esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Implant Design and Clinical Outcome  
#### 3.1.1 First Generation  
The design of BCR-TKA is especially important to ensure proper kinematics and implant survival. A primary concern for first-generation BCR-TKA includes tibial baseplate stability, as there is an absence of a tibial keel and reduced tibial coverage. First-generation BCR-TKA, for which the longest follow-up is available, included a femoral component with cobalt-chromium with an asymmetric trochlear groove requiring right and left components. The tibial component consisted of a flat symmetric polyethylene (PE) insert on a single-piece horseshoe-shaped titanium tibial tray with two round fixation pegs and a flat symmetric polyethylene (PE) bearing [4]. [PERSON] presented the largest case series (214 arthroplasties in 160 patients) for this implant (Townley Anatomic, Biopro Inc, Port Huron, MI, USA) with clinical outcomes with a minimum follow-up of 20 years [11]. The Kaplan-Meier curve showed a revision-free survival of 89% (95% confidence interval (CI): 82-93%). Twenty-two knees in twenty-one patients (5.6%) were revised. Reasons for revision were polyethylene wear (n = 7), aseptic loosening of the femoral (n = 4) or tibial component (n = 3) and infection (n = 4). The Cloutier BCR-TKA (Ceraver Osteal, Paris, France), which included a tibial component with two flat symmetrical polyethylene (PE) bearings on a single-piece notched titanium tibial tray with two round 15 mm fixation pegs, showed a survivorship of 82% at 20 years with aseptic component loosening of 4.3% (7 of 163) [12].
## 2 System Structure
### Rx/Tx
The propagation time is the same since all transmitting antennas are at the same location. Therefore, in an ideal case of no delay in any channel, \(\Delta\rho_{i}\) are the same in all channels and \(\Delta\rho_{i}=\Delta\rho_{2}=...=\Delta\rho_{N}\) can be obtained. In practice, we likely obtain that \(\Delta\rho_{1}\
eq\Delta\rho_{2}\
eq...\
eq\Delta\rho_{N}\) due to the system delay biases. Thus, the correction of system delay bias for each channel can be estimated using the difference of \(\Delta\rho_{i}\). For instance, the correction of the \(i\)-th channel is \(\Delta\rho_{i}-\Delta\rho_{1}\) without loss of generality. It should be noticed that this method estimates the different portions of system delay biases, but the common portions are cancelled by subtraction processing. The common portion will be included in the user clock error and estimated in positioning estimation in the user terminal. (2) On-line correction using a reference indoor GPS station  
As discussed in the pre-correlation, \(\Delta\rho_{i}\) includes the system delay bias and the distance \(d_{i}\) from the transmitting antenna to the indoor receiver. Herein, the correction of system delay bias can be estimated as \(\Delta\rho_{i}-d_{i}\). \(\Delta\rho_{i}\) can be estimated from the pseudorange measurements of indoor receiver and Tx/Rx. If the indoor receiver's location can be obtained, \(d_{i}\) will be known. Thus, in the on-line correction algorithm, a reference GPS station must be placed at a known location. Its distance to each antenna is a known constant and can be pre-estimated correctly. Using \(\Delta\rho_{i}-d_{i}\), the correlation of system delay bias of each channel can be calculated. The correlation value can be deliver to user terminal via Wi-Fi. In Figure 2, the demodulated navigation data \(D_{i}(k)\) from the Rx is delayed by one epoch in Tx, where one epoch equals to one integration time \(T_{coh}\) of tracking loop in the Rx. The reason for the one-epoch delay is that the navigation data bit can only be obtained after demodulation. In other words, the current navigation data bit cannot be obtained in the real-time system. Thus, the previous moment navigation data bit is used to reconstruct the RF signal and the minimum time delay is 1 ms (\(T_{coh}=1\) ms). Since the delay occurs in every Tx, it will be considered as a part of receiver clock error and does not affect the positioning result.
