esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Nonlinear Damping on Vibrations of Microbeam
## 3 Results
### Primary Resonance  
When the frequency of the load \(\Omega_{2}\) approaches the modal frequency of the microbeam \(\omega\), the beam will appear as a relatively large amplitude response. Under this condition, let \(f_{1}=0\) and \(\Omega_{2}=\omega+\varepsilon^{2}\sigma\), thus the solvable condition of Equation (17) can be written as:  
\[-i2\omega\big{(}A^{\prime}+cA\big{)}-(ic_{2}\omega+3d)A^{2}\overline{A}+\frac {1}{2}f_{2}\exp(i\sigma T_{2})=0 \tag{23}\]  
Figure 2: Time evolution of the microbeam’s midpoint displacement of the non-resonance for \(\Omega_{1}=1.5,\ f_{1}=0.5\).  
Substituting Equation (19) into Equation (23), then separating the real and imaginary parts, we obtain:  
\[\alpha^{\prime}=-c_{1}\alpha-\frac{1}{8}c_{2}\alpha^{3}+\frac{f_{2}}{2\omega} \sin\gamma,\ \alpha\gamma^{\prime}=\sigma\alpha-\frac{3}{8\omega}d\alpha^{3}+\frac{f_{2}}{2 \omega}\cos\gamma \tag{24}\]  
where \(\gamma=\sigma T_{2}-\beta\). The steady-state motions occur when \(\alpha^{\prime}=\gamma^{\prime}=0\), which correspond to the singular points of Equation (24). The steady-state solutions can be obtained by the following algebraic equation [26]:  
\[\left[c_{1}^{2}+\sigma^{2}+\frac{1}{4}\left(c_{1}c_{2}-\frac{3d\sigma}{\omega} \right)a^{2}+\frac{1}{64}\left(c_{2}^{2}+\frac{9d^{2}}{\omega^{2}}\right)a^{4 }\right]a^{2}=\left(\frac{f_{2}}{2\omega}\right)^{2} \tag{25}\]  
The stability of the steady-state solutions can be judged by an investigation of the nature of the singular points of Equation (24). Letting \(a=a_{0}+a_{1}\) and \(\gamma=\gamma_{0}+\gamma_{1}\), substituting them into Equation (24), then expanding the small \(a_{0}\) and \(\gamma_{0}\), and maintaining the linear terms in \(a_{1}\) and \(\gamma_{1}\), we obtain:  
\[\begin{array}{c}\alpha^{\prime}_{1}=\left(-c_{1}-\frac{3}{8}c_{2}\alpha_{0} ^{2}\right)\alpha_{1}+\frac{\gamma_{1}f_{2}\cos\gamma_{0}}{2\omega}\\ \gamma^{\prime}_{1}=-\left(\frac{f\cos\gamma_{01}}{2\omega\alpha_{0}^{2}}+ \frac{3d\alpha_{0}}{4}\right)\alpha_{1}-\frac{\gamma_{1}f_{2}\sin\gamma_{0}}{ 2\omega\alpha_{0}}\end{array} \tag{26}\]  
where \(a_{0}\) and \(\gamma_{0}\) are the singular points of Equation (24), which are used in Equation (26). The stability of the steady-state motions depends on the eigenvalues of the coefficient matrix on the right-hand side of Equation (26). If the real parts of the eigenvalues are greater than zero, the solutions are unstable [25]. Therefore, the steady-state motions are unstable when  
\[\left(\frac{3c_{2}a_{0}^{2}}{8}+c_{1}\right)\left(\frac{c_{2}a_{0}^{2}}{8}+c_ {1}\right)+\left(\frac{3d\alpha_{0}^{2}}{8\omega}-\sigma\right)\left(\frac{9 d\alpha_{0}^{2}}{8\omega}-\sigma\right)<0 \tag{27}\]  
Equation (27) indicates that nonlinear damping affects the stability of the steady-state solutions.
