esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Final Remarks and Outlook  
This review examines the role of amine type in the CO\({}_{2}\) transport behaviors through amine-functionalized silica/organosilica membranes. Based on a simple summary of reported studies in this field and on a systematic comparison of our previous studies, the term \"membrane affinity\" is likely to be regarded in a broad sense that comprises not only the favorable interactions of CO\({}_{2}\) molecules with the membrane matrix, but also their promoted/inhibited diffusion within the membrane matrix and from the interface of the membrane permeate side. Hence, the best solutions to synthesize CO\({}_{2}\) separation membranes with high CO\({}_{2}\) transport efficiency should include an optimization of the kinetics of both the sorption and diffusion processes for CO\({}_{2}\). Under mild conditions such as ambient or sub-ambient temperatures, and in the case of comparable membrane microstructures, membranes with hindered amines seem to be the best choice to achieve a good balance between sorption and diffusion processes for CO\({}_{2}\) molecules due to the low-to-moderate CO\({}_{2}\) binding energies that may improve the CO\({}_{2}\) mobility in a membrane matrix. However, more in-depth studies are needed to evaluate the roles of membrane affinity and/or amine type as well as their synergistic effect with the geometrical microstructures of membrane matrices in CO\({}_{2}\) separation performance. In addition, more studies should also be devoted to how temperature affects the amine-CO\({}_{2}\) interactions (or CO\({}_{2}\) mobility in the membrane matrix) to achieve a comprehensive understanding of the role of amine type in CO\({}_{2}\) transport behaviors in both single and mixed gas systems. Studies on the thermal stability of amine groups, relationships between the accessibility of amine groups, and on the microstructures of membranes are also of importance and significance to direct the preparation of high-efficiency CO\({}_{2}\) separation membranes.  
[PERSON] wrote the manuscript in consultation with [PERSON]. All authors discussed the results and commented on the manuscript.  
A part of this research was supported by JSPS KAKENHI Grant Numbers JP15H02313 and JP18H03855.  
L.Y. sincerely acknowledges financial support from the Special Postdoctoral Researcher Program provided by the Global Career Design Center, Hiroshima University. Authors thank for the financial support from JSPS KAKENHI Grant Numbers JP15H02313 and JP18H03855.  
The authors declare no conflict of interest.  
Figure 19: Relationships of \(E_{p}(CO_{2})\) vs. \(E_{p}(CO_{2})-E_{p}(N_{2})\) for amine-functionalized organosilica membranes. The preferred region shown in the figure demonstrates lower values for both \(E_{p}(CO_{2})\) and \(E_{p}(CO_{2})-E_{p}(N_{2})\). Results were reorganized from refs. [21; 22; 23].
# Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots
## 3 Homography Estimation for a Center-Aligned Image Pair
### Training and Results  
When training the HomographyFpnNet model, we used a momentum optimizer with a momentum value of 0.9, a batch size of 64, and an initial learning rate of 0.05. During the first 1000 training iteration steps, we linearly increased the learning rate from 0.0 to the initial learning rate of 0.05. We then continued training the model for an additional 90,000 iteration steps and simultaneously updated the learning rate from 0.05 to 0.0 using a cosine decay method [25].  
We tested the mean corner error of the trained model on the 30,000 test samples generated from the COCO 2014 test dataset. The mean corner error was obtained based on the calculation of the L2 distance between the estimated and the ground truth corner locations, for the average of the four corners and all test samples. As shown in Figure 4, our HomographyFpnNet model had a mean corner error of 3.22 pixels on the 30,000 test samples generated from the MS-COCO 2014 test set. Compared with HomographyNet, the mean corner error decreased from 9.42 pixels (our own implementation, 9.2 pixels in the published report [20]) to 3.22 pixels, that is, the mean corner error decreased by 65%. Compared with the hierarchical method [22], our HomographyFpnNet model with an error of 3.22 pixels performed better than their four-stage model with an error of 3.91.  
Figure 5 shows some of the prediction results of HomographyFpnNet on some MS-COCO 2014 dataset images and the associated wall images of our wall-climbing robots.  
Figure 4: Mean corner pixel error comparison of various methods for homography estimation of a center-aligned image pair.
