esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study
### Hardware and Software Integration  
Using the block design, each data source block has a corresponding physical sensing device (e.g., a camera or a microphone) that connects to the computing platform. Here, we define our customised sensing devices (that are compatible with REIP's default data source blocks), and implement any custom blocks required. The prototypical sensor system shown in Figure 6 uses two 5 MP USB cameras providing a 160\({}^{\circ}\) horizontal field of view (85\({}^{\circ}\) max per camera) and 15 fps recording, satisfying our video capture use case requirement. The compute core is the NVIDIA Jetson Nano Developer Kit [5], which offers edge intelligence capabilities from its 128-core GPU, quad-core 1.43 GHz CPU and 4 GB of LPDDR4 RAM. The majority of the sensor's hardware is enclosed within an aluminum weatherproof housing.  
The custom acoustic front-end has been designed to capture synchronised 12 channel audio from its 4 \(\times\) 3 array of digital pulse density modulated (PDM) Micro Electro Mechanical Systems (MEMS) microphones. It uses the USB MCHStreamer [51] as an audio interface, so the same Microphone block from the REIP audio library can be reused to read data from this USB audio class compliant device. In the given application pipeline, a single microphone channel is used as input to the audio processing blocks. For data transmission, a USB LTE modem is integrated containing a SIM card providing a bandwidth limited cellular plan.  
Figure 5: Multimodal smart traffic event detection pipeline. This pipeline handles both audio and video. It captures video, passes it to an object detection Block, and then writes the detection outputs to JSON files. It also captures audio at short intervals (1 s) and computes SPL at the 1 s resolution, as well as accumulates the audio into longer clips and writes them to disk as audio files. Finally, the salient event detection block is monitoring these files, preserving only those that had an event of interest detected in them.
## 1 Introduction
The functional inversion can be performed with the Lagrange inversion method for some particular forms of \(t(u)\) and with a general procedure described in a paper by [PERSON] [17] through recursive application of nested derivatives on the kernel \(f(u)\). The result is expressed in terms of the Taylor expansions of \(u(t)\) at a given point. One of the authors (GF) of the present paper is preparing a new efficient inversion technique based on an auxiliary exact function and through the fast direct derivative of \(t(u)\) followed by a linear triangular recursive inversion algorithm. In his method, the nested derivatives are not necessary. In the present paper, we introduce logistic wavelets and describe their properties in terms of Eulerian numbers. We add the second logistic wavelet into Matlab's Wavelet Toolbox, to be able to use the continuous wavelet transform (CWT). We then perform CWT analysis for the second differences of a smoothed total number of people reported as infected with the COVID-19 virus. We model the total number of people infected with the COVID-19 virus by the sum of several logistic functions (the so-called multilogistic function). The CWT analysis allows to estimate parameters of the successive logistic waves, which together form the multilogistic function. All three parameters (i.e., the inflection point, the steepness and the saturation level) of each logistic function can be read from the CWT scalograms. Then we use the non-linear generalized reduced gradient method, minimizing the RMSE error, to optimize the parameters, mainly the saturation levels. To show the accuracy and effectiveness of our method, we apply it to the cases of COVID-19 infection in several countries, as well as around the world. This method, in addition to the curve-fit property to existing data, has also a predictive value, and, in particular, allows for an early assessment of the size of the emerging new wave of the epidemic. Thus it can be used as an early warning method. This paper is organized as follows. In Section 2.1, we discuss basic properties of Riccati's equation, logistic equation, and logistic curve. For this purpose, we use Eulerian numbers. Sections 2.2 and 2.3 are devoted to introduction and study of the logistic wavelets. In Section 3 we model the cumulative number of persons reported to be infected by COVID-19 in several countries and also in the whole world. The results are discussed and concluded in Section 4. We use the following convention for the Fourier transform:  
\[\hat{f}(\xi)=\frac{1}{\sqrt{2\pi}}\int_{-\infty}^{\infty}f(x)e^{-i\xi x}dx, \tag{4}\]  
where \(f\in L^{1}(\mathbb{R})\cap L^{2}(\mathbb{R})\) (the intersection of the space of integrable functions and the space of square integrable functions defined on the set of real numbers \(\mathbb{R}\)).
