esa-sceva/satcom-chunk-collection huggingface dataset:
# Small Target Radiometric Performance of Drone-Based Hyperspectral Imaging Systems
## 3 Methodology
### Data Processing and Analysis
The mirror background signal is an additive contribution to the point targets signal and is subtracted during the point target extraction process. The mirror background spatial uniformity and reflectance contribute to the ability to accurately isolate the point target predicted spectral radiance. The low reflectance background (i.e., \(\sim\)2% average hemispherical reflectance) provides the best scenario for optimizing the extraction accuracy. It is critical to fully understand the background stability and spatial variation during the experiment, for this could lead to a signal bias in the point target. Figure 8 contains an average spectral radiance measurement of the two mirror backgrounds, \"Bkg1\" and \"Bkg2\". For each overpass, the mirror background is extracted at the center of the panel and averaged over roughly a \(5\times 5\) pixel box. The black felt material used to construct the multi-layer mirror background provides for excellent spatial uniformity (i.e., has volumetric scattering properties). The point target entrance aperture-reaching spectral radiance can be isolated from the backgrounds by defining a small bounding box around the point target that contains all of the measurable signal. Equation (4) defines the ensquared energy from a point target, over a bounding box with \(N\) pixels, after an average background signal is subtracted:  
\[L_{mirror}(\lambda)=\sum_{p=1}^{N}\left(L_{img}(p,\lambda)-\overline{L_{bkg}( \lambda)}\right) \tag{4}\]  
An overlooked feature within Equation (4) is the averaged background subtraction. This process is critical for extracting only the point target's signal. We also note that the background subtraction process additionally eliminates the path radiance, including adjacency effects. Because the point target produces a spatial response of the imaging system, the ensquared energy is spread over the SPSF, and only the ensquared energy can be compared to the predicted entrance aperture-reaching spectral radiance as defined in Equation (3). Figure 9 illustrates the result of Equation (4), where a point target within an optimized bounding box highlighted in orange is summed after background subtraction. If  
Figure 7: The spectral radiance curve from each Permanflect panel, highlighted in the image with the yellow box, observed during the experiment is displayed as a grouping of the 18 overpasses. The signal variations reflected off each Permanflect panel is indistinguishable for a given panel reflectance. the background signal is accurately estimated, a varying bounding box will not contribute to the ensquared energy but will only impact the noise within the signal (i.e., [PERSON]'s identity). Because small targets will be assessed in a similar way to the point targets, the ensquared energy defines the limit of radiometric target detection from the background.
###### Abstract
This approach is distinguished by higher accuracy and resilience of the obtained solutions and has proven itself suitable for modeling multi-step stochastic processes of various natures. The approach considered in this article passed the experimental assessment for some of the most well-known and popular types of attacks. Network topology spoofing attacks are typical examples of passive-type attacks that do not cause damage to the SDN, but reveal important information that an attacker can use to carry out more serious attacks. The controller hack/crash attack is a typical example of active attacks that significantly disrupt the performance of SDN. These types of attacks will be considered in this article as objects for analytical modeling. The theoretical contribution of the results discussed in this article lies in the further development of methods for analytical modeling of cyber attacks against SDN and their application to assess resilience as a very important property of a data transmission net work, in which the network control layer is separated from data transmission devices and implemented in software for network virtualization. The novelty of the results obtained is determined both by the methods used and by the scope of their application. In methodological terms, the novelty of the results is determined by the integration of methods, each of which has been individually well studied by the authors in relation to cybersecurity problems. These methods include statistical methods, in particular, the methods of the theory of Markov processes [7; 8; 9], the method of topological transformation of stochastic networks (TTSN) [10; 11; 12; 13; 14] and methods of deep machine learning using neural networks of the Long Short-Term Memory (LSTM) type [7; 15]. Markov models make it possible to obtain analytical expressions for assessing the stability of SDN under various cyber attacks. The TTSN method provides these models with input data. LSTM networks ensure the ability to monitor network traffic in order to effectively detect computer attacks. Thus, in terms of their scope, the results obtained cover almost all stages of the operation of the SDN computer security system, which is responsible for detecting, evaluating and eliminating the consequences of computer attacks. The further structure of the article is as follows. Section 2 provides an overview of related works. Section 3 reveals the content of the approach to assessing SDN resilience based on the TTSN method. Section 4 contains the results of analytical modeling using the examples of the two most characteristic types of attacks. The results of the experimental assessment of SDN resilience are presented in Section 5. Section 6 contains the main conclusions and discussion about the directions for future research.