# Review of Recent Phased Arrays for Millimeter-Wave Wireless Communication
## 2 Millimeter-Wave Antenna Array for 5G Communication  
Even though mm-wave technology is generally acknowledged as being promising for 5G wireless communication systems, there exists a gap between the current mm-wave designs and the proposed commercial mm-wave cellular networks [4; 5; 6; 9; 11; 13; 22]. A few basic modifications are needed in the practical implementation of mm-wave cellular networks. To improve the performance of wireless communication systems, advanced antenna array architectures are used, with names such as phased arrays,  
Figure 3: 5G heterogeneous mobile network scenario.  
beamforming arrays, and multi-input multi-output (MIMO) transceivers [1; 7; 9; 10; 12; 14; 15; 16; 17; 19; 22; 24; 25; 28]. However, user mobility, the random movement of mobile devices, as well as indoor and outdoor propagation in urban environments are essential factors that affect the architecture of base station (BS) and mobile station (MS) antennas in mm-wave cellular communication systems [13; 20; 25; 29]. Thus, to obtain a good link quality, beam-steering should be applied on both the BSs and MSs, as illustrated in Figure 4.  
Generally, most of the recent mm-wave 5G research efforts are based on: (1) SiGe compound semiconductor circuits and integrated chips (ICs) [30; 31; 32; 33; 34] and (2) large-scale mm-wave phased arrays and beam-switching antennas to increase the antenna and amplifier gains within the mm-wave 5G wireless communication links [22; 23]. These studies have stimulated interest in applications that focus on compact and low-power consumption, such as the mm-wave 5G backhaul, access terminals, and BSs [12; 14; 16]. With the help of prominent mm-wave 5G antenna studies, these applications are further specified as planar phased arrays [35], grid arrays [36], substrate-integrated waveguides (SIWs) [37], and planar lens architectures [38; 39].
# Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory
## 3 Recognition Model
### Loss Functions
#### 3.3.2 Prototype Loss (PL)  
To further improve the recognition performance of the network while completing open-set recognition, the distance between the intra-class sample features can be reduced by PL [32] during the training period, which expands the inter-class distance by compacting the intra-class signals. Meanwhile, the spatial distribution of the unknown class is expanded based on reducing the feature space of known classes through the constraint of the loss function, which is more favorable to the detection and rejection of unknown classes. PL is defined as:  
\[\text{pl}((x,y);\theta,A)=\left\|f(x)-a_{y}\right\|_{2}^{2} \tag{7}\]  
where \(a_{y}\) is the prototype of the class \(y\). Minimizing \(\text{pl}((x,y);\theta,A)\) reduces the distance between the sample features and the prototypes to which they belong.
###### Abstract
If a significant drop in accuracy is observed, surpassing a predefined threshold, it signals the presence of concept drift. This detection triggers an immediate retraining of the classifier using the most recent data captured in the adaptive window. In addition, the framework also employs an optimization function using Particle Swarm Optimization (PSO) for choosing optimal hyperparameters for the drift adaptation algorithm as well as the anomaly classification model. The idea is based on the work by [PERSON] and and [PERSON] [14] that was originally developed for high-dimensional (\(\approx\)80 features) network traffic analysis, which we have adapted for the less complex, yet highly variable data dimensions (features) of cleanroom CPSs. Comparing with [14], we also applied off-the-shelf popular drift adaptation techniques (see details in Section 4.3) to compare their performance against that of TWIN-ADAPT's continual learning framework. Other significant contributions of this work include the following:  
* Designing a more comprehensive dataset featuring diverse anomaly distributions to robustly train and evaluate the TWIN-ADAPT model under simulated drift conditions. * Leveraging the PSO enhanced continual learning models for real-time anomaly classification within a cleanroom's CPSs, like Vacuum Pumps, Fumehoods and Lithography Units. Each CPS component's monitoring model represents its digital twin that accurately reflects the current operational states and anomalies. When concept drift is detected--indicating changes in operational conditions or emerging anomaly patterns--the digital twin's continual learning model dynamically adapts by retraining on new data samples captured through an adaptive sliding window. This ensures that the digital twin remains aligned with the dynamic and non-stationary data distributions, providing robust, up-to-date anomaly monitoring in the dynamic environment of cleanrooms. It is also worth mentioning that TWIN-ADAPT is primarily deployed within a supervised setting, tailored for anomaly (binary) classification. This approach holds similarity to fault classification processes, where faults are identified through deviations from established normal behaviors--similar to detecting anomalies. The rest of this paper is organized as follows. Section 2 provides a comprehensive background on the concept and importance of digital twins in Industry 4.0. In Section 3, we discuss related works pertaining to the application of digital twins, concept drift detection, adaptation and continual learning strategies for scientific cleanroom laboratories. Section 4 elaborates on this study's methodology, including the dataset description, anomaly injection techniques, model selection and optimization for online drift adaptation. Next, we present a complete system overview in Section 5 that explains the proposed TWIN-ADAPT algorithm and experimental setup. Experimental results are discussed in Section 6 followed by the Discussion, Future Directions and Conclusion in Section 7, Section 8 and Section 9, respectively.
