esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The proper screw geometry and pilot-hole size remain controversial in current biomechanical studies. Variable results arise from differences in specimen anatomy and density, uncontrolled screw properties and mixed screw brands, in addition to the use of different tapping methods. The purpose of this study was to evaluate the effect of bone density and pilot-hole size on the biomechanical performance of various pedicle screw geometries. Six screw designs, involving three different outer/inner projections of screws (cylindrical/conical, conical/conical and cylindrical/cylindrical), together with two different thread profiles (square and V), were examined. The insertional torque and pullout strength of each screw were measured following insertion of the screw into test blocks, with densities of 20 and 30 pcf, predrilled with 2.7-mm/3.2-mm/3.7-mm pilot holes. The correlation between the bone volume embedded in the screw threads and the pullout strength was statistically analyzed. Our study demonstrates that V-shaped screw threads showed a higher pullout strength than S-shaped threads in materials of different densities and among different pilot-hole sizes. The configuration, consisting of an outer cylindrical shape, an inner conical shape and V-shaped screw threads, showed the highest insertional torque and pullout strength at a normal and higher-than-normal bone density. Even with increasing pilot-hole size, this configuration maintained superiority.  
pedicle screw; screw loosening; pilot hole; bone density +
Footnote †: journal: Applied Sciences  
Article  
Biomechanical Comparison of Fixation Stability among Various Pedicle Screw Geometries: Effects of Projection Shape and Thread Profile  
[PERSON], [PERSON][PERSON], [PERSON], [PERSON][PERSON], [PERSON][PERSON], [PERSON][PERSON], [PERSON][PERSON], [PERSON] Comparison of Fixation Stability among Various Pedicle Screw Geometries: Effects of Screw Outer/Inner Projection Shape and Thread Profile
## 2 Materials and Methods
### Western Blot Analysis  
HDP cells were treated with _S. deserticola_ JD052 extracts for 24 h. Subsequently, total cell lysates were obtained by lysing the cells in radioimmunoprecipitation assay (RIPA) buffer supplemented with protease inhibitors. The extracted proteins were then separated via sodium dodecyl sulfate-polyacrylamideamide gel electrophoresis (SDS-PAGE) and subsequently analyzed by immunoblotting. For intracellular fractionation into nuclear and cytoplasmic components, NE-PER reagents (Thermo Fisher Scientific) were employed. The primary antibodies utilized for Western blot analysis were as follows: \(\beta\)-catenin (1:1000, Cell Signaling Technology; CST, Danvers, MA, USA), phospho-\(\beta\)-catenin (S33/S37/T41) (1:1000, CST), GSK3\(\beta\) (1:1000, CST), phospho-GSK3\(\beta\) (S9) (1:1000, CST), phospho-GSK3\(\beta\) (T390) (1:1000, CST), AKT (1:1000, CST), phospho-AKT (S473) (1:1000, CST), p38 MAPK (1:1000, CST), phospho-p38 MAPK (T180/Y182) (1:1000, CST), ERK1/2 (T20/T204) (1:1000, CST), phospho-ERK1/2 (1:1000, CST), SAPK/JNK (1:1000, CST), phospho-SAPK/JNK (T183/Y185) (1:1000, CST), VEGF (1:500, Santa Cruz Biotechnology; SCBT), IGF-1 (1:500, SCBT), FGF2 (1:500, SCBT), FGF7 (1:500, SCBT), and \(\beta\)-actin (1:1000, SCBT). After incubation with the primary antibodies overnight at 4 \({}^{\circ}\)C, the membranes were incubated with goat anti-mouse or goat anti-rabbit horseradish peroxidase (HRP)-conjugated secondary antibodies (CST). Following subsequent extensive washes, protein expression was detected using an enhanced Clarity MAXTM Western chemiluminescence (ECL) substrate reagent (Bio-Rad, Hercules, CA, USA) and visualized using the ChemiDoc Touch Imaging System (Bio-Rad).
