esa-sceva/satcom-chunk-collection huggingface dataset:
# An Elliptical-Shaped Dual-Band UWB Notch Antenna for Wireless Applications
## 4 Discussion and Outcomes
### UWB Antenna Results
#### 4.1.2 Current Distribution  
Figure 11 illustrates a simulation and study of the current distribution to illustrate the functionality of the proposed UWB antenna. At 3.6, 6.8, and 9.7 GHz, the resonance qualities are examined. Figure 11 shows how different antenna parts have varied resonance patterns for different operating modes. In Figure 11a, the lower portion of the feed is active at 3.6 GHz, where the current is highest. In Figure 11b, the feed at 6.8 GHz has the highest current. Figure 11c reveals that the lower portion of the patch and feed are most active at 9.7 GHz. Thus, the antenna resonates at 3.6, 6.8, and 9.7 GHz.  
Figure 10: VSWR, simulated and measured.
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c l l l l l l} \hline \hline \multirow{2}{*}{**Embedding**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{6}{c}{**Different Metrics for Different Variants**} \\ \cline{3-6}  & & **Silhouette** & **Calinski–** & **Harabasz Index** & **Davies–Bouldin** & **Runtime (s)** \\ \hline \multirow{6}{*}{\(k\)-means} & \(k\)-means & 0.48 & 13,089.36 & 1.38 & 553.95 \\  & \(k\)-means + Boruta & 0.34 & 17,478.82 & 1.4 & 20.01 \\  & \(k\)-means + & 0.48 & 13,112.61 & 1.38 & 61.78 \\  & LASSO & & & & & \\  & \(k\)-means + RFF & 0.25 & 4855.80 & 2.04 & 28.02 \\ \cline{2-6}  & \(k\)-modes & 0.26 & 6626.82 & 2.59 & 198,926.91 \\  & \(k\)-modes + Boruta & 0.33 & 13,146.09 & 1.89 & 6646.35 \\  & \(k\)-modes + & 0.28 & 9596.01 & 1.92 & 36,435.04 \\  & LASSO & & & & & \\  & \(k\)-modes + RFF & \(-0.15\) & 1389.75 & 1.07 & 1370.85 \\ \cline{2-6}  & Fuzzy & 0.27 & 8821.95 & 2.13 & 29,193.81 \\  & Fuzzy + Boruta & 0.33 & 13,397.80 & 1.89 & 664.19 \\  & Fuzzy + LASSO & 0.26 & 8663.69 & 2.17 & 3958.60 \\  & Fuzzy + RFF & 0.19 & 12,069.50 & 1.09 & 395.95 \\ \cline{2-6}  & Hierarchical & 0.44 & 10,516.02 & 1.84 & 106,092.34 \\  & Hierarchical & + 0.32 & 13,324.79 & 1.60 & 2761.91 \\  & Boruta & & & & & \\  & \(k\)-modes + RFF & 0.42 & 10,529.10 & 1.61 & 8650.66 \\  & LASSO & & & & & \\  & \(k\)-modes + RFF & 0.26 & 5010.25 & 1.00 & 2542.87 \\ \cline{2-6}  & \(k\)-modes + RFF & 0.26 & 5010.25 & 1.00 & 2542.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Clustering quality metrics for different clustering methods with number of clusters \(=5\). Best values are shown in bold.
## 2 Methods  
To analyze and assess the BDS-2 and BDS-3 broadcast ephemeris, we begin with the evaluation and calculation methods of broadcast ephemeris and the SISRE. For BDS broadcast orbits, BDS interface control document (ICD) gives the calculation methods, which will not be discussed in this contribution [17; 18; 19]. As for the evaluation and calculation methods of satellite clock offsets, a deep analysis in detail is offered in this section. Finally, the traditional BDS SPP and new BDS-2 and BDS-3 combined SPP models considering ISB are developed in this section.
# Retrieving Freeze/Thaw Cycles Using Sentinel-1 Data in Eastern Nunavik (Quebec, Canada)
## 3 Results
### Seasonal Reference Winter versus Summer  
As one of the most important basic conditions in the algorithm, seasonal references have to be meaningful and representative. As shown in Figure 4, the references calculated from VV and VH for the 39 test sites shared almost the same curve for the same season, while the VH references were lower than the VV references overall, which also continues to confirm the observation in the previous section.  
By comparing the references of summer (August) and winter (January + February), obvious seasonal differences can be observed in both polarizations. At most sites, the value of the summer reference was higher than that of the winter reference. There were also some counterexamples, mainly on the sites with controversial data quality mentioned in the previous site. In comparison, there were more sites from VH polarization that did not meet expectations than from VV polarization.  
