esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 2 Signal Model and Potential Error Sources
### Potential Error Sources  
There are various potential error sources that degrade the navigation performance, and these are categorized as multi-path and NLOS reception, ionospheric irregularities, tropospheric delay, clock biases and receiver noise. GNSS signals are low-powered, and therefore, they are vulnerable to various noises and errors. Among all error sources, multipath and NLOS, being the most prominent source of error in GNSS, contribute to reduced navigation performance.  
In ideal conditions, signals acquired by the GNSS receiver are LOS. However, in a challenged environment due to buildings, surroundings structures, narrow streets, and trees, the received signal can be reflected, diffracted, or completely blocked before reaching the ground receiver. When the signals are received through various paths, multipath (MP) signal and NLOS reception arise, when the signal is acquired via reflections, as shown in Figure 2. MP/NLOS is one of the major contributors to GNSS positioning errors in urban environments, as it enhances the uncertainties and inaccuracies in the positioning and navigation performance. Hence, received signal quality drastically varies in such an environment, and this results in inaccurate positioning accuracy. In severe urban environments, GNSS fundamental parameters significantly vary from their standard values. The presence of NLOS/MP may lead to limited satellite availability, continuity and disrupted signal quality, which may result in degraded or interrupted navigation performance. Advanced and modern GNSS receivers have greatly enhanced the GNSS availability in highly urbanized areas. However, positioning accuracy stills remains a problem due to blocked, reflected and interrupted signals. MP/NLOS receptions are the most challenging and crucial task in GNSS positioning if no countermeasures are taken.
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 7 Results of All-Bands BadNets Attack and Defense
### Results of Defense Mechanisms  
#### 7.2.1 Neural Cleanse  
We poisoned samples from EuroSat with triggers of sizes from \(2\times 2\) to \(10\times 10\) and trained a backdoored model with a backbone of ResNet-20, ResNet-56, DCNN, and EfficientNet architectures. Note that the trigger was embedded in all of the 13 bands in the EuroSat data samples. We then applied Neural Cleanse to reverse engineer the trigger and detected whether the models were backdoored. Results by Neural Cleanse are shown in Table 3. For most of the cases, Neural Cleanse failed to detect the backdoor, especially for the ResNet-56 architecture. Neural cleanse performed better for the Efficient-Net architecture than other architectures, as it succeeded five times out of nine scenarios. It also proved that the effectiveness of the Neural cleanse method depends on the backbone architecture.
# Reviews on Corrugated Diaphrags in Miniature Fiber-Optic Pressure Sensors
## 4 The Application of CDs in Miniature Fiber-Optic Pressure Sensors  
CDs have been studied for quite a long time and were first widely used in capacitive pressure sensors and RF MEMS switches [41; 42; 43; 44; 45; 56; 52; 53; 54]. Introducing the corrugated structure into the fiber-optic sensor can significantly improve the performance of sensors. The CD-based EFPI fiber-optic pressure sensors have been favored by researchers because of their advantages, such as high sensitivity, large dynamic range, good linearity, and easy integration with signal processing circuits.
# A Generic Block-Level Error Confinement Technique for Memory Based on Principal Component Analysis
## 3 PCA-Based Error Confinement Technique
### Error Detection and Confinement Method
#### 3.4.2 Error Confinement  
When there is an error in \(\mathrm{v_{ij}}\) (i from 1 to n, and j from 1 to k) of the eigenvectors matrix \(\mathrm{V_{Ki}}\), the error bit can be replaced by the corresponding data \(\overline{\mathrm{v}_{i}}\) (i from 1 to n) of the average vector \(\overline{\mathrm{V}_{i}}\). The method of correcting error data of the eigenvectors \(\mathrm{V_{Ki}}\) with the average vector \(\overline{\mathrm{V}_{i}}\) is shown in Figure 6a. When an error is detected in element \(\mathrm{y_{ij}}\) of the projection matrix data \(\mathrm{Y_{i}}\), the scale of the error is confined by replacing the erroneous data with the corresponding element in the confinement vector \(\overline{\mathrm{Y}_{i}}\). Such procedure is shown in Figure 6b.  
