esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Validation
### Experimental Validation
#### 3.2.2 Description of the Test Setup  
The beam specimen was then connected to a dedicated setup. A picture of the setup and its schematic are provided in Figure 9. PXIe 5423 was used to generate the excitation signal, which was then amplified through the PI E472 module to adequately supply the square piezo transducer. The signal used for the tests, a swept sine excitation in the range 0-3 kHz, excited the dynamic response of the beam, whose deformation was detected by the two thin piezo transducers. The excitation and the strain-related signals were collected by the PXIe 5105 module and elaborated through a PC.  
The main characteristics of the experimental setup were collected in Table 9, while the instrumented prototype is shown in Figure 10. The piezoelectric transducers adopted for this application meet the European standard EN 50324-2:2004. The accuracy of the piezoelectric patches used as sensors depends on several parameters discussed in [29]; here, the accuracy is conservatively assumed to be equal to 1%. This kind of transducer was chosen because of its high sensitivity (at least 3 orders of magnitude larger than for resistive strain gauges), as well as its large frequency bandwidth (in the range of several MHz), making it able to follow the characteristic phenomena associated with ultrasound-wave-based de-icing systems. Other types of sensors, such as fiber optic sensors, even if more sensitive, would require dedicated acquisition systems presenting practical limitations in terms of the max frequency bandwidth (generally below 20 kHz), making them hardly compatible with ultrasound applications.  
Figure 8: Test specimen with the piezoelectric ceramic transducer and the curved beam.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & Material & PVC (see Table 1) \\  & Mean radius (mm) & 76.35 \\ Beam & Thickness (mm) & 2.7 \\  & Width (mm) & 30 \\  & Angular extension (deg) & 90 \\  & Constraint condition & Cantilevered \\ Piezo actuator & Material & PK-11 (see Table 1) \\  & Dimensions (mm) & 20 \(\times\) 20 \(\times\) 3 \\  & Material & PIC-151 \\ Piezo transducers & [PERSON]’s modulus (GPa) & 15.7 \\  & g31 (Vm/N) & \(-\)10.1 \(\times\) 10\({}^{-3}\) \\  & Dimensions (mm) & 12 \(\times\) 5 \(\times\) 0.3 \\  & Material & Araldite 420 \\  & Thickness (mm) & 0.1 \\  & Density (g/cm\({}^{3}\)) & 1.20 \\ Adhesive & [PERSON]’s modulus at 23 \({}^{\circ}\)C (GPa) & 1.5 \\  & Shear modulus at 23 \({}^{\circ}\)C (GPa) & 0.73 \\  & Poisson ratio & 0.33 \\  & Tensile strength at 23 \({}^{\circ}\)C (MPa) & 29 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Main features of the experimental setup.  
Figure 9: Experimental setup (**top**) and relevant block diagram (**bottom**).
## 4 Implementation of Off-Chip Components
### Class-E Power Amplifier and Its Power Supply  
The primary coil is driven by a Class-E power amplifier with a supply voltage generated by a DC-DC converter that is controlled by an 8-bit DAC. Since power regulation is mainly adapted to slow coupling variation and power boosting is scheduled in advance for fast loading variation, the tracking speed of the DC-DC converter does not need to be very fast. However, if the ASK data modulation for the downlink data transmission is directly implemented by changing the supply voltage of the DC-DC converter, the tracking speed requirement will be much higher. In \"0\" transmission, the PA's supply has a negative voltage pulse of only 2.5 \(\mathrm{\upmu s}\) that is difficult to be generated by the DC-DC converter. Instead, the narrow negative pulse is generated using the circuit shown in Figure 9c. Through a resistor string, a lower voltage \(\mathrm{V_{D2}}\) is generated from the higher DC-DC output voltage of \(\mathrm{V_{D1}}\). For most of the time, \(\mathrm{S_{1}}\) is turned on and \(\mathrm{V_{D1}}\) supplies power to the PA. Only in \"0\" transmission that \(\mathrm{S_{2}}\) is turned on and the capacitor \(\mathrm{C_{L2}}\) provides \(\mathrm{V_{D2}}\) to generate the negative pulse. As the downlink is not frequently activated, the power overhead of sending the data is minimal. The resistor string is designed to be large enough such that its quiescent current is less than 10 \(\mathrm{\mu A}\), which is negligible.
