esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Formulation of the Optimization Algorithm
#### 3.7.2 Decision Variables  
The decision variables are those that the optimization algorithm finds to optimize the objective function.  
The battery power setpoint is defined as Equation (6), where positive values of power mean that the battery is discharging. It is also distinguished if the battery is charging or discharging. The battery charging and discharging powers are defined as Equations (7) and (8), respectively. To prevent obtaining a solution where the battery could simultaneously charge and discharge, a binary variable is defined in Equation (9). The SOC is shown in Equation (10). In the EMS algorithm, it is assumed that the battery setpoint is the same as the real battery power generation/consumption.  
\[P_{t,p}^{bat^{*}},\;\forall\;t\in T^{EMS},\;\forall\;p\in T^{intra} \tag{6}\]  
\[P_{t,p}^{bat_{char}},\;\forall\;t\in T^{EMS},\;\forall\;p\in T^{intra} \tag{7}\]  
\[P_{t,p}^{bat_{disch}},\;\forall\;t\in T^{EMS},\;\forall\;p\in T^{intra} \tag{8}\]  
\[X_{t,p}^{char},\;\forall\;t\in T^{EMS},\;\forall\;p\in T^{intra};X_{t,p}^{char} \in\{0,1\} \tag{9}\]  
\[SOC_{t,p}^{bat},\;\forall\;t\in T^{EMS},\;\forall\;p\in T^{intra} \tag{10}\]  
The diesel connection/disconnection setpoint and the power generation of each diesel generator are denoted as Equations (11) and (12), respectively. \(ON_{t,p}^{dies}\) is 1 if the diesel generator \(d\) at the EMS period \(t\) and the intra period \(d\) is connected (and 0 otherwise).  
\[ON_{t,p}^{dies},\;\forall\;t\in T^{EMS},\;\forall\;d\in N^{Diesel},\;ON_{t,p}^{dies }\in\{0,1\} \tag{11}\]  
\[P_{t,p,s,d}^{dies},\;\forall\;d\in N^{Diesel},\;\forall\;s\in S,\;\forall\;t \in T^{EMS},\;\forall\;p\in T^{intra} \tag{12}\]  
The PV power generation of each scenario is written as Equation (13), while the maximum PV power setpoint is expressed as Equation (14).  
\[P_{t,p,s}^{pv},\;\forall\;s\in S,\;\forall\;t\in T^{EMS},\;\forall\;p\in T^{intra} \tag{13}\]  
\[P_{t,p}^{PV_{max}},\;\forall\;t\in T^{EMS},\;\forall\;p\in T^{intra} \tag{14}\]
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 2 Circuit Structure and Operation Analysis
### Description of Operating Range  
Figure 3 shows the voltage gain profile in the forward mode of the proposed isolated bidirectional symmetric resonant converter. It is conducted and completed by applying Mathcad and simulated software. The magnitude of voltage gain depends on the ratio of the switching frequency with respect to the resonant frequency. According to the frequency ratio, Region I only ensures ZVS. Region II ensures ZVS of power switches and ZCS of the diodes. Region III exhibits large voltage variation with only ZCS of the diodes. Therefore, Region III should be avoided for voltage control consideration [18]. Due to the symmetric feature, the description is suitable for the reverse mode as well.  
### Description of Operating Range  
Figure 4 shows the power propagation of the forward charging mode. The energy on the DC grid \(V_{DC\_Grid}\) supplies energy via power switches \(Q_{I}\), \(Q_{2}\), \(Q_{3}\), and \(Q_{4}\); it passes from the transformer primary side to the secondary side and thus is rectified by the intrinsic diodes \(D_{BS}\), \(D_{B6}\), \(D_{B7}\), and \(D_{BS}\) to charge the battery \(V_{Bat}\). By adjusting the switching frequency, the output voltage and output power are controlled accordingly. The trigger signals of \(Q_{I}\)-\(Q_{4}\) are a pair of symmetric square waves with each being around a 50% duty ratio. With \(Q_{I}\) and \(Q_{4}\) being a group and \(Q_{2}\) and \(Q_{3}\) being another, the trigger signals of \(Q_{I}\) and \(Q_{2}\) are displaced by 180 deg, and an appropriate dead time is introduced therein. Due to the symmetric feature, the reverse discharging mode will apply the same operation. Therefore, only the forward mode operating in Region II is used to describe the operating principle. Figure 5 illustrates the important theoretic waveforms. For simplifying the analysis, the following assumptions must be made:  
Figure 3: Voltage gain profile in the forward mode of the proposed bidirectional symmetric resonant converter.  
