esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In addition, processing the received signal over the total integration time yields a single Doppler frequency and code phase estimation for each satellite. However, the accuracy of this time-delay estimation is limited by the dynamic motion of both the satellites and the receiver during the correlation time [3]. Acquiring weak GNSS signals requires significant computational effort due to the need for extended integration. The most time-consuming operation during the acquisition phase is correlating the input signal with a locally generated replica. This process involves multiplying thousands of samples every millisecond for each tested code phase, Doppler frequency, and satellite [3]. Consequently, developing a real-time High Sensitivity GNSS (HS-GNSS) receiver presents a major challenge. Several strategies have been explored to reduce the computational demands of GNSS signal acquisition algorithms, with some examples provided below. The Double Block Zero Padding (DBZP) approach, as detailed in [13], employs partial correlations of short durations (typically a few tens of chips) to calculate the CI using multiple small Fast Fourier Transforms (FFTs) instead of a single large FFT. Another strategy, proposed by [14], focuses on the joint acquisition of the in-phase and quadrature components of the GNSS signal. It utilizes a cascaded FFT technique for acquiring both the primary and secondary codes. Refs. [15; 16] introduced two algorithms designed to extend the CI time to multiples of the data bit interval, without requiring prior knowledge of the transmitted modulated data. These algorithms are named circular correlation with multiple data bits (CCMBB) and modified DBZP. Both approaches aim to predict the most likely data bit combination, using this prediction to adjust the data signs accordingly. The CCMDB algorithm reduces computational demands by initially setting a short CI time, then gradually increasing it, selectively eliminating Doppler frequency bins based on their likelihood. The modified DBZP technique offers a means to account for the Doppler effect over the PRN code's duration with only a slight increase in processing needs. Another method, utilizing a double-FFT algorithm [3], performs CI over a bit period without prior knowledge of bit transitions. This approach applies a sliding FFT window equal to one bit's duration, facilitating the detection of bit transitions and enabling coherent integration of correlation outputs.
## 4 Technologies for Nanocellulose Production
### Production of Nanocellulose from Cellulose
#### 4.2.2 TEMPO-Mediated Oxidation  
Another chemical method which is used for the production of NC is 2,2,6,6-tetramethyl piperidine-1-oxyl (TEMPO, (CH\({}_{2}\))\({}_{3}\)(CMe\({}_{2}\))\({}_{2}\)NO)-mediated oxidation, and is usually coupled with a mechanical disintegration method. Alkaline conditions (pH between 9 and 11) are typically used for TEMPO-mediated oxidation treatment, along with bleaching agents such as NaClO, and catalysts such as sodium bromide (NaBr) and the oxidation initiates when NaClO is added. During the process, the oxidation of primary hydroxyl groups of C6 carbon of native cellulose takes places, which get converted into carboxylate groups, causing the repulsion between the ionized carboxylate groups and separating the cellulose fibrils to form NCs. The process also inhibits the aggregation ability of the NCs and increases the water dispersion of cellulose [18; 99; 100]. Using the TEMPO/NaBr/NaClO system to hydrolyze lemon seeds in an aqueous solution at pH 10, followed by centrifugation, dialysis and sonication, a CNC yield of 52.01% was obtained [101]. Although the TEMPO hydrolysis technique is employed for the production of NC, it has certain drawbacks, including hazardous and toxic chemicals and a narrow range of the oxidation position [101].
# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 5 Research Challenges and Future Research Directions
### FAS Based Unsupervised Domain Generalization  
Challenge: Currently, the majority of FAS algorithms are based on supervised learning, primarily relying on labeled datasets to distinguish between genuine faces and attack samples. However, obtaining labeled data is labor-intensive and costly, leading to the well-known problem of data scarcity in FAS, which hinders practical applications. In recent years, some studies have begun to explore semi-supervised [118] or self-supervised [125; 134; 216; 217] learning methods aimed at reducing dependence on fully supervised data by leveraging limited labeled data or self-generated pseudo-labels. The model's generalization capability may be poor if the self-supervised pretext tasks are not highly correlated with the FAS task. A significant challenge for few-shot weakly supervised learning is whether the small number of samples can effectively cover various attack types and ensure the model's generalization ability. However, these methods are based on pretext tasks, resulting in limited performance and an inability to alleviate actual domain shifts. Furthermore, unsupervised domain generalization (UDG) benchmarks for various scenarios, such as cross-attack types, have yet to be established. Therefore, unsupervised algorithms show great potential in reducing data labeling costs and improving cross-domain generalization capabilities.  
