esa-sceva/satcom-chunk-collection huggingface dataset:
# Motion Control System of Unmanned Railars Based on Image Recognition
## 2 Detecting Predefined Signs with Image Recognition
### Image Pre-Processing Using OpenCV  
OpenCV library [17] provides many useful utilities for image processing and is very popular in the industry. For development effectiveness and maintenance, in this research a variety of OpenCV utilities are applied in main program so that the designed system is able to quickly and correctly identify four different predefined signs from the images captured by a Metal-Oxide-Semiconductor (CMOS) camera. Careful consideration of image pre-processing is fundamental for computer vision applications because raw image data direct from a camera may be noisy and unevenly lighted. Therefore, proper image pre-process tasks should be performed to handle the noisy and unevenly lighted situations.  
In practical, like the application of this research, many object features in image are brightness, contrast, edges, shape, contours, texture, perspective or shadows related, rather than color related. To increase the speed of particular object detection, the captured colorful RGB images are converted to gray level images because the data volume of a gray level image with 8-bit resolution is significantly less than that of its original colorful RGB image. Therefore, the first step of image processing is to convert the captured color images to gray level images. The conversion between color RGB images to gray level Y images is based on the formula in (1) [20]:  
\[Y=0.299\text{R}+0.587\text{G}+0.114\text{B}. \tag{1}\]  
In this research, the image recognition and motion control program was developed in Python [21]. The images captured by the camera were applied OpenCV function _cv2.cvtColor_[22] to convert the input color RGB images to gray level images.  
Secondly, since the captured images are usually noisy, we applied OpenCV function _cv2.GaussianBlur_[23], which convolves the gray level images with the specified Gaussian kernel to reduce the image noises.  
Thirdly, in gray level images, image features usually relate to image contrast. When contrast values are very close, the features in images are difficult to detect. Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. Through this adjustment, the intensities are better distributed on the histogram. This allows for areas of lower local contrast to gain a higher contrast.  
Histogram equalization accomplishes this by effectively spreading out the most frequent intensity values. In other words, this algorithm normalizes the brightness and increases the contrast of the image. The _cv2.equalizeHist_[24] is the dedicated function to handle the histogram equalization of gray level images in OpenCV.
# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 2 Background
### CNNs  
Convolutional neural networks (CNNs) are widely established NN architectures, capable of generalizing with a relatively low number of free parameters, when compared to traditional NN architectures. CNN-based approaches have led to state-of-the-art results in various areas, most prominently in computer vision [20] and speech processing [21]. The training of a CNN is very similar to the training of a standard NN. It consists of forward data propagation, followed by gradient-based backward error propagation for tuning NN weights. CNNs employ a non-linear activation function, such as rectified linear unit (ReLU):  
\[f(x)=max(0,x) \tag{1}\]  
where \(f\) is the ReLU and introduces nonlinearities in the decision function, as well as in the entire network. Other functions, such as hyperbolic tangent and sigmoid, are also used to increase non-linearity. ReLU is more often used since it reduces training time without a considerable cost in terms of generalization.  
CNNs comprise convolutional, pooling and fully connected layers:  
Convolutional layers are responsible for feature extraction. Each convolutional layer consists of a group of neurons, forming a rectangular grid. This grid is convolved with a given part of the input and the result is passed to the next layer.  
Pooling layers are usually placed between single or multiple convolutional layers and progressively reduce the representation size. Each output block of the convolutional layer is subsampled, reducing the complexity of the network and controlling overfitting.  
Fully-connected layers are the top-level layers of every CNN-based architecture, performing the high-level reasoning of the entire network, i.e., combining features detected from input parts (such as image regions). The output of the pooling layer is transformed into an \(N\)-dimensional vector, where \(N\) denotes the number of classes considered. It could be argued that the fully-connected layers are the actual network, whereas the previous layers are feature extractors. This last layer is tied to a loss function, used to provide an estimation of the classification error, which affects the weight tuning performed by means of backward error propagation.
# Influence of Cargo Luggage on the Vertical Drop Crashworthiness of Aircraft Mid-Fuselage Section
## 5 Influence of Cargo Luggage on Fuselage Section Crash Response
### Energy Absorption Characteristics  
The structures under the cabin floor are the primary parts that absorb energy during a crash. When an aircraft crash-lands during an emergency, the stronger the energy absorption capacity of the structures under the cabin floor, the lower the impact of the kinetic energy transferred to the cabin area, and the higher the probability of safe survival of the passengers. Therefore, the evaluation of the energy absorption capacity of the main energy-absorbing components of the fuselage should be focused on the evaluation of the crashworthiness of an aircraft.  