## 1 Introduction
The constrained solution space can accelerate the convergence of the algorithm and obtain a reliable solution. Some prior distributions for some parameters were widely used as the regularization terms, such as total-variation (TV) priors [18], Student-t priors [19], Laplacian priors [20]. The stronger the sparse prior knowledge is, the better the image reconstruction ability is. TV prior is often used in CT image reconstruction [13]; however, TV prior causes over smoothing or blocky artifacts in the reconstructed images. Therefore, a compound Gaussian distribution and a multivariate Gaussian distribution can be chosen as priors for modeling two unknown parameters, the sparse representation and the point spread function, respectively. In this paper, we utilized a hierarchical Bayesian Maximum-a-Posteriori (MAP) formulation to estimate the sparse representation and the point spread function, respectively. The hierarchical Bayesian-MAP formulation is a statistical model written in multiple levels that can estimate the parameters of the posterior distribution using the Bayesian-MAP method [21]. The hierarchical model for the inverse problem was introduced into the problem of CT image reconstruction with blind image restoration in this paper. The problem of CT reconstruction with the blind restoration was transformed into the estimation of some specific parameters. All of the parameters were estimated by the hierarchical Bayesian MAP. Experiments simulate the fewer data environment by sampling complete projection data. This paper is organized as follows. Section 2 presents the basic problem of medical CT image blind restoration and reconstruction with different parameters and derives the stochastic hierarchical Bayesian maximum for solving the medical CT image reconstruction with blind restoration and presents a proposed algorithm. Section 3 reports the experimental results of applying the proposed algorithm to medical CT image blind restoration. Section 4 concludes this paper.
# Bistatic Forward ISAR with DVB-T Transmitter of Opportunity
###### Abstract  
The radar geometry defined by a spatially separated transmitter and receiver with a moving object crossing the baseline is considered as a Bistatic Forward Inverse Synthetic Aperture Radar (BFISAR). As a transmitter of opportunity, a Digital Video Broadcast-Terrestrial (DVB-T) television station emitting DVB-T waveforms was used. A system of vector equations describing the kinematics of the object was derived. A mathematical model of a BFISAR signal received after the reflection of DVB-T waveforms from the moving object was described. An algorithm for extraction of the object's image including phase correction and two Fourier transformations applied over the received BFISAR signal--in the range and azimuth directions--was created. To prove the correctness of mathematical models of the object geometry, waveforms and signals, and the image extraction procedure, graphical results of simulation numerical experiments were provided.  
bistatic forward ISAR; DVB-T waveform; BFISAR signal model; BFISAR image reconstruction +
Footnote †: journal: Physics Letters A  
1
Footnote 1: email: [PERSON]; [PERSON]; Bistatic Forward ISAR with DVB-T Transmitter of Opportunity.  
20 September 2021  
20 October 2021  
DUDI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
###### Abstract  
The combination of collaborative deep learning and Cyber-Physical Systems (CPSs) has the potential to improve decision-making, adaptability, and efficiency in dynamic and distributed environments. However, it brings privacy, communication, and resource restrictions concerns that must be properly addressed for successful implementation in real-world CPS systems. Various privacy-preserving techniques have been proposed, but they often add complexity and decrease accuracy and utility. In this paper, we propose a privacy-preserving deep learning framework that combines Instance Reduction Techniques (IR) and the Restricted Boltzmann Machine (RBM) to preserve privacy while overcoming the limitations of other frameworks. The RBM encodes training data to retain relevant features, and IR selects the relevant encoded instances to send to the server for training. Privacy is preserved because only a small subset of the training data is sent to the server. Moreover, it is sent after encoding it using RBM. Experiments show that our framework preserves privacy with little loss of accuracy and a substantial reduction in training time. For example, using our framework, a CNN model for the MNIST dataset achieves 96% accuracy compared to 99% in a standard collaborative framework (with no privacy measures taken), with training time reduced from 133.259 s to 99.391 s. Our MLP model for MNIST achieves 97% accuracy compared to 98% in the standard collaborative framework, with training time reduced from 118.146 s to 87.873 s. Compared to other studies, our method is a simple approach that protects privacy, maintains the utility of deep learning models, and reduces training time and communication costs.  
deep learning; privacy-preserving; collaborative deep learning; Privacy-Preserving Cyber-Physical Systems; Restricted Boltzmann Machines; Instance Reduction Algorithms
## 3 Transmission Experiment Comparing Analog and Digitized RF-over-Fiber Transport
### Experimental Results and Discussions  
Shown in Figure 5a is the measured error vector magnitude (EVM) plotted as a function of received optical power (dBm) for digitized RF-over-transport after transmission over 20 km of SMF for a bit resolution of 8 bits. Also plotted are results for an ADC bit resolution of 4 and 6 bits [34]. The dotted line indicates error-free reception for 4-QAM signal (corresponding to a bit-error-rate of \(10^{-12}\)). The results indicate that the performance of the link is not sensitive to the received optical power until the received optical power goes beyond the link sensitivity whereby the performance degrades drastically with decreasing optical power. For a 4-bit resolution, a minimum received optical power of \(-13\) dBm is needed to achieve error-free transmission. The same trend was seen for 6-bit and 8-bit resolution but with better EVM performance and lower received optical power requirement (\(-13.5\) dBm and \(-15\) dBm).  
