esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Evaluation
### DeepGun vs. Deep Architectures as Feature Extractor
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Classifier** & **Metric** & **Nas-NetLarge** & **Dense-Net201** & **VGG19** & **Mobile-NetV2** & **Res-Net50** & **AE** & **DeepGun** \\ \hline \multirow{4}{*}{_IFC_} & \(F_{1}\) & 68.4 & 62.2 & 45.6 & **69.4** & 44.7 & 46.4 & 65.0 \\  & \(ACC\) & 85.6 & 83.1 & 78.8 & **85.8** & 78.4 & 77.3 & 84.1 \\  & \(P\) & 88.6 & 86.6 & 81.7 & **89.2** & 81.5 & 82.0 & 87.6 \\  & R & 92.6 & 91.8 & 92.3 & **92.9** & 92.3 & 89.5 & 92.0 \\  & \(AP\) & 87.6 & 85.7 & 81.2 & **88.1** & 81.4 & 81.3 & 86.6 \\ \hline \multirow{4}{*}{_SVM_} & \(F_{1}\) & 30.4 & **35.9** & 32.0 & 28.1 & 11.6 & 10.0 & 21.9 \\  & \(ACC\) & 75.0 & **75.9** & 75.0 & 74.4 & 71.3 & 65.4 & 73.1 \\  & \(P\) & 78.4 & **79.5** & 78.7 & 78.0 & 75.3 & 73.6 & 76.9 \\  & R & **92.2** & 91.7 & 91.7 & 92.0 & 92.0 & 84.4 & 92.0 \\  & \(AP\) & 78.2 & **79.1** & 78.4 & 77.8 & 75.5 & 73.9 & 76.8 \\ \hline \multirow{4}{*}{_MCDE_} & \(F_{1}\) & 84.3 & 83.3 & 83.3 & **83.5** & 83.2 & 83.3 & 83.3 \\  & \(ACC\) & **74.4** & 72.0 & 72.0 & 72.2 & 71.9 & 72.1 & 72.0 \\  & \(P\) & **77.3** & 75.6 & 75.7 & 75.5 & 75.7 & 75.7 & 75.7 \\  & R & 92.6 & 92.7 & 92.7 & **93.4** & 92.2 & 75.8 & 92.7 \\  & \(AP\) & 77.2 & 75.5 & 75.6 & 75.5 & 75.5 & 75.7 & **75.7** \\ \hline \multirow{4}{*}{_GMM_} & \(F_{1}\) & 24.1 & 37.3 & 18.4 & 33.4 & 37.9 & **38.0** & 18.3 \\  & \(ACC\) & 25.3 & 28.4 & 71.4 & 28.8 & 29.9 & 32.2 & **71.5** \\ \cline{1-1}  & \(P\) & 16.0 & 23.8 & 30.9 & 21.6 & 24.2 & 24.5 & **31.0** \\ \cline{1-1}  & \(R\) & 48.3 & 86.9 & 13.0 & 72.6 & **87.2** & 84.7 & 13.0 \\ \cline{1-1}  & \(AP\) & 20.4 & 23.9 & 25.3 & 22.4 & 24.2 & 24.5 & **25.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comprehensive quantitative analysis of firearm detection results obtained with different deep architectures as feature extractor and four state-of-the-art OCCs implemented on frames with visible _handgun_ extracted from UCF-Crime shooting video dataset.
## 1 Introduction  
Introducing a novel technology in Engineering, especially in the Aeronautics Industry, requires a comprehensive study of all related disciplines in an interchangeable manner to allow their real-life applications. Given this fact, morphing wing technology [1, 2], a novel wing technology designed for next-generation aircraft, has been the subject of meticulous studies over the past few decades. It has been evaluated through several disciplines, including aerodynamic design [3, 4, 5, 6], structural analysis [7, 8], flight dynamics [9, 10, 8], actuation systems [11, 12, 13, 14] and control [15, 16, 17]. The development of a morphing wing requires the contribution and integration of multiple disciplines. The promising advantages obtained from aerodynamic and structural studies in recent decades have encouraged further exploration of the practical application of morphing wings in the future. Considering the challenges concerning the practical applications of morphing wings [18, 19, 20], comprehensive research covering all disciplines involved is needed. These studies include investigating the reliability of morphing wings in real flight, their manufacturing possibilities and their maintenance cost. A limited number of morphing wing technology applications in real flight have been tested in recent decades: a joint project between Flexsys and the US Air Force Research Laboratory [21]; the CRIAQ MDO-505, a Canadian-Italian collaborative project [22]; the CRIAQ 7.1, a Canadian collaborative project [23], SARISTU [24] and Clean Sky 2 [25] in Europe are just a few. Several studies conducted by testing different types of morphing wings in wind tunnels can be found in the literature.
# Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion
## 5 Experiments  
### Dataset Description  
The proposed algorithm is experimentally validated on both our self-constructed kitchen garbage, recyclable garbage, hazardous garbage, and other garbage (KRHO) dataset and the public TrashNet [36] dataset, thereby demonstrating its superiority. The KRHO dataset was primarily gathered through web searches and web crawlers, followed by multiple rounds of meticulous data cleaning. It comprises four categories: kitchen garbage, recyclable garbage, hazardous garbage, and other garbage. Specifically, there are 24 subcategories of kitchen garbage with a total of 3512 images; 24 subcategories of recyclable garbage with 3414 images; 7 subcategories of hazardous garbage with 888 images; and 15 subcategories of other garbage with 1308 images, amounting to 9122 images overall. To ensure fairness in the experimental evaluation, the dataset is partitioned into training and testing sets in an 8:2 ratio. For instance, as detailed in Table 1, using hazardous garbage as an example, the images from the seven subcategories of hazardous garbage are merged into the respective training and testing sets, resulting in 710 training images and 178 testing images. Applying the same partitioning strategy to the remaining three categories yields a final split of 7310 images for training and 1812 images for testing. Although the original resolutions of the images vary, they are uniformly resized to 224 \(\times\) 224 pixels during preprocessing to align with the input requirements of our model. Detailed information regarding the self-constructed KRHO dataset is provided in Figure 9.  
The public TrashNet dataset comprises 2527 RGB images spanning six garbage categories, with the following distribution: 501 images of glass, 594 of paper, 403 of cardboard, 482 of plastic, 410 of metal, and 137 of general garbage. In our experiments, we selected only four categories--glass, paper, cardboard, and plastic--for evaluation. The processing approach is identical to that used for our self-constructed KRHO dataset: each category is split into training and testing sets in an 8:2 ratio, and all images are uniformly resized to 224 \(\times\) 224 pixels prior to input.
# Prediction of Vacant Parking Spaces in Multiple Parking Lots: A DWT-ConvGRU-BRC Model
## 3 Methodology
### Prediction Model  
The DWT-ConvGRU-BRC model provided in this study consists of four components, namely, the DWT component, three ConvGRU components, the meta-info feature extraction component, and the BRC component (Figure 4). The first component is the DWT module, which performs noise reduction on VPS data by means of the db3 wavelet basis function. The second component is the ConvGRU module. A CNN can capture spatial correlation well but not temporal correlation. A GRU and LSTM can both model temporal correlation well, but a GRU maintains the prediction accuracy and reduces the running speed compared to LSTM. Therefore, an integration of a CNN and GRU to form a three-layer ConvGRU network can capture both temporal and spatial correlations. The third component is a two-layer linear layer module that incorporates external factors such as temperature, wind  
Figure 3: Impact of weather on VPS changes (7 a.m. to 7 p.m.). (a) Lot1 parking lot; (b) St5 parking lot; (c) St7 parking lot.  
Figure 2: Impact of holiday events (weekdays or weekends) on VPS changes (7 a.m. to 7 p.m.). (a) Lot1 parking lot; (b) St5 parking lot; (c) St7 parking lot.  
speed, humidity, weekdays, and vacations into the model to enhance the accuracy of long-term forecasts. Finally, feature fusion is performed using the BRC layer to obtain predictions via the sigmoid function.
# Map Matching Based on Seq2 Seq with Topology Information
## 4 Experiment
### Experimental Result
#### 4.4.2 Model Performance Comparison  
The overall model training accuracy rate and loss curve in this paper are shown in Figure 14.  
The absolute accuracy of the model on the training set is 89.93%, and the absolute accuracy of the model on the verification set is 89.88%. Through Equation (10), the accuracy of the verification set is re-converted, and the accuracy of the converted model is 95.45%. It shows that the road network matching method proposed in this paper is effective.  
Figure 14: Accuracy and Loss.  
Figure 12: Matching results of complex intersection sections. The green line represents the wrong trajectory and the red line represents the corrected trajectory.  
Figure 13: Wrong match. The green line represents the wrong trajectory and the red line represents the corrected trajectory.  
