esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Materials  
Sample #1 consisted of a GaAs substrate wafer that was used as a reference. Samples #2 consisted of Al0.33 Ga0.67 As thin film alloys grown on GaAs substrate wafers while samples #3 and #4 consisted of AlAs/GaAs superlattices grown on GaAs substrate wafers. The GaAs substrate wafers had a 3\" (76 mm) diameter with a (001)-orientation and were nominally undoped. The AlxGa(1 - x)As semiconductor alloys and superlattice samples were grown via molecular beam epitaxy (MBE). The GaAs substrate temperature in the MBE chamber during growth was 600 \({}^{\circ}\)C (measured by a pyrometer) and the As-flux (beam equivalent pressure BEP) was 9.6 \(\times\) 10\({}^{-6}\) Torr in the MBE machine. The growth of all wafers was proceeded by careful oxide desorption at 600 \({}^{\circ}\)C and the growth of a GaAs buffer layer. The metal layers were deposited by thermal evaporation in a separate deposition chamber with current-heated evaporation boats. Sample #2 had a 200 nm GaAs buffer layer with a nominally 2000 nm thick Al0.33 Ga0.67 As layer. Sample #3 and #4 and D had a 150 nm GaAs buffer layer and a superlattice consisting of 10 repetitions of nominally 26 nm AlAs and 26 nm GaAs (with the superlattice ending with a GaAs layer). Samples #2 and #4 were covered by a 100 nm thin Au-layer, while sample #1 and #3 are covered by a Ti  
Figure 3: FD-TR setup.  
layer with a thickness of 50 nm. The deposition processes on all samples were done in a vacuum with pressure less than \(10^{-6}\) mbar.  
Figure 4 presents Scanning Electron Microscope image (SEM) of superlattice sample.  
The difference in film thickness measurements using SEM and reflectometry is due to slight differences in film thickness across the sample surface. The samples measured using reflectometry were used for the measurements. The samples properties are presented in Table 1.
# A Neural Network Approach to a Grayscale Image-Based Multi-File Type Malware Detection System
## 4 Results
### Additional Validation Results  
As discussed in Section 3.1, further testing has been applied to all models in order to detect any overfitting and to ensure a good generalization. A comparison has been achieved between classic static analysis and the image-based approach proposed in this work. The testing datasets used include unseen samples from the Contagio and Dike datasets. Table 5 illustrates the validation results.  
Lower levels of accuracy were noted, especially when dealing with PDF files. This may be attributed to the limited number of validation samples available. Additionally, it is worth highlighting that the PDF files used for validation exhibited significant variations in terms of length, number of pages, and content type when compared to the files used for training. Thus, enhancing the model's robustness to accommodate such variations is a potential area for future research.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**File Formats** & **No. of Files** & **Train** & **Test** & **Model** & **Accuracy** & **Size** \\ \hline PDF and MS & 25,659 & 20,529 & 5130 & AlexNet & 93.76\% & 227 MB \\ \hline PDF, MS, PE & 35,611 & 28,491 & 7120 & AlexNet & 96.88\% & 227 MB \\ \hline PDF, MS, PE & 35,611 & 28,491 & 7120 & GoogleNet & 96.39\% & 24 MB \\ \hline PDF, MS, PE & 35,611 & 28,491 & 7120 & SqueezeNet & 96.69\% & 5 MB \\ \hline PDF, MS, PE & 35,611 & 28,491 & 7120 & MobileNet-v2 & 96.74\% & 13 MB \\ \hline PDF, MS, PE & 35,611 & 28,491 & 7120 & VGG-16 & 97.56\% & 515 MB \\ \hline \hline \end{tabular}
\end{table}
Table 4: Result summary for multiple file type malware detection model.  
Figure 7: Confusion matrices for the model trainings using multiple file types (L to R, T to B): AlexNet, GoogleNet, SqueezeNet, MobileNet-v2, VGG-16.  
Nonetheless, it is important to note that, for the field of MS documents, the traditional static analysis technique failed to detect any of the malicious samples accurately. In contrast, the proposed image-based combined method achieved 88.46% accuracy, demonstrating its capability to extract significant underlying data from the samples, which can be valuable for model training.
