esa-sceva/satcom-chunk-collection huggingface dataset:
# Affordable Broadband with Software Defined IPv6 Network for Developing Rural Communities
## 3 Affordable Broadband for Rural Communities
### Subsidization  
Even if the network infrastructure is ready for rural communities, service providers will not be attracted to provide services to rural areas due to a lack of guarantee of return on investment. Hence government grants and loan in the form of subsidization are to be provided to run their digital infrastructure under the community network. These subsidies could be used to operate health centers, hospitals, schools, community farms, etc., so that they will be encouraged to use rural utilities and other broadband services [12]. Access to government information to be delivered to citizens like.np domain content, educational materials, health and agricultural farming related information should be available free of cost to rural communities in the beginning so that rural people will be attracted to ICT based services. For example, fully subsidized IT lab setup at government schools throughout Nepal is a good initiation by the NTA that helps to establish IT based education centers in rural areas of Nepal [13]. Free access to content can also be ensured via a geographical limit in which marginalized citizens of those areas can be set as a priority to provide free access to internet and services. Digital content production in their own language and sharing among the rural communities can stimulate societal development from within the societies [2].  
Developed countries like America and South Korea had put direct investment as well as guarantee loans into the rural communities to reduce the digital divide via reliable broadband infrastructure and services [14]. Discounts or direct funding to end users as incentive based subsidies may be more effective via the provision of broadband vouchers to economically marginalized people in rural areas [15].  
Service providers will be encouraged to expand their infrastructure and services to rural areas only if they are guaranteed some subsidies like tax exemptions on the network equipment that is imported for rural broadband expansion. Hence, exemption on local taxes and or taxes while importing equipment as well as direct subsidies through public private partnerships (PPP) can be considered as an important avenue for social development and reduce the digital divide by means of reliable and sustainable broadband services [16]. As a priority, subsidies need to be guaranteed first for building a new network rather than upgrading the existing network [17].
###### Abstract
Although these studies are helpful to understand the influence of the voids on the lining structure, there are still some limitations. For example, the stress variation along the thickness direction of the lining is not considered in these methods. Moreover, there is no accurate understanding of the void caused by the insufficient thickness of the lining. Therefore, it is of great significance to study the circumferential stress characteristics and failure modes of the void region. Studies show that structure strengthening is an effective treatment for the vault void problem. Currently, structure strengthening methods can be mainly divided into three categories, including pasting a high-strength fiber layer [14; 15], sticking a steel plate [16; 17; 18; 19; 20], and constructing an umbrella arch [21; 22]. In an operating highway tunnel with a void problem, numerous auxiliary facilities such as lighting and fans are installed near the vault, limiting the available space margin for the strengthened structure. In these cases, sticking a steel plate on the lining surface has unique advantages in the treatment of voids in the vault. With an increase in the traffic volume, the demand for large section tunnels is gradually increasing [23]. Studies show that large cross-section tunnels are subjected to greater surrounding rock pressure. Accordingly, the secondary lining in these tunnels is generally thicker, thereby increasing the risk of void problems during the concrete pouring process. In the present study, the highway tunnel is considered the research object, and it is studied from the following three aspects:  
1. A model experiment was carried out to study the stress characteristics and the failure modes of the vault lining with different thickness voids. 2. A mechanical model is established based on the experiment. Then the model is analyzed and verified through experimental results. 3. The strengthening effect of the steel plate on the void lining is studied by making steel plate strengthening models. In the second chapter of this paper, the details of the experimental device and specimen will be introduced. Then, in the third chapter, the void lining and strengthened lining will be analyzed from the perspective of damage, stress and deformation according to the experiment results. In addition, a simplified mechanical model will be established in the fourth chapter to calculate and verify the rules obtained from the experiment.
