esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Markov Evaluation of TSS Dynamic
Whether the progress of the pixel-based TSS or the regional average TSS between 2014 and 2017, the number of levels changed by TSS still stays the same which is 1 and 0, respectively, according to Table 3.  
To intuitively reflect the detailed change process of the large-scale dynamic in water quality, this study combines the remote sensing method and Markov model into mapping the spatial-temporal distribution of water quality changes in Hedi Reservoir from 2014 to 2018 (in Figure 11). Specifically, the progress degree calculated by the method in Section 2.3.3 is used to represent the change in water quality. It can be seen that the progress degree of water quality in the upstream reservoir is relatively low, which was \(-\)8.236 in 2014, \(-\)7.851 in 2015 and \(-\)8.632 in 2016. However, since 2017, lower progress degree levels of the downstream reservoir near the shore and in some bays have been found, which were \(-\)10.346 in 2017 and \(-\)3.448 in 2018. According to the statistics of the progress of the reservoir area from 2014 to 2018, it was found that the parts with the progress degree at more than 1 accounted for 46.2%, 22.17%, 22.33%, 59.28% and 49.45% and show an increasing trend. The part where the progress degree is at more than 1 indicates that the TSS concentration has decreased and the corresponding \(\Delta L\) can be ensured according to the color scales in Figure 12. In contrast, the progress degree which is less than \(-\)1 means that the TSS concentration has increased, and the corresponding proportions account for 10.08%, 3.04%, 9.28%, 11.43% and 0.63%. In particular, the proportion of water quality deterioration decreased by 94%, from 11.43% in 2017 to 0.63% in 2018. It can be seen from Figure 12f that the progress within these five years (from 2014 to 2018) is between 1.219 and 10.771, which means that the TSS concentration has not increased and the water quality has improved during this period. Figure 11: The evaluation results of the progress degree of TSS in the Shijiao Town area of Hedi Reservoir from 2014 to 2018. The blue part is based on the region-averaged TSS concentration in the Shijiao Town area, and the orange part is based on the pixel-based TSS concentration in the Shijiao Town area. Figure 12: The spatial distribution map of water quality variation calculated by the progress degree combining the Markov model and the remote sensing method from 2014 to 2018 in Hedi Reservoir, where (**a–e**) show the annual spatial variation distribution based on the progress degree and (**f**) presents the spatial variation within the whole five years. The color scale marked as \(\Delta L\) indicates the difference in the number of changes in the two TSS levels during the change in the TSS level status. It means that the TSS level has increased and the water quality has improved when \(\Delta L\) is greater than 0, otherwise the TSS level has decreased and the water quality has deteriorated. Especially, \(\Delta L\) is 0, which means that the TSS level has not changed and the water quality remains stable.
## 2 Related Work
By mathematically modeling these parameters and their influences, the study develops a weighted model that optimizes the selection of relays, thereby improving energy efficiency and overall network Quality of Service (QoS). The LP model is used to calculate the optimal contributions of each parameter, resulting in a more reliable and energy-efficient D2D communication system. [PERSON] et al. [32] provide a comprehensive review of interference mitigation techniques that utilize Software-Defined Networks (SDNs) in wireless communication to explore existing research trends and evaluating existing solutions within the context of B5G networks. The analysis begins with discussing the present perspectives of mobile communication concerning routing-based interference mitigation followed by past and future perspectives. The study then delves into the potential of various SDN topologies--centralized, decentralized, and hybrid for mitigating interference, highlighting their strengths and limitations. By critically analyzing prominent SDN-based interference mitigation schemes, this review identifies gaps in current methodologies and proposes an Intelligent Interference Mitigation architecture for utilizing controllers in distributed SDNs. The review concludes by suggesting future research directions, emphasizing the need for advancements in some areas. These areas include Fog Federation, Lightweight SDN implementations in drone networks, Cognitive Radio Access Networks (C-RANs), 5G Ultra-Reliable Low-Latency Communication (URLLC), and compatible SDN controllers.
# Mechanical and Dielectric Properties of Aligned Electrospun Fibers
## 3 Mechanical and Dielectric Properties of Nanofibers
### Mechanical Properties  
Among many nanofibers that have mechanical properties, PAN nanofibers are widely preferred because of their excellent tensile strength and modulus. In this section, mechanical properties of PAN nanofibers (in general) and carbon nanof fillers are discussed. The mechanical properties of electrospun nanofibers, including PCL, polyvinyl pyrrolidone (PVP), and PEO, are also reviewed. Post-treatment techniques, such as drawing and annealing processes, are discussed, since these post-treatments can improve molecular orientation and crystallinity [134]. Moreover, PAN nanofibers in composite applications and other types of nanofibers are discussed in the end.
