esa-sceva/satcom-chunk-collection huggingface dataset:
# Generating Activity-Based Mobility Plans from Trip-Based Models and Mobility Surveys
## 2 The Related Literature and Problem Statement
### The Related Literature
#### 2.1.2 Activity-Based Mobility Models  
Activity-based models aim to realistically represent the interdependencies of activities, times, locations, modes, and routes on a microscopic level. Most activity-based models share their two-step structure. They first generate a synthetic population to then generate schedules for each member or household within the population. A prototypical example for an activity-based model was presented by [PERSON] and [PERSON] [43]. Activity-based models are predominantly disaggregated.  
Utility-based models were the first activity-based models to predict full-scale mobility plans. They simulate the human decision-making process as a two-step process of first generating a choice set and forming the solution space, followed by selecting the best alternative [42]. The decisions are modeled to imitate the human desire to maximize utility [41]. While the participation in activities yields a positive utility, the financial and temporal cost associated with traveling result in negative utility [3]. The research community further differentiates utility-based models into econometric models [4; 43; 44] and utility-based microsimulations [4].  
Computational process models, occasionally termed rule-based, equal utility-based microsimulations in their focus on a complex search heuristic and the application of sequential decision-making processes, resulting in a specific solution. The two modelsdiffer in their decision-making processes: utility-maximizing approaches assume optimal decision making by humans, which has been deemed unrealistic by the authors of computational process models [41]. Hence, computational process models strive to select the first sufficient option rather than the globally optimal one [44].  
Driven by the advances in the field of artificial intelligence, numerous models that leverage vast sets of trace data to train artificial mobility plan generators have recently emerged as new means to model human mobility. Some of these generative models generate artificial mobility plans that are indistinguishable from real ones. In contrast, others learn behavior from traces and allow the model developer to alter behavioral parameters. Applied techniques include generative adversarial networks [45], variational autoencoders [46], decision and regression trees [47], Markov models [48], hidden Markov models [49], and recurrent neural networks [50]. The downside of these approaches, however, is their reliance on large data sets for the target region to be modeled. Mining existing data sets to this end entails privacy challenges.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs  
This section presents detailed analysis of all the routing schemes considered in this survey. The protocols have been divided into two categories based on their localization requirement, i.e., localization-based routing schemes, and localization-free routing schemes. Each of the two categories are further subdivided into protocols that consider mobility (limited or free), and those that do not consider mobility of sensor nodes (Figure 3).  
Figure 3: Classification of routing protocols based on location requirement and mobility.
## Materials and Methods
### Exclusion of Unreliable RR Intervals  
The reliability of the detected R points is considered in two aspects; i.e., (1) whether detected R exists in the noise region, or (2) whether the first R in the current sliding window overlaps with the R in the previous sliding window. Figure 13 shows an example for the exclusion of unreliable RR intervals. In the figure, R(\(n\)) represents the position of the \(n\)-th detected R point in the entire ECG signal. If R (16) is an unreliable R point detected in the noise region, RR (15) and RR (16) thus calculated are also unreliable and should be excluded from the ECG analysis. Also, if R (18) is the first detected R point in the current sliding window and does not overlap with the R points in the previous sliding window, the detected R points between the sliding windows lack continuity. As a result, RR (17) calculated by R (18) is unreliable and excluded from the ECG analysis. However, R (18) and R (19) are guaranteed to have continuity, so RR (18) is a reliable RR interval. This relationship is expressed as follows:  
\[\left\{\begin{array}{ll}\mathrm{RR}(n-1),\ \mathrm{RR}(n)\ \mathrm{is unreliable\ RR},&\mathrm{if\ R}(n)\ \mathrm{is\ in\ noise\ region}\\ \mathrm{RR}(n-1)\ \mathrm{is unreliable\ RR},&\mathrm{if\ R}(n)\ \mathrm{is\ the\ 1 st\ R\ and\ not\ overlap}\end{array}\right., \tag{10}\]  
Figure 12: QRS detection result using a pair of derivative filters and a max filter for Record (**a**) 108, (**b**) 111, (**c**) 118, (**d**) 217, (**e**) 214 sections in MIT-BIH DB.