Through the comparison of LSTM, Bi-LSTM, Bi-LSTM-ATTN, and other model structures under the experimental conditions of the same parameters in Table 4, the parameters are shown in Table 5, and all accuracy rates are absolute accuracy rates in the verification set. It can be seen that after 20 iterations, the accuracy of the model trained by the bidirectional LSTM is 1.81 points higher than that of the unidirectional LSTM, and the accuracy of the model with the addition of attention mechanisms on this basis is 86.71 points, and the accuracy of the model in this paper is 89.93 points. It shows that the accuracy of the model can be improved to some extent by using bidirectional recurrent neural network and attention mechanisms.
# Unfrozen Skewed Turbulence for Wind Loading on Structures
## 4 Results
### Flow Characteristics of Wind Storm Aina (2017)
An additional source of noise may be probe-induced flow distortion due to the geometry of the transducer. Although high-frequency anemometer noise has a limited impact on the study of the root-coherence, it can affect the estimation of the normalized one-point velocity spectra. In particular, this noise can challenge the identification of the inertial subrange. For this reason, the one-point turbulence spectra were studied using one of the two anemometers located 49 m above ground, that was least affected by aliasing and/or flow distortion. The 3-h event displayed in Figure 3 was characterized by a mean wind direction of 293 deg, an angle of attack (AoA) of 23 deg and a mean wind speed of 24 m s\({}^{-1}\). The turbulence characteristics were studied after removing the linear trend in the time series. Using the double rotation technique, the flow was studied in a coordinate system where the vertical mean wind speed is zero. The one-point velocity spectra and cross-spectra were estimated using [PERSON]'s algorithm [79] with a Hamming window, six segments of 30 min and 50% overlapping. Further smoothing was applied by bin averaging the spectral estimates on logarithmically-spaced bins. The root-coherence was also estimated using [PERSON]'s algorithm but with segments of 90 s and 50% overlapping. The estimated turbulence intensities are summarized in Table 1. In this table, the friction velocity was computed as by [PERSON] [37]. An identical friction velocity was found using the method by [PERSON] [80], indicating that the double rotation technique is here appropriate. This comparison also suggests that the flow is not substantially affected by lateral shear stress, which can become important in mountainous terrain [81]. The integral flow characteristics identified during this event are consistent with those observed in uniform terrain with moderate roughness length. However, these values differ from those reported in [PERSON] et al. [76], collected in May 2016 for a wind direction between 320 deg and 340 deg, which were more representative of flow in complex topography. Table 2 summarizes the skewness \(\mu_{i}\) and kurtosis estimates \(\kappa_{i}\) (\(i=u,v,w\)). Although the distribution of the \(w\) component is slightly skewed toward negative values, the third and fourth-order statistical moments indicate fairly Gaussian fluctuations. Velocity records from the anemometer located 33 m above ground provided similar skewness and kurtosis values as reported in Table 2. Therefore, the turbulence generation scheme described in Section 3 is applicable for the case at hand.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 2 Image Encryption Algorithms
### Chaotic Systems
#### 2.2.1 Logistic Map  
A logistic map is one of the simplest non-linear and deterministic dynamical systems that are used to generate chaotic sequences. The logistic map can be defined as follows:  
\[X_{n+1}=rX_{n}(1-X_{n}), \tag{1}\]  
where \(X_{0}\) represents the initial parameter or starting value of the logistic map, with \(0\leq X_{i}\leq 1\). The parameter, \(r\), is the control parameter or growth rate of the logistic map, and it has a range of (0, 4). However, chaotic behavior is observed only when the control parameter, \(r\), is within the range of (3.56995, 4) [19]. The bifurcation diagram of the logistic map is illustrated in Figure 2.  
[PERSON] et al. [8] proposed an image encryption algorithm based on the logistic map, permutations, and an AES S-box. The proposed algorithm employs SHA-2 hash based on the plaintext image, a pre-shared key, and an initialization vector (IV) to generate the initial parameters for the logistic map. The pseudorandom sequences generated by the logistic map are used to chaotically permute and substitute the image at the pixel level.  
In [20], [PERSON] et al. proposed an image encryption algorithm for medical applications, using the key sequences of triple logistic maps. In the proposed algorithm, the logistic map is utilized to generate three different key sequences, \(X1_{i}\), \(X2_{i}\), and \(X3_{i}\), with different initial values, where \(X1_{0}\
eq X2_{0}\
eq X3_{0}\). These key sequences are converted into discrete key sequences, \(K1_{i}\), \(K2_{i}\), and \(K3_{i}\), within the range of (0, 255). Then, a combined key sequence \(Ki\) is obtained through a bit-by-bit logical XOR operation between \(K1_{i}\), \(K2_{i}\), and \(K3_{i}\), which is then used to encrypt the image by performing a logical XOR operation between image pixels and the key sequence, \(Ki\). Logistic maps are popular in image encryption algorithms due to their minimal complexity and fast computation speed. However, a potential disadvantage is their limited range of chaotic behavior.
