esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper proposes using neutral axis locations to monitor and quantify the prestress force in post-tensioned precast segmental beams. Strain measurements are used to obtain the neutral axis locations of specific cross-sections of the precast prestressed segmental beams, based on the plane-remains-plane and linear strain distribution assumption. A theoretical calculation method based on the static equilibrium of a specific cross-section is developed to calculate the prestress force in segmental beams based on the neutral axis location. To verify the accuracy of the proposed method, a post-tensioned prestressed segmental beam is built and tested in the laboratory. A corresponding high-fidelity finite element model is also developed based on the beam design and material properties. Experimental studies and numerical simulations are conducted to verify the feasibility and accuracy of the proposed method in quantifying the prestress force in precast segmental beams. Both experimental and numerical results demonstrate that the proposed method can reliably estimate the prestress force, which can be used to monitor the prestress force loss in post-tensioned structures.  
preast segmental beam; prestress force; quantification; post-tensioning; neutral axis location Article
## 5 Conclusion  
In this paper, the characteristics of flow around two cylinders with roughness strips and their FIV under turbulent condition were studied in detail by a numerical method. The amplitude, frequency, and wake vortex pattern of the cylinders were analysed. The influence of the gap distance ratio along flow direction and cross flow direction on FIV characteristics of two cylinders was discussed. The influence mechanism of gap flow on two cylinders was understood. The main conclusions are as follows:  
1. For the two stationary cylinders, the spacing \(dc\) at which the gap flow can be observed is \(3D\) when \(Re=2000\), \(d_{c}=2.5D\) at \(Re=6000\)-\(14\),\(000\). When gap distance ratio \(d=d_{c}\), a gap flow is generated, and the lift-drag coefficient and the \(St\) of the cylinders increase sharply.
2. For the two staggered cylinders, the vibration modes include: Periodic vibration mode (small reduced velocity), double-periodic vibration mode (moderate reduced velocity and small staggered distance), multi-periodic vibration mode (moderate reduced velocity and large staggered distance) and quasi-periodic vibration mode (large reduced velocity).
3. When the staggered distance is changed, the amplitude of the upstream cylinder changes slightly, and the maximum amplitude can be obtained at \(U^{*}=6\). The amplitude of the downstream  
Figure 14: The wake vortex structure of two cylinders with roughness strips at different reduced velocities at \(T=0.6D\).  
Figure 15: The wake vortex structure of two cylinders with roughness strips at different reduced velocities at \(T=1.0D\).  
cylinder varies significantly, and its maximum amplitude is obtained at \(T=0.6D\) and \(U^{*}=12\). The variation of vibration frequency of two cylinders is consistent. With the increase of staggered distance, the vibration frequencies of the two cylinders get closer.
4. At the same inflow velocity, the gap flow between the two cylinders is formed as the staggered distance increases. When \(T>0.6D\) and \(U^{*}<8\), gap flow becomes the main factor affecting the vibration of the two cylinders, which can be divided into the dominant region of gap flow. In other regions, the two cylinders can be approximated as the arrangement in series.  
Conceptualization, Z.-M.Y., L.D., and Q.-Y.Y.; methodology, Z.-M.Y., L.D., Q.-Y.Y., and L.Y.; validation, Z.-M.Y. and Q.-Y.Y.; formal analysis, Z.-M.Y. and L.D.; investigation, Z.-M.Y., Q.-Y.Y., and L.Y.; resources, L.D. and L.Z.; data curation, Z.-M.Y. and L.D.; writing--original draft preparation, Z.-M.Y. and L.D., writing--review and editing, L.Z. and Q.-Y.Y.; supervision, L.Z.; project administration, L.D.; funding acquisition, L.D.  
This research was funded by National Natural Science Foundation of China, grant number 51776021.  
The authors acknowledge the help from VIVACE group of Marine Renewable Energy Laboratory of the University of Michigan, and special thanks to [PERSON] and [PERSON].  
The authors declare no conflict of interest.
