esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Photocatalytic Studies
#### 3.7.3 Reusability Performance of ZnO NPs in Removal Efficiency of COD in Paper  
To address the catalysts' reusability issue, the recovered catalyst was added with fresh paper mill water solutions after centrifugation. All of the experimental conditions were kept constant, and the procedures were repeated three times with fresh water from a paper mill. It was noticed that the recovered weight of catalyst is decreased gradually in each set due to the adherence of part of the catalyst on the container wall, thus the author settled for only four reusable sets. Therefore, after centrifugation, washing and drying the catalyst, the weight was decreased slightly but still could be collected and reused. Reusing ZnO NPs three times in the degradation of paper mill effluent was used to test their reusability. As demonstrated in Figure 9, the photocatalytic treatment of paper mill effluent with ZnO NPs reduces COD concentrations by 74.30%, 63.23%, and 54.96% for the first, second, and third cycles, respectively. Due to the larger number of active sites on the ZnO NPs surface available for efficient photon absorption, good COD removal efficiency is observed. However, the photocatalytic activity of ZnO NPs was reduced due to the photocorrosion effect that occurs when exposed to UV light. After three cycles, the photocatalysis of COD decreased by around 20%, indicating that ZnO NPs can be reused as an efficient photocatalyst numerous times because photo corrosion is negligible [33].  
Figure 8: Effect of the contact time on % COD reduction by ZnO NPs (\(n=3\)).
## 2 Requirements Specifications and Measurements Gaps
### Atmospheric Pressure over the Sea Surface  
Atmospheric pressure oscillations produce SST variations, and its monitoring provides a better understanding of the factors disturbing climate variations, contributing to accurate marine weather forecasts. Microwave radiometers can infer this measurement using several channels in the 50-60-GHz frequency bands. Among these, future microwave sounders in the time frame from 2020-2030 to measure the atmospheric pressure over the sea surface are: the Advanced Microwave Sounding Unit-A (AMSU-A), the Advanced Technology Microwave Sounder (ATMS) and nanosatellites, such as the Microwave Radiometer Technology Acceleration (MiRaTA) [23], and the Earth Observation Nanosatellite-MicroWave (EON-MW) missions [23]. AMSU-A is a whisk broom line scanner instrument in a SSO, with a global coverage of twice per day. It has 14 channels in the oxygen band (50-60 GHz). It is operated by NOAA, NASA and EUMETSAT, and the utilization period is from 2006-2024 on MetOp. ATMS on the Join Polar Satellite System (JPSS) is a cross-track scanning microwave sounder in a Sun-synchronous orbit, with a global coverage of twice per day. It is able to measure atmospheric pressure (over the sea surface). Its utilization period is from 2018-2021. It is also operated by NASA, NOAA and EUMETSAT. ATMS is the functional equivalent to AMSU-A with improved coverage thanks to a swath of 2600 km. The EON-MW mission has been proposed by MIT Lincoln Laboratory to extend the JPSS. It aims at demonstrating ATMS quality on a low-cost CubeSat platform in order to mitigate the gaps in weather observations. This mission is scheduled for launch in 2018-2019. The Infrared (IR) sounder/spectrometer also can measure this variable. The IR sounder on a CubeSat is feasible (6U). A good example of the compact from of this type of sensor is the Earth Observation Nanosatellite Infrared (EON-IR) on the CIRAS (CubeSat Infrared Atmospheric Sounder) mission [47]. In this regard, a constellation of microwave sounders or IR sounders as the payloadcould be considered, in order to cover the low revisit time (\(<\)3 h). These data will be complementary to those obtained by sensors onboard large platforms.