## 4 Experimental Results and Error Analysis
### Experimental Validation and Analysis  
Figure 18 shows the reconstruction results using the four different reconstruction algorithms for the temperature field generated with the butane burner. The upper part of the figure shows the 3D distribution of the reconstruction results, and the lower part of the figure shows the corresponding isothermal contour maps. In these figures, the peak value of the reconstructed flame distribution using the ART and Landweber algorithms is obviously low, and the fluctuations at the edge are more severe, whilst with MARRT and MIARO, the reconstructed images effectively represent the true temperature distribution in the high-temperature region. However, the MIARO algorithm performs better than MARRT in the flame's center section.  
Figure 17: Experimental platform of the butane torch burner. (**a**) Three-dimensional model of the experimental platform; (**b**) local view of the model; (**c**) experimental platform photo; (**d**) photo of local flame view; (**e**) field optical path arrangement.  
Figure 18: Reconstruction results of different reconstruction algorithms for butane torch.  
The temperature values obtained using different algorithms for the corresponding cross-section in Figure 18 are shown in Figure 19. The broken lines in Figure 19 represent the measurement results using the thermocouples. With the ART and Landweber algorithms, the constructed results become flattened in both high- and low-temperature zones over this cross-section, which does not truly reflect the flame distribution, whilst the reconstructed temperatures based on the MAART and MIARO algorithms are much closer to the thermocouple measurements. In fact, in general, the temperature reconstruction using MIARO has smaller deviations from the reference thermocouple measurements.
###### Abstract  
A novel tracking and monitoring system for ISO 668 dry containers was realized by the ESA-funded \"5G SENSOR6 SEA\" project, integrating 5G cellular technologies for massive Internet of Things with a GEO satellite-optimized backhauling link. The scope is the development of monitoring and tracking new services for multimodal container shipping. With the cooperation of four industrial partners and a telecommunication research center, the so-called \"5 GT System\" was designed, developed, tested and validated up to field trials. Several modules of the system were designed, built and finally installed on the ship and in the teleport: the container tracking devices placed on the containers, the NB-IoT cellular network with optimized satellite backhauling, the Ku-band satellite terminals and the maritime service platform based on the OneM2M standard. The field trial conducted during the intercontinental liner voyage of a container ship showed primary technical achievements, including fair switching between terrestrial and satellite networks, reduction in packet loss in the open sea scenario and seamless integration of the BLE mesh network over the container tracking devices as NB-IoT/BLE LE Mesh gateways.  
Keywords:NB-IoT; non-terrestrial networks; Bluetooth low-energy mesh; container logistics; maritime transport +
Footnote †: journal: _Iot_
## 2 Materials and Methods
### Data Collection and Description  
In a prospective multi-center clinical study, for the PREDICTion of BE neoplasia (PREDICT study), VLE data was acquired in vivo from 45 patients undergoing BE surveillance at the Amsterdam UMC (AMC; Amsterdam, The Netherlands), the Catharina Hospital (CZE; Eindhoven, The Netherlands), and the St. Antonius Hospital (ANZ; Nieuwegein, The Netherlands) from October 2017 to November 2018, using a commercial VLE system (NinePoint Medical, Inc., Bedford, MA, USA). Patients undergoing surveillance of NDBE, or patients referred for work-up and treatment of BE with early neoplasia (HGD and/or EAC), were eligible for this study. The study was approved by the institutional review boards at AMC, CZE, and ANZ. Written informed consent was obtained from all patients prior to VLE imaging.  
For each patient, one or several regions of interest (ROIs) were extracted in the following manner. First, four-quadrant laser-mark pairs were placed at 2 cm intervals using the VLE system, according to the Seattle biopsy protocol [20; 21]. Next, a full VLE scan was performed, after which the VLE balloon was retracted from the esophagus. Then, regular endoscopy was used to obtain biopsies in between the laser-mark pairs. Finally, ROIs were cropped from the full scan in between the same laser-mark pairs, and were labeled according to pathology outcome, ensuring histology-correlation [22] of the extracted ROIs. The histopathological correlation was assumed to apply over 1.25 mm, conform a small biopsy specimen, comprising 25 cross-sectional images, in both vertical directions (i.e., distal and proximal), and thus resulting in 51 images per ROI.  