Figure 5: Adding parity bits to projection data \(\mathrm{Y_{i}}\).  
Figure 6: Correcting erroneous data with the average vectors. (**a**) Correcting error in the eigenvectors \(\mathrm{V_{Ki}}\) with average vector \(\overline{\mathrm{V}_{i}}\), (**b**) Correcting error in the projection data \(\mathrm{Y_{i}}\) with average vector \(\overline{\mathrm{Y}_{i}}\).
## 1 Introduction
These network models have obtained satisfactory results in the corresponding fields, but they do not involve the processing of high-noise and other interference components in the signal. This is because the network is sensitive to the sparse level of the signal and cannot be directly grafted into other outdoor, high-noise signal reconstruction fields. Inspired by the CS signal reconstruction method based on an unfolded network [51; 52; 53; 54], we propose a new signal reconstruction method, called ADMM-1 DNet, for the online monitoring of bearing parts of mechanical equipment working in an open environment. Each iteration of the classical ADMM algorithm is unfolded into the layers of a deep neural network (DNN), and the network simultaneously learns a redundant analysis operator for sparse representation of the signal to address the impact of high-noise interference on reconstruction accuracy. The parameters of the ADMM algorithm are translated into network parameters. The network's forward propagation is comparable to performing a limited number of iterations of the ADMM algorithm, followed by back-propagation training of the network to learn the model parameters from the training set. All parameters in the network are learned end-to-end, so the problem of large errors caused by relying on empirical values can be avoided. In addition, the establishment of the neural network follows the guidance of the traditional ADMM reconstruction algorithm. The expansion algorithm can not only efficiently reconstruct the signal but also make the signal reconstruction process have a certain interpretability [55; 56]. The rest of this paper is organized as follows: two traditional convex optimal CS reconstruction algorithms are introduced in Section 2 and compared with the proposed method as the baseline method. Section 3 introduces the specific theory of the ADMM-1 DNet network and the detailed signal online monitoring implementation scheme. In Section 4, the parameter setting and signal sparse level recovery performance of the network are explored on the simulation signal, and then the proposed method is compared with the traditional algorithm by taking the real bearing vibration signal as an example. Section 5 summarizes and discusses the results of this paper.
# Time-Dependency in the Corrosion Process of Reinforced Concrete Subjected to a Chloride Solution
## 4 Results and Discussion  
### Corrosion Potential with Time  
The HP monitoring results were obtained from 42 days of concrete curing, at which point the chloride solution was exposed to the concrete, for approximately 385 days, as shown in Figure 3. After immersion in a chloride solution, a sharp decrease in the potential was observed for all samples, resulting from an increase in the electrical conductivity of the concrete owing to the contamination of the chloride solution [6]. In the passive state, the potentials fluctuated with the concrete conditions caused by the wet-dry regimes before reaching a critical corrosion potential of \(-\)350 mV vs. CSE. The fluctuation was more severe for the 0.4 OPC concrete than for the 0.6 OPC concrete. It appeared that having more restrained pore structures in the concrete affected the corrosion potential determination. The results indicated that concrete and steel conditions affect the corrosion potential of the steel, as in a previous study [24]. In addition, the corrosion potentials of the steels in the 0.4 OPC concrete grow after 240 days, which may indicate an improved corrosion resistance, such as growth of the passive layer [25]; however, this cannot be determined based only on the trend of corrosion potential.  
When the critical corrosion potential for corrosion initiation was set to \(-\)350 mV vs. CSE, only the onsets of corrosion for the 0.6 OPC concrete were detected during the experiments. Corrosion initiation typically occurs when the corrosion potential decreases.  
Figure 3: Corrosion potential of (**a**) W/C = 0.4 and (**b**) W/C = 0.6 OPC concrete with time.  
The fluctuations for the 0.6 OPC concrete in Figure 3b were reduced after the onset of corrosion. Furthermore, the following cases regarding the corrosion behaviour or depassivation process were observed, as shown in a previous study [25]: (1) a sharp decrease in corrosion potential; (2) a slow decrease in corrosion potential with the corrosion potential maintained between \(-250\) mV to \(-350\) mV. The time to onset of corrosion with steel was 171-269 days of concrete age, even though the steel is present in the same concrete. This implies that local effects, such as steel conditions and concrete, significantly influence the depassivation process in addition to environmental conditions, including chloride content, oxygen availability, and moisture content.  
