esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
## 5 Simulation Results
### Performance of the Msg3 PR Scheme
#### 5.1.4 The Average Energy Consumption  
Figure 10 presents the average energy consumption of the devices that completed the RA. We plot the performance when \(V=20\) and \(N_{max}=8\). The average energy consumption of the devices increases with increasing the number of accesses; this is because the RA success probability decreases due to the congestion. Since most energy consumption occurs during transmission, the Msg3 PR scheme consumes more energy than the conventioal case. However, increasing the \(PRS\) does not necessarily increase the energy consumption. Surprisingly, the Msg3 PR with \(PRS=3\) dB or 4 dB consumes less energy than the Msg3 PR with \(PRS=1\) dB or 2 dB. For example, the Msg3 PR consumes approximately 13% more energy than the conventional case when \(PRS=3\) dB or 4 dB, while it consumes approximately 30% more energy than the conventional case when \(PRS=1\) dB or \(PRS=2\) dB. From this observation, we can argue that the energy consumption reductions due to the increase in the RA success probability are greater than the increased energy consumption due to increased transmission power results from \(PRS\) increment. In conclusion, choosing proper values of \(PRS\) and \(N_{max}\) for the Msg3 PR scheme can improve the RA throughput and lower the connection failure probability with relatively little additional energy consumption.  
Figure 10: Average energy consumption of devices that completed the RA procedure (\(N_{max}=8\), \(V=20\)).  
Figure 9: Connection failure probability (\(N_{max}=8\), \(V=20\)).
## 3 Experiment  
The experimental system was built as shown in Figure 1. The detection light source is a NewOpto-633-2-P helium-neon laser from Hangzhou New Power Optoelectronics Technology Co., Ltd. (HangZhou, Zhejiang, China), with a wavelength of 632.8 nm, an optical power of 2 mW, and a spot size of 2 mm. The polarizer and analyzer are Glan-Taylor polarizers, and the extinction ratio is up to 10\({}^{5}\):1. PEM1 and PEM2 are both single-drive octagonal symmetrical PEMs developed by us, the piezoelectric driver is piezoelectric quartz crystal, and the optical crystal is fused quartz crystal. The length, width and thickness of the fused silica crystal of PEM1 and PEM2 are 60 mm \(\times\) 60 mm \(\times\) 16 mm and 54.2 mm \(\times\) 54.2 mm \(\times\) 16 mm, respectively. The resonant frequencies of the two PEMs are 44.912 kHz and 49.953 kHz, respectively. The motorized scanning stage is a precision two-dimensional electric translation stage (EPSD150-150, Wuhan Hongxingyang Technology Co., Ltd., Wuhan, China) with an XY stroke of 150 \(\times\) 150 mm, a maximum scanning speed of 6 mm/s, and a positioning accuracy of 50 \(\upmu\)m.  
The motorized scanning stage is equipped with a clamping fixture whose size could be flexibly adjusted according to the sample size. The used detector is a silicon photodetector (DH-GDT-D020V, Beijing Daheng New Era Technology Co., Ltd., Beijing, China). The PEM driving and data processing circuit is manufactured using an Altera EPC IV FPGA chip as the core. The two PEM driving signal sources used the DDS module of the FPGA to provide driving square wave signals, which are amplified by the LC resonant circuit to drive the PEMs. The modulated light intensity signal is detected using a photodetector. Signal acquisition is implemented using a 12-bit high-precision analog-to-digital converter (ADC), and the digital signal sequences are input into the FPGA digital phase-locked data processing module [15; 16]. The data processing of the harmonic terms of \(V_{\omega_{1}}\), \(V_{\omega_{2}}\), and \(V_{\omega_{2}-\omega_{1}}\) is then simultaneously completed.
## 2 Materials and Methods  
In this study, drilling experiments were performed using the multi-spindle drill head type MH30/13 to increase productivity and reduce time by producing multi-holes simultaneously. A manual milling machine that has a maximum spindle speed of 3450 rpm with constant feeds was used for drilling operations. Therefore, drilling parameters include the spindle speeds of 1007, 2015, and 3025 rpm whereas the selected feeds were 0.04, 0.08, and 0.14 mm/rev. These drilling parameters were considered similar to the previous study by [PERSON] et al. [19]. All drilling tests were conducted in a dry condition. The workpiece material used was Al2024, which is used in the aerospace industry due to its good machinability, high fracture toughness, excellent damage tolerance and resistance to fatigue crack [22]. The carbide twist drills with TiN and TiCN coatings were used for the drilling operation. All drills were 6 mm with a helix angle and point angle of 30\({}^{\circ}\) and 140\({}^{\circ}\), respectively. The carbide drill was selected due to its combination of high hardness and toughness [23], where the high point and helix angles gave high-quality holes [8]. The 6 mm diameter is a common size for making holes in aerospace structures for creating rivets [3]. The coatings were selected to enhance the wear resistance and tool life, and improve the hole quality [24].  
