esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Novel Technologies of 6G for Smart Grid Applications
### AI-Based Ultra-Dense Networks (UDNs)  
In this study, we explore the integration of AI techniques like reinforcement learning, deep learning, and machine learning in 6G-enabled UDNs for smart grid applications. The UDN is capable of handling a vast number of small cells. The fundamental concept behind deploying small cells is to ensure enhanced spectral efficiency, capacity, energy efficiency, and data rates while minimizing latency [1]. AI-based UDNs are another promising technology to meet the demands of modern communication systems by efficiently utilizing resources and enhancing connectivity. However, effective resource allocations and interference mitigation are the major difficulties in the case of UDNs.  
Furthermore, effective resource allocation can be performed by applying AI in UDNs [94]. In addition, an AI-based UDN provides improved spectral and energy efficiency, network efficiency, and less jitter as well [1].
## 3 Simulation Examples
### Residual STFT-Based Mode Shape Extraction  
#### 3.2.1 Effect of Window Size  
In this section, the Hann window is used with three different sizes of 64, 128, and 256 with an overlap ratio of 99% to investigate the effect of window size under the sample frequency of 200 Hz. Meanwhile, the modal assurance criterion (MAC) [43] values is provided for the evaluation of the estimated mode shape (\(\phi_{e}\)) with reference to the analytical one (\(\phi_{u}\)).  
As Figure 6 shows, the first three beam modes could fit well with the analytical ones. (All corresponding MAC values are larger than 0.91.) However, there are still some differences among the estimated mode shapes from these window sizes. Using a larger window size results in the loss of longer signal segments at the beginning and end. So, the window size of 64 could provide the longest mode shapes (from 1.0 m to 24.0 m of the beam length). Meanwhile, the window size of 256 could only provide the shortest mode shapes (from 3.5 m to 21.5 m of the beam length). However, for the second and third mode shapes, the window size of 64 contains a slightly larger deviation from the analytical ones when compared to the other two window sizes, even if the MAC values are larger than 0.95. In other words, a sufficient window size should be used to guarantee that the necessary information is contained [12]. The window size of 128 could be a balanced option, providing mode shapes from 2.0 m to 23.0 m along the beam and small deviations on the mode shapes, similar to the window size of 256. Then the Hann window with a window size of 128 and an overlap ratio of 99% is applied in the following sections.  
Figure 6: The first three beam mode shapes using the residual STFT-based extraction with window sizes of 64, 128, and 256, compared to the analytical first mode shape (**a**), the second mode shape (**b**), and the third mode shape (**c**), as well as the MAC values.
# Experimental Validation of Non-Marker Simple Image Displacement Measurements for Railway Bridges
## 4 Verification Results by Model Bridge
### Discussion for Practical Application I: Camera Distance  
The measurement accuracy of the midspan 35 Hz excitation was 0.03 pixels at a distance of 2.5 m from the camera to the object to be measured; the displacement was 44 \(\upmu\)m. Assuming that the measurement accuracy in pixels does not vary with the measurement distance (i.e., the influence of distance attenuation and air fluctuation is insignificant), the measurement limit displacement at any measurement distance \(x\) (m) can be calculated as 0.0176 \(\times\)\(x\) (mm). If bridge displacement when the train passes is larger than this measurement limit displacement, the non-marker image measurement examined in this study can be applied. Here, the feasibility rate is the probability that the measured bridge displacement will exceed the measurement limit displacement.  
Figure 14 shows the relationship between the maximum displacements and the bridge span [28] used for calculating the feasibility rate. We observed all of the 246 bridges constructed for high-speed railways. The maximum displacement value at low speed was used as a representative value in each case. The span length of the bridges in Figure 14 was 10 to 110 m, and the measuring distance was set every 5 m from 5 to 50 m. The feasibility rate was computed as the proportion of bridges whose maximum displacement exceeded the measured limit displacement calculated at 0.0176 \(\times\) (mm) for each span length. Table 7 shows the number of bridges used for the trial calculation in this study for each span length.  
