esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Content-Aware Multi-Resolution Disparity Estimation Using Semi-Global Block Matching  
#### 2.2.1 Overview  
Deriving disparity from EIs using SGBM presents challenges, mostly attributed to the lack of texture and low resolution. Upsampling the EIs would lead to data loss, resulting in the introduction of noise and a decrease in image quality. Many studies [14,15] have investigated the computation of disparity at various resolutions to enhance the accuracy of disparity maps, particularly in the context of developing deep learning models. While rescaling EIs may result in a loss of image quality, calculating the disparity at multiple resolutions instead of merely one upsampled resolution still is an effective approach for handling varying levels of details and textures. Lower resolutions may result in the loss of some details in the scene, while higher resolutions may exhibit an inconsistent overall structure. This indicates a trade-off between maintaining structural consistency and capturing high-frequency details based on the input resolution [15].  
Content information can vary across different regions within EIs, particularly at varying resolutions. By utilising a content-aware approach, the disparity window size can be dynamically modified according to the characteristics of each location, resulting in more accurate disparity estimations. When working with areas that have a high level of  
Figure 4: EI before and after applying bilateral filtering. As seen in the texture map of the original image, there is a noticeable stepping effect in the background. Although the filter did not eliminate it, it did assist in reducing the impact while maintaining edge information.  
texture, using a smaller window size would be advantageous in capturing intricate details. Conversely, in areas that lack texture, a larger window size can be used to minimise noise.
## 5 Results
### Comparison of RL Algorithms in Quasi-Static Environment  
We used the light coupling performance obtained from Shack-Hartmann wavefront sensor data as the reference for comparison with the refined RL algorithms outlined in Section 4. This comparison was conducted under the quasi-static turbulence condition of \(D/r_{0}=5\). The results are presented in Figure 8. Employing the Shack-Hartmann wavefront sensor with 12 lenslets across the aperture diameter for a total of 112 lenslets as a benchmark enabled us to comprehensively evaluate the effectiveness of the proposed RL algorithms in improving light coupling performance in the presence of atmospheric turbulence.  
The SAC and DDPG algorithms rely on the randomness introduced by their replay buffers. To ensure that the results of these algorithms can be replicated, a fixed seed is used  
Figure 7: Comparison of the selection of clipping parameter \(\epsilon\) in PPO applied on 20 randomly selected quasi-static atmospheric turbulence of \(D/r_{0}=5\).  
as the initial parameter. To avoid the possibility of reusing the same set of random values in every iteration, the fixed seed value is incremented by 1 after each iteration.  
The results shown in Figure 8 indicate that the PPO algorithm outperforms the SAC and DDPG algorithms in this experiment. Specifically, when we consider the randomly selected quasi-static atmospheric turbulence with a ratio of \(D/r_{0}=5\), the PPO algorithm achieved a maximum reward of 67%, which is close to the maximum reward obtained by the Shack-Hartmann sensor, approximately 73%. Although SAC and DDPG still demonstrated acceptable performance with a maximum reward of 53% in the early training episodes, the PPO algorithm consistently demonstrated superior results. The improved performance of PPO over SAC and DDPG can be attributed to its ability to sample from the action distribution, which helps it avoid suboptimal local minima in high-dimensional observation spaces. In contrast, in SAC and DDPG, the actions that look nearly optimal can have an equal likelihood of being tried as those that appear highly suboptimal.
## 3 Results
### Hand Gesture Recognition Performance
#### 3.2.2 User Experience and Usability  
The hand gesture control system provided an intuitive and engaging way to interact with the drone, especially for users with little to no prior experience in drone piloting. By eliminating the need for a traditional controller, the system allowed users to fly the drone using natural hand movements, making the experience more accessible.  
Most users required some initial practice to become comfortable with the system. The main learning curve involved the following:  
* Memorizing multiple gestures and associating them with their corresponding drone commands.
* Maintaining proper hand positioning in front of the camera to ensure accurate recognition.
* Balancing attention between their hand gestures and the drone's movement, particularly when not relying on the AI deck's streamed FPV footage.  
Once users became familiar with the system, they found it to be a responsive and effective alternative to traditional control methods. The ability to adjust hand positioning and recognize gestures contributed to an overall smooth and immersive control experience.