## 3 Data Acquisition and Analysis
### Data Processing  
Magnetotellurics (MT) is a geoelectrical sounding method used for estimating the Earth's electrical conductivity structure on the basis of electromagnetic induction [36]. The impedance tensor, \(Z\), relates the horizontal electric and magnetic fields recorded at a specific location, at the Earth's surface. Additionally, we examine the impedance of the Earth:  
\[Z=a\ \frac{E}{H}, \tag{1}\]  
where \(E\) and \(H\) are the electric and magnetic field variations, respectively. The apparent resistivity \(\rho\) can be given:  
\[\rho=\frac{1}{\omega\mu_{0}}\bigg{|}\frac{E}{H}\bigg{|}^{2}, \tag{2}\]  
where \(\omega\) is the angular frequency, \(\mu_{0}\) is the magnetic permeability of free space. The phase \(\phi\) is given by  
\[\phi=\arctan\bigg{(}\frac{E}{H}\bigg{)} \tag{3}\]  
The recorded time series at MT-Stations were transformed from the time domain to the frequency domain by Fourier transform. The robust algorithm was used to compute the magnetotelluric response functions [37]. The robust processing scheme results in a period band ranging from 0.001 to 1000 s are shown in Figure 2. The apparent resistivity and phase curves of MT points in the study area show the different electrical characteristics in different tectonic units. The apparent resistivity and phase curves of MT-01 represent the electrical structure of the Wunlungu Depression, which has low resistivity with typical basin resistivity characteristics. The apparent resistivity and phase curves of MT-40 reveal the electrical structure of the Yemaquan island arc, which has low resistivity in the shallow part and high resistivity in the deep part of the crust. The apparent resistivity and phase curves of MT-80 reveal the electrical structure of the Zhaheba complex, which has the high resistivity in general. The apparent resistivity and phase curves of MT-102 show the electrical structure of the Dulate arc, which has the low-high-medium resistivity.  
Figure 2: The apparent resistivity-phase curves of MT stations from different parts of the study area.
# From GPS Receiver to GNSS Reflectometry Payload Development for the Triton Satellite Mission
## 4 Conclusions  
The introduction of the Triton mission is described at the beginning of the paper. The development of the NSPO GPSR, which is ready to fly onboard the Triton satellite, is then illustrated. Moreover, in order to satisfy the requirements of future NSPO missions and university CubeSats, several miniaturized versions of the NSPO in-house built GPSRs were developed without performance degradation [29]. Afterward, the paper presented not only the design and development of the Triton's GNSS-R mission payload but also the flight tests for the validation of the GNSS-R payload. Moreover, a science team has been formed to perform the tasks of data calibration, data archiving, data retrieval, and data utilization to support the Triton mission. The Triton program started in 2012 and passed several milestones, such as a system design review (SDR), a preliminary design review (PDR), a critical design review (CDR), and an integration & test review (ITR), before 2018. The NSPO team also has completed the installation of most components, harness routing,  
Figure 17: (**a**) Antenna gain pattern and setup configuration (**b**) Link budget analysis flowchart.  
Figure 18: Comparison of the theoretical model (blue) and real collected (red) DDM SNR.  
and integration. The entire environmental tests are planned to be conducted in 2021 so that the Triton satellite is ready to launch in 2022.  
Conceptualization, Y.-F.T. and W.-H.Y.; data curation, D.-S.Y.; writing--original draft preparation, Y.-F.T.; writing--review and editing, Y.-F.T. and W.-H.Y.; supervision, J.-C.J.; project administration, C.-T.L. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the National Space Organization, Taiwan.  
The authors would like to thank Center Weather Bureau (CWB), Taiwan and Drifter Technology Co., Ltd., Taiwan for providing the buoy data used in this paper.  
The authors declare no conflict of interest.
# A Methodology to Design Quantized Deep Neural Networks for Automatic Modulation Recognition
## 2 Background
### Quantization  
Quantization is a mapping operation between a real value \(r\) and an integer value. Quantization reduces the numerical precision of a value in bit-width, or what is the same, to reduce the number of bits used to represent a number. In the case of CNN, reducing the bit-width representation in inputs, activations, and weights results in a model that requires less memory space. In symmetric quantization, Equation (1) defines the clip range in which the real value is mapped to the integer value using \(q\)-bit quantization.  
\[(-s,+s)=(-2^{q-1}+1,2^{q-1}-1) \tag{1}\]  
Once the limits of the clipping range are defined, the second step is to map the full-scale range of floating-point values to a range of integer format values by defining a threshold, as indicated in Equation (2). Then, positive values of the input are mapped to positive values of the integer representation.  
