esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Synthesis of Optical Coherence Function
### Quasi-Distributed Sensing with SOCF Based on Long-Length FBGs  
Distributed measurement of the Bragg wavelength in a long-length FBG can be done with the SOCF technology, as shown in Figure 12[29; 30; 31]. For example, the distributed measurement in a 10-cm long-length FBG with a two-cm spatial resolution was demonstrated. The SOCF spatial resolution was set by adjusting the FM modulation amplitude. Moreover, a quasi-distributed measurement system with multiplexed long-length FBGs was also demonstrated, as shown in Figure 13[31]. To measure the FBG spectral shape, the lasering frequency has to be swept with a saw-tooth waveform. Then, this modulation creates the additional frequency difference between the sensing lightwave and the reference lightwave, because the FBG reflection and the reference have the OPD, as shown in Figure 10. This makes the intermediate frequency shift in the heterodyne interferometer. To compensate for the frequency shift, the AOM driving frequency is changed, as shown in Figure 13[31]. This system is an example of the quasi-distributed measurement that is shown in Figure 2c.  
Figure 12: Measurement of Bragg wavelength distribution along a long-length FBG with SOCF technology [29].  
Figure 13: Experimental setup of a quasi-distributed sensing system with multiplexed long-length FBGs by using a synthesized delta function-like optical coherence function [31].  
Figure 14 demonstrates the quasi-distributed measurement function of the system [31].
## 3 Proposed Schemes
### Delay Minimization Routing (DMR) Scheme  
#### 3.1.1 Network Initialization:  
The network consists of sensor nodes, four minor sink nodes, a major sink node and offshore data center as shown in Figure 1. The distribution of sensor nodes is random, which are capable of sensing data and responsible for forwarding the packets towards the minor sinks from where they are transferred to the major sink node. The entire network is split into four equal zones. To deploy minor sink nodes, the minor sink node \(S_{1}\) is launched from the observation point \(a\) at sea surface and it travels downward with a speed of \(v\) m/s. It stops after a time interval \(t_{1}\) calculated as below  
\[t_{1}=\frac{D_{1}}{v} \tag{1}\]  
where \(D_{1}\) is the depth from the surface observation points \(a\) to the point where \(S_{1}\) stops. The interval \(t_{1}\) is set in such a manner that the minor sink node \(S_{1}\) stops at the top left subzone of the network. The minor sink node \(S2\) takes the same interval of time to reach to the top right subzone of the network from the reference point \(b\). Likewise, the time interval \(t_{2}\) is taken by the minor sinks \(S_{3}\) and \(S_{4}\) to reach to the lower left and right subzones of the network from the surface reference points \(a\) and \(b\), respectively. The time interval \(t_{2}\) is calculated as  
\[t_{2}=\frac{D_{2}}{v} \tag{2}\]  
where \(D_{2}\) is the depth of the minor sinks \(S_{3}\) and \(S_{4}\) from the sea surface. This depth is the same for both \(S_{3}\) and \(S_{4}\). The minor sink node \(S_{3}\) forwards the information towards the \(S_{1}\) that sends it to the major sink node as shown in Figure 2. On the other hand, the minor sink node \(S_{4}\) broadcasts the received packets to \(S_{2}\), which are further forwarded towards the major sink as indicated in Figure 3. The minor sink nodes \(S_{1}\) and \(S_{2}\) recognize the information coming from each other by their IDs. Therefore, \(S_{1}\) and \(S_{2}\) forward packets to major sink only when they receive them from \(S_{3}\) and \(S_{4}\), respectively. Conversely, \(S_{3}\) and \(S_{4}\) do not accept packets from \(S_{1}\) and \(S_{2}\).
# Determination of Cyanotoxins and Prymnesins in Water, Fish Tissue, and Other Matrices: A Review
## 4 Sample Preparation and Analytical Methods for Cyanotoxins and Prymnesins Detection
### Sample Preparation
#### 4.1.4 CYNs  
Cylindrospermopsins are a significantly more hydrophilic toxin than MCs, without as much structural diversity, and as a result extraction procedures have generally been more straightforward. In Table 3, three procedures are listed for the analysis of CYNs, and these all use mechanical homogenization or lyophilization, followed by extraction with methanol or water.
## 2 Materials and Methods
### The Methodology
#### 2.1.5 Online Condition Monitoring  
The output of the offline training consists of (1) the best feature subset, which contains the minimum information for class separation; (2) the best classification model, which can accurately classify the known conditions in a short time; and (3) the best anomaly detection model, which can distinguish the nominal condition from all other conditions. These outputs will be used in the online phase to assess the current condition of the monitored machinery and establish if a novel behavior occurred.  