## 2 Materials and Methods
### Electrodialysis Desalination Performance Testing  
A batch-cycle ED system was assembled with a pump (Cole-Parmer. Vernon Hills, IL, USA, Model: 7519-00), one-liter stream reservoirs stirred by non-heating magnetic stirrers (Fisher Scientific, Waltham, MA, USA, model: Fisher 14-955-150), two pH/conductivity meters (Thermo Scientific, Bartleville, OK, USA, model: Orion Star A325), a digital scale (Meller Toledo, Columbus, OH, USA, model: XS2002S), a programmable DC power supply (B&K Precision, Yorba Linda, CA, USA, Model: 9123A), and a MicroED stack (PCCell/PCA, GmbH, Heusweiler, Germany, model: 08002-001). The active cross-sectional area of membranes assembled in the micro-ED was 7.48 cm\({}^{2}\) (2.8 cm \(\times\) 2.8 cm). The thickness of the polyester spacer was 0.45 mm used to separate the AEMs and CEMs [26].  
The feed water used was a 3.0 g/L NaCl solution. The flow velocity of the diluate stream was maintained at 3 cm/s, and the stack voltage was set to 0.8 V per cell pair. The membrane combinations tested included PES with Neosepta AMX76 (ASTOM, Tokyo, Japan), and sPES with Neosepta AMX76.
## 2 Materials and Methods
### Artificial Neural Networks
RMSE has a squaring mechanism to magnify large errors while suppressing smaller errors, MAPE relates the magnitude of error to the magnitude of the observed value, and lastly, CC evaluates the closeness of predicted values' trend to that of the observed ones. These measures have been used in different studies to evaluate accuracy of ANN models [15; 32; 34]. They were calculated as per Equations (3)-(5). \[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(P-O\right)^{2}}{n}} \tag{3}\]  
\[MAPE=\frac{\sum_{i=1}^{n}\left|P-O\right|/O}{n}\times 100 \tag{4}\]  
\[CC=\frac{n\sum_{i=1}^{n}P(O)-\left(\sum_{i=1}^{n}P\right)\left(\sum_{i=1}^{n}O \right)}{\sqrt{n\left(\sum_{i=1}^{n}P^{2}\right)-\left(\sum_{i=1}^{n}P\right) ^{2}}\sqrt{n\left(\sum_{i=1}^{n}O^{2}\right)-\left(\sum_{i=1}^{n}O\right)^{2}}} \tag{5}\]  
where:  
\(P\) = Predicted values;  
\(n\) = Number of samples;  
\(o\) = Observed values. Figure 2: ANN Model. * Refer to Table 2 for details of variables in input layer. It is also important to use appropriate sampling technique for training and validation dataset to avoid bias in accuracy because of specific data samples. This issue was addressed by using the bootstrap sampling in this study. This sampling technique adopts replacing the samples with the appropriate values based upon functional distribution for parameters, consequently removing outliers [35; 36]. ANNs have been reported to have problem of convergence on local minima. Ensemble learning approach has been adopted in this study to address this issue, in which ANNs of slightly inferior performance can be combined synergistically to achieve better performance [37]. In this study, the ensembles were developed using the averages of outputs from MLP and RBFNN models.
# A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position
## 3 Materials and Methods
### A Robust Filter Model
When the vector \(Z_{k}\) has a gross error in the first situation, the robust vector \(\beta_{k}\) is constructed as follows:  
\[\beta_{k}=\left[ \begin{array}{cc}\frac{\lambda_{\Delta N}}{\delta_{\Delta N }}&1\end{array} \right]^{T} \tag{41}\]  
When the vector \(Z_{k}\) has a gross error in the second situation, the robust vector \(\beta_{k}\) is constructed as follows:  
\[\beta_{k}=\left[ \begin{array}{cc}1&\frac{\lambda_{\Delta N}}{\delta_{\Delta E }}\end{array} \right]^{T} \tag{42}\]  
When the vector \(Z_{k}\) has a gross error in the third situation, the robust vector \(\beta_{k}\) is constructed as follows:  
\[\beta_{k}=\left[ \begin{array}{cc}\frac{\lambda_{\Delta r}}{\chi^{2}_{m, \alpha}}&\frac{\lambda_{r}}{\chi^{2}_{m,\alpha}}\end{array} \right]^{T} \tag{43}\]  
When the vector \(Z_{k}\) has a gross error in the fourth situation, the robust vector \(\beta_{k}\) is constructed as follows:  
\[\beta_{k}=\left[\frac{\lambda_{\Delta N}}{\delta_{\Delta N}}+ \begin{array}[] {cc}\frac{\lambda_{r}}{\chi^{2}_{m,\alpha}}&\frac{\lambda_{\Delta E}}{\delta_{ \Delta E}}+\frac{\lambda_{r}}{\chi^{2}_{m,\alpha}}\end{array} \right]^{T}ss \tag{44}\]  
According to the above method, the robust filter can effectively restrain the gross error in the observation vector \(Z_{k}\). In practice, it is possible for the matrix \(R_{k}\) to be modified even there is no gross error in the observation \(Z_{k}\). At last, we proposed the BLE/PDR integrated System localization framework in the following diagram of Figure 7. In the PDR position method, the gyroscope readings are used to compute the heading angle and the accelerations and magnetometer are utilized to compensate for accumulated error as well. To improve the accuracy of the heading angle, people's motion status is considered in correcting the control parameters of MCF. The accelerations are also used to detect the steps and estimate the step length. With the step length and heading angle, a position is computed by the PDR method. The other position is obtained by the BLE fingerprint method at the same time. Then, two positioning estimations are input into the Extend Kalman filter to resolve the fusion position. To improve the integrated system robustness and scalability, a robust filter is used to restrain the gross error of BLE observational position results.