## 3 Data Augmentation and Model Design
### The Modified Attention Mechanism Module
\[\mathbf{w}=\mathbf{w}_{0}\oplus\mathbf{w}_{1} \tag{7}\]  
where the symbol \(\oplus\) in Equation (7) is the cross-dimensional fusion operation. We recalibrate the weights of the multi-scale feature maps, as shown in Equation (8). \[\mathbf{w}_{i}^{*}=\text{Softmax}(\mathbf{w}_{i})=\frac{\exp(\mathbf{w}_{i})}{\sum_{i=0}^{ 1}\exp(\mathbf{w}_{i})},i=0,1 \tag{8}\]  
The Softmax function is used to recalibrate the channel direction attention vector \(\mathbf{w}_{i}\) to obtain \(\mathbf{w}_{i}^{*}\), which contains all the spatial position information of the feature maps and the attention weight in channels, realizing the interaction between local and global channel attention. The channel attention vector \(\mathbf{w}_{0}^{*}\) and \(\mathbf{w}_{1}^{*}\) recalibrated by the feature are contacted to obtain the channel attention weight \(\mathbf{w}^{*}\) of the whole feature maps, as shown in Equation (9). \[\mathbf{w}^{*}=\mathbf{w}_{0}^{*}\oplus\mathbf{w}_{1}^{*} \tag{9}\]  
Last, we multiply the recalibrated multi-scale attention weight \(\mathbf{w}^{*}\) by the feature graph \(F^{*}(i)\), obtaining an advanced feature graph \(F_{\text{out}}\) with rich multi-scale feature information, as shown in Equation (10). \[F_{\text{out}}=F^{*}(i)\otimes\mathbf{w}^{*}=\text{Cat}([\,F_{0}\otimes\mathbf{w}_{0}^ {*},F_{1}\otimes\mathbf{w}_{1}^{*}]) \tag{10}\]  
ResNet50 is a residual network consisting of multiple bottleneck modules that contain cascaded convolutional layers. The MSE module is used to replace the Conv \(3\times 3\) layer in bottlenecks and introduce the DuBIN layer to replace the batch normalization layer (BN), obtaining an improved bottleneck module. The improved ResNet50 (i_ResNet50) serves as our backbone network, as shown in Figure 6.  
Figure 6: The improved bottleneck module in ResNet50: (**a**) Description of the bottleneck in ResNet50; (**b**) Description of the bottleneck embedded with the MSE module and DuBIN layer. The i_ResNet50 has a powerful multi-scale feature extraction capability. It can adaptively recalibrate the weights across forged images' channel dimensions. Furthermore, it better obtains the information interaction between local and global channels. Meanwhile, the backbone can disentangle the instance-level heterogeneity and reduce computational effort during the training stage by the DuBIN layer.
# Collaborative Optimization of Aerodynamics and Wind Turbine Blades
## 3 Discipline Analysis
### Structure Discipline
#### 3.2.2 Lay-Up Design  
Lay-up design plays an important role in the structural performance of wind turbine blades due to the different mechanical behavior of different parts. As stated above, the blade is formed of three parts, the leading edge, spar, and trailing edge. Comparatively, the material thickness of the spar is larger than those of the contiguous two edges since the bending moment is the main internal force that the wind turbine blade has to resist. Around the area of the blade root, the thickness of the three parts should be much larger than in other areas since flapwise bending should also be considered.  
For the leading edge and trailing edge, the internal forces include the bending moment and torsion. Based on the material selection of E-glass UD composite for the skin and the PVC foam for the sandwich core, the fiber orientation is \(\pm\)45\({}^{\circ}\). The same fiber orientation is used for the spar, which resists most of the bending load imposed on the wind turbine blade. E-glass UD composite is selected for the skin of the spar and spar cap mixture is selected for the spar cap.  
The Ansys Composite PrepPost (ACP) component in Workbench is employed to conduct the lay-up design. First, the blade is partitioned alongside the length of the blade and the length of the chord. Afterward, finite element analysis (FEA) meshing is performed. A typical shell181 element is adopted and 46024 elements are obtained. A detailed lay-up design is then carried out in the Setup module of the ACP component. The lay-up results of a section are shown in Figure 16. The thickness distribution alongside the length of the blade is shown in Figure 17. As can be seen, the thickness decreases from the root to the tip and from the spar to the edges.
## 3 Methodology
### Field Trial Measurement  
To develop realistic prediction models that reflect the real mobile radio propagation characteristics, we first conducted a set of aerial drive tests and then used the measurements to evaluate the cellular link performance in a UAV-to-BS communication scenario. The following describes the measurement setup/methods, study area, and validation strategies for the results.  
Figure 2 depicts the general concept of the utilized measurement method. The developed multirotor drone in [24] has been used to perform the drive-test. For measuring cellular key performance indicators (KPIs), a smartphone with an installed drive-test application is required. Plenty of drive-test applications are available on Google Play Store for Android devices, such as GNet Track Pro [32], RF Signal Tracker [33], and Network Cell Info [34]. In this study, the GNet Track Pro has been used due to its accuracy and capability to measure and monitor the essential KPIs of the cellular network compared to other existing applications [24].  
The measurement was conducted at the National University of Malaysia (UKM). The campus environment can be considered a tropical suburban metropolitan area with a geographic terrain of undulating hills and dense vegetation. Figure 3 shows an aerial image of the considered area.  