## 5 Equivalent Fixed-Based Structure (EQ) Model  
The proposed formulation was herein applied to perform the equivalent fixed-based structure (EQ model) that represents Model 2 (H = 20 m): S21 (density = 1.8 kN/m\({}^{2}\) and Vs = 100 m/s). Parameters \(\alpha\) and \(\beta\) were calculated by (9)-(10) and resulted as 0.03 and 0.001469, respectively. Two assumptions were considered: (1) structural masses were proportionally increased to obtain 0.80 s as the fundamental period (compare with Figure 9b) and (2) the parameter \(\eta\) (\(\eta\) = 0.778, [29]) was applied to account for the increased damping of the system (11.5%, instead of the conventional 5%).  
Ten input motions were selected from the NGA database on the basis of the Eurocode prescriptions to be compatible with the code-specified spectrum for the life-safety limit state (corresponding to an earthquake with a return period of 475 years, lat.: 42.333 N, 14.246 E, S = 1.50714, Tb = 0.257 s, Tc = 0.770 s, Td = 2.538, Cc = 2.02847, NTC). Figure 10 shows the selected elastic response spectra (pseudo-spectral acceleration vs. period) and the mean spectral ordinates. Note that the mean spectrum (calculated among the selected ones) is included between the \(-\)10% and +30% of the code-based spectral shape, as prescribed in [30]. Table 7 shows the characteristics of the selected input motions in terms of peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). Note that five of the selected input motions were taken from near field (NF) motions and five from far field (FF) ones. In addition, the set was selected to have significantly dispersion of intensities to significantly affect the dynamic characteristics of the system.  
The non-linear dynamic analyses were performed with the longitudinal component of the input motions that was applied at the base of the model. As in [31], the large dimension of the 3D model needs to consider several steps in the analyses. In the first step the soil (without the structure and foundation) was loaded and linear properties (weight, shear and bulk modulus) were considered. In step 2, the soil properties were changed from elastic to plastic (in 25 load steps). In step 3 structural loads were applied and finally dynamic analyses were performed in step 4. The NewtonLineSearch algorithm was used to increase the speed of the solution.  
Figure 10: Selected input motions.
## 3 Experimental Results
### Performance Evaluation of the Sigmoid Calibration Function  
The performance of the sigmoid calibration function in reconfiguring UAV radiometric properties to potholes was evaluated by analyzing AUC results generated by the ROC model. Figure 8 presents the AUC-ROC curve results explaining the performance of the sigmoid calibration model.  
Based on the results, the AUC values for blue, green, red, NIR and red edge spectral channels of UAV imagery were found to be 0.53, 0.35, 0.71, 0.19 and 0.35, respectively. By implication, the sigmoid calibration function showed better performance in radiometrically reconfiguring the red channel of UAV imagery (Figure 7c). Moreover, the performance of the sigmoid calibration function on blue, green, NIR and red edge spectral channels was weak (Figure 7a,b,d,e).
## 4 Enterprise Network Functionality
### Network Monitoring and AI-Driven Analytics  
Enterprise networks depend on AI-driven analytics and monitoring technologies to guarantee reliable performance and security [74]. Network managers are constantly alerted of any problems by network performance management (NPM) tools, which constantly track traffic patterns in order to identify abnormalities. Consequently, this process is improved by artificial intelligence (AI) and machine learning (ML) through identification of malicious patterns, activation of self-healing mechanisms [75; 76; 77], and prediction of bottlenecks within the network [1].
# Controller-Pilot Data Link Communication Security
## 2 Related Work  
The research community is questioning the security of many of the aviation systems [5]. In [6] [PERSON] et al. discuss the current lack of security within the automatic dependent surveillance - broadcast (ADS-B). In [1] [PERSON] mentions that CPDLC, along with other systems, is inherently insecure as it provides unauthenticated and unencrypted data links which are easy to eavesdrop upon and attack. In [7] the authors present an interesting experiment where a possible attack was simulated in a testing environment demonstrating how the CPDLC system can be attacked through a man-in-the-middle approach using freeware and open source tools. Although they did allow the attacker to have physical access to the network, which is unlikely to happen in real life, the work demonstrates that the system is insecure at the application level even before it goes into the wireless medium.  
