esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup
### Vibration Signals  
The test rig was designed to run the slew bearing from brand new until final failure. The vibration signal was monitored continuously for 139 days from February to August 2007. Each day, the vibration signal was acquired automatically using task scheduler software that automatically triggers the Pico data acquisition equipment to collect the signal for approximately 3 min. Once the vibration data are saved in the PC, a nine-features extraction executable file is also run automatically using task scheduler software at about 30 min after the digitized vibration signal is stored in the PC. An example plot of the vibration signal from accelerometer #1 (radial) is presented in Figure 3 and the feature extraction result is presented in Section 3.3.  
In steel making industries, slew bearings are operated in dusty environments. Typical micro size particles from coal as the energy source in the industry are visually found inside the bearing when the bearing is dismantled from the rig during the maintenance work. In this study, the coal dust is inserted into the slew bearing to accelerate the failure in mid-April (58 days from the beginning). An example of the original vibration signal is presented in Figure 3a. The signal is collected before the final failure on 1 September. It can be seen from Figure 3b that the signal is dominated with unknown high frequency signals. None of these frequencies are associated with bearing fault frequencies. According to the FFT result, some degradation parameters, namely features, are necessary to quantitatively monitor the bearing condition, especially to identify when the impending damage occurs.  
Figure 1: (**a**) Slew bearing rig picture; (**b**) Schematic of laboratory slew bearing rig showing a slew bearing attached in the drive ring and the applied load from the hydraulic.  
Figure 2: A detailed sketch of the location of four accelerometers and two AE sensors on slew bearing.
# A Hybrid DSCNN-GRU Based Surrogate Model for Transient Groundwater Flow Prediction
## 2 Methodology
### Deep Learning-Based Surrogate Models
#### 2.2.4 Proposed Method (DSCNN-GRU)  
After confirming the feasibility of CNN as a surrogate model, an optimized coupling methods should be determined by systematically combining DSCNN, LSTM, and GRU network architectures. During this experimental phase, CNN is combined with LSTM to form CNN-LSTM, while DSCNN is fused with LSTM to create DSCNN-LSTM. Furthermore, the combination of DSCNN and GRU results in DSCNN-GRU.  
To enhance the processing ability of surrogate models for time series, this study combines CNN and RNN. Traditional RNN networks struggle to capture long-term dependencies in time series data due to vanishing gradients. While LSTM mitigates the memory issue, it shares the inefficiency of CNN when handling large-scale datasets. To boost the training efficiency and accuracy of surrogate models, DSCNN offers higher computational efficiency and comparable prediction accuracy to CNN, as described in Sections 2.2.2 and 2.2.3. On the other hand, GRU provides higher computational efficiency and retains robust long-term memory compared to LSTM. Thus, this study combines DSCNN with GRU to create a new model.  
This study employs [PERSON] TimeDistributed layer to merge DSCNN and GRU. The input consists of chronologically ordered image sequences. TimeDistributed applies the DSCNN model to each time slice of the sequence. The output flows into a 128-unit GRU layer, integrating information from the entire sequence. This allows the model to process image sequences while learning spatial features (using DSCNN) and temporal features (using GRU).  
As shown in Figure 9, after DSCNN and GRU processing, the image dataset, now enriched with temporal features, is supplemented with hydraulic conductivity field and pumping rate data. These two datasets are first passed through a fully connected layer to extract temporal features. The processed image data and these features are then input into another fully connected layer to learn their temporal relationships. This allows the model, given hydraulic conductivity field and pumping rate data, to predict groundwater level fields for the entire study area at each time step. The prediction is based on the model's understanding of the temporal and spatial characteristics of the image data, alongside the relationship between these features and the mentioned data types. To facilitate evaluation, as detailed in Section 2.2.1, a reshape operation is incorporated at the end of the model. This operation generates visual representations of the water level field at six specific time points across the entire time period.
_applied_  
_sciences_  
_TDGVRPSTW of Fresh Agricultural Products Distribution:_  
Considering Both Economic Cost and Environmental Cost_  
**[PERSON] 1,2** and [PERSON] 1,*  
1 College of Economics and Management, Shanghai Ocean University, Shanghai 201306, China; [EMAIL_ADDRESS]  
2 Nanchang Institute of Technology, Economic and Technological Development Zone, Nanchang 330044, China  
* Correspondence: [EMAIL_ADDRESS]; Tel: +86-021-61900856  
**Abstract**: The time-dependent vehicle routing problem of time windows of fresh agricultural products distribution have been studied by considering both economic cost and environmental cost. A calculation method for road travel time across time periods is designed in this study. A freshness measure function of agricultural products and a measure function of carbon emission rate are employed by considering time-varying vehicle speeds, fuel consumptions, carbon emissions, perishable agricultural products, customers' time windows, and minimum freshness. A time-dependent green vehicle routing problem with soft time windows (TDGVRPSTW) model is formulated. The object of the TDGVRPSTW model is to minimize the sum of economic cost and environmental cost. According to the characteristics of the model, a new variable neighborhood adaptive genetic algorithm is designed, which integrates the global search ability of the genetic algorithm and the local search ability of the variable neighborhood descent algorithm. Finally, the experimental data show that the proposed approaches effectively avoid traffic congestions, reduce total distribution costs, and promote energy conservation and emission reduction.  