In the considered study area, cellular coverage is provided by different telecommunication service providers and, in this study, only one of the cellular operators with better coverage was selected for the measurement. The measurement area is covered by four LTE BSs with a carrier frequency of 2.6 GHz and a system bandwidth of 20 MHz. Table 2 presents the information on BSs.  
Figure 2: Illustration of the drive test method in three different routes, A-C, and four different heights, 65–125 m.  
The smartphone was mounted on the drone and served by the LTE network during the measurement. At the same time, GNet measured the LTE-related parameters such as drone position, RSRP, RSRQ, and network latency. The measured KPIs were then stored on the phone and retrieved after landing. It should be noted that during the tests, the drone speed was maintained at an average speed of 20 km/h.  
Drive tests were conducted on three different paths and four elevations (65, 85, 105, and 125 m). Meanwhile, to enhance measurement accuracy, each flight set was repeated twice. Moreover, the starting point's elevation was considered the reference point, about 40 m above sea level. For example, flying at an altitude of 65 m means flying at an altitude of 65 m above the starting point or equivalent to 105 m above sea level.
## 4 Flying Base Station Channel Capacity and Positioning
### Base Station Positioning
#### 4.1.2 The Optimal Location for Transmission  
Our scheme is adaptive to the user's location. However, in contrast to the previous research, which focused only on the link between the user and the flying base station (Section 2), the flying base station location also depends on that of the stationary base station. We define the _transmission-optimal location_ to be the flying base station location, which yields the least power consumption for communication signal transmissions. Any leftover transmission power beyond the requirement from what is required can be used to improve the SNR performance.  
Theorem 4.1: _The transmission-optimal location is halfway between the user and the stationary base station, so that \(d_{FU}=d_{FS}=0.5\;d_{SU}\)._  
Proof: Equation (3) when \(\gamma\geq 2\) (for practical wireless channels, as discussed in Section 3.3) yields that the required transmission power \(P\) (or \(P_{\text{F}}\) for the flying base station) is convex with respect to the distance \(d\), i.e., for any distances \(d_{1}\), \(d_{2}\), and \(\theta\), where \(0\leq\theta\leq 1\), \(P(\theta d_{1}+(1-\theta)d_{2})\leq\theta P(d_{1})+(1-\theta)P(d_{2})\). (Alternatively, \(P\) is a power function with respect to \(d\), where the exponent is the fixed \(\gamma\), i.e., \(P=\beta d^{\gamma}\). A power function is convexin the positive domain if the exponent is greater than one, for example, \(\gamma=2\) yields a quadratic function with \(d\), where the non-dominant linear and constant terms are zero; \(\gamma=2\) yields cubic; and \(\gamma=2\) yields quartic. Thus, if \(\gamma>2\), then \(P\) is strictly convex with positive \(d\)). From Jensen's inequality, the transmission power consumption of the expected value of the distances \(d\) is less than or equal to the expected value of the transmission powers across distances, i.e., \(\text{E}[P_{\text{F}}(d)]\leq P_{\text{F}}(\text{E}[d])\). If \(d_{FU}\
eq d_{FS}\), then \(P_{\text{F}}(\frac{d_{FU}+d_{FS}}{2})\leq\frac{1}{2}P_{\text{F}}(d_{FU})+ \frac{1}{2}P_{\text{F}}(d_{FS})\). Because the flying base station relays the communication in both directions (to the user and the stationary base station), \(d_{FU}=d_{FS}=\frac{d_{FU}+d_{FS}}{2}=\frac{d_{SU}}{2}\) yields the minimum power required for \(P_{\text{F}}\) and the transmission-optimal location.
## 5 Results and Discussion
### Refinement of ML Models  
With reference to the feedback and data collected, the ML models go through iterative refinement. Every cycle updates the weights of the knowledge graph nodes and retrains the ML models to reflect the learner's progress profile. The system's prediction accuracy and adaptability increased through an iterative process and escalation of the learner's personalized learning experience. Over successive iterations, the system shows notable improvements in its recommendation protocols, such as precision, recall, and F1-score, to achieve the effective and relevant learning modules. The chart illustrates the model refinement in terms of F1-score, precision, and recall over 10 iterations as mentioned in Figure 10.  
Figure 10: Refinement of DKPS.  
Figure 9: Impact of feedback integration in DKPS.
# Automatic Methodology for Forest Fire Mapping with SuperDove Imagery
## 3 Results
### Burned-Area Maps  
The results of the burned-area maps derived from Planet images using the described methodology contrast the data provided by CEMS reports. This comparison was carried out both qualitatively and quantitatively, through visual evaluation and surface measurement, respectively.  
