esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Incremental Nonlinear Dynamic Inversion Control Law Design
### Overall Control Structure
#### 3.5.2 Inner-Loop Control  
The inner loop is responsible for controlling the roll and yaw angular rates, which are the main control objectives. Building upon the previous research, the overall control structure is depicted in Figure 9. It can be observed that the control structure comprises five key components: the reference model, error controller, angular acceleration estimation, control synchronization, and control allocation. While the aforementioned sections focused on discussing the former four components, the latter (control allocation) will be addressed separately.  
Figure 8: Outer-loop control structure: (**a**) Bank angle control loop and (**b**) Lateral acceleration control loop.  
In the control allocation module, a hard cross-connection method [7] is employed to effectively decouple the control coupling between the aileron and the rudder. By referencing the control input matrix in Equation (3), it is possible to reformulate the roll-yaw control coupling matrix as follows:  
\[B_{c}=\begin{bmatrix}\overline{L}_{\delta_{a}}&\overline{L}_{\delta_{b}}\\ \overline{N}_{\delta_{a}}&\overline{N}_{\delta_{b}}\end{bmatrix} \tag{38}\]  
Assuming no control cross-connection between the aileron and rudder, the desired decoupled control matrix \(B_{des}\) for the roll and yaw channel can be expressed as follows:  
\[B_{des}=\begin{bmatrix}\overline{L}_{\delta_{a}}&0\\ 0&\overline{N}_{\delta_{b}}\end{bmatrix} \tag{39}\]  
The pseudo-inverse method is applied to realize control decoupling, and the hard cross-connection matrix \(K_{hc}\in R^{2\times 2}\) can be calculated as  
\[K_{hc}=B^{\prime}B_{des} \tag{40}\]  
where \(B^{\prime}\) is the pseudo-inverse of \(B_{c}\), which can be obtained using the singular value decomposition. Furthermore, it is worth noting that other control allocation methods based on optimization [39] can also be utilized in conjunction with the control structure illustrated in Figure 9. Ultimately, the control output to the actuator can be determined by dividing the control efficiency parameter, as follows:  
\[u_{cmd}=B_{des}^{-1}K_{hc}v_{cmd} \tag{41}\]  
where \(u_{cmd}=[\delta_{ac},\delta_{rc}]\).
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 5 Proposed Classification
### Classification of RL-Based Routing Protocols Based on Routing Algorithm
#### 5.2.4 Routing Process  
In this section, the routing methods are divided into two categories based on the routing process: centralized and distributed. These methods are compared in Table 6. In the following section, we explain these techniques in summary.  
* **Centralized routing:** In this routing method, a central server manages the routing process. This process is shown in Figure 20. This scheme assumes that the central server has global knowledge of the entire network. The central server is responsible for managing all UAVs and calculating the optimal routing paths on the network. The most important advantage of these methods is that the central server can fully control the entire network and obtains optimal routes at the lowest computational cost. However, this routing technique has disadvantages, such as server maintenance cost, lack of fault-tolerance, single point of failure, high delay, and high routing overhead. In highly dynamic networks such as FANET, it is difficult or even impossible to obtain complete knowledge of the network by the central server. For this reason, these methods are not successful in FANETs, and are not scalable.  
Figure 19: Route discovery technique.  
* **Distributed routing:** In this routing method, UAVs share their information with their neighboring UAVs to obtain local knowledge from the network. Then, each UAV participates in the routing process and decides on routing paths based on this limited knowledge. This process is shown in Figure 21. This technique is scalable and flexible because UAVs can quickly and locally react to any issue related to the network dynamics. Therefore, these methods are more suitable for real-time applications. Due to relying on local information, distributed routing methods may form sub-optimal routes and distribute loads using an unbalanced manner in the network. In addition, these methods have more computational overhead compared to centralized routing methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Routing** \\ **Scheme** \\ \end{tabular} & **Scalability** & \begin{tabular}{c} **Routing** \\ **Overhead** \\ \end{tabular} & \begin{tabular}{c} **Computational** \\ **Overhead** \\ \end{tabular} & \
\begin{tabular}{c} **Single Point of** \\ **Failure** \\ \end{tabular} &
\begin{tabular}{c} **Adapting with** \\ **Dynamic** \\ **Topology** \\ \end{tabular} & **Fault-Tolerance** \\ \hline \hline
**Centralized** & Low & High & Low & Yes & No & No \\ \hline
**Distributed** & High & Low & High & No & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of centralized and distributed routing schemes.  