Figure 15 shows the feasibility rate calculated from the measured bridge and the limit displacements in Table 7 and Figure 14. Figure 15 shows that the bridges with a span length of 20 m have a feasibility rate of \(\
eg\)90% or more at measurement distances up to 50 m. However, because the bridge displacement is small for bridges with a span length of \(\
eg\)20 m, it is necessary to provide a measurement distance below 20 m. If a bridge with a short span  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bridge Spans** & **Bridge Number** & **Bridge Spans** & **Bridge Number** \\ \hline Less than 10 m & 4 & 30–40 m & 66 \\
10–20 m & 13 & 40–50 m & 54 \\
20–30 m & 44 & Over 50 m & 65 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Number of bridges and the span length used in the calculation.  
Figure 14: Relationship between the span length and the maximum measured bridge displacement (246 high-speed railway bridges).  
length must be measured from a distance of 20 m or more, it may be necessary to increase the displacement per pixel using a telephoto lens. However, the above consideration disregards the influence of distance attenuation and fluctuation, and separate verification is required when the measurement distance is long; this is a matter for future research.
## 3 Dual-Stack Single-Radio Communication Architecture (DSSRCA)
### UAV-to-Sensor Communication  
The second partition of DSSRCA aims at providing an efficient and small-footprint protocol to support the communication between the UAV and the WSN. To reduce the protocol footprint it is necessary to bypass the 6 LoWPAN stack, which can be done by using Linux _raw sockets_.  
Such protocol is based on the frame format shown in Figure 2. _Sensor ID_ is a number that identifies the message sender. _Data Type_ specifies what type of phenomenon the sensor is observing. _Time Stamp_ is used to identify the sensors local time of observation. Finally, the payload is the sensed data. The sensor header requires 6 bytes of the 802.15.4 payload size using short address mode, leaving 110 bytes for the data payload.  
Figure 2: Message structure for Unmanned Aerial Vehicle (UAV)-to-WSN communication.  
To collect data from the WSN, it is suggested that the network is organized in clusters [33], _i.e._, there are cluster head (CH) nodes which collect and aggregate data from other sensor nodes in range. At the proper moment, the UAV triggers the CH with a _request message_ for transmitting its data. In fact, this moment depends on the application under consideration.  
Whenever a CH receives the _request message_ and has data to transmit, it does so using unslotted CSMA-CA in the frame format previously described. When the UAV receives a message, an acknowledgment frame (\(ack\)), at the MAC level, is transmitted to confirm receipt. The CH continues to transmit until it has no more data or up to the moment it stops receiving the \(ack\). Three attempts of re-transmissions are performed until the CH gives up transmitting. If this happens, the CH must wait for a new _request message_ to re-start the transmission.  
An important detail about this protocol regards the need for a modification in the UAV transmission power if compared to UAV-to-Sink communication. At the maximum UAV transmission power of +19 dBm, the CH would receive the _request message_ even at a distance of hundreds of meters away from the UAV. In this case, the sensor would try to perform transmissions but would fail to reach the UAV due to the long distance. To tackle this issue, a mechanism named _Power Control_ in the PHY level was implemented to switch the transmission power of the UAV radio to a lower level (-2 dBm) when communicating with the WSN.
## 2 Theory
### Data Dimensionality Reduction Based on Factor Analysis  
The acoustic emission data of a valve contains a large number of high-dimensional features. These high-dimensional features bring many problems to the establishment of the quantitative model of the valve leakage rate. For example, the high-dimensional feature set contains a lot of redundant features, which increases the complexity of the model algorithm greatly, and increases the time and space complexity of the algorithm. Therefore, data dimensionality reduction technology has been introduced into high-dimensional data processing, aiming at reflecting most of the information of the original data with low-dimensional independent samples.  
The factor analysis method is a kind of data dimensionality reduction method. Its core is to use a few sample sets to express almost all the information of high-dimensional data. Here the R-type factor analysis model is used. Let \(X=(X_{1},\,X_{2},\,\ldots,\,X_{p})\) be the raw sample data, \(F=(F_{1},\,F_{2},\,\ldots,\,F_{m})\) is an unknown vector. The raw sample data can be described as:  
\[X_{i}=\mu_{i}+\alpha_{i1}F_{1}+\cdots+\alpha_{im}F_{m}+\varepsilon_{i}, \tag{1}\]  
Equation (1) can be expressed as matrix variance:  
\[X-\mu=AF+\varepsilon, \tag{2}\]  
where \(F=(F_{1},\,F_{2},\,\ldots,\,F_{m})\) is the common factor of original sample data, the coefficients of which are factor loads. \(\varepsilon=(\varepsilon_{1},\,\varepsilon_{2},\,\ldots,\,\varepsilon_{m})\) is the special factor, the part that cannot be contained by the \(m\) common factors. \(F_{i}\) is the \(i\)th common factor of the original sample data. \(\alpha_{i\bar{\jmath}}\) is the factor load. \(F_{i}\) is independent with each other, and their variance is \(1\).
