esa-sceva/satcom-chunk-collection huggingface dataset:
# Electroless Copper Patterning on TiO\({}_{2}\)-Functionalized Mica for Flexible Electronics
## 2 Materials and Methods
### Preparation of TiO\({}_{2}\)-Coated Mica Substrates  
Natural mica sheets (14 mm \(\times\) 50 mm \(\times\) 0.05 mm) were used as substrates. The sheets were decreased using 2-propanol and subsequently coated with TiO\({}_{2}\) via sol-gel dip-coating. The sol was composed of TTIP, AcAc, and 2-propanol in a 1:3:15 molar ratio. The detailed preparation and characterization of this sol have been previously published [32].  
The dip-coating process was performed using a custom-built apparatus, with a withdrawal rate of 1 mm s\({}^{-1}\). Three coating cycles were applied, each followed by heat treatment at 350 \({}^{\circ}\)C for 15 min and 500 \({}^{\circ}\)C for 1 h. The mass change in the TiO\({}_{2}\)-coated mica was measured gravimetrically, yielding an average of 14.6 \(\pm\) 1.3 ug cm\({}^{-2}\) per cycle, resulting in a total mass of 43.9 \(\pm\) 2.6 \(\upmu\)g cm\({}^{-2}\) after the third cycle (n = 20 samples). Assuming a TiO\({}_{2}\) layer density of 3.9 g cm\({}^{-3}\), the estimated film thickness was approximately 112 nm.
# On the Use of Neuro-Swarm System to Forecast the Pile Settlement
## 3 Model Development
### ANN Design  
This section describes the implementation of the ANN model to predict pile settlement. Prior to start ANN modeling for prediction of pile settlement, all datasets regarding training or testing feature must be separated. [PERSON] and [PERSON] [63] based on their studies suggested that (20%-30%) of whole datasets should be considered as testing datasets. Accordingly, we allocated 20% (19 datasets) of whole datasets to testing datasets. Since prosperous applying of Levenberg-Marquardt (LM) training algorithm has noticed in many researches [64; 65], it was also used in this study in order to design ANN. As an undeniable fact related to number of hidden layer, according to several researchers [29; 66; 67], almost all problems can be approximated through the use of only one hidden layer. On the other hand, based on results of the study conducted by [PERSON] et al. [68], the maximum number of nodes in a hidden layer is \(2\times N_{input}+1\), where \(N_{input}\) is the number of model inputs. Replacing \(N_{input}\) = 5 in this equation indicates that the problem of pile settlement can be solved by a range of 1 to 11. Therefore, several ANN models with hidden node numbers in the mentioned range were constructed and the results of analyses are shown based on coefficient of determination (\(R^{2}\)) in Table 1. The formula of \(R^{2}\) is presented in follows:  
\[R^{2}=1-\frac{\sum_{i}\bigl{(}x_{imens}-x_{ipred}\bigr{)}^{2}}{\sum_{i}(x_{imens }-\overline{x})^{2}} \tag{3}\]  
where, \(x_{imens}\) and \(x_{ipred}\) are the measured and predicted values, respectively. \(\overline{x}\) represents the average measured values. According to results of Table 1, an ANN model with only three hidden nodes performs better compared to the others. This model with \(R^{2}\) of 0.809 and 0.816 for train and test stages, respectively, shows an acceptable performance prediction in forecasting pile settlement proposing ANN model. Hence, an architecture of ANN with the size of \(5\times 3\times 1\) was found to offer more enhanced results. Note that, in the suggested ANN architecture, 5 is the number of input parameters, 3 is the number of hidden neurons and 1 is number of output parameter.
