic loads. By using a power series polynomial to represent the NRF, a data-based approach was proposed for the identification of chain-like structure equipped with magneto-rheological (MR) damper [29; 30] and shape memory alloy (SMA) damper [31]. The generalized two-variable Chebyshev polynomials and their relevant relations were further discussed by [PERSON] and [PERSON] [32; 33]. A data-based nonlinear identification approach was proposed by [PERSON] et al. [34] for mass and NRF identification under incomplete loadings. By introducing an equivalent linear structural system and a power series polynomial, [PERSON] et al. [35] proposed an EKF-based model-free NRF identification approach for the identification of the unknown coefficients of the power series polynomials and other structural parameters. [PERSON] et al. [36] proposed a two-step approach to identify the nonlinearities of model-free MR damper with partial response data. Based on the Hilbert transform, two novel nonparametric identification approaches were proposed by [PERSON] et al. [37] for nonlinear piezoelectric mechanical systems. [PERSON] et al. [38] proposed a zero-crossing method for nonparametric identification of systems with asymmetric NRFs. As the rapid development of computers and their associated computational capabilities, the intelligent black-box methods for nonparametric nonlinear identification using artificial neural networks, fuzzy networks, genetic algorithms and so forth have received increasing attention. The literature review on these natural computing methods with different emphasis can be found [4; 39].
# Blockchain-Based Lightweight Trust Management in Mobile Ad-Hoc Networks
###### Abstract  
As a trending and interesting research topic, in recent years, researchers have been adopting the blockchain in the wireless ad-hoc environment. Owing to its strong characteristics, such as consensus, immutability, finality, and provenance, the blockchain is utilized not only as a secure data storage for critical data but also as a platform that facilitates the trustless exchange of data between independent parties. However, the main challenge of blockchain application in an ad-hoc network is which kind of nodes should be involved in the validation process and how to adopt the heavy computational complexity of block validation appropriately while maintaining the genuine characteristics of a blockchain. In this paper, we propose the blockchain-based trust management system with a lightweight consensus algorithm in a mobile ad-hoc network (MANET). The proposed scheme provides the distributed trust framework for routing nodes in MANETs that is tamper-proof via blockchain. The optimized link state routing protocol (OLSR) is exploited as a representative protocol to embed the blockchain concept in MANETs. As a securely distributed and trusted platform, blockchain solves most of the security issues in the OLSR, in which every node is performing the security operation individually and in a repetitive manner. Additionally, using predefined principles, the routing nodes in the proposed scheme can collaborate to defend themselves from the attackers in the network. The experimental results show that the proposed consensus algorithm is suitable to be used in the resource-hungry MANET with reduced validation time and less overhead. Meanwhile, the attack detection overhead and time also decrease because the repetitivity of the process is reduced while providing a scalable and distributed trust among the routing nodes.  
blockchain; distributed trust; mobile ad-hoc network (MANET); optimized link state routing protocol (OLSR) +
Footnote †: journal:  
_Article_  
Blockchain-Based Lightweight Trust Management in Mobile Ad-Hoc Networks
## 2 T-S Identification Method
### Fuzzy Clustering
#### 3.1.1 FCM Clustering Algorithm
Step 3: Calculate the centers of the clusters such that: \[v_{i}^{(l)}=\frac{\sum_{k=1}^{\text{N}}u_{ik}^{(l-1)}x_{k}}{\sum_{k=1}^{\text {N}}u_{ik}^{(l-1)}}\quad 1\leq i\leq c,\] (37) where \(\text{I}\) is the current iteration number of the algorithm. Figure 2: Flowchart of the identification and control process using the FCM clustering algorithm. Step 4: Define a desired cluster center within the calculated centers in order to force its value. This center will be the same in all iterations. Step 5: Calculate the distances of the samples to the centers of the clusters such that:  
\[D_{ikA}^{2}=\|x_{k}-v_{i}\|^{2}=(x_{k}-v_{i})^{T}A(x_{k}-v_{i}),\ \ \ 1\leq i\leq c,\ 1\leq k\leq N. \tag{38}\]  
Step 6: Update the partition matrix U such that  
\[u_{ik}=\frac{1}{\sum_{j=1}^{c}\left(D_{ikA}/D_{jkA}\right)^{\frac{2}{m-1}}}. \tag{39}\]  
Step 7: Repeat steps 3, 4, 5, and 6 for \(l=1,\ 2,\ \dots\) until fulfilling  
\[\left\|U^{(l)}-U^{(l-1)}\right\|<\varepsilon. \tag{40}\]  