## 3 Experimental Results and Analysis
### Experiments of Complex Noise Removal on ICVL Dataset  
As previously described, in the final training stage, the network is trained to handle five complex noise types simultaneously: non-i.i.d Gaussian noise, Gaussian stripe noise, Gaussian dead pixel noise, Gaussian impulse noise, and mixed noise. Figure 7 illustrates the denoising results of the network under these complex noise conditions. A comparison of the denoising performance metrics in Table 2 shows that the proposed network outperforms LLRT, which, being based on low-rank matrix methods, loses some essential structural information during the denoising process. Additionally, the proposed network performs better than other deep-learning-based methods, such as MAFNET, SQAD, and QRNN3D. As shown in the figure, under Case 2 noise, the proposed network effectively preserves the clarity of the text, while under Case 4 noise, where interleaving of power lines and building windows lead to detail loss in the results of other networks, the proposed network can effectively distinguish between different features.  
Figure 6: The Gaussian noise removing results of SSIM at 13 th band of the HSI under noise level \(\sigma=\text{Blind}\) (ICVL dataset).
## 5 Conclusions
\begin{tabular}{l l} \(C_{b}\) & Black-body radiation constant, Equal to 5.67 (W/m\({}^{2}\)-K\({}^{4}\)) \\ \((\varepsilon\rho)_{a^{\prime}}(\varepsilon\rho)_{r}\) & Unit heat capacity of outdoor and indoor air (kJ/m\({}^{2}\)-K) \\ \(E_{0},E_{i}\) & Long wave radiation intensity of the outer and inner surfaces (W/m\({}^{2}\)) \\ \(F_{k}\) & Inner surface area of building envelope (m\({}^{2}\)) \\ \(h\) & Heat transfer coefficient of gas between glass layers (W/m\({}^{2}\)-K) \\ \(HE_{s}(n)\) & Heat removal capacity of air conditioning system at \"n\" moment (W) \\ \(I_{D\theta},I_{d\theta}\) & Direct radiation and sky-scattered radiation absorbed by envelope (W/m\({}^{2}\)). For vertical plane, \(\theta=90\)\({}^{0}\); For horizontal plane, \(\theta=0\)\({}^{0}\). \\ \(I_{R\theta}\) & Ground reflected radiation intensity accepted by an inclined plane with a horizontal inclination of \(\theta\) (W/m\({}^{2}\)) \\ \(I_{SH}\) & Total solar radiation intensity to be accepted by the ground (W/m\({}^{2}\)) \\ \(K\) & Heat transfer coefficient of envelope (W/m\({}^{2}\)-K) \\ \(k_{1},k_{2}\) & Heat transfer coefficient of the outer glass and inner glass of a double-glazed window (W/m\({}^{2}\)-K) \\ \(L_{a}(n)\) & Amount of air permeability at \"n\" moment (m\({}^{3}\)/h) \\ \(N_{i}\) & Total number of inner surfaces of rooms with different building envelopes \\ \(q_{S}\) & Solar radiation absorbed by the outer surface of the envelope (W/m\({}^{2}\)) \\  & Ground reflected radiant heat absorbed by outer surface of the building envelope \\ \(q_{R}\) & (W/m\({}^{2}\)) \\ \(q_{B}\) & Intensity of long wave radiation absorbed by the outer surface (W/m\({}^{2}\)) \\ \(q_{g}\) & Ground radiation heat absorbed by envelope (W/m\({}^{2}\)) \\ \(q_{0}\) & Heat transfer of the outer surface of the envelope to the inner surface (W/m\({}^{2}\)) \\ \(q_{ca}\) & Convective heat transfer of the outer surface of the envelope to the surrounding air (W/m\({}^{2}\)) \\  & Amount of heat radiation from the outer surface of the envelope to the surrounding environment (W/m\({}^{2}\)) \\ \(q_{ra}\) & surrounding environment (W/m\({}^{2}\)) \\  & The amount of heat transfer obtained by \(i\)-th inner surface of the building \\ \(q_{i}(n)\) & envelope (W/m\({}^{2}\)) \\  & Solar radiation and internal disturbance radiation heat in the inner surface of the \\ \(q_{i}^{\tau}(n)\) & \(i\)-th building envelope (W/m\({}^{2}\)) \\  & Heat from illumination, human body heat and device display heat at the _n-th_ \\ \(q_{1}^{c}(n)\) & moment (W) \\ \(q_{2}^{c}(n)\) & Room heat loss caused by evaporation of water at \"n\" moment (W) \\ \(S_{1},S_{2}\) & Radiant intensity absorbed by the outer surface and inner surface of the outer \\  & glass of a double-glazed window (W/m\({}^{2}\)) \\  & Radiant intensity absorbed by the outer surface and inner surface of the inner \\ \(S_{3},S_{4}\) & glass of a double-glazed window (W/m\({}^{2}\)) \\ \(T_{g}\) & Ground temperature (K) \\ \(t_{a},t_{i},t_{0}\) & Temperature of outer surface and inner surface of the envelope, and outdoor air \\  & temperature (K) \\ \(t_{r}(n)\) & Indoor air temperature (K) \\ \end{tabular}
