esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Aging of Lithium-Ion Batteries
### Cycling Aging  
Cyclic aging refers to the aging caused by the charging and discharging cycle of the battery, and its aging performance has a great relationship with cycle depth (CD). Cycle depth indicates the change in charge state during a cycle and is related to the amount of charge gained or lost during the charge and discharge process.  
Figure 5: Calendar aging curves.  
Based on the research in [13], the relationship between capacity fade and the operation conditions, including cycle depth, average SOC, and the number of cycles (NC), under the conditions of 25 \({}^{\circ}\)C storage temperature, is shown in Equation (2):  
\[C_{f\_cyc}=0.021\cdot e^{-0.01943\cdot SOC}\cdot cd^{0.7162}\cdot nc^{0.5} \tag{2}\]  
where \(C_{f\_cyc}\) represents the percentage of capacity fade caused by cycling aging, _SOC_ represents the average SOC level during the cycle, and _cd_ and _nc_ represent the cycle depth and the number of cycles, respectively. Figure 6 shows the calendar aging curve drawn by Equation (2).  
Compared with calendar aging, the rising slope of cycle aging curve is steeper when the number of cycles is low but tends to be flat when the number of cycles increases. In some cases, the batteries with a deeper cycle depth will have a faster aging rate than the ones with a shallower cycle depth. Sometimes, the difference can be several-fold.
## 3 Network Positioning
### Wi-Fi RTT Ranging  
Another form of network positioning is Wi-Fi round-trip time (RTT), which measures the distance from a device to nearby Wi-Fi access points to determine a location. The IEEE 802.11 mc standard made Wi-Fi RTT possible in 2016 with the fine timing measurement (FTM) protocol [31]. Wi-Fi RTT works by measuring the distance from a device to nearby Wi-Fi access points. More specifically, the RTT is the time it takes for a signal to be sent to the access point, plus the time it takes for the access point to communicate that the signal was received. Wi-Fi RTT allows the user to achieve 1-2 m accuracy in indoor scenarios [32]. This method is superior to Wi-Fi fingerprinting, as there is no need to build a radio map and maintain a large database. Instead, the location of the access points can be configured and provided upon connection [33]. Android added support for Wi-Fi RTT ranging in 2018, although the lack of access points that have implemented the IEEE 802.11 mc standard has proved that this technology is still in its early stages. Once more access points support this standard and the technology is more developed, Wi-Fi RTT may grow to be a leading indoor positioning technique with future possibilities of sub-meter accuracy. Although this could be revolutionary for indoor drone positioning, the limited number of access points currently supporting this standard prompts the authors to focus on the current capabilities of traditional Wi-Fi and cell localization instead.
# A Self-Paced Multiple Instance Learning Framework for Weakly Supervised Video Anomaly Detection
## 1 Introduction
* Unlike the widely used top-\(k\) instance selection strategy, which may result in a biased classifier, we propose to adaptively select the video instances (i.e., segments) from easy to hard according to the principle of self-paced learning. By alternatively updating the subset of segments used for training and parameters of the classifier, we can achieve an enhanced classifier with the ability of unbiased prediction. Figure 1: Schematic diagram of the baseline model and the proposed **self-paced** multiple-instance learning (SP-MIL) framework. * Extensive experiments conducted on the UCF-Crime, ShanghaiTech, and XD-Violence datasets demonstrate the effectiveness of the proposed SP-MIL framework across different video features and baseline models, and the proposed framework achieved the best performance compared with the state-of-the-art methods.
# Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar
## 2 Target Detection in Passive Forward Scattering Radar  
Radar cross section (RCS) information and pattern is one of the parameters in target's detection process and in feature's extraction for classification techniques of the proposed passive FSR system. The second parameter is the target's Doppler frequency in the received signal due to relative movement between the target and radar. This section will explain the target detection method and practical experimental set up for a passive FSR system.
## 3 System Model
### System Propagation Model
#### 3.3.1 Satellite Channel Model  
In this paper, different packets are transmitted through different beams of one satellite or different beams of different satellites. Therefore, we mainly consider the influence of satellite channels on signal transmission in two different scenarios. One is the difference of channels in different beams of one satellite. The other is the difference of channels in different beams of multiple satellites.  