## 3 Results
### Classification
#### 3.4.1 Classification: ERPs  
A Friedman test showed no statistically significant differences in the mean balanced test accuracy distributions among the no jitter (\(Mdn=0.756\)), small (\(Mdn=0.747\)), or large jitter (\(Mdn=0.781\)) calibrations, \(\chi^{2}(2)=1.750\), \(p=0.417\). The slow calibration had lower model accuracies (\(Mdn=0.669\)) than any of the three experimental 5 Hz conditions (all \(p\) values \(<0.001\)). PCA/RDA/KDE classification estimates of the ERP signals (i.e., mean balanced accuracies) showed significant positive associations with the N200 attention effects measured in the pooled signal in the no jitter (\(r_{\rm s}(22)=-0.634\), \(p<0.001\)), small jitter (\(r_{\rm s}(22)=-0.535\), \(p=0.007\)), and large jitter (\(r_{\rm s}(22)=-0.509\), \(p=0.011\)) calibrations. At site Pz, however, there was only a marginal relationship between N200 target effects and classifier balanced accuracies in the no jitter calibrations (\(r_{\rm s}(22)=-0.371\), \(p=0.074\)). Classification accuracies were significantly positively correlated with P300 target effects in all experimental conditions, both at Pz and in the pooled signal (all \(r_{\rm s}(22)\) values \(\geq 0.479\); all \(p\) values \(\leq 0.018\)). Lastly, classifier-balanced accuracies were strongly positively correlated with accuracy during the copy phrase tasks in all test conditions: no jitter (\(r_{\rm s}(22)=0.795\), \(p<0.001\)); small jitter (\(r_{\rm s}(22)=0.541\), \(p=0.006\)); and large jitter (\(r_{\rm s}(22)=0.547\), \(p=0.006\)).
## 3 Results and Discussion
### Mechanical Properties  
The mechanical properties of the samples are shown in Figure 8. The compressive strength of ZK60, ZK60-0.5 La, ZK60-1.0 La, ZK60-1.5 La, and ZK60-2.0 La was 134.5 \(\pm\) 4.5 MPa, 151.2 \(\pm\) 4.3 MPa, 169.4 \(\pm\) 5.1 MPa, 163.7 \(\pm\) 4.5 MPa, and 158.8 \(\pm\) 5.0 MPa, respectively. The compressive strength increased as the La content increased, and ZK60-1.0 La possessed the highest compressive strength. The enhanced compression strength was partly ascribed to the strengthening effect of grain refinement and uniformly dispersed MgZnLa phase with short-bar shape. When the La content increased further, the compressive strength decreased. It was ascribed to the fact that continuity of the Mg matrix was destroyed by the semi-continuous network MgZnLa phase along the grain boundary, and stress accumulation could form at the interfaces between the MgZnLa phase and matrix during plastic deformation. In addition, with the increase of La content, the hardness of the samples increased from 83.9 \(\pm\) 3.5 Hv for ZK60 to 104.9 \(\pm\) 5.2 Hv for the ZK60-2.0 La. It was attributed to the strengthening effect of grain refinement and precipitation strengthening of the high hardness MgZnLa phase.