**Keywords**: green vehicle routing problem; fresh agricultural products; genetic algorithm; variable neighborhood descent algorithm
## 4 Analysis and Findings of Research Questions
#### 4.1.5. Smartphone User Activities Based on Application Modality  
According to [100], users' password-entry activity may be observed by others, captured on small recording devices or public surveillance cameras when they use their mobiles in public. An adversary can immediately access sensitive data if a mobile is stolen or lost, as most user authentication mechanisms do not support post-login authentication. As system usability is crucial [107], user authentication mechanisms are continually constrained by usability and security trade-offs.  
In a study by [106], smartphone wallet software frequently facilitated consumer contact with cryptocurrency. Static code analysis and network data analysis were adopted to investigate and reveal that a lower rate of security vulnerabilities characterizes traditional banking applications compared with cryptocurrency applications. Furthermore, Google Play Store and other third-party app shops tackle illegally modified free versions of costly products. The dearth of sufficient security measures permits the rapid proliferation of mobile malware within these markets [113]. Moreover, one of the most pervasive and severe errors on these devices is the digitalized copies of traditional agendas and contact books. They are also the primary and ultimate interfaces to what is popularly referred to as the \"mobile cloud\" [103].  
Relevant to the first research question, smartphone users' activities have been categorized into five groups: sensors; services; physical; software; and applications. Each group is associated with the activities or behaviors connected to the sensors based on the permission granted. Notably, user behaviors contribute to cyber attacks due to unawareness, lack of knowledge, and unskilled employees inside a business, resulting in targeting smartphone assets such as contacts, bank information, and images. Providing permissions requested by applications exposes the mobile user to privacy threats [114].  
Email, services, and sensors are the mediums for spear phishing on mobile devices.  
The sensors serve as a means for data exchange, shared services provision, and message transmission between various electronic devices.  
Nevertheless, attackers can take advantage of this vulnerability to attack by either sharing malicious links or sending malware to a targeted victim in the form of an.apk file. Victims are more likely to click a URL than malware since malware needs to request access to specific permissions, such as the Internet, to obtain access to the data [53]. Consequently, smart devices face numerous issues due to threats that can be easily implemented, gain access to the sensors, and be exploited due to a lack of awareness.  
Researchers have proposed numerous solutions to strengthen smartphone security against sensor-based threats. These solutions range from imposing strict permissions for sensors to analyze information flow between devices. On the other hand, these suggested solutions rely on either the decisions made by users or the accessibility of the applications' source code [115].
# An Intelligent Warning Method for Diagnosing Underwater Structural Damage
## 2 Methodology
### Gray Level Co-Occurrence Matrix  
The gray level co-occurrence matrix (GLCM), which specifies a certain condition in the gray space, counts the number of probability occurrences. Additionally, it meets the specified condition in the entire image of the pixel pair. Suppose the pixel pair coordinates are in order \((x,y)\), \((x+dx,y+dy)\) and the pixel values are \(f(x,y)=i\), \(f(x+dx,y+dy)=j\). When the image gray level is \(g\), the criterion of the sliding window is defined as follows. The pixel pair spacing value is \(d\), and the angle between the connecting line and the horizontal direction is \(\theta\). Among them, \(g\), \(d\), and \(\theta\) are generative criterion of the GLCM. \(P(i,j,d,\theta)\) is the probability that pairs of pixels appear at the same time. The GLCM can be expressed by Equation (1):  
\[P(i,j,d,\theta)=\Big{\{}\Big{[}(x,y),(x+dx,y+dy)\Big{]}f(x,y)=i,f(x+dx,y+dy)=j \Big{]}\Big{\}}, \tag{1}\]  
where \(\theta\) is \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\).  