## 1 Introduction  
With the increasing employment of aspheric and free forms with high surface form accuracy, low surface roughness, and free defects, a number of classical deterministic sub aperture polishing techniques have been developed [1; 2; 3]. Examples include, but are not limited to, small tool polishing [4; 5; 6], stressed-lap polishing [7; 8], magnetorheological finishing [9; 10; 11], bonnet polishing [12; 13; 14; 15; 16; 17], ion beam figuring [18; 19], and fluid jet polishing [20].  
In 2008, the German engineer [PERSON] proposed a wheel polishing technology (WPT) based on the principle of a wheel grinder [21]. The outer contour of the polishing wheel is spherical. The polishing wheel has a three-layer structure: the inner layer is a rigid hub, the middle layer is a flexible rubber layer, and the outer layer is a polyurethane polishing pad. However, the polishing wheel with this three-layer structure has a disadvantage: when the polishing pressure is greater than a certain value, the TIF of the polishing wheel is concave in the center [15]. This can cause mid and high frequency errors. In 2016, Opto Tech designed an Advanced Wheel Polishing Tool [22] and [PERSON] designed an Orthogonal Velocity Polishing Tool [23]. The polishing wheel is simultaneously rotated about two orthogonal axes by two independent motors, and this form of motion produces a Gaussian shape TIF. However, since the deepest part of the instantaneous TIF of the polishing wheel is not the center of revolution, a wavy machining trajectory is generated when it moves with a straight line.  
In order to overcome the aforementioned shortcomings, we developed a new and patented polishing tool called Self-rotating Wheel Polishing Tool (SWPT). The polishing wheel is an innovative two-layer structure: a rigid hub inside and a flexible polishing pad outside. The dynamic pressure distribution model between the polishing wheel and the workpiece is Gaussian shape, and the correctness of the model is verified by an experiment. Finally, the optical fabrication was performed, and the form accuracy achieved the expected target of RMS-0.02\(\lambda\), which proves that the SWPT has the characteristics of high convergence rate and high fabrication accuracy. The SWPT has a broad application prospect in the field of high-precision optical fabrication.
# Quantum Stream Cipher Based on Holevo-Yuen Theory
## 5 Concrete Applications of Quantum Stream Cipher
### Optical Fiber Communication  
Large amounts of important data are instantaneously exchanged on the communication lines between data centers where various data are accumulated. It is important from the viewpoint of system protection to eliminate the risk that the data are copied in their entirety from the communication channel. We believe that the Y-00 quantum stream cipher is the best technology for this purpose (see Figure 4). On the other hand, this technology can be used for optical amplifier relay system. Hence, it can apply to the current optical communication systems. Transceivers capable of cryptographic transmission at speeds from one Gbit/s to 10 Gbit/s have already been realized, and by wavelength division multiplexing, a 100 Gbit/s system has been tested. Furthermore, communication distances of 1000 km-10,000 km have been demonstrated. In offline experiments, 10 Tbit/s has been demonstrated. In general, a dedicated line such as dark fiber is required. If we want to apply this technology to network function, then we need the optical switching technology developed by the National Institute of Advanced Industrial Science and Technology (AIST). Thus, in collaboration with AIST and other organizations, we have successfully demonstrated the feasibility of using the Y-00 transceiver in testbed optical switching systems (see
## 3 Methods  
Figure 2 shows the detailed mapping procedure of our proposed method for glacial lakes. We first gathered time-series Sentinel-1A/1B images and pre-processed them for water identification. Then, based on the median composite of the backscattering coefficients, an improved segmentation method was developed for extracting the glacial lake outlines from a single date. Finally, the obtained Sentinel-based glacial lake outlines were validated with the manual digitization of lake boundaries from GF-2 PMS images. We describe each process in more detail in the following subsections.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Purpose** & **Platform and Sensor** & **Pass Direction** & \begin{tabular}{c} **Time Period or Date** \\ **(dd/mm/yyyy)** \\ \end{tabular} \\ \hline \hline \begin{tabular}{c} Segmentation method \\ development and \\ high-frequency glacial \\ lake mapping \\ \end{tabular} & Sentinel-1A/1B & Ascending/Descending & May to October, 2018 \\ \hline \hline \begin{tabular}{c} Some glacial lake \\ mapping results used for \\ accuracy assessment \\ \end{tabular} & Sentinel-1A/1B & \begin{tabular}{c} Ascending \\ Ascending \\ Descending \\ Ascending \\ \end{tabular} & \begin{tabular}{c} 3 July, 2018 \\ 15 July, 2018 \\ 21 July, 2018 \\ 2 August, 2018 \\ 2 August, 2018 \\ 20 August, 2018 \\ \end{tabular} \\ \hline \hline \begin{tabular}{c} Validation \\ \end{tabular} & GF-2 PMS & \
\begin{tabular}{c} Descending \\ Descending \\ Descending \\ \end{tabular} &
\begin{tabular}{c} 7 July, 2018 \\ 12 July, 2018 \\ 22 July, 2018 \\ 1 August, 2018 \\ 6 August, 2018 \\ 16 August, 2018 \\ 16 August, 2018 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of Sentinel-1A/1B images for the method development and glacial lake mapping experiment, and corresponding GF-2 PMS images used for validation.