## 3 Brief Overview of Iron Metabolism, the Labile Iron Pool and How It Is Altered in Cancer
### Altered Iron Metabolism in Cancer  
Cancer cells have a higher requirement for iron in order to have a proliferation advantage, which is a cancer hallmark [4; 5]. Thus, cancers have adapted to increase the overall iron content in the cells. They have achieved this adaption by increasing iron intake and decreasing the efflux, for example, by increasing the transferrin receptor and decreasing ferroportin [64; 65; 66]. The increase in transferrin can be the result of the oncogene c-Myc. C-Myc is essential as it can be transitionally upregulated in TFR. Alternatively, it can result from hypoxia as iron is needed to deliver oxygen to cells in low oxygen conditions or by IRP2 upregulation [67; 68]. Many critical enzymes involved in importing iron are upregulated in cancer, such as membrane receptors such as DMT1, which are overexpressed in several cancers [69]. Proteins that facilitate endosomal uptake of the di ferric transferrin complex are also increased in cancer, and the subsequent STEAP 1-4 proteins that are involved in reducing iron in endosomes are also overexpressed in cancer [46; 70; 71; 72].  
As stated above, cancer cells can limit the volume of iron that can efflux; an example of this is the downregulation of ferroportin in several cancers such as ovary, lung myeloma and many more [73; 74; 75]. Hepcidin can also modulate ferroportin by binding to it, resulting in its eventual degradation; this has shown to be upregulated in prostate cancer cells and shows one method in which ferroportin can be decreased in cancer cells [76]. Irons decreased efflux in cancer cells and increased influx results in a much-increased labile iron pool, as shown in Figure 4. This satisfies its need for iron for the essential process, resulting in further genomic instability by iron-induced ROS. The increased labile iron pools in cancer allow more Fe-S synthesis for crucial cell functions such as DNA repair and replication. Simultaneously, it potentially results in more genetic instability by enhanced Fenton chemistry, aided by the abundance of iron. Therefore, iron chelators have been designed to target labile iron pools, remove iron, and disrupt one of cancer cells' main adaptive mechanisms.
## 4 Discussion
### Transverse Arch Dimensional Changes  
The distinctive expansion of the arches in passive self-ligating bracket (PSLB) appliances, particularly Damon, is associated with a uniform arch shape in both arches. This arch shape is typically widened in the premolar region to minimize the appearance of \"black lateral corridors\" when smiling. It is stated that the PSLB technique's treatment causes a greater expansion of the dental arches compared to other bracket systems [2; 3]. [PERSON] et al. [10] conducted a study to analyze the effects of a PSLB system using three-dimensional digital casting analysis. The study found that the largest increases in arch dimensions occurred at the premolar level in both the maxillary and mandibular arches, ranging from 2.0 to 2.2 mm. This was associated with significant increases in maxillary and mandibular arch perimeters (2.3 and 2.5 mm, respectively) and buccal crown torque of the upper premolars with appropriate torque control of other teeth. The study concluded that the PSLB system resulted in a modest yet statistically significant widening of both maxillary and mandibular dental arches, accompanied by significant net gains in maxillary and mandibular arch perimeters.  
However, older studies also reached similar conclusions to the newer ones regarding arch development in PSLBs and CBs. In both the Damon self-ligating and conventional orthodontic bracket systems, there was an increase in inter canine width, a reduction in arch length, and proclination of mandibular incisors associated with alignment, but the differences between the two appliances were not significant, according to a randomized clinical trial ([PERSON] et al., 2008) [60]. A study comparing two preadjusted edgewise appliances found little overall difference in the arch alignment and leveling pattern. Still, the group treated using the passive self-ligating appliance showed a statistically greater increase in interolar width, although the difference was small at 0.91 mm ([PERSON] et al., 2009) [61]. According to a single-center randomized controlled research ([PERSON] et al., 2011) [71], the use of conventional or passive self-ligating brackets did not seem to be a significant predictor of mandibular interolar width in non-extraction patients when the same wire sequence was utilized. A study evaluating the [PERSON] system found that both maxillary and mandibular intercanine, interpremolar, and interimolar widths increased significantly after treatment, while mandibular incisors were significantly advanced and proclined, contradicting the lip bumper theory of [PERSON]. However, there was no significant difference in posttreatment incisor inclinations between the Damon group and the control group (conventional bracket systems) ([PERSON] et al., 2011) [21].
# Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
###### Abstract  
Automatic modulation classification (AMC) plays an important role in intelligent wireless communications. With the rapid development of deep learning in recent years, neural network-based automatic modulation classification methods have become increasingly mature. However, the high complexity and large number of parameters of neural networks make them difficult to deploy in scenarios and receiver devices with strict requirements for low latency and storage. Therefore, this paper proposes a lightweight neural network-based AMC framework. To improve classification performance, the framework combines complex convolution with residual networks. To achieve a lightweight design, depthwise separable convolution is used. To compensate for any performance loss resulting from a lightweight design, a hybrid data augmentation scheme is proposed. The simulation results demonstrate that the lightweight AMC framework reduces the number of parameters by approximately 83.34% and the FLOPs by approximately 83.77%, without a degradation in performance.  
automatic modulation classification (AMC); lightweight neural network; data augmentation; deep learning 2023  
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# Status of Pure Electric Vehicle Power Train Technology and Future Prospects
## 9 Discussion  
The purpose of this paper is to focus on the power train system and subsystems of EV. Different technical findings are analyzed and the possibilities future trends of these sectors are discussed. The major findings of this paper are:  
* Different configurations are available for the drive train architecture in EVs. EVs can have front wheel drive, rear wheel drive, single motor drive, dual motor drive, or even all-wheel drive. In-wheel drive vehicles offer distinct advantages such as avoiding transmission as a major one. Different configuration of drive trains has not commercially penetrated now, but they do have scopes for use in future EVs.
* Varieties of electric machines of different designs and configurations can be employed for use in EVs. Induction motors, permanent magnet synchronous motors, and synchronous reluctance motors are the eminent machines to propel EVs. Induction motors have been mostly used by present electric vehicles like Tesla and Mahindra Electric, while PMSM is currently being widely used with brands like Hyundai, Kia, BYD, etc. The next few years will be interesting to see the battle between induction and PMSM motors in electric drive trains.
* Power electronics have developed to a great extent and different control systems have been produced and adopted for driving motors, managing energy, and charging the batteries. With increased penetration of new EV drive systems, energy sources, and charging technologies in the future, there will be greater opportunities for more efficient control mechanisms.
* Drive train optimization has been a trend for research. Consequently, efficient drive components could be developed. Different simulation tools have been in use for this approach for the design and optimization of the drive train, control unit, and sizing battery pack as well. Recently, multi speed transmission systems, especially two-speed transmission systems for EVs have been a hot topic, although major EV manufacturers are still using single speed fixed gear. In-wheel drive systems and their different configurations have been explained in order to avoid mechanical transmission systems. Most of the research has pointed the positive results with two-speed transmission systems when compared with fixed gear transmissions, as they can minimize the size of the drive train unit as well as increase the efficiency and advantages of in-wheel drive systems. Both of these systems may be in use in future EVs, but a lot of research and experiments in the real world might need to be undertaken as it adds weight and complexity to drive units, as well as adding control complexity to in-wheel drive systems.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 5 NS-3 Simulation Tool
### Channel Transfer Function  
In order to develop efficient PLC systems and to propose improvements to the existing technology, it is necessary to accurately characterize the electrical infrastructure. The characteristics of the Channel Transfer Function (CTF) and the stationary noise give an indication of the PLC channel capacity and allow for evaluation of the PLC system performance.  
The NS-3 Simulator uses a deterministic frequency domain method to compute the CTF of two-port networks. A frequency-domain-based method can be applied if the topology is well known and a detailed knowledge of the power line topology exists.  
The transfer function and insertion loss of the power line channel can be calculated as a ratio of the load voltage to the source voltage, using the following equation (see Figure 3):  
\[\begin{array}{c}H=\frac{V_{2}}{V_{1}}=\frac{Z_{\text{S}}+Z_{\text{L}}}{A \cdot Z_{\text{L}}+B+Z_{\text{S}}\cdot(C\cdot Z_{\text{L}}+D)}\\ H(f)=\frac{V_{2}(f)}{V_{1}(f)}=\frac{Z_{\text{S}}+Z_{\text{L}}}{A(f)\cdot Z_{ \text{L}}+B(f)+Z_{\text{S}}\cdot(C(f)\cdot Z_{\text{L}}+D(f))}.\end{array} \tag{1}\]  
\[\begin{array}{c}IL=-20\cdot\log\left|\frac{V_{2}}{V_{1}}\right|=-20\cdot \log\left|\frac{Z_{\text{S}}+Z_{\text{L}}}{A\cdot Z_{\text{L}}+B+Z_{\text{S}} \cdot(C\cdot Z_{\text{L}}+D)}\right|\\ IL(f)=-20\cdot\log\left|\frac{V_{2}(f)}{V_{1}(f)}\right|=-20\cdot\log\left| \frac{Z_{\text{S}}+Z_{\text{L}}}{A(f)\cdot Z_{\text{L}}+B(f)+Z_{\text{S}} \cdot(C(f)\cdot Z_{\text{L}}+D(f))}\right|\end{array} \tag{2}\]  
\(A\), \(B\), \(C\), and \(D\) are frequency-dependent coefficients, which are calculated from the secondary parameters: characteristic impedance \(Z_{C}\) and propagation constant \(\gamma\)[51].  