# Electromagnetic and Thermal Phenomena Modeling of Electrical Discharges in Liquids
## 2 Discharge Models in Liquids  
When a high voltage of appropriate value is applied to an electrode system filled with dielectric material, filamentary discharges called \"streamers\" appear at the first stage of breakdown in gases, liquids, and solids. Environmental conditions of the discharges determine the streamers structures including their shape, speed of propagation, current values, electric field intensity at the streamer's head, plasma channel radius, and degree of ionization [14]. According to the large number of factors that have influence on electric breakdown process, uniform theory for the development of discharges in liquids cannot be presented. The discharge process depends on the type and purity of the liquid material, electrostatic pressure, viscosity, conductivity, thermal conductivity, and dielectric permittivity [14; 15; 16]. Therefore, a large number of theories have been proposed [12; 14; 16]. Discharges in liquids are similar, under certain conditions, to discharge process in gas [16]. In gaseous environments most important factor for discharge process is impact ionization that depend on kinetic energy of electrons in the discharge area [17; 18]. In most cases it has been assumed that the discharges can be initiated by the absorption of radiation quantum [19]. These assumptions are generally acceptable for gaseous environments [14; 19]. In liquids, however, such correlations cannot be found so far, because of the difficult performance of studies of the mentioned effects [20]. Therefore, the proposed descriptions are burdened with greater uncertainty. The most popular descriptions of discharges development, important from their applicability point of view, are presented below.
## 2 Proposed Solution
### AmoebaNet Solution for the BigData Express (BDE) Project  
AmoebaNet is originally designed for BigData Express application. A major goal of BigData Express is to apply AmoebaNet's networking service to provide \"Application-aware\" networks and facilitate large-scale data transfer. BigData Express has been designed for large scientific computing centers. It has been deployed in many government supported computing centers such as Fermi National Accelerator Laboratory supported by the US Department of Energy. Figure 3 is illustrating two typical sites connected by BigData Express. These sites consist of high-performance DTNs using a dedicated cluster, a large-scale storage system, and an SDN-based AmoebaNet campus networking service in BigData Express. An on-demand WAN-based service requires that BigData Express establishes the site-to-site connection. It also enables guaranteed bandwidth by creating paths between source and destination sites in designated time slots.  
Figure 2: VLAN operations at Gateways.  
The scheduler of BigData Express (BDE) is logically centralized and mainly responsible for programming the entire networks dynamically and performs data transfer jobs. The purpose of the BDE scheduler is to coordinate with each BigData Express site to properly program the underlying networks. It is also responsible for scheduling and managing the local site's resources such as storage, DTNs, and the AmoebaNet for campus networks through agents (storage agents, DTN agents and AmoebaNet). AmoebaNet will keep track of the BigData Express LAN topology and traffic status with the aid of SDN controllers. As requested by the BigData Express scheduler, it will program a network at run-time for data transfer tasks.
# Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm
## 5 Experiments
### Extension of Our Method  
Although distance was used as the weight of paths in this work, actually, any characteristics (i.e., slope, width, capacity, disturbances,...) and traffic flow, even the user behaviors could be modeled into weight of different paths to adopt our SODPP algorithm to find out the optimum path under certain situations. In Figure 11, when no congestion happens, the path searching result for a request from the place is denoted as a dot to the place denoted with a star.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Standard** & **Dijkstra** & **A*** & **ACA** & **SODPP** \\ \hline Start Point & 33,766 & 33,766 & 33,766 & 33,766 \\ End Point & 33,966 & 33,966 & 33,966 & 33,966 \\ Total Points & 826 & 826 & 826 & 826 \\ Load Points & 826 & 826 & 826 & 257 \\ Load Time & 860 ms & 875 ms & 801 ms & 410 ms \\ Construct Time & 1186 ms & 1167 ms & 1109 ms & 264 ms \\ Search Points & 348 & 212 & 394 & 134 \\ Total Time & 2564 ms & 2110 ms & 18,057 ms & 1142 ms \\ Path Length & 21,415.83 m & 21,415.83 m & 22,365.50 m & 21,415.83 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of results for Experiment 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Standard** & **Dijkstra** & **A*** & **ACA** & **SODPP** \\ \hline Start Point & 33,898 & 33,898 & 33,898 & 33,898 \\ End Point & 34,100 & 34,100 & 34,100 & 34,100 \\ Total Points & 826 & 826 & 826 & 826 \\ Load Points & 826 & 826 & 826 & 156 \\ Load Time & 890 ms & 860 ms & 801 ms & 210 ms \\ Construct Time & 1208 ms & 1162 ms & 1093 ms & 172 ms \\ Search Points & 204 & 147 & 224 & 92 \\ Total Time & 176 ms & 152 ms & 2438 ms & 85 ms \\ Path Length & 2832.47 m & 2832.47 m & 3086.84 m & 2832.47 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of results for Experiment 2.  
If we include the traffic flow characteristics into the weight of any path, we can take the impact of congestion into account when looking for the best path. Figure 12 shows the path searching result for the same request when there is congestion detected in places denoted by \(A\) through analyzing real-time traffic data.
## 2 Measurement Methodology
### Measurement Equipment
#### 2.1.2 Terragraph Platform  
Next to power measurements using the channel sounder presented in Section 2.1.1, we also use the TG sounder platform presented in [54] to perform PL, PER, and throughput measurements. The TG platform consists of a pair of customized IEEE 802.11 ad nodes each having an antenna array of 36 by 8 elements with an interspacing of 0.55 \(\lambda\) and the possibility for beamsteering via a phase shifter. When using all antenna elements, the antennas' HPBW is 2.8\({}^{\circ}\). The azimuthal scanning range is \(-45^{\circ}\) to 45\({}^{\circ}\) in steps of 1.4\({}^{\circ}\).  
The nodes transmit and receive IEEE 802.11 ad waveforms at RF channel identifier 2, with a center frequency of 60.48 GHz and a channel bandwidth of 2.16 GHz. At the transmitting node, we calculate the effective isotropically radiated power (EIRP) by combining antenna gain (which depends on the beam configuration) and power settings. The EIRP in our PL, throughput, and PER measurements is 32.8 dBm. The transceiver chipset of the receiving node reports received signal strength information (RSSI) which is combined with the receiver gain settings to obtain received power. The receiver sensitivity is \(-81\) dBm. The nodes are calibrated and can be used to measure PL, by subtracting the received power from the EIRP and taking into account the RF and IF gain. Prior to the measurement campaign, the TG platform is validated in an anechoic room for a distance of 2 m between the nodes, as can be seen in Figure 2.  
The measured PL is 74.22 dB which differs less than 1 dB from the free space PL (FSPL) of 74.09 dB, but sidelobes are present in the antenna radiation pattern, which results in a cross pattern when visualizing PL as a function of angle of departure (AoD) and angle of arrival (AoA).  
In addition to PL measurements, we also use the TG platform for PER and throughput measurements. Prior to these measurements, a scan is performed over all TX-RX beam  
Figure 2: IEEE 802.11 ad Terragraph platform validation in anechoic room, with a distance of 2 m between the antennas. (**a**) Measurement setup; (**b**) Angular path loss profile.  
combinations, and the beam combination for which the maximum SNR is obtained, is selected. The MCS index can be set from 1 to 12, all using a single-carrier physical (PHY) layer. The PER is measured by sending packets using the best beam combination, for all MCS indices and counting the number of packets that are correctly received. For the throughput measurements, frames are continuously sent using the best beam combination, and throughput is estimated based on the MCS that the IEEE 802.11 ad transceiver would select in a real deployment. The adaptive rate mechanism of IEEE 802.11 ad is not standardized, and the MCS selection is done in the transceiver's baseband, based on the channel information, mainly SNR, embedded with the data frames. The resulting throughput estimation provides an upper bound, based on the MCS index. Actual throughputs can be lower, due to packet loss.