## 2 Mathematical Formation of the Time-Based Markov Model
However, the starting location for the scattering-based Markov chain model is always where scattering happens, which is a random location in the layer. In comparison, for the time-based model, if the photon is not scattered in a specific event, then the ending location of this photon is actually fixed rather than randomized. This error can be reduced with finer spatial meshes. Figure 1: Problem formation using the Markov chain method. (**a**) Scattering-based model; (**b**) Time-based model. The governing equations for the time-based model are also similar to those under the scattering-based model, and the following equations are still applicable:  
\[T((z_{m},\theta_{i}),(z_{n},\theta_{j}))=P(z_{m},z_{n},\theta_{i})\cdot P(\theta_ {i},\theta_{j},z_{n}) \tag{2}\]  
\[\alpha=\arccos(\cos\theta_{i}\cos\theta_{j}\cos\varphi+\sin\theta_{i}\sin\theta _{j}) \tag{3}\]  
\[P(\theta_{i},\theta_{j},n)=\int_{0}^{2\pi}\Gamma_{n}[\arccos(\cos\theta_{i} \cos\theta_{j}\cos\varphi+\sin\theta_{i}\sin\theta_{j})]d\varphi \tag{4}\]  
In the equations, \(P(z_{m},z_{n},\theta_{i})\) is the probability for the photon that starts its propagation at \(z_{m}\) with the propagating direction of \(\theta_{i}\) and finishes at \(z_{n}\) during a specific time step; \(P(\theta_{i},\theta_{j},\,n)\) is the probability for the photon that propagates in the direction of \(\theta_{i}\), \(sn\)th layer, and then propagates in the new direction of \(\theta_{j}\); and \(\Gamma_{n}(\alpha)\) is the phase function in the \(n\)th layer. The implications of Equations (2)-(4) have been introduced elsewhere [15; 16; 17; 18], and we will briefly introduce these equations. Equation (2) describes the transition probability \(T\) from one state (\(z_{m},\,\theta_{i}\)) to another state (\(z_{n},\,\theta_{j}\)). This probability is co-determined by the probability of the path length the photon propagates before being scattered again and the probability of the new scattering angle \(\theta_{j}\) after being scattered. The relationship between \(\theta_{i}\)
# Review Quantum Circuit Synthesis for [PERSON]'s Algorithm Oracle
## 1 Introduction  
The vast amount of information available on the network requires efficient search methods [1]. While numerous classical search algorithms have been developed, the increasing volume of data demands innovative approaches to data traversal [2]. Quantum computing emerges as a promising alternative, offering potential improvements in computational time and complexity over traditional methods [3]. Current quantum computers leverage quantum state properties, such as superposition and entanglement, to achieve these enhancements [4]. These properties have led to the development of a new set of algorithms that redefine the search problem through a quantum lens [5].  
Implementing these algorithms has demonstrated a quadratic improvement in query complexity for specific cases compared to existing classical algorithms [6]. A notable example is [PERSON]'s search algorithm, introduced in 1997 [7]. This algorithm utilizes an oracle, a black box that functions as a unitary operator capable of recognizing the solution to the search problem [8]. For [PERSON]'s algorithm to function correctly, the oracle must be explicitly mapped from classical information [9]. Additionally, the performance of quantum computers executing this oracle is contingent on the number of qubits available and the number of gates used when making the implementation [9].  
The underlying complexity of mapping oracles to functional quantum circuits remains a significant challenge in applying these algorithms to industrial problems [10]. That is why this article reviews the current methodologies available for generating quantum oracles for [PERSON]'s search. It begins with the classical synthesis of reversible quantum circuits [11] and then explores trends in circuit generation, such as metaheuristic methods [12] and the generation of quantum circuits using neural networks [13]. This article provides insights into the methodologies for generating quantum circuits, their current limitations, and the efforts to bridge the gap between practical applications of quantum algorithms and traditional software [10].