## 2 Development History of Traffic Prediction  
In this section, we conduct a thorough review and survey of significant research on traffic prediction throughout its development history. The field of traffic prediction has evolved over nearly six decades, during which various prediction methods have emerged. Traffic prediction is a multidimensional research field encompassing a diverse classification of methods and applications (Figure 1). Methodologically, traffic prediction is divided into three major classes: statistical methods, traditional machine learning methods, and deep learning methods. Moreover, traffic prediction involves multiple sub-areas of applications, such as traffic flow prediction, passenger flow prediction, traffic speed prediction, and travel time prediction. These sub-areas will be explored in detail in Section 5. This section will systematically review traffic prediction's historical development and technological progress, following the aforementioned methodological classification. Statistical methods are particularly suitable for handling smaller datasets due to their clear and simplified computational frameworks compared to more advanced machine learning techniques. Meanwhile, traditional machine learning methods excel in capturing complex non-linear relationships in traffic data and processing high-dimensional data. With the enhancement of computational power and improvement in data acquisition methods, deep learning methods are increasingly popular due to their complex structures and ability to outperform many traditional methods given sufficient data.
# Digital Forensics Readiness in Big Data Networks:
## 5 Proposed Framework  
### Requirements Analysis  
To ensure that the system effectively meets the needs of digital forensics investigations, a comprehensive requirements analysis was conducted, guided by previous work [2; 5; 7; 8; 9; 10; 11; 16; 18; 20]. This analysis was crucial to identify and define the required functionalities and features that the proposed system must possess to address the specific challenges and requirements of DFR in big data environments. Additionally, the analysis also considered four key factors discussed below:  
* Architecture: Research on DFR and big data frameworks informed the identification of necessary system components and their functionalities.
* Operational needs: The practical needs of digital forensics investigations, such as data collection, secure storage, efficient processing, and access control, were key drivers in defining the system requirements.
* Technological capabilities: Leveraging existing technologies like Hadoop and Apache Spark ensured that the system could meet the performance and efficiency demands of both BI and forensics analytics.
* Compliance and security: Ensuring compliance with legal standards and maintaining robust security measures were paramount in defining the system requirements.  
For the proposed system, seven key DFR requirements were identified and are outlined as follows:  
1. Data Collection and Storage: The system must be capable of capturing and securely storing network access logs, firewall logs, IDS logs, SIEM logs, and Hadoop cluster data. It should centralise the storage of all relevant forensic data to facilitate easy management and analysis.
2. Network Segmentation and Security: Network segmentation and security of both the big data network and its DFR segment must be implemented.
3. Efficient Data Processing: The system must leverage technologies that effectively enforce both BI and forensics analytics, ensuring efficient processing and analysis of large data volumes.  
Metadata Management: The system should provide efficient metadata management through secure centralised nodes, crucial for precise and efficient forensic analysis.
* Access Control and Compliance: Strict access control measures must be enforced to ensure that only authorised personnel can interact with the forensic data. Furthermore, the system should log all security policies and maintain an up-to-date terms and conditions big data collection policy.
* Time Synchronisation: Effective synchronisation with the network's network time protocol (NTP) server is essential to adhere to digital forensics admissibility requirements.
* Resource Allocation: The forensic nodes must have sufficient storage and processing capabilities to handle the volume of data collected.
# The Progress of Cobalt-Based Anode Materials for Lithium Ion Batteries and Sodium Ion Batteries
## 4 Application of Cobalt-Based Anode Materials in LIBs/SIBs
### Cobalt Phosphide and Its Composites
With regard to the CoP electrode, a capacity of only 429 mA h g\({}^{-1}\) was retained after 100 cycles. This situation can be ascribed to the framework of CoP/RGO, which possesses more free space in favor of the volume change during cycling. [PERSON]'s group synthesized a carbon-coated CoP\({}_{3}\) nanocomposite and tested it as an anode material for SIBs [80]. The cycling performances of CoP\({}_{3}\)@C and CoP\({}_{3}\) at 100 mA g\({}^{-1}\) are displayed in Figure 12g. The CoP\({}_{3}\)@C  
Figure 12: Electrochemical performances of cobalt-based anode materials. (**a**) Charge-discharge profiles of Co\({}_{2}\)P@N-doped C (N-C)@reduced graphene oxide (rGO) in various cycles [143]. (**b**) Cycle performances of CoP hollow and solid nanoparticles [79]. (**c**) Charge-discharge profiles of N, P co-doped porous carbon sheet (CoP\(\subset\)NPPCS) hybrids at various current densities [141]. (**d**) Rate performances and (**e**) EIS spectra of Co\({}_{2}\)P-Co/graphene and Co\({}_{2}\)P-Co composites [139]. (**f**) Cycle performances of CoP/RGO and CoP [135]. (**g**) Cycle performances of CoP\({}_{3}\)@C and CoP\({}_{3}\)[80]. (**h**) Cycling performance of CoP@C/biomass-derived carbon (BC) and pure BC [142]. electrode exhibited a higher capacity than CoP\({}_{3}\) after 80 cycles, suggesting that the presence of the carbon layer contributed to the electrochemical performance of the electrode. Furthermore, [PERSON]'s group successfully embedded ultrafine CoP nanoparticles into the carbon nanorod, which delivered a superior cycling performance when tested as an anode material of LIBs at 1 A g\({}^{-1}\) (Figure 12h) [142]. Well-utilized CoP was the main reason for this result.