## 3 Results and Discussion
### Temporal Analysis of Post-Fire Vegetation Trajectory
#### 3.2.2 Yearly Dynamics of Post-Fire Vegetation  
The entire study period (1984-2006) includes three parts: pre-fire years (1984-1986), fire event year (1987) and post-fire years (1988-2006). Figure 7 shows the evolution of RRI, which was calculated using NDVI differences between fire scars and unburned control areas for the growing season average (April to October) and June (the month right after the fire in 1987) time series. Zero on the time scale represents the burn year, _i.e._, relative year 0 was the year of the wildfire event. Relative year \(-1\) and \(+1\) were years immediately before and after the fire event, respectively. In both series, there was an abrupt decrease in RRI values from 0 to \(-0.1\) and \(-0.25\) for the growing season average and June, respectively. Both series had low temporal variability in the pre-fire period that progressively increased in the post-fire period.  
The June series RRI values decreased abruptly after the Daxing'anling fire in 1987. This decrease was larger than for the growing season average time series. The RRI underwent a progressive increase during the first four years following the fire. Different recovery trends have been explained in relation to the species types installed and the biophysical constraints of the sites [23]. Generally, understory grass can recover to very high RRI values within several months following a fire. D  
Figure 6: Monthly NDVI trajectories of (**a**) a normal year and (**b**) the fire year. The points within the green box represent the growing season (April to October), while the points within the black box represent the snow cover season (November to March). The red point represents the month that the fire occurred.  
rapid regrowth capabilities, which can reach the saturating biomass stage within a short time scale (3-5 years). Therefore, the increase in this phase was mainly the result of survived dwarf shrub and understory grass. There is a slight decrease in the relative year 5, which was mainly a consequence of post-fire logging and planning. The RRI value was less than 0 during the post-fire period. However, the recovery trend was well pronounced and the system tended to recover to pre-disturbance conditions. Figure 7 shows a good recovery at relative year 19 with RRI reaching zero, which indicates there was no difference between burn scar and control plot. However, to confirm this result over a much longer period, additional observations are required.
# Automated Optical Inspection System for O-Ring Based on Photometric Stereo and Machine Vision
###### Abstract  
This research developed an AOI (automated optical inspection) system for the O-Ring inspection. First, an AOI system was implemented to detect surface scratches, flow marks, non-fills, and indentations on elastomer O-Rings with high surface reflection coefficients. The proposed system employed multiple light source structures to realize the photometric stereo. This method eased the identification of the O-Ring contour and Bezier control points. Then, by applying an interpolation process on these control points, we estimated the paths of the object surface. Simultaneously, regression analysis was conducted to produce a new smooth surface and then a constructed surface was compared with the model built by the photometric stereo method. The difference was deemed the candidate defect location. Overall, the detection recall rate was 100% and accuracy reached 96.56%. This paper also developed an AOI system for the O-Ring dimension measurement. The system analyzed the contour of the O-Ring and reversely calculated the Bezier curve control points. Then, those control points were used with [PERSON]'s algorithm to estimate the O-Ring dimension with high accuracy.  
photometric stereo; AOI; Bezier curve; rubber; homography; defect detection Article
## 2 Related Work
However, it is worth noting that this progressive adaptation approach often necessitates training several semantic segmentation models. For instance, in [5], three models were trained separately for three different domains, potentially making the training process less efficient. Building upon this methodology of using intermediate domains for progressive domain adaptation, some studies have trained some additional image translation models. CycleGAN [18] is a good case in point and enables the inter-transfer of daytime and nighttime images before training semantic segmentation models, thus introducing different visual features through diverse augmented data and aiding in the adaptation of the transferred models to various scenes and environments. Furthermore, ref. [30] introduced a nighttime semantic segmentation method based on image translation by translating nighttime images into daytime ones and utilizing semantics with the model trained on the daytime domain. More recently, to improve the semantic segmentation of night scenes, researchers have explored the use of different sensors to capture the same image as an auxiliary input. [PERSON] et al. [31] proposed to utilize the insensitivity of thermal infrared to changes in illumination as a supplemental input to segmented images to provide additional information for nighttime semantic segmentation. Additionally, other studies have devised specialized scene semantic segmentation methods. For example, [PERSON]. [32] proposed a two-stage adversarial training approach that employs domain adaptation techniques to transform between pairs of daytime and nighttime scenes, particularly for rainy and nighttime scenarios. Likewise, [PERSON]. [33] introduced an adaptive network capable of automatically adapting its internal architecture based on the attributes of input images to different environmental conditions, including nighttime and rainy ones. Differing from the above methods, this paper introduces a network structure designed to train semantic segmentation for low-light images via end-to-end adversarial learning without resorting to intermediate domains or auxiliary images.