Figure 20: Centralized routing scheme.
## 5 5G FWA Field Trials
### 5G FWA Field Trial Overview
Moreover, Deutsche Telekom and Magyar Telekom conducted FWA trials in Hungary in order to assess Terragraph as a feasible high-speed broadband connectivity solution. For a fair comparison of home connectivity applications, they employed mm-wave technology for subscribers with the existing copper/DSL broadband solution. The reported trial results are promising as the Terragraph solution considerably changed customers' internet experience and offered reliable speed improvement from approximately 5 Mbps to about 650 Mbps. This signifies more than two orders of magnitude enhancement [261]. In addition, Orange in conjunction with Samsung and Cisco publicized the first 5G multi-vendor FWA test results carried out between 1 June 2018, and 16 July 2018, in Floresti, Cluj county, Romania. The test was performed in real environment conditions and showed the 5G technology capabilities in the European market development framework [329; 347; 348]. In the subsequent subsections, we use the 5G multi-vendor FWA trial executed by Orange in conjunction with Cisco and Samsung as a case study to expatiate on the implementation.
# GAN-Based Inversion of Crosshole GPR Data to Characterize Subsurface Structures
###### Abstract  
The crosshole ground-penetrating radar (GPR) technique is widely used to characterize subsurface structures, yet the interpretation of crosshole GPR data involves solving non-linear and ill-posed inverse problems. In this work, we developed a generative adversarial network (GAN)-based inversion framework to translate crosshole GPR images to their corresponding 2D defect reconstruction images automatically. This approach uses fully connected layers to extract global features from crosshole GPR images and employs a series of cascaded U-Net structures to produce high-resolution defect reconstruction results. The feasibility of the proposed framework was demonstrated on a synthetic crosshole GPR dataset created with the finite-difference time-domain (FDTD) method and real-world data from a field experiment. Our inversion network obtained recognition accuracy of 91.36%, structural similarity index measure (SSIM) of 0.93, and RAscore of 91.77 on the test dataset. Furthermore, comparisons with ray-based tomography and full-waveform inversion (FWI) suggest that the proposed method provides a good balance between inversion accuracy and efficiency and has the best generalization when inverting actual measured crosshole GPR data.  
subsurface structure; crosshole ground-penetrating radar (GPR); inversion; deep learning; generative adversarial network (GAN); finite-difference time domain (FDTD) +
Footnote †: journal: remote sensing  
1
Footnote 1: Copyright** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# Possibilities of Powering Military Equipment Based on Renewable Energy Sources
## 2 Literature Review
### Personal Photovoltaic Power Device Application Concept  
The development of flexible photovoltaic panels technology means that they have even higher efficiency, mechanical strength, and a lower price. For the concept of building a personal power supply device (RUPD) it is likely that one would use a flexible PV panel. OUZ is designed to charge power sources for devices that soldiers are equipped with, such as a low-power personal radio (e.g., R3501, GP340 radiotelephone, light sources, or GPS). It was assumed that the proposed solution will have a modular structure able to be freely configured depending on the user's needs. The diagram block is presented in Figure 5.  
The personal charging device will consist of the following blocks:  
1. Power distribution module;
2. Energy storage;
3. Flexible PV panel;
4. A charging module that powers:  
Figure 5: Block diagram of the personal power device (source, prepared by the authors).  
\begin{tabular}{l l} \(\bullet\) & R3501 radio battery; \\ \(\bullet\) & GP340 radio; \\ \(\bullet\) & GPS receiver; \\ \(\bullet\) & USB socket; \\ \(\bullet\) & Other accumulators (e.g., for light sources). \\ \end{tabular}  
The individual parts should be attached to the power distribution module with flexible hoses having connectors meeting MIL standards. The PDM module is the central element of the system; it integrates all parts of the system. The module's task is to distribute the power from the energy storage and the PV panel to the equipment used, depending on their energy needs. The energy storage system is a battery that has been selected as the basic criterion for the ratio of its capacity to its weight. Another criterion is the resistance of the battery to weather conditions, mechanical shocks, and shocks themselves. The main source of power is supplemented by a flexible PV panel, which should be folded or rolled up for transport. PDM is equipped with dedicated cables for charging one type of terminal device battery, for example the R3501 radio battery, another for charging the radio (e.g., GP340). The USB socket provides +5 V for charging other electronic devices.