## Communication protocol
### Mobile Network Infrastructure
#### 2.5.1 4G/5G Core Network  
The Evolved Packet Core (EPC) consists of the most common components for the user authentication, the central database for all subscriber-related data and the routing to other networks. In addition, it supports mobility management as well as call and session building:  
* Packet Data Network Gateway (PGW)
* Serving Gateway (SGW)
* Mobility Management Entity (MME)
* Home Subscriber Server (HSS)
## 2 Materials and Methods
### Large Mask Inpainting--LaMa
#### 2.2.1 Fast Fourier Convolution  
FCCs are fully differentiable and are an excellent alternative to conventional convolutions due to their easy use. In addition, FCCs have receptive fields that cover the entire image, which allows the generator to notice the global context from the initial layers  
Figure 1: Application example of the inpainting approach. (**a**) Image for restoration; (**b**) detail of areas (in red) for reconstruction; (**c**) reconstructed output image.  
Figure 2: Images stacking with the image \(x\), the mask \(m\) and the resultant four-channel input tensor \(x^{\prime}\).  
of the network, which is crucial for high-resolution images treated with the inpainting approach [22].  
According to [16], FCC works by dividing the channels into two parallel branches. The first is the local branch, which uses conventional convolutions, and the second is the global branch, which uses the fast Fourier transform (FFT) to take into account the global context. The real FFT is applied to real-value signals, while the inverse real FFT ensures that the output is a real value. Finally, the results of the local and global branches are merged and the process is repeated until all the residual FFC blocks are completed and the image is reconstructed. The process of applying the FFC can be seen in Figure 3, where the lower output arrow of the FFC block in the flowchart refers to the result of merging the local and global branches.  
The LaMa network architecture has a ResNet-Like proceeding [16]. It uses three down-sampling blocks, 6-18 residual blocks and three up-sampling blocks. The residual blocks use FFC, while the discriminator architecture uses pix2 pixHD, which incorporates segmentation information in object instances and allows for manipulations. They also use the Adam optimization algorithm [23], with a learning rate of 0.001 for the inpainting network and 0.0001 for the discriminator. All models are trained for 1M iterations, with a batch size between 2 and 30. The hyper-parameters were selected using the coordinate beam search strategy, resulting in \(\kappa=10\), \(\alpha=30\), \(\beta=100\) and \(\gamma=0.001\).  
In order to obtain a better reconstruction, ref. [16] also proposed Big LaMa, a scalable LaMa model that treats real high-resolution images with large dimensions and more resources. The model differs from LaMa in three respects: the depth of the generator, the training dataset and the batch size. This model has 18 residual blocks, all based on FFC, resulting in 51 million parameters.