Step 8: Denormalize the obtained cluster centers in order to properly place them within the ranges of the different dimensions. After the centers have been obtained using the algorithm, it is necessary to include fuzzification based on the FCM formulas for the T-S and fuzzy controller identification stages. The fuzzification process for each sample is as follows:  
\[X_{n}(k) =\frac{x(k)-\hat{X}}{\sigma\chi}\] \[D_{i}^{2}(k)=\|x_{n}(k)-v_{i}\|^{2} =(x_{n}(k)-v_{i})^{T}A(x_{n}(k)-v_{i}),\ \ \ 1\leq i\leq c \tag{41}\] \[\mu_{i}(k) =\frac{1}{\sum_{j=1}^{c}\left(D_{i}(k)/D_{j}(k)\right)^{\frac{2} {m-1}}}.\]
## 3 Results and Discussion
### Suitability of the Electrical Method for Controlling Quality Parameters
The obtained results of measurements of electrical parameters of honeys and the analysis of their correlations with quality parameters confirm the results of the research by [PERSON] et al. [40], who found that conductivity parameters, compared to dielectric parameters, are of greater utility value in everyday quality analyses of honeys. The RCC model may be valuable in more detailed studies on the quality and authenticity of honeys, especially where the analysis of complex dielectric properties and differences in composition is crucial. The analysis of measurement results shows that the electrical method applied to assess the quality of honeys has both significant advantages and certain limitations. It is characterized by high sensitivity, allowing distinction of differences between honey species based on electrical parameters of conductivity (Z, G) and capacitance (Cp, Cs). The results also suggest that this method is particularly useful for botanically homogeneous honeys. Low values of standard deviations of the analyzed parameters confirm the stability and repeatability of measurements. Compared to physicochemical methods, the proposed approach is less time-consuming and does not require complicated and destructive sample preparation, which makes it a viable practical tool in honey diagnostics on a large scale. The method should be considered more user-friendly and allows maintenance of sample integrity. Its efficiency is, however, limited by external factors, such as temperature variability, which affects the results of impedance measurements. In a narrow measurement frequency range, it is also necessary to take into account diffusion effects, which may be significant in the case of honeys with a higher water content. Therefore, further research will be aimed at fitting the results to more advanced electrical circuit models, such as the modified Randles model, and at assessing the accuracy of the fit.
## 3 Data and Method
### Experimental Settings  
#### 3.3.1 Experiment Design  
To validate the effectiveness of the proposed model, a series of experiments were designed. The following provides a detailed description of the experimental design, including dataset division, baseline model selection, optimizer choice, hyperparameter settings, and ablation studies.  
The division of the dataset is of paramount importance, as proper partitioning ensures the model's generalizability and effectiveness in real-world applications. The entire dataset was divided chronologically into training, validation, and test sets. Specifically, 70% of the dataset was chosen as the training set, followed by 15% as the validation set, and the remaining 15% as the test set. This method of division ensures the model's predictive capability in real-world scenarios and prevents potential data leakage.  
In all experiments, the Adam optimizer was employed for model optimization. The Adam optimizer, merging the advantages of Adagrad and RMSprop, can adaptively adjust the learning rate and has been demonstrated to perform well in deep learning models. Hyperparameter choices were based on performance on the validation set. The learning rate was initially set to 0.001 and reduced by 10% every 5 epochs based on validation loss. The batch size was set to 32. For Transformer models, an 8-head self-attention mechanism was implemented, with the hidden layer size set to 512. To prevent overfitting, a dropout rate of 0.1 was incorporated into the model.  
Ablation studies were designed to further validate the effectiveness of various components of the proposed model. Specifically, the memory attention module and long-distance loss function were removed separately to observe how these modifications impact model performance. Such experiments offer insights into the contributions of different model components to the final performance, providing guidance for future improvements. In summary, through meticulously designed experiments, this research aims to comprehensively and deeply evaluate the performance of the proposed model. These experiments reveal the model's performance in real-world scenarios and its comparison with current mainstream techniques.