According to [PERSON] et al. [8], air traffic navigation (ATN) systems should support authentication for peers, while securing the data integrity in order to ensure that the information has not been modified or duplicated. The questions are not new and several proposals on how to secure the ATN system were presented more than 15 years ago. In [8] the authors proposed how to secure the communication through encryption using an asymmetric key system to securely communicate with a symmetric session key. In addition, in the same year [PERSON] [9] designed a complete model demonstrating how to secure the ATN system while minimizing security overhead. Some years later the authors of [10] suggested a secure protocol design based on elliptic curve cryptography (ECC). None of the proposed security solutions were adopted by the authorities, leaving CPDLC together with many other ATN means without any form of protection against cyberattacks.
# Recommender Systems in the Real Estate Market--A Survey
## 2 Background
### Collaborative Filtering  
Collaborative filtering (CF) is a group of RSs which infers the preferences of a user using the preferences of other users in the system. Therefore, this type of RS assumes that users with similar history have similar tastes. The main advantage of CF-based RSs is that, unlike CB that provides obvious and over-specified recommendations, they are able to provide non-obvious and therefore more surprising recommendations. Generally there are two main categories of CF-based RSs: model-based and memory-based ones.  
Model-based CF methods such as FunkSVD [2], BPR [3], and NeuMF [4] use the given users' feedback to train a model and learn parameters to provide recommendations. These methods learn two low-rank matrices for users and items that represent them in a dense latent feature space. The given users' feedback can be explicit (e.g., rating) or implicit (e.g., clicks). Model-based CF methods can be categorized based on their learning approaches to learning to predict ratings/interactions [2,4,5] and learning-to-rank methods [3,6,7]. Unlike the model-based CF, memory-based methods such as user-based KNN (UKNN) [8] and item-based KNN (IKNN) [9] are heuristic approaches that do not learn parameters but form neighborhoods based on user or item similarities to generate recommendations.
# Encapsulated Probiotics: Potential Techniques and Coating Materials for Non-Dairy Food Applications
## 3 Probiotic Encapsulation Techniques
### Drying Method
#### 3.4.2 Freeze-drying  
Freeze-drying, also known as lyophilization or cryodesiccation, is a process whereby the water vapor in the frozen sample is removed through the sublimation of ice. This technique produces capsules with sizes of 1-1.5 mm. It is commonly used to preserve thermosensitive components such as probiotics. The process of freeze-drying can be divided into three phases, (i) the initial freezing process of the probiotics (together with the carrier material), (ii) the primary drying (sublimation) phase, and (iii) secondary drying to eliminate the remaining traces of water due to absorption [43].  
[PERSON] et al. [23] used freeze-drying and spray-drying to encapsulate _E. faecalis_ incorporated in carrot juice using gum Arabic and maltodextrin as coating materials. Compared to spray-drying, heat injuries to the probiotics are lower in the freeze-drying technique. [PERSON] et al. [59] reported that _L. acidophilus_ microencapsulated in the form of emulsification/internal gelation followed by freeze-drying using a blend of pectin micro-particles with prebiotic rice bran maintained probiotic viability for 120 days at 25 \({}^{\circ}\)C. In another study, [PERSON] et al. [25] used freeze-drying to encapsulate _L. acidophilus_ and _L. casei_ using whey protein isolate and fructologosaccharides as wall material in the development of probiotics-enriched freeze-dried banana powder. During the 30 days of refrigerated (at 4 \({}^{\circ}\)C) storage, the encapsulated probiotics had higher survivability compared to the free cells. The encapsulated probiotics were also more resistant to simulated gastric intestinal fluid.  
However, in another investigation by [PERSON] et al. [60], the authors did not obtain the same positive findings using microencapsulation of _L. acidophilus_ Lac-04 through complex coaervation followed by freeze-drying. The authors observed a significant decrease in viability (\(p<0.05\)) after 30 days at 37 \({}^{\circ}\)C. The microencapsulated probiotics failed to withstand the pH condition of the human stomach. Although the freeze-drying technique has been reported to provide shelf stability to probiotics, sometimes the crystal formation during the freezing process can result in cell injury and eventually lead to cell death. Therefore, cryoprotectants that exert protection for the probiotics are necessary [61]. Cryoprotectants protect probiotics from freezing damage by inhibiting rapid cellular dehydration and ice formation during freeze-drying. Furthermore, freeze-drying is an expensive procedure with high operational and maintenance costs and is not easy to scale up [40].