## 2 Raw Materials and Test Methods
### Test Methods  
#### 2.3.1 Specimen Preparation and Curing  
Cement, sand, aggregate, agent, and fibers were dry mixed in a mixer for 1 min, followed by wet mixing with added water for 3 min. After discharge, the slump and air content were measured, and the mixture was manually poured and vibrated to form prism specimens with dimensions of 40 mm \(\times\) 40 mm \(\times\) 160 mm. Each proportion is to make 18 prismatic specimens and to be tested in 3 kinds of RH. The first set of prismatic specimens is used for the flexural strength test, and the second set of prismatic specimens is used for the freeze-thaw cycle test. Therefore, the number of 40 mm \(\times\) 40 mm \(\times\) 160 mm prismatic specimens is at least 108 pieces. [PERSON] et al. [16] and [PERSON] et al. [17] suggested that the standard anifreeze specimen of 100 mm \(\times\) 100 mm \(\times\) 400 mm is unsuitable for testing external bending stress in the limited space of the freeze-thaw cycle tank. Therefore, a smaller specimen of 40 mm \(\times\) 40 mm \(\times\) 160 mm is recommended for the freeze-thaw test. Comparing test results from two different specifications, it can be concluded that both specimens exhibit the same level of freezing resistance. Moreover, at least 108 cubic specimens sized 100 mm \(\times\) 100 mm \(\times\) 100 mm were prepared to assess the compressive strength of concretes with varied mix ratios at both 28 and 180 days under different RH conditions.  
The specimens underwent moist curing, were demolded after 1 day, and then transferred to a standard curing chamber with a temperature of (20 \(\pm\) 3) \({}^{\circ}\)C and RH above 95% for an additional 7 days of curing. Subsequently, the specimens were cured for 180 days under three different conditions: (20 \(\pm\) 3) \({}^{\circ}\)C with RH of 30%, 50%, and above 95%, respectively. Performance tests were conducted at specified intervals under the three curing conditions.  
To determine the strength class of the concrete series and the strength growth pattern under different humidity conditions, this paper measured the cubic compressive strength at 28 and 180 days, as well as the flexural strength of prisms at 180 days.
# EE-UWSNs: A Joint Energy-Efficient MAC and Routing Protocol for Underwater Sensor Networks
## 3 The Proposed EE-UWSNs Protocol
### The Main Principles of Energy Saving  
The purpose of the proposed EE-UWSNs protocol is to save energy and balance the consumption of all nodes in order to prolong the lifetime of underwater sensor networks. The main idea of the proposed protocol is to use several principles in order to save energy. These principles are:  
* Using finite levels of power, from minimum power level (P1) to maximum power level (PN). The objective of the use of several levels of power is to transmit data to nearby nodes with less energy than farther nodes. This leads to energy saving.
* Applying the multi-hops transmission method when sending data to a surface sink. Several references have proved that multi-hop transmission saves power. Using multihops leads to reducing the distance between the transmitter and the receiver. Thus, if the handshake is used, short distances lead to reducing the duration of RTS/CTS exchange.
* Narrowing the scope of transmission to a specific area by using a cone angle. When nodes are absent in this area, the angle can be shifted. The idea of narrowing the transmission scope leads to minimizing the number of nodes responsible for forwarding packets. Therefore, this leads to reducing collisions and thus decreasing the energy consumed. In addition, a shifting angle is used in order to avoid loss of data in case of the absence of nodes in a specific cone angle.
* Using the principle of inactivation, where some sensor nodes in the underwater sensor network become inactive. During the period of activation, nodes are powered off (i.e., do not send nor receive). The choosing of inactive nodes is based on the distance to the surface sink, as well as the energy consumption.
* Balancing energy consumption in order to avoid draining the energy of some nodes. This can be accomplished by taking into account the issue of the energy consumed when choosing relay nodes and inactive nodes. The objective of balancing the energy consumption is to prolong the life of the network by avoiding the early death of some nodes.
# High-Resolution, Broad-Range Detection Setup for Polarimetric Optical Fiber Sensors
## 3 Results
### Range and Resolution
The response of the detection setup was plotted against the set retardation, and it is shown in Figure 10.  