# Session-Based Recommendations for e-Commerce with Graph-Based Data Modeling
## 5 The Experimentation Procedure and Results
### The Experiments  
The same session-based datasets were used for all the experimental implementations and recorded the results for MRR@\(k\) and Rec@\(k\) for the top items \(k=\{10,20,30\}\). More specifically,  
* We compared our methods against three state-of-the-art GNN recommendation models, namely, SR-GNN [7], GCE-GNN [8], and IC-GAR [9]. We trained the models using the code available from the respective GitHub and recorded the time from the moment the training starts to receiving the results. For each model, we used the hyperparameters proposed in the corresponding Github codes. We executed the GNN models in the Google colab environment with a Tesla T4 GPU accelerator (16 GB) and Intel Xeon CPU @ 2.2 GHz;
* We also performed the same experiments using HSP and RIC and recorded the execution times from the moment we read the data until the generation of the results. The HSP method has no hyper-parameter that requires adjustment. It is worth mentioning that the HSP method extends the Pair Popularity approach and achieves better results. For the RIC method, the values of the parameter \(\alpha\) were set to 0.1, 0.3, 0.5, 0.7 and 0.9 during the experiments. Table 3 shows the optimal parameter value per dataset. Our models were executed in Google colab in a CPU-only machine with an Intel Xeon CPU @ 2.2 GHz;
* Additionally, we run the experiments for the Pair Popularity algorithm [6] in the same scenarios of the top k recommendation items.
# A New Top-Mounted Shear-Hinge Structure Based on Modal Theory and Rubber-Pad Damping Theory
## 2 TMSH Field Research
### Structural Design Optimization  
As a highly elastic material with good vibration damping and low cost, rubber is widely used in subway lines, including for rubber floating-slab tracks [23], typical rubber-to-metal bonded components for rail suspension systems [25], and new vibration isolators that combine the particle damping and vibration absorption of rubber materials with bandgap vibration resistance [26]. The shear hinge is mainly subjected to vibration from the floating slab in the vertical direction during service, so only the vertical degree of freedom of the shear hinge is considered. The shear hinge's natural frequency can be expressed as:  
\[f=\frac{1}{2\pi}\sqrt{\frac{k}{m}} \tag{1}\]  
The formula for calculating the vertical-support stiffness of the rubber bearing is as follows [27]:  
\[K_{v=}\frac{E_{cb}A}{nt_{r}} \tag{2}\]  
where \(K_{v}(k)\) is the vertical-support stiffness, \(E_{cb}\) is the modified elastic compression modulus, \(A\) is the effective cross-sectional area of the support, \(n\) is the number of rubber pads, and \(t_{r}\) is the thickness of the rubber pad.  
\[E_{cb}=\frac{1}{E_{c}}+\frac{1}{E_{b}} \tag{3}\]  
\[E_{c}=E_{0}\Big{(}1+2\kappa S_{1}^{2}\Big{)} \tag{4}\]  
Figure 5: Design of new TMSH structure.  
where \(E_{c}\) is the longitudinal modulus of elasticity of rubber under pure compression, \(E_{b}\) is the bulk modulus of elasticity of rubber, \(\kappa\) is the rubber hardness correction factor (the specific values are given in [28]), and \(S_{1}\) is the primary shape factor (\(S_{1}=\text{D}/4i_{r}\)). After measurement, the total weight of the TMSH is 60.34 kg, and the parameters of the rubber pads are shown in Table 2. In order to reduce the natural frequency of the TMSH to the low-vibration region of the floating slab, the target frequency is set to 80 Hz (in Figure 17b,d of [29], it is shown that the low-vibration region of the floating slab will be entered after 80 Hz), and Equations (1)-(4) are then substituted to calculate the results. Twelve ring-shaped rubber pads with an inner diameter of 24 mm, an outer diameter of 48 mm, and a thickness of 18 mm are supported through the bolts between the shear hinge and the metal pad to reduce the natural frequency of the TMSH, as shown in Figure 5.  