## 2 Methods  
The presented study contains the analysis of experimental data (performance and emissions) obtained from the engine running in PC-HCCI combustion mode, where HCCI is initiated by the pre-chamber and comparison of PC-HCCI with PCSI and conventional HCCI combustion mode is obtained by well-validated 1D/0D simulation. The compression ratio of the experimental engine is set to 17.5, while the engine was run at an engine speed of 1600 rpm, which enables the comparison with previous experiments conducted on the same experimental setup. At a defined compression ratio, HCCI combustion in the main chamber can be achieved only if the intake air temperature is increased compared to ambient conditions. Therefore, experiments were conducted at different intake air temperatures of 80 \({}^{\circ}\)C, 90 \({}^{\circ}\)C, and 100 \({}^{\circ}\)C for 3 and 3.5 bar IMEP. This also enables the study of controllability and the ability to control combustion timing with changeable boundary conditions. A spark sweep was conducted for every load and intake air temperature within the window of possible spark timings, which are limited by combustion stability and knock tendency, i.e., pressure rise rate. Combustion is considered unstable when CoV IMEP is higher than 5%, while excessive knocking combustion is considered when the pressure rise rate exceeds 8 bar/\({}^{\circ}\)CA. In post-processing, the knock tendency was monitored by analyzing the maximum amplitude of pressure oscillations (MAPO). Based on the analysis, it was concluded that the operating point should be considered as being over the knock limit if MAPO exceeded 1.5 bar in more than 5% of the measured cycles, in a window of 300 consecutive cycles, which correlates well with the average MAPO being over 0.8 bar.  
Different loads were obtained with wide-open throttle, while fuel mass in the pre- and main chambers was varied, which then changed the excess air ratio. Pre-chamber fuel mass was controlled to achieve a near-stoichiometric local excess air ratio in the pre-chamber at spark timing, while the main chamber fuel mass was controlled to achieve a predefined load. The excess air ratio in the pre-chamber cannot be directly measured during the experiment; hence, a look-up table with the help of a cycle simulation tool was prepared during the design of the experimental phase. The pre-chamber fuel mass required for stoichiometric conditions in the pre-chamber at spark timing is then calculated taking into account the intake air temperature and the measured global excess air ratio.  
For comparison results of HCCI combustion mode, the previously published experimental results were used for the validation of the 1D/0D simulation model. This was chosen because the experimental measurements of conventional HCCI combustion were conducted only at compression ratio 16, while the PC-HCCI experimental results are obtained at compression ratio 17.5. Therefore, the validated model at CR = 16 was used to prepare HCCI results at CR = 17.5.
# A _uv_-Cellular Network: Design, Implementation and Experiments
## 5 Experiments Study
### Performance Metrics & Experiment Results
#### 5.3.1 Test for Network Packet Delivery Fraction & Data Rate  
_Network Packet Delivery Fraction_ indicates the ratio of packets successfully received to the total sent in the network. As the experiments were conducted in a real underwater environment, there are a variety of factors that can cause packet loss. The following two metrics were tested along with the Network Packet Delivery Fraction.  
Figure 11: The records of the field test.  
Figure 10: The area of _uw_-Cellular network deployed.  
Data Rate represents the number of bits of data transmitted on the channel per second, also known as the data rate or bit rate. The unit of rate is bps (bits per second) or b/s. The main purpose of this group of experiments is to test the data rate (1) from the terminal node to the surface gateway nodes, (2) terminal nodes within the same subnet, and (3) terminal nodes in different subnets. The test results can be seen in the Table 2.  
The results of this experiment verify the feasibility of the CLA protocol proposed in this paper in a real underwater environment. All network nodes, whether fixed or mobile, can perform path selection based on the communication link state evaluation strategy mentioned above. For example, we had deliberately deployed the AUV 01 outside the reliable communication range of the L1-BS 01. It selected the suitable relay node to forward data to node L1-BS 01 based on its real-time location. And finally need 3 hops and 5 hops to forward packets to the Surface Gateway Node and Terminal Node 22. However, due to the adverse effect of the Doppler and Multipath on signal demodulation, continuous and serious packet loss occurred when the waves become stronger or the mobile terminal node move faster.  
It is worth noting that the performance of the communication between two fixed network nodes is obviously better than that between fixed and mobile nodes. That is because the statues of the communication links between fixed nodes is relatively stable and occasional transmission failures do not have a serious and lasting impact on these communication links. However, for mobile nodes, the potential for packet loss is greatly increased by adverse factors such as the Doppler and multipath effects, resulting in relatively low data rates. What's more, the attitude of AUVs in the underwater also can have a negative impact on communication performance. Figure 12 illustrates the possible attitudes of AUVs in the underwater. The AUV itself, acting like a sound baffle, causes masking of the acoustic signal, which leads to a low \(SNR\) of the received signal from the UAM.