Future Research Directions: UDG [218; 219] aims to learn a model from an unlabeled set of source domains such that it can semantically cluster images in an unseen target domain. Recently, [PERSON] et al. [220] applied the batch and instance normalization techniques to UDG where there were no labels in the training domains to acquire invariant and transferable features. [PERSON] et al. [146] proposed the first unsupervised domain generalization framework for FAS. In addition, contrastive learning (such as MoCo [221] and SimCLR [222]) can enforce invariance to various augmentations. Therefore, combining contrastive learning with other unsupervised learning strategies to investigate UDG FAS algorithms is possible.
## 4 Results and Discussion
### Consensus on Benefits  
There were a number of challenges for AR implementation that were consistently reported among the identified papers, but there were also several beneficial attributes that demonstrated the effectiveness of AR in construction research for communication. These reports showed the potential of AR and the opportunities that AR supports in the industry. The following sections review the seven main benefits for which there is consensus in the literature about AR.
## 5 Research Methodology and Empirical Findings
### Reliability Estimation and Prediction  
Service delivery status is for the reference of decision makers. In the establishment of reliability assessment, subsequent simulations and predictions are based on the empirical reliability function. Considering the possibility of censored data, we will use the Kaplan-Meier methodology to conduct a preliminary reliability analysis. Because right censoring occurs in most cases, univariate censorship and survival analysis will be used to explore reliability studies [64]. The reliability derived from the failure rate fits the nonparametric analyzing function. [PERSON] and [PERSON] proposed the Kaplan-Meier methodology to conduct incomplete observations [65]. During the experiment, the decreasing survival status and the ratio will be measured. Converting the survival rate into the system failure rate, the reliability estimation becomes [66]  
\[\hat{\mathrm{R}}(\mathrm{t})=\prod_{\mathrm{j=0}}^{\mathrm{d}}\left(1-\frac{ \mathrm{M_{j}}}{\mathrm{S_{j}}}\right). \tag{7}\]  
Defining the event time interval [0, d], malfunction \(\mathrm{M_{j}}\) and the survival observation \(\mathrm{S_{j}}\), we can measure the probability of system malfunction. In the data extraction, the time and process of each event will be recorded and sorted. Regarding the system failure model, rearrange the failure results, which help analysts conduct model examining. While failures or accidents may occur, the system still has an opportunity to operate normally through foolproof and safety mechanisms [66]. Therefore, estimating the remaining functioning system and promptly supporting for troubleshooting can improve the service quality.
## 2 Materials and Methods
### Time Synchronization  
One of the requirements established by the agronomists for this study was that all the nodes should take the sensor measurements at the same time every 15 min so that they could be compared. Therefore, a synchronization algorithm was implemented: Once a day, the nodes connected to a time server (NTP) and update their real-time clock (RTC), which is very inaccurate (it has a daily drift of approximately 50 s). This inaccuracy, together with the drift of the internal timer in deep sleep mode (about 0.3%), generated a time drift that resulted in desynchronized measurements between nodes. To solve this problem, a time correction algorithm was developed. This algorithm determined the time the system requires to perform the measuring and data sending processes, working out the remaining time needed to be in deep sleep mode before waking up at the exact minute defined by the sampling period. In this deployment, measurements were performed every 15 min (minute 0, 15, 30 and 45 of each hour) and measurements were sent every 30 min (minute 0 and 30 of each hour).
## 2 Materials and Methods
### Machine Learning Methods  
This section describes applying five ML algorithms to predict the PL values of fully grouted rock bolts: XGBoost and four hybrid models incorporating metaheuristic optimisation algorithms (HHO, JSO, DA, and FA). These algorithms were trained on a comprehensive dataset consisting of 72 rock bolting samples, where each sample was defined by six critical variables related to the rock bolt and mechanical grout characteristics, including (W/G) ratio, CT, UC, GA, DC, and FA content.  