# Memory-Non-Linearity Trade-Off in Distance-Based Delay Networks
## 3 Methods  
### DDNs  
DDNs were introduced in [17] as an extension to conventional ESNs. They were inspired by the computational properties of biological axons. Instead of instantly transmitting a signal from one neuron to another, axons apply a finite signal propagation delay, depending on various factors such as the length and diameter [26]. Similarly, DDNs simulate this phenomenon in a simplified way, by introducing distance-dependent signal delays. Neurons are defined with a corresponding spatial location, and the Euclidean distance between each neuron pair dictates the amount of delay applied to connection \(w_{i,j}\). DDNs are formalized as follows.  
\[\mathbf{x}(n)=(1-\alpha)\mathbf{x}(n-1)+\alpha f\left(\sum_{d=0}^{D_{\text{ pass}}}\left(\mathbf{W}_{D=d}^{\text{res}}\mathbf{x}(n-d)+\mathbf{W}_{D=d}^{\text{in}} \mathbf{v}(n-d)\right)+\mathbf{b}_{\text{res}}\right) \tag{2}\]  
Here, \(\mathbf{x}(n)\) is the reservoir state at time \(n\), \(\alpha\) is the decay parameter (also referred to as the leak rate), \(f(\cdot)\) is the activation function (in this case, the hyperbolic tangent), \(\mathbf{v}(n)\) is the task input at time \(n\), and \(\mathbf{b}_{\text{res}}\) is the vector of reservoir bias weights. Instead of a single full weight matrix, as in Equation (1), we now sum over multiple sparse weight matrices that only contain the weights corresponding to connections of a (discretized) distance \(d\) between any two neurons, with all other elements in the matrix set to 0. The elements of this delay-specific weight matrix \(\mathbf{W}_{D=d}\) are defined by  
\[W_{i,j,D=d}=\delta_{d,D_{i,j}}\cdot W_{i,j} \tag{3}\]  
where \(\delta_{\cdot,\cdot}\) refers to the Kronecker delta operator.
# Tourism Demand Forecasting Based on an LSTM Network and Its Variants
## 4 Results and Discussion
### Series 1
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Month** & **Actual** & **[PERSON] et al. (2019)** & **LSTM** & **Bi-LSTM** & **GRU** \\ \hline May-00 & 216,692 & 219,138 & 209,198 & 207,674 & 206,979 \\ Jun-00 & 225,069 & 217,519 & 208,465 & 206,980 & 206,283 \\ Jul-00 & 217,302 & 224,546 & 215,417 & 213,552 & 212,875 \\ Aug-00 & 220,227 & 219,122 & 208,971 & 207,459 & 206,763 \\ Sep-00 & 221,504 & 220,453 & 211,399 & 209,754 & 209,065 \\ Oct-00 & 249,352 & 221,995 & 212,459 & 210,756 & 210,070 \\ Nov-00 & 232,810 & 247,299 & 235,545 & 232,562 & 231,973 \\ Dec-00 & 228,821 & 235,712 & 221,837 & 219,618 & 218,966 \\ Jan-01 & 199,800 & 230,085 & 218,529 & 216,493 & 215,828 \\ Feb-01 & 234,386 & 222,144 & 204,655 & 203,378 & 202,671 \\ Mar-01 & 251,111 & 232,287 & 233,190 & 230,340 & 229,739 \\ Apr-01 & 235,251 & 249,710 & 249,466 & 245,695 & 245,187 \\ May-01 & 227,021 & 238,079 & 234,424 & 231,505 & 230,910 \\ Jun-01 & 239,878 & 228,914 & 228,374 & 225,793 & 225,169 \\ Jul-01 & 218,673 & 238,869 & 239,059 & 235,879 & 235,309 \\ Aug-01 & 224,208 & 240,966 & 221,670 & 219,460 & 218,807 \\ Sep-01 & 193,254 & 224,076 & 228,381 & 225,799 & 225,175 \\ Oct-01 & 192,452 & 215,560 & 208,305 & 206,830 & 206,132 \\ Nov-01 & 190,500 & 193,244 & 205,900 & 204,556 & 203,851 \\ Dec-01 & 210,603 & 191,470 & 209,408 & 207,872 & 207,177 \\ Jan-02 & 217,600 & 208,926 & 230,045 & 227,370 & 226,754 \\ Feb-02 & 233,896 & 217,268 & 209,218 & 207,693 & 206,997 \\ Mar-02 & 281,522 & 232,541 & 222,738 & 220,469 & 219,820 \\ Apr-02 & 245,759 & 279,376 & 262,144 & 257,641 & 257,220 \\ May-02 & 243,941 & 267,961 & 232,569 & 229,753 & 229,149 \\ Jun-02 & 241,378 & 244,875 & 231,063 & 228,331 & 227,720 \\ Jul-02 & 234,596 & 242,421 & 228,939 & 226,326 & 225,705 \\ Aug-02 & 246,079 & 236,270 & 223,318 & 221,017 & 220,371 \\ Sep-02 & 233,613 & 245,205 & 232,834 & 230,003 & 229,401 \\ Oct-02 & 258,360 & 236,077 & 222,503 & 220,247 & 219,598 \\ Nov-02 & 255,645 & 256,345 & 243,001 & 239,598 & 239,051 \\ Dec-02 & 285,303 & 256,724 & 240,755 & 237,478 & 236,918 \\ Jan-03 & 238,031 & 283,235 & 265,265 & 260,579 & 260,181 \\ Feb-03 & 259,966 & 240,587 & 226,166 & 223,707 & 223,073 \\ Mar-03 & 258,128 & 258,138 & 244,330 & 240,851 & 240,312 \\ Apr-03 & 110,640 & 259,062 & 242,809 & 239,417 & 238,868 \\ May-03 & 40,256 & 111,762 & 120,256 & 123,504 & 122,983 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of forecast.
# _Applied sciences_
## 1 Introduction
[12] from the University of Freiburg proposed U-Net as a convolutional network that relies heavily on data augmentation to use the available annotated samples more efficiently and has a more significant advantage in the semantic segmentation of biomedical images. [PERSON] et al. [13] used convolutional neural networks for the semantic segmentation of 7-channel multispectral images to successfully recognize vegetation and calculate its coverage based on high-resolution aerial images of a park. [PERSON] and [PERSON] [14] proposed a multispectral encoder fused aurous spatial pyramid pooling (EFASPP) U-Net to merge the features of visible and thermal images recorded in night traffic scenes. [PERSON] et al. [15] used a U-Net model for semantic segmentation of RGB imagery in maize plants, and the results showed that the U-Net had an excellent segmentation accuracy for maize tasks under various complex situations. [PERSON] et al. [16] took cucumber spectral images as the research object, and a U-Net was utilized to construct the semantic segmentation model for cucumber target leaf spot segmentation. Although extensive research has been carried out on U-Net, it is mostly used for a set of object classes, and no single study exists that adequately addresses color recognition. Furthermore, there is excellent potential for the semantic segmentation of painted images based on U-Net. The architecture consists of a contracting path to capture context and an expanding path for precise localization. U-Net relies on the strong use of data augmentation to learn more data features when the training samples are insufficient. However, when outputting semantic segmentation images for each color, it is easy to generate probabilistically unstable salt-and-pepper noise, which affects the segmentation accuracy. It is obvious that previous convolutional neural networks have limitations when semantic segmentation is performed on painted colors. This paper presents a model called UR-Net to address the issue encountered by previous methods, which found it difficult to achieve color consistency when segmenting painted images. UR-Net adds an improved residual block to the contracting path and expanding path of U-Net for deeper training. UR-Net is additionally enhanced with the ability to learn data features based on the original U-Net and perform accurate segmentation to achieve color consistency. This is achieved based on semantic segmentation and by making a finer division of the semantics so that each color is treated as a semantic category. Images for each color are obtained by training the UR-Net to recognize each color pixel in color painting samples. Compared with the previous convolutional neural networks, the optimized semantic segmentation model derived from the trained UR-Net in this paper is more accurate in recognizing color pixels. The proposed UR-Net is more applicable to painted images and can be used as a technical basis to achieve the color reconstruction of cultural color paintings.