A Kistler 9257 BA force dynamometer connected with a control unit and data acquisition was used for measuring and analysing the thrust force. A support plate was used to avoid any damage to the dynamometer. Surface roughness is generally measured as the average roughness (\(R_{a}\)), which is commonly used in the mentioned industries [25]. Therefore, in this study, \(R_{a}\) was measured using the surface roughness tester and the burrs were inspected using a digital microscope. The details of the equipment are given in Figure 1. Moreover, the analysis of variance (ANOVA) technique was performed to find the percentage contribution of spindle speed, feed and coatings on the thrust force and \(R_{a}\). ANOVA is a decision-making tool used for determining the impact of input parameters from experimental results [26]. Finally, an artificial neural network (ANN) technique was used for modelling and predicting the \(R_{a}\) using the MATLAB(r) 2017b environment.
# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 4 Results and Discussion
### Feature Extraction  
From the alpha and beta wavelet coefficients, intervals labeled were selected as seizure and non-seizure. Mean, variance, skewness, kurtosis, entropy, and energy metrics were computed for sample sets within one-second epochs. Additionally, intermediate features were extracted between two epochs or with overlapping of half-epochs. By using this, interactions between each epoch were also considered.
# In Vitro Antioxidant Effects of Coenzyme Q10 on Cellular Metabolism in Aged Mesenchymal Stem Cells
## 4 Discussion
CoQ10 also supports fatty acid oxidation by upregulating peroxisome proliferator-activated receptors (PPARs) and CAT, which are key regulators of \(\beta\)-oxidation and lipolysis [33]. Fatty acid \(\beta\)-oxidation predominantly occurs in mitochondria, with peroxises playing a complementary role, particularly for very long-chain fatty acids. Unlike mitochondrial \(\beta\)-oxidation, which generates ATP, peroxisomal \(\beta\)-oxidation directly transfers high-energy electrons to oxygen, producing hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) and releasing heat. Catalase, a peroxisomal enzyme, converts H\({}_{2}\)O\({}_{2}\) into water and oxygen, mitigating oxidative damage. Intracellular, H\({}_{2}\)O\({}_{2}\) also facilitates the degradation of unwanted proteins and defends against foreign particles [47]. CoQ10 supplementation appears to enhance fatty acid \(\beta\)-oxidation in an age-dependent manner, as evidenced by increased PPAR-\(\gamma\) and catalase activity. This mechanism likely reduces oxidative stress, particularly in older cells, where oleic acid serves as a primary substrate. In younger cells, CoQ10 supplementation elevates the levels of lauric acid, myrstic acid, and palmitic acid. Conversely, late passage cells exhibit significant increases in palmitic acid, stearic acid, and elaidic acid, suggesting a shift from unsaturated fatty acids, such as oleic and elaidic acids, toward saturated fatty acids. This shift may enhance membrane fluidity and rigidity, as saturated fatty acids, with their straight chains, pack more efficiently within bilayer membranes [47, 48]. The observed increase in stearic acid, known for its antioxidant-enhancing properties, highlights its role in reducing oxidative stress and improving cellular resilience. Additionally, CoQ10 supplementation increases pentadecanoic acid (C15:0, PDA), a rare odd-chain fatty acid with a low synthesis rate in humans [49]. PDA is associated with improved health span and longevity through mechanisms involving mTOR inhibition and AMPK activation, pathways shared with rapamycin and metformin [50]. PDA can be fully \(\beta\)-oxidized, yielding six acetyl-CoA molecules and one propionyl-CoA [49].