In the LEO satellite communication system, as shown in Figure 3, the signal of the packet \(a_{qi}^{c}\) is mainly influenced by large scale fading (including free space loss, scintillation effect, cloud and rain attenuation, and oxygen absorption), small scale fading, transmission delay, and doppler effect. The channel impulse response of the beam \(b_{pj}\) of the satellite \(s_{p}\) which is used to transmit the packet \(a_{qi}^{c}\) can be expressed as:  
\[h_{a_{qi}^{c}s_{p}}b_{pj}=PL_{pathloss_{qi}^{c}s_{p}}b_{pj}\bullet PL_{scintillation }\bullet PL_{rain}\bullet PL_{oxygen}\bullet\delta_{a_{qi}^{c}s_{p}}\bullet e^{ j2\pi f_{a_{qi}^{c}s_{p}}b_{pj}}*h^{\prime}, \tag{1}\]  
where \(PL_{pathloss}\bullet PL_{scintillation}\bullet PL_{rain}\bullet PL_{oxygen}\) is large scale fading parameter. \(PL_{pathloss}\) represents free space loss. \(PL_{scintillation}\), \(PL_{rain}\), \(PL_{oxygen}\) are scintillation effect, rain attenuation, and oxygen absorption, respectively, which we set to 1, 1.585, and 1. \(\delta_{a_{qi}^{c}s_{p}}\) is the transmission delay. \(e^{j2\pi f_{a_{qi}^{c}s_{p}}b_{pj}}\) is the doppler effect. \(f_{a_{qi}^{c}s_{p}}\) is the value of doppler shift in the beam \(b_{pj}\) of the satellite \(s_{p}\). \(h^{\prime}\) is small scale fading defined as TDL channel in 3 GPP.  
As shown in Figure 3b, at the current time, the satellite \(s_{1}\) can only provide user \(u_{q}\) with one beam, and other beams of satellite \(s_{1}\) cannot be used for transmission because the band of other beams is occupied or the channel fading is serious. However, the user \(u_{q}\) needs to transmit a large number of packets and has the highest priority, the adjacent satellite \(s_{2}\) will be scheduled to serve the user \(u_{q}\). As shown in Figure 4, the distance between the satellite \(s_{1}\) and user \(u_{q}\) is 1500 km and the distance between the satellite \(s_{2}\) and user \(u_{q}\) is 1700 km. When the distance between satellite and user is different, the free space fading, doppler shift, and transmission delay are different.  
Figure 3: Propagation model. (**a**) Single-satellite multi-beam. (**b**) Multi-satellite multi-beam.
## 3 Results
### Changes in Gait Ability  
Table 3 shows the changes in gait parameters based on walking speed. The analyzed parameters included paretic and non-paretic stride length, stance and swing phases, single  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Parameters** & & & & **Mean \(\pm\) SD or Frequency (\%)** \\ \hline \multirow{6}{*}{MMT (1/2/3/4)} & \multirow{2}{*}{Knee} & \multirow{2}{*}{Hip} & Flexors & 0/0/21/3 (0/0/87.5/12.5) \\  & & Extensors & 0/1/19/4 (0/4.17/79.17/16.67) \\  & & Flexors & 0/0/20/4 (0/0/83.33/16.67) \\  & & Extensors & 0/1/19/4 (0/4.17/79.17/16.67) \\  & & Dorsiflexors & 1/7/11/5 \\  & & & (4.17/29.17/45.83/20.83) \\  & & & Plantar flexors & 1/7/11/5 \\  & & & (4.17/29.17/45.83/20.83) \\ MAS (1/1+/2) & & Plantar flexors & 4/12/8 (16.67/50.00/33.33) \\ MMSE-K (points) & & & 25.70 \(\pm\) 4.43 \\ BBS (points) & & & 42.16 \(\pm\) 9.25 \\ MBI (points) & & & 75.41 \(\pm\) 12.02 \\  & & Slow & 0.51 \(\pm\) 0.24 \\ Walking speed (m/s) & & Self-selected & 0.67 \(\pm\) 0.24 \\  & & Fast & 0.90 \(\pm\) 0.36 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Parameters** & & **Slow Speed** & **Self-Selected** & **Maximum Speed** & **F(_p_)** & **Post hoc** \\