To establish the prediction model, the primary equation used in this study was based on a regression framework that relates the dependent variable, i.e., PL values, to the mentioned independent input variables. Equation (1), used as the foundation for the modelling process, is expressed as:  
\[\text{PL}=\text{f}(\frac{\text{W}}{\text{G}},\text{CT},\text{UCS},\text{GA}, \text{DC},\text{FA}) \tag{1}\]  
After defining the independent and dependent variables, The XGBoost algorithm was subsequently applied to predict PL values. This process enhanced the accuracy of predictions by minimising the loss function, typically measured by Mean Squared Error (MSE) in this study. Each hyperparameter, including the learning rate, maximum depth, and number of estimators, was fine-tuned to improve the model's performance. Also, four other hybrid models, including HHO, JSO, DA, and FA, leveraged optimisationalgorithms to refine the hyperparameters of XGBoost further. These optimisation techniques allowed the models to explore a larger space for potential solutions, enhancing accuracy and generalisation.  
The detailed application of these algorithms within the geotechnical engineering context allows for more accurate and robust predictions of the axial load bearing capacity of the fully grounded rock bolting systems. Specifically, by incorporating mechanical parameters of grout such as UCS into the model, we could directly link the mechanical behaviour of grout and the rock bolting systems and their predicted performance under different real-field conditions from weak to strong media. This will help geotechnical engineers decide on grout selection and rock bolt design.
## 3 The Proposed HCDBAVNS Algorithm
### Fitness Function  
The delivery path among suppliers, manufacturers, and warehouses can be obtained through encoding and decoding the bat position. However, it cannot be proved that the derived delivery path is a feasible solution, because the path may violate the constraint conditions of Formulas (13) and (14). Therefore, this paper deals with the constraints of Formulas (13) and (14) using a punishment mechanism to punish the infeasible solution. Further, it is found that there is no quantitative relationship among Formulas (5), (13), and (14). If the sum of the three formulas is directly used for fitness comparison, it is not conducive to controlling the optimization direction of the algorithm. Second, it is found that the optimal path must first satisfy Formulas (13) and (14) and then converge to the optimal direction of Formula (5). Therefore, on the basis of the punishment mechanism, a vector mechanism has been added to the fitness function in this paper. After using the punishment and vector mechanisms, the fitness function of bat \(i\) can be derived from Formula (5):  
\[G_{i}{=}\{G_{i1},G_{i2},G_{i3}\} \tag{24}\]  
\[G_{i1}=\sum_{s\in S\in S}\sum_{l\in SL}\max\left\{0,\sum_{m\in SM}\sum_{k\in SK }\sum_{p\in SP}g_{smkp}{l}^{\prime}{{}_{pl}}-e_{sl}\right\} \tag{25}\]  
\[G_{i2}=\sum_{m\in SM}\sum_{k\in SK}\max\left\{0,\sum_{p\in SP}g^{\prime\prime} {{}_{mkp}}{{}_{0}}{{}_{p}}-u_{mk}\right\} \tag{26}\]  
\[\begin{split}\text{G}_{i3}{=}\sum_{s\in S}\sum_{m\in SM}\sum_{k \in SK}\sum_{p\in SP}\sum_{l\in SL}g_{smkp}{l}^{\prime}{{}_{pl}}{{}_{cl}}{_{ sl}}{_{cl}}{+}2\times\sum_{s\in S}\sum_{m\in SM}g^{\prime}{{}_{sm}}d_{sm}b_{s}{ +}\\ \sum_{m\in SM}\sum_{k\in SK}\sum_{p\in SP}g^{\prime\prime}{{}_{ mkp}}{{}_{0}}{{}_{p}}{{}_{cl}}{{}_{m}}{+}\sum_{m\in SM}\sum_{k\in SK}\sum_{p\in SP }{{}_{(}y_{mkp}+y^{\prime\prime}{{}_{mkp}}{{}_{p}}{{}_{mk}}{{}+}}\\ \sum_{m\in SM}\sum_{k\in SK}\sum_{p\in SP}\sum_{p^{\prime}\in SP }y^{\prime}{{}_{mkp^{\prime}}}{{}_{pkp^{\prime}}}{{}_{pk}}{{}_{mk}}\end{split} \tag{27}\]  
where \(G_{i1}\) and \(G_{i2}\) are the punishments for violating the constraint of Formulas (13) and (14) respectively, and \(G_{i3}\) is the original target.  