## 6 Simulation Experiments and Performance Analysis  
The performance of the proposed approach was simulated in the MATLAB environment using RMASE, an application platform running under PROWLER. The simulator, as an event-driven WSN simulator, utilizes codes that are compatible with those of MATLAB. RMASE has well-defined metrics, the values of which are generated as the simulation is being run. The simulations are conducted considering various numbers of sensors, cluster heads, and optimal points located in a network with a size of 1000 \(\times\) 1000 square meters. The nodes are deployed randomly, each with an initial energy of 2J. The DCVs have no energy constraints and can move at a speed of 10m/s. Our experiment presents an improved energy-efficient ant-based routing (IEEABR), which is compared with three other swarm-intelligence-based routing protocols namely, Energy-Efficient Load Balancing Ant-based Routing Algorithm (EBAR) [66], Bee-Sensor-C [67], and Flooded Forward Ant-based Routing (FF) [68]. The performance of the proposed approach was tested with the aforementioned algorithms using some evaluation metrics. We note that modeling IoT-based systems as swarm intelligence comes with some additional benefits that allow such systems to share in the benefits inherent in swarm intelligence systems, such as robustness, flexibility, and scalability [54]. These major features have the potentials to help the IoT-based system to overcome its main architectural challenges, such as scalability, robustness, flexibility, and interoperability [69]. A summary of the simulation parameters is presented in Table 4.
## 4 Design Consideration and Simulation  
In this study, the PL TB405B is the HPLD type-number (Osram Opto Semiconductors Inc., Regensburg, Germany). Its specification is listed in Table 3 from the datasheet [12].  
As shown in Figure 2, at the HPLDDC input side, the minimum input voltage, \(V_{in(min)}\), and the maximum input voltage, \(V_{in(max)}\), from the on-board batter were set as 9 and 16 V; at the HPLDDC output side, three blue-beam HPLDs were series connections, and the HPLD's forward voltage was 14.4 V from \(V_{LD2}\) in Table 3. Hence, the minimum output voltage of HPLDDC \(V_{o(min)}\) was set as 13 V, and the maximum output voltage of HPLDDC \(V_{o(max)}\) was set as 16 V. The HPLDDC output current, \(I_{o}\), was 1.2 A for the CC operation. In addition, the switching frequency of  
Figure 10: Control block diagram of CLC: (**a**) Proportional-integral (PI) control; (**b**) PIAP control.  
was 400 kHz. \(\alpha\) was set as 1%, and \(\beta\) was set as 20%. These parameters and specifications are listed in Table 4.  
The power converter topology was the SBBC, which could be operated in both buck and boost conversion modes, when variable battery voltages input to the HPLDDC. In different input and output voltages, the SBBC operating mode can be changed by the SBBC controller. To ensure the estimating inductor and output capacitor can address the buck and boost operating modes in practical applications, this study provides the design considerations and calculating rule to obtain the single element value for the SBBC circuit assembly.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 2 FSO Communication Channel Effects and Their Mitigation Techniques
### FSO Communication Channels
Since the transmitter/receiver stations are positioned at roof/tower tops, building/tower movement due to high winds as well as temperature variations and ground vibrations impair optical beam pointing accuracy, i.e., they introduce optical beam center wandering on the detector plan. (ii) Slant link: preferred for ground and UAV, ground and aircraft, ground and top-of-mountain connectivity or in high towers and where we cannot set up a horizontal link. For slant link, the effects of atmosphere changes on the propagating light signal are not uniform along the direction. (iii) Up/downlink: preferred for earth surface and satellites, and earth surface and spacecraft data links. It is important to compute the atmospheric turbulence effects considering the altitude in this link. (iv) Indoor link: preferred for office voice/message communication, computers/phones/ printers, and other electronic gadgets such as Li-Fi short-range data connectivity. This form of indoor connection is gaining popularity due to its easy installation and high data rates. The \(C_{n}^{2}\) variation measured for the first 100 m is insignificant (varies from 10-11 to 10-15 m\({}^{-2/3}\)), but it is significant for a medium-/long-range link since we can obtain a good FSOC system installation at any place where we can measure the \(C_{n}^{2}\) with reasonably good accuracy. Present studies on combining several separate orbital angular momentum (OAM) beams will greatly increase FSO device capability and spectral performance, while reducing cross-talk [41]. Through combining OAM with wavelength division multiplexing (WDM), researchers have shown that a 100.8 Tbps FSOC is feasible [40].