# Image Degradation Modeling for Real-World Super Resolution via Conditional Normalizing Flow
## 3 Materials and Methods
### Datasets  
To conduct experiments on IDFlow, we chose three different image datasets: RealSR [8], DF2K, and DPED [22]. Moreover, we split these datasets used for training and testing. The summarized information about the datasets are reported in Table 1.  
RealSR: The RealSR [8] dataset contains 559 real-world paired HR-LR images taken by DSLR cameras in real scenarios. We used the third version of this dataset, which contains HR and LR images at different resolutions with a scale factor of \(\times 4\). Following [8], we split the dataset into two parts: 459 images for training, denoted as the RealSR-train, and 100 images only for testing, denoted as the RealSR-Test. RealSR-Train is used for the training of blur degradation modeling in IDFlow.  
DF2K: The DF2K dataset combines both the DIV2K [13] and Flicker2K [4] datasets, totally containing 3450 high-resolution images of diverse scenes. We used the DF2K dataset as HR source images to generate HR-LR training pairs for SR model training.  
DPED: The DPED [22] dataset is a real-world image dataset that has 5614 images directly taken by an iPhone 3 mobile phone, denoted as DPED-Train. Moreover, it has a testing set that contains 100 cropped images, denoted as DPEP-Test. The images have sensor noise, blur, and unknown artifacts. We collected about 3500 noise patches from DPED-Train with a size of \(256\times 256\) for the training of real-world nosie modeling in IDFlow, denoted as DPED-noises. Specially, the DPED dataset has no HR ground-truth available.
## 4 Experiment Setup  
The schematic diagram of the developed PAS system with a multi-pass cell is depicted in Figure 4. A 1653 nm laser (DFB1653, Sichuan Zhiyuanguang Electronics Technology, Chengdu, China) was used as the excitation source. Figure 5 shows the performance of the DFB laser measured by an optical spectrum analyzer (AQ6317, ANDO Electric, Tokyo, Japan). The emitting wavelength of the DFB laser is tuned from 1652.8 nm to 1654.4 nm with the injected current from 70 to 170 mA at the temperature of 23.89 \({}^{\circ}\)C. The corresponding light power is changed from 9 to 24 mW. The temperature and current of the DFB laser were precisely controlled by a laser driver (TED200 and LDC205, Thorlabs, Newton, MA, USA). An analog modulation signal combined with a low-frequency sawtooth and a high-frequency sine wave was generated by a data acquisition card (USB-6211, National instrument, Austin, TX, USA) and imposed on the laser driver. A fiber collimator with a focal length about 20 mm was employed to collimate the laser beam. The collimated laser beam enters from the edge of one side of the reflecting mirror at a designed incident angle, and then exits the photoacoustic system after 21 round trips of reflection.  
Figure 4: Schematic diagram of the multi-pass cell photoacoustic system.  
Figure 3: (**a**) Schematic diagram of the designed photoacoustic cell. (**b**) Image of the designed photoacoustic cell.  
Methane has strong absorption intensity at 1653 nm. At 1653.72 nm, its absorption cross-section reaches 1.25 \(\times\) 10\({}^{-20}\) cm\({}^{2}\)/mol. Figure 6 shows the absorption cross-section of methane at 1653.5-1654 nm (the data are simulated by the HITRAN database) [31]. To cover the absorption line of methane at 1653 nm, the temperature and current of the laser were precisely controlled by the laser driver.  