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 4 Simulation Experiment Settings
### Packet Delivery Ratio (PDR)  
The _PDR_ is the ratio of total packets received to the total packets transmitted between the source and destination. This metric indicates the efficiency of the scheme in transferring the packets between the source and destination. A higher _PDR_ value indicates that the scheme is more reliable, in that more packets are successfully transmitted and received. The _PDR_ can be computed by using Equation (13) [22].  
\[PDR\;(\%)=\frac{\sum_{i=1}^{n}R_{i}}{\sum_{i=1}^{n}S_{i}}\times 100 \tag{13}\]  
where \(R\) denotes the received packets and S denotes the packets sent.
# Adaptive Clustering of Users in Power Domain NOMA
### Paper Organization  
This paper is organized as follows: Section 2 presents the fundamentals of downlink NOMA systems. Section 3 describes the models used to generate the fading channel gains. In Section 4, we describe the proposed clusterization algorithm. Section 5 details the proposed model, including the clustering and power allocation algorithms. Finally, the results of the experiments are discussed in Section 6, including an evaluation of the system performance. Our conclusions for the work are presented in Section 7.
# Influences of Extreme Weather Conditions on the Carbon Cycles of Bamboo and Tea Ecosystems
## 3 Results and Discussion
### Observed and Modeled Soil Water Content  
The model reproduced the seasonal dynamics of the observed soil water content at various time scales (hourly, daily, and monthly as shown in Table 1; Figure 5 and Figure S3). The exponent in the soil water retention curve, Clapp and Hornberger's \"\(B\)\", is a sensitive soil water content modeling parameter used in the CLM [33]. It was adjusted to improve the soil water content simulation at these two plots in the present study. To compensate for the effects of gravel on the soil water content at the two plots [34], it was adjusted to the lower boundary of the reasonable range for silt loam provided by [PERSON] and [PERSON] [35]. The coefficients of determination (\(R^{2}\)) between the observed and simulated hourly soil water contents at depths of 5, 10, and 30 cm from January 2015 to December 2017 were 0.58-0.61 (0.22-0.64), 0.50-0.63 (0.09-0.65), and 0.36-0.55 (0.36-0.52) at the bamboo (tea) plot, respectively. Similar \(R^{2}\) values were obtained at daily and monthly scales (Table 1; Figure S3). The simulated monthly soil water content was always within or near the ranges of the observations at a given depth (5, 10, 30, and 40 cm) at these two plots (Figure 5).  
Figure 4: Schematic of the method used in the study. FSDS, P, T, RH, and Wind represent incoming solar radiation, rainfall, air temperature, relative humidity, and wind speed, respectively.  
Both the observation and modeling results illustrated an obvious decrease in soil water content during August and September 2016, which was caused by the limited precipitation during August 2016 (monthly precipitation: 16.7 mm; Figure 3g). In comparison, the soil water content was larger in August and September 2017 because of greater precipitation in August 2017 (219 mm). The limited precipitation and low soil water content in August and September 2016 had a clear influence on the C cycle (see Section 3.4). Interestingly, the mean observed soil water contents at depths of 5, 10, and 30 cm at the bamboo (tea) plot were 27.95 (21.81)%, 28.14 (22.66)%, and 28.24 (24.33)%, indicating wetter soil in the bamboo than in the tea plot.
## 1 Introduction  
The Global Air Navigation Plan (GANP) [1] developed by the International Civil Aviation Organization (ICAO) specifies that the future development trend of air traffic management (ATM) system is a network-centric intelligent sensor network that provides network-enabled services and applications to form a networked ATM system, as shown in Figure 1. It integrates cyberspace (airborne communication/navigation satellite networks, airborne self-organizing networks, and ground-based system wide information management) with the physical environment (airlines, airports, and ATM operation centers/departments) through communication, network, satellite, and data link technologies to form a multi-dimensional and complex intelligent system, a cyber-physical system (CPS) [2].  