Based on data shown in Figure 11 and the behavior of the experimental setup, it can be concluded that the resolution of presented setup is approximately 2 mrad (0.11\({}^{\circ}\)) or 2\(\pi\)/3000 rad. The conversion factor \(k_{\varphi}\), links the response of the setup expressed in the number of pixels \(\Delta N\) with measured retardation \(\Delta\varphi\), i.e.,  
\[k_{\varphi}=\frac{\Delta N}{\Delta\varphi} \tag{22}\]  
was determined to be 57.8 pixels/rad, which corresponds to 363 pixels per 2\(\pi\) rad of the measured phase shift. Figure 11: Response of the detection setup to set retardation. Figure 10: Fragment of measured data pattern recorded by one row of pixels and quadratic function approximating that fragment of the pattern.
## 2 Materials and Methods
### Spectrofluorimetric Assay  
Human serum albumin, at a concentration of 0.19 mg/mL [3], was incubated with or without different concentrations of TMA (3.26 nM, 0.82, 2.46, 5.74, 24.6 mM) and/or TMAO (29.2 nM, 2.86, 6.68, 9.54, 28.6 mM) in 20 mM Tris/HCl, 0.1 mM EDTA and 15 mM NaCl, pH 7.4. These concentrations were selected considering the plasma mean value of TMA and TMAO in CVD patients, as measured and reported in [PERSON] et al. [15]. A ratio of mean value plasma HSA with TMA or TMAO was calculated; the ratio was converted in order to achieve the same in vitro ratio that would mimic physiological and pathological conditions (i.e., physiological condition was obtained at 3.26 nM TMA and 29.2 nM TMAO). Four samples were prepared for each experiment: the first with only albumin (control), the second with albumin and TMA, the third with albumin and TMAO, the fourth with albumin, TMA and TMAO. The samples were incubated at room temperature (25 \({}^{\circ}\)C); the measurements were taken at an excitation wavelength 295 nm at time 0 and at 60 min. The emission spectra were measured in the range from 310 nm to 450 nm in a spectrofluorometer Hitachi F-4500. All experiments were repeated at least three times under the same conditions. Data were presented as the % of variation of Trp emission spectra peak of samples containing TMA, TMAO or both compounds versus the control.
## 3 Disturbance-Rejection Controller Design Based On Linear Extended State Observer (LESO)  
The whole control scheme is illustrated in Figure 3. The dynamic model is Equation (2), \(H(\xi)\) is Equation (1), the designed LESO is described in Section 3.1, and the designed controller is described in Section 3.2.
# Improving Real-Time Position Estimation Using Correlated Noise Models +
###### Abstract  
We provide algorithms for inferring GPS (Global Positioning System) location and for quantifying the uncertainty of this estimate in real time. The algorithms are tested on GPS data from locations in the Southern Hemisphere at four significantly different latitudes. In order to rank the algorithms, we use the so-called log-score rule. The best algorithm uses an Ornstein-Uhlenbeck (OU) noise model and is built on an enhanced Kalman Filter (KF). The noise model is capable of capturing the observed autocorrelated process noise in the altitude, latitude and longitude recordings. This model outperforms a KF that assumes a Gaussian noise model, which under-reports the position uncertainties. We also found that the dilution-of-precision parameters, automatically reported by the GPS receiver at no additional cost, do not help significantly in the uncertainty quantification of the GPS positioning. A non-learning method using the actual position measurements and employing a constant uncertainty does not even converge to the correct position. Inference with the enhanced noise model is suitable for embedded computing and capable of achieving real-time position inference, can quantify uncertainty and be extended to incorporate complementary sensor recordings, e.g., from an accelerometer or from a magnetometer, in order to improve accuracy. The algorithm corresponding to the augmented-state unscented KF method suggests a computational cost of \(O(d_{x}^{2}d_{t})\), where \(d_{x}\) is the dimension of the augmented state-vector and \(d_{t}\) is an adjustable, design-dependent parameter corresponding to the length of \"past values\" one wishes to keep for re-evaluation of the model from time to time. The provided algorithm assumes \(d_{t}=1\). Hence, the algorithm is likely to be suitable for sensor fusion applications.  
GPS; uncertainty quantification; sensor fusion; noise models; embedded computing; system performance evaluation; positioning algorithms +
Footnote †: This paper is an extended version of Real-time uncertainty quantification using correlated noise models for GNSS positioning. In Proceedings of the 2017 IEEE Sensors, Glasgow, UK, 29 October–1 November 2017.