In total, 233 NDBE and 80 neoplastic (HGD/EAC) ROIs were laser-marked under VLE guidance and subsequently biopsied for histological evaluation by an expert pathologist for BE. Out of the total cohort of patients, the first 22 patients were used as the training dataset (134 NDBE and 38 HGD/EAC ROIs, totalling 8772 VLE images) and the remaining 23 were treated as the unseen test dataset (99 NDBE and 42 HGD/EAC ROIs, totalling 7,191 VLE images).
Due to the great complexity of the direct calculation of mutual information, it is almost impossible to obtain a closed-form solution. Monte Carlo trials are usually used for direct and accurate calculation [14]. In order to reduce the computational complexity, a bit-level algorithm using PDF of a log-likelihood ratio (LLR) to calculate mutual information is proposed [15]. However, each modulation mode of the algorithm requires a lot of prior simulations, and it is only suitable for specific scenarios without universality. To optimize linear precoding with finite-alphabet inputs, the authors in [16; 17] deduced the closed-form lower and upper bounds of mutual information as alternatives, which reduce the computational effort by several orders of magnitude compared to calculating the average mutual information directly. Then the bounds of mutual information are applied to multiple antennas, secure cognitive radio networks [18], and relay networks [19]. The study in [20] utilized the cutoff rate (CR) as the alternative of mutual information (MI) to design the linear precoders. Mutual information was also used to develop a two-step algorithm to enhance the achievable secrecy rate of cooperative jamming for secure communication with finite-alphabet inputs in [11]. However, the gaps between approximation and accurate mutual information are still ambiguous, which limits the range where mutual information can be applied. Recently, the authors of [21] approximated ergodic mutual information based on multi-exponential decay curve fitting under M-ary quadrature amplitude modulation (M-QAM) signaling, but other modulation modes were neglected. This study takes a step toward evaluating accurate mutual information for finite-alphabet-based transmissions over doubly correlated MIMO fading channels. After discussing the applicability of the saddle point method, we obtain the approximate solution of mutual information for any known CSI and modulation mode by using this method, which is universal. On this basis, the mutual information expectation of doubly-correlated MIMO Rayleigh fading channels is further derived. This proposition highlights the considerable accuracy with radically reduced complexity. The outline of this paper is as follows. The second section introduces the MIMO transmission model and its preliminary research. In the third section, the saddle point approximation method is used to estimate the mutual information, and then we calculate the mutual information expectation over doubly correlated MIMO Rayleigh fading channels. Then the validity and accuracy of the proposed method are verified under different doubly correlated MIMO fading channels scenarios in the fourth section. Finally, the fifth section gives conclusions.
# C-MWCAR: Classification Based on Multiple Weighted Class Association Rules
###### Abstract
Its core idea is to classify an instance by selecting a set of CARs that are most similar to the instance and then utilizes their weighted classification abilities to obtain the final classification results. C-MWCAR mainly contains three innovative components, respectively named as dictionary order-based CAR mining algorithm (DOCMA), branch-based CAR selection algorithm (BCSA), and multiple weighted CARs-based classifier (MWCC). Concretely, DOCMA aims to extract the complete set of CARs as fast as possible; BCSA is used to select a representative and concise set of CARs from the extracted CARs based on their distribution, coverage, and redundancy; then, MWCC carefully picks out a few CARs from the obtained concise set of CARs for each unknown instance, and finally uses them to classify each unknown instance as hypertension or normoten. To evaluate the effectiveness of the proposed C-MWCAR, it is applied to a real-world and important classification task, i.e., hypertension diagnosis. The reason why this task is chosen for experiments is because hypertension is a typical cardiovascular disease and affects more than 40% of adults in the world, according to a report from the World Health Organization [18]. The rest of this paper is organized as follows. The background and related literature are reviewed in Section 2. The materials and methods are then described in Section 3. The process of applying the C-MWCAR to the hypertension diagnosis task is described in Section 4, followed by the evaluation of the experimental results in Section 5. Next, the advantages and limitations of the proposed method are discussed in Section 6. Finally, the contribution of this study is concluded in Section 7.