A pair of mirrors coated with protective silver were used in the multi-pass system. To minimize the loss of light intensity, two ultraviolet fused silica lenses coated with SWIR antireflection film (900-1700 nm) were selected as the window of PA cell. The PA cell was made of stainless steel with a resonant frequency of 4560 Hz. Four microphones (FG-23329-P07, Knowles, Istacka, IL, USA) were installed in the center of the resonant cavity. All four microphones were connected with a preamplifier circuit to obtain stronger signals. The microphone signal was amplified and sent to a phase-locked amplifier (LIA-BVD-150-L, Femto, Berlin, Germany). The photoacoustic signal was demodulated using the lock-in amplifier with a reference sine signal generated by the data acquisition card. The air inlet and outlet of PA cell were set at the left and right buffer tanks. Two mass flow controllers (GM50A013103 SMM020, MKS Instruments, Andover, MA, USA) with a maximum flow rate of 1 L/min and an error of 1 mL/min were used to control the proportion and speed of incoming gas.  
Figure 5: The laser wavelength and the output power tuned by the injected current of the experiment at the temperature of 23.89 \({}^{\circ}\)C.  
Figure 6: The absorption cross-section value of methane from 1653.5 nm to 1654 nm.
## 3 Results and Discussion
### Structural Interpretation  
From the above analyses, the suggested structures of these complexes are given in Figure 6 and are matched with previous research. The Schiff base ligand coordinated to metal ions Mn(II), Ni(II) through the N, N, O system [11; 37] while Co(II) and Zn(II) [38], was bidentate and bonded through the N, N, O atoms. For Cu(II), the structure is presented in Figure 6.
# ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias
## 3 Materials and Methods  
This study seeks to classify ECG signals for the diagnosis of various arrhythmia diseases using an interpretable KAN. The methodology comprises three primary components: data preprocessing, model architecture, and evaluation analysis. Sample balanced and variance sorting are employed to preprocess ECG signals. Subsequently, the classification performance of ECG signals is compared between the KAN and MLP models, highlighting the advantages of the interpretable KAN model through data analysis techniques such as feature clustering and fuzzy matrices. Figure 1 outlines the entire workflow, which is elaborated upon in subsequent sections.
# A Study of Forest Phenology Prediction Based on GRU Models
## 2 Method
### Pre-Built Model Selection for Vegetation Index  
The GRU model consists of two control gates (Figure 4): the update gate (\(Z_{t}\)) and the reset gate (\(r_{t}\)) [36], which utilize an update gate to perform the functions of both the forget gate and the input gate and utilize the reset gate to directly act on the previous hidden state. The reset gate determines how to combine the new input information with the previous memory, while the update gate defines the amount of previous memory to be preserved up to the current time step. Essentially, these two gate control vectors determine which information can ultimately be output from the gated recurrent unit. The unique feature of the GRU model is its ability to maintain information in long-term sequences without clearing or removing it over time, even if it is not relevant to the prediction. The update equation is computed as follows:  
\[Z_{t}=\sigma(W_{xx}\chi_{t}+W_{zh}h_{t-1}+b_{z}) \tag{3}\]  
\[r_{t}=\sigma(W_{rx}\chi_{t}+W_{nh}h_{t-1}+b_{r}) \tag{4}\]  
\[\widetilde{c}_{t}=\tanh(W_{cx}\chi_{t}+W_{ch}(r_{t}*h_{t-1})+b_{c}) \tag{5}\]  
\[c_{t}=(1-Z_{t})c_{t-1}+Z_{t}\widetilde{c}_{t} \tag{6}\]  
Figure 4: The structure of the GRU neural network.  
In the formula, \(Z_{t}\) is update gate, \(r_{t}\) is reset gate. \(c_{t}\) is the output vector of the hidden layer at time \(t\). \(\widetilde{c}_{t}\) is the updated candidate vector. The sigmoid function maps the result between 0-1. The closer it gets to 1, the easier it is to retain information.
# Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction
## 1 Introduction
The CNN model extracts relevant spatial features, further enhancing prediction accuracy, while the GRU model utilizes past datasets to predict future trends. The experiment proves that the CNN-GRU model has a better performance than the CNN and GRU model, with an improvement of about 45%. The proposed method provides a new idea for hydraulic safety management. The rest of this paper is organized as follows: Section 2 introduces the principles, processes, and details of the methods used in this study. Section 3 validates the effectiveness and superiority of the proposed methods through experiments. Section 4 summarizes the rationality, superiority, and possible issues of the proposed methods. Finally, Section 5 concludes the entire paper, providing analysis and conclusions.