## 2 Principle
### Analysis of Anti-Dispersion Ability
In order to more intuitively analyze the relationship between the ICI and the transmitter filter, we converted the CP size into the filter bandwidth and center frequency interval. When the CP was 0.17, 0.23, 0.29, and 0.33, it was converted when the symbol rate was 10 Gbaud, and the bandwidth and center frequency spacing of the Sinc-shaped filter were 12 GHz, 13 GHz, 14 GHz, and 15 GHz, respectively. Although the transmission performance of the system could be improved after the CP was inserted, this also reduced the spectrum efficiency, so we will not discuss the case of inserting a larger CP into the AO-OFDM system. Figure 8 shows the ICI of the adjacent sub-carrier to the target sub-carrier after the target sub-carrier of the AO-OFDM system with a symbol rate of 10 Gbaud was affected by 800 ps/nm accumulative dispersion. The blue line in Figure 8 represents a system based on a Gauss-shaped filter, and the red line represents a system based on a Sinc-shaped  
Figure 5: Change in \(g(t)\) affected by different accumulative dispersions. Figure 6: Change in \(g^{\prime}_{\zeta\gamma}(t)\) affected by different accumulative dispersions. filter. The dotted line represents the influence of ICI on the optimal sampling point of the current symbol period and the solid line represents the influence of ICI on the optimal sampling point in the adjacent symbol period. On the whole, although the AO-OFDM systems based on Sinc-shaped filters were orthogonal to each other during back-to-back transmission without ICI, their ICI exceeded that of systems using Gauss-shaped filters after being affected by dispersion. Figure 8 shows that, although the size of the inserted CP could affect the ICI of the AO-OFDM system, the ICI of the AO-OFDM system based on the Gauss-shaped filter was always smaller than that of the system based on the Sinc-shaped filter after 40 km of transmission. In the next part, we will explain the superiority of the Gauss-shaped filter over the Sinc-shaped filter from another aspect. \[G_{k}(z,\Omega_{k})=G_{k}(0,\Omega_{k})exp(-\frac{\dot{j}}{2}\beta^{\prime\prime }{\Omega_{k}}^{2}z) \tag{25}\]0 The ICI size of adjacent sub-carriers of AO-OFDM signals based on two filters with carrier spacing of 12, 13, 14, and 15 (CP = 0.17, 0.23, 0.29, 0, and 33) after being affected by 800 ps/nm accumulative dispersion. \[G_{k}(z,\Omega_{k})=G_{k}(0,\Omega_{k})exp(-\frac{\dot{j}}{2}\beta^{\prime\prime }{\Omega_{k}}^{2}z) \tag{25}\]1 After being affected by 800 ps/nm accumulative dispersion, the filter of the two AO-OFDM systems at the receiving end received the pulse-shaping function of the corresponding sub-carrier and the adjacent sub-carrier.
## 5 Prototype Measurement Results  
The final prototype of the SA array is shown in Figure 18.  
Figure 17: Realized gain pattern for a \(23^{\circ}\) beam tilt in both the E plane (\(\varphi=0^{\circ}\)) and the steered plane (\(\varphi=23^{\circ}\)). The pattern is identical for both RHCP and LHCP ports.  
Figure 18: Photographs of the first four prototypes of the SA array.  
A measurement campaign was conducted to evaluate the radiative properties of the SA array. The input impedance was obtained using a Vector Network Analyzer (VNA), calibrated at the two ports of the feeding network. To reduce environmental influence on the measurement, tests were carried out in an open-site setup with a reference ground plane. The measurement procedure involved alternating connections: port 1 was connected to the output of the RHCP network, while port 2 corresponded to the LHCP path. During each measurement step, one port was connected to the SA network while the other was terminated with a matched 50 Ohm load, and vice versa. The results, expressed in terms of reflection coefficient magnitude, are presented in Figure 19 and show values below \(-10\) dB across the entire frequency band of interest, from 407 MHz to 417 MHz.  