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
###### Abstract
Regarding the offline phase, a high spatial density for RPs and a large number of measurements collected at each RP are typically desirable, as both factors contribute to increasing the positioning accuracy. As a result, the number of RPs defined for the offline phase as well as the number of measurements taken at each RP is typically the result of a trade-off between the time and efforts required to collect the measurements and the desired accuracy. In the case of an outdoor fingerprinting system, given the larger areas to be covered compared to indoor scenarios (up to tens of km\({}^{2}\) vs. hundreds of m\({}^{2}\), typically), this trade-off might not be achievable: the need to keep the efforts for the acceptable measurement collection might result in unacceptably low accuracy (Challenge 1). Regarding the online phase, the WNN algorithm is characterized by a computational complexity that grows quadratically with the number of RPs [4]; in considering the coverage area of an outdoor system, the number of RPs might be in the order of thousands or tens of thousands of points. Given the expected high density of devices characterizing the IoT scenarios and the corresponding number of positioning requests, the processing workload during the online phase might be difficult to sustain (Challenge 2). Finally, a challenge common to both phases is related to the density of eNBs/NPCIs available in a commercial deployment: current NB-IoT networks are in fact deployed so as o satisfy coverage requirements defined in terms of communication availability and may not provide enough signal sources to support the accurate positioning by fingerprinting (Challenge 3). The early attempts to apply RF fingerprinting to NB-IoT did not explicitly assess the above challenges. Fingerprinting in combination with NB-IoT was first investigated in [6], where. however. an indoor scenario similar to the ones typically addressed in WiFi-based fingerprinting was considered. Outdoor fingerprinting in NB-IoT using commercial networks was first analyzed in [7; 8], although, due to the hardware limitations of off-the-shelf NB-IoT modems, the RF fingerprint was defined by the received signal strength indicator (RSSI) of only one cell, i.e., the cell to which the NB-IoT device was connected. More recently, fingerprinting using NB-IoT was investigated in [9] using data collected with development boards to avoid the limitations characterizing commercial hardware;however, a limited accuracy was reported. The possibility of adopting fingerprinting in NB-IoT networks as a viable alternative to GNSS positioning remains, therefore, to be fully explored, and the feasibility of this approach in a realistic scenario is yet to be assessed. A step forward in this direction was taken in [10] by proposing novel design solutions that take into account the information available for NB-IoT signals. This work extends [10] by providing an in-depth analysis of the feasibility and the performance of fingerprinting in NB-IoT networks.
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 4 Results
### Analysis of Estimated Drifts in TOC Products
#### 4.3.1 Analysis of Temporal Series over Four Representative Sites
Yet, obvious drifts can be observed in the two scattering directions. The compensation effect is very clear over this site, since the estimated drifts in the backward and forward scattering directions--0.0039 and \(-0.0014\), respectively--reduce to \(0.0000013\) reflectance/year, when observations from all angles are merged. As observed for the other vegetated sites, the BRDF normalization allows removing completely the azimuthal asymmetry in the estimated drifts; in fact, the drifts in the different angular conditions now have the same sign and comparable magnitude. The BRDF normalization performs extremely well over this site by strongly reducing the scattering of the data, although some unexpected variations are observed during the winter periods for all spectral bands, particularly in 2019. However, as discussed for the shrubland site, these localized anomalies do not appear to impact the estimation of the drift over the considered seven-year time frame.