esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 5 Deep Learning Approaches
### Fusion and Cascading Architectures
#### 5.3.2 Lidar Fusion
[105] also leverages lidar submaps for radar-based localization on a lidar map. However, in contrast to [104], individual radar and lidar backbones are implemented using a Recurrent All-Pairs Field Transformer (RAFT) feature extractor [132] to generate a BEV feature representation and a unique descriptor for each sensor. This method utilizes a place recognition head for loop closure detection and a metric localization head to find the 3D pose \(T\in SE(3)\) on the lidar map. During training, the place recognition head is trained with a triplet loss between the current radar descriptor and a positive and negative example of a lidar submap to enforce the similarity of descriptors between similar places recorded with different sensors. Interestingly, the application of a final NetVLAD layer did not produce better localization results than the final CNN layers. The encoded positive lidar submap is further used in the metric localization head to predict the complete flow in the radar scene. Akin to [89], this method also utilizes the correlation volume between the two BEV encodings as input for a GRU to predict the scene flow. Utilizing this flow and the localization head, the ego-pose of the system in the lidar map can be inferred from a single radar scan. [PERSON] et al. [106] implement a cross-modal model using 4D automotive radar sensors, a lidar sensor, camera images, and IMU odometry sensors for a supervised scene flow approach. The primary objective is to predict the complete scene flow of a scan, encompassing the rigid ego-motion and motion of other dynamic objects. Relevant stationary points of consecutive scans and corresponding weights are estimated from 4D radar point clouds processed through a backbone network consisting of _set conv_ layers [133], an initial flow estimation head, and a motion segmentation head. Similar to [89], an SVD decomposition is applied to the found coordinates to derive a rigid transformation matrix, which is trained using the transformation found with the IMU sensor.
# Analysis and Design of Tunable THz 1-D Leaky-Wave Antennas Based on Nematic Liquid Crystals
## 2 Theoretical Analysis  
### Closed Waveguide  
We start from the analysis of the PEC/PMC air-filled waveguide (see Figure 1, left, with the relevant coordinate systems), which differs from the one analyzed in [43] for having the PEC and PMC walls along the short and long sides, respectively, (in our geometry equivalent to the horizontal and vertical sides, respectively). A simple eigenvalue analysis of this structure reveals that the fundamental mode is a transverse electric and magnetic (TEM) mode (the limit case of a transverse electric (TE) mode, as opposed to the TEM mode of the parallel-plate PEC waveguides, which is the limit case of the transverse magnetic (TM) mode), whereas the first higher-order mode is the TM\({}_{10}\) (the first and second subscript refer to modal indexes along the \(z\)- and \(y\)-axes, respectively).  
In the PEC/PMC air-filled waveguide, due to the closed and lossless nature of the problem, the propagation wavenumber \(k_{x}\) of the TM\({}_{10}\) mode can only be either purely real or purely imaginary [44], depending on whether the mode is above or below the cutoff. Hereafter, we will focus exclusively on this TM\({}_{10}\) mode as the radiating properties of the proposed LWA will be determined by its perturbed (partially open) version.  
The eigenvalue analysis also reveals that the TM\({}_{10}\) mode has only \(H_{y}\), \(E_{z}\), and \(E_{x}\) components, with \(\{H_{y}\), \(E_{z}\}\propto\cos(k_{z}z)\) and \(E_{x}\propto\sin(k_{z}z)\), \(k_{z}\) being the transverse wavenumber. We note here that this modal configuration has consequences on the required feeding scheme of the waveguide. As shown in [45], the sinusoidal profile of the \(E_{y}\) field of the \(\mathrm{TE}_{10}\) mode couples efficiently with the Gaussian profile of the impinging transversely polarized \(\mathrm{THz}\) beam commonly used as a free-space excitation source. Conversely, the \(\mathrm{TM}_{10}\) mode has a non-negligible \(E_{x}\) field component that can be efficiently excited with a longitudinally polarized \(\mathrm{THz}\) beam. Therefore, sources such as those proposed in [46; 47] are considered here for efficient excitation of the desired \(\mathrm{TM}_{10}\) mode.
# Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser
## 3 Proposed Fog-Based CDN Framework
### Fog-Based CDN Framework Components
#### 3.1.1 Fog Registry (\(F\)r)  
The Fog Registry (\(FR\)) is a service that stores all fog-node-related data, as well as fog services available for offloading. Communication between the \(FR\) and other components takes place using the RESTful API. The fog nodes register themselves once they boot up. Any web service can integrate with the fog-based CDN framework by registering itself into the \(FR\) as well. The \(FR\) also keeps track of active event channels that are geographically distributed. Based on the fog node's geographic location, the \(FR\) instructs the nodes to subscribe to specific event channels. Furthermore, the \(FR\) publishes the registered services for the registered fog nodes once they get registered or updated.  
Since the Fog Registry is keeping track of the registered fog nodes, it can also be extended to compute other fog node related aspects, such as: importance of the fog node and its reliability to other nodes [17; 18].
# Spinal Cord Segmentation in Ultrasound Medical Imagery
## 3 Proposed Method
### Atrous Spatial Pyramid Pooling  
As the shape and the size of objects inside the image may differ, the concept of image pyramid [31] had been introduced to improve segmentation precision. It consists on extracting features from different scales in a pyramid like approach before interpolating and merging them. But calculating the feature maps for every scale separately increases the size of the network and leads to heavy computations with risk of over-fitting. This is why a relevant method that combines the multiscale information in an efficient way is needed. Spatial pyramid pooling (SPP) [32] is proposed to treat this problem. SPP was first proposed to solve the issue of random input size of proposals in object detection [32]. SPP divides the randomly sized images into spatial bins before applying the pooling operation on every bin and concatenating them to obtain the fixed feature map size associated with this input image. Although its efficiency in capturing multi-scale features from the image, SPP is not well adapted to image segmentation because the pooling operations lose the pixel details needed for this task. Hence, we substituted the normal pooling layers in SPP by atrous convolutions with different sampling rates. Then, the extracted features from every sampling rate are merged to obtain the final  
Figure 7: Spinal Cord semantic segmentation using Fully convolutional DensenNets.  
feature vector. This method is called Atrous Spatial Pyramid Pooling (ASPP). The atrous convolution helps to have different receptive field for a convolution kernel by merely changing the sampling rate. This approach is the base of the state of the art model DeepLabv 3 plus [33], which is a segmentation model different in architecture from U-Net and FC-DenseNet. It is currently the best algorithm tested on PASCAL VOC dataset [34]. This is why we decided to study the effect of inserting an ASPP module inside the FC-DenseNet. Figure 8 illustrates the version of ASPP used in DeepLab v3 plus and in our experiments. Figure 9 shows the insertion of the ASPP module inside the DenseNet block to form an ASPP Dense Block. We will study the effect of this insertion inside the experimental part.
## 3 Results
### Future Projections of Urban Expansion  
#### 3.3.1 Future Projection Results  
In future urban projections, the Markov chain is utilized to forecast the additional urban land in 2024 and 2031 based on the land use data from 2010 to 2017. The total area of newly added urban land was 703.58 km\({}^{2}\) between 2010 and 2017, between 2017 and 2024 it was 633.13 km\({}^{2}\), and between 2024 and 2031 it was 651.98 km\({}^{2}\). By employing the RF-SNSCNN-CA model, simulations are conducted using the real land use data of 2010 as the initial state to predict the spatial patterns of urban expansion in 2024 and 2031. The final simulation results are shown in Figure 12. The findings of future urban development trends are as follows:  
1. The overall results of the Markov chain prediction indicate a slowdown in the urban development rate, with the urban expansion rate in the period of 2017-2031 being lower than that of 2010-2017.