# Nanosatellite Autonomous Navigation via Extreme Learning Machine Using Magnetometer Measurements
## 3 Parameter Optimization  
### GPS-Based EKF Configuration  
When available, the GPS data received by the satellite were processed by the first EKF, and a state vector, \(\boldsymbol{x}_{GPS}\), was returned. The EKF was run using the configuration shown in Table 1.  
The received GPS data \(\theta_{k}^{GPS}=[GPS_{x},GPS_{y},GPS_{z}]\) were simulated by adding a zero-mean Gaussian noise, \(GPS_{noise}\), with the standard deviation \(\sigma_{GPS}\) to the real Cartesian  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Measurement noise, \(\sigma_{GPS}\) & 0.033 km \\ Initial position error, \(\Delta x\) & 5 km = 3\(\sigma_{x}\) \\ Initial velocity error, \(\Delta v\) & 0.5 km/s = 3\(\sigma_{v}\) \\ Process noise, \(u_{v}\), \(u_{a}\) & 0 km/s, 10\({}^{-5}\) km/s\({}^{2}\) \\ Measurements noise matrix, \(\mathbf{R}_{GPS}\) & \(\mathrm{diag}\{c_{GPS}^{2},\sigma_{GPS}^{2},\sigma_{GPS}^{2}\}\) \\ Covariance matrix, \(\mathbf{P}\) & \(\mathrm{diag}\{c_{x}^{2},\sigma_{x}^{2},\sigma_{y}^{2},\sigma_{y}^{2},\sigma_{y }^{2},\sigma_{y}^{2}\}\) \\ Process noise matrix, \(\mathbf{Q}\) & \(\mathrm{diag}\{w_{v}^{2},w_{v}^{2},w_{v}^{2},w_{v}^{2},w_{v}^{2},w_{v}^{2}\}\) \\ Measurement sampling time, \(T_{s}^{GPS}\) & 1 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: GPS-based Extended Kalman Filter configuration.  
Figure 1: ELM Neural Network structure.  
position in the ECI of the spacecraft \(\mathbf{r}_{true}=[x_{true},y_{true},z_{true}]\) given by the propagated true orbit:  
\[\mathbf{\varrho}_{k}^{GPS}=\mathbf{r}_{true}+\mathbf{GPS}_{noise} \tag{19}\]
## 4 Result Analysis
### Sentiment  
In order to know the sentiment of participants in the SNS-based approach and the traditional RE approach, we collected data by asking the opinion regarding their satisfaction about the performance of the proposed approach. The opinion was expressed by following a Likert scale from 1 (very low) to 5 (very high) concerning (i) how the proposed approach is effective and efficient and (ii) how easy it was to implement. The mean of these two scores is shown in Table 9. The mean value of team T1 for effective and efficient (EE) is 4.26 and 3.76 for easiness of the SNS-based proposed approach. The feedback shows that the proposed approach is effective and efficient, but participants still prefer the traditional RE approach because of its easiness. The feedback mean-value of team T2 for the traditional RE regarding efficiency and effectiveness was 3.86. The mean value for easiness was 4.40. In summary, the proposed approach was globally judged positive by team T1.  
Figure 13: Boxplot for total time taken by two teams to determine Variability and Commonality.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Participants** & **Effective and Efficient** & **Easy** \\ \hline \multirow{3}{*}{**T1**} & Mean & 4.2667 & 3.7667 \\  & N & 15 & 15 \\  & Std.Deviation & 0.49522 & 0.59362 \\ \hline \multirow{3}{*}{T2} & Mean & 3.8667 & 4.4000 \\  & N & 15 & 15 \\  & Std.Deviation & 0.66726 & 0.60356 \\ \hline \hline \end{tabular}
\end{table}
Table 9: T-test for Sentiment.