# A Comparative Study of Local Descriptors and Classifiers for Facial Expression Recognition
## 3 An Approach for Optimized Facial Expression Recognition
### Feature Extraction and Feature Vector Calculation  
In this study, six local descriptors were investigated: Local Binary Patterns (LBP), Compound Local Binary Patterns (CLBP), Local Directional Patterns (LDP), Angled Local Directional Patterns (ALDP), Weber's Local Descriptor (WLD), and Histogram of Oriented Gradients (HOG). Feature extraction and feature vector calculations are key stages of this FER method. Although the two processes were portrayed separately in the preamble of this section, their workings are very interlinked. The method used in this study divides the face image into several equally sized non-overlapping sub-regions, and a local descriptor is applied to each sub-region, producing several histograms. The final feature vector is generated by normalizing the histograms and concatenating them into a single 1D vector. It must be noted that the sub-region histograms are calculated separately and normalized such that the frequencies sum to one (this rule is applied to all the considered descriptors except the HOG descriptor, where the blocks are normalized in groups using L2-Hys [26]). The normalized histograms are then concatenated to form the final feature vector. Suppose \(I_{j}\) is a \(W_{S}\times W_{S}\) matrix of intensities representing the \(j\)-th sub-region in the image, and the function \(D(x,y)\) computes the descriptor at a position \((x,y)\) in \(I_{j}\), and then the histogram associated with this sub-region is defined as  
\[H_{j}=\{h_{i}\}_{i=0,1,\ldots,N_{b}-1} \tag{14}\]  
\[h_{i}=\sum_{x=0}^{W_{S}-1}\sum_{y=0}^{W_{S}-1}\delta(D(x,y),i)\]  
\[\delta(x,i)=\begin{cases}1,&\text{if $L(i)\leq x<U(i)$}\\ 0,&\text{otherwise}\end{cases},\ \ L(i)=i\times\frac{n}{N_{b}},\ \ \ U(i)=(i+1)\times\frac{n}{N_{b}},\]  
where \(n\) is the number of possible descriptor values, and \(N_{b}\) is the number of histogram bins.  
The final feature vector is given by  
\[H=\left\{\frac{H_{0}}{(W_{S})^{2}},\frac{H_{1}}{(W_{S})^{2}},\ldots,\frac{H_{ m-1}}{(W_{S})^{2}}\right\} \tag{15}\]  
where \(m\) is the number of sub-regions.
# Gaussian-Distributed Spread-Spectrum for Covert Communications
## 1 Introduction
Other non-conventional LPD techniques include methods based on (1) exploiting the multiplicity of users scattered across the wireless network and the channel variations caused by their mobility [33], (2) directional transmission using multiple antennas [34], (3) opportunistic power control similar to conventional power control with an on-off switch that turns off the transmitter when the channel gain falls below a threshold [35], (4) artificial noise generation to disguise the existence of covert channels [36], and (5) millimeter-wave communications that use feature steerable narrow beams operating in the frequency band of 30-300 GHz [37]. The proposed scheme in this paper uses thermal white noise of the system to obscure signals generated by a DSSS transmitter and produce a spread-spectrum waveform that follows a Gaussian distribution (GDSS). The design of the GDSS scheme ensures that the receiver does not need any information about the sequences used to mask the signal to retrieve the message. To the best of our knowledge, this study represents the first investigation into utilising naturally occurring thermal noise for spread-spectrum communication. We conducted a performance evaluation of the proposed GDSS scheme and compared it to the widely used DSSS technique, which has several weaknesses that make it vulnerable to exploitation by adversaries. These vulnerabilities arise from the use of fixed modulation and repeatable spreading sequences. The paper is structured as follows. Section 2 describes the standard DSSS system, and Section 3 presents the proposed GDSS waveform scheme. In Section 4, we evaluate the LPD performance of the proposed scheme using higher-order moments, a modulation stripping signal detector, and cyclostationary analysis. Section 5 investigates the error-rate performance of both coded and uncoded systems and compares it with DSSS. Furthermore, a numerical expression for the bit error rate performance of the GDSS system is derived and presented in this section. Finally, we conclude the paper in Section 6 and provide limitations of this research and suggestions for future work.