# Biomechanical Analysis of Posterior Ligaments of Cervical Spine and Laminoplasty
###### Abstract  
Cervical laminoplasty is a valuable procedure for myelopathy but it is associated with complications such as increased kyphosis. The effect of ligament damage during cervical laminoplasty on biomechanics is not well understood. We developed the C2-C7 cervical spine finite element model and simulated C3-C6 double-door laminoplasty. Three models were created (a) intact, (b) laminoplasty-pre (model assuming that the ligamentum (LF) between C3-C6 was preserved during surgery), and (c) laminoplasty-res (model assuming that the LF between C3-C6 was resected during surgery). The models were subjected to physiological loading, and the range of motion (ROM), intervertebral nucleus stress, and facet contact forces were analyzed under flexion/extension, lateral bending, and axial rotation. The maximum change in ROM was observed under flexion. Under flexion, ROM in the laminoplasty-pre model increased by 100.2%, 111.8%, and 98.6% compared to the intact model at C3-C4, C4-C5, and C5-C6, respectively. The ROM in laminoplasty-res further increased by 105.2%, 116.8%, and 101.8% compared to the intact model at C3-C4, C4-C5, and C5-C6, respectively. The maximum stress in the annulus/nucleus was observed under left bending at the C4-C5 segment where an increase of 139.5% and 229.6% compared to the intact model was observed for laminoplasty-pre and laminoplasty-res model, respectively. The highest facet contact forces were observed at C4-C5 under axial rotation, where an increase of 500.7% and 500.7% was observed compared to the intact model for laminoplasty-pre and laminoplasty-res, respectively. The posterior ligaments of the cervical spine play a vital role in restoring/stabilizing the cervical spine. When laminoplasty is performed, the surgeon needs to be careful not to injure the posterior soft tissue, including ligaments such as LF.  
laminoplasty; finite element method; ligamentum flavour +
Footnote †: journal: _Sci. 2021_, 11, 7645. [[https://doi.org/10.3390/app11167645](https://doi.org/10.3390/app11167645)]([https://doi.org/10.3390/app11167645](https://doi.org/10.3390/app11167645))
## 5 Results and Discussion  
### Computational Time  
The computer program was launched 100 times with different population sizes on the personal computer with AMD Ryzen 7 2700X Eight-Core Processor at 3.70 GHz CPU, 16 GB RAM and Windows 10 operating system. The population size was 100 individuals. The mutation rate was 2%, and the crossover rate was 75 % of the population (the number of selection/crossover operations in each generation was 0.75 of the population number) in 200 generations. The average running time is shown in Table 4. The software has two modes: \"normal\" and \"log\". In the \"log\" mode, the software keeps logs of some operation/calculation, and that makes it approximately 30 % slower. The values in Table 4 were achieved in the \"normal\" mode.  
Increasing the GA parameters over 200 generations and 100 individuals in the population did not produce better results, and the calculation time was still acceptable, so these GA parameters were used in the further calculation.  
For the comparison, the results and speed of the steady-state algorithm (SSga) that was used in this research were compared with the steady-state generation algorithm (SSGa). After consecutive 100 runs, the average calculated makespan value was lower (better) with the steady-state (SSga) algorithm. On the other hand, the steady-state generation algorithm (SSGa) had a better performance regarding the software run-time, as shown in Table 5.  
Despite the much lower computational time of the steady-state generation algorithm, priority was given to the quality of the results of the steady-state algorithm because the duration of the execution is acceptable in both cases.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Population Size [pcs]**} \\ \hline  & & 50 & 100 & 200 & 300 & 500 \\ Generations & 100 & 00:09.273 & 00:20.211 & 00:44.316 & 01:01.513 & 01:46.551 \\  & 200 & 00:12.772 & 00:35.135 & 01:26.321 & 02:17.244 & 03:00.225 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Running time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **avg. Time [sec]** & **avg. Makespan [h]** \\ \hline steady-state algorithm (SS-GA) & 36.778 & 160.087 \\ steady-state generation algorithm (SSG-GA) & 19.181 & 162.103 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Makespan and running time (SS-GA and SSG-GA).