## 2 Materials and Methods
### Photoacoustic Imaging Experimentation and Machine Learning-Based Crack Detection
#### 2.1.5 Implementation Details  
The tasks of image preprocessing and augmentation were implemented using Python libraries OpenCV (cv2) and Scikit Image (skimage). The CNN models (VGG16, ResNet50, and GoogleNet) with pretrained ImageNet parameters were implemented in Pytorch, and these models were fed with the batch of input images to extract the features. These features' embeddings were concatenated over the whole training set, and the resultant feature matrix was used for PCA-based feature selection. The top 128 features (k = number of components) were selected on the basis of an optimal explained variance against a minimum number of components. These selective training features were used to train three regression models. The whole dataset was split using a ratio of 8:2 for generating the training and testing datasets, respectively. A total of 10% of the data from the training dataset were used for cross-validation. Python library SciKit Learn (sklearn) was used to perform regression and hyperparameter tuning. In the case of linear regression, generally, hyperparameter tuning is not needed because its performance primarily depends on how well input data follow a linear distribution [41]. To improve the performance of the linear regression model, data were normalized, and regularization was added into the objective function. A grid search method was used to find the optimal hyperparameters for RFR and SVR. RFR only requires the number of trees, maximum depth, and number of evidential features (m) to generate a prediction model. The model's accuracy may be improved by reducing correlation among trees by reducing the parameter m [42]. To find an optimal combination of these parameters, the range of trees was set between 100 and 300 at intervals of 50 and the search depth between 10 and 30 at an interval of 10. The value of m = 11 was selected using the square root of the input feature size. The best results were obtained for 300 trees, 20 search depth, and m = 11 using GridSearch. After a certain number of trees and depth, improvement in the model's accuracy is negligible but computational overhead is significant [43]. Root mean square error (RMSE) is used for reporting the performance results of the proposed machine learning models. RMSE is a well-known and reliable performance metric when assessing the accuracy of a predictive model. RMSE measures the average magnitude of the errors between predicted values and actual values by taking the square root of the average of the squared differences between predicted and actual values. Mathematically, it can be expressed in Equation (7) as follows:  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}{\left({y_{i}-\hat{y}_{i}}\right)}^{2}}} \tag{7}\]  
where \(n\) represents the number of observations, \(y_{i}\) represents the actual values, \(\hat{y}_{i}\) gives the predicted values, and the resulting \(RMSE\) gives the root mean squared error for the model.
# Enhancement of a Spent Irrigation Water Recycling Process: A Case Study in a Food Business
## 1 Introduction
[PERSON] (2003) claimed that RO could support plant growth with less variability, as the water quality is constant and free from impurities [20]. There is, however, an argument that RO removes useful components from the water. [PERSON] (2008) highlighted that RO removes both harmful contaminants present in the water as well as some desirable minerals [21]. RO generally removes salt, manganese, iron, fluoride, lead, and calcium [22]. This raises the issue that for the purpose of growing crops, desirable nutrients would be required to be added back into the treated water, adding additional processes, materials, and therefore, costs. Another drawback of RO is that the process wastes between 3 and 4 L of water for every litre purified [20; 23]. Furthermore, although RO does remove some bacteria, it may not remove all bacteria [24]. KK Water Purification Ltd. (2019) claims that RO alone is not recognized as an exhaustive barrier to microbiological contamination [23]. Since RO has the highest level of suspended-solid removal of all the membrane technologies, regardless of the membrane filtration process adopted, a further process is required to achieve the best results in water cleaning. Chlorine dioxide is commonly used to disinfect water. The chlorine dioxide used in these applications is commonly generated onsite, usually from sodium chlorite mixed with hydrochloric acid in chlorine dioxide generators. Removal of chlorine dioxide from the water treatment process not only removes the risk of harmful chemical by-products such as chlorates being created but also removes a particular health and safety risk. However, chlorine dioxide is a reactive gas that may affect human health and is explosive at concentrations above 10% _v/v_ in the air [25]. Ultraviolet (UV) technology has been successfully used for the control of pathogen microorganisms, with the most common application for UV light in water treatment systems being disinfection. UV successfully attacks chlorine-resistant microorganisms by disinfecting water as it flows through a chamber containing a UV lamp. As the water flows past the lamp, microorganisms receive a lethal dose of UV light. UV water disinfection is, therefore, a safe, chemical-free way to treat water [23]. In this context, the purpose of this article is to assess the current spent irrigation water recycling process used in a food business, to then, propose a solution to improve the filtration system and, consequently, remove the requirement to treat the spent irrigation water with chemicals.