## 3 Materials and Methods
### Data Collection  
#### 3.3.1 Simulation Environment and Scenarios  
In this study, the steps followed to simulate the VANET network are as follows:  
Step 1: The preparation of a road network by importing a map from Open Street Map (OSM) [26]. The simulation zone is defined on a map form the Moroccan city El Jadida (Figure 2).  
Step 2: The generation of mobility trace files using SUMO (Simulation of Urban Mobility) [9] which simulate realistic vehicle movements. SUMO provides a variety of python tools such as netconvert, randomTrips, poly-convert, and traceExporter used for the creation, execution, and evaluation of traffic simulations. The generated mobility trace file is then fed to the network simulator NS-3 (Figure 3).  
Figure 2: The simulation map from Open Street Map.  
Step 3: Simulate the network components using the NS-3 network simulator, which is used to model the VANET communications' whole protocol stack. Simulation scenarios are configured in NS-3 using scripts written in the C++ programming language. Simulations are implemented using parameters exhibited below (Table 1).  
We consider two VANET scenarios using the simulation parameters previously outlined in Table 1.  
In the first scenario, 50 vehicles are involved, with 10 random source-destination pairs, which produce CBR traffic with fixed-size packets. The AODV routing protocol is used to route packets considering all nodes as legitimate vehicles. In this case, no black hole node is simulated.  
In the second simulated scenario, we implement AODV routing protocol with black hole attacks wherein 1 to 3 nodes are selected to act as attackers and are configured at different times to launch the black hole attacks. The remainder of the nodes are trustworthy and have legitimate communications to the other vehicles in the network.
## 5 Evaluation
### Initial Accuracy  
The model was initially trained with a small percentage of the labeled dataset until convergence. The initial achieved model accuracy is shown in Figure 5 for Kaggle and NLST datasets. Early stopping was implemented, which stops the algorithm execution once the model starts converging. The model started converging around 350 epochs for Kaggle dataset and 230 epochs using the NLST dataset, as observed in the initial accuracy plots presented in Figure 5. The model stabilized after 400 epochs for the Kaggle dataset and 300 epochs for the NLST dataset. The model showed very good stability thereafter up to 500 epochs.  
We conducted experiments with 25%, 30%, 40%, and 50% of total training labels. The initial models were trained until convergence. Subsequently, there were 10 EM iterations in which each iteration in the active learning step selects 10 unlabeled samples for which ground truth labels are provided by oracles (experts). Thus, the total number of labeled samples add 9.1%, 3%, and 2% to the initial labels for the Kaggle, NLST, and LIDC datasets, respectively. For example, when the initial model was trained in the active learning phase with 50% of training labels from the Kaggle dataset, by the final iteration, the model would have used 59.1% of the labels (with additional labels being added at each iteration). The fully supervised learning models use 100% of the training labels. The model performances were tested using unseen testing data (20% of dataset). For each dataset, all models were tested using the same unseen test data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Images** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Initial Model** \\ **(Minutes)** \\ \end{tabular} } & \multicolumn{2}{c}{**ASEM-CAD**} & \multirow{2}{*}{
\begin{tabular}{c} **Total Time** \\ **(Minutes)** \\ \end{tabular} } & \multirow{2}{*}{**\% Increase**} \\  & & & & & \\ Kaggle & 1357 & 26 & 8 & 34 & 31 \\ \hline NLST & 2538 & 47 & 15 & 62 & 32 \\ \hline LIDC & 4253 & 8 & 4 & 12 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training time for the ASEM-CAD algorithm estimated based on 10 EM iterations.
# Data Interpretation Technology of GPR Survey Based on Variational Mode Decomposition
## 2 Theory and Methodology
### The Instantaneous Frequency Based on VMD Theory
#### 2.1.2 The Instantaneous Frequency Spectrum Solution  
The solution condition of instantaneous frequency needs the IMF components that can be obtained with VMD. When the input signal is decomposed into several IMFs that are based on VMD, the solution of instantaneous frequency can be implemented. The Hilbert transform is used to construct the analytical signal, and the instantaneous frequency and amplitude of each modal function are obtained. The Hilbert instantaneous frequency spectrum is constructed based on the instantaneous frequency and the amplitude of each modal function. Figure 1 shows the process of instantaneous frequency spectrum implement.  