As early as 1996, the ICAO Working Committee on Aeronautical Telecommunications Network (ATN) clearly stated that \"ATM messages transmitted over the data chain are at risk of Modification, Replay and Masquerade attacks\", and emphasized that \"All aviation communication, navigation and surveillance network/air traffic management (CNS/ATM) applications are vulnerable to Distributed Denial of Service (DDoS) attack\". Therefore, the networked ATM is bound to face serious information security threats, and the security situation is not optimistic. With the development of networked ATM, the number of intrusions and attacks that take advantage of the vulnerabilities and security risks of networked ATM is increasing year by year. Experts from European Organization for the Safety of Air Navigation (EuroControl) and the European Aviation Safety Agency (EASA) say aviation security is facing unprecedented challenges. Statistics show that in the first quarter of 2019 alone, aviation networks were hacked as many as 30 times, and each attack on aviation networks caused an average economic loss of approximately USD 1 million [3]. In addition, the statistics for 2020 show that the number of hacking attacks against civil aviation systems (including airlines, airports, ticket sales, etc.) reached a staggering 1000 worldwide. After 9/11, [PERSON], then director of the National Infrastructure Protection Center, said that \"if the terrorists of 9/11 had combined a physical attack (crashing planes into buildings) with a cyber-attack (hijacking U.S. ATM), the result would have been a much worse disaster than 9/11\" [4]. Therefore, information security for networked ATM is critical and significant in terms of ensuring national security and preventing air transportation security incidents, as well as in reducing the social impact of security incidents and property damage.
# Determination of Cycle to Cycle Battery Cell Degradation with High-Precision Measurements
## 3 Experiments
### Effect of Different Aging Mechanisms on Capacity Degradation  
In this section, the relationship between capacity degradation, calendrical aging, cycle aging and leakage charge will be established. Figure 10 shows charging and discharging capacity over the respective cycles.  
When a cell is cycled, its capacity decreases during charging and discharging which can be associated with cycle aging. In addition, the cell is permanently discharged by a leakage current. The magnitude of the leakage current most likely depends strongly on the SoC. In contrast, the leakage charge only causes the charge and discharge capacities to be measured incorrectly. It can be seen that the discharge capacity is always measured lower than the actual cell capacity at that time because the leakage charge leads to premature discharge termination of the cell (red arrow pointing down). Furthermore, it turns out that the charge capacity is always measured larger than it actually is because, due to the leakage charge, more charge has to be pushed into the cell until it is full (red arrow pointing up).  
Figure 10: Breakdown of capacity degradation into its individual components: discharge capacity loss, charge capacity loss and leakage charge.  
Figure 9: Cycling scheme with open circuit holding period at 100% SoC (“high-SoC-scheme”) to find out calendrical aging and leakage charge at a high state of charge in contrast to the C/x-scheme which always has an average state-of-charge of 50%.  
Regardless of whether the cell is cycled or stored, a second time-dependent aging effect occurs--the so-called calendrical aging. Calendrical aging depends strongly on the state of charge of the cell and typically increases with increasing SoC. It is assumed that calendrical aging is shared between both the discharge and charge losses shown in Figure 10.  
As also shown in Figure 10, the total capacity loss between two successive charge measurements is the sum (magenta arrow + cyan arrow) of damage caused by charging, discharging and by the calendrical aging that occurred in this time interval.  
In the next Sections 3.3 and 3.4, the total charge losses due to calendrical aging, cycle aging and leakage charge between successive cycles are determined by evaluating high-precision measurement data.
# Vulnerability Assessment of Ubiquitous Cities Using the Analytic Hierarchy Process
## 2 The Components of U-Cities and Hazards
### Information and Communication Technologies (ICTs)
#### 2.3.4 Internet of Things (IoT)  
The concept of U-cities eventually creates a network, called the Internet of Things (IoT). The purpose of IoT is to utilize all the information and data from all the ICTs into a big database by using the middleware in simulation technology (MIST) as the bridge to connect with portable devices, telematics, and system on a chip [47] (refer to Figure 3). To connect each group, certain devices/sensors will be used, for example, telematics will help to connect between Satellite and WAN, SoC is used to connect between LAN and Satellite, whereas portable devices are used to link between WAN and LAN. Utilizing these devices and sensor allow the city to collect data and information, by using MIST to produce meaningful database and develop management models for the city, which communicate sensed data seamlessly and omnipresent, to create a network that information can be exploited in automating and streamlining processes. As the advancements in ICTs continue further, developments are made in Ubiquitous city concepts.  
Middleware in Simulation Technology (MIST): MIST represents computing software that adapts programs and applications to work simultaneously and seamlessly. Its main uses are to amalgamate complex applications. Thus, including web servers, application servers, content management systems, and similar tools that support application development and delivery [48].  