Considering the original quality and the performance of the timeseries data after preliminary calculations, some sites were carefully excluded in subsequent tests. The most relevant problem, leading to exclusion, was the limited data amount, at sites such as stations 12, 33, and 38, as well as the 10 cm layer of station 8. Here, the overlap between the  
Figure 3: Backscatter signal time series of chosen test sites, **left**: stations **2**, **6**, and **36**; **right**: stations **4**, **31**, and **38**; blue, VV polarization; green, VH polarization.  
in situ measured data and SAR data was too small to support the testing process. There were also arbitrary issues, such as the location pointed to by the coordinates of site 4 (in the water), which was inconsistent with the site description (on a sandy plain with seashells). Although these sites were not used for this work, they are still very valuable data in the Nunavik area; thus, they were kept in the summary of data to provide information for future work.
## 1 Introduction
3D Doppler OCT used for regional cerebral blood flow (CBF) measurements in rat cortexes has been reported to obtain the absolute CBF [32]. OCT angiography revealed microvascular hemodynamics in the rat somatosensory cortex during subject activity [33]. The refractive index in the somatosensory cortex of in vivo rats has also been measured using FF-OCT [34]. In vivo imaging of neuronal cell bodies and the cortical myelination of rats up to depths of 1.3 mm were demonstrated using sealed cranial windows and a 1.3 \(\upmu\)m spectral domain OCM [35]. Single-myelin-fiber imaging of in vivo rat cortexes at a depth of 0.34 mm was also performed using OCM with high resolution and cranial windows [36]. The imaging of cytoarchitecture and myeloarchitecture in the rodent cortex in vivo was demonstrated using spectral domain OCM [37]. It is known that white matter is crucial for cognitive functions, and that age-related white matter alterations and the loss of myelinated fibers and oligodendrocytes may have important implications in age-related cognitive and mnemonic impairments [38; 39]. Therefore, it is necessary to study the white matter at a cellular level [40]. Using the window on a skull, OCT images of in vivo rat brain have been obtained at depths of 1.3 mm with axial resolution of 4.7 \(\upmu\)m. It is necessary to have in vivo and ex vivo imaging of deeper areas under the cortex with high spatial resolution. As the thickness of the cortex is approximately 1.5 mm [41], low-invasive thin probes with a spatial resolution of a few micrometers are necessary; however, using such a probe for in vivo imaging has not been reported yet. In this study, we demonstrate the OCM imaging of in vivo rat brain by means of FF-OCM using an SMMF probe with an OD of 125 \(\upmu\)m and a length of 7.4 mm. First, the imaging conditions as the basic characteristics of SMMF are shown; subsequently, the experimental setup, spatial resolution, and influences of linearly polarized (LP) modes on the images in SMMF are described. Finally, experimental procedures to insert the SMMF into the brain, and 3D OCM images in tissues are demonstrated.
## 2 Theoretical Background and Hypotheses Development
### Stimulus (S): Social Robot (MOCCA)'s Features
#### 2.3.3 Perceived Risk--Information Privacy Risk (IPR)  
Previous research has demonstrated that humans frequently attribute human-like characteristics to social robots [29], often anthropomorphizing them. This tendency facilitates the integration of social robots into daily life [54], as individuals are more likely to perceive them as friends or companions. As a modern social robot, MOCCA is equipped with advanced processors and innovative sensors, enabling it to analyze user data with precision and engage in emotionally responsive interactions. Users may develop a sense of emotional connection with MOCCA and feel more comfortable sharing personal or sensitive information. However, while MOCCA can provide emotional support and companionship, it may also expose users to perceived risks related to information privacy [55; 56], particularly when users are not fully aware of how their personal data are collected, used, or shared.  
Information privacy risk refers to users' concerns about potential privacy loss due to the disclosure or exposure of personal information to third parties or institutions [10; 57]. Equipped with advanced sensors, social robots are capable of collecting not only contextual information but also detailed personal data about users [5; 58]. For instance, MOCCA engages in continuous interaction with users, during which it transfers and processes large volumes of personal information. These data may include users' behavioral patterns, preferences, or even sensitive information exchanged during interactions. The extensive collection and transmission of such personal data raises serious concerns about data security, particularly regarding how these data are secured and whether they might be subject to unauthorized access or misuse [9].
# Plate Size Effects in Gravelly Soil Based on In Situ Plate Load Tests and Finite Element Analysis
## 4 Finite Element Analysis
### Results and Analysis
#### 4.2.3 Coefficient of Subgrade Reaction  
As illustrated in Figure 11, the \(K_{p}\) decreased with the increasing test plate diameter in a power-law function. Based on [PERSON]'s [25] correction formula for the coefficient of subgrade reaction in clay, a correction relationship was derived for Equation (3). Using the test plate with an 800 mm diameter as a reference, the coefficient of subgrade \(K_{s}\) for different diameters was fitted, yielding an exponent \(n\). The fitting result for Equation (4) resulted in \(n=0.953\), which is highly consistent with [PERSON]'s [25] formula, indicating that the subgrade modulus of gravelly soil can be extrapolated through [PERSON]'s method from PLT.  
\[K_{s}=K_{p}\bigg{(}\frac{B_{p}}{B}\bigg{)}^{n} \tag{3}\]  
Figure 11: Relationship between \(K_{s}\) and diameters.  
Figure 10: Relationship between \(E_{PLT}\) and diameters.  
Here, \(K_{p}\) represents the subgrade modulus of the reference test plate, \(B_{p}\) denotes the diameter of the reference test plate, and \(n\) is the exponent that needs to be determined.
## 2 Materials and Methods
### Transfer Learning  
In machine learning, transfer learning is the process of transferring knowledge from a model that performs well at a certain task to a new model used for a different task. Generally, the model from which transfer occurs is trained and tuned in a high-performance computing environment. However, due to the lack of resources, similar performance cannot be achieved using consumer-level hardware. Additionally, the models from which transfer occurs are generally trained on millions of samples over a long period of time. Transfer learning has been proven to be an efficient technique that helps newer models to generalize faster by benefiting from previously acquired knowledge [36].  
For CNNs, there are multiple ways to reuse features or transfer knowledge from a trained network to a new model. The most widely used techniques are:  
1. CNN feature extraction: A fully trained CNN can be repurposed and treated as a feature extractor by removing its output layer. For a network such as the VGG16 [37], the first dense layer of 4096 units can be seen as a CNN features vector. A lightweight linear classifier can be used such as a Support Vector Machine (SVM) that takes this CNN features vector as input and outputs the data classes. In most cases, CNN features can provide a fast way of implementing an accurate image classifier with few data samples on low-end hardware.
2. Fine-tuning: Fine-tuning consists of retraining or replacing the classification layer of a CNN (final layer). There are multiple fine-tuning approaches. The first is straightforward, and consists of removing the last layer, freezing the weights of the already trained network and training the last classification layer. The second approach consists of removing the last classification dense  
Figure 6: A Convolutional Neural Network (CNN) architecture.  
layer, connecting a new output layer and then training the whole network with a smaller learning rate; this helps the model to converge faster as it benefits from the weight initialization of the previously trained network. The third approach consists of freezing only the weights of the first few convolutional layers and then training the remaining layers. Previous work [38] has shown that the first layers perform very accurate universal low-level feature extraction. As a result, transferring these features from a well-trained network is beneficial.  
In this work, we used the ResNet50 network [39], which is a residual network with fine-tuning of all its layers. We removed the last dense layers and used average pooling to compress the features of the last convolutional layer. The output layer of the network consists of 2048 ReLU (Rectified Linear Unit) activated units.  
In the following section, we present an overview of residual networks and justify their selection in our approach.
# A Filter Pruning Method of CNN Models Based on Feature Maps Clustering
## 5 Experiments and Results
### Preparation in Advance
#### 5.1.2 Training and Evaluation before Pruning  
1. Metrics: In our experiments, since the face position is critical for future applications, for instance, detecting the face, IoU (intersection over union) for a bounding box that indicates the accuracy of the face position is adopted. The IoU can be calculated as \[\text{IoU}=\frac{A_{o}}{A_{u}}\] (6) where \(A_{o}\) represents the area of the overlap for the ground truth bounding box and detected bounding box. \(A_{u}\) represents the area of the union for the ground truth bounding box and detected bounding box. To evaluate the performance of face detection, we use the mean average precision (MAP) to calculate the mean value of average precision (AP) for each query \(Q_{l}\). MAP is calculated as \[\text{MAP}=\frac{1}{|Q|}\sum_{i=1}^{|Q|}AP(Q_{i})\] (7)
2. Results:  
The training dataset is trained with the RetinaNet network for 80 epochs from scratch in Nvidia Titan-X GPU with i7-7700K CPU and 16 GB RAM. For tuning the parameters, the IoU threshold is set to 0.5, which is considered a good location predictor for iterating out the inaccurate predicted face position. When the training process finishes, the classification loss and the regression loss curves both converge as shown in Figure 11.  
Figure 10: Wider face Dataset Samples.  
Then, we evaluate the trained model with the processed validation set. The evaluation results, including the precision, speed, and number of parameters, are shown in Table 1. It can be seen that the initial precision MAP equals 71.75%, and the number of parameters is 19,195,117. In addition, GPU time and CPU time represent the time of the network detection for one picture; they are 0.143 s and 0.388 s, respectively. These results will be used for the following comparisons with the pruned models.
# Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models
## 2 The Methodology
### Simplification Rules
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Simple Rule** & **Simplification Action** & **Example** \\ \hline plane & (a) _voxel_(a) & \\  & (b) _center_(_voxel_) \(\in S\) & \\  & (c) _alpha_\_shape_(\(R\)) \(\in S\) & \\  & & \\ \hline sphere & (a) _center_(_sphere_) \(\in\) _sured_ & \\  & (b) if \(\Delta_{curvature}<\varepsilon_{cur}\), then _voxel_(\(\alpha\)) & \\  & (c) if \(\frac{\|p_{i}-\emph{center}(\emph{sphere})\|=}{\emph{c}_{cur}}\), then _voxel_(\(\alpha\)) & \\  & (d) if \(\Delta_{curvature}\geq\varepsilon_{cur}\), then \(p_{i}\in S\) & \\ \hline ellipsoid & (a) _center_(_ellipsoid_) \(\in\) _sured_ & \\  & (b) _voxel_(\(\alpha\times\overline{h}\)) & \\  & (c) _alpha_\_shape_(\(P\)) \(\in\) _sured_ & \\ \hline cylinder/cone & (a) if (\(\|p_{i}-V_{0}\|\leq\gamma\times\max\)), then _voxel_(\(\alpha*\overline{h}\)) & \\  & (b) if (\(\|p_{i}-V_{0}\|\geq(1-\gamma)\times\max\)), then _voxel_(\(\alpha\)) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simplification Rules. (4) **Rule for cylinders/cones:** If the area is matched with a cylinder/cone, the top and bottom of the cylinders/cones is important to shape-keeping. The distance (\(\|p_{i}-V_{0}\|\)) between points and the axis of the cylinder/cone can be seen as the criterion by which the location of a point relative to the cylinder/cone is judged. \(\gamma\) is the threshold of distance, and the \(max\) is the maximum of distance (\(\|p_{i}-V_{0}\|\)) in the last row of Table 1. The points are classified into two types during the point cloud simplification. The points which are next to the top or bottom of cylinder/cone, are simplified as the voxels at the fixed resolution \(\alpha\). The points which are next to the body of the cylinder/cone, are simplified as the voxels at the variable resolution \(\alpha\times\overline{h}\). An example of standard cone simplification is shown in the last row of Table 2.
### Contributions  
The major contributions of this work are summarized as follows:  
* A novel D2D aided two-phase CRS exploiting SM-MIMO (termed D2D-CRS-SM) is proposed, where interference cancellation and management techniques are not required at any of the communication nodes. Two different scenarios are investigated considering information reception criteria at NU during \(P_{1}\).
* The proposed protocol demonstrates how interference free information reception is possible at multiple cellulars and D2D users in the same frequency band considering downlink transmission scenario. Moreover, unlike other popular method such as NOMA, no fixed portion of transmitted power is required to be allocated for a user.
* In conventional SM [12], both symbol and antenna indices of a transmitter are the desired information of a single receiver. However, in D2D-CRS-SM, symbol and antenna indices are the desired information of two different receivers; the protocol, thus, facilitates multi-user communication.
* The performance of D2D-CRS-SM is analyzed and investigated, in terms of bit error rate (BER), spectral efficiency (SE), and sum SE considering \(M\)-PSK and \(M\)-QAM over independent Rayleigh fading channels. Moreover, comparative results with Vertical-Bell Laboratories layered space-time (VBLAST) spatial multiplexing MIMO technique [49] with zero-forcing (ZF) detector [50] (termed VBLAST-ZF) is also provided.  
* The efficiency of D2D-CRS-SM is validated via Monte Carlo simulation. It is observed that sum SE in both scenarios is almost identical for similar set of parameters.
# Directional Paging for 5G Communications Based on Partitioned User ID
## 3 Proposed PIDP Paging
Thus, multiple POs are required in each IDRX cycle. Since the network is aware of paging load and other factors, it configures numerous POs in the LTE/LTE (A) system. In directional paging, as well, multiple POs would be required for the same reasons. However, in the PIDP mechanism, we propose that the network configures multiple POs in each IDRX cycle based on \(N1\) bits (that is, UE ID Part 1). Let \(N_{PIDP}\) be the number of POs that can be configured per IDRX cycle in PIDP, and it is evaluated as: \[N_{PIDP}=2^{N1}=2^{(N-N2)}\] (6) where \(N1\) are the number of bits by which each UE ID size is cropped. The POs are subsequently mapped to all the possible bit configurations that can be attained using \(N1\) bits. Thus, distinct PO in the IDRX cycle has a distinct \(N1\) bit configuration associated with it. Unlike PO/PF evaluation in LTE (Section 2.2), in the PIDP mechanism, we propose a novel and simplified mapping between POs and the least significant bits of the UE ID. 3. Distributing: In the legacy system, multiple POs are configured by the network in the IDRX cycle, and the UE are distributed over these POs. Similarly, in the PIDP mechanism, the UE are distributed over \(N_{PIDP}\) number of POs configured for each IDRX cycle. We propose that the UE with the same least significant bits are configured in the same PO. Thus, in PIDP, while only \(N2\)  
Figure 3: Partitioning of UE ID in the partitioned UE ID-based directional paging (PIDP) mechanism. most significant bits of the UE ID (Part 2) are included in the paging message, \(N1\) least significant bits (Part 1) are used to distribute the UE over different POs. This ensures that the IDs of all the UE that are paged are uniquely identified by the network even though only partial bits are included in the paging message. Figure 4 gives an example for the configuration of POs and the distribution of UE over the configured POs when \(N1=2\). For \(N1=2\), four POs are configured each with distinct bit sequence \(00,01,10\) and \(11\). All the UE with the same Least Significant Bits (LSBs) (e.g., 00) are distributed over the same PO (e.g., \(PO_{1}\)). In this example, only \(N2=N-2\) bits per UE are included in the paging message. Though the reduction in the UE ID size per UE is expected to reduce the overall paging resource overheads in directional communication, at the same time, the configuration of POs and the distribution of the UE would also change. It is therefore important to analyze the performance of the PIDP mechanism considering all the related parameters to ascertain its gains. In the subsequent numerical analysis, we evaluate the PIDP gain taking into account the aforesaid partition and configuration.
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 4 Results  
In this section, we present the results of our study. We begin with the presentation of the results of the plume segmentation model in Section 4.1. Appropriate segmentation quality is necessary for a correct estimation of NO\({}_{2}\) produced by ships. In Section 4.2, we validate the concept presented in this paper. In the Subsection, we compare the obtained on the basis of segmentation model results of ship NO\({}_{2}\) estimation with the theoretical ship emission proxy.
## 2 Materials and Methods
### Phenolic Characterization by HPLC-DAD-IT-MS  
Phenolic characterization was conducted using an Agilent HPLC series 1100 (Agilent Technologies, Santa Clara, CA, USA) equipped with an autosampler and a diode array detector (DAD) connected to a Bruker Esquire 3000 Ion Trap Mass Spectrometer (Bremen, Germany) with an Electrospray Ionization Source (ESI).  
For the separation process, a ZORBAX Eclipse XDB-C18 column (150 \(\times\) 4.6 mm, 5 \(\upmu\)m particle size) was employed from Phenomenex. The mobile phases consisted of 0.1% (\(v/v\)) formic acid in water (A) and 0.1% (\(v/v\)) formic acid in methanol (B), with the following gradient: 2% B isocratic (0-2 min), 2-12% B (2-5 min), 12% B isocratic (5-15 min), 12-20% B (15-16 min), 20-35% B (16-36 min), 35-50% B (36-47 min), 50-98% B (47-61 min), 98% B isocratic (61-62 min), and 98-2% B (62-64 min). Detection wavelengths for the analysis were 280 nm, 320 nm, and 520 nm.  
The mass spectrometer's ion source operated in both positive and negative modes, scanning a mass range from \(m/z\) 50 to 1000. The MS parameters were set as follows: capillary voltage at 3500 V, drying gas flow rate at 10 L/min, nebulizer pressure at 45 psi, and dry gas temperature at 350 \({}^{\circ}\)C. The collected data were analyzed in detail through a comprehensive review of the scientific literature and the MS database (FOODB) to verify the potential presence of the identified compounds in the extracts and assess whether these compounds had been previously reported in the studied plant.