In the online phase, data are collected continuously in streaming, while the online inference is made by the fault detection and diagnosis models at each cycle. Hence, the selected feature subset is extracted every 800 ms, and the so-obtained feature vector is input to the anomaly detection model, which determines if it belongs to the nominal class or not. In the first case, the algorithm ends and goes to the next cycle. Otherwise, a counter is incremented by one, and the algorithm goes to the next cycle. When the value of the anomaly counter is equal to 2, the trained classification model is applied to determine the belonging class of the past two points. Then, if the maximum Class Probability Estimation (CPE) is higher than a certain threshold, the fault has been diagnosed, and the machine can be stopped. Otherwise, the detected anomaly does not correspond to any of the known conditions. In this case, an alarm is triggered, and a check by the machine user may be required to determine whether the novel behavior is a fault condition or not.
# Millimeter-Wave Multi-Channel Backscatter Communication and Ranging with an FMCW Radar
## 8 Conclusions  
The proposed multi-channel backscatter communication with FMCW radar is tested with commercial E-band frontend modules at 75.2 to 78.2 GHz. A novel package solution for the RF tag is proposed. The tag performance is tested for different modulation schemes and different data rates. For the single tag measurement, it demonstrates the highest data rate of 8 Gbps and the highest modulation order signal of 32-QAM compared to other related works. The frequency offset between the transmitter and the receiver can be used for range measurement. Its accuracy is related to the SNR and chip length of signature codes in use. For the multi-tag measurement, signature codes are used to separate signals from two tags. Blind equalization is applied to further decrease the EVM. By using signature codes, tags can be freely deployed.  
Conceptualization, S.A. and Z.S.H.; Data curation, S.A.; Funding acquisition, H.Z.; Methodology, S.A.; Project administration, H.Z.; Supervision, H.Z. and Z.S.H.; Validation, S.A. and Z.S.H.; Writing--original draft, S.A.; Writing--review & editing, X.B., H.W., H.Z. and Z.S.H. All authors have read and agreed to the published version of the manuscript.  
The research was funded in part by Car2 TERA project of the European Union's Horizon 2020 research and innovation programme under grant agreement No. 824962, European Union's Horizon 2020 project Int5 Gent (957403), European Union's Horizon 2020 project RISE6G (101017011), the National Natural Science Foundation of China under Grants 61620106001, and Chalmers Transport Area of Advance.  
Figure 20: EVM and frequency estimation error with different chip length of two-tag measurement.
# Distributed Interference-Aware Power Optimization for Multi-Task Over-the-Air Federated Learning
## 2 System Model
### Federated Learning Model  
Each AP-device cluster forms a standalone FL system, with the \(m\)-th cell learning a cell-specific model parameterized by the vector \(\mathbf{e}_{m}=[e_{m,1},\ldots,e_{m,\mathcal{C}}]^{T}\), where \(\mathcal{C}\) is the model dimension. Each device \(k\in\mathcal{K}_{m}\) holds a private local dataset \(\mathcal{D}_{k}\). The sample loss function evaluates the prediction error of model parameter \(\mathbf{e}_{m}\) on an input-label pair \((\mathbf{x}_{i},\xi_{i})\in\mathcal{D}_{k}\). Accordingly, the local loss function on device \(k\) is defined as  
\[H_{k}(\mathbf{e}_{m})=\frac{1}{|\mathcal{D}_{k}|}\sum_{(\mathbf{x}_{i},\xi_{i})\in \mathcal{D}_{k}}H_{k}(\mathbf{e}_{m},\mathbf{x}_{i},\xi_{i}). \tag{1}\]  
Figure 1: Parallel multi-task Air-FL system.  