# Tracking Extended Targets: Novel TPMB Filter Driven by Model and Data Collaboration
## 5 Simulation Results and Discussions
### Simulation Scenario  
This algorithm primarily addresses the tracking of extended targets under the conditions of multi-model switching and unknown noise influences. In this section, we present the results of 100 Monte Carlo simulations conducted in the first scenarios. We tested the proposed IMM-TPMB-GBP-ASCKF filter in two target motion scenarios and compared it with the GGIW-TPMB filter from [24], which used clustering and assignment, as well as with the TPMB-BP [20], TPMB-GBP, and IMM-TPMB-GBP-CKF filters.  
The linear programming (LP) metric was used to compare the performance of these filters [25; 26]. Thus, we set different numbers of maneuvering targets in two scenarios, each under different motion models. The target motion models include Constant Velocity (CV) and Constant Turning (CT), with their corresponding motion equations as follows:  
\[F_{\text{CV}}=\begin{bmatrix}1&0&T&0\\ 0&1&0&T\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix},\,F_{\text{CT}}=\begin{bmatrix}1&0&\sin(\theta T)/\theta& -[1-\cos(\theta T)]/\theta\\ 0&1&[1-\cos(\theta T)]/\theta&\sin(\theta T)/\theta\\ 0&0&\cos(\theta T)/\theta&-\sin(\theta T)/\theta\\ 0&0&\sin(\theta T)/\theta&\cos(\theta T)/\theta\end{bmatrix},\,Q_{\text{CV}}= Q_{\text{CT}}=\begin{bmatrix}T^{2}/3&0&T^{2}/2&0\\ 0&T^{2}/3&0&T^{2}/2\\ T^{2}/2&0&T&0\\ 0&T^{2}/2&0&T\end{bmatrix}\,\sigma^{2}\]  
Each object consists of a two-dimensional position and velocity, which we represent using the target matrix \(\mathbf{x}_{k}=[x_{k},\dot{x}_{k},y_{k},\dot{y}_{k}]^{\text{T}}\cdot(x_{k},y_ {k})\) denotes the plane position coordinates, and \((\dot{x}_{k},\dot{y}_{k})\) is used to represent the planar position. We consider a monitoring area of 200 m \(\times\) 200 m, with a total motion duration of 100 s. For the following two scenarios, we set the sampling period \(T=1\) s and the mean clutter rate \(\gamma=10\). The Poisson rate of PPP birth is 0.01, the clutter is distributed in the region. For the birth density, the velocity follows a Gaussian distribution with zero mean and covariance 100 \(I_{2}\), where \(I_{2}\) represents an identity matrix of size 2. For the GGIW implementations, the position is Gaussian distributed with zero mean and covariance 150 \(I_{2}\). In the two experimental scenarios, different numbers of target trajectories are set to test the algorithm's performance. Assume a detection probability of \(P^{D}=0.9\), a survival probability of \(P^{S}=0.95\), and a forgetting factor for the measurement rate state set to \(\eta=1.25\). The experimental results indicate that using 1000 particles in our filter achieves a good balance between the runtime and estimation performance.
# Antioxidants
## Results
### Evaluation of the Oxidative Stability of Bulk Oil
#### 3.2.3 Peroxide Value (PV)  
The kinetics of PV in CO, HO, and EO oil samples during the seven days of storage is depicted in Figure 4. At day 0, the three bulk oils displayed similar PV (\(p>0.05\)) equal to \(2.45\pm 0.02\), \(3.26\pm 0.18\), and \(3.91\pm 0.66\) meq Q\({}_{2}\)/kg oil for CO, HO, and EO, respectively (see Table S7, Supplementary Materials, for detailed PV). Afterward, EO displayed a very different pattern with respect to CO and HO. In fact, while CO and HO showed a lag phase of one day (\(p>0.05\) between day 0 and day 1) (Table S7, Supplementary Materials), in EO after 6 h of storage, the PV was twice the value found in CO and HO; again, after 1 day, the PV was threefold higher and reached the highest amount of \(45.46\pm 0.74\) meq O\({}_{2}\)/kg oil on day 2. It should be pointed out that PV was used to define the lag phase; thus, after 2 days of storage, EO was not analyzed.  