## 2 Materials and Methods
### Models of the Auditory Nerve Firing Rates  
#### 2.1.1 Acoustical Stimulation  
A biophysical model was used to predict stimuli firing rates of the AN fibers [8] from acoustic input. Version 1.2 of the model was used is and summarized as follows [12]. First, the acoustic stimulus passes a middle ear filter [19]. Second, the signal is put into an electrical equivalent circuit transmission-line model of the basilar membrane. The transmission line model is a cascade of serial impedances and parallel admittances [20]. The admittances are characterized by frequency- and level-dependent poles. Level dependence reflects the cochlear gain. At low stimulus levels, the poles are close to the imaginary axis of the s-plane, resulting in small-bandwidth high-gain cochlear filters. At high levels, the poles are apart from the imaginary axis, resulting in large-bandwidth low-gain cochlear filters. At levels in between, the pole positions are interpolated hyperbolically in a dynamics-compressing way. Tonotopy refers to a frequency-place mapping, i.e., low and high frequencies excite basilar membrane regions close to its apical and basal end, respectively. Subsequent processing is organized tonotopically. Third, the IHC membrane's electrical potential is obtained from the deflections of the stereocilia, using a non-linear non-instantaneous electrical equivalent circuit with parallel branches for the direct mechanoelectrical transduction channel, as well as for the fast and the slow activating potassium channels of the cell [21]. Fourth, the synapse's emission rate of neurotransmitters, i.e., the exocytosis rate, is another non-linear non-instantaneous function of the cell's membrane electrical potential. Synapses with low, middle, and high exocytosis rates are modelled. In the absence of CS, each IHC is connected via synapses to \(\mathrm{N_{HSP}}=13\) high-rate fibers, \(\mathrm{N_{MSR}}=3\) middle-rate fibers, and \(\mathrm{N_{LSR}}=3\) low-rate fibers [22]. The fiber spontaneous rates before refractoriness are 1, 10, and 70 spikes/s, and their maximal rates are 800, 1000, and 3000 spikes/s, respectively. These rates are pre-refractoriness rates since they are well above the maximal steady-state rates. The adaptation of the exocytosis rate is modelled by a classic three-store diffusion model [23], and refractoriness is modelled by reducing the firing rate by short-term and long-term integrals over past firing rates [24].
## 3 Experiments
### Objective Performance Measures  
To compare the voice quality performance of the proposed speech enhancement methods and the baseline methods, we adopted four objective measures in the temporal, frequency, and perceptual domains.  
1. Cepstral distance (CD): This is used to evaluate the cepstral distance between the converted and target frames. We evaluated the source (ES-SRC) and the different types of converted stimuli, which is calculated as: \[CD[dB]=\frac{10}{Mlog10}\sum_{(\mathcal{C},C)}\sqrt{2\sum_{i=1}^{D}(\tilde{C} _{i}-C_{i})^{2}},\] (19)where \(\hat{C}_{i}\) and \(C_{i}\) represent the \(i\)th component of the aligned converted and target cepstral vectors, respectively. \(D\) is the dimension of the cepstral vectors and \(M\) is the number of \((\hat{C},C)\) couples.
2. Perceptual Evaluation of Speech Quality (PESQ): referred to by the ITU-T recommendation in the P.862 standard [35]. PESQ is a suitable means to evaluate the subjective voice quality of codecs (waveform and CELP-like encoders) and end-to-end measurements [36]. The range for the PESQ score is between \(-0.5\) and \(4.5\).
3. Short-Time Objective Intelligibility (STOI): this is a function that compares the temporal envelopes of normal and converted speech in segments of short duration using a correlation coefficient. A greater STOI value indicates better intelligibility of enhanced speech.
4. Segmental Signal to Noise Ratio (segSNR): it defines the average of SNRs computed from aligned converted and target cepstra and was determined by Equation (20). \[segSNR=\frac{10}{M}\sum_{(\hat{C},C)}log_{10}\frac{\sum_{i=0}^{N-1}C_{i}^{2}}{ \sum_{i=0}^{N-1}(\hat{C}_{i}-C_{i})^{2}}\] (20) where \(\hat{C}_{i}\) and \(C_{i}\) are, respectively, the \(i\)th component of the aligned converted and target cepstral vectors. \(N\) (512) is the cepstrum length.