During the vertical crash of the fuselage section, the fuselage frame, skin, cargo luggage, joints, and cargo floor beam are the main deformation structures. The energy absorption ability of these components affects the crashworthiness of the fuselage. Figure 18 shows the proportion and value of the energy absorption of each component. The energy absorption of the cargo luggage was the greatest, and the maximum energy absorption was 14,900 J, which accounted for 39.2% of the total energy absorption. The energy absorption of the fuselage frame was the second largest, and the energy absorption was 5270 J, accounting for 13.9% of the total energy absorption. Moreover, 74.84% of the total energy was absorbed by the cargo luggage, fuselage frames, fasteners, and skin, which were the main energy absorption components. The energy absorption of fasteners accounted for 11.4% of the total energy absorption. This shows that the modeling of structural fasteners cannot be ignored when the accuracy of the simulation analysis of structural energy absorption design is required to be high in a crashworthiness study.
## 1 Introduction
We analyze two years (2013 and 2016) of dense time series of the COSMO-Skymed constellation and all three years so far available from Sentinel-1 (2016, 2017, 2018) over central Yamal. The start of records from COSMO-Skymed coincides with the beginning of soil thaw in both years and for Sentinel-1 in one year what allows both a direct comparison of the X and C-band records and investigation for early season gap filling. We eventually discuss the results obtained in the context of ALT monitoring and implications of temperature extremes as recently recorded on Yamal.
# An Uplink Channel Estimator Using a Dedicated Instruction Set for 5G Small Cells
## 3 Algorithm and Implementation of Channel Estimation  
### The Methodology of Designing a Dedicated Instruction Set  
After selecting the use of a dedicated instruction set as the research method of channel estimation in this study, it follows a flow chart [18] which is suitable for the design of the dedicated instruction set, as shown in Figure 3.  
The first step is demand analysis. The performance of the wireless communication system is largely affected by the wireless channel, which makes the propagation path between the transmitter and the receiver very complex. The wireless channel is very random, so the receiver needs to constantly evaluate and predict the channel environment. For example, the receiver estimates the channel response of the entire subspectrum using known signals (pilots) distributed on different subspectra. The basic process of pilot-based channel estimation is to estimate the channel at the pilot first, then obtain the channel estimation between pilots by interpolation or filtering. Finally, the channel estimation of the whole channel is used for channel equalization to eliminate the distortion of the received data.  
After understanding the requirements and basic methods of channel estimation, we can choose the algorithms with low cost and good enough performance as the basis of the follow-up work. The selected algorithms are shown in Sections 3.2 and 3.3. After selecting the algorithms, we need to test the function and performance of the algorithms. The functional verification and performance of the algorithms are shown in Section 3.4. After the test is passed, we need to analyze the code in order to have a comprehensive understanding of the instruction set that needs to be designed and analyze the possibility of code acceleration. The work of code analysis is shown in Sections 4.2 and 4.3. The next step is to design a dedicated instruction set that contains accelerated instructions, as well as the arrangement and verification of assembly instructions, which can be seen in Sections 4.3-4.5. After designing the instruction set and assembly code and verifying them, the final work is to carry out the microarchitecture design of the hardware module and gate-level circuit design to realize the hardware function according to the previous research. The research on hardware design is not in the scope of this paper, but it relates to the goal of the follow-up research of this paper.
## 4 Results and Discussion
have little influence on the in-plane modes, while they have a considerable impact on the flexural modes [31]. For the same branch of the dispersion spectra, the red dot dash is the lowest (FG-O), the green dash (FG-X) is the highest, yet the blue solid (UD) and the black dash (FG-V) are quite close to each other. Compared with the other patterns, the FG-X plate has the highest frequencies. This is due to the symmetric configuration in the thickness direction. Meanwhile, the top and bottom surfaces with the maximum normal stress are GPLs rich and the neutral layer with the minimum normal stress is pure PMMA. This distribution pattern is the most effective since it makes better use of GPLs reinforcements and determines the higher bending stiffness. On the contrary, FG-O patterns performs the lowest frequency due to the weakest bending stiffness. Figure 6 analyzes the effect of the boundary conditions on the dispersion spectra of the composite plates with different boundary conditions at \(\xi=0,1\) in which SS, CC and FF represent both edges, simply supported, clamped and free. As we can see, the boundary condition has little effect on the in-plane modes of the dispersion spectra, and the flexural modes are quite sensitive to the boundary conditons. Especially the first two black dash branches are unique for the FF plate, the corresponding branches for the SS and CC conditions are absent. Figure 7 depicts the first four dispersion spectra of the composite plates with different aspect ratios (s = 0.1, 0.15, 0.2), which include two in-plane and two flexural modes. Similar to the phenomenon shown in Figure 6, the dispersion relations are insensitive to the aspect ratio for the two in-plane modes, while the aspect ratio noticeably affects the flexural modes. The frequency grows with the aspect ratio increases for the flexural modes. The in-plane mode branches go steeper, while the flexural mode branches become milder. Concluded from Figures 4-7, the GPLs distribution patterns, boundary conditions and aspect ratios influence the flexural modes more obvious than the in-plane modes, while the GPLs weight fraction affects both the flexural and in-plane modes remarkably.