In the solution of instantaneous frequency, the original signal \(f\) (\(t\)) can be decomposed into \(n\) number of IMF while using the VMD method:  
\[f(t)=\sum_{k=1}^{n}u_{k}(t) \tag{5}\]  
The Hilbert transform was performed on IMF. The transform equation is expressed as:  
\[H[u_{k}(t)]=\frac{1}{\pi}P\int_{-\infty}^{+\infty}\frac{u_{k}(\tau)}{t-\tau} \tag{6}\]  
where \(P\) is the Cauchy principal value and \(\tau\) is time.  
The analytical signal can be constructed using Formula (6) and IMF. The formula is expressed as:  
\[A[u_{k}(t)]=u_{k}(t)+iH[u_{k}(t)] \tag{7}\]  
In the signal's Equation (7), its amplitude and phase is:  
\[a_{k}(t)=\sqrt{u_{k}^{2}(t)+H^{2}[u_{k}(t)]} \tag{8}\]  
\[\theta_{k}(t)=\arctan\frac{H[u_{k}(t)]}{u_{k}(t)} \tag{9}\]  
The phase of the signal was derived. Then, the instantaneous frequency of the signal can be obtained:  
\[\omega_{k}(t)=\frac{1}{2}\frac{d\theta_{k}(t)}{dt} \tag{10}\]  
where \(\omega_{k}(t)\) is the instantaneous frequency.  
Then, \(f(t)\) can be expressed as the following:  
\[f(t)=\text{Re}\!\left(\sum_{k=1}^{n}a_{k}(t)e^{j\omega_{k}(t)}\right) \tag{11}\]  
Formula (11) was expanded and the Hilbert instantaneous frequency is expressed as the following:  
\[H(t,\omega)=\text{Re}\!\left(\sum_{k=1}^{n}a_{k}(t)e^{j\int a_{k}(t)dt}\right) \tag{12}\]  
Figure 1: Hilbert instantaneous frequency amplitude spectrum solving process.  
where \(H(t,\omega)\) is the Hilbert instantaneous frequency spectrum and \(\omega\) is the frequency.  
Based on VMD, the Hilbert instantaneous frequency spectrum can be solved from Formula. \(H(t,\omega)\) describes the amplitude of the signal in the entire frequency domain with the time and frequency of the transformation. In Fourier analysis, the energy representation at a certain frequency is a harmonic wave that exists for the entire time. By contrast, in [PERSON]'s instantaneous frequency analysis, the frequency energy at some point may indicate its occurrence in the entire time. A frequency wave appears and it represents the probability of occurrence.
## 3 Beam Steering/Beam Forming Using Reconfigurable Components  
Several studies are available in the literature [73; 74; 75; 76; 77; 78; 79; 80] to demonstrate the operations and functions of transmitarray beam steering. These are realised by utilizing different reconfigurable devices, materials, element designs, and operational parameters. However, every structure needs a requisite to obtain the beam steering/beam forming of the transmitarray antenna. Each transmitarray unit cell must acquire a transmission phase that can be  
Figure 3: (a) Conceptual diagram of the wavefront of fixed beam transmitarray and variable source antenna; (b) phase variation concept of fixed transmitarray and variable source antenna.  
Figure 2: Conceptual diagram of the wavefront of (a) fixed beam transmitarray and fixed source antenna (b) active transmitarray with fix source and variable phase shift of TA.  
tuned (varied) up to 360 degrees. Moreover, the transmission coefficient should remain constant throughout the operational bandwidth. This section focuses on demonstrating the electronically reconfigurable beam of transmitarray, transmission phase distribution on the transmitarray surface, and the phase shift of each unit cell. Reconfigurable beam steering can be achieved with different techniques. The most prominent one is electronic control accomplished, which is achieved by loading one or more active devices into the resonant elements designed on the unit cell such as (PIN diodes, varactors, and MEMS switches). Other techniques are on the utilization of tunable materials (liquid crystal, microfluidic systems). There is a detail Summary of beam steering reconfigurable transmitarrays with PIN diodes is presents in Table 1.