2. Discrete areas within the central city gradually become connected, forming a more cohesive urban landscape.
3. Peripheral urban areas exhibit a tendency to develop towards the central area.
## 3 Results
### Grain Yield, Water Consumption and Water Use Efficiency  
Grain yield, water consumption and water use efficiency (WUE) of winter wheat were 7528-8387 kg ha\({}^{-1}\), 327-367 mm and 2.11-2.56 kg m\({}^{-3}\), respectively (Table 4). A quadratic function was fitted to the relationship between grain yield and WUE and between water consumption and WUE (Figure 6). Compared with the same border, grain yield with a 75 mm irrigation quota was relatively stable, but total ET\({}_{\rm c}\) was high, leading to a lower WUE than that of a 45 mm irrigation quota. When border width increased, the difference in grain yield and ET\({}_{\rm c}\) between 45 mm and 75 mm irrigation quota treatments gradually became larger, whereas the difference in WUE was from 0.12 kg m\({}^{-3}\) (border width 2.3 m) to 0.40 kg m\({}^{-3}\) (border width 5.3 m). Maximum grain yield and WUE were obtained with a 45 mm irrigation quota. During the growing season of 2020-2021, spike number, ineffective spike number, and grain number per spike were 19.3-19.7, 2.98-3.75 and 28.5-31.5, respectively. Furthermore, it was observed that both the maximum and minimum values of the above-mentioned indicators were observed with the 45 mm irrigation quota treatments, indicating lower irrigation quota induced larger variations in yield components, which was not beneficial for winter wheat to obtain stable yields. The analysis results showed that the water consumption of different border widths was significantly different. There was also a significant difference in water consumption and water use efficiency among different irrigation quotas, respectively. The yield was less affected by border width and irrigation quota, and the difference between treatments was not significant.  
Figure 6: Relationship between grain yield and water use efficiency, and between water consumption and water use efficiency for winter wheat.
## 4 Simulations and Discussion  
In this section, experiments were conducted to evaluate the capacity of the proposed BA-ELM compensation method. The experiments included three steps. Firstly, we collected training samples before employing the BA-ELM to estimate the function. Secondly, some parameters which have an impact on estimating function performance were determined. The cross-validation method was utilized to optimize these parameters and obtain the BA-ELM model whose fitting accuracy and prediction accuracy met the requirements. Thirdly, we used the simulated star images to validate the BA-ELM systematic error compensation method in improving the accuracy of the CoM method. Finally, the performance of our method and that of other compensation methods were compared and discussed. All these simulations were written in Matlab R2015a and performed on an Intel Core 3.4 GHz computer.
## 3 Results
### Historical Comparison Cohort and Analysis of Outcomes
Three-dimensional tumor-intersecting tractography is displayed using pre-set tracking parameters (angular threshold of 45\({}^{\circ}\), step size of 0.3 mm, maximum tract length of 300 mm, (**C–D**)). Four additional representative examples of tumor-associated tractography are depicted in (**E–H**), with T2-weighted MRI axial image demonstrating the tumor shown to the left of the arrow and the post-processed three-dimensional tumor ROI with intersecting tractograms shown to the right of the arrow within in each pane. Mean tract length (TL) is reported for each. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Patient Characteristics (\(n=66\))** & \(n\) **(\%)/Mean (\(\pm\)SD)** & \(\beta\) **(95\% CI)** & \(p\)**-Value** \\ \hline Overall Survival (Days) & 714 (\(\pm\)664.8) & – & – \\ Surgical Resection (vs. biopsy) & 60 (90.9\%) & – & – \\ Chemotherapy (Temozolomide) & 63 (95.5\%) & – & – \\ Radiation & 64 (97.0\%) & – & – \\ Multifocal Tumor at Diagnosis & 10 (15.2\%) & – & – \\ \hline \multicolumn{4}{c}{Demographics} \\ Age (Years, Mean \(\pm\) SD) & 59.4 (\(\pm\)12.1) & \(-\)13.24 (\(-\)26.55, 0.08) & 0.051 \\ \hline \multicolumn{4}{c}{Tumor Location and Structural Features} \\ Location, Frontal & 22 (33.3\%) & – & 0.417 \\ Location, Temporal & 27 (40.9\%) & – & 0.853 \\ Location, Parietal & 29 (43.9\%) & – & 0.679 \\ Location, Occipital & 10 (15.2\%) & – & 0.496 \\ Location, Corpus Callosum & 4 (6.1\%) & – & **<0.001** \\ Location, Basal Ganglia/Thalamus & 3 (4.5\%) & – & 0.922 \\ Laterality, Left & 33 (50\%) & – & **0.029** \\ Number of Tumor foci & 1.2 (\(\pm\)0.4) & 61.37 (\(-\)338.88, 461.62) & 0.760 \\ Tumor Volume (cm\({}^{3}\)) & 43.1 (\(\pm\)37.9) & –3.59 (\(-\)7.87, 0.69) & 0.099 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Associations of patient characteristics and imaging features with overall survival in University of Texas, M.D Anderson Cancer Center (MDACC) glioblastoma comparison cohort (\(n=66\)). Figure 2: Projecting tumor-associated tractography observed bridging between distinct tumor foci in multifocal glioblastoma. In a minority of cases presenting with multifocal glioblastoma, tumor-associated tractography sometimes demonstrated directionally oriented fiber bundles passing between anatomically distinct tumor regions of interest suggesting possible functional connectivity between the regions. An example of this phenomenon is illustrated from a single patient from the Rhode Island Hospital cohort. FLAIR images depict multifocal disease at two axial levels, (**A**,**B**). After generating tumor regions of interest (ROI) and associated ROI-intersecting tractography, extending tract bundles appear to span between distinct tumor foci (arrows in (**C**,**D**)). In this case, two separate tumor ROI were separately evaluated with ”reciprocal” tracts observed to span between the two ROI.