A previous article [31] described an extension of the framework for PLC communication simulations in NS-3 environment. The extension was an implementation of other cables and related calculations of primary parameters of power cables that have a big influence on the shape of the transfer function and channel capacity.  
NS-3 Simulator could, according to user specified grid topologies, generate channel transfer functions and noise power spectral densities. From these quantities, signal-to-noise power ratio (SNR) and channel link capacity could be derived.
# Finding Bottlenecks in Message Passing Interface Programs by Scalable Critical Path Analysis
## 4 Methods and Algorithms for Finding Critical Path
### Topological Sorting
In particular, the _threshold_ parameter is introduced, which defines the minimal time between subsequent MPI calls, or the granularity unit size of the inter-communication computational operations. All of the communications and computations within the granularity unit are treated as a single composition block; computations of smaller sizes are not considered individually. This minimizes the overheads on counting data transmissions at the highest precision; however, it allows us to focus on meaningful parts of the program, neglecting other parts until they become meaningful, and, at the same time, allows us to establish a balance between the performance of the algorithms and the scale of the parallel execution. This solution is approximate, but it is good enough for practical applications and is in line with how real-world applications are profiled, diagnosed, and modified afterwards. From a mathematical point of view, this algorithm finds the longest edge in each group of overlapping edges. If the program satisfies the BSP model, then groups of computation edges are interleaved with groups of communication edges (the communication step always follows the computation step). Therefore, the longest edge in each group of computation edges will indeed be part of the critical path, and groups of communication edges can be skipped because we can simply connect the end of the previous computation edge to the start of the next one. The imbalance can only occur if the parallel processes start/finish their computations for the current BSP superstep at different points in time. The only way to affect the topological sorting's correctness is to have unsynchronized clocks across the MPI processes. We addressed this shortcoming by implementing our own synchronization inside the program.
## 2 Materials and Methods
### Inverse Task-Velocity Kinematics  
Using (10) and (12) and properties \(\hat{R}_{x,\hat{\varphi}}R_{x,\hat{\varphi}}^{T}=[i\times]\hat{\varphi}\), \(\hat{R}_{y,\hat{\theta}}R_{y,\hat{\theta}}^{T}=[j\times]\hat{\theta}\), \(\hat{R}_{z,\hat{\varphi}}R_{z,\hat{\varphi}}^{T}=[k\times]\hat{\psi}\) and \(R[a\times]R^{T}=[(Ra)\times]\), the angular velocity vector becomes, in terms of the task variables,  
\[\omega_{a}^{(w)} =[R_{w}^{a}(y)-I]k\dot{x}-R_{z,\hat{x}}^{T}\dot{\beta}\] \[={}^{w}\!J_{\omega_{a}}(y)\dot{y} \tag{20}\]  
where the operator \({}^{w}\!J_{\omega_{a}}(y)\) arises as  
\[{}^{w}\!J_{\omega_{a}}(y)=\frac{\partial\omega_{a}^{(w)}}{\partial\dot{y}}= \begin{bmatrix}c_{a}c_{\beta}&-s_{a}\\ -s_{a}c_{\beta}&-c_{a}\\ s_{\beta}-1&0\end{bmatrix} \tag{21}\]  