Due to the long-chain molecular structure of rubber and the small secondary force between molecules, it has viscoelastic properties and can absorb the vibration load transmitted from the floating slab. It is believed that rubber pads can attenuate the vibration through inertial motion and then transmit it to the shear hinge so that the natural frequency of the shear hinge can withstand the peak vibration frequency of the floating slab. It is expected that this can reduce the vibration level of the shear hinge.
# Evaluation of Coupling PV and Air Conditioning vs. Solar Cooling Systems--Case Study from Jordan
## 2 Materials and Methods
### Air Conditioning Systems  
The energy efficiency of newly developed air conditioning units has increased significantly. They utilise variable speed motors for the purpose of maximising the airflow efficiency by operating at different speeds to correspond with the output and also include an automatic fan delay switch such that any residual cool air can be pushed into the building to be reused. Thermal expansion valves limit the refrigerant flow when required in order for the consumption of energy to be minimised.  
Air conditioners come in five different types, each with a different purpose: basic central AC, ductless (mini-split air conditioners), portable unit, window unit and portable and window unit (hybrid). The least costly air conditioning option is a portable or window AC unit to cool an individual room. Mini-split air conditioners offer cost-effectiveness when only a few rooms or offices must be cooled. A more costly alternative is the central air conditioning unit, which is more suitable for large buildings with multiple rooms.  
Natural, i.e., non-synthetic, refrigerants that can be used as refrigerants in air conditioning systems include various natural hydrocarbons, carbon dioxide, ammonia, and water. They are replacements for artificial refrigerants (chlorofluorocarbon (CFC), hydrofluorocarbon (HFC) and hydrochlorofluorocarbon (HCFC)) [47].  
Both the seasonal coefficient of performance (SCOP) value for heating and the seasonal energy efficiency ratio (SEER) value for cooling are reflective of the actual amount of energy consumed by a heat pump. Both temperature variations and periods of standby are considered in the longer term to provide a clear and reliable picture of the average energy efficiency that can be achieved across a whole heating or cooling season. The energy efficiency rating (SEER) is an indicator of the volume of BTUs that are removed each hour for every watt of power used, whereby an increased rating denotes that the energy efficiency is higher and the SEER values of central air conditioners range between 13 and 24.
# Few-Shot Charge Prediction with Data Augmentation and Feature Augmentation
## 3 Method
### Feature Fusion Network  
Some feature splicing layers usually use simple feature splicing or feature summing without considering the relationship between external knowledge and internal descriptions, while we observed that the charge label features obtained by feature augmentation did not have exactly the same impact on the charge prediction task, and further analysis of the relationship between the charge label features and the fact descriptions could achieve a reasonable use of external knowledge. Therefore, we designed a layer of a feature fusion network to select more relevant information from the charge labels. As shown in Figure 5, formally, we defined an alignment matrix \(U\in R^{n\times k}\) between charge label features and fact description features. Specifically, this paper first took charge graph information \(c\) through a layer of a fully connected network to get \(g\) as the hidden layer representation of the fusion network, then measured the importance of words through the fact description and the similarity between labels, and obtained the standardized importance weight through softmax function. The specific formula is as follows:  
\[g_{i}=\tanh(W_{u}c_{i}+b_{u}) \tag{12}\]  
\[U_{i}=\frac{\exp(g_{i}T^{W}W_{v}v)}{\sum_{t}\exp(g_{i}T^{W}W_{v}v)} \tag{13}\]  
where \(W_{u}\in R^{d_{w}\times d_{S}}\) represents the weight matrix of the charge label feature and \(W_{v}\in R^{d_{w}}\times d^{d_{w}}\) represents the weight matrix of fact description. Based on \(U\), we computed the charge label embedding \(e_{c}=(cU^{T})\). Finally, we obtained the semantic-enhanced fact representation \(f\in R^{d_{w}}\) by concatenating the fact description \(v\) with charge label embedding.  
\[f=W_{f}[v;e_{c}] \tag{14}\]  
where \(W_{f}\in R^{d_{w}\times(d_{w}+d_{S})}\) denotes weight matrix and \([;]\) means the concatenate operation.