# An Inverter Topology for Wireless Power Transfer System with Multiple Transmitter Coils
## 2 System Description and Operation
### Mode of Operation
#### 2.2.1 Normal Mode Operation
The equivalent AC resistance of the load and rectifier circuit [30] is defined as:  
Figure 1:3  
The reflected impedance, \(\overset{\rightarrow}{Z}_{R}^{Normal}\), as shown in Figure 5b, is given as:  
Figure 1:4  
The total transmitter coil current, \(\overset{\rightarrow}{I}_{PT}\), is obtained by:  
Figure 1:5  
The magnitude of the equivalent AC output voltage, \(\left|\overset{\rightarrow}{V}_{out}^{ac}\right|\), is expressed as:  
Figure 1:6  
Figure 1:7 Simplified equivalent circuit of the proposed system when operated in normal mode: (a) Overall circuit; (b) Reflected impedance in primary circuit. where \(\overset{\rightarrow}{Z}_{S}=R_{S}+R_{L}^{ac}+j\big{(}\omega L_{S}-\frac{1}{\omega C_{ S}}\big{)}\). The DC output voltage in Figure 2 is obtained as:  
Figure 1:8  
where \(\delta=R_{PT}\left|\overset{\rightarrow}{Z}_{S}\right|^{2}+\omega^{2}M^{2} \big{(}R_{S}+R_{L}^{ac}\big{)}\) and \(\lambda=\left|\overset{\rightarrow}{Z}_{S}\right|^{2}\left(\omega L_{PT}- \frac{1}{\omega C_{PT}}\right)-\omega^{2}M^{2}\big{(}\omega L_{S}-\frac{1}{ \omega C_{S}}\big{)}\). The DC output voltage in Equation (7) can be regulated by adjusting the angle \(\alpha\), which can be realized by controlling the duty cycle of the gate signal of the upper switch, \(S_{U}\). Note that three out of six switching operations in Figure 4, namely stages 4, 5, and 6, are in the free-wheeling mode. Even though the inverter voltage is equal to zero, the fundamental component of the inverter voltage remains sinusoid. With the resonant compensation arrangement, the system exhibits a high-quality factor (Q) circuit behavior. Only the fundamental component of the inverter voltage in Equation (2) contributes to the current flowing in the circuit. Therefore, the power transfer to the load is provided as:  
Figure 1:9  
The transfer efficiency is slightly decreased when the system is operated in the free-wheeling mode due to the additional turn-off switching loss at \(t=t_{c}\), as shown in Figure 2:0
## 3 Methods
### Mesoscale Hydrological Model  
The Mesoscale Hydrological Model (mHM) is a physically-based, distributed hydrological model designed to understand and model the components of the water cycle, particularly for assessing water resources over large areas and extended periods [86]. The mHM utilizes a distributed approach to model spatially variable hydrological processes using meteorological (e.g., precipitation, temperature, evaporation) and soil data to compute hydrological processes for each grid cell [87]. The importance of SM modeling is the rainfall-runoff module, which simulates processes such as precipitation falling on the soil surface, infiltration, surface runoff, and river flow [86]. The soil moisture module calculates the distribution and changes in moisture within the soil profile. In this study, SMCI was calculated using the SM values generated by the mHM. Precipitation, temperature, and PET were obtained from ERA-5 for use in the mHM. SM values were produced at a spatial resolution of 1 km for each grid cell within the study area. Figure S1 provides an insight into the working principles of the mHM. During the development of the mHM, the calibration was carried out using measurements from 10 streamflow stations within the study area, which includes four provinces: Aksaray, Karaman, Konya, and Nigde. The calibration set achieved a R of 0.78 and an RMSE of 3.2 m\({}^{3}\)/s between the simulated and observed values. For the validation set, an R of 0.77 and an RMSE of 3.3 m\({}^{3}\)/s were obtained.