Notice that, with this formulation, the product \(B(y)\,\,{}^{w}\!J_{\omega_{a}}(y)=I_{2}\); thus, the operator \({}^{w}\!J_{\omega_{a}}(y)\) qualifies as the right pseudo-inverse of \(B(y)\), with the additional advantage of  
such that the desired generalized velocity vector becomes the following  
\[\dot{q}^{d}\triangleq A_{y}(d,R)\;\dot{y}^{d} \tag{27}\]  
**Remark 2**.: _Notice that the evaluation of \(A_{y}(d(q),R(q))\) needs platform pose feedback, either from a forward kinematics estimation or direct measurements. However, if the control guarantees a small tracking error, i.e., \(y\approx y^{d}\), then \(d(y),R(y)\approx d^{d}(y^{d}),R^{d}(y^{d})\), such that there could be implemented an open-loop evaluation of the TVO using desired trajectories instead of real ones--that is, in such circumstances,_  
\[\dot{q}^{d}=A_{y}(d^{d}(y^{d}),R^{d}(y^{d}))\;\dot{y}^{d} \tag{28}\]  
_could be used reasonably instead of (27), as we have to test the simulation study._
# An Insight to the Outage Performance of Multi-Hop Mixed RF/FSO/IWOC System
## 3 Outage Probability Analysis  
The outage probability performance of the proposed mixed multihop communication system is investigated in this section. For the proposed system, the end-to-end instantaneous SNR of the received signal at node \(D\), \(\gamma_{DF}\), is given as [34]  
\[\gamma_{DF}=\min[\gamma_{RF},\gamma_{FSO},\gamma_{UW}], \tag{5}\]  
where, \(\gamma_{FSO},\gamma_{RF},\gamma_{UW}\) represent the instantaneous SNRs of FSO, RF, and UWOC links, respectively. Using (5), the equivalent CDF of the \(\gamma_{DF}\) can be written, as given in [35], as \[F_{\gamma DF}(\gamma)=1-(1-F_{\gamma RF}(\gamma))(1-F_{\gamma FSO}(\gamma))(1-F_{ \gamma UW}(\gamma)). \tag{6}\]  
Further, the probability of the outage can be an important tool in evaluating the reliability of the communication systems. It can be defined as the probability when \(\gamma_{DF}\) is lower than the specific value of the threshold, \(\gamma_{th}\) such that the system performance is considered as insufficient. Accordingly, the \(P_{out}^{DF}\) may be evaluated from (6) by substituting \(\gamma\) by \(\gamma_{th}\), that is, \(P_{out}^{DF}(\gamma_{th})=F_{\gamma DF}(\gamma_{th})\). Therefore, substituting \(F_{\gamma RF}(\gamma)\), \(F_{\gamma FSO}(\gamma)\), and \(F_{\gamma UW}(\gamma)\) from (1), (3), and (4), respectively, and putting \(\gamma_{th}\) in place of \(\gamma\), we obtain the closed-form expression for the probability of outage of the proposed mixed RF/FSO/UWOC system, shown as  
\[\begin{split} P_{out}^{DF}&=1-\Bigg{[}\Bigg{[}1- \frac{1}{\Gamma(m)}\Gamma\bigg{(}m,\frac{mL_{RF}\gamma_{th}}{\tilde{\gamma}_{ RF}}\bigg{)}\Bigg{]}\\ &\times\Bigg{[}1-wG_{1,2}^{1,1}\Bigg{(}\frac{1}{\lambda}\bigg{(} \frac{\gamma_{th}}{\mu_{r}}\bigg{)}^{\frac{1}{2}}\bigg{|}\begin{array}{c}1 \\ 1,0\end{array}\Bigg{)}-\frac{1-w}{\Gamma(a)}G_{1,2}^{1,1}\Bigg{(}\frac{1}{b^{c} }\bigg{(}\frac{\gamma_{th}}{\mu_{r}}\bigg{)}^{\frac{5}{2}}\bigg{|}\begin{array} []{c}1\\ a,0\end{array}\Bigg{)}\Bigg{]}\\ &\times\Bigg{[}1-\exp\Big{(}-\frac{\theta_{FOV}^{2}}{2\sigma_{ \theta}^{2}}\Big{)}-\Bigg{(}1-\exp\Big{(}-\frac{\theta_{FOV}^{2}}{2\sigma_{ \theta}^{2}}\Big{)}\Bigg{)}\\ &\times\Bigg{(}\frac{\xi^{2}}{\Gamma(\alpha_{f})\Gamma(\beta_{f})}G_{2,4}^{3,1}\Bigg{(}\frac{\alpha_{f}\beta_{f}}{A_{o}h_{l}}(\sqrt{\frac{\gamma_{th }}{\tilde{\gamma}_{FSO}}})\Big{|}\begin{array}{c}1,1+\xi^{2}\\ \xi^{2},\alpha,\beta,0\end{array}\Bigg{)}\Bigg{)}\Bigg{]}\Bigg{]}.\end{split} \tag{7}\]