Telematics: Telematics is the communication of multiple devices allowing sending, receiving a string of information. Telematics most common use is in GNSS technology relaying information between computers and mobile devices. Telematics provides services via telecommunications, which allows the user to access real-time traffic data to allow better trip plans [22].  
System-on-a-Chip (SoC): SoC integrates a full computer system into a micro fully functional circuit. It can process several different technologies as well as possessing an array of different sensor functionalities such as RFID, GNSS, Location-Based Service (LBS), and so on. Generally, any embedded sensor will contain and utilize this technology [49].  
Portable devices: Portable devices allow individuals to access the services from different U-city areas, at the same time act as part of the IoT network on providing feedback information to the big database. Different to SoC's focus on processing information, portable devices is to transfer digital or analog information signals wirelessly and allows more flexibility on customizable information by using application software [50].
## 4 Experiments and Results
### The Comparison of Bit Error Ratios  
The BER is a crucial index for evaluating the performance of detection algorithms. Figures 7-9 compare the BER of four algorithms under QPSK modulation with different numbers of antennas. When the number of antennas was two for both transmitters and receivers, the AWOA achieved a BER of \(1.07\times 10^{-3}\) dB. When the number of antennas increased to four, the AWOA's BER was \(4.0\times 10^{-3}\) dB. When it was eight, the BER dropped to \(4.3\times 10^{-2}\) dB. As the average received signal-to-noise ratio increased, the BER gradually decreased. Although the increasing number of antennas leads to more interference signals, AWOA detection maintains certain advantages and exhibits noticeable BER performance, which can offer practical insights.  
Figure 6: Communication distance verification.  
Figure 9: BER in \(8\times 8\) MI-MIMO.  
Figures 10-12 illustrate the BER performance comparison of the four detection algorithms in the MI-MIMO system with 16-QAM modulation using different numbers of antennas. When two transmitters and four receivers were used, BER further decreased to \(2.81\times 10^{-2}\) dB. When there were four transmitters and si\(\times\) receivers, the AWOA's BER was \(4.62\times 10^{-2}\) dB. With eight receivers, the BER decreased to \(3.09\times 10^{-2}\) dB. These figures reveal that as the number of receiving antennas increased, the BER decreased following the diversity gain law. Therefore, the BER performance in MI-MIMO systems is closely related to the modulation scheme.  
Figure 8: BER in \(4\times 4\) MI-MIMO.  
Figure 7: BER in \(2\times 2\) MI-MIMO.
## 4 Results and Discussion
Figure 10: Graphs of the level of recognition of punches of a group of entry-level boxers. The obtained data are in good agreement with the data of other authors. In work [3], where nine people were used for the dataset, and 20% of them were the control group, they only participated to determine the accuracy of the model. The recognition accuracy varied for different types of movement from 87% to 99%. In [11], based on video data analysis, Multi-class SVM classifiers and a Breiman Random Forest are utilized for punch classification. The experimental results illustrate an accuracy of 97.3% for previously seen athletes and 96.2% for unseen athletes. It should be noted that, in our work, the accuracy of the previously seen group of boxers was 100%. In [22], based on video motion capture using Convolutional Neural Networks with a Long Short-term Memory Network, the accuracy of motion recognition was up to 78%. The classification of karate strikes in a study by the authors of [12] based on video capture of movements showed a strong difference in accuracy, depending on the type of movement, ranging from 81 \(\pm\) 15% to 97 \(\pm\) 4%. In [16], one person participated in the study. This work used data that were obtained from IMUs. These data were used to recognize punches employing a variety of deep learning methods, including MLP. For the experiment configuration, when IMUs were attached to the boxer's wrists, the accuracy was 0.98. In work [16], configuration 2 was also investigated, in which an additional IMU sensor was installed in the third thoracic vertebrae region. The authors obtained an interesting result: \"sensor 1 configuration demonstrated greater overall classification accuracy for all models\".
## 2 RO Measurement Requirements
### The Cion RO Receiver  
Early RO-based satellites such as the FORMOSAT-3/COSMIC RO constellation carried the IGOR (Integrated GPS Occultation Receiver) GNSS receiver, which is based on the BlackJack receiver designed at JPL, and flown on such missions as PAZ, CHAMP, SAC-C, and GRACE. The recently deployed (June 2019) six-satellite COSMIC-2 constellation [(39)] carries the TriG GNSS receiver (the name implies the first letter of each of three common GNSS systems, namely GPS, Galileo and GLONASS), the follow-on to BlackJack [(40)]. However, IGOR or TriG are much too large to be flown on small satellites, such as a 6U cubesat.  