# Bifurcation Flight Dynamic Analysis of a Strake-Wing Micro Aerial Vehicle
## 6 Numerical Verification of Predicted MAV Behaviour
### \"Cobra\" Manoeuvre Simulation
This causes a rapid transition of the MAV through C, G, E and F branches of steady flight states (Figures 5 and 6). Figures 13 and 14 show the results of a digital simulation of the \"Cobra\" manoeuvre, taking into account the occurrence of limit cycles. Based on the analysis of these graphs, it can be concluded that all flight parameters are significantly changed during a Cobra figure, increasing their values. Using the terminology of the Dynamical System Theory, it can be said that the \"Cobra\" is unstable due to the presence of Hopf bifurcation (at an elevator displacement angle of \(\delta_{e}=-23.1^{\circ}\)) and torus bifurcation (for \(\delta_{e}=-16.5^{\circ}\)). Figure 14 shows Poincare maps for selected state parameters. It can be concluded when the non-stationarity (hysteresis) of aerodynamic coefficients were taken into account, MAV motion equation solution irregularities of quasi-harmonic nature were obtained. The digital simulation took into account the fact that at high angles of attack, the aircraft is spirally unstable (branch C, Figures 5 and 6) and has a tendency to wing-rock oscillations (branch B, Figures 6 and 7). In the course of a manoeuvre, the aircraft attack and pitch angles rapidly increase, reaching maximum values of approximately \(84^{\circ}\) (for an attack angle of \(\alpha\)) and \(100^{\circ}\) (for a pitch angle of \(\Theta\)). The MAV bank and yaw angle waveforms (Figure 13) indicate that these angles increase over time. This is associated with the present area of spiral instability (branch C in bifurcation diagrams--Figures 5 and 6). The development of wing-rock oscillations is also visible. The period of these oscillations varies at approx. 0.2 s. It should be noted that the amplitude and frequency of these oscillations are irregular (quasi-harmonic). Figure 13: “Cobra” manoeuvre simulation. Time waveforms of selected flight parameters.
###### Abstract  
This study aimed to investigate how external perturbations caused by the treadmill belt's deceleration during the pre-swing phase affect gait kinematics and kinetics in young adults. Twenty-one healthy young females walked on a treadmill in a virtual environment (GRAIL, Motek), where unexpected perturbations were applied to the left belt, mimicking a 'trip-like' effect at toe-off. The spatiotemporal, kinematic, and kinetic parameters were analyzed during two cycles. The first cycle involved the first perturbation and the response to it. The second included a gait cycle without the perturbation (treadmill gait). The perturbation resulted in an increased stride duration for both limbs when compared to the treadmill gait. The perturbed limb had a longer support phase, while the reactive limb had the longest double stance phase. The responding limb exhibited more than double the ankle plantar flexion compared to the normal treadmill gait and the perturbed limb. At the hip joint, both limbs showed significantly higher values, with a 40.8% increase in flexion and a 227% increase in extension for the perturbed limb, and a 24.5% increase in flexion and a 212% increase in extension for the responding limb, compared to the treadmill gait. Muscle torque was generally lower in most joints for both limbs, except for notably higher hip and knee extensor values for the perturbed limb. The responding limb exhibited lower values for the ankle, knee, and hip joints, indicating unexpected muscle activity patterns. Studying treadmill belt deceleration during pre-swing gait can provide valuable insights into biomechanical adaptations and motor control strategies.  