Figure 1: General scheme of an SDR device. Two general stages are identified, the first is the RF-front-end, and the second is the baseband processing stage. FPGA is the closest hardware accelerator to the RF-front-end; it is here where DL algorithms can be downloaded to fulfill specific CR tasks. Taken from [16].  
\[INT_{threshold}=\begin{cases}+s&\text{if }r_{Value}>0\\ -s&\text{if }r_{Value}<0\end{cases} \tag{2}\]  
Then, scale of the quantization is defined as shown in Equation (3), where \(Value_{range}=[r_{max},r_{min}]\).  
\[Scale=\frac{Value_{range}}{INT_{threshold}} \tag{3}\]  
Finally, the quantified value is as follows:  
\[r_{INT}=\frac{r_{FLOAT}}{Scale} \tag{4}\]
# Enhancing Reliability of Tactical MANETs by Improving Routing Decisions
## 4 LQA-AODV Routing Protocol
### Overview of AODV  
AODV is a on-demand reactive routing protocol. Thus, AODV does not start route discovery process until a network node requests such service in order to transmit its data. Specifically, when source node (S) wants to transmit data to destination node (D), it first checks its routing table to determine whether there is already a route to node D. If no such path is available, AODV protocol starts a route discovery process to determine an up-to-date path to destination. A route request (RREQ) packet is initiated by S, and it is forwarded to all node S neighbors who forward the packet further until the intended destination is reached. The RREQ packet contains the information fields listed in Figure 2 (see reference [15] for more details).
###### Abstract  
The advent of the Internet of Things (IoT) has revolutionised our interaction with the environment, facilitating seamless connections among sensors, actuators, and humans. Efficient task scheduling stands as a cornerstone in maximising resource utilisation and ensuring timely task execution in IoT systems. The implementation of efficient task scheduling methodologies can yield substantial enhancements in productivity and cost-effectiveness for IoT infrastructures. To that end, this paper presents the IoT-mist bat-inspired algorithm (IMBA), designed specifically to optimise resource allocation in IoT environments. IMBA's efficacy lies in its ability to elevate user service quality through enhancements in task completion rates, load distribution, network utilisation, processing time, and power efficiency. Through comparative analysis, IMBA demonstrates superiority over traditional methods, such as fuzzy logic and round-robin algorithms, across all performance metrics.  
Internet of Things; mist; edge; cloud; load balance; scheduling 2024 16 93 10.390/116030093 10.390/116030093 10.  
introducing the IoT-mist bat-inspired algorithm (IMBA), which provides efficient resource optimisation and scheduling for IoT tasks.  
The proposed IMBA aims to achieve six objectives. They are to (1) increase the successful rate of task completion; (2) enhance the processing time; (3) reduce the dependency on external computing in cloud and edge, as these require more task waiting time, more offloading steps, a slow response time, and a greater distance for data to travel; (4) allocate more tasks to the mist layer, aiming to provide fast response times and shorter waiting times for performing tasks, as mist notes are the nearest computing resources; (5) reduce the energy consumption of external resources in cloud and edge layers by functioning the internal resources in the mist layer without showing high energy consumption in the mist layer; and (6) reduce the dependency on external networks (WAN networks) to minimise the risk of security vulnerabilities by functioning the tasks in internal networks (LAN network).  
The paper consists of five sections. Section 1 introduces the paper. Section 2 analyses recent studies relevant to task scheduling and previous studies on the usage of the bat algorithm to address IoT network challenges. Section 3 gives an overview of our proposed approach. Section 4 presents the results of and validates IMBA. The last section concludes the paper.
## 4 Case Study  
### Simulation of Improved MMG Model  
After finishing the MMG factor modification, a panama maximum size bulk carrier is designated as the simulated ship, with detailed information as indicated in Table 2. Initially, the speed and course are 12.5 knots and 000\({}^{\circ}\), respectively.  
Figure 8: Decision-making of collision avoidance.  
As illustrated in Figure 9a, turning one circle takes 927 s, and the final speed is 5.75 knots (2.96 m/s), a 54 percent drop in speed. The advance and transfer of the ship are, respectively, 785 m (3.48 L) and 430 m (1.91 L), where L represents the length overall (LOA) of OS. Figure 9b shows that the first decrease in speed is rapid and that the speed at the last trend is constant. When comparing the decrease in speed, transfer, advance, and other factors under the turning circle in the real world, the precision meets the collision avoidance criterion at sea. Figure 10 shows that under different RPMs of the propeller, the real ship speed and the simulation speed in MMG are closer, and the parameter setting of the MMG model has fulfilled the requirements of ship collision avoidance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline Length Overall/m & 225 & Propeller number & 1 \\ Breadth/m & 32.5 & Propeller advance/m & 4.738 \\ Draft/m & 14.5 & Average of Rudder/m2 & 56.88 \\ Block coefficient & 0.8715 & Propeller blade number & 4 \\ Propeller diameter/m & 6.8 & Propeller type & Fixed pitch \\ Propeller pitch/m & 2.81 & blade ratio & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Principal particulars and manoeuvrability characteristics of OS.  
Figure 10: Difference between real ship and simulation ship.  
Figure 9: Simulation of turning circle. (**a**) ship trajectory at starboard 35\({}^{\circ}\); (**b**) tendency of speed.  
Four turning circles are illustrated in Figure 11a, which is taken by different telegraphs. The gap between different turning circles is small because the ship's speed has minimal influence on the advance of the ship's turn in actuality. When the speed tends to be steady, a ship controlled by telegraph from \(D\) to \(F\) at 1200 s. The tendency of speed change is in alignment with the practice of navigation in reality. Therefore, the accuracy of improved MMG conforms to the requirement of intelligent collision avoidance.
## 4 Test Results and Discussion
### Evaluation of Pullout Strength  
The pullout-resistance characteristic of inextensible reinforcement could be determined by the front displacement range as follows: (1) smooth strip, 1.2 mm, and (2) bar mats and welded wire meshes, 12-50 mm [30]. The pullout strength (\(\tau_{\text{p}}\)) of Types A (SN) and B (PS1-1, PS2-1, and PS3) could be calculated by using the determined displacement range. Figure 5 shows the calculated pullout strength and evaluated pullout parameters as listed in Table 3. The pullout strength of all cases had a linear increment as the increment of normal stress; the increment of normal stress and the transverse member caused greater increment. This means that the incremental effect of pullout resistance was due to the  
Figure 4: Results of pullout tests by change in installation location of transverse members (normal stress, 100 kPa): (**a**) number of transverse members—1 and (**b**) number of transverse members—2.  
Figure 3: Results of pullout tests: (**a**) SN, (**b**) PS1-1, (**c**) PS2-1, and (**d**) PS3.  
bearing resistance of transverse members. Friction strength (\(\tau_{\text{p(fr)}}\)) due to longitudinal members (SN) was subtracted from total pullout strength (\(\tau_{\text{p}}\)) in order to evaluate the bearing strength (\(\tau_{\text{p(br)}}\)) of transverse members in the cases of PS1-1, PS2-1, and PS3. Figure 6 shows the relationship between normal stress and bearing strength. The effect of bearing strength is increased in total pullout strength because of the increment of normal stress and transverse member. However, the incremental effect of bearing strength becomes almost constant if the normal stress is over a certain value, even though bearing strength increases as an increment of transverse members, because the bearing strength has not subsequently increased.
## 4 Advancements in Remote Sensing Technologies for Coastal Monitoring
### UAV Technology for Precise Coastal Erosion Monitoring
[88], which mapped erosion and beach recovery prior to and subsequent to typhoon events with centimeter-scale accuracy, a challenging task to achieve with satellites alone. A comparative framework (Table 7) is presented below, outlining the strengths and limitations of UAV-based and satellite-based shoreline monitoring methods in terms of resolution, accuracy, revisit rate, and integration potential based on the content of this research study.
## 2 Problem Formulation  
Consider nonlinear systems as  
\[\left\{\begin{array}{l}\dot{\xi}_{i}=\xi_{i+1}+f_{i}(\xi_{1},\cdots,\xi_{i} ),\quad i=1,\cdots,n-1,\\ \dot{\xi}_{n}=u+f_{n}(\xi_{1},\cdots,\xi_{n}),\\ y=\xi_{1},\end{array}\right. \tag{1}\]  
where \(\xi=[\xi_{1},\cdots,\xi_{n}]^{T}\), \(u\in\mathbb{R}\) and \(y\in\mathbb{R}\) are the system states, the system input and system output, respectively. Define \(\vec{\xi}_{i}=[\xi_{1},\cdots,\xi_{i}]^{T}\) (\(i=1,\cdots,n\)). \(f_{1}(\cdot),\cdots,f_{n}(\cdot)\) are nonlinear smooth known functions.  
The expression for the tracking error \(e(t)\) in this article is as follows:  
\[e(t)=y(t)-y_{0}(t), \tag{2}\]  
where \(y_{0}(t)\) is a smooth and bounded reference signal with \(n\) differentiable times.  
According to reference [19], the NOTC means that the sign of the tracking error \(e(t)\) in \(t\in[0,+\infty]\) is constant; that is,  
\[e(t)\geq 0\quad or\quad e(t)\leq 0. \tag{3}\]  
Therefore, the control goal of this paper is to use the design state constraint non-overshoot controller, and the controlled system meets the following conclusions:  
1. All signals of the CLS are bounded, and the tracking error satisfies \(\lim_{t\to\infty}e(t)=0\);
2. The tracking error \(e(t)\) satisfies the condition of NOTC; that is, \(e(t)\) always keeps \(e(t)\geq 0\) or \(e(t)\leq 0\);3. The constraint property of the BLFs is not violated; that is, the system state \(\xi_{i}\) (\(i=1,\cdots,n\)) satisfies the following constraints \[k_{i1}<\xi_{i}(t)<k_{i2},\] (4) where \(k_{i1}<0\) and \(k_{i2}>0\) are constraint parameters.  
Define the following two functions.  
\[Y_{1}(t)=\ln\frac{y_{1}(t)-k_{1}}{k_{2}-y_{1}(t)},\quad Y_{2}(t)=\ln\frac{y_{2 }(t)-k_{1}}{k_{2}-y_{2}(t)}, \tag{5}\]  
where \(k_{1}<0\) and \(k_{2}>0\) are constants, and the functions \(y_{1}(t)\) and \(y_{2}(t)\) satisfy \(k_{1}<y_{1}(t)<k_{2}\) and \(k_{1}<y_{2}(t)<k_{2}\), respectively.  
If \(\lim_{l\to\infty}(Y_{1}(t)-Y_{2}(t))=0\), then  
\[\lim_{t\to\infty}(y_{1}(t)-y_{2}(t))=0. \tag{6}\]  
Define \(Y(t)=Y_{1}(t)-Y_{2}(t)\). According to (5), we have  
\[\begin{split} Y(t)&=\ln\frac{y_{1}(t)-k_{1}}{k_{2} -y_{1}(t)}-\ln\frac{y_{2}(t)-k_{1}}{k_{2}-y_{2}(t)}\\ &=\ln\frac{(y_{1}(t)-k_{1})(k_{2}-y_{2}(t))}{(k_{2}-y_{1}(t))(y_{ 2}(t)-k_{1})}.\end{split} \tag{7}\]  
From \(\lim_{l\to\infty}(Y_{1}(t)-Y_{2}(t))=0\), \(k_{1}<y_{1}(t)<k_{2}\) and \(k_{1}<y_{2}(t)<k_{2}\), we have the following derivation  
\[\begin{split}\lim_{t\to\infty}Y(t)=0&\Rightarrow \lim_{t\to\infty}\ln\frac{(y_{1}(t)-k_{1})(k_{2}-y_{2}(t))}{(k_{2}-y_{1}(t))( y_{2}(t)-k_{1})}=0\\ &\Rightarrow\lim_{t\to\infty}\frac{(y_{1}(t)-k_{1})(k_{2}-y_{2}( t))}{(k_{2}-y_{1}(t))(y_{2}(t)-k_{1})}=1\\ &\Rightarrow\lim_{t\to\infty}(y_{1}(t)-y_{2}(t))(k_{2}-k_{1})=0 \\ &\Rightarrow\lim_{t\to\infty}(y_{1}(t)-y_{2}(t))=0.\end{split} \tag{8}\]
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 6 Benefits of Using a Mobile Manipulator for Railway Track Maintenance  
### Environmentally friendly and sustainability  
Current maintenance techniques in railways are heavily dependent on humans, a fact which poses severe health and life risks. This dependency on humans also reduces the efficiency of the rail network. Train services are greener means of public transportation compared to other public transportations system. For example, modern Eurostar rail only emits 6 g of CO2 per passenger per kilometer (pppk), which is almost zero and insignificant compared to other transport systems such as small and medium petrol cars, which emit 96 g and 192 g CO2 PPPk, respectively [153]. Adopting train services for public transport will contribute towards the sustainable development goals (SDG) by reducing carbon footprint. However, delays and cancellations of trains impact customer satisfaction negatively and discourage users. In the UK, 3.3% of the train services were canceled, whereas 10.7% were delayed by at least 3 min during 2021-2022 Q4 [154]. Of these, almost 17% of delays could be attributed to track faults [155]. Therefore, timely track maintenance is essential to maximize the availability of railway networks. A flexible mobile manipulator system with inspection and repair capability, capable of working out of hours and in adverse weather conditions, could provide a considerable positive impact.