# On-Orbit Absolute Radiometric Calibration and Validation of ZY3-02 Satellite Multispectral Sensor
## 4 Results
### Cross-Validation with Landsat-8 OLI Using the Three Determined Radiometric Calibration Coefficients
The average relative difference (ARD) between the TOA radiance from Landsat-8 OLI after SBAF correction \(L_{OLI}^{SBAF}\) and the TOA radiance of the ZY3-02 MUX \(L_{measured}^{MUX}\) is:  
\[ARD=mean(\frac{L_{OLI}^{SBAF}-L_{measured}^{MUX}}{L_{OLI}^{SBAF}})\times 100\% \tag{11}\]  
Finally, the calibrated ZY3-02 MUX with coefficient C shows the best cross-validation results, and the average relative difference between ZY3-02 MUX Measured TOA Radiance and Landsat-8 OLI TOA radiance after SBAF correction is less than 3.5% in all bands of ZY3-02 MUX (Table 9, Figure 17). The Dunhuang Gobi cross-validation results also show good agreement with the Baotou desert area validation results. The results show that radiometric calibration coefficient C of combining the permanent artificial targets and the radiometric calibration tarps is best.
# Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection
## 5 Experimental Evaluation
### Performance Measures  
Assessing the proposed model's performance through testing is crucial for gaining insight into its capabilities and grasping its strengths and limitations more comprehensively. In this research, we executed model testing using a five-fold cross-validation method and a testing dataset of around 4000 samples. We then systematically evaluated the model's performance, utilizing established evaluation metrics throughout the training, validation, and testing phases. Figure 5 summarises the customary performance evaluation criteria employed in this study.  
The proposed model is assessed using a set of performance metrics derived from the confusion matrix.  
\[Recall=\frac{TP}{TP+FN}\]  
Figure 5: Confusion matrix.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**F\#** & **Feature Name** & **F\#** & **Feature Name** & **F\#** & **Feature Name** \\ \hline F1 & Duration & F15 & Su attempted & F29 & Same srv rate \\ F2 & Protocol type & F16 & Num root & F30 & Diff srv rate \\ F3 & Service & F17 & Num file creations & F31 & Srv diff host rate \\ F4 & Flag & F18 & Num shells & F32 & Dst host count \\ F5 & Source bytes & F19 & Num access files & F33 & Dst host srv count \\ F6 & Destination bytes & F20 & Num outbound cmds & F34 & Dst host same srv rate \\ F7 & Land & F21 & Is host login & F35 & Dst host diff srv rate \\ F8 & Wrong fragment & F22 & Is guest login & F36 & Dst host same src port rate \\ F9 & Urgent & F23 & Count & F37 & Dst host srv diff host rate \\ F10 & Hot & F24 & Srv count & F38 & Dst host error rate \\ F11 & Number failed logins & F25 & Serror rate & F39 & Dst host srv error rate \\ F12 & Logged in & F26 & Srv error rate & F40 & Dst host error rate \\ F13 & Num compromised & F27 & Rerror rate & F41 & Dst host srv error rate \\ F14 & Root shell & F28 & Srv error rate & F42 & Class label \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of features of the NSL-KDD dataset.  
\[Precision=\frac{TP}{TP+FP}\]  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN}\]  
\[f\text{-}measure=2\times\frac{Precision\times Recall}{Precision+Recall}\]
## 3 Numerical Results
### Deterministic Analyses  
The two QoIs (i.e., the volumetric strain in the outer liner and the equivalent stress in the concrete layer) are firstly studied in a deterministic framework with the parameters of Table 1.  