# Sustainable IoT Sensing Applications Development through GraphQL-Based Abstraction Layer
## 7 Energy-Wave Problem and High Power Communication  
The energy-wave problem can be raised in dense IoT networks where data are communicated to gateway nodes in multi-hop fashion due to communication range limitations of constrained devices. We focus on IoT networks with high data rate above, either due to multimedia sensors or dense deployment with frequent reporting scenarios. Such networks using multi-hop data collection, e.g., in Scenario 3 of our experimentation, face the energy-wave problem. Our proposed approach can help in overcoming the energy-wave problem through traffic management in constrained IoT networks with high data sending rate. We considered the energy-wave problem and tested an added proposal for overcoming it. Our approach was using direct high power communication instead of low power multi-hop data sending. We obtained supporting results for this test case by changing the communication range of involved devices.  
IoT devices are constrained in energy supply and communication range. Multi-hop communication is used for long range IoT applications connectivity. Channel contention is increased when the data flow towards the neighbourhood of the gateway node and hierarchical connection may face bottleneck  
Figure 12: Scenario 3: Average delay, energy consumption, PDR, and throughput comparisons with increasing number of sender hopes.  
issues. This phenomenon is also commonly referred as the energy-wave problem, as shown in Figure 2. We propose high power single hop connectivity as efficient alternative to multi-hop data delivery and resolution of energy-wave problem. We discuss supporting results obtained on real experiments based on our implementation of GraphQL-based abstraction layer using our IoT testbed in outdoor environment.
# A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain
## 3 Problem Formulation  
In this section, we define the problem from three aspects: system model, threat model, and security requirements.
# Amplitude and Phase Information Interaction for Speech Enhancement Method
## 3 The Proposed Method Architecture
In terms of the included features, \(F_{sub}\) contains the spectral information surrounding the corresponding frequency axis and the temporal correlation features, i.e., local information. \(F_{mp}\) contains the global information extracted from the amplitude and phase spectrum, including the full-band amplitude information and phase information. In order to enable the model to learn the detailed sub-band features of the audio signal spectrum along the frequency axis, a sub-band module is introduced at the end of the model. The structure of this module is depicted in Figure 4, and it consists of two unidirectional LSTM layers with 384 hidden units each. No activation layer is included between these layers. The sub-band module predicts the complex ratio mask (CRM) by weighting the sub-band noise with respect to different frequency bands and combining it with the output of the previous full-band feature extractor. The same loss function as the baseline model [11], mean square error (MSE) loss, was used. Additionally, the impact of two other loss functions, mean absolute error (MAE) loss and Huber loss, on the model's training effectiveness was also tested. The formulas for these three loss functions are as follows:  
Figure 1:2  
Figure 1:3  
Figure 1:4  
Figure 1:5  
\[HuberLoss(b,f,t,m)=\left\{ \begin{array}{ll}\frac{1}{2}{(M_{cIRM}-M_{cRM})}^{ 2},&{|M_{cIRM}-M_{cRM}|<\delta}\\ \delta{|M_{cIRM}-M_{cRM}|}-\frac{1}{2}\delta^{2},&{|M_{cIRM}-M_{cRM}|>\delta} \end{array} \right. \tag{12}\]  
where \(b\) represents the number of audio samples in each training batch, \(f\) represents the number of frequency bins in the spectrum, \(t\) represents the length of the time axis in the spectrum, and \(m=(1,2)\) denotes the real and imaginary parts of the mask. Huber loss is a combination of the other two loss functions, with \(\delta\) as a hyperparameter. The value of \(\delta\) determines the emphasis of Huber loss on MSE loss and MAE loss. CRM is generated by the model when inputting the noisy speech, and CIRM is the complex ideal ratio mask constructed from the real and imaginary parts of the clean and noisy speech spectrum; it is calculated by the following formula:  
\[M_{r}=\frac{Y_{r}S_{r}+Y_{i}S_{i}}{Y_{r}^{2}+Y_{i}^{2}} \tag{13}\]  
Figure 4: Structure of sub-band. The LSTM (hidden size = 384) block denotes an LSTM layer with 384 hidden units. \[M_{i}=\frac{Y_{r}S_{i}-Y_{i}S_{r}}{{Y_{r}}^{2}+{Y_{i}}^{2}} \tag{14}\]  
where \(Y\) represents the noisy speech, \(S\) represents the clean speech, and \(M\) represents the complex ideal ratio mask, with the subscripts \(r\) and \(i\) denoting the real and imaginary parts, respectively.