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 5 Experimental Verification
### Test Devices and Site  
In Figure 13, there are devices for thermal balanced test including flight model, heating devices, auxiliary equipment, temperature measuring device, power supply, control unit, thermal vacuum tank, etc. Test site is shown in Figure 14. The temperature of chamber is under 100 K, and the pressure is below 10\({}^{-5}\) Pa. Heat flux of the sun is mainly performing on the solar panels, other surfaces in this direction is covered with MLI, influence of heat flux can be ignored. The effect of the sun is simulated by heating the solar panels to reach the temperature level on orbit.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Location** & **Instrument** & **Condition I (\({}^{\circ}\)C)** & **Condition II (\({}^{\circ}\)C)** & **Required Temp (\({}^{\circ}\)C)** \\ \hline \multirow{6}{*}{Inside the satellite} & OBDH & 19.1–23.4 & 10.2–15.3 & \(-\)10–45 \\  & Reaction wheel I & 21.0–27.2 & 12.7–16.6 & \(-\)10–45 \\  & Reaction wheel II & 14.0–17.5 & 8.2–12.1 & \(-\)10–45 \\  & Navigation unit & 22.4–28.0 & 13.5–17.8 & \(-\)10–45 \\  & Signal processing unit & 18.8–20.9 & 9.5–14.4 & \(-\)10–45 \\  & MEMS gyroscope & 16.8–18.3 & 10.2–11.3 & \(-\)10–45 \\  & Magnetometer & 10.1–12.6 & 5.7–7.9 & \(-\)10–45 \\ Outside the satellite & Star tracker A & 14.8–16.2 & 6.7–9.2 & \(-\)30–30 \\  & Star tracker B & 15.5–17.4 & 9.7–12.1 & \(-\)30–30 \\ \hline \end{tabular}
\end{table}
Table 12: Analysis result of the temperature variation conditions of instruments inside and outside the satellite cabin.  
Figure 13: Schematic diagram of the testing devices.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 2 Main Types of Hollow-Core Fibers  
### Basic Designs  
Dielectric hollow-core fibers include central round, gas-filled cavity (core) surrounded by a photonic barrier (cladding), preventing the escape of radiation. HCFs were first proposed in 1976 (Bragg slab waveguides) [6], 1978 (Bragg fibers) [7] and 1995 (Photonic Bandgap Fiber-PBGF) [8]. Fibers developed so far can be split into three groups depending on the light confinement mechanism employed:  
1. Bragg Fibers, where the core is enclosed with multiple alternating layers of transparent materials with high and low refractive index constituting a Bragg mirror (Figure 1a);
2. Hollow-core Photonic Bandgap Fibers (HC-PBGFs), where multiple layers of periodic glass/air structure around the core block the radial propagation of light (Figure 1b). PBGFs with solid core are not covered here;
3. Anti-Resonant Fibers (ARFs), where the optical anti-resonance in multiple flat membranes or walls of tubes blocks the radial propagation of light. (Figure 1c).  
In fibers (b) and (c), the structure blocking escape of light (cladding) is normally made of gas and pure fused silica; other glasses and polymers are less useful, primarily due to low mechanical strength and gas permeability.  
The first anti-resonant fibers were Kagome fibers, where the cladding is a lattice of hexagonal and triangular cells (Figure 2a). This fiber was modified by changing the surfaces around fiber's core from flat to curved ones, resulting in \"negative curvature\" fiber (Figure 2b), reduced overlap of guided light with glass, and lower loss [9; 10]. Further development led to the replacement of Kagome cladding with a single layer of thin-walled, single or nested glass tubes, resulting in NANF fibers (Figure 1c). [11].
# Performance of Ship-Based QKD Under the Influence of Sea-Surface Atmospheric Turbulence
## 3 The Influence of Sea-Surface Turbulence on the Performances of a Ship-Based QKD  
In a ship-based QKD system based on the BB84 protocol, performance is affected by sea-surface atmospheric turbulence. Moreover, the downlink is expected to experience lower attenuation since the uplink beam is much more affected by atmospheric turbulence [5,18]. If the uplink can accomplish communication, the downlink can also realize communication. Therefore, this study focuses on the performances of the uplink of a ship-based QKD system. Two kinds of ship-based QKD systems are studied in this paper: one where transmitter is on a ship and the receiver is on a satellite, and one where; both the transmitter and receiver are on ships. The specific schematic diagram is in Figure 3 as follows:  
Figure 1: Atmospheric scintillation versus transmission distance.  
Figure 3: Schematic diagram of QKD system based on a ship.  
Figure 2: Variance in aiming error angles versus altitude.  
For a BB84-based system, the default parameter settings are as follows: the pulse repetition frequency at the transmitting end is 625 M; the number of individual pulse photons is 0.8; the diameter of the telescope at the transmitting end is 0.2 m; the transmitting end shading loss is 0.3; the diameter of the telescope at the receiving end is 0.6 m, the shading loss at the receiving end is 0.18; the wavelength is 1550 nm; the girdle radius of the laser beam is 0.005 m; the sifted efficiency of the BB84 protocol is 0.5; and the field-of-view angle is 100 \(\upmu\)rad. To minimize the effect of background light on quantum light performance, the background parameter was set to that of a clear night.