gait; perturbations; treadmill; GRAIL; Motek Article
# Seismic Effect of Marine Corrosion and CFRP Reinforcement on Wind Turbine Tower
## 5 Conclusions  
In this study, the influence of corrosion and different earthquake types on a 3 MW offshore wind turbine tower is studied by numerical simulation field test and shaking table experiment, and the corresponding influence of near-field earthquakes and far-field earthquakes on the structural dynamics of wind turbine towers is compared. At the same time, the incremental dynamic analysis method is used to study the influence of corrosion on the structural buckling and collapse of wind turbine towers. The research conclusions of this paper are universal for single-pile fans. Other types of single-pile fans can refer to the conclusions of this paper for reinforcement design.  
1. The dynamic response of wind turbine towers is more sensitive to near-field earthquakes. Under the same PGA increment, the acceleration and displacement increment increase by 11% and 78%, respectively, compared with the far-field earthquake.  
Figure 34: Comparison under the Hyogoken-Nanbu earthquake: (**a**) Comparison of acceleration peak; (**b**) Comparison of displacement peak.  
Figure 33: Comparison under the Nihonkai-Chubu earthquake: (**a**) Comparison of acceleration peak; (**b**) Comparison of displacement peak.  
2. The plastic ratio of the structure with serious corrosion is smaller than that without corrosion. Under the earthquake, the area surrounded by the moment curvature hysteretic curve of the splash area increases gradually.
3. The shaking table test results show that the dynamic response of the structure under different corrosion conditions is significantly different, and the peak acceleration decreases with the increase in corrosion degree.
4. Using CFRP to reinforce the corrosion area of the tower can effectively reduce the dynamic response of the wind turbine tower and improve the seismic performance of the structure.
5. In order to better understand the corrosion condition of the wind turbine tower in service, it is recommended to regularly use a long-range laser Doppler vibrometer and other equipment to monitor the change of the natural frequency of the wind turbine or the thickness of the steel pipe pile. According to the monitoring data, a reasonable reinforcement scheme should be designated as early as possible.  
Conceptualization, B.S.; methodology, D.W.; software, D.W. and C.L.; validation S.D.; writing--original draft preparation, D.W. and S.D.; writing--review and editing, D.W. and C.W.; supervision, B.S. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China (52078038).  
Not applicable.  
Some or all the data and models that support the findings of this study are available from the corresponding author upon reasonable request.  
Thanks to Jiangsu Rudong Electric Power Co., Ltd. for its help in this study.  
The authors declare no conflict of interest.
## 4 Cost Prediction Performance Analysis
### Predictive Performance of Cases  
Case 1 is the result of machine learning using the collected construction cost data as it is. As expected, it can be seen that the data deviations were severe, making the machine learning process very unstable, and the result also had very poor accuracy.  
The construction cost was predicted in two ways: Total Cost and Sum Cost, which combines the predictions of seven sub-construction costs. The error rate was analyzed compared to the Actual Total Cost. The average value of the R-square score of Case 1 was negative, which means that the Case 1 model cannot be used to predict construction costs. Figure 6 shows the machine learning process of Case 1, which shows that the machine learning executed was very unstable.  
In case 2, scaling was applied only to the input data, and the output data was used as it was. Even if scaling was applied only to input data in this way, it was found that machine learning performance was greatly improved compared to case 1.  
The R2 scores for the prediction results of Sum Cost, which were predicted by summing the sub-construction costs, and Total Cost, which directly predicted the total construction cost, were 0.80 and 0.65, respectively. The process of changed loss value in the machine learning process of case 2 is shown in Figure 7.  
Figure 6: Loss of unscaled features (Case 1).  
Figure 7: L2 Regulation Result (Case 2).  
In Table 5, the R-square of each case can be compared in more detail. As illustrated in Table 4, when both scaling and normalization are applied, the R-square value became higher, the standard deviation decreased, and thus the prediction error range decreased.