**(\%RVC)** & & **Walking (A)** & **Speed Walking (B)** & **Walking (C)** & **F(_p_)** & **Post hoc** \\ \hline \multirow{6}{*}{Stance phase} & RF & 659.95 \(\pm\) 725.60 & 797.15 \(\pm\) 1169.16 & 1319.67 \(\pm\) 1286.18 & 5.459 (0.007) & A \textless{}C \\  & BF & 592.00 \(\pm\) 505.34 & 668.03 \(\pm\) 499.54 & 1013.80 \(\pm\) 950.94 & 5.698 (0.006) & \\  & TA & 569.36 \(\pm\) 579.40 & 854.86 \(\pm\) 831.86 & 1082.51 \(\pm\) 1626.82 & 1.716 (0.191) & \\  & GN & 347.63 \(\pm\) 444.21 & 367.34 \(\pm\) 347.68 & 469.60 \(\pm\) 380.73 & 1.770 (0.182) & \\  & GM & 3114.99 \(\pm\) 8154.58 & 1254.40 \(\pm\) 1807.01 & 2114.04 \(\pm\) 4878.40 & 1.636 (0.206) & \\  & RF & 407.49 \(\pm\) 481.03 & 476.76 \(\pm\) 528.75 & 849.66 \(\pm\) 978.37 & 5.971 (0.005) & A \textless{}C \\  & BF & 604.73 \(\pm\) 563.99 & 743.73 \(\pm\) 675.04 & 1118.23 \(\pm\) 1069.70 & 5.143 (0.010) & A \textless{}C \\ Swing phase & TA & 459.32 \(\pm\) 580.81 & 605.41 \(\pm\) 611.73 & 763.35 \(\pm\) 1114.47 & 0.930 (0.402) & \\  & GN & 329.56 \(\pm\) 286.61 & 392.68 \(\pm\) 299.73 & 576.36 \(\pm\) 299.73 & 6.487 (0.003) & A \textless{}C \\  & GM & 2836.31 \(\pm\) 6223.01 & 1226.62 \(\pm\) 1909.08 & 1792.19 \(\pm\) 4168.99 & 2.406 (0.101) & \\ \hline \end{tabular}
\end{table}
Table 2: Changes in paretic side lower limb muscle activity according to differences in walking speed (n = 24).  
limb support time, and the GAI. A one-way repeated-measures ANOVA with a Bonferroni post hoc test was conducted to compare walking speeds. Stride length significantly increased with faster walking speeds on the paretic side (46.22 to 60.00 %height, 29.81%) and the non-paretic side (45.99 to 59.09 %height, 28.48%) (_p_ < 0.05 for both). However, no statistically significant changes were observed in other gait parameters, including stance and swing phases, single-limb support time, and the GAI (_p_ > 0.05).
## 3 Results
### Effect of JAUN on ROS Generation  
As shown in Figure 4, ROS production in HaCaT cells increased by 56.56% following UVB irradiation. The results showed that the administration of JAUN led to a significant and dose-dependent decrease in intracellular ROS levels. Notably, after treatment with JAUN at a concentration of 100 \(\upmu\)g/mL, intracellular ROS levels decreased by 35.46%. This reduction rate is higher than the 34.09% observed in the positive control group.  
Figure 4: JAUN inhibits intracellular ROS levels in photoaging model. A plot of cell number against DCFH-DA detected in the FL-2 channel (**a**). Results are shown as a histogram (**b**). Findings are displayed as the mean \(\pm\) SD from three replicates. ***\(p\) < 0.05 compared with the unirradiated group. ***\(p\) < 0.01, ***\(p\) < 0.001 compared with the group treated only with UVB irradiation.  
Figure 3: Cell viability of photoaging model with or without JAUN. Ascorbic acid was used as a positive control. Findings are displayed as the mean \(\pm\) SD from three replicates. *\(p\) < 0.05 compared with the group only subjected to UVB irradiation.