After the corrosion initiation, the corrosion potentials decreased continuously with exposure time. Considering that the chloride effect is negligible after the onset of corrosion [26], the continuous decrease in corrosion over time, that is, the ongoing corrosion process, is attributed to the consistent supply of oxygen and moisture for the wet-dry phase. However, a continuous drop in the corrosion potential does not necessarily indicate a propagation phase in corrosion, which is an ongoing corrosion process, because the HP technique is considered a qualitative method. Therefore, quantifying the corrosion rate through HP monitoring is difficult.
## 2 Full-Duplex Transceiver with Passive/Active Eavesdrop Transmission System
### Security Gap  
In the practical context of the wiretap channel when the short-packet is used for transmission, the typical BER performance criteria are usually used to ensure two aspects of performance such as reliability and secrecy conditions [23]. Let us denote \(BER_{A}\) and \(BER_{E}\) as the average BER of user A and user E, respectively. While \(BER_{A,max}\) and \(BER_{E,min}\) are the maximum BER that user A can achieve and the minimum BER that user E can obtain, respectively. The reliability condition holds when \(BER_{A}\leq BER_{A,max}\), which means that the BER of user A should be maintained at a low value to enhance the reliability condition. Meanwhile, the security condition is achieved when \(BER_{E}\geq BER_{E,min}\), which means that the BER of user E should remain at a sufficiently high value to guarantee the security.  
According to [7; 17], the security gap, which is the minimum difference of SNRs (in dB) required to guarantee the legitimate receiver security over the eavesdropper, is calculated as:  
\[S_{g}(dB)=SNR_{A,min}-SNR_{E,max} \tag{1}\]  
where \(SNR_{A,min}\) is the minimum SNR corresponding to \(BER_{A,max}\), where user A has to operate to make sure the BER is below some reliability thresholds, i.e., \(BER_{A,max}=10^{-5}\), which is a sufficient level for practical applications [23]. Similarly, \(SNR_{E,max}\) is the maximum SNR corresponding to \(BER_{E,min}\) in which the BER of user E can approximately reach a threshold, that is, \(BER_{E,min}=0.5\), which is called the security threshold because user E cannot exactly decode the information message in this region [7].  
The graphical presentation of security gap is shown in Figure 3. In fact, the size of the security gap \(S_{g}\) indicates the minimum cost of the difference in SNRs between user A and user E that maintains the possibility of secure communication, the higher values of \(S_{g}\) will lead to a higher transmission cost. Therefore, the objective of this paper is to reduce the size of the security gap \(S_{g}\) as much as possible. In particular, the SNR of user A, \(SNR_{A}=p_{B}/\sigma_{A}^{2}\) (dB), on the main channel must be small enough to ensure that user A can correctly decode the information message from user B assuming the lowest possible power. In contrast, the SNR of user E, \(SNR_{E}=p_{B}/\sigma_{E}^{2}\) (dB), on the wiretap channel must be as large as possible to guarantee that the self-jamming broadcasting from user A still affects the decoding process of user E.  
Next, we will consider the first case with passive eavesdropper and the presence of a blind feedback algorithm.
## 3 Materials and Method
### Software  
As mentioned, the software used in this study was MATLAB. The written code's functionalities are as follows. Initialization Step: the program commences by establishing a serial port (COM6 in our computer) through the'serial' command, operating at a bit transfer rate of 115,200 bits per second. Subsequently, the Arduino data file is opened using the 'fopen' command, enabling data retrieval.  
* Data Acquisition: data from the Arduino are read from the file using the 'fscant' command. The program defines parameters related to the accelerometer sensor, including 'MaxValue', 'Resolution', 'Fs' (sampling frequency), and 'MaxAcc_g'.
* Data Storage Loop: a loop is established to collect accelerometer sensor values, specifically, the acceleration values in the Earth's gravity direction (\(x\), \(y\), and \(z\) axes); these values are stored in the 'xyz' variable over a period of 120 s.
* Visualization Step: the program employs the 'plot' command to draw a blue circle at the center of the monitor screen to serve as a reference point, and maintains it on the screen using the 'hold on' command.
* Angle Calculation: the accelerometer sensor supplies data regarding the Earth's gravitational acceleration in the \(x\), \(y\), and \(z\) directions. Using these values, the program computes the head's angle for both forward and backward bending as well as left and right bending directions using Equations (2) and (3), respectively. \[\theta =\tan^{-1}\left(\frac{A_{x}}{\sqrt{A_{y}^{2}+A_{z}^{2}}}\right)\] (2) \[\psi =\tan^{-1}\left(\frac{A_{y}}{\sqrt{A_{x}^{2}+A_{z}^{2}}}\right)\] (3)  
The real-time position of the sensor is visually represented by a red circle on the same page. The sampling rate was set at 100 Hz. Following resampling, the data are subjected to a  
Figure 2: Experimental setup: the experimental setup, including accelerometer (ADXL345) mounted on the head of the subject, Arduino UNO, MATLAB, and PC, is illustrated in this figure.  
10 Hz low-pass filter to reduce noise. Finally, the program generates visual representations of the sensor's movements. As depicted in Figure 2, the ADXL345 accelerometer sensor is firmly affixed to a plastic holder to eliminate unintended movements. Subsequently, it is connected to the Arduino UNO board using the appropriate protocol connectors. The Arduino board is further connected to a computer or laptop through the USB port. When the sensor is positioned on the individual's head, the data related to head angle are transmitted via the board to the MATLAB software running on the computer. The individual is then guided to adjust their head position based on the displayed angle information.
## 3 Influence Metrics
### Effectiveness Analysis of the Proposed Metrics
#### 4.2.1 Local Metrics  
We compared Dynamic Degree during the studies to two benchmark measures, namely, Degree and Weighted Degree. Figure 7 uses the Reality Mining datasets to show the outcomes of these studies. The top-N devices are sorted according to their Weighted Degree and Dynamic Degree, and Figure 7 shows the plotted curves reflecting the average number of nearby devices among those devices.  
An effective local impact metric in Figure 7 should show a diminishing trend since a node with strong local influence often shows a high number of nearby nodes. The curves for the two measures shown in the picture, however, are rather near to one another. This resemblance could result from certain traits or distinctive qualities that the dataset itself possesses.  
Figure 7 shows the plotted curves for the top-N devices ordered by their Degree and Dynamic Degree, which reflect the average number of connections. In light of the fact that a device with a strong local effect should interact with its neighbors often, the Dynamic Degree curve has a smoother downward slope and performs better than the Degree metric. Therefore, of the three influence measures, the Dynamic Degree meter performs the best since it establishes a balance between the number of nearby devices and the frequency of encounters.
###### Abstract  
Photodynamic therapy (PDT) provides a potential therapeutic approach for killing malignant cell/solid tumors, but currently approved photosensizers (PSs) are generally excited by visible light, limiting the penetration depth in tissues. It is necessary to develop a near-infrared (NIR) responsive photodynamic platform, providing maximum tissue penetration. Here, we present a gold nanopeanut platform exhibiting dual functions of NIR PDT and two-photon luminescence imaging. The nanopeanut with a size less than 100 nm exhibits two distinct NIR surface plasmon absorption bands at approximately 1110 and 1300 nm. To perform PDT, we conjugated commercial toluidine blue O (TBO) PS on the surface of the nanopeanuts. With spectral overlap, the 1230-nm femtosecond Cr: forsterite laser can excite the surface plasmons of nanopeanuts, transfer energy to TBO, and generate singlet oxygen to kill cells. Moreover, the plasmon resonance-enhanced two-photon luminescence of nanopeanuts can be used to map their delivery in vivo. These results demonstrate that the PS-conjugated gold nanopeanut is an effective theranostic system for NIR PDT.  