Figure 4: Kinetics of peroxide value (PV) (meq O\({}_{2}\)/kg oil) in stripped linseed oil without antioxidants (CO), with hemp extract (HO), and with \(\alpha\)-tocopherol (EO) and results of ANOVA with [PERSON]’s test. Each point corresponds to the mean \(\pm\) standard deviation (\(n=3\)). Different letters a,b within the same day mean significantly different results at \(p<0.05\) among different samples. n.s. = not significant.  
Figure 3: Kinetics of \(\alpha\)-linolenic acid (ALA) in stripped linseed oil without antioxidants (CO), with hemp extract (HO), and with \(\alpha\)-tocopherol (EO) during the storage treatment. For each point (mean \(\pm\) standard deviation of three independent replicates), results of ANOVA with Duncan’s test are reported between the samples within the same day. Different letters a–c mean the results were significantly different at \(p<0.05\). n.s. = not significant.  
With regard to CO and HO, until day 3, the PV was not significantly different (\(p>0.05\)). However, from day 3 onward, their kinetics differentiated. In fact, the PV in CO was significantly higher than that detected in HO (\(p<0.05\)), and, at day 7, the CO already reached the value of \(49.57\pm 0.22\) meq O2/kg oil, while, in HO, a PV of \(21.19\pm 1.44\) meq O2/kg oil was found. Moreover, PV in HO from day 3 to 7 remained indistinguishable (\(p>0.05\)) (Table S7, Supplementary Materials).  
According to the induction period (the time required to reach a peroxide value equal to 20 meq O2/kg oil (CO, 2 days; HO, 7 days; EO, 1 day) [33]), the resulting protection factor (Pf) was equal to 3.5 and 0.5 for HO and EO, respectively, attesting the antioxidant potential of the hemp extract.
# A Review of Evaporite Beds Potential for Storage Caverns: Uncovering New Opportunities
## 3 Properties and Geomechanics of Bedded Evaporites
### Main Geomechanical Considerations in Bedded Evaporite Caverns
#### 3.3.2 Solution Mining
A multi-step horizontal leaching method was proposed in [215], while ref. [232] analyzed the influence of multi-stage leaching, including factors like water injection rate, tubing withdrawal distance, and existing air cushions, on the shape and ultimately the stability of caverns. In the study in [214], the authors outlined four stages of cavern leaching: sump leaching, leaching of the main chamber, leaching of the cavern dome, and neck leaching. The authors also proposed creating niches in well walls to expedite leaching and achieve desired cavern shapes. With respect to multi-step retreating (MSR), ref. [212] conducted experiments to determine fluid velocity distribution and cavern shapes in simulated horizontal caverns. Several studies have focused on predicting cavern stability during leaching. For instance, ref. [233] applied catastrophe theory to understand interbed failure during solution mining. In another model based on the mechanical properties of rock salt, ref. [19] developed a simulator to model cavern construction and integrity. The authors of [215] developed a mathematical model for multi-step horizontal cavern construction, incorporating factors such as dissolution rate, brine concentration, and accumulation of impurities and their distribution in the cavern. The stability of caverns as a function of cavern height was analyzed in this study. Another mathematical model developed in [234] investigated leaching in salt formation with high amounts of impurities, coupling cavern shape, brine concentration, and brine velocity. Another mathematical model was proposed in [235] to simulate the repair of the irregular salt caverns using a re-leaching method. A multiphysics coupled model based on heat transfer during horizontal cavern leaching was developed in [236], while coupled thermo-hydro-mechanical-chemical processes were described for solution mining in [237]. In addition, the study in [238] investigated various factors affecting deformation in brine-filled salt caverns after solution mining, including salt creep, brine thermal expansion, brine penetration, salt dissolution, and cavern compressibility. It is crucial to monitor changes in pressure due to these factors to ensure cavern stability. Although most of the studies on solution mining are considering salt beds, a few studies are available on solution mining in potash and trona beds. The authors of [239] provided an overview of the geology of solution mining of potash in Saskatchewan, Canada. A feasibility study for horizontal solution mining was performed in [240] for trona beds in Wyoming, United States.
# Defect Detection in Solid Timber Panels Using Air-Coupled Ultrasonic Imaging Techniques
## 3 Results and Discussion
A 70 mm \(\times\) 70 mm sample area shown in Figure 7b contains a natural crack in the middle of a knot. The amplitude (C-scan) image is presented in Figure 10b and the TOF (D-scan) image in Figure 12, respectively. Comparing Figure 7b to Figures 10b and 12, both imaging results (C-scan and D-scan results) are essentially consistent with the actual combined defects. It is thus demonstrated that the ACU method is a reliable tool for detecting combined defects. However, as the defect shown in Figure 7b consists of both a crack and a knot, Figure 10b demonstrates that the C-scan method works well for such combined defects. Moreover, the diameter of the knot and the length of the crack detected in Figure 10b, are equal to the actual diameters and length in Figure 7b. It is clear that both the amplitude and the TOF methods can detect the combined defects, including their size and location. However, as seen in Figure 12, it is difficult to distinguish the knots and the cracks if the TOF is used. Comparing Figures 7, 11 and 12, it is concluded that the C-scan method demonstrates better performance than the D-scan method when knot and crack defects need to be evaluated since imaging results of C-scan perform more accurate size information and higher resolution when combined defects need to be distinguished. Figure 11: Amplitude attenuation (**a**) and TOF images of the sample with knot defect (**b**). Figure 12: TOF image of the sample with a combined knot and crack defect. To further explore the potential of the ACU for NDT of timber products, artificial wormhole-like defects were simulated by drilling holes. Figure 13 shows a C-scan image of drilled holes with different diameters. Again, it is demonstrated that the ACU method is a reliable inspection tool for wormhole defect detection. However, it is also concluded that defects smaller than 3 mm in diameter (area B) may not be detected due to the limited precision of the system and noise interference. Comparing Figures 11-13, it is easy to distinguish knots from the other two types of defects because of the large attenuation of the wave amplitude. However, distinguishing between cracks and wormholes using attenuation characteristics is more challenging. Nevertheless, it is expected to be able to identify them with the help of pattern recognition methods (e.g., support vector machine), since cracks and wormholes usually have obvious shape differences [8]. Pattern recognition methods may also help to differentiate areas with defects from those without defects. Due to the non-contact nature of the ACU method, the measuring process is more efficient and does not contaminate the samples or scratch the surface of the samples, especially in the continuous manufacturing process, which is critical for successful industrial applications.
## 2 Materials and Methods
### MR Imaging Protocol  
MRI including DWI was performed in follow-up examinations with a mean value of 2.72 years (range 1.2 years to 7.5 years) after surgical treatment. We think that the effect of the time on imaging in terms of detecting the scar versus recurrence can be expected.  
MRI was performed with a 1.5T scanner (MAGNETOM Symphony, Siemens Healthcare, Erlangen, Germany), phased-array body coil and parallel imaging. Patients were scanned in a supine position on a flat table-top insert. DW images were acquired in axial plan with a single-shot spin-echo diffusion-weighted echo-planar pulse sequence at b-values of 0, 50, 100, 150, 300, 600, 1000 s/mm\({}^{2}\) with the following sequence parameters: TR 2700 ms; TE 83 ms; no phase oversampling; Field of View 270 mm \(\times\) 230 mm; pixel spacing 1.70 mm \(\times\) 1.70 mm; slice thickness 4.0 mm; gap between slice 0.0 mm; receiver bandwidth, 1250 Hz/pixel.  
In order to reduce respiratory artefacts, each patient received bowel preparation and antispasmodic medication. The entire MR imaging protocol was reported in detail in [22; 23].  
Rectal cancer recurrences were histo-pathologically proven in the 34 patients while fibrosis/scar tissue were histo-pathologically proven in 6 patients (27.3%) while in 16 patients (72.7%) were confirmed  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Patients Description** & **Numbers (\%)/Range** \\ \hline \multirow{2}{*}{Gender} & Men 37 (66.1\%) \\ \cline{2-3}  & Women 19 (33.9\%) \\ \hline Age & 62.5 y; range: 35–82 y \\ \hline \multicolumn{2}{c}{**Local recurrence Description**} \\ \hline Presacral site & 14 (41.2\%) \\ \hline Anastomotic site & 7 (20.6\%) \\ \hline Rectal lumen & 10 (29.4\%) \\ \hline Precocrygeal site & 3 (8.8\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the study population.  
by the clinical follow-up. The clinical follow-up ranged from 20 to 108 months and consisted of the following visits for 5 years: Every 3 months in the first 2 years, and then every 6 months in the following 3 years: Visit with rectal exploration; rectoscopy; CEA and blood chemistry routine; 4. MRI every 6 months for 5 years; annual thoracic abdominal CT scan; colonoscopy after 1 year from treatment; if negative, after 3 years (4 years after treatment); if negative, every 5 years.