Although in most cases, the initial stress state in the horizontal direction is smaller than the vertical stress (i.e., \(\lambda<1\)), it is also stated the higher horizontal stress in some cases [33]. Thus, in order to better investigate the anisotropic effect of initial far field stress, two other values of the anisotropic ratio (\(\lambda=1\) and \(\lambda=1.2\)) are considered and their results are compared with the reference case. It is noted that \(\lambda=1\) refers to isotropic initial stresses. As illustrated in Figure 2, the QoIs, at the intrados of the corresponding liner, are calculated as a function of the inclined angle ranging from \(0^{\circ}\) (springline) to \(90^{\circ}\) (crown). As expected, for the case of \(\lambda=1\), one observes uniform stresses and deformations from the springline to the crown in each liner. An increase of the anisotropic degree of initial stresses (case of \(\lambda=1.2\)) induces a higher compressive orthoradial stress at the tunnel roof and hence the equivalent stress in the concrete liner is maximum at this position. Inversely, the volumetric strain in the flexible liner is highest at the springline. However, at the same position, this deformation decreases quickly and becomes lowest for \(\lambda=0.66\). The same trend can be stated for the equivalent stress at the concrete liner roof. However, for both cases of anisotropic stress states, the maximum equivalent stress in the principal support element of tunnel (i.e., concrete liner) is higher than the one of the isotropic stresses. This expected observation confirms the discussions in the literature [5; 6; 18; 19]. Following that, the anisotropic initial stress generates a non-uniform deformation distribution in the outer liner and of the transmitted load/pressure at the extrados of the concrete liner. This non-uniform pressure induces bending moments in the concrete liner and results in, as a consequence, higher compressive hoop stresses in this last support element.  
An investigation with different values of anisotropic ratio in the range \(\lambda\in[0.66,\ 1.2]\) as depicted in Figure 3 is then performed. Only the results at the springline and crown are captured in this figure, which confirms the above-mentioned observations. Regarding the maximum equivalent stress in the concrete liner (Figure 3b), these results demonstrate the important role of the anisotropy of initial stress state on the stability of this support element. The maximum equivalent stress in this tunnel support element is the lowest in the isotropic stress case.  
Figure 2: Distribution of volumetric strains at the intrados of the outer flexible liner (**a**) volumetric strain and of equivalent stress at the intrados of the concrete liner (**b**) equivalent stress.
## 3 Results
### Devices Featuring Ni-ALD Dielectric-Ni Junctions
#### 3.2.1 SET Devices with Ni-SiO\({}_{2}\)-Ni Tunnel Junctions
The spectral density of this noise abruptly becomes about two orders of magnitude larger than that at low \(V_{ds}\) and it appears to be of Lorenzian shape, typical for RTS trap recombination noise [47]. However, unlike the RTS noise visible in in Figure 10a, this noise is only present outside of the Coulomb blockade region and has a distinctly different frequency cut off (several hundred Hz), suggesting that some other mechanism, other than trap-modulation of Coulomb blockade, is responsible for its appearance. For example, it may be caused by population/depopulation of relatively deep traps located about \(\sim\)7.5 meV above the Fermi level in the remaining NiO layer. Figure 11: **(a)** Coulomb charging diagram of an Ni-SiO\({}_{2}\)-Ni SET in which the ALD dielectric (\(\sim\)1 nm of SiO\({}_{2}\)) was exposed to remote hydrogen plasma (RHP) (measured at \(T\) = 0.4 K) along with a \(G(V_{ds})\) cross section (red curve) at a peak of CBO (\(V_{g}\) = 0.45 mV). Strong asymmetry, non-MIM SET conductance out of blockade region, as well as “switching noise” are visible. **(b)** CBOs of the same device. Figure 12: **(a)** Coulomb charging diagram and \(G(V_{ds})\) cross section at \(V_{g}\) = 10 mV of a Ni-SiO\({}_{2}\)-Ni device fabricated with two RHP steps (measured at \(T\) = 0.4 K). Barrier composition is 12 cycles (\(\sim\)1 nm) of SiO\({}_{2}\) by plasma-enhanced ALD (PEALD); **(b)** CBOs of the same device. To summarize, we have demonstrated that two hydrogen-based treatments are required to achieve restoration of MIM characteristics of the junctions: by using two FGA or two RHP we achieved improvements in the device performance but both techniques have their limitations. The double FGA technique results in the appearance of large switching RTS noise and may lead to agglomeration, resulting in discontinuities, while the double RHP technique, though mitigating the RTS noise, leads to incomplete NiO reduction accompanied with thinning of the ALD dielectric layer.