Assuming uniform dataset sizes within each cell, i.e., \(|\mathcal{D}_{j}|=|\mathcal{D}_{k}|,\forall j,k\in\mathcal{K}_{m}\), the union of all local datasets in cell \(m\in\mathcal{M}\) is denoted as \(\mathcal{D}_{m}=\cup_{k\in\mathcal{K}_{m}}\mathcal{D}_{k}\). Then, the corresponding global loss function for cell \(m\in\mathcal{M}\) is expressed as  
\[H_{m}(\mathbf{e}_{m}) =\frac{|\mathcal{D}_{k}|}{|\mathcal{D}_{m}|}\sum_{k\in\mathcal{K} _{m}}H_{k}(\mathbf{e}_{m})=\frac{1}{K}\sum_{k\in\mathcal{K}_{m}}H_{k}(\mathbf{e}_{m}). \tag{2}\]  
The training goal of cell \(m\in\mathcal{M}\) is to find the optimal model parameter \(\mathbf{e}_{m}^{\star}\) that minimizes the global loss function \(H_{m}(\mathbf{e}_{m})\), formulated as  
\[\mathbf{e}_{m}^{\star}=\arg\min_{\mathbf{e}_{m}\in\mathbb{R}^{C}}H_{m}(\mathbf{e}_{m}). \tag{3}\]
## 4 Experimental Results and Comparisons
### Findings and Further Analysis  
(1) In this paper, a new network training strategy was proposed. Unlike previous research methods, this paper proposed a new network model training strategy that considers mislabeled samples directly in the network training process. The experimental results  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**FP Rate (\%)**} \\ \cline{3-6}  & & **Inception\_v2** & **Proposed** & **Inception\_v2** & **Proposed** \\ \hline \multirow{4}{*}{**Defects**} & Rolling & \multirow{2}{*}{81.60} & \multirow{2}{*}{90.25} & \multirow{2}{*}{0.79} & \multirow{2}{*}{0.55} \\  & marks & & & & \\ \cline{1-1}  & Scratches & 79.85 & & & \\ \cline{1-1}  & Cracks & 77.44 & & & \\ \cline{1-1}  & Hole & 82.19 & & & \\ \cline{1-1}  & Edge defects & 80.35 & & & \\ \cline{1-1}  & Average & 80.23 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of defect recognition accuracy.  
demonstrated that this method can effectively alleviate the adverse effects of noisy labels on model training and improve the accuracy of network classification without changing the architecture of standard deep networks. However, in this paper, only Inception_v2 network was adopted for comparison. Further analysis should focus on investigating the superior performance of proposed method on other classical deep neural networks.  
(2) In this paper, a new learning curriculum was designed to measure the complexity of the data by using the distribution density of the data in the feature space, and the complexity of the data was ranked in an unsupervised manner. The data were then divided into three subsets, namely easy-to-classify samples (clean samples), relatively easy-to-classify samples, and hard-to-classify samples (noisy samples), and input into the network \"progressively\" according to their complexity. Further analysis should focus on investigating whether dividing samples into more subsets according to the complexity of the data can achieve a better classification performance.  
(3) We proved that the proposed method can achieve a state-of-the-art performance on the benchmark dataset and a real-world defect detection case through statistical analysis. However, the selection of hyperparameters of the network and the image quality of the initial experiments may affect the training and performance of the network. Further analysis should focus on conducting more experiments to verify the significance of the proposed method.
# Evaluation of the Urban Weather Generator on the City of Toulouse (France)
## 3 Experimental Results  
In this section, we analyze UWG under the three points mentioned above: impact of the rural station, sensitivity analysis, and UWG performances.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Param.**} & **Description (Short Name)** & **Unit** & **Family** & **Min.** & **Max.** & **Step \(\Delta_{j}\)** \\ \hline \multirow{2}{*}{\(\theta_{1}\)} & Building height & & & & & & \\  & (bldHeight) & meters (m) & urban surface \(\Theta_{u}\) & 3 & 70 & 7.4 \\  & Building density & & & & & & \\  & (bldDensity) & & & & urban surface \(\Theta_{u}\) & 0.1 & 1 & 0.1 \\  & Grass cover & & & & & & \\  & (grasscover) & & & & & & \\  & Tree cover (treecover) & & & & urban surface \(\Theta_{u}\) & 0 & 1 & 0.11 \\  & Facade length (facade & meters (m) & urban surface \(\Theta_{u}\) & 4 & 4270 & 474 \\  & length) & & & & & & & \\  & Roof albedo (albRoof) & & & & urban surface \(\Theta_{u}\) & 0.1 & 0.7 & 0.07 \\  & Road albedo (albRoad) & & & & urban surface \(\Theta_{u}\) & 0.1 & 0.7 & 0.07 \\  & Wall albedo (albWall) & & & & urban surface \(\Theta_{u}\) & 0.1 & 0.7 & 0.07 \\  & Vegetation albedo & & & & & & & \\  & (albVeg) & & & & & & \\  & Daytime urban & & & & & & \\  & boundary layer & meters (m) & atmosphere \(\Theta_{a}\) & 800 & 2000 & 133.34 \\  & (blday) & & & & & & \\  & Nighttime urban & & & & & & \\  & boundary layer & meters (m) & atmosphere \(\Theta_{a}\) & 30 & 100 & 7.7 \\  & (ublnight) & & & & & & \\  & Reference height (href) & meters (m) & atmosphere \(\Theta_{a}\) & 100 & 200 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 5: **UWG input parameters’ variation ranges** used in the sensitivity analysis. The short name associated with the description of parameters corresponds to the names used in the figures in the experimental part.