Recently, a low-cost, low-power, and low-mass GNSS receiver (Cion) was developed at JPL [(41)]. The Cion receiver was designed for two commercial firms, Tyvak and GeoOptics, for use in the GeoOptics CICERO (Community Initiative for Continuing Earth Radio Occultation) constellation [(42)]. Cion uses a commercial off-the-shelf (COTS) computer along with existing space-qualified RF downconverters, software, and firmware to produce the precise timing needed to generate RO profiles (Figure 5). The Cion is capable of recording both setting and rising occultations, and the processing uses the  
Figure 4: The geographical distribution of the upper percentile (top 2%) of the measured \(\Delta\phi\) from all ROHP observations between 60S and 60N latitudes. Each color denotes a vertical region where the \(\Delta\phi\) from all rays were averaged. Black, orange and red symbols represent cases where the average \(\Delta\phi\) was obtained by averaging all RO rays whose tangent point lies between 0–5, 5–10 and 10–15 km height, respectively. The top panel includes all data for June–July–August (JJA); the lower panel for December–January–February (DJF). The color background map depicts the average rain rate during these months derived from the GPM IMERG precipitation product.  
open-loop (OL) tracking implemented in the receivers for COSMIC and COSMIC-2 for extended water vapor profiling through the moist lower troposphere [43]. A JPL version of the Cion receiver suitable for NASA small satellite missions has been designed and is currently being built for the SigNals of Opportunity P-band Investigation (SNOOPI) In-Space Validation of Earth Science Technologies (InVEST) mission and for the Navigation Technology Satellite (NTS-3), a technology demonstration for the next-generation GNSS transmitter. It has also been proposed for numerous other missions, primarily in conjunction with other observations that require precise time tags at the level of a few picoseconds. A follow-on NASA Instrument Incubator Program (IIP) called Genesis is building on this design to enable a next-generation GNSS reflections receiver for small satellites as well.
## 5 Simulation Verification  
### Simulation Model  
To validate the effectiveness of the proposed pilot protection scheme, the grid-forming PV system illustrated in Figure 2 is implemented in PSCAD/EMTDC. The transmission line voltage level is 220 kV, the line length is 40 km, and the unit line impedance is z = 0.0178 + /0.314 \(\Omega\)/km. The rated power of the PV plant is 80 MW. The data window of the proposed protection is 20 ms, the sampling frequency is 10 kHz, and the protection threshold \(D_{\text{set}}\) is 0.3. Five fault locations are configured in the PV system: F1 (PV side) and F5 (grid side) are external faults, while F2 (1 km from the PV terminal), F3 (20 km), and F4 (39 km) are internal faults.
# Time Series Feature Selection Method Based on Mutual Information
###### Abstract  
Time series data have characteristics such as high dimensionality, excessive noise, data imbalance, etc. In the data preprocessing process, feature selection plays an important role in the quantitative analysis of multidimensional time series data. Aiming at the problem of feature selection of multidimensional time series data, a feature selection method for time series based on mutual information (MI) is proposed. One of the difficulties of traditional MI methods is in searching for a suitable target variable. To address this issue, the main innovation of this paper is the hybridization of principal component analysis (PCA) and kernel regression (KR) methods based on MI. Firstly, based on historical operational data, quantifiable system operablility is constructed using PCA and KR. The next step is to use the constructed system operablility as the target variable for MI analysis to extract the most useful features for the system data analysis. In order to verify the effectiveness of the method, an experiment is conducted on the CMAPSS engine dataset, and the effectiveness of condition recognition is tested based on the extracted features. The results indicate that the proposed method can effectively achieve feature extraction of high-dimensional monitoring data.  
time series; feature extraction; mutual information; system operablility; condition identification +
Footnote †: journal: Applied Sci. 2024, 14, 1960. [[https://doi.org/10.3390/app14051960](https://doi.org/10.3390/app14051960)]([https://doi.org/10.3390/app14051960](https://doi.org/10.3390/app14051960))