## 3 QoS Self-Provisioning and Interference Management Concept
### Step 3--The HS-DSCH Power Calculations  
The data channels power of the HNB has direct impact on the data rate available for HUE connected to the HNB. Thus, calibration is based on agreed requirements, e.g., data rates expected at the femtocell edge. The transmission power of the data channels, \(P_{\textit{HSDSCH}}^{opt}\), to reach the required downlink throughput at the cell edge is calculated as  
\[P_{\textit{HSDSCH}}^{opt}=\text{sinr}\big{(}\text{Req}\text{Thp}_{\textit{ DL}}\big{)}.\frac{PL_{\textit{femuo}}\cdot I_{\textit{HNB}}}{SF} \tag{13}\]  
where \(I_{\textit{HNB}}\) is the interference measured by the HNB and \(SF\) is the spreading factor and equals 16 for the HS-DSCH. It is assumed that the interference level from the surrounding network is the same in the whole femtocell area, which is only an estimate since it depends especially on whether interference from other femtocells is strongly present.  
Estimating this interference is rather complicated as it depends on the active load. We therefore base the estimation on the RSCP CPICH NLM measurements of all macrocells in the vicinity: \(RSCP_{CPICH\_k}\). We only consider macrocells not to put neighboring femtocells into a race condition where they keep increasing their power to fulfill cell edge rates. This would only lead to worse network performance and similar cell edge data rates for the competing femtocells. Utilization of RSCP makes the approach independent on network load during NLM measurements and we can then apply modification afterwards. The interference at the HNB, \(I_{\textit{HNB\_estimated}}\), is estimated as  
\[I_{\textit{HNB\_estimated}}=\eta_{\textit{Total\_CPICH}}\cdot F_{\textit{ load}}\cdot\sum_{k=1}^{K}RSCP_{\textit{CPICH\_k}} \tag{14}\]  
where \(F_{\textit{load}}\) is the assumed average load of the surrounding cells and \(\eta_{\textit{Total\_CPICH}}\) is the assumed ratio between the average total transmit power of a base station and its pilot power. We assume \(\eta_{\textit{Total\_CPICH}}=10\) for the simulations but it is a parameter that is set by the operator depending on the macro network configuration.
## Literature Review
### Politics  
Politics is an interesting application field of sentiment analysis and opinion mining on micro-blogs. Here, we report a few works.  
In [17], the authors proposed a framework to predict the popularity of political parties in Pakistan in 2013 public election, by finding the sentiments of Twitter users. The proposed framework is based on the following steps: (1) collection of tweets; (2) pre-processing of tweets; (3) manual annotation of the corpus. Then, to perform sentiment classification, supervised machine learning techniques such as Naive Bayes (NB), k Nearest Neighbors (kNN), Support Vector Machines (SVM) and Naive Bayes Multinomial (NBMN) were used to categorize the tweets into the predefined labels.  
In [18], authors utilized tweets to reveal the views of the leaders of two democratic parties in India. The tweet data-set was collected by using the public twitter accounts, and Opinion Lexicon [19] was used to compute the number of positive, negative and neutral tweets. They proposed a \"Twitter Sentiment Analysis\" framework, which, after pre-processing of the crawled data-set from Twitter, accumulated opinion lexicon along with classification of tweets into three classes, i.e., positive, negative and neutral, for the evaluation of sentiments of users.  
To discover the sentiments of Twitter users, with the aim of exploring their opinions regarding political activities during election days, the authors of [20] proposed a methodology and compared the performance of three sentiment lexicons, i.e., W-WSD, SentiWordNet, TextBlob and two well known machine learning classifiers, i.e., Support Vector Machines (SVM) and Naive Bayes. They achieved better classification results with the W-WSD sentiment lexicon.  
In [10], authors utilized tweets to predict the sentiment about Islamic State of Iraq and Syria (ISIS); opinions are organized based on their geographical location. To perform the experimental evaluation, they collected tweets for a period of three days and used [PERSON]'s algorithm with data mining algorithms such as Support Vector Machine, Random Forest, Bagging, Decision Trees and Maximum Entropy to classify tweets related to ISIS.  
The paper [8] presents a study where the authors exploit tweets to find out political friends. They named their approach a \"Politic Ally\" which identifies the friends having the same political interest.