Besides, \(dx,dy\) are the offset vectors; the equation is as follows:  
\[dx=d\cos\theta,\;dy=d\sin\theta. \tag{2}\]
## 2 Materials and Methods
### Bacterial Identification and Antibiotic Sensitivity Assessment of the Bacteria by VITEK 2 System  
In this study, the Vitek 2 system (bioMerieux, Marcy l'Etoile, France) was used to identify and test the antibiotic sensitivity of all species at Alborg Medical Laboratories in Riyadh City, Saudi Arabia, following the manufacturer's instructions. Gram-negative identification (GNI) cards, Gram-positive identification (GPI) cards, and antibiotic susceptibility testing (AST) cards were utilized for this purpose. To conduct the identification technique, 2-3 fresh colonies were completely dissolved in a sterilized physiological salt solution (0.45% NaCl in water, pH 4.5 to 7.0). The McFarland turbidity was adjusted from 0.50 to 0.63 using DensiChekTM (BioMerieux, Marcy-l'Etoile, France). Five milliliters of the adjusted suspension were added to the Vitek 2 ID-GNI and ID-GPI cards. Finally, the Vitek 2 cassette with cards and suspension tubes was inserted into the device. The unidentified bacteria were compared with the reference strains recorded in Vitek 2 software to ensure correct identification. Similarly, the AST-GP67, AST-N292, and AST-N291 cards were used in the same manner of identification to determine the AST by replacing the ID cards with AST cards. Based on the interpretation guidelines provided by NCCLS [35], the MICs classified bacterial susceptibility into susceptible, moderate, or resistant categories.
# Experimental Investigation of Vertical Density Profile of Medium Density Fiberboard in Hot Press
## 4 Comparative Analysis
### Internal Bonding and Strength of the Sheet  
The use of tests allows for the strength of sheets and the ways by which they resist external force to be determined. Multiple industries perform bonding tests to standardize final sheets. Prior to optimization, we used an iron pattern, but this consumed a lot of time in the heating process and in conducting the test. Now, however, we use a steel pattern, which speeds up the heating process and is less time consuming.  
Several authors have worked on medium density fiberboard properties in many ways, but this study experimentally investigates the properties of MDF board by examining the effect that changing the various process parameters has on bending, internal bonding, and the vertical density profile. IN a previous study, [PERSON] et al. worked on the modeling of the development of the vertical density profile of MDF during hot pressing [26]. [PERSON] et al. worked on modeling and provided a comparison of vertical density profiles. There is various MDF manufacturing plant types, such as single daylight opening plants, multi-opening plants, and continuous production plants [27].  
The results show that the internal bonding and strength of the sample before optimization are lower than the standards, which means that the sheet can easily be rupture at the center and also from the side. In contrast, by setting the press parameters of pressure, temperature time, and dampness content, internal bonding reaches its highest level of quality.
## 5 Experimental Results and Discussion
### Verification of the Health Factor
#### 5.2.4 Results Analysis of OCV  
Figure 13 showed that the SOC interval and the OCV-SOC curve influence the capacity estimation result.  
(1) The SOC value is calculated on the basis of OCV value, thus the accuracy of the OCV-SOC curve will affect the prediction result. Figure 13 shows that the estimation errors of different OCV-SOC curves are different;  
(2) The OCV test will obtain multiple corresponding points of OCV value and SOC value and these corresponding points are used to fit the OCV-SOC curve. In generally, the errors between the fitting value and the real value of the two ends of the fitting curve are relative larger. The above SOC interval 1 (100%-35%) has a large error due to use the 100% SOC value. For SOC interval 3 (95%-25%) and SOC interval 5 (90%-25%), the lower SOC of these two intervals is 25%, with the degradation of the battery, this 25% SOC will reduce to a relative small value. For example, if the nominal capacity of the battery is 1500 mAh, when the fresh battery with full charged is discharged for 1125 mAh, then the SOC is changed from 100% to 25%. However, when the fresh battery degenerates into an old one and its life will end; its available capacity is degraded into 1200 mAh, if the battery with full charged is still discharged for 1125 mAh, then the SOC is changed from 100% to 6.25% which is below 10% and the OCV-SOC fitting error is larger. Figure 13 showed, in the later battery life, the estimating error is larger for the two SOC intervals than the error in the early battery life;  
Figure 13: Capacity Prediction.  
(3) The SOC interval is greater than 60%, the estimation results are more accurate [18]. The SOC interval 4 (90%-35%) and the SOC interval 6 (90%-50%) are less than 60%. Figure 13 indicated the estimate error of such two SOC interval are relative larger for the three OCV-SOC curve with different orders.  
In summary, the SOC interval 2 (95%-30%) not only satisfies that the SOC interval is more than 60% but also satisfies that the 100% SOC and the SOC below 10% are not used. Form Figure 13, it can be seen that the estimating errors using such SOC interval are relative small and the estimate error of the OCV-SOC curve with 11 order polynomial is the smallest.