###### Abstract  
Autonomous vehicle path tracking accuracy faces challenges in being accomplished due to the assumption that the longitudinal speed is constant in the prediction horizon in a model predictive control (MPC) control frame. A model predictive control path tracking controller with longitudinal speed compensation in the prediction horizon is proposed in this paper, which reduces the lateral deviation, course deviation, and maintains vehicle stability. The vehicle model, tire model, and path tracking model are described and linearized using the small angle approximation method and an equivalent cornering stiffness method. The mechanism of action of longitudinal speed changed with state vector variation, and the stability of the path tracking closed-loop control system in the prediction horizon is analyzed in this paper. Then the longitudinal speed compensation strategy is proposed to reduce tracking error. The controller designed was tested through simulation on the CarSim-Simulink platform, and it showed improved performance in tracking accuracy and satisfied vehicle stability constrains.  
autonomous vehicle; path tracking; model predictive control; longitudinal speed compensation +
Footnote †: journal: Applied  
_Article_  
A Model Predictive Controller with Longitudinal Speed Compensation for Autonomous Vehicle Path Tracking  
[PERSON] and [PERSON] *
# CNN-Based Model for Landslide Susceptibility Assessment from Multispectral Data
## 5 Discussion
From the geomorphological point of view, it is important to take into account that the area of analysis is located in a sector of the Eastern Cordillera of Colombia (belonging to the Andes Mountains), where high slopes dominate and where geoforms of structural origin have an areal distribution close to 90%. Therefore, it is considered that the factors that most influence landslide susceptibility are those of a geomorphological nature. Among these factors are the slope of the terrain, curvature, morphogenesis and other denudational processes that are indicators of instability. Understanding that susceptibility is closely related to geomorphology, whose components are essential parameters of the dynamics of the earth's surface, the DEM, therefore, constitutes a fundamental input to obtaining the geometry of the slopes and detecting other changes in the terrain that may be associated with active morphodynamic processes. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy** & **F1-Score** & **Dataset Size** & **No. of Parameters** & **Patch Size** \\ \hline MLP [17] & 61.0 & 38.0 & 222 samples & Not given & Not given \\ Decision Tree [17] & 80.0 & 80.0 & 222 samples & Not given & Not given \\ CNN-DNN [17] & 84.8 & 87.0 & 222 samples & Not given & Not given \\ Proposed & 87.73 & 88.11 & 137,942 samples & 8972 & \(5\times 5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with state-of-the-art approaches. This table is included as a qualitative type comparison since the compared methods have been designed and trained under data sets with a significant difference in both the source and size of the datasets. Following this, the model obtained from the proposed methodology shows a high correlation with the susceptibility map by geomorphology, where the areas corresponding to high susceptibility in the map have a similar distribution to the resulting areas, indicating that for similar geomorphological conditions, the proposed method can be used as a first indicator of the susceptibility to landslides in a given area. According to this discussion, the proposed method presents a performance similar to state-of-the-art methods, being validated at pixel level with balanced data and metrics according to such distribution. In addition, the proposed architecture is both conceptually and technically simpler than other architectures, with a low computational cost.
# Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery
## 5 Discussion
### River-Foodplain Connectivity of the Yukon Flats
An added contributor for the observed heterogeneity in lake area changes may be substrate geology, as recent work has shown lakes located farther from the floodplain, along permeable alluvial terraces or over coarser soils, are more likely to experience long-term shrinkage [23; 56; 57]. A regional mechanism for the observed pattern of stable and decreasing lakes co-existing in close proximity may be the influence of permafrost, which occurs discontinuously within our study area [32]. The presence of permafrost tends to impede infiltration of lake water to groundwater systems, plausibly contributing to lake stability in this area. Development of talks (thaw bulbs) beneath lakes promotes infiltration, and permafrost research has identified through-talik formation and permafrost degradation around some lakes in the Yukon Flats [24; 31; 58]. Lakes underlain by through-taliks are connected to the groundwater system, enabling water loss to the sub-surface [19; 21; 54]. The spatially heterogeneous patterns in surface water extent may thus be associated with discontinuous permafrost, though we observe no evidence of large lakes draining completely during the summer season. In sum, we conclude that hydrologically connected lakes respond primarily to changing water levels in the main-stem Yukon River, whereas the sub-seasonal dynamics of other water bodies in the surrounding Yukon Flats wetlands are more complex, owing to a combination of intricate flow paths, underlying geology, and permafrost. According to the long-term (1951-2016) Yukon River discharge record at Eagle, the flow in 2016 was higher than average but well within normal variability, suggesting our observed results reflect sub-seasonal inundation patterns in wet years. Given the improving temporal frequency of Planet imagery, we anticipate future research can combine even denser time series of lake surface area with river discharge observations and maps of substrate geology and permafrost to improve scientific understanding of the controls on inundation in Arctic and sub-Arctic wetlands. Our observed variability further highlights that sub-seasonal patterns may obscure long-term trends in lake extent, the magnitude of which is often much smaller than sub-seasonal cycles driven by spring recharge, precipitation and late summer high discharge events [26; 59]. Considering the interest in understanding long-term temporal changes in surface water extent in the Yukon Flats(e.g., [20; 22; 23]) and other high latitude environments (e.g., [17; 31; 56]), this variability can critically impact reported trends in lake extent, especially when observed patterns are based on only a handful of images per year. We therefore also suggest that spatially heterogeneous intra-seasonal variability needs to be considered in long-term trend analyses.