## 2 Methodology and Related Works
### Methodology
#### 2.1.1 Urban Data Analysis  
Data analysis is the process of collecting and processing raw data to extract meaningful information to provide supporting evidence to help decision-making. Alternative workflow proposals exist to describe the data analysis process. This work applies a workflow proposed by [PERSON] and [PERSON] [6] (Figure 1).  
The data analysis process starts and ends in the current reality. In urban contexts, this implies collecting raw data from a given city and, in the end, communicating findings to authorities and citizens. To this end, the data analysis process is comprised of several phases. Initially, raw data must be processed, including placing data into tables, inspecting datasets, and cleansing data to detect corrupt or inaccurate records. After that, Exploratory Data Analysis (EDA) [7] is performed. EDA aims at describing what data can tell, beyond a formal modeling and hypothesis testing phase. Urban data tends to come from a variety of diverse and dynamic sources (e.g., sensors, mobile phones, social media), thus EDA becomes mandatory for urban data analysis to detect  
Figure 1: Data analysis workflow applied in this work.  
inaccuracies. After EDA, statistical models and algorithms (e.g., inferential statistics, machine learning) are applied to identify relationships among the data. Finally, results are communicated, usually through visualization techniques.  
EDA makes an intensive use of data visualization with the goal of efficiently displaying measured quantities through graphics. Traditionally, data visualization techniques were mainly dominated by charts and diagrams comprised of numerical data. However, areas such as urban data analysis, which demand combining quantitative and qualitative data, require more advanced means of visualizing results for effective communication. Since urban data usually has a prevalence of geographic components, urban data visualization combines classic statistical graphics with Geographic Information Systems (GIS).
# A Dual-Band 8-Antenna Array Design for 5G/WiFi 5 Metal-Frame Smartphone Applications
## 4 Experimental Results and Discussion
### Effects of Hand  
When designing wireless electronic products like mobile phone antennas and wearable device antennas for signal transmission and reception, it is crucial to not only assess antenna performance within the device's physical structure but also account for the influence of the human body on antenna performance. Ansys HFSS 15.0, an electromagnetic simulation software, offers a human body structure model. Within this model, material properties such as relative permittivity and bulk conductivity are predefined as global variables, specifically denoting the average values of relative permittivity and volume conductivity. Considering the impact of human body tissues on antenna performance due to their lossy properties and high permittivity, Figure 12a,b depict the effects of hand phantoms on the current distribution of the proposed MIMO antenna under single-hand mode and double-hands mode. Simulated S-parameters of the user's hand are presented in Figure 13. For the single-hand mode (Figure 13a), the hand's close proximity to Ant. 2 and Ant. 3 led to decreases in the simulated reflection coefficient for these antennas. In the double-hands mode (Figure 13c), Ant. 2, Ant. 3, Ant. 6, and Ant. 7 performance deteriorated because they were mostly covered by fingers. Notably, Figure 13b,d demonstrate that all isolations between different antenna elements remained higher than 16.5 dB. The remaining mobile phone antenna units, unaffected by finger coverage, effectively covered the desired frequency bands while also maintaining satisfactory isolation between each port. These results affirm that the antenna maintained good performance whether held by one or two hands.  
Table 1 provides a comprehensive comparison between the current design and previously reported smartphone MIMO antennas. Notably, the proposed antenna exceled in fulfilling the requirements of dual-band MIMO operations, showcasing commendable performance in terms of isolation, efficiency, and ECC. The majority of the referenced works in Table 1 focus on single-mode antennas covering either one or two sub-6 GHz bands, whereas the proposed antenna extends its coverage to three sub-6 GHz bands. Furthermore, when compared to the most referenced antennas integrated into metal-frame smartphones, the proposed antenna exhibited superior isolation, higher efficiency, and a broader bandwidth. Despite the ECCs in the 3.5 GHz frequency band being slightly higher than those found in the references, the ECC values of the proposed antenna still met the operational demands of the MIMO system.  