Figure 5b shows the measured EVM for the analog RF-over-fiber transport of the OFDM signal for transmission over 0, 10 and 20 km of SMF [34]. The performance of the analog link degrades steadily with increasing transmission distance and with decreasing received optical power. Error-free reception is only possible for a received optical power of \(>\)\(-11\) dBm for a transmission distance of 10 km and is not achievable for 20 km of SMF for received optical power \(<\)\(-10\) dBm.  
Figure 4: Experimental setup for (**a**) digitized RF-over-fiber transport and (**b**) analog RF-over-fiber transport of wideband OFDM signal. © [2008] IEEE. Reprinted, with permission, from _IEEE OSA Lightwave Technology, Journal._from Figure 5a,b, we can see that for an error-free transmission over 20 km of SMF analog RF-over-fiber requires higher received optical power compared (\(>\)\(-\)10 dBm) to digitized RF-over-fiber transport (\(-\)15 dBm for 8-bit resolution). Even for a 4-bit ADC resolution, optical bit rate of 4 Gb/s and received optical power of \(-\)13 dBm, the digitized RF-over-fiber link outperforms the analog link distributing the same OFDM signal at 10 GHz with an received optical power of \(-\)10 dBm.
# RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach +
## 6 Discussions and Conclusions
Finally, in this work we have assumed that the system operator is in charge of detecting low vs high volatility periods in the training RSSI time-series and divided the training set into two subsets for training the proposed bi-modal LSTM autoencoders. However, one could argue that due to variability in RSSI during the presence vs absence of moving objects, it is possible to detect two periods (for separating the training datasets for building the multi-model classifiers) usingunsupervised clustering approaches such as k-means instead of relying on the judgment of a system operator for creating such separation. This is certainly an interesting future work that can further enhance our proposed _crossover model switching indicator_. Conceptualization, P.M. and N.V.; methodology, P.M.; software, P.M.; validation, P.M. and N.V.; data curation, P.M.; writing--original draft preparation, P.M.; writing--review and editing, P.M. and N.V.; supervision, N.V.; project administration, N.V. Both authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors declare no conflict of interest.
# Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
## 3 Results and Discussion
### Input Data
#### 3.1.2 Jasper Ridge Dataset  
Jasper Ridge has 224 bands ranging from 380 nm to 2500 nm with the spectral resolution of 9.46 nm. Due to the dense water vapor and atmosphere effect, bands 1-3, 108-112, 154-166 and 220-224 are removed, and 198 bands are reserved for experiments. Additionally, a sub-image of 100 \(\times\) 100 pixels starting with the (105,269) pixel from the original image is used, as shown in Figure 3. There are four endmembers in this data: road, soil, water and tree.
# End-to-End Mispronunciation Detection and Diagnosis Using Transfer Learning
## 4 Experiments and Results
### Experimental Results
#### 4.4.2 Comparison with Conventional Methods  
Table 3 lists the MDD results of the proposed methods and other conventional methods. Our pretrained model based methods surpass the well-known GOP based method [53] and CTC based methods [7, 20]. As for the training data, in CTC-ATT [7], the researchers also included a small portion of the Librispeech corpus, in addition to the L2-arctic and TIMIT datasets, to build a three-stage model that is complicated. Compared to their work, our XLSR based model trained with less data increase the F1-score by 4.44% (56.02% vs. 60.44%). Even without using the TIMIT training data, our XLSR based model can still achieve an F1-score of 59.37%. As for the model architecture, our model is similar to that in [20] whereas our model replaced their Bi-LSTM with transformer based architecture and made full use of acoustic knowledge transferred from wav2 vec2.0 models. A close observation shows that the benefit of using pretrained models mainly comes from the improvements of the precision metric while the recall metric does not improve too much. This means that our proposed models made fewer false-rejection errors while detecting slightly more real mispronunciations.  
Figure 4: Confusion matrices of the XLSR-based MDD models trained with the _default_ (the upper two subplots) and \(-66\%\) (the lower two subplots) data configurations.
# Model for Quantifying the Quality of Secure Service
###### Abstract  
Although not common today, communications networks could adjust security postures based on changing mission security requirements, environmental conditions, or adversarial capability, through the coordinated use of multiple channels. This will require the ability to measure the security of communications networks in a meaningful way. To address this need, in this paper, we introduce the Quality of Secure Service (QoSS) model, a methodology to evaluate how well a system meets its security requirements. This construct enables a repeatable and quantifiable measure of security in a single- or multi-channel network under static configurations. In this approach, the quantification of security is based upon the probabilities that adversarial listeners and disruptors may gain access to or manipulate transmitted data. The initial model development, albeit a snap-shot of the network security, provides insights into what may affect end-to-end security and to what degree. The model was compared against the performance and expected security of several point-to-point networks, and three simplified architectures are presented as examples. Message fragmentation and duplication across the available channels provides a security performance trade-space, with an accompanying comprehensive measurement of the QoSS. The results indicate that security may be improved with message fragmentation across multiple channels when compared to the number of adversarial listeners or disruptors. This, in turn, points to the need, in future work, to build a full simulation environment with specific protocols and networks to validate the initial modeled results.  
communication model; security; metrics; probability; confidentiality; integrity Article
## 2 Passive Radar Performance Principle
### Bistatic Doppler Resolution  
For monostatic and bistatic Doppler resolutions, \(1/_{T_{int}}\) is conventionally considered as an adequate Doppler separation between two target echoes at the receiver, where \(T_{int}\) is the coherent integration time. In Figure 5, two targets sharing the same bistatic bisector are represented, as well as their speed component along the bistatic bisector. The required \(\Delta V=|V_{P1}-V_{P2}|\) is given by expression Equation (4) [1]:  
\[\Delta V=\frac{\lambda}{2T_{int}\cos(\beta/_{2})} \tag{4}\]
# A Coordination Technique for Improving Scalability of Byzantine Fault-Tolerant Consensus
## 3 A Coordination Technique for Scalable BFT Consensus
### Step 3. Processing Equal/Unequal Transactions  
Based on collected transactions from each node, this step decides the execution of the BFT-based consensus algorithm. Figure 3 shows processing steps for collected transactions. At first, the coordinator checks if all transactions collected from each node are equal to those from other nodes. Thus, when all transactions are equal, it executes the _Handling Equal Transactions_ step, and then this third step is terminated. When transactions are not the same, it first classifies transactions into _common_ and _trouble_ transactions. The coordinator then requests the prime node to execute a consensus algorithm only for trouble transactions and gets _agreed_ transactions. Finally, the coordinator merges and sorts common and agreed transactions in time order.
## 2 Smart High-Voltage Equalizing Detection  
In this section, the principle of high-voltage equalizing detection (HVED) via an LED band gap, based on the voltage reference, will be considered. This paper proposes both an equalizing and monitoring system. The monitoring system detects if one cell is fully charged or all cells are fully charged and the equalizing system tops off each cell at the desired voltage. To achieve this, a light-emitting diode (LED) band gap is used as a voltage reference to detect the cell voltages and to inform the user if any cell is at its high voltage. Also, a smart-monitoring system using a liquid crystal display (LCD) displays to the user if one cell is at the desired high-voltage level or if all cells are at the desired high-voltage level by displaying the text messages, \"one cell is high\" or \"all cells high\" on the LCD. This detection also gives a signal to the microcontroller to turn on, or to turn off the charger if all cells are at high-voltage level. In addition, a Bluetooth module was designed to command the microcontroller to turn on or to turn off the charger via voice or text message by using a smartphone. Other technologies were tried out, such as supply level detectors, but these had an even higher cost, the leakage current was higher, and the voltage accuracy was lower.  
Typical Zener diodes do not have a very sharp knee as the threshold voltage for equalization (discrete or integrated circuit e.g., LM329). The aim is to obtain a low-leakage current at low voltage (below threshold voltage) and high-equalizing current at a constant voltage, or simply put, a sharp knee in the I-V characteristic. The most often used integrated circuit (IC) in commercially available BMS systems is a three-terminal TL431, in which the third terminal is used to set the voltage. However, a minimum regulating current of 400 \(\upmu\)A is required, with a voltage divider needed to get an adjustable output voltage. With a 2.495 V reference (1%) and the assumption of a 10 k\(\Omega\) reference resistor in the voltage divider, this requires 0.2495 mA. This causes a 2.185 Ah capacity decrease in one year, and usually, there are also other leakage currents in a system. For the newer technologies, such as the junction gate field-effect transistor (JFET) pinch-off references, this problem arises with a 560-800 \(\upmu\)A quiescent current for the ADR 430 series. While obtaining a high accuracy, low-temperature coefficients, and noise, JFET pinch-off references cost more. One of the most recent entries in the voltage reference sweepstakes is a floating gate voltage reference. The idea here is to introduce some charge into the buried and well-insulated gate of a metal-oxide-semiconductor field-effect transistor (MOSFET) during manufacturing, thereby putting it at a certain voltage. The ISL21009 series exhibit a typical regulating current of 100 \(\upmu\)A, which is acceptable.