As can be seen from Formula (24), the fitness function \(G_{i1}\) is a three-dimensional value, and the commonly-used comparison method of one-dimensional numerical values is not suitable for fitness comparison in this paper. Hence, this paper has proposed a comparison method based on vector mechanism.  
Supposing that the fitness of delivery path 1 is \(G_{1}=\{G_{11},G_{12},G_{13}\}\), the fitness of delivery path 2 is \(G_{2}=\{G_{21},G_{22},G_{23}\}\), and the optimal fitness is \(G_{best}\), the flow chart is shown in Figure 7.
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 7 IoT and Blockchain Integration
### Application Areas of IoT and BCN Integration  
There are different challenges concerning evolving IoT. They include the problems of IoT data storage and exchange, IoT devices and user identity management, device/user authentication, and access control. With blockchain technology, well integrated with IoT, many application areas are realized for an IoT. In this section, we distinctively discuss three chief application areas of IoT-blockchain integration.
## 3 Results and Discussion
### Surface Hydrophilicity  
The surface hydrophilicity was characterized by the water contact angle. As shown in Figure 3, it can be seen that the pristine magnesium alloy exhibited relative hydrophobicity and its water contact angle was about 86.8\({}^{\circ}\). After sodium hydroxide treatment, the water contact angle was reduced to about 21.6\({}^{\circ}\), indicating that the hydrophilicity was enhanced obviously. According to the FTIR results, more hydrophilic hydroxyls can be introduced on the surface after NaOH treatment, which can significantly enhance the surface hydrophilicity. In contrast, as compared to the pristine magnesium alloy, the water contact angle was increased to 103.7\({}^{\circ}\) after HF treatment, suggesting that hydrogen fluoride treatment can enhance the surface hydrophobicity. It was considered that the magnesium fluoride layer contributed to the improved hydrophobicity. Therefore, the excellent hydrophilic chemical conversion layer can be produced by sodium hydroxide treatment, while a relative hydrophobic chemical conversion layer can be obtained after hydrogen fluoride treatment. In the following studies, the influences of different hydrophilicity on corrosion behaviors, blood compatibility and endothelial cell behaviors were explored in the present study.
# Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities
## 2 Materials and Methods
### Data Preprocessing  
The datasets used to determine the efficiency of this study's classification model were taken from patients' ECG recordings. Each segment had the baseline wander adjusted and noise removed with a high-pass and low-pass filter with 0.5 Hz and 50 Hz cut-off frequencies, respectively. A cycle of P, QRS, and T waves has been reported as one method to evaluate patients' cardiac condition. First, the QRS complex (the maximum amplitude) was detected based on the maximum slope. Next, the T-wave and the P-wave (the minimum amplitude and the location before the QRS complex) were detected using continuous wavelet transform (CWT) techniques [26]. Finally, the algorithm, based on the CWT in MATLAB, was used to determine each P, QRS, and T wave with a varying scale for each wave cycle, as shown in Figure 3.  
Figure 3: Example of P-QRS-T detection using the CWT technique.
# Interest Forwarding in Named Data Networking Using Reinforcement Learning
## 6 Results and Discussion
### Probe Parameter  
We investigate the impact of the probe parameter (\(p\)) of the NDNFS-RLRNN algorithm. This parameter determines how often alternative paths are explored by a CR. In this simulations, we consider a uniform content popularity model across the network with Mzipf parameters set at \(\alpha=1.0\) and \(q=5.0\). By uniform popularity, we mean that request arrivals at each CR in the network are characterized by the same MZipf parameters. We fix the catalogue size at 1000 data objects, and each CR is equipped with a cache size of 1% of the catalogue.  