## 5 Case Analysis
### Reliability Analysis of Early Warning Results  
To testify the reliability of the predicted results in Section 5.3, the Bland-Altman analysis was used to analyze the predicted values and the measured values. The Bland-Altman analysis method was first put forward by British scholars [PERSON] and [PERSON] in 1983. Its basic principle is to analyze the differences between the results calculated by two different methods within a 95% confidence interval (consistency limit). This method has become an authoritative statistical method to judge the consistency of the results calculated by two data calculation methods. In this research, the calculation results based on QPSO-LSSVM and the real construction risks were compared. If the calculation results based on QPSO-LSSVM were consistent with the real construction risks within a 95% confidence interval, it showed that the risk warning results based on QPSO-LSSVM had good reliability. In the empirical study, the Bland-Altman diagram is shown in Figure 3.  
In Figure 3, the vertical axis of the diagram was the difference between the measured value and the predicted value, and the horizontal axis was the average of the measured value and the predicted value of the model. From Figure 3, 26 groups of values were within \((-1.96\text{SD},+1.96\text{SD})\), and only one group was absent. According to the basic principle of the Bland-Altman analysis method, it could be known that the calculated results in Section 5.3 and the measured values met 95% of the predicted points within the consistency range. Therefore, the early warning results of construction risks in Section 5.3 had high reliability.
## 7 IoMT Healthcare Services and Applications
### Glucose Monitoring in Healthcare  
Monitoring glucose levels is important for people with diabetes to help them manage their blood sugar levels. It can also be used to monitor other conditions, such as hypoglycemia, which can be caused by certain medications or illnesses. By monitoring glucose levels closely, individuals with diabetes are proactive in managing their health andavoiding the risk of developing potentially serious complications. It takes a great deal of time to measure and record glucose levels manually, and this is not only inconvenient; it also only reports a patient's glucose level at the moment of the test. In cases where levels fluctuate widely, periodic testing may not be sufficient to detect problems. By providing continuous, automatic glucose monitoring to patients, IoT devices help address these challenges [99]. Patients are alerted when their glucose levels are abnormal when glucose monitoring devices replace manual record-keeping. For instance, if a person notices their glucose levels starting to decline, they take steps to increase them before they become dangerously low. However, there are potential risks associated with continuous glucose monitoring. Improper transmission or misinterpretation of data from the device can result in false alarms or inaccurate readings. This may cause unnecessary anxiety for patients or, worse, lead to dangerous situations if patients do not take appropriate actions in response to the alarms.
## 3 Aberration Simulation of Each Imaging Lens
In the following simulation, the spot size of the beam in the detector plane was calculated. The spot diagrams were displayed in Figures 4 and 5. For the ADL, the root mean square (RMS) spot radius is \(\sim\)3 \(\upmu\)m at the central wavelength and extended to \(\sim\)20 \(\upmu\)m at the edge wavelength of the FWHM spectral bandwidth, as seen in Figure 4. However, as seen in Figure 5, the RMS spot radius of the TFL was less than 2 \(\upmu\)m at the center wavelength and increased to \(\sim\)5 \(\upmu\)m at the edge wavelength. Because the length of the single pixel is 10 \(\upmu\)m, the spectrometer with the ADL may experience intensity loss and spectral crosstalk. Unlike the TFL, the ADL causes fringe visibility degradation at wavelengths shorter or longer than the central wavelength. The scale for the spot diagram in Figures 4 and 5 was 100 \(\upmu\)m and 20 \(\upmu\)m, respectively. The modulation transfer function (MTF) curve as a function of spatial frequency is given in Figure 6. The MTF curve helps us to understand the contrast characteristics of the imaging lenses. High MTF values at high spatial frequencies indicate that the SD-OCT system is able to provide bright, high-contrast imaging at deep depths. When looking at the MTF curve calculated at the three different incident angles, which correspond to the center and each edge wavelength, the two imaging lenses showed significant differences as the spatial frequency increased. The TFL presented a similar decreasing trend, regardless of incident angles, and maintained a similar MTF value at the maximum spatial frequency. However, the ADL provided very different curves depending on the incident angles and finally reached almost zero at the maximum spatial frequency. Using the TFL for the spectrometer could improve the achievable image depth and contrast. The MTF value for the TFL was between 0.4 and 0.6 at the Nyquist frequency of the LSC (50 lines/mm).