Figure 2: Voltage gain of LLC resonant converter at forward and reverse modes.  
1. The circuit operates in the steady state.
2. Only intrinsic diode \(D_{B}\) and parasitic capacitance \(C_{OSS}\) are taken into account with others being ignored.
3. The effect of the parasitic capacitances \(C_{OSS1}\)-\(C_{OSS8}\) is neglected.
4. All devices are ideal.
5. The output capacitances \(C_{DC\_Grid}\) and \(C_{Bat}\) are large enough to look upon \(V_{DC\_Grid}\) and \(V_{Bat}\) as the ideal voltage source.
## 2 Experimental Process  
Our specimens were initially cored with a diamond bit to 16 mm in length and 32 mm in diameter from a large coal mass recovered from mining operations in the Karaganda basin in Kazakhstan. The coal samples were first cut using a rock saw and then carefully abraded to achieve appropriate dimensions while minimizing the presence of external fissures. The coal samples were drilled in a way that the axial direction of each coal rock was vertical to its bedding plane. The permeability of original coal specimens is very small (~1 mD), which shows that Karaganda coal cannot supply satisfactory gas production without stimulation. Given the limited water resources in Kazakhstan, conventional water stimulation methods are not feasible. Moreover, the use of water for hydraulic fracturing would prolong the dewatering period, worsen water-blocking issues, and hinder methane production.  
After preparing the coal samples, they were placed in a drying oven for 24 h and immediately immersed in LN\({}_{2}\). Implementing LN\({}_{2}\) use was divided into freezing time (FT) and freezing-thawing cycles (FTC). The freezing times were set as 30, 60, 90, 120, and 150 min, whereas as many as five freezing-thawing cycles were included in FTC experiments, labeled as C1-C5 cycles. Each freezing-thawing cycle included 30 min of freezing and 30 min of thawing at room temperature. Figure 2 shows the region and the coal mine from which samples were taken, and Figure 3 presents the experimental process.  
Figure 1: Geological map of the region north of the city of Karaganda, where the Firma Rapid mine site is located. The Carboniferous K10 and K12 coal seams are commercially exploited at the site. D: Devonian basement; C-KS: Carboniferous Karaagandinskaya Suite; TJ-SF: Triassic-Jurassic Saranskaya Formation; J-DF: Jurassic Dubovskaya Formation; J-KF: Jurassic Kumyskudukskaya Formation; N: Neogene clays.
## 3 Results and Analysis
### Simulation Experiment and Result Analysis
#### 3.3.2 Performance Comparison in Different Dimensions
The hybrid algorithm trades time for optimization capability and stability. The running time relationship between algorithms is as follows: T (MDBAS) < T (AFS-MDBAS) < T (AFS-MMSBAS) < T (AFS).
## 4 Results and Application
### Application of Coherent Integration Time Optimization Model
#### 4.2.1 Model Application: Airborne Experiment Scenario  
In the airborne altimetry scene described in Section 4.1, the traditional integration parameters cannot yield the optimal precision. In constructing a coherent integration time optimization model based on the same experimental parameters, the optimal coherent integration time is calculated to be 7.5 ms, as shown in Figure 5. By applying this integral parameter to the processing of the measured data, the precision is 0.81 m. As compared to the empirical coherent integration (10 ms) [21], the altimetric precision is improved by 0.09 m.
## 5 Discussions  
We consider the energy efficiency as an evaluating indicator for the proposed MC-MDCSK-MCS system. In References [32; 33], the definition of the data-energy-to-bit-energy ratio (DBR) is defined as follows:  
\[DBR=\frac{E_{data}}{E_{b}} \tag{25}\]  
where \(E_{data}\) presents the energy of a data-bearing signal, and \(E_{b}\) denotes the required total energy to carry each bit. As the diagram of \(n\) parallel parts is similar, we only consider one of them.  
The symbol for energy of the transmitted signal in the MC-MDCSK-MCS can be obtained as follows:  
\[E_{s}=E_{data}+E_{ref} \tag{26}\]  
where  
\[E_{s}=P\log_{2}(M)E_{b} \tag{27}\]  
\[E_{data}=PE_{ref} \tag{28}\]  
\(M\) is the number of M-ary DCSK. \(P\) shows the number of M-ary constellation. In the proposed system, one reference energy \(E_{ref}\) is shared with \(P\) transmitted bits.  
Subsequently, by using (26)-(28), the DBR in (25) can be given as follows:  
\[DBR=\frac{E_{data}}{E_{b}}=\frac{P^{2}}{P+1}\log_{2}(M) \tag{29}\]  
Comparisons of DBRs between the proposed MC-MDCSK-MCS system and other DCSK-based chaotic communication systems are given in Table 1. Clearly, the proposed MC-MDCSK-MCS system is superior to other systems in terms of energy efficiency. As \(P\) groups of transmitted bits share one reference, the rate of the reference energy in the total emission energy is reduced.  
Figure 8: BER performance of the MC-MDCSK-MCS with different \(N_{c}\) over the AWGN channel.  
The energy efficiency of the proposed MC-MDCSK-MCS system is very high; however, its lack of security is a shortcoming similar to those in Reference [34]. The reason for the lack of security is the sectional similarity that exists in the information signal and the reference signal. Many methods in the existing literature could solve this security problem. Reference [35] introduced a permutation transformation in time for improving the security of the DCSK system. An orthogonal scrambling matrix is designed for improving the security of the SR-DCSK system in Reference [36]. Reference [34] proposed an interleaving and spreading in frequency and time to increase the security of the MC-DCSK system. In subsequent studies, one of the methods above will be used to make the proposed MC-MDCSK-MCS system more secure.
# An Intelligent Access Channel Algorithm Based on Distributed Double Q Learning
## 2 System Model and Problem Formulation  
### System Model  
In order to facilitate the research, this paper makes the following assumptions:  
* As shown in Figure 1, we consider a wireless communication network. There are \(N\) users, one base station, and one jammer. The number of channels available is \(M\). The set of channels available is \(\mathbf{C}=[C_{1},\ldots,C_{m},\ldots C_{M}]\). Each user distributed computingand distributed execution, and hardware performance requirements are not high with good scalability and strong processing power.
* A MAC frame consists of \(K\) subframes, and each subframe consists of \(B\) time slots. Therefore, the time slot of a MAC frame is \(N_{slot}=KB\). Each subframe is followed by a feedback signal from the base station. The content of the feedback signal is the current subframe and the current channel occupancy information, so that all users can make full use of the global resource occupancy information.
* The jamming mode of the jammer is multi-channel intelligent blocking interference [23]. The jammer can quickly sense the channel occupied by the user and select \(Z(Z<M)\) channels that have been occupied for the longest time in the previous multiple subframes for jamming. The jamming channel set can be defined as \(\mathbf{J}=\{J_{1},\cdots,J_{z},\cdots,J_{Z}\}\).
* It is assumed that the subframe and time slot of the system are synchronized, and the subframe is the basic transmission unit in this paper. Each user has \(L\) packets to be transmitted. Each time slot can successfully transmit one packet.
## 2 Forward Model Development
### Backward Phase Function \(\beta(\psi)/b_{b}\)  
The scattering parameters in Equation (7) must be expanded into water and particulate components. Expanding \(\beta(\psi)/b_{b}\) gives:  
\[\frac{\beta(\psi)}{b_{b}}=\frac{\beta_{p}(\psi)+\beta_{w}(\psi)}{b_{bp}+b_{bw}}, \tag{9}\]  
where the \(p\) and \(w\) subscripts represent particles and molecular seawater, respectively. In Equation (9), the pure seawater terms, which are temperature and salinity specific, can be directly computed with an estimated error of no more than 2% [52]. Introducing a term for the particulate phase function in the backward direction, \(P_{bb}(\psi)=\beta_{p}(\psi)/b_{bp}\), Equation (9) then becomes:  
\[\frac{\beta(\psi)}{b_{b}}=\frac{P_{bb}(\psi)b_{bp}+\beta_{w}(\psi)}{b_{bp}+b_{ bw}}. \tag{10}\]Note \(b_{bp}\) and \(b_{bw}\) both have spectral dependencies as does \(\beta/b_{b}\), and the unknowns are \(b_{bp}(\lambda)\) and \(P_{bb}(\psi)\). Equation (10) can be easily rewritten in terms of \(\eta_{bb}\), with the same unknowns. In coastal waters where \(\eta_{bb}\) is near zero, \(\beta_{p}>>\beta_{w}\), \(b_{bp}>>b_{bw}\), and \(\beta(\psi)/b_{b}\) will be approximated by \(P_{bb}(\psi)\). For clear ocean waters, phase functions are represented by a mixture of both particles and pure seawater with VSF shapes dependent on \(\eta_{bb}\).
## 4 Closer Look into PPP
### Additional Support for GLS by Numerical Example  
The fact that the GLS estimate is the BLUE estimate (and also the MVUE estimate if the data is Gaussian) and that it lies below the range of \((y_{1},y_{2})\) suggests two features. First, \(\mu\) must be likely to fall outside the range of \((y_{1},y_{2})\). Second, there must better than random chance capability to guess on which side of the range of \((y_{1},y_{2})\) that \(\mu\) lies.  
In addition to GLS's BLUE property, we can add support for GLS by numerical example to illustrate features one and two. Figure 1 plots the contours of the bivariate normal density having \(\Sigma\) given by Equation 1 and \(\mu=0.88\) which as shown previously is the OLS estimate of the mean in the case \(y_{1}=1.5\) and \(y_{2}=1.0\). Informally, we can integrate this density over regions 1 and 3 to see that there is a large probability that both \(y_{1}\) and \(y_{2}\) lie either above the mean or below the mean, so indeed \(\mu\) is likely to fall outside the range of \((y_{1},y_{2})\). Integration of the bivariate normal for \(\Sigma\) given by Equation 1 indicates that with probability approximately 0.40, \(\mu\) lies below the minimum of \((y_{1},y_{2})\) and with the same probability \(\mu\) lies above the maximum of \((y_{1},y_{2})\). This is a total of approximately 0.80 probability that \(\mu\) falls outside the range of \((y_{1},y_{2})\), which is an example of feature one. Having an estimate lie outside the range of the data is therefore defensible, provided (feature two) that one can guess with better than random chance performance whether \(\mu\) lies below the minimum or whether \(\mu\) lies above the maximum of \((y_{1},y_{2})\). To see that one can beat random guessing performance, suppose \(y_{1}>y_{2}\) as in our case (\(y_{1}=1.5\) and \(y_{2}=1.0\)). Then, because \(\sigma_{y_{1}}^{2}=0.1125\) is larger than \(\sigma_{y_{2}}^{2}=0.05\) in Equation 1, \(\mu\) is more likely to fall below \(y_{2}\) because if instead \(\mu>y_{1}\) then the distance from \(y_{1}\) to \(\mu\) would be smaller than the distance from \(y_{2}\) to \(\mu\), contradicting the fact that \(\sigma_{y_{1}}^{2}>\sigma_{y_{2}}^{2}\). To confirm this line of reasoning, in 10,000 simulations (in the statistical computing language R) of \((y_{1},y_{2})\) pairs having \(\mu=0.88\), 57% of the simulation runs for which \(y_{1}>y_{2}\) did in fact also have \(\mu<y_{2}\). On the basis of 10,000 simulations, 57% is repeatable to within \(\pm 1\%\) or less, so this is better than random chance (50%) guessing. This is not a formal proof but does suggest a direction to understand when a GLS estimate falling outside the range of the data is effective. Note that \(y_{1}=1.5\) and \(y_{2}=1.0\) in the PPP statement, and the GLS estimate is \(\hat{\mu}=0.88<y_{2}\).  
Figure 1: Contours of example bivariate normal density of \(y_{1}\) and \(y_{2}\) illustrating that \(\mu=0.88\) is likely to fall outside the range of \(y_{1}\) and \(y_{2}\) because of the large probability of (\(y_{1},y_{2}\)) falling in region 1 or 3.
# Glass Defect Detection with Improved Data Augmentation under Total Reflection Lighting
## 5 Conclusions  
To enable the industrial inspection of mobile phone flat glass, solve the problem of dust interference in the production line, and reduce the error rate of follow-up supporting products, this paper independently designed a total reflection-grazing incidence light source according to the unique optical characteristics of point defects, including scratches, stains, and dust, which can effectively distinguish these four defects. In addition, the deep learning network YOLOv5 was introduced into the defect detection of mobile phone flat glass. Aiming at the problem of small target defect detection and inconspicuous  
Figure 21: Test result diagram of testing set.  
Figure 20: Training MAP value graph.  
defect features, the CBAM was embedded into the feature extraction module, and a small target detection layer was added. As for the problems of manual labeling, inaccurate labeling boxes, and data deficiency, a data augmentation method based on segmented image fusion and the automatic output of position information and labels was put forward. The comparative ablation experiments on the training set and testing set prove that the proposed algorithm provides a better performance than the YOLOv5 in the defect detection of mobile phone flat glass. The average precision of the optimal model on the validation set reaches 98.36%, the missed detection rate is 1.27%, the over-detection rate is 2.47%, the detection speed stands at 64 fps, and its average accuracy on the testing set reaches 98.75%. All of the data suggest that the model is applicable to multi-scale and multi-type glass defect detection and that it successfully meets the industrial requirements for detecting mobile phone flat glass defects. It is worth noting that the research in this paper is based on the flat region of mobile phone flat glass, and the task of detecting defects in the edge region is ongoing. Therefore, future research will include the task of detecting defects at the edges of mobile phone flat glass and establishing how to quickly deploy and bring online inspection models into different industrial environments.  