# Design, Manufacturing, and Flight Testing of an Experimental Flying Wing UAV
## 4 Aircraft Aerodynamic and Stability Design
### Aerodynamic Configuration and Stability Design  
In aerodynamic configuration and stability design, the wing configuration greatly influences the aircraft's aerodynamic characteristics and efficiency. It is necessary to understand the wing and airfoil parameters to ensure that the aircraft has good aerodynamic characteristics under mission requirements. In this section, the aerodynamic configuration design is presented. Wing planform and airfoil selection are both important procedures in aerodynamic configuration design. The airfoil is designed to generate a suitable pressure distribution on the wing's top and bottom surfaces to maximize the lift and minimize the drag and pitching moment. The following parameters are important in airfoil selection: (1) the maximum lift coefficient (as large as possible); (2) the drag coefficient corresponding to the designed lift coefficient (as small as possible); (3) the corresponding drag coefficient should not change greatly near the design lift coefficient; and (4) the moment coefficient should not be too large and should be easy to trim.  
The aerodynamic design of a wing mainly involves determining a wing planform that can maximize the lift and minimize the drag. The main planform parameters are the aspect ratio, sweep angle, and taper ratio. The aspect ratio affects the induced drag coefficient, zero-lift drag coefficient, and slope of lift coefficient. An increase in the aspect ratio can reduce the wing's induced drag. A decrease in the aspect ratio prevents wing tip stall under high angle of attack and reduces the wing structure's weight and the bending moment at the wing root.  
The wing's induced drag is related to the induced drag factor that is in turn affected by the lift coefficient. When the lift coefficient increases, the wing's induced drag also increases. An increase in the aspect ratio can decrease the induced drag factor. An elliptical wing has the lowest induced drag factor; however, it is not easy to manufacture. The induced drag factor can be adjusted with appropriate planform parameters. In this study, the wing is designed to reduce the induced drag with a tapered ratio. The root chord is 0.54 m, tip chord is 0.22 m, wingspan is 2.83 m, and aspect ratio is 7.48.  
Fuselage design mainly considers the internal space, and fuselage drag should be minimized. For a low-speed experimental flying wing UAV, the internal space configuration and manufacturing costs are the main considerations. The aerodynamic configuration design of the fuselage is not as important.  
Wings with large sweep angles are commonly designed for aircrafts that fly at high speeds. The wing's critical Mach number can be increased by increasing the sweep angle. Although the experimental flying wing UAV flies at low speed, we design the wing with a sweep angle that satisfies the aircraft's static stability requirements. To increase the flying wing UAV's lateral stability, its dihedral angle is designed suitably.
## 2 System Model  
### Partially Connected Hybrid Beamforming  
Figure 1a shows the conventional PC-HBF architecture for downlink massive MIMO systems. The base station (BS) is equipped with \(N_{t}\) transmit antennas and \(N_{RF}\) RF chains. The BS simultaneously transmits \(U\) data streams to \(U\) mobile stations (MSs), where each mobile station has one antenna, and \(N_{RF}=U\) is assumed. An RF chain is connected to a subarray, which contains \(M\) transmit antennas. The data streams are precoded using a digital precoder \(\mathbf{F}_{BB}\in\mathbb{C}^{N_{RF}\times U}\) at the baseband, followed by a VPS-based RF precoder \(\mathbf{F}_{V}\in\mathbb{C}^{N_{t}\times N_{RF}}\). Then, the signals are amplified using power amplifiers (PA) before they are transmitted via antennas. The signals received at \(U\) MSs can be stacked into \(\mathbf{y}=[y_{1},y_{2},...,y_{U}]^{T}\), where \(y_{u}\) is the received signal at user \(u\), to yield \(\mathbf{y}=\mathbf{H}\mathbf{F}_{V}\mathbf{F}_{BB}\mathbf{s}+\mathbf{n}\), where \(\mathbf{H}=[\mathbf{h}_{1},...,\mathbf{h}_{U}]^{T}\) is a composite downlink channel. Here, \(\mathbf{s}\in\mathbb{C}^{U\times 1}\) is a vector of the transmitted symbols that satisfies \(\mathbb{E}\{s_{u}s_{u}^{*}\}=1\) and \(\mathbb{E}\{s_{u}s_{j}^{*}\}=0\) for \(i\
eq j\), where \(s_{u}\) is a symbol transmitted to user \(u\). The noise vector \(\mathbf{n}\) consists of independent and identically distributed noise signals \([\mathbf{n}]_{u}\sim\mathcal{CN}(0,\sigma^{2})\). The RF precoding matrix \(\mathbf{F}_{V}\) is a block diagonal matrix, where each block is an \(M\)-dimensional vector with unit modulus elements; \(M=N_{t}/N_{RF}\). The phase of the \(n\)th VPS in the \(u\)th subarray can be set to the phase of the corresponding channel coefficient, that is, \(\angle[\mathbf{F}_{V}]_{n,u}=\angle[\mathbf{h}_{u}]_{n}\) for \(n\in\{(u-1)M+1,...,uM\}\)[17].