# Identification of C-Band Radio Frequency Interferences from Sentinel-1 Data
## 6 Conclusions  
The paper proposed the use of the first \"rank\" pulses per burst to derive a very sensitive RFI monitoring from Sentionel-1 SAR. In fact that, thanks to TOPSAR varying squint, those pulses are ideally unaffected by clutter. This has been demonstrated by a careful analysis of S1 acquisition scheme, and checked by processing hundreds of scenes. The achievement of a high sensitivity, in the order of 8 mW of minimal detectable ground power, requires a careful calibration to remove ripples and spurious tones, and frequency domain multi-looking to suppress random fluctuation of noise power.  
Detection of RFI has been then approached by exploiting both Fisher Z test, for the strongest RFI, and Kullback-Leibler divergence, sensitive to diffused RFI with low power, like RLAN. The best results have been found by jointly exploiting the two figures of merit.  
Figure 11: Cumulated histograms of RFI power distributions with frequency, normalized to S1 noise power, estimated by processing data from all-Europe. Vertical scale is probability in \(\log_{10}\).  
The power estimation has been applied to a repeat coverage of Europe comprising ascending and descending passes for a total of 32,000 bursts (each of \(\sim\)20 km along track and 100 km across track), and producing the first map of C-band RFI power. The analysis evidenced many low power sources, spread over the whole bandwidth, and just a few very high power ones, that are active for only part of the time, and on very precise frequency bands.  
The force of this approach is the ubiquitous time and space coverage of S1 constellation, which would ensure the capability of producing and updating a world-wide RFI map.  
This map could be exploited by policy makers to understand how important is the RFI issues, and to drive the evolution of the RLAN regulation for WIFI.  
The production of these map could be done at a negligible computational cost, and in very short time, if rank pulses are made available apart form the whole raw-datasets (that are totally useless for such goal). This upgrade in S1 products is expected for early 2018.  
The research and the open publication are funded by Italian Space Agency (ASI).  
[PERSON] designed and tested the algorithms and wrote the paper. [PERSON] had the original idea to exploit the rank-burst, revised the paper, and provided information on Sentinel-1 performance. [PERSON] wrote the Matlab code to extract rank pulses from Sentinel-1 raw data, the table and schemes of Sentinel-1 calibration, and made the long strip analyses in Figure 2.  
The authors declare no conflict of interest.
# Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain
## 4 Experiments
### Robustness Experiment
#### 4.2.4 Multiple Attacks  
Considering that the images are vulnerable to multiple types of attacks in the actual transmission process, the algorithm's robustness can be further verified by conducting multiple attack experiments on the images. We randomly selected three attack methods to superimpose attacks on the slice images in each gradient direction of the DWI, and the BER values of the watermark extraction under multiple attacks are shown in Table 5. It can be seen that almost all the BER values of the images are below 0.09 after three different types of superimposed attacks, indicating that our proposed algorithm still has good robustness against multiple attacks.
## 5 Experiment and Results
### Execution and Results  
To prove our proposed solution, we ran the entire process cycle. The encryption key was generated in a secured environment; then, it was transmitted a bit string to the USB blinking device. The mobile phone camera recorded the video of the blinking sequence. The mobile phone signed, encrypted, and transmitted the blinking LED video. The mobile receiver device in the target location accepted the blinking video and transformed it into a bit string. Then, the bit string was translated into the ASCII code to retrieve the original encryption key. This key was used to decrypt the corresponding transmitted messages.  
We performed an experiment of a key transfer (e.g., \"a b c d\") between a host with an optical USB device and a smartphone with a camera, where each character had 8 bits.  
Figure 14 depicts the key transmission example [\"abcd\"] used in the experiment. The four screen images were captured during the live key transmission stages. In image a, the stream of the 0 xA5 bit string was accepted by the mobile device connected to the sending computer. Image b captured the sender computer screen shot during the key transmission to its associated mobile device. Image c is the captured mobile screen while accepting the \"abcd\" key and image d depicts the complete acceptance of the transmitted key.
