esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Key Enabling Techniques
### Millimeter-Wave Communications (mmWave)
[65] developed an adaptive low-latency approach that uses cooperative networking to overcome end-to-end latency and boost systems channel capacity. In [23], a new  
Figure 3: 5G key enabling technologies. technique mmWave small cell network (mmWave-SCN) is proposed that is a combination of mmWave, mMIMO, and network densification to combine additional infrastructure nodes and spectrum. The resulting mmWave-SCN contains features from mmWave and SCN, such as flexible deployment and management, ultra-high data rate support, and ironic obtainable spectral resources. [PERSON]. [PERSON] et al. [66] proposed the mmWave Aerial Base Station (mAeBS) by the grouping of the aerial base station (AeBS) and mmWave. This technique provides efficient and strong maneuverability, adequate spectrum resources, and also overcomes the shortcoming of proneness to obstruction by flexible adjustment of mmWave position. In [67], smaller antennas are made by combining even smaller antenna arrays which demonstrate the effectiveness of beam steering and narrowing (BSN) techniques for enhanced channel capacity. [PERSON] et al. [68] presented a framework to incorporate the high data rate and growing user demand in mmWave cellular network in the 28 and 73 GHz band by dividing a dense mmWave hexagonal cellular network into various smaller cells with their own base stations. The analysis shows a better spectrum and energy efficiency.
# A Compact Size Antenna for Extended UWB with WLAN Notch Band Stub
## 3 Results and Discussions
### Reflection Coefficient  
A comparison among simulated and measured reflection coefficient of the antenna is presented in Figure 5. It can be seen clearly that the antenna offers an ultra-wideband spectrum of 3-14.32 GHz when measured, having a notched band ranging from 4.59-5.82 GHz. Moreover, a strong comparison between simulated and measured results validates the findings.
# Construction of Geosynthetic-Reinforced Pavements and Evaluation of Their Impacts
## 2 Field Construction  
### Overview  
The project site is located on Snyder Road East, Baden, Ontario. The test section is 45 m in length in total, composed of three trial sections that are each 15 m long. As shown in Figure 1, the pavement structure was composed of a 200 mm thick asphalt concrete layer and a 450 mm thick granular base layer, underlain by the subgrade. The asphalt concrete layer is composed of one lift of 50 mm surface course using a Superpave (SP) 12.5 asphalt mixture compacted over two 75 mm binder course lifts using an SP 19 asphalt mixture. The \"12.5\" and \"19\" in the designations of the asphalt mixtures refer to the maximum aggregate size (MAS), which are 12.5 mm and 19 mm, respectively. These mixes are commonly used in pavement design standards in Southern Ontario [27]. The base for all three sections is composed of an unbounded granular layer [28]. The pavement structural design is based on an Average Annual Daily Traffic (AADT) of 9573 and cumulative Equivalent Single Axle Loading (ESAL) of 3,608,000 ESALs with 5% trucks, 2% growth rate, and 20 MPa estimated resilient modulus [29].  
The use of fibreglass geogrid located at either the centre of the asphalt or geogrid composite on the subgrade can be an optimum solution for reconstructing a poor-condition pavement [30]. To better study this effect, three pavement structures were designed for the  
Figure 1: Section side view of field instrumentation and construction.  
trial sections in this project. These sections include a control section without reinforcement (referred to as Section 1, or the control section), a section with fibreglass geogrid installed within the two binder course lifts (referred to as Section 2, or the geogrid section), and the third section with geogrid composite laid at the interface of base and subgrade (referred to as Section 3, or the geogrid composite section). The fibreglass geogrid was installed in the asphalt layer to study its capability to reinforce asphalt concrete, while the geogrid composite bonded to geotextile has drainage and filtration functions, which is more crucial in the subgrade where water and fine particles can have a larger impact on the soil during freeze-thaw cycles.  