P.D.: supervision and writing--reviewing; P.D.: writing--reviewing and editing, revising the manuscript, and software; L.Y.: editing and plotting the figures; L.Y.: providing valuable input and discussions in the early stages of the research. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors would like to thank the anonymous reviewers of this article for their valuable comments and suggestions.  
The authors declare no conflict of interest.
## 4 Discussion  
The present study examined the effects of four months of HWE on BMD and bone metabolic markers in middle-aged premenopausal women. Our main findings showed that a significant decrease in OC and an increase in CTX were found only in the CON group while these values did not change in the HWE group. However, four months of HWE were not sufficient to improve BMD in middle-aged premenopausal women. Substantial empirical evidence has shown that resistance and step exercise have several benefits, which work as non-pharmacological interventions for the improvement of bone health in children/adolescents, adulthood, and the elderly [31, 43]. However, the effects of high-impact exercise on BMD in middle-aged pre-menopausal women remain unknown [36]. The present study showed no significant effects on femur, lumbar, and forearm BMD after four-month of high-impact weight-bearing exercise. The International and National Osteoporosis Foundation, and other organizations recommend weight-bearing exercises to prevent osteopenia and osteoporosis [44]. There are different types of body weight-bearing exercises including high-impact exercises such as running and jumping as well as low-impact exercises such as body weight training, water aerobics, and walking [44]. It has been recently reported that trochanter and femoral neck BMD were significantly improved after high-impact weight-bearing exercise in pre-menopausal women [30]. Additionally, high-impact weight-bearing and resistance training either in combination or alone, induced gains in BMD by 1-2% at the femoral neck and lumbar spine with a minimal effect on other body sites in pre-menopausal and post-menopausal women [45]. Similar results in other studies have found that high-impact exercise for 12 months (3 times/week) resulted in a significant improvement in femoral neck BMD by 0.6-1.1% in pre-menopausal women [46, 47]. Although the present study did not show a significant change, there was a tendency where the femur BMD increased following four months of HWE (0.47%) without any improvement on other sites of the body. [PERSON] and [PERSON] [48] reported that a bone remodeling cycle takes about 3-4 months and a minimum of 6-8 months is required for the bone to reach a new, measurable steady-state bone mass. Therefore, the absence of the significant improvement in BMD is likely due to the insufficient duration of an exercise intervention to promote bone formation in the present study. In fact, combined
## 3 Home Area Network
### Resiliency
#### 3.2.4 Energy Optimization  
Power Requirements  
This factor in wireless portable devices has always been one of the most important. The main motives for waste of energy embrace overhearing, idle listening, collision and control overhead. Over the last decade, significant developments were achieved in this area; however, an evolution towards more power-efficient elements must continue, especially for monitoring sensors, battery-powered devices, remote control and mobile handheld equipment so as to extend the durability of these devices by saving as much energy as possible.
# Research on Optimal Landing Trajectory Planning Method between an UAV and a Moving Vessel
## 1 Introduction
The above-mentioned UAV flight path planning research is mainly focused on land-based UAVs; thus further improvements are needed for meeting the requirements of sea flight and landing. However, when the flight procedure is executed, depending on the result of the current monitoring information, the change in the weather condition or the movement path of the carrier, the UAV movement set by the flight task can be significantly changed according to commands from the control panel. In this regard, it is necessary to control the possibility of the drone returning to the vessel carrier during the flight. In order to do this, it is necessary to determine the length of the minimum return path, taking into account the handling of the drone steering carrier and the proximity landing device. The ship-borne landing device used in this work is similar to the SideArm system published by Defense Advanced Research Projects Agency (DARPA) [17], with the current research proposing a method for calculating the optimal approach landing trajectory between the UAV and the small vessel. In this project, the possibility of a UAV return in any moment of the mission flight was analyzed. The planned landing flight trajectory can be divided into several stages, and in each stage the corresponding landing track parameters are calculated according to the flight mission and UAV flight performance. The simulation results demonstrate the effectiveness of our proposed method allowing the UAV to consistently fly along the designed track with a smooth, safe, and reliable altitude. The proposed landing trajectory planning for fixed-wing UAVs can ensure for a safe and efficient UAV recovery task.