## 2 Flash Storage Framework  
### Overview of NAND Flash Types  
The types of NAND flash used in SSDs differ based on the number of bits each flash cell can store [8]. Each type presents its own balance of performance, cost, endurance, and data density. Single-level cell (SLC) stores 1 bit per cell with 2 voltage levels (0 and 1), delivering the highest write performance and endurance but at a higher cost and lower density. Multilevel cell (MLC) stores 2 bits per cell with 4 voltage levels (00, 01, 10, and 11). Triple-level cell (TLC) and Quad-level cell (QLC) store 3 and 4 bits per cell, requiring 8 and 16 voltage levels, respectively. As data density increases from SLC to QLC, cost efficiency improves, but write performance and endurance are progressively reduced.
# Position Sensing with a Compact 0.1 THz Amplitude Modulated Source
## 2 Materials and Methods
### THz Triangulation Measurement Setup  
The proposed layout of the measurement setup can be seen in Figure 1, which shows the source, two focal lenses, the sensor plane and the object of interest represented by a horizontal plane. THz illumination propagates in all directions, unlike a laser beam, and its direction is defined by the direction of the horn antenna at the end of the source. A focal lens is needed to focus the THz illumination on the object of interest and to focus the signal reflected from the object on the sensor plane.  
In Figure 1a, the horizontal plane is in the mid vertical position and the signal is focused on it. With the proposed setup, the reflected signal is directed to the centre of the sensor plane. When the horizontal plane is shifted vertically, simulating the change of height of the observed object (which depends by the application), the focus position of the reflected THz signal on the sensor plane changes.  
Figure 1: The proposed layout of the measurement setup: (**a**) THz signal propagation when the horizontal plane is in the middle position; (**b**) THz signal propagation when the horizontal plane is raised vertically; (**c**) THz signal propagation when the horizontal plane is lowered vertically.  
Figure 1b shows an example where the horizontal plane is raised vertically, closer to the source. Due to the setup and angle of incidence, the THz signal is reflected from a point closer to the source, compared to Figure 1a, so the reflected signal will hit the lens on the left side. In this case, the signal goes through the central part of the lens at a certain angle. Therefore, the signal is focused on a different part of the sensor plane and is further to the right, as in the case shown in Figure 1a.  
Similarly, if the horizontal plane is vertically further away from the THz source, as shown in Figure 1c, the reflected signal is horizontally further away from the source and, consequently, the reflected signal passes the lens further to the right part. As a result, the signal is focused on the left part of the sensor plane. A more detailed look at the sensor plane is shown in Figure 2.  
Figure 2: Cont.  
When a series of sensors is used or the sensor plane is scanned with a single sensor, the proposed measurement setup can transfer the vertical/height detection of the observed object to the horizontal detection, where the focus of the received signal is on the sensor plane.
# Key Bit-Dependent Side-Channel Attacks on Protected Binary Scalar Multiplication +
## 3 Materials
#### 3.1.1 Key Bit-Dependent Properties  
Binary scalar multiplication consists of iterative operations determined according to the \(i\)-th bit \(d_{i}\) value of secret scalar \(d\) (Figure 2). Therefore, there exists a key bit identification phase in which the \(i\)-th scalar bit value is extracted from a \(\lambda\)-bit scalar string \(d=(d_{\lambda-1},d_{\lambda-2},\cdots,d_{1},d_{0})_{2}\) and stored in a \(d_{i}\) variable at the beginning of each \(i\)-th iteration. Thus, power consumption associated with the \(d_{i}\) value occurs. We can categorize these properties according to hamming distance (HD) and hamming weight (HW), mainly used as power-consumption models as follows.  
**Property 1**.: _In hardware implementations, power consumption in the key bit identification phase is simultaneously affected by the hamming distance between two consecutive bits \(d_{i+1}\) and \(d_{i}\), i.e., \(d_{i+1}\oplus d_{i}\ (0\leq i<\lambda-1)\). Thus, if two consecutive bits are the same, i.e., \(d_{i+1}=d_{i}\), power consumption related to \(d_{i+1}\oplus d_{i}=0\) occurs. Otherwise, power consumption related to \(d_{i+1}\oplus d_{i}=1\) occurs._  
**Property 2**.: _In software implementations, power consumption in the key bit identification phase is affected by the hamming weight of \(d_{i}\ (0\leq i\leq\lambda-1)\). Thus, if the value of \(i\)-th secret bit is 0, i.e., \(d_{i}=0\), then power consumption is related to 0. Otherwise, power consumption related to 1 occurs._