# Extension of DBSCAN in Online Clustering: An Approach Based on Three-Layer Granular Models
## 2 Related Work on DBSCAN Variants
### Basic DBSCAN Algorithm  
DBSCAN was first proposed in 1996 [14], and is a density-based clustering algorithm. It generates clusters by the connectivity analysis based on density, namely clustering via the distinction between high-density regions and low-density regions. The main idea is based on the neighborhood of a point, which belongs to a cluster, and contains at least a minimum number of data samples. Therefore, it is easy to determine any arbitrary clusters through this idea. Due to these properties, DBSCAN algorithms are usually applied in clustering data with arbitrary shapes, e.g., in fields involving spatial analysis, such as civil engineering, meteorological engineering, spectroscopy engineering, and diagnostics on medical images. In [23], DBSCAN was applied to study the issue of drinking water distribution pipe breakage in American infrastructure systems. In [24], the DBSCAN algorithm was applied to group stationary GPS trace data into driving destination clusters. In meteorological engineering, DBSCAN was utilized in the application of weather conditions [25; 26]. In a spectroscopy application, DBSCAN was used for clustering single particle mass spectra at New York City [27]. Moreover, DBSCAN was proved to be practical in many medical applications, e.g., identification of skin lesion(s), clustering in the diabetes mellitus type II data, and the detection of brain diseases by MRI images [28; 29; 30]. Moreover, DBSCAN could be utilized in clustering some non-spatial high-dimension databases. For instance, DBSCAN was used in remote sensing to cluster three-dimensional images in [31]. In [32], DBSCAN was used to monitor anomaly states in high-dimension wind turbine data.
## 2 Results and Discussion
### Odorants Quantitation
These results are in agreement with results obtained by sensory assessors, as ratings of descriptors honey, malty, and floral were rated relatively high in those meads. Despite the dominance of the same odor-active compounds in meads made of tilia, buckwheat, and acacia honeys, concentration levels for those compounds were different in all tested samples. The highest concentrations of floral 2-phenylethan-1-ol were noted for TNY and ANS meads (4.29 and 4.19 mg/L, respectively) and the lowest for BNY mead (2.0 mg/L). A high concentration of this floral odorant in the ANS sample resulted in the highest OAV for all compounds, which suggests it is the most important odorant in this type of mead. Floral 2-phenylathan-1-ol is a widely identified compound in meads and wines [18; 19], and it is derived from phenylalanine in the fermentation process [20]. Visibly higher concentrations of malty, solvent-like 2- and 3-methyl-1-butanol were noted for buckwheat-based meads when compared to other mead types. The highest concentration was determined for BBY (0.0462 mg/L), followed by BNY (0.0413 mg/L), BNSM (0.0387 mg/L), and BNS (0.0334 mg/L). The differences in the concentrations of this compound are also reflected in the sensory evaluation of the discussed meads, as the notes of the malty descriptor for all buckwheat-based meads were visibly higher than those presented for other meads. Malty, solvent-like 2- and 3-methyl-1-butanol also showed the highest OAVs in all buckwheat-based meads, making it the most important and characteristic odor-active compound in meads prepared from buckwheat honey. It was also an important odor compound for two acacia-based meads, ANSM and ANY (OAV, 34 and 49, respectively), and two tilia-based meads, TNS and TNY (OAV, 44 and 60, respectively). In the rest of the tested samples, the concentration of this compound only slightly exceeded its odor threshold. The second important aldehyde identified in tested meads was 2-phenylacetaldehyde with a honey-like odor. This compound was detected in all tested mead samples, and its OAVs were comparable among all meads. The highest OAV was noted for TBY (20) and the lowest for ANSM and TNY equally (10). The concentration ranged from 0.0512 mg/L to 0.101 mg/L. Honey-like 2-phenylacetaldehyde formed during Strecker degradation is reported to be an important odorant in wines [21]. The last of
# Optimum Slot and Pole Design for Vibration Reduction in Permanent Magnet Synchronous Motors
## 3 Results
### Analysis of the Influence of the Number of Slots on the Natural Frequencies  
The natural frequencies of the assembly of the stator and the windings were calculated by means of the model defined in Section 2.3 for several numbers of teeth (or slots) of the stator. The other geometry parameters were not altered, thus, the outer shape, inner diameter, and teeth height remained unchanged. Furthermore, the number of teeth were changed but the teeth volume to slot volume ratio was kept constant. The tooth width was changed in the same proportion but inversely to the proportion at which the number of teeth is changed. For instance, if the number of teeth was doubled, the tooth width was halved.  
Four cases were analysed: 18, 36, 48, and 72 teeth. The first three modes in Figure 6 were compared for the abovementioned four models.  
In Table 3, the results obtained are shown. In each mode, almost the same natural frequencies were obtained.  
The experimental natural frequencies measured for the stators of 18 and 48 teeth are compared in Table 4.  
Considering that the number of slots and the windings are not the same, and that the manufacturing process leads to a certain variability in the behaviour of individual units, the differences in natural frequencies are negligible.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **18 Teeth** & **48 Teeth** & **Difference (\%)** \\ \hline Mode 1 & 395.17 & 444.54 & 11.11 \\ Mode 2 & 574.03 & 561.70 & \(-\)2.20 \\ Mode 3 & 568.96 & 600.02 & 5.18 \\ Mode 4 & 631.83 & 700.06 & 9.75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the first four natural frequencies (in Hz) of the stators with different numbers of teeth.  
Figure 6: First three mode shapes of the assembly of the stator and windings.
# An Efficient Adaptive Data-Link-Layer Architecture for LoRa Networks
## 4 Data Link Layer Design
### Network Topology  
The designed link layer architecture uses single-channel LoRa devices as packet forwarders, as observed in Figure 2, instead of more complex multi-channel LoRaWAN gateways, in order to reduce the overall network cost.  
The proposed solution is based on a two-level multi-layer topology where each set of end nodes \(EN_{PFp}\) sends the collected data to their respective packet forwarder \(PF_{p}\) using a LoRa network based on the star topology. The packet forwarder relays the data received from its end nodes to a web server using any available connection to the Internet, such as Wi-Fi, cellular or Ethernet. In the developed prototype, a Wi-Fi connection was used. Through the use of multiple packet forwarders in range of the respective end nodes, the coverage area of the system can be increased.  
In the following sections, for simplicity, the term \"network\" is used to refer to a single LoRa subnetwork composed of a packet forwarder and its associated end nodes instead of the larger network composed of all LoRa subnetworks.  
Figure 1: Components of the proposed data-link-layer architecture.  
Figure 2: Network topology of the proposed link layer architecture.
# SDN-Based Routing for Backhauling in Ultra-Dense Networks
## 1 Introduction
The routing algorithm runs on the SDN controller, and two different types of traffic flow are considered: data and control plane coordination traffic. The goal is to avoid or reduce the amount of traffic generated by the small cells, which the backhaul network is not able to support, distributing in a fair way the eventual lack of bandwidth among different access points. Simulation results show that with the considered approach the performance significantly improves, and the network can reconfigure the traffic routing depending on the changing conditions.