Figure 11: Simulated and measured total efficiency.  
Figure 12: (**a**) The current distribution of the single-hand mode in different bands. (**b**) The current distribution of the double-hands mode in different bands.  
Figure 13: _Cont._
## 2 Literature Review
### Selection of Interaction Behaviour Analysis Methods  
In the initial screening phase of the study, a series of common pattern mining algorithms were compared, including, but not limited to, Lagged Sequence Analysis (LSA), Generalized Sequential Patterns (GSP), the Sequential Pattern Discovery Algorithm (SPADE), and the Event Tree-based Mining Algorithm (EP-Tree). It is evident that these algorithms exhibit considerable proficiency in data processing within their designated domains of applicability. However, the central objective of this study is to examine the interaction behavior between the GAI (under the assumption that it is an intelligent assisted instruction system or platform) and the learner. Consequently, when selecting algorithms, particular emphasis is placed on their sensitivity and accuracy in capturing and analyzing such interaction behaviors.  
Whilst the algorithms GSP [24], SPADE [25], and EP-Tree [26] demonstrate efficacy in general pattern mining tasks, they are not well-suited to the requirements of this study in certain scenarios. For instance, GSP has been observed to prioritize global pattern mining over local interaction details [27], while SPADE is susceptible to computational efficiency challenges when dealing with large-scale datasets [28]. Additionally, while EP-Tree demonstrates proficiency in handling complex event sequences, it may not be as intuitive and effective as LSA in capturing subtle time lag effects.  
More importantly, LSA has been employed to investigate shared cognitive development in online collaborative learning [29] and to examine visual behavioral patterns in online learning activities [30]. This study was conducted based on an online learning platform, so we ultimately chose LSA as the research method.  
It is undeniable that LSA offers distinctive advantages in elucidating interdependencies in behavioral sequences and investigating behavioral patterns. However, it is important to recognize that LSA primarily concentrates on lagged relationships between behaviors [31], which may be more intricate and diverse in authentic scenarios. Consequently, in this study, in addition to LSA, we employed controlled experimental design and visual analysis methods as supplementary techniques, with the objective of comprehensively capturing and analyzing the multidimensional relationships among behaviors.  
It is noteworthy that, in contrast to previous studies that employ LSA to examine a range of aspects related to online learning, this research is distinctive in its focus on the interactional behaviors between learners and GAI. By capturing these specific behaviors, the study seeks to reveal how learners engage with GAI as an interactive partner, shedding light on their problem-solving processes and the role of GAI in facilitating learning within the context of PBL.
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 3 Approach
### Implementation in CityGML  
In our research, we concentrate on the derivation of the floor numbers and their heights. This does not allow us to create a complete LoD4 model. As a result, we enhance the LoD2 model geometry with the hull geometry for each floor. For this purpose, we introduce LoD2+, as visualized in Figure 11.  
In LoD2 and higher LoDs, the outer facade of a building can be modeled semantically by the _BoundarySurface. The _BoundarySurface is a part of the building's exterior shell with an assigned function such as the wall WallSurface, roof RoofSurface, ground plate GroundSurface, outer floor OuterFloorSurface, outer ceiling OuterCeilingSurface or ClosureSurface. For indoor modeling FloorSurface, InteriorWallSurface, and CeilingSurface can be used [27]. In [11], the authors enhance the CityGML scheme with a new feature class, Storey, which has five attributes: class, function, usage, storeyHeightAboveGround and storeyVolume.  
To model the indoor geometry, we keep the LoD2 representation using _BoundarySurface and add indoor geometry for each storey using FloorSurface, InteriorWallSurface, and CeilingSurface, as well as the feature class Storey introduced by [11]. In addition, we propose a further attribute of the feature class Storey: storeyAltitude. This attribute is necessary for our application, as the output of a navigation device is an altitude and not the height above the ground. This extension is not included in the current version of the CityGML specification, however we suggest to include it in the next release.  
For the dynamic generation of the CityGML model, citygml4j [28] was used. This is an open-source library for Java, which binds the XML Schema definitions of CityGML to a Java object model.  
Figure 11: The proposed level of detail two plus (LoD2+) model, which carries information about the number of stores as proposed by [11] and their corresponding altitudes.