Figure 6 presents the computed burned areas for the four distinct considered fire events. The inner section of each graph (in black) represents the computed burned areas using the automatic methodology described in Figures 2 and 3. In the surrounding areas, depicted in red, it is possible to appreciate the boundaries of the burned areas obtained from the CEMS reports. This color-coded representation provides a comprehensive view of the extent of the fires and the additional areas identified by the CEMS. Except for the image in Figure 1a, the boundaries of the areas generated by the proposed methodology align well with those obtained from the geodata provided by the CEMS.  
Figure 6: Comparative visualization of the burned areas computed using the described methodology (in black) and those extracted from the CEMS report (in red) for the fires in (**a**) north Attica, (**b**) Portbou, (**c**) Euobea, and (**d**) Sierra de los Guájères.  
In Figure 7, an enlarged view showing the details of the area for the north Attica fire, in which the edges of the burned areas do not coincide with the pre- and post-fire images, can be seen. The red line corresponds to the delimitation of the fire according to the CEMS reports, while the yellow line corresponds to the boundaries generated with the described methodology. The analysis of the images before and after the fire allows us to identify that, in most locations, the vegetation has not been burned and remains vigorous. However, it is true that there is a change in the bare ground that could be attributed to the fire.  
A comparison between the estimation of the burned area (in hectares) and the area delimitation provided by CEMS reports appears in Table 3 for each study area. The applied methodology automatically classifies image pixels into two groups that correspond to the burned areas and untouched vegetation. Subsequently, the sum of the pixels labeled as \"burned area\", weighted by the slope of each pixel's central location, is calculated to obtain the total burned surface. However, the data provided by the CEMS refer to the delimitation of the burned area. This represents a different approach that involves reducing the number of objects based on their size [36] (p. 10), [37] (p. 12), [38] (p. 12), [39] (pp. 9-10). Consequently, although, as seen in Figure 6, the delimitation of the areas is reasonably similar (except in the case already mentioned in Figure 6a), the measurement of the surface affected by the fire may appear to be more different. In general, there is a trend towards overestimation in CEMS reports, which increases as the area affected by the fire increases.
## 2 Materials and Methods  
### Preparation of Plasma Rich in Growth Factors (PRGF), Hematogy Parameters, and Growth Factor Content  
PRGF was prepared according to a method described by [PERSON] et al. [17; 18]. The blood was withdrawn from the cephalic vein of a healthy volunteer (male, age 45, originated from Bihor County, Romania, previously informed about the purpose of the procedure), in two sterile test tubes, 9 mL each,containing 0.4 mL of 3.8% sodium citrate solution as an anticoagulant. The tubes were centrifuged at 580 G for 8 minutes at room temperature. The blood was thus separated into its three basic components: 1) red blood cells, which appeared at the bottom of the tube; 2) leukocytes, as a thin layer above the erythrocytes; and 3) plasma, above the white cells. PRGF (fraction 2) is represented by the first 2 mL of plasma located above the white cells. The PRGF fraction from each tube was carefully collected and transferred to sterile graduated tubes (1.3 mL of PRGF in each tube), and then 50 uL of 10% calcium chloride was added to PRGF. After an activation time of 15 minutes, the PRGF gel was formed and ready to be used in combination with collagen membranes. In order to characterize the quality of collected PRGF, the platelet, erythrocyte, and leukocyte concentration was assessed by using a hematology analyzer (Micros 60, Horiba ABX, Montpellier, France). Additionally, the amounts of several key growth factors were quantified immediately after preparation, by commercially available enzyme-linked immunosorbent assay kits (DSX(r)--Dynex Technologies Inc. Denkendorf, Germany). The selected growth factors and cytokines/chemokines quantified for this study were: TGF-81, VEGF, IGF-1, PDGR, RANTES, IL-18, and MCP-1.
## Materials and Methods
### Data Analysis  
Chi square test was used to determine the significance of difference of GSU features between benign and malignant nodules, whereas the significance of difference of SWE indices between benign and malignant nodules was calculated by Mann Whitney U test. The diagnostic performance of GSU was evaluated by deducing the frequency tables of true-positive, true-negative, false-positive, and false-negative cases. Receiver operating characteristic (ROC) curves were used to determine the optimal cut-off of different SWE indices in distinguishing benign and malignant nodules, and the associated diagnostic performance of the optimal cut-off. The diagnostic performance of combining GSU and SWE was determined based on the principle that a thyroid nodule was considered malignant when it was presented with at least one suspicious GSU feature (i.e., hypoechogenicity, tall to width ratio \(\geq\)1, irregular border or microcalcification) and had SWE indices (\(E_{\text{maximum}}\geq\) 67.3 kPa or \(E_{\text{mean}}\)\(\geq\) 23.1 kPa) equal to or greater than the corresponding optimal cut-off values. Thyroid nodules did not fulfill these criteria were categorized as benign. All statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) software (Version 20, IBM Corporation, Armonk, NY, USA) and two-tailed \(p\) value < 0.05 was significant.
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 3 Spatiotemporal Feature Enhancement Methods
### Spatiotemporal Feature Enhancement Based on Spatial Features  
Spatiotemporal feature enhancement method based on spatial features is widely applied in the earliest deep learning methods for lip-reading by typically employing the sole mode and integration of 2D CNNs and 3D CNNs to extract spatiotemporal features, which are then enhanced in networks such as ResNet, DenseNet, EfficientNet, and ShuffleNet.  
Temporal modeling is subsequently performed using RNNs, GRUs, LSTMs, etc. According to the kind of networks for feature extraction, spatial feature enhancement methods are based on ResNet, DenseNet, lightweight network, and other methods.
## 4 Proposed Methodology of Hybrid Channel Modeling
### Electromagnetic Propagation in a Mine Gallery  
The transmission of EM signals in the mine can be observed in the form of a superposition of various modes with varied distributed field patterns and attenuation coefficients. The complete distributed EM field patterns of every mode can be derived by sorting [PERSON]'s expression in the form of Eigen-function [31], as given in (4):  
\[E_{m,n}^{eigen}(x,y)\cong sin(\frac{m\pi}{2a}x+\varphi_{x})cos(\frac{n\pi}{2b} y+\varphi_{y}) \tag{4}\]  
where the values for \(\varphi_{x}\) and \(\varphi_{y}\) are calculated using  
\[\begin{cases}\varphi_{x}=0&\text{if $m$ is even}\\ \varphi_{x}=\pi/2&\text{if $m$ is odd}\\ \varphi_{y}=0&\text{if $n$ is odd}\\ \varphi_{y}=\pi/2&\text{if $n$ is even}\end{cases} \tag{5}\]  
The distribution field is calculated against any position \((x,y,z)\) in the mine by the addition of particular EM fields of each substantial mode, and the expression is given below in (6):  
\[E^{Rx}(x,y,z)=\sum_{m=1}^{\infty}\sum_{n=1}^{\infty}C_{mn}E_{m,n}^{eigen}(x,y)e ^{-(\alpha_{mn}+j\beta_{mn})z} \tag{6}\]  
where \(C_{mn}\), \(\alpha_{mn}\) and \(\beta_{mn}\) represent intensity of modes, attenuation and coefficient of phase shift, respectively [32; 33]. The expressions for the coefficient of attenuation and phase shift are given below  
\[\alpha_{mn}=\frac{1}{a}(\frac{m\pi}{2 ak})^{2}Re\frac{\overline{k_ {v}}}{\sqrt{\overline{k_{v}}-1}}+\frac{1}{b}(\frac{n\pi}{2 bk})^{2}Re\frac{1}{ \sqrt{\overline{k_{h}}-1}} \tag{7}\] \[\beta_{mn}=\sqrt{k^{2}-(\frac{n\pi}{2a})^{2}-(\frac{n\pi}{2b})^{ 2}} \tag{8}\]  
The waveguide model declares the lower-order mode in the environment of underground mine as \(C_{11}=1\) and \(C_{mn}=0\) if \((m,n)\
eq(1,1)\) but multiple modes exist in the near zone of the transmitting antenna with particular intensities. To find the field distribution and mode intensity \(C_{mn}\) on the excitation plane, the GO model is used in the current study.
# Segmentation of Liver Anatomy by Combining 3D U-Net Approaches
## 3 Experimental Setup
### Liver Vessel Segmentation
#### 3.2.1 Full 3D Approach  
In the full 3D setup, the models were trained using complete 3D liver volumes (cf., Figure 1a) to predict a vessel probability map for each input image. Each time, 19 volumes of size \(512\times 512\times 120\) voxels were used for training (with three volumes used for validation). Volumes with a number of slices smaller than 120 were extended to fix this size by adding zeros (zero padding).  
A full 3D approach allows taking advantage of complete spatial information contained in a volume. However, it also results in limited train data and requires the heaviest computational workload. Both the slab-based setup and the box-based setup diminish these limitations at the cost of limiting spatial information to selected subvolumes.