# A pH Monitoring Algorithm for Orifice Plate Culture Medium
## 3 Algorithm Principle and Result Analysis
### Algorithm Principle
#### 3.1.4 Color Moments in HSV Color Space  
As the time in cell culture increases, carbon dioxide in the incubator undergoes a neutralization reaction with buffer substances in the culture fluid. The phenol red color development in the culture fluid gradually changes from red to orange and finally to yellow. Its color feature is often the standard to determine whether to change the liquid. The color feature is a global feature that describes the surface properties of the corresponding field within an image or image area. Color feature descriptions can be divided into color histograms, color moments (color distribution), color sets, color aggregation vectors, and color correlation plots [22; 23]. A color moment in HSV color space was used as a color feature in this study [24; 25; 26]. As the color information is mainly distributed in low-order moments, the first, second, and third-order moments were sufficient to represent the color distribution of the image. The advantage of this method is that color space quantization is not required, and the dimension of the eigenvector is low. Its mathematical definition is:  
**Theorem 1**.: _Color moment eigenvalue [27]._  
\[\begin{split}\mu_{i}=\frac{1}{N}\sum\limits_{j=1}^{N}{p_{i,j}} \\ \sigma_{i}=\left(\frac{1}{N}\sum\limits_{j=1}^{N}{\left({p_{i,j}- \mu_{\text{i}}}\right)^{2}}\right)^{\frac{1}{2}}\\ s_{i}=\left(\frac{1}{N}\sum\limits_{j=1}^{N}{\left({p_{i,j}-\mu_{ \text{i}}}\right)^{3}}\right)^{\frac{1}{3}}\end{split} \tag{1}\]  
Figure 5: Algorithm flow for cropping the liquid map in the hole.  
Figure 6: Cropped \(500\times 500\) images.  
where, represents the \(i\) color component of the \(j\) pixel of a color image. \(N\) represents the number of pixels in the image. The first three-order color moments of RGB images form a 9-dimensional histogram vector. The color features of the image are expressed as:  
\[F_{color}=[\mu_{r},\sigma_{r},s_{r},\mu_{a},\sigma_{a},s_{a},\mu_{b},\sigma_{b},s _{b}] \tag{2}\]  
The steps for establishing a fitting curve model are as follows:  
1. Convert the image from RGB to HSV (hexconemodel) [28] format to obtain the HSV component of the image; calculate the number of image pixels;
2. Then, the average, variance, and skewness of HSV are obtained;
3. Coexist the obtained value with the measured pH value. For example, in the table file, the table is arranged according to the value of pH;
4. Find out the color characteristics that meet the linear relationship, establish a functional model, and analyze the functional relationship between HSV and pH.
# Analysis of Pre-Driver and Last-Stage Power--Ground-Induced Jitter at Different PVT Corners
## 2 Methodology: PSIJ under PVT Comers and Jitter Sensitivity
For this reason, this paper explores:  
* The consideration of PVT corners to analyze the separate contribution of PGSIJ in the pre-driver or last-stage circuits and their combined PGSIJ contribution, as both driver's IO stages can share the same PDN or have a distinct PDN design, where the decoupled PG supply noise can have different noise waveforms. * The derivation of experimental frequency- and amplitude-dependent jitter transfer functions of the pre-driver and last stage IO buffer circuits from transient TL circuit simulation under the worst-case corner determined from the above first analysis. Moreover, the two-tone jitter superposition validity under small and large supply voltage variations for both of the driver's stages is studied and analyzed.
###### Abstract  
Nowadays, the Internet of Things (IoT) performs robust services for real-time applications in monitoring communication systems and generating meaningful information. The ZigBee devices offer low latency and manageable costs for wireless communication and support the process of physical data collection. Some biosensing systems comprise IