esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Validation Methods
#### 3.3.3 Sensitivity Analysis  
A sensitivity analysis can be used to analyze the stability of a model and observe the stability of the evaluation criteria weights. The one-at-a-time method is a widely used sensitivity analysis method for spatial multi-objective decision technology [45; 46], where the sensitivity increase/decrease of the spatial evaluation results is calculated by way of the aggregate mean, which has been applied to the risk evaluation model in this paper.  
The main steps of the method are as follows. First, for criteria \(a\), the weight value is increased and decreased using 10% equal intervals. The scaling range is [\(-\)50%, 50%], and the new weight of criteria \(a\) is obtained using Equation (17):  
\[\overline{w_{a}}(cr)=(1+cr)*w_{a}\, \tag{17}\]  
Figure 9: Distribution of CALIFSO-VFM verification points around UTC 05:00 from January to September in 2018.  
where \(\overline{w_{a}}(cr)\) represents the new weight of criteria \(a\), \(cr\) is the increased/decreased value, and \(w_{a}\) represents the original weight. To ensure that the summed weights equal 1, Equation (18) is calculated for the remaining criteria:  
\[\overline{w}_{h}(cr)=w_{h}\times\frac{1-\overline{w_{a}}}{1-w_{a}}, \tag{18}\]  
where \(\overline{w}_{h}(cr)\) represents the weights adjusted for the remaining criteria, \((h\
eq a)\), and \(w_{h}\) represents the original weight of criterion \(h\).  
Then, the new risk evaluation values are calculated using the adjusted weights, as shown in Equation (19):  
\[\mathrm{R}(w_{a},cr)=\overline{w_{a}}\times x_{a}^{\prime}+\sum_{a\
eq h}^{n} \overline{w_{h}}\times x_{h}^{\prime}, \tag{19}\]  
where \(\mathrm{R}(w_{a},cr)\) represents the new risk evaluation value for each grid, \(x_{a}^{\prime}\) represents the standardized value of criterion \(a\) for each grid, and \(x_{h}^{\prime}\) represents the standardized value of criterion \(h\) for each grid.  
The mean of the absolute change rate (MACR) of the whole region is calculated to obtain the change rate of criterion \(a\) with changes \(cr\), following Equation (20):  
\[\mathrm{MACR}(w_{a},cr)=\sum_{j=1}^{S}\frac{1}{S}\bigg{|}\frac{R_{j}(w_{a}, \mathrm{cr})-R_{j}^{0}}{R_{j}^{0}}\bigg{|}\times 100\%, \tag{20}\]  
where \(\mathrm{MACR}(w_{a},cr)\) represents the MACR of the risk evaluation in the whole region, \(R_{j}(w_{a},\mathrm{cr})\) represents the risk evaluation value of the \(j\)-th grid after weight adjustment, and \(R_{j}^{0}\) represents the original risk evaluation value, \(S\) represents the total number of grids.  
If the adjustment amplitude of a criterion weight is far less than the change amplitude of the model result, it indicates that the proposed model has good stability. The results of the sensitivity analysis are shown in Section 4.3.
# Backscatter Assisted NOMA-PLNC Based Wireless Networks
### Contributions  
The main contributions of this paper are:  
* proposal of low power 5G wireless network using NOMA and PLNC schemes with ambient backscattering.
* derivation for optimal power allocation to the near node and the far node with ABS.
* closed form expression for the end-to-end outage probability of the proposed wireless network with ABS.
* closed form expression for the end-to-end average BER of the proposed wireless network with ABS.  
The rest of the paper is organized as follows: In Section 2, the system model of the proposed wireless network with ABS is discussed. The derivation for the optimal power allocation of the downlink NOMA with ABS is presented in Section 3. The end-to-end outage probability expression is derived in Section 4 and average BER expression is given in Section 5. Numerical results of the proposed wireless network is presented in Section 6 and the concluding remarks are given in Section 7.
# LaRoAwan CubeSat with an Adaptive Data Rate: An Experimental Analysis of Path Loss Link Margin
## 2 Related Work  
LoRa is a versatile and promising technology for connecting low-powered sensor devices because of its versatility. In recent years, a considerable number of studies have been conducted. Researchers are investigating every possible element and application of LoRa technology. One of the factors to consider is how far the signal can travel and LoRa's maximum range before the signal degrades. In [6], the study proposed the internet in space for small satellites. They demodulated the signal using Semtech SX1276 and transmitted it from LoRa MAC to Ethernet. Despite the fact that this design satisfies the Direct-to-Satellite (DtS) requirements, the greatest calculated distance was 30 km when tested using a High-Altitude Balloon (HAB). Using a microchip LoRa transmitter, [PERSON] et al. [7] designed a communication subsystem for the Unmanned Aerial Vehicle (UAV) wireless sensing network. It was thoroughly tested by UAV, but the longest distance recorded was 8.62 km at an altitude of 30 m above the ground, which does not meet the satellite communication requirements. Using an IMST IC880A LoRaWAN concentrator and an RN2483 LoRa node, [PERSON] et al. [8] tested the LoRaWAN gateway. It was tested indoors, and the highest node distance observed was 60 m, which is again restricted to terrestrial connectivity. [PERSON] et al. provided a solution for marine monitoring activities by using LoRaWAN network architecture [9]. The onshore signal performance degrades due to the collision of air molecules and dense humid environment. The proposed system satisfies the performance needs; however, the maximum recorded distance was 8.3 km and the authors suggested to increase the height of antenna for the increase in coverage area. [PERSON] et al. designed a DtS-IoT ground terminal for environmental monitoring using the Xingyun IoT satellite [10]. The design of the transceiver is based on the Xingyun satellite communication protocol with a much lower transmit power of 30 dBm with both uplink and downlink reception. The research satisfied the DtS-IoT connectivity by using the Xingyun satellite and obtaining the data from the ground station over the internet. The research was focused on using the Xingyun satellite services using patented satellite transceiver names such as LEOBIT. Meanwhile, [PERSON] et al. [11] suggested an ADR algorithm based on fuzzy logic for LoRaWAN gateway systems. Even though that research demonstrated superior performance in terms of energy consumption and transmission power compared to the Semtech ADR scheme, it showed that the Semtech ADR scheme is ideal in terms of energy consumption. However, in satellite LoRaWAN communication systems, it can only be helpful when the handoff between coupled satellites occurs.
# Development Status of Key Technologies for Optoelectronic Integrated Circuit Manufacturing
## 4 Packaging of Optoelectronic Integrated Circuits
### Monolithic Package  
Monolithic packaging is the packaging of monolithic integration formed by the simultaneous processing of optical and electrical devices on the same tape-out platform [76]. With the continued demand for smaller and superior optoelectronic integrated circuit devices causing manufacturers to increase component density and reduce component size wherever possible within the device, the development of package structures should be on the chip level, wafer level, and even quantum level to achieve minimal package occupancy area. In 1983, [PERSON] et al. [77] of STC Company detailed a package scheme, based on a ceramic substrate. The heat sink is electrically connected to the top of the laser by wire bonding technology. In 1995, the Korea Institute of Electronics and Communications disclosed a butterfly packaging scheme [78], which can transmit high-speed signals to the micro pin lines of package pins installed on the micro pin lines and the front surface of the feedback optical interrupter, and fixed them through laser welding to minimize the optical fiber in heat transfer path to improve the reliability of module packaging. In 2005, Taiwan Semiconductor Manufacturing Company revealed a packaging structure for optical elements [79]. The chip has optical components and a plurality of pads arranged on its active surface; holes are formed through the chip and electrically connected with the pads. The cover is attached to the active surface through an adhesive ring, and a plurality of metal wires are arranged on the rear surface of the chip. This packaging structure can be used for wafer-level mass manufacturing, which not only reduces the packaging cost considerably but also improves the reliability of packaging. In 2016, [PERSON] et al. [80] invented a monolithic packaging scheme to prepare an insulating film on the microchannel cooler to achieve water-electric separation, which can reduce the requirement for cooling water resistivity and incorporate the use of ordinary cooling water, which is conducive for improving the environmental adaptability of the laser and extending the lifetime of the laser. In 2018, [PERSON] et al. invented a package structure design scheme for quantum cascade lasers [81], as depicted in Figure 2, which not only improves the collimated coupling efficiency and the flexibility of replacing the laser chip but also reduces the package cost and assembly difficulty and reduces the package volume. Monolithic packaging schemes are increasingly prioritizing higher reliability and smaller size, which are simple for monolithic packaging technology but also lead to a higher loss of optical waveguide and a lower response rate of photoelectric conversion, which results in the optoelectronic device becoming unable to perform optimally [82].
# Active Gait Retraining with Lower Limb Exoskeleton Based on Robust Force Control
## 2 Materials and Methods  
### Exoskeleton Description  
In order to perform the rehabilitation exercises, this work used the anthropomorphic lower limb exoskeleton shown in Figure 1a, which has two robotic legs with two links and two elastic joints each. The anthropometric design of the exoskeleton allows for easy adjustment to the patient's legs, ensuring that the patient's movements are appropriately adapted to the exoskeleton's legs (Figure 1b). The elastic element in the exoskeleton's flexible joints acts as a shock absorber, reducing the actuator inertia felt by the user and thereby increasing safety and comfort. The flexible coupling between the user and the motor protects the user's joints from impact loads and other undesirable interactions. Additionally, it allows for small patient movements, further enhancing comfort.  
In addition to the robotic legs, the exoskeleton has an additional loading system so that the legs of the exoskeleton do not support the patient's weight during rehabilitation routines but the loading system does this task, leaving the exoskeleton legs only to move the patient's legs to complete the rehabilitation routine. The exoskeleton lifting system consists of a double four-bar mechanism operated by two linear actuators (model LACT6P), each capable of withstanding a load of 50 kg. The system is anchored at one end to the hand supports of a standard treadmill and at the other end to the mechanical legs, allowing them to move up and down while maintaining vertical alignment. The patient is supported by a harness attached to the upper bar of the harness frame, which is connected to the four-bar system at the same point as the mechanical legs.  
Figure 1: Anthropomorphic lower limb exoskeleton. (**a**) Real prototype; (**b**) conceptual scheme.  
To generate movement in each leg, the exoskeleton is equipped with FHA-14C-100 electric actuators at the hip and knee joints, capable of delivering up to 28 Nm of torque. Each actuator is coupled to a torsional spring with a stiffness of 138.65 Nm/rad, through which mechanical power is transmitted to the links of each leg. The angular displacement at each joint is measured directly by two AMT203-V absolute rotary encoders from CUI devices. Two encoders per joint are used to simultaneously measure the angular position of the motor (before the spring) and the angular position of the link (after the spring) at the same joint. Angular velocity is obtained via software by numerically differentiating the angular position.  
Data acquisition and processing, as well as the generation of control statements, are carried out on a myRIO card from National Instruments.
## 5 Results and Discussion  
This study addresses the application of two optimization algorithms of genetic algorithm (GA) and imperialist competition algorithm (ICA) for improving the performance of the artificial neural network (ANN) for modeling the heating load (HL) and cooling load (CL) of a residential building. Note that, the mentioned algorithms are employed to find the best computational parameters (i.e., the weights and biases) of the ANN. Eight influential factors, including relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution of a residential building were selected to form the input dataset. Among 768 samples, 80% (614 records) were randomly selected and devoted to the training phase, and the remaining 20% (154 records) were used to evaluate the accuracy of ANN, GA-ANN, and ICA-ANN models. In this sense, to evaluate and compare the efficiency of the applied models, three accuracy criterions namely, R\({}^{2}\), MAE, RMSE were defined, which have been extensively used in earlier studies [44; 45]. Equations (2)-(4) formulate the mentioned statistical indices.  
\[R^{2}=1-\frac{\sum_{i=1}^{N}\bigl{(}Y_{i_{observed}}-Y_{i_{predicted}}\bigr{)}^{2 }}{\sum_{i=1}^{N}\bigl{(}Y_{i_{observed}}-\overline{Y}_{observed}\bigr{)}^{2}}, \tag{2}\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\Bigl{|}Y_{i_{observed}}-Y_{i_{predicted}}\Bigr{|}, \tag{3a}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\bigl{(}Y_{i_{observed}}-Y_{i_{predicted}} \bigr{)}^{2}}, \tag{3b}\]  
where \(Y_{i\,observed}\), and \(Y_{i\,predicted}\) denote the actual and predicted values of HL (or CL) of the efficient building, respectively. The term N represents the number of instances, and \(\overline{Y}_{observed}\) stands for the average of the actual values of HL (or CL).  
Figure 6: The histogram of the Y1 (**a**) and Y2 (**b**) and the correlation (**c**) between them.
# Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management
## 2 Review of the State-of-the-Art Technology
### Active Detection Radars

Table 1: ART Midrange 3D specifications. \begin{table}
\begin{tabular}{c c} \hline \hline
**Radar** \\ \hline Ku-band, FMCW \\ Scan 360 degrees/second \\ Sectorized RF blanking \\ Doppler and Clutter Map techniques \\ True track report (position, course and speed) \textgreater{}2 km for smallest target of RCS = 0.1 m\({}^{2}\), \\ once per second \\ X-Band alternative for longer ranges \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ARMS radar specifications. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Specification** & **Spexer 2000 3D MkII Radar** & **Spexer 2000 3D MkIII Radar** \\ \hline Maximum UAV & 9 km & 9 km \\ detection range & 6 km & 6 km \\ Radar technology & Full coherent pulse & Full coherent pulse \\ Proppler Radar & Doppler Radar & Doppler Radar \\ Frequency range & X-band & X-band \\ Azimuth coverage & 120\({}^{\circ}\) & up to 360\({}^{\circ}\) \\ Elevation coverage & 15\({}^{\circ}\) & up to 90\({}^{\circ}\) \\ Track while scan & \textgreater{}300 targets & \textgreater{}300 targets in 120\({}^{\circ}\) \\ Power consumption & \textless{}550 W & Antenna: 1700 W \\  & & Processing: 400 W \\ \hline \hline \end{tabular}
\end{table} 
Table 3: HENSOLDT radar specifications. rate) and searching for additional targets in its coverage. Its specifications are detailed in Table 4.  
An alternative solution is the Ranger R8 SS-3D from Flir [16], whose specifications can be seen in Table 5.  
RST enterprise has another radar solution to detect UAVs, and it is called Doruk: UAV detection radar [17]. Its basic functions are a low-altitude moving target detection over land and sea. It provides detection, classification, azimuth and range measures, RCS, radial velocity, heading and width of Doppler Frequency Spectrum of targets. Its main specifications can be seen in Table 6.
## 4 Simulation Analysis
### Results and Analysis  
The WPE method is utilized to extract the fault characteristics of the faulty signal. The wavelet function db3 is used, decomposition layers are selected to be 3, then 8 decomposition bands are generated and the frequency range of 3-layer wavelet packet displayed in Table 2. The proportional relationships of the energy bands are analyzed and calculated; the comparison result is illustrated in Figure 6 by histograms. It can be seen that the changes in the WPE distribution is presented in the node [3,2] and the node [3,3]. Figure 7 illustrated the results of the square envelope spectrum the proposed method, and \(f_{o}\) represents the fault frequency of the bearing outer ring.  
1. By selecting the appropriate wavelet function and the number of decomposition layers, the measurement signal is decomposed by WPT and passed to obtain the WPT coefficient of each node;
2. Calculate the WPT energy value of each node;
3. Select the decomposed signal with the concentrated energy for reconstruction;
4. Calculate the SK map of the acquired reconstructed signal and select the center frequency and bandwidth corresponding to the maximum kurtosis value;
5. Filter the reconstructed signal using the optimized center frequency and bandwidth as the band-pass filter parameters;
6. Perform square envelope demodulation on the filtered signal to obtain the envelope spectrum of the signal;
7. Analyze the envelope spectrum to obtain the fault diagnosis result.
## 4 Results and Discussion
### The Comparative Analysis of SASW Modulus and FWD Modulus  
In Section I and Section II, 11 measurement locations were selected to conduct the FWD tests. Table 3 summarizes the concrete layer modulus at the same measurement locations for the SASW and FWD methods. The modulus value shows that the modulus measured by the SASW method is generally larger than the FWD modulus. Considering the sensitivity of modulus is highly related to frequency, the modulus at high frequency is larger than that at low frequency. Thus, the reason for the difference between these two types of moduli is related to the measured frequency. The measured frequency range of the SASW method can reach up to 7 kHz, while the frequency range in FWD measurements is generally between 0 and 60 Hz.  
A linear regression analysis is carried out to develop the correlation between the modulus measured by FWD and the modulus obtained from laboratory compression tests. Figure 5 shows the correlation relationship between these two types of moduli, with a correlation coefficient R\({}^{2}\) of 0.6451, which is less than the correlation coefficient of the SASW method and the compression modulus (0.7622). However, the trends of the correlation relationship between the FWD modulus and the compression modulus are inversely proportional, with a negative Pearson coefficient of \(-\)0.8032. The negative coefficient indicates that the modulus from the FWD method is not consistent with the compression modulus. A correlation analysis is also conducted on the SASW modulus and FWD modulus (Figure 6). The correlation coefficient R\({}^{2}\) between these two types of moduli is only 0.4891, and the Pearson coefficient is also negative (\(-\)0.6993), which indicates there is no direct correlation.  
Figure 4: Correlation relationship between SASW modulus and compressive modulus.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pavement Section** & **Measurement Location** & **SASW (MPa)** & **FWD (MPa)** \\ \hline \multirow{6}{*}{Section I} & X1 & 43,291 & 36,217 \\  & X2 & 34,075 & 39,003 \\  & X3 & 34,958 & 36,384 \\  & Y1 & 49,675 & 22,541 \\  & Y2 & 46,224 & 29,460 \\  & Y3 & 46,996 & 28,870 \\ \hline \multirow{6}{*}{Section III} & A & 47,922 & 23,277 \\  & B & 49,196 & 34,155 \\ \cline{1-1}  & C & 41,020 & 28,259 \\ \cline{1-1}  & D & 44,844 & 25,993 \\ \cline{1-1}  & E & 46,246 & 28,918 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The summary of concrete modulus from the SASW method and FWD method.
## 7 Related Work  
In this section, we introduce three state-of-the-art IoUT-related works. These works can provide more information of IoUT, and offer the potential research directions in the near feature.  
* In [4], the research effort has focused on the MAC-layer packet scheduling for IoUT. Considering the design challenges of MAC protocols, the authors presented an Energy-Aware scheduling with Spatial-Temporal reuse (EAST) protocol for IoUT. The authors mentioned the design challenges of the IoUT MAC protocols, including: (1) sensor network lifetime; (2) acoustic channel utilization; and (3) packet loss problems. EAST utilizes the information of data buffer and traffic load to switch the energy modes (i.e., the listen and sleep mode) of underwater sensor nodes, and thereby achieves both high energy efficiency and high channel utilization.
* In [5], the authors concentrated on the network-layer routing protocol for IoUT. Considering the IoUT challenges of data transmission and of energy efficiency, the authors proposed an Enhanced Channel-Aware Routing Protocol (E-CARP) for IoUT. E-CARP exploits the greedy strategy to deliver packets hop-by-hop. Specifically, E-CARP simplifies the relay-node selection method and reduces control packets, and thereby reduces the communication cost and energy consumption. As a result, E-CARP can reserve energy and lengthen the lifetime of the underwater sensor system for IoUT.
* In [6], the study focused on the design and implementation of the smart-city IoUT system. The authors proposed the Smart Environment Monitoring and Analytics in Real-time system (SEMAR). SEMAR is a combination of water quality monitoring system, coral reef monitoring system, wireless mesh network system, and Big Data system. SEMAR collects data by using the underwater sensors, remotely operated vehicles (ROVs), and underwater cameras. The collected data would be delivered to the monitoring center. For data processing (in the monitoring center), the authors point out that using Hadoop Distributed File Systems (HDFS) can perform better than using SQL systems, especially in the cases of processing large/huge amounts of data.
# A Quad-Band RF Circuit for Enhancement of Energy Harvesting
## 4 Design and Simulation
### Quad-Band Circuit Design  
The applied input power of the RF source was set to offer a power of 0 dBm. The frequency was adjusted to be varied between 0 and 5 GHz to cover the interested quad-bands. Power probes were inserted at the input and output points to compute the power  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Parameter** & **GSM-900** & **GSM-1800** & **UMTS-2100** & **Wi-Fi 2.45** \\ \hline \(\ell_{1}\) (mm) & 78.56 & 36.87 & 31.16 & 25.92 \\ \(\ell_{2}\) (mm) & 33.10 & 27.59 & 24.55 & 21.64 \\ W (mm) & 4.2 & 4.2 & 4.2 & 4.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions of transmission lines and microstrip lines.  
and then calculate the RF-DC conversion efficiency. On the other hand, the load resistor (\(R_{L}\)) for the two scenarios was selected to be 5 K Ohms since it is a typical value for loads of many applications.