The radiation pattern of the SA array was measured by placing it on a reference ground plane and recording the received power as a function of the incidence angle of the transmitting antenna over the two principal orthogonal planes. A schematic diagram of the measurement setup is shown in Figure 20.  
The transmitting site antenna, operating in circular polarization, was positioned at approximately 15 m to ensure far-field conditions. The angular scans were performed in two cuts: one in the azimuth plane (Phi = 0\({}^{\circ}\)) and the other in the elevation plane (Phi = 90\({}^{\circ}\)). The radiation pattern for each SA polarization port was derived from the received power at each scanning angle, under both RHCP and LHCP transmission. The gain values were obtained using the two-antenna method, with the single patch element of the SA array used as the reference antenna. Figures 21 and 22 present the realized gain patterns corresponding to the two circular polarizations, evaluated over the principal planes (the E plane and H plane). Co-polar and cross-polar components were normalized with respect to the maximum received power and are expressed in dBi. As expected, the  
Figure 19: The measured reflection coefficient of the SA prototype for the RHCP and LHCP feeding ports.  
Figure 20: Schematic diagram of the SA prototype measurement setup.  
maximum realized gain is approximately 16 dBi, with an HPBW of 17\({}^{\circ}\), indicated by the horizontal line in the graphs.  
The co-polar and cross-polar components are plotted together for each principal plane, allowing the evaluation of the polarization isolation at each antenna port. In all cases, the isolation is found to be greater than 10 dB.
## 2 Materials and Methods
### Circuit Design
#### 2.3.2 Electrical Signal Conversion Module  
The conductive yarn interwoven in the back layer of the fabric acts as a dual-port variable resistor. By pressing the sensing area on the fabric's surface, the resistance value of the conductive yarn can be significantly changed. However, the resistance value cannot be directly input into the printed circuit board (PCB). Therefore, an electrical signal conversion module is needed to convert the varying resistance value into a measurable voltage signal that can be processed.  
The most common method currently used is the resistive voltage divider. However, resistive voltage dividers have drawbacks such as poor linearity and compatibility issues with different sensor models. On the other hand, an electrical signal conversion module based on operational amplifiers offers high linearity in signal output. It also provides adjustable amplification and high measurement accuracy, making it suitable for various fabric sensors. The structure of the operational amplifier-based electrical signal conversion module is simple and plug-and-play, as shown in Figure 6. It outputs a voltage signal ranging from 0 to 3.3 V, making it compatible with both 5 V and 3.3 V control systems.  
Its electrical signal conversion calculation curve is given by Equation (1):  
\[U_{0}=\bigg{(}\,1+R_{AO-RES}\times\frac{1}{R_{X}}\,\bigg{)}\times 0.1, \tag{1}\]  
where \(U_{0}\) is the output voltage value of the module, \(R_{AO-RES}\) is the feedback resistance value, and \(R_{X}\) is the output resistance value of the fabric conductive part.
# High Throughput Data Relay in UAV Wireless Networks
## 4 Proposed Solution  
In this section, an iterative algorithm based on the general optimization framework mentioned in Section 2 for solving the problem (5) subject to constraints (5a)-(5f) is discussed. The overall problem is separated into two sub-problems based on BCD. To be specific, for a given UAV trajectory \(\mathcal{C}\), we optimize the victim bandwidth scheduling \(\mathcal{A}\). On the other hand, for any given victim bandwidth scheduling \(\mathcal{A}\), the UAV trajectory \(\mathcal{C}\) is optimized with the help of SCA. Furthermore, to ensure the best decoding performance of the DS method by the UAV, the received signal power from the paired victims are the same, as stated in (5d). We call this the power balance and it is implemented to link the two sub-problems. Finally, the overall algorithm is presented as a combination of the two sub-problems and power balance.
# Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids
## 4 Optimization Scheduling
### Stackelberg Game for EMOs
#### 4.2.2 Achievement of Game Equilibrium
\right.. \tag{30}\]  
In order to optimize the objective function in (30), we define a two-dimensional coordinate system for the selling price and buying prices, as shown in Figure 3. The critical values in (26) of the sellers and the power grid prices are located in a transverse axis, such as \([p_{gb}^{h},p_{cs,i}^{h,\min},p_{cs,i}^{h,\min},...,p_{cs,k}^{h,\max},p_{cs,i}^ {h,\max},p_{gs}^{h}]\), and the critical values in (27) of the buyers and the power grid prices are located in a longitudinal axis, such as \([p_{gb}^{h},p_{cb,q}^{h,\min},p_{cb,p}^{h,\min},...,p_{cs,m}^{h,\max},p_{cs,n}^ {h,\max},p_{gs}^{h}]\). The feasible region \(S\) of the profit function (30) is divided into a certain number of sub-regions, such as sub-region \(\forall s\in S\). For each sub-region \(s\in S\), it is easily known that the profit function in (30) is concave (the proof can be seen in Appendix A). The optimal buying and selling prices in sub-region \(s\) can be found as \([p_{cb,s}^{h*},p_{cs,s}^{h*}]\) by using CPLEX which is the commercial solver for MIP problem. Thus, the optimal prices in \(S\) can be obtained as  
\[[p_{cb}^{h*},p_{cs}^{h*}]=\arg\max\limits_{(p_{cb,q}^{h},p_{cs}^{h})}{\left( Pr_{MGC}^{h}(p_{cb,s}^{h*},p_{cs,s}^{h*}),\forall s\in S\right)}. \tag{31}\]  
Furthermore, the optimal prices \([p_{cb,s}^{h*},p_{cs,s}^{h*}]\) will be sent to MGOs. The MGOs can calculate the optimal load demands \(L_{i}^{h*}\) according to formulas (22)-(27). Thus, the SE of the proposed game is reached. The sensitivity analysis of MGs' utilities over the internal prices adopted by the market operator are shown in Appendix B.
###### Abstract  
We developed a new strategy for a synchronous generation of real-time (RT) and near real-time (NRT) tropospheric products. It exploits the precise point positioning method with Kalman filtering and backward smoothing, both supported by real-time orbit and clock products. The strategy can be optimized for the latency or the accuracy of NRT production. In terms of precision, it is comparable to the traditional NRT network solution using deterministic models in the least-square adjustment. Both RT and NRT solutions provide a consistent set of tropospheric parameters such as zenith total delays, horizontal tropospheric gradients and slant delays, all with a high resolution and optimally exploiting all observations from available GNSS multi-constellations. As the new strategy exploits RT processing, we assessed publicly precise RT products and results of RT troposphere monitoring. The backward smoothing applied for NRT solution, when using an optimal latency of 30 min, reached an improvement of 20% when compared to RT products. Additionally, multi-GNSS solutions provided more accurate (by 25%) tropospheric parameters, and the impact will further increase when constellations are complete and supported with precise models and products. The new strategy is ready to replace our NRT contribution to the EUMETNET EIG GNSS Water Vapour Programme (E-GVAP) and effectively support all modern multi-GNSS tropospheric products.  
t 10 232018 10 232 2018 10.3390/rs10020232
# Optical-OFDM VLC System: Peak-to-Average Power Ratio Enhancement and Performance Evaluation
## 2 Multicarrier Modulation Schemes Based on the VLC System
### Flip-OFDM  
In Figure 3, a schematic of a Flip-OFDM is depicted. The data input process in Flip-OFDM follows a pattern similar to that discussed in DCO-OFDM, extending up to the output generated by the IFFT stage. Flip-OFDM utilizes half of the available OFDM subcarriers for data transmission, leading to a real bipolar output \(x(n)\) from the IFFT process [29]. The signal \(x(n)\) is divided into two separate components: one comprises the positive samples, denoted as \(x_{p}(n)\), and the other comprises the negative samples, denoted as \(x_{n}(n)\), as specified in Equation (10).  
\[x_{p}(n)=\begin{cases}x(n),&\text{if }x(n)\geq 0\\ 0\,,&\text{otherwise}\end{cases}\text{ and }x_{n}(n)=\begin{cases}x(n),&\text{if }x(n)<0\\ 0\,,&\text{otherwise}\end{cases} \tag{10}\]  
Figure 2: ACO-OFDM block diagram based on a VLC system.  
Here, n ranges from 0 to \(N-1\). The \(x_{p}(n)\), is sent within the first OFDM segment, whereas the second OFDM segment is allocated for conveying the flipped \(x_{n}(n)\), as visualized in Figure 3. The first and second OFDM segments have CPs appended to them, with a duration of \(C\), since communication occurs across a dispersive optical channel [30]. Subsequently, the second segment is temporally delayed by (N + C) and synchronized after the first segment. At the Flip-OFDM receiver, the bipolar OFDM frame is reconstructed using the two received segments, as demonstrated in Figure 3. Initially, the CPs associated with each segment are released, facilitating the regeneration of the original bipolar samples as follows:  
\[y(n)=y_{p}(n)+y_{n}(n) \tag{11}\]  
Here, \(y_{p}(n)\) represents the samples of the first received segment, and \(y_{n}(n)\) represents the samples of the second segment [22].