Au nanorod; nanopeanut; two-photon fluorescence; photodynamic therapy; fluorescence resonance energy transfer +
Footnote †: journal: Applied Sciences
# Brewing with 10% and 20% Malted Lentils--Trials on Laboratory and Pilot Scales
## Results and Discussion  
### Laboratory-Scale Brewing Trial  
Results of the analyses performed are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **BM** & **BM + 10\% LM** & **BM + 20\% LM** \\ \hline Mash analyses & & & \\ Odor & Normal & Normal & Normal \\ Saccharification time (min) & 10–15 & 20–25 & 25–30 \\ Filatrion time (min) & 25\({}^{\text{b}}\)\(\pm\) 1.5 & 22\({}^{\text{a}}\)\(\pm\) 1.5 & 21\({}^{\text{a}}\)\(\pm\) 0.6 \\ Wort analyses & & & \\ pH & 5.58\({}^{\text{a}}\)\(\pm\) 0.01 & 5.82\({}^{\text{b}}\)\(\pm\) 0.01 & 5.98\({}^{\text{c}}\)\(\pm\) 0.01 \\ Color (EBC unit) & 4.66\({}^{\text{a}}\)\(\pm\) 0.04 & 5.70\({}^{\text{b}}\)\(\pm\) 0.10 & 7.55\({}^{\text{c}}\)\(\pm\) 0.09 \\ Extract (\({}^{\text{Plato}}\)) & 8.87\({}^{\text{c}}\)\(\pm\) 0.06 & 8.60\({}^{\text{b}}\)\(\pm\) 0.02 & 8.30\({}^{\text{a}}\)\(\pm\) 0.04 \\ Turbidity (EBC unit) & 4.0\({}^{\text{c}}\)\(\pm\) 0.3 & 3.1\({}^{\text{b}}\)\(\pm\) 0.1 & 1.5\({}^{\text{a}}\)\(\pm\) 0.4 \\ Beer analyses & & & \\ pH & 4.74\({}^{\text{a}}\)\(\pm\) 0.03 & 4.83\({}^{\text{b}}\)\(\pm\) 0.01 & 4.90\({}^{\text{c}}\)\(\pm\) 0.01 \\ Color (EBC unit) & 4.56\({}^{\text{a}}\)\(\pm\) 0.05 & 5.40\({}^{\text{b}}\)\(\pm\) 0.08 & 7.11\({}^{\text{c}}\)\(\pm\) 0.14 \\ Apparent extract (\({}^{\text{Plato}}\)) & 1.90\({}^{\text{a}}\)\(\pm\) 0.04 & 1.89\({}^{\text{a}}\)\(\pm\) 0.07 & 1.98\({}^{\text{b}}\)\(\pm\) 0.04 \\ Apparent degree of fermentation (\%) & 78.57\({}^{\text{b}}\)\(\pm\) 0.28 & 78.03\({}^{\text{b}}\)\(\pm\) 0.72 & 76.11\({}^{\text{a}}\)\(\pm\) 0.51 \\ \hline \hline \end{tabular} Values are means \(\pm\) standard deviation (n = 3). Superscript letters indicate significantly different groups (\(p\leq 0.05\)), starting from \({}^{\text{a}}\) for lower values (BM = Barley Malt; LM = Lentil Malt).  
\end{table}
Table 2: Results of analyses during and after the brewing process—Brewing with 10% and 20% malted lentils at the laboratory scale.
# A Review of In-Situ and Remote Sensing Technologies to Monitor Water and Sanitation Interventions
## 2 Sensing WASH
### Unmanned Aerial Vehicles  
Increasingly, unmanned aerial vehicles (UAVs, or drones), are being used by researchers to map watersheds. Structure from Motion (SfM) three-dimensional point clouds can be derived from digital images collected on board UAVs, similar to the type of data produced by Light Detection and Ranging (LiDAR) technology [19]. The technological development of UAVs and their ability to derive high-resolution 3D information (and with much lower operational and up-front costs than manned airborne platforms) and satellite imaging has made UAV image acquisition appealing in several applications. Research applications utilizing UAVs for environmental monitoring, management, and evaluation have been increasingly explored in the past 10 years. The use of UAVs for data collection in the monitoring of natural resources [20; 21], biomass [22], forests [23; 24; 25], and vegetation [26; 27] have been found to have significant impacts on the temporal and spatial resolution of data at a more cost-effective price than traditional monitoring practices.