# Integrity Monitoring of PPP-RTK Positioning: Part II: LEO Augmentation
## 2 Measurement Geometry under LEO Augmentation
### The LEO Configuration  
Based on the various advantages provided by LEO satellites as mentioned in the introduction, some LEO satellite constellations have been designed for navigation purposes and considered as augmentation systems to GNSS, for purposes of both navigation and IM. Recently, the German Aerospace Center has proposed a new GNSS named Kepler, consisting of 24 MEO satellites and 6 LEO satellites [24]. CentiSpace in China has also designed a future LEO satellite constellation to augment the GNSS-based PNT service [15], which plans to transmit GNSS-interoperable signals on L1 and L5 [25]. The first test satellite, named Xiangrikui 1, was launched in September 2018 and had dual-frequency GNSS receiver as well as instrument for laser inter-satellite links equipped onboard. It was shown to be able to achieve a cm-level POD accuracy based on internal validation methods [26]. CentiSpace has currently released two planned layers of LEO satellites based on the Walker constellation [27], located at altitudes (\(H\)) of 975 and 1100 km, respectively (see Table 1) [15; 28]. \(N\) and \(P\) in Table 1 denote the total number of satellites in the corresponding layer, and the number of orbital planes, respectively. The satellites are uniformly distributed on each plane with the number \(S=N/P\). The parameter \(F\) for the Walker constellation, which is used to describe the phase shift between adjacent orbital planes, should be an integer between 0 and \(P-1\). It is set to 0 in this study as an example.  
Figure 1: Frequency structure of the GPS, Galileo and BDS. The satellite numbers of different constellations/generations were retrieved on 1 February 2022.  
The symbols \(I\) in Table 1 denotes the orbital inclination, and all LEO satellites within the constellation are expected to have circular orbits with an eccentricity of zero. The right ascension of ascending node \(\Omega\) of the orbital plane \(i\), denoted as \(\Omega_{i}\), can be calculated as follows for Layers A and B (near-polar orbits), respectively:  
\[\text{Layer A}:\ \Omega_{i}=\frac{2\pi(i-1)}{P},\ \text{Layer B}:\ \Omega_{i}= \frac{\pi(i-1)}{P} \tag{1}\]  
For the \(j\)th satellite on orbital plane \(i\), the mean anomaly \(M_{ij}\) at a defined starting time \(t_{0}\) can be expressed as [29]:  
\[M_{ij}=\frac{2\pi}{S}\times\frac{F}{P}\times(i-1)+\frac{2\pi(j-1)}{S}=2\pi \times\bigg{(}\frac{F\times(i-1)}{N}+\frac{j-1}{S}\bigg{)}. \tag{2}\]  
The two-layer LEO constellation is also shown in Figure 2, where the red and the yellow lines illustrate the two layers of 975 and 1100 km, respectively.
## 3 Results and Discussion
### Limiting Oxygen Index (LOI)  
The flame performance of the nanocomposites was determined generally by the Limiting Oxygen Index (LOI). The LOI is the least concentration of oxygen, found in a flowing oxygen-nitrogen mixture, that supports a material's combustion. It signifies the capacity of the polymer to endure the fire. A higher LOI value means that it has a higher ability for fire resistance, i.e., it is difficult to ignite [31]. The following equation was used to calculate the LOI values.  
\[\text{n}\%=\text{O}_{2}\;(\text{O}_{2}+\text{N}_{2})^{-1}\times 100, \tag{1}\]  
where n% = LOI;  
\(\text{O}_{2}=\text{Volumetric speed of flow of O}_{2}\;\text{gas (mL s}^{-1})\);  
\(\text{N}_{2}=\text{Volumetric speed of flow of N}_{2}\;\text{gas (mL s}^{-1})\)[12].  
The flame-retardant behavior of the nanocomposites is described in Table 3. The LOI value of the TPU was enhanced when the flame retardants were added. The LOI value was the lowest (19) in TPU-1, where no flame retardant was added. In TPU-2, Benzoflex was added to the TPU matrix, which is a plasticizer and not a flame retardant; so, the LOI remained the same, i.e., 19. The LOI increased to 21 with the addition of the antimony trioxide flame retardant in TPU-3. The inorganic Sb\({}_{2}\)O\({}_{3}\) improved the flame retardancy of the composites because it is noncombustible and decreased the thermal conductivity and delayed the heat transfer to protected blends. It melted as the combustion started and formed a protective layer on the surface of the polymer nanocomposite; hence, it kept the polymer from contacting the oxygen. At elevated temperatures, antimony trioxide vaporizes and dilutes the oxygen present in the vicinity, and in this way, it acts as a flame retardant [27].  
Further enhancement in the LOI up to 26% was noticed when as little as 0.2% of mmT nanoclay was added (TPU-4), keeping the above fillers the same. The maximum value of the LOI, 29%, was obtained when the quantity of mmT nanoclay was increased to 0.5%. This is because organo-clay forms a thermally insulating layer of carbonaceous (inorganic) residues while burning, which shields the heat and retards the burning of the underlying polymeric material [20]. It can be concluded that a large quantity of flame retardants is needed to achieve the self-extinguishing features of polymers [32]. The above results indicated good synergic effects of the antimony trioxide and mmT nanoclay on the flame retardance of the TPU polymer.