## 1 Introduction
Department in clean water has been proved to be the only reliable method for off-flavor removal. Unfortunately, the removal of off-flavors is a slow process, and even in the optimal conditions, it can take from days to weeks [17; 19; 22]. The off-flavor compounds are typically removed by keeping the fish without feed in flow-through tanks until no off-flavor can be perceived by organoleptic testing. Other approaches have been examined to decrease the off-flavors in water and in fish, and reduce the time of deprivation. These approaches include addition of peracetic acid and hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) [23], and the ozonation of circulating water or deprivation water [18; 24; 25], and photocatalysis [26]. Due to the increasing demand for sustainable food production, including eco-friendly seafood and vegetables for the growing human population, more research is needed to understand the potential benefits of aquaponic systems. One of the problems with RAS is the long start-up time for a fully functioning biofilter. It appears that no attention has been paid to the possibility of using plants to shorten the duration of the onset of the nitrification process in RAS or to buffer the sharp increase of ammonia and nitrite caused by the maturing biofilter. On the other hand, in our unpublished organoleptic tests, rainbow trout (_Oncorhynchus mykiss_) reared in an aquaponic system tasted rather normal as compared to those reared in RAS, which possessed a very strong muddy taste. This suggests that the plants could potentially be used as absorbers of compounds causing off-flavors in fish, and bacteria from the genus _Streptomyces_ have been found to be absent in aquaponics but not in RAS [27]. Consequently, our study hypothesized (1) that the onset of the nitrification process is faster in the aquaponics treatment compared to RAS, (2) that rainbow trout grown in an integrated system with baby spinach (_Spinacia olercea_) have lower concentrations of off-flavor compounds compared to those reared in RAS, (3) that the plants in an aquaponicsystem contain a higher concentration of GSM and MIB than plants grown in hydroponics, and (4) that the plants and fish grown in an aquaponic system grow equally well than in hydroponics and RAS, respectively.
## 2 Transmission Model and Simulation Conditions  
### Application Example  
Higher-order modulation schemes require higher levels of received SINR to correctly demodulate transmission signals, because the higher-order modulation scheme is very sensitive to noise, nonlinear distortion, interference, and multipath fading. To keep higher received SINR, higher-order modulation schemes such as 1024- and 4096-QAM can be implemented in point-to-point systems, e.g., in backhaul links with line-of-sight propagation conditions. Figure 1 shows an application example of higher-order modulation schemes used in HetNet, where a large macro-cell operated by macro-eNB is combined with pico-cells operated by pico-eNB. In this case, the downlink SINR received at pico-eNB will be higher because the transmission link between the macro-eNB and pico-eNB is fixed and the antennas of pico-eNB are located higher than UE. Consequently, a higher-order modulation scheme can be implemented to the backhaul link between the macro-eNB and pico-eNB, as shown in Figure 1. Figure 1 also includes a relay system operated by a relay node, where the relay node is installed around a macro-cell edge to recover, amplify and retransmit to the UE, a now strengthened but previously poor signal received from the macro-eNB. Similarly, a higher-order modulation scheme can be implemented to the backhaul link between the macro-eNB and relay node [26; 27]. It is also expected that higher-order modulation schemes will be used to directly link between eNBs and UE over limited areas, e.g., in small-cell systems with very little interference, in indoor environments, and so on. However, in the near future, higher-order modulation schemes will diffuse through the use of enhanced technologies such as powerful coding and highly precise 3D beamforming.
# A Low-Ambiguity Signal Waveform for Pseudolite Positioning Systems Based on Chirp
## 1 Introduction
As will be demonstrated later, ChPN inherits the attractive features of both PRN code and chirp signals, such as excellent auto-correlation and cross-correlation properties, fine multipath resolution, and high compression gain. Our contributions are as follows: (1) the signal model as well as ACF and PSD are derived; (2) the signal performances are evaluated by theoretical analysis and simulation; (3) the ChPN with orthogonal chirps is also discussed. Since the ACF side-peaks of the ChPN signal are much lower than that of the BOC modulation, the wrong acquisition and false lock problems caused by the ACF peak ambiguity can be mitigated. Although the slightly sharper ACF main peak of the BOC signal promises a little higher tracking precision, the multipath-induced tracking biases converge much slower than the ChPN signal with the multipath delay increasing. The near-far effect can be mitigated by the ChPNs with orthogonal chirps. However, its candidates amount is subject to signal bandwidth. From the perspective of receiver complexity, the ChPN receivers can be implemented by a slight modification of the BOC receiver. Thus, ChPN modulation provides a good candidate for pseudolite ranging signal design. The remainder of the paper is organized as follows: the time-domain model of the ChPN signal will be provided in Section 2, followed by its ACF and power spectrum density (PSD). Section 3 will analyze the generation and detection methods of the ChPN signal. Section 4 will provide a theoretical analysis for signal performance. The simulation is done in the Section 5. Conclusions will be given in Section 6. Finally, Appendix A gives the detailed derivation of the chirp spectrum.