## 1 Introduction
In fact, the short spreading codes including the GPS L1 C/A code (and also BDS B1-I code), are more accurately modelled as a cyclostationary random process using two-parameter ACF [12]. Although simple, the existing analytical methods generate results worse than (typically with accuracy of 0.2 degrees (1 \(\sigma\)) for carrier tracking standard deviation over a 10-s sliding window) or even different from those from pure simulation methods using a constellation simulator, a software receiver, and a software signal generator [13]. In addition, to date all analytical models on self-interference are for GPS L1 C/A code, and there are no models for self-interference within the BDS constellation. To evaluate BDS self-interference, a simple analytical model is developed from an existing model for GPS self-interference [12] to predict the impact of B1-I code self-interference on BDS receiver functions that are dependent primarily on the sum of the correlations (e.g., carrier phase tracking and data demodulation). The focus is on self-interference between BDS MEO and IGSO satellites. Self-interference effects between BDS GEOs are not studied because GEO signals do not interfere with MEO signals due to the high symbol rate [14]. The effects of the NH code [15], only present in the BDS MEO and IGSO B1-I signal, on self-interference evaluation, is addressed during the development of the analytical model. The proposed model is based on the perspective that self-interference can be modelled by the introduction of an extra white noise in receiver operations. The noise has the same effects on code and carrier tracking as self-interference. The accuracy of the model is ensured by using a two-parameter ACF to characterize a B1-I code either with or without navigation data. This type of cyclostationary analytical model will be validated as an accurate method to evaluate self-interference for short codes, producing excellent agreement with actual receiver observables, using hardware simulator intermediate frequency (IF) data and a software receiver. The Section \"Signal and Correlator Model\" provides basic assumptions and signal models to be used in the development of the proposed analytical model. Section \"SSC and Equivalent White Noise Level\" derives analytical models for the variance at the correlator output for three situations: the data bits of the desired and undesired signals are (1) aligned or (2) misaligned by an integer number of code chips or (3) misaligned by a fraction of a code chip. These three models are the basis of the proposed analytical model. Section \"Proposed Model for BDS MEO/IGSO B1-I Self-Interference\" presents the proposed model based on SSCs derived in the previous section. Section \"Model Validation\" demonstrates the validity of the model using live BDS data record and a modified software receiver. Section \"Results\" analyzes typical behavior of B1-I self-interference and its impact on receiver range observables using the developed model. Section \"Conclusions\" summarizes the expected contributions and presents a framework for further efforts.
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 3 Reflector Antenna Configurations in Spaceborne SAR Systems
### Feed Configurations for Reflector Antenna
#### 3.2.3. Phased Array Feed (PAF)  
The PAF technique combines the beam-scanning and beamforming capabilities of phased arrays with the high-gain characteristics of reflector antennas, offering an advanced solution for high-performance radar and communication systems. In spaceborne SAR applications, PAF reflector antennas provide HRWS imaging with flexible beam control, making them a key development direction.  
PAF reflector antennas typically adopt a hybrid architecture, where the phased array feed illuminates the reflector, enabling beam agility and dynamic adjustments to meet the demands of high-resolution SAR. Unlike multi-beam feeds described in Section 3.2.2, which require complex high-power feed-switching networks to achieve multiple beams of different widths and directions required for SARs, the reflector antenna with PAF has the advantages of both phased array antennas and reflector antennas, mainly in the following aspects [58]:  
(1) Improved System Reliability--The distributed transmission of a PAF ensures that all beams are formed simultaneously by all feed elements, eliminating the need for high-power microwave switches or feed-switching networks, thereby improving system reliability.  
(2) Reduced Feed Losses--PAF designs allow transmission/reception (T/R) components to be placed closer to the horn elements, reducing feed loss and enhancing antenna efficiency [59; 60].  
(3) Enhanced Beam Flexibility--By applying amplitude and phase weighting to feed elements, PAF reflector antennas can achieve limited electronic scanning and beam broadening, offering greater flexibility than multi-beam reflector antennas [61; 62].  
Although the PAF technique is still relatively uncommon in SAR applications, China's environmental and disaster monitoring satellites (HJ-2-E/F) have adopted a one-dimensional phased array feed, with its basic configuration shown in Figure 12. The PAF technique introduces new capabilities to reflector antennas, such as beam scanning, beamforming, and multi-beam generation, significantly expanding their applicability. In spaceborne SAR systems, PAF reflector antennas offer great potential for HRWS imaging, adaptive beam control, and multi-functional SAR operations. As technology advances, PAF-based reflector antennas are expected to play an increasingly important role in future spaceborne SAR systems.  
Figure 12: Phased array feed reflector antenna basic configuration.
# Electric Load Forecasting Based on Deep Ensemble Learning
## 1 Introduction
### Related Works  
Power load forecasting can be categorized into traditional and intelligent methods. Traditional methods typically encompass the elasticity coefficient, consumption unit, catalog, and load density methods [3]. While these methods are not sufficiently accurate for prediction, some power companies still employ them due to practical considerations. Techniques such as regression analysis and time series analysis are also used for daily forecasting. Further details are provided below.