# Online Algorithm for Deriving Heart Rate Variability Components and Their Time-Frequency Analysis
## 2 Materials and Methods
### Data and Preprocessing
It consists four components \(x_{c}\), each simulated as amplitude (AM)- and frequency (FM)-modulated continuous signals:  
\[ \begin{split} x_{c}(t)\ =\ A_{c}(t)\sin(\varphi_{c}(t)),\\ A_{c}(t)\ =\ A_{am0}+A_{am}\sin(2\pi f_{am}t),\\ \varphi_{c}(t)\ =\ 2\pi A_{fm0}t+A_{fm}/f_{fm}\sin\left(2\pi f_{fm}t \right),\end{split}  \tag{1}\]  
where \(A_{am0}\), \(A_{am}\) and \(A_{fm0}\), \(A_{fm}\) represent the range of magnitude variation in amplitudes and frequencies and \(f_{am}\), \(f_{fm}\) are their modulation frequencies (Table 1). The values of these parameters were estimated from the study of three real HRV signals [39]. Then, the continuous HRV, sampled at 1 kHz, was created as the sum of all four simulated components (HF, LF, VLF, and ULF), shifted up by 0.95 s. Then, irregularly spread HRV samples were derived from it, taking into account the relationship between the HRV values and the timeline. Next, the signal was disturbed by adding Gaussian noise with a standard deviation of 0.01 s. To obtain the final uniformly sampled HRV signals, a necessary condition for using the intended signal processing procedures, cubic spline interpolation at 2 Hz was used, and an antialiasing zero-phase filter with a cutoff frequency of 0.5 Hz was applied to minimize HRV spectrum distortions. In the rest of this work, we assume that the HRV signal is ready for use, and new samples appear successively as a result of preprocessing.
## 6 Performance of Retrofitted Column against Vehicle Impact
### Performance of AFRP-Retrofitted Column  
Column C300, with 2.6% reinforcement, is retrofitted with ARFP sheets. The properties of the AFRP are the same as the test column described earlier in the numerical model verification section. The column is jacketed by the AFRP sheet at the bottom half (up to 2 m height) of the column because the damage from the car impact occurs near the base (Figure 17a). Two to five layers of AFRP sheets are used to observe the effect of the AFRP layers on the impact performance of the column. The FE simulations were conducted for car impact velocities of 40 and 50 km/h.  
The displacement-time history of the AFRP retrofitted column is shown in Figure 18. With the addition of the AFRP layer to the column, the impact performance of the column is improved. At a car impact velocity of 40 km/h and 2 layers of AFRP sheets, the maximum and residual displacements at the height of 1740 mm are reduced by 12% and 15%, respectively (Figure 18a). Again, at a car impact velocity of 50 km/h, column C300 fails, as indicated in Figure 18b. The failure can be prevented by jacketing the column with two or more layers of AFRP sheets. However, the existence of a large residual displacement (13.5 to 16.5 mm) demonstrates the internal damage of the column. This indicates that the AFRP sheet can reduce the damage of the column but cannot completely eliminate the residual damage for a slender column at moderate car impact velocity. Furthermore, the addition of more than two layers of AFRP does not show any significant improvement in the impact performance of the column, as can be seen in Figure 18. A similar conclusion was drawn by another study, where it was shown that the increasing FRP layers did not improve blast performance significantly [39].  
Figure 17: Column protection by: (**a**) AFRP sheet; (**b**) RC jacket.
## 4 Signal Feature Extraction Method Based on Intrinsic Mode Function Energy Permutation Entropy  
Comparing the four signal decomposition methods in Section 2, it can be seen that the line spectrum detection method based on EMD can realize the initial extraction of the target signal features but cannot identify the signal features between different vessels. To effectively extract different target signal features and solve the problem of screening different typical ship target signals, this paper proposes a kind of arrangement entropy based on EMD of IMF.