In Figure 3, we observe that for uniform content popularity in the network, increasing the exploration of the network, as a result of increasing \(p\), increases the cache hit rates at the cost of increased network overload. In general, the _ProbCache_ policy produces the best cache hit performance because it combines better redundancy and eviction properties compared with the other policies. The LCE produces the worst performance because of its characteristic high redundancy and high eviction rate. Although _Betw_ reduces redundancy compared with LCE, there is a high rate of eviction at the most \"central\" nodes.  
Interestingly, while we observe a steadier increase in the hit rates as \(p\) increases for both the LCE and _ProbCache_ policies, for _Betw_, the benefit reduces as \(p\) is increased. In fact, 90% of the improvement from increasing \(p\) from 0.1 to 1.0 is already attained at \(p=0.6\) for Betw compared with about 70% for both LCE and _ProbCache_. Since _Betw_ caches contents mostly at specific nodes, NDNFS-RLRNN can search these locations considerably well enough such that further increasing exploration becomes less cost-effective. As expected, Figure 3b shows the overhead cost of increasing the probe parameter.
## 4 Discussion  
Ideally, detection of in-hospital AKI would be prospective, automated, based on routinely collected hospital data, and available to the entire healthcare team. In order to advance clinical detection and research, the medical community requires a carefully crafted consensus definition of AKI using SCr criteria and instructions on how to reliably operationalize these SCr criteria to build clinical alerts, analyze clinical databases, or  
Figure 8: **Adjusted odds ratio of in-hospital mortality by narrowest observation window for detecting AKI (\(n\) = 126,367).** AKI, acute kidney injury. Adjusted odds ratios (dots) and 95% confidence intervals (lines) were calculated using logistic regression, where the reference category were patients who did not develop AKI. Logistic regression was adjusted for hospital admission status (elective/unknown, urgent, and emergency), estimated GFR in categories of 15 mL/min/1.73 m\({}^{2}\) based on first hospital serum creatinine, gender, hospital type (academic medical center vs. community hospital), and baseline comorbidities of diabetes, anemia, hypertension, congestive heart failure, liver disease, and peripheral and visceral atherosclerosis.  
develop treatments or prevention strategies for AKI [17; 18]. As a step towards this ideal, we previously reported the impact of magnitude of SCr change and suggested separating KDIGO AKI stage 1 into stage 1a and 1b [9]. The current study characterizes the time components of KDIGO AKI SCr criteria and explores how to operationalize these time components in a large clinical database.  
This study demonstrated that expanding the timing windows of the KDIGO AKI criteria increases the incidence of AKI detected and is meaningfully associated with the outcomes of in-hospital death and LOS. An observation unique to the present study was the demonstration that increases in SCr over a 24-h period and particularly within one calendar day are associated with a substantial increased risk for mortality and increased hospital LOS. However only 54.7% of non-index SCr assessments were drawn less than 24 h from the previous SCr assessment, suggesting that AKI associated with very high mortality might have been more frequent than the reported 7.4% had there been more SCr levels drawn within 24 h.
## 2 Project Overview
### Temporal Distribution Characteristics of MS Events  
The MS monitoring system was installed and formally put into operation on 19 July 2018. Figure 4 shows the temporal distribution of the MS events. In particular, 2881 valid MS events were collected as of 31 October 2020, and the incidence of the MS events was closely related to the construction conditions. From 19 July 2018 to 31 July 2019, bench I of the underground powerhouse was excavated. The natural state of the surrounding rock was destroyed, and a new free face was formed, which changed the stress state of the surrounding rock. Moreover, the surrounding rock saw a stress redistribution that resulted in the generation of MS events. Overall, the MS events occurred frequently, with an average of 9 per day and up to 19 in some localities. Specifically, the MS events were highly active during high-frequency blasting and construction from July to November 2018. In contrast, the MS events were inactive when the blasting and excavation activities of the underground powerhouse ceased from mid-January 2019 to late March 2019; however, there were still MS events, indicating that the surrounding rock experienced a long stress adjustment under high geostress. From August 2019 to 31 October 2020, bench II and part of bench III of the underground powerhouse were excavated. Due to the completion of bench I and the slow progress of blasting, construction, and excavation, the surrounding rock was less disturbed, and its internal stress was fully adjusted and released; thus, the MS event rate was low, with an average of five events per day.  
Figure 3: Network topology of the MS monitoring system for the underground powerhouse.