## 4 Case Study
### Machine-Learning Results
#### 4.1.2 Binary Classification Model  
The Binary classification model results for the three cases are shown in Figures 8-10, respectively. It shows the accuracy and precision for various ML algorithms while predicting when the machine might fail in days. The best performing algorithm in all three cases is the NN followed by the Decision Forest algorithm, while the remaining ones show similar results. However, the Two-Class Support Vector Machine algorithm scored a 100% in precision which is significantly high. The worst performing algorithm is the Boosted Decision Tree algorithm. As for the computational time, there is no significant change between the normal case and the half case while the double case took more than the normal case by 12.5%.  
Figure 8: Binary classification model results for normal case  
Figure 7: Regression model results for double case.  
In conclusion, the parameters of the normal case would be the ones that will be used for binary classification model if computation time is not an issue. Since the normal case has been proven to be the best case, no further investigations would be conducted concerning the other two cases: the half case and double case.
###### Abstract
In contrast, decentralised systems offer enhanced security, scalability, and user control but are more complex and costly to implement [8; 11].
# Priority Pricing for Efficient Resource Usage of Mobile Internet Access
## 1 Introduction
For example, if a service level becomes bad, some users may decide to pay more and use a higher-level service, and vice versa. If there is no level whose service is worth the price, the user may decide not to send any traffic. The dynamics of a priority pricing system have often been modeled using equilibrium analysis [11,21,22,23,24,25,26,27,28]. The proposed scheme is built on top of the existing equilibrium analysis methods. The scheme adjusts the results of the equilibrium methods to account for the distortions created by realistic mobile internet access environments. The existing equilibrium analysis methods commonly assume that: (a) the individual user's impact on the system is infinitesimal, and (b) users have up-to-date global knowledge of the system status, such as the queue length or the job arrival rate at each service level, which is crucial if users are to make optimal decisions [29]. However, when a finite (potentially small) number of users makes suboptimal decisions, as a result of inaccurate knowledge of the system status, a stable equilibrium may not always exist. Such a scenario is very possible in the case of wireless Internet access, as the number of users attached to a base station is typically small, and up-to-date knowledge of the system status is not available to the users. The impact of using outdated information to determine network service pricing has been addressed in [11,30]. However, these studies deal with dynamic pricing, in which the service provider dynamically updates prices at run time. My scheme considers a static pricing system in which the prices do not change at run time, which is much more practical than dynamic pricing. This work differs from the equilibrium analysis of unobservable queues [27,28,31,32,33], in that it models explicit queues that are observable but have a delay in feeding the queue information to users. My analysis indicates that the system often fails to converge to a stationary equilibrium in realistic environments. As a result, equilibrium methods may produce suboptimal prices. The suggested scheme effectively adjusts the suboptimal price selection of the equilibrium methods when the system does not converge to a stationary equilibrium. It uses a heuristic search algorithm with pruning built on a sliding window transition model. Note that [PERSON] et al. [11] showed the existence of multiple equilibrium delays, whereas this work deals with equilibrium prices. The remainder of this paper is organized as follows: Section 2 describes the general system framework used in this study. Section 3 describes the model of system behavior. Section 4 describes the analysis of the results of the existing equilibrium method usingthe model presented in Section 3. Section 5 describes how to adjust the results of the equilibrium method. Finally, Section 6 concludes the paper by presenting an overview of the paper and suggesting a future research.
# Effectiveness of Laser-Assisted Teeth Bleaching: A Systematic Review
## 3 Results
### General Characteristics of the Included Studies
#### 3.2.3 Neodymium/Yttrium-Aluminum-Garnet (Nd:YAG) Laser-Assisted Bleaching  
The use of the Nd:YAG laser for whitening non-vital teeth has proven to be as effective as both a diode laser at 810 and 980 nm and the walking bleaching method [33]. In in vivo tooth whitening, the results were comparable to those achieved without activation. However, in vitro studies showed less tooth color change with Nd:YAG laser activation compared to halogen light [59; 60]. [PERSON] et al. [61] attempted to enhance the whitening effect by pre-treating the enamel with this laser before applying the whitening agent, but this approach did not yield the expected results [61].