Instrumentation was prepared to monitor the temperature, moisture, and mechanical behaviour of the pavement; instrumentation will be further detailed in Section 2.3. The cross-section side view showing detailed instrumentation profile and pavement structures is shown in Figure 1. All the sensors testing mechanical behaviour were installed along the right wheel path of the westbound lane. The data from the sensors were logged using a data logger installed in a cabinet located on the side of the roadway.
## Materials and Methods
### Environmental Variables
#### 2.3.1 Remote Sensing Variables  
The remote sensing images used in this study included SAR and optical data. The SAR image used in this study was Sentinel-1A that was downloaded from the ESA (European Space Agency), whereas the optical data was Landsat-8 OLI that was downloaded from the USGS (US Geological Survey). Designed by the ESA, Sentinel-1 is composed of two satellite constellations, including Sentinel-1A and Sentinel-1B (S  
map STN. A total of eight environmental covariates were extracted from remote sensing images, four of which were from optical images and the remainder from SAR data.
## 2 Mathematical Model
### Computational Algorithm  
The computational procedure of the presented method may be summarized, as follows,  
1. Let the search index n = 0, and arbitrarily choose a set of initial values of a vector as \(\mathbf{w}_{(0)}\).
2. Substitute \(\mathbf{w}_{(n)}\) into transmission Equations (1)-(4) using numerical solver as finite difference method to obtain the values of \(\mathbf{x}(t;\)\(\mathbf{w})\) and \(\mathbf{i}(t;\)\(\mathbf{w})\), and use Equation (8) to obtain \(J(\mathbf{w})\).
3. Solve Equations (13) and (14) for \(\mathbf{g}_{(n)}\) and substitute it into Equation (12) to obtain \(\mathbf{E}_{(n)}\). Let \(\mathbf{A}_{(0)}=\mathbf{I}\) when \(k\) = 0; otherwise, substitute \(\mathbf{E}_{(n)}\) into Equation (11) to obtain \(\mathbf{A}_{(n)}\). Then, substitute \(\mathbf{g}_{(n)}\) and \(\mathbf{A}_{(n)}\) into Equation (10) to get search direction \(\mathbf{P}_{(n)}\).
4. For the step length, the two methods are proposed. The first is to solve Equation (16) to obtain step length \(\beta_{(n)}\). The second is to use the [PERSON]'s method (Equation (17a)), which is described in Section 2.5 to get \(\beta_{(n)}\).
5. After obtaining \(\mathbf{P}_{(n)}\) and \(\beta_{(n)}\), the new \(\mathbf{w}_{(n+1)}\) can be obtained from Equation (9). If constraint equations in Section 2.6 are applied, substitute \(\omega_{0}\), \(Z_{T}\), and \(\mathbf{w}_{(n+1)}\) into Equations (20)-(23) to make sure \(\mathbf{w}_{(n+1)}\) are matching the constraints, if not, update the \(\mathbf{w}_{(n+1)}\) from constraint Equations (20)-(23).
6. Set the search iteration to n = n + 1 and determine whether \(J(\mathbf{w})\) is smaller than the preset tolerance \(\epsilon(\leq 1\times 10^{-7})\) of convergence criteria or the specified number of iterations. If the constraint of the convergence criteria is satisfied, end the iteration process; otherwise, start from Step 2 again.
## 4 Power Control For Multiple Sink Cluster WSNs (MSCWSNs-PC)
### MSCWSNs-PC Algorithm Implementation  
In this section, we describe the implementation details of MSCWSNs-PC protocol using flowchart. The two main processes are route request and route reply. A flowchart of this distributed power control algorithm is shown in Figure 9.  
Figure 9: Flow chart for MSCWSNS-PC in each sink.  
Figure 10 gives a simple illustration of a radio radius calculation. For the underside border of the work area, the first sink \(A\) has the euclidean distance \(|Aa_{2}|\) as its transmission range \(R_{A,t}\), then sink \(B\) can calculate \(R_{B,2}\) according to \(R_{A,t}\) by:  
\[R_{B,2}=\sqrt{(x_{B}-x_{A})-\sqrt{R_{A1}^{\ 2}-{y_{A}^{\ 2}}^{\ 2}+{y_{B} }^{\ 2}}} \tag{11}\]  
From what has been discussed above, we may safely draw a conclusion that the \(R_{i,j}\), \(i\in M\), \(j\in\{\ \
umsink_{Topside}\ \
umsink_{Underside},\
umsink_{Lefiside}\ \
umsink_{Rightside}\}\), can be calculated by:  
\[\begin{cases}R_{i,j}=\sqrt{(x_{j}-x_{j,t})-\sqrt{({R_{j,t}}^{\ 2}-{y_{j,t}}^{\ 2})^{\ 2}+{y_{j}}^{\ 2}}}\ \ \ \ i\in M,j\in\{\ \
umsink_{t_{\text{path}}}\ \sigma j\in\
umsink_{t_{\text{path}}}}\\ R_{i,j}=\sqrt{({y_{j}-{y_{j,t}}})-\sqrt{({R_{j,t}}^{\ 2}-{y_{j,t}}^{\ 2})^{\ 2}+{y_{j}}^{\ 2}}}\ \ \ \ i\in M,j\in\
umsink_{t_{\text{path}}}\ \sigma j\in\
umsink_{Rightside}\end{cases} \tag{12}\]  
Finally, \(r_{i}\), \(i\in M\), be calculated by:  
\[r_{i}^{\ \tau}=\max\{R_{i,j}\}\ \ \ \ \ i\in M,j\in\{\
umsink_{Topside},\
umsink_{ unnsink_{t_{\text{path}}}},\
umsink_{t_{\text{path}}},\
umsink_{t_{\text{path}}}\} \tag{13}\]
## 3 Test Results and Analysis  
### Analysis of Low-Pressure \(CO_{2}\) Adsorption Test Results  
For coal samples, the micropore specific surface area is the dominant factor of gas adsorption capacity in coal [29; 31]. In order to study the gas adsorption capacity of PE coal samples and AB coal samples, the two types of coal samples were subjected to low-pressure CO\({}_{2}\) adsorption tests from the angle of their micropore structural characteristics. [32].  
As shown in Figure 3, the pore size distribution of AB and PE coal samples was obtained through tests. It could be seen that AB coal samples had the maximum peak at 0.4-0.7 nm and PE coal samples at 0.4-0.6 nm, and AB coal samples showed several small peaks at 0.7-1.2 nm and PE coal samples at 0.7-0.9 nm, indicating the appearance of many pores in this range. Meanwhile, it was known that AB coal samples were anthracite with relatively developed micropores, mainly because anthracite had a high degree of coalition and metamorphism, while PE coal samples were bituminous coal with a low degree of metamorphism and a low level of micropore development. The development of micropores was positively correlated with the specific surface area, and the larger the specific surface area, the stronger the gas adsorption, directly proving that the maximum gas adsorption capacity (39.842 cm\({}^{3}\cdot\)g\({}^{-1}\)) of AB coal samples was greater than that (17.9779 cm\({}^{3}\cdot\)g\({}^{-1}\)) of PE coal samples.  
Figure 2: Experimental equipment.
# Evaluation of Coastal Sea Level Offshore Hong Kong from Jason-2 Altimetry
## 5 Results
### Sea Level Trend
#### 5.2.2 The Tide Gauge Reference  
Using Equation (5), we computed the tide gauge trend over the 6.5-yr time span considered in this study. We found a linear sea level trend value of +5.2 \(\pm\) 2.0 mm/yr (after eliminating the outlier corresponding to the storm-surge event). However for further comparison with the altimetry-based sea level data, the tide gauge time series needs to be corrected for vertical land motion (VLM) [36]. This can be done using GPS precise positioning techniques. [PERSON] et al. (2008) [37] analyzed data from a GPS station named HKOH, which is close to the tide gauge. VLM at HKOH was estimated to +0.22 \(\pm\) 0.46 mm/yr. [PERSON] et al. (2010) [38] analyzed GPS data (2006-2009) from HKOH and obtained a VLM estimate of +0.3 mm/yr (uncertainty not provided). The time spans of these studies are different from ours, but because the VLM at HK appeared to be fairly stable over the last decade, we assume a VLM value of +0.3 mm/yr. After correcting for VLM, we find a trend of +5.5 \(\pm\) 2.0 mm/yr at the tide gauge site.
## 4 Multiple Objective Motion Optimization Problem
### Combining the Acceleration Constraints  
In practice, due to the power and motion constraints of the MASS, the acceleration is restricted by two aspects:  
1. The maximum acceleration, i.e., \(ACC_{max}\), which is determined by the driving power and breaking force of the ship; \[\sqrt{A_{t,x}^{2}+A_{t,y}^{2}}\leq ACC_{\text{max}}\] (10)
2. The ship has a minimum turning radius, which requires that the orthogonal acceleration over the forwarding velocity cannot be too large to avoid ship roll-over during turning. \[\frac{\left\|A_{t}-\frac{\langle v_{X}\cdot A_{t}\rangle}{\|v_{X}\|\|v_{X}\|} \right\|v_{X}\right\|}{\|v_{X}\|}\leq H\] (11) where \(\left\|A_{t}-\frac{\langle v_{X}\cdot A_{t}\rangle}{\|v_{X}\|\|v_{X}\|}v_{X}\right\|\) is the magnitude of acceleration orthogonal to the ship direction; \(H\) is a threshold.
# User-Information-Aware D2D Multicast File Distribution Mechanism
## 6 Cluster Head Selection Mechanism  
In the fourth section, the probability of the user requesting the video and the willingness for the potential service requester to accept the pushed video were deduced by analyzing the user's historical viewing information, the popularity of the video and the state information about the user's device. Using the user-demand-aware D2D multicast clustering algorithm, the potential service requesters that satisfy the condition of the willingness threshold were selected to form a D2D multicast cluster together with the service requesters. In D2D multicast communication, the selection of cluster head has a great influence on the performance of data transmission. Therefore, in this section, we propose a D2D multicast utility metric to measure the transmission performance of D2D multicast communication, and then a cluster head selection algorithm is proposed to optimize the system's D2D multicast utility value.
# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 4 Deep Learning-Based Detectors
### Network Architecture Design
Additionally, we have included dropout layers to introduce stochasticity during training, forcing the models to learn robust features and preventing them from relying too heavily on specific patterns present in the training data. These measures collectively contribute to enhancing the generalization capabilities of the models and mitigating overfitting concerns. Considering the critical importance of computational efficiency in cooperative spectrum sensing, the proposed models underwent an optimization process. The numbers of parameters and hyper-parameters were systematically tuned to strike a balance between performance and computational resource requirements. Through extensive simulations, a thorough exploration of the models' sensitivity to different parameter configurations was conducted. This optimization process aimed at achieving a model that not only excels in terms of accuracy but is also practical in real-world scenarios, where computational resources are often limited. The obtained results, particularly in terms of the detection time, suggest that the computational costs associated with the models are relatively modest.
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 4 Results
### Results Obtained from the CIC-IoT 2023 Dataset Using a Unsupervised Machine Learning Approach
#### 4.4.1 Research Analysis According to [52]  
Research [52] has investigated anomaly detection in IoT networks, utilizing the CIC-IoT 2023 dataset. The methodology encompasses several key steps.  
Step 1: Data Preparation and Processing  
The CIC-IoT 2023 dataset was preprocessed, which included converting symbolic values to numeric values and normalizing the data, selecting the most relevant features using the Information Gain Ratio (IGR) method and applying the Gaining-Sharing Knowledge (GSK) optimization algorithm to reduce the dimensionality of the data.  
Step 2: Model utilization  
The Autoencoder model was used for anomaly detection. Autoencoder architecture consisted of five layers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **ML Approach** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Source** \\ \hline Autoencoder & Unsupervised & 98.76\% & 99\% & 99\% & 99\% & [52] \\ \hline Isolation Forest & Unsupervised & low & low & low & 64\% & [53] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification report summary on the CIC-IoT 2023 dataset (own elaboration).  
Step 3: Model evaluation  
The performance of the Autoencoder model was evaluated using standard metrics. Evaluation was carried out for both a binary (normal/attack) and multi-class classification task (distinguishing between different types of attacks).  
Step 4: Result verification  
The results obtained with the AE model were compared with those of other models, such as Multilayer Perceptron (MLP). Analysis of the results from the source indicates that Autoencoder can be an effective tool for intrusion detection in the network, especially for binary classification.
## 4 Future Directions for Technology
### Multi-Source Data Fusion and Enhanced Expressive Power  
Reservoir seepage response prediction relies on a wide range of data sources, covering geological modeling data (e.g., lithology, porosity, permeability), engineering development data (e.g., injection and extraction intensity, well network layout), and dynamic monitoring data (e.g., pressure, WC, and daily fluid production), and it often presents heterogeneous characteristics across scales, modes, and time periods [30]. In reservoirs with extra-high WCPs, there are often problems such as format inconsistency, different acquisition frequencies, and missing spatial locations among the data, which seriously constrain the training quality and generalization ability of Deep Learning models [31]. In the current research, although multimodal fusion modeling was initially explored, most of the models still remained at the level of \"data side-by-side input\", failing to deeply explore the correlation structure and weighting mechanism between different data. Therefore, future research should strengthen structural alignment, semantic complementation, and spatial-temporal matching at the data fusion level, explore algorithmic mechanisms such as graph structural fusion and cross-modal alignment [32], and introduce domain knowledge-driven noise perturbation, simulation sample generation, counterfactual sample construction, and other strategies at the data enhancement level, so as to improve sample diversity and generalization ability and, thus, further improve the adaptability and stability of the Deep Learning model [33].
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 4 Modulation Recognition Based on Deep Learning in Small Sample Environment  
In order to overcome the problem of poor modulation recognition accuracy in a small sample environment, some scholars have begun to focus on research on modulation recognition algorithms based on deep learning in a small sample environment. There are two main methods currently being studied. One is to perform data enhancement on the modulated signal to generate enough data and then use the neural network for training and the second is to combine transfer learning with deep learning techniques and apply excellent models in other fields that deal with small samples of data to the field of modulation recognition. Therefore, we will introduce the modulation recognition algorithm based on deep learning in the case of small samples in this section, as shown in Figure 13.
## 3 Results and Discussion
### Effluent Characterization at Optimal Conditions
#### 3.4.1 Fractionation of Humic Substances  
Under optimal COD removal conditions, the EC process removed fractions of humic substances, measured as COD and DOC, in the same order, from highest to lowest: HA, FA, and HyL. Measured as COD, 69% HA (from 1900 \(\pm\) 100 mg L\({}^{-1}\) to 600 \(\pm\) 20 mg L\({}^{-1}\)), 63% FA (from 770 \(\pm\) 30 mg L\({}^{-1}\) to 290 \(\pm\) 10 mg L\({}^{-1}\)), and 61% HylL (from 870 \(\pm\) 30 mg L\({}^{-1}\) to 340 \(\pm\) 10 mg L\({}^{-1}\)). Measured as DOC, 75% of HA was removed from the EC process (850 \(\pm\) 40 mg L\({}^{-1}\) to 270 \(\pm\) 10 mg L\({}^{-1}\)), 67% of FA (330 \(\pm\) 10 mg L\({}^{-1}\) to 100 \(\pm\) 5 mg L\({}^{-1}\)), and 46% of HylL (260 \(\pm\) 10 mg L\({}^{-1}\) to 82 \(\pm\) 5 mg L\({}^{-1}\)). These differences in removal were related to the different molecular weights and surface loads of the three organic fractions [36]. The surfaces of humic substances were negatively charged because of the abundance of carboxyl and hydroxyl functional groups [90]. In the EC process, HA was preferentially eliminated by the adsorption mechanism, which implied an electrostatic attraction between humic substances and metal hydroxides, which were positively charged [36].  
The greater removal of HA could be explained by its greater degree of polymerization, which had a greater ability to interact with metal hydroxides than the other two fractions evaluated (FA and HyL) [20; 91]. These results were consistent with those of several studies that have characterized dissolved organic leachate materials [36; 92; 93]. In the crude leachate, the sum of HA and FA represented 76% and 82% of the COD and DOC, respectively, confirming the high degree of stabilization of the sampled leachates [88]. [PERSON] et al. [36] characterized biologically pretreated leachates in an anaerobic peat and wood biofilter and obtained removal values of HA and FA of 92%, which were higher than those obtained in this study, although the leachates used were not of the stabilized type. In the EO process, the removal of different fractions of humic substances occurred in an inverse order to that of the EO, from lower to higher: HA, FA, and HyL. Measured as COD, 40% of HA was removed (620 \(\pm\) 30 mg L\({}^{-1}\) to 370 \(\pm\) 10 mg L\({}^{-1}\)), 55% of FA was removed (290 \(\pm\) 10 mg L\({}^{-1}\) to 130 \(\pm\) 6 mg L\({}^{-1}\)), and 68% of HyL was removed (320 \(\pm\) 10 mg L\({}^{-1}\) to 100 \(\pm\) 40 mg L\({}^{-1}\)). Measured as DOC, the EO process removed 60% HA (210 \(\pm\) 10 mg L\({}^{-1}\) to 80 \(\pm\) 4 mg L\({}^{-1}\)), 65% FA (111 \(\pm\) 5 mg L\({}^{-1}\) to 39 \(\pm\) 1 mg L\({}^{-1}\)), and 76% HylL (141 \(\pm\) 7 mg L\({}^{-1}\) to 34 \(\pm\) 1 mg L\({}^{-1}\)). This could be explained by the fact that, in this case, elimination was generated by the action of \(\bullet\) OH radicals, which more easily degraded the less complex molecules, in order of structural complexity from lower to higher, HyL, FA, and HA [94; 95].
# The Feasibility of Shadowed Image Restoration Using the Synthetic Aperture Focusing Technique
## 2 Experimental Setup
### Physical Model  
The physical model created to perform this shadowing scan consists of a skew driver rod and seven fishing lines (Figure 1a). The rod (T\({}_{\text{OBS}}\)), 5.5 mm in diameter, is used as an obstacle for three fishing lines (T\({}_{1}\), T\({}_{2}\), and T\({}_{3}\)). The other four fishing lines (T\({}_{4}\), T\({}_{5}\), T\({}_{6}\), and T\({}_{7}\)) are used to serve as a comparison. The diameter of the fishing line (T\({}_{\text{N}}\)) is 0.7 mm. Taking gallstone ultrasound as an example, T\({}_{1}\), T\({}_{2}\), and T\({}_{3}\) can be considered to be small gallstones shadowed by a big one, T\({}_{\text{OBS}}\). To create the shadowing effect and, as a contrast, T\({}_{\text{OBS}}\), T\({}_{1}\), T\({}_{2}\), T\({}_{3}\), T\({}_{4}\), T\({}_{5}\), T\({}_{6}\), and T\({}_{7}\), respectively, are aligned inline. For this purpose, T\({}_{1}\), T\({}_{2}\), and T\({}_{3}\) are positioned behind T\({}_{\text{OBS}}\) to obtain a shadowed image for the scan position SP\({}_{\text{OBS}}\). Additionally, SP\({}_{\text{FR}}\), T\({}_{4}\), T\({}_{4}\), T\({}_{5}\), and T\({}_{7}\) are also aligned inline for an obstacle-free scan. Bothof the alignment lines are perpendicular to the scan surface (Figure 1b). The pink dot-line shown in Figure 1 indicates the scan line. SP\({}_{\text{OBS}}\) and SP\({}_{\text{FR}}\) located on the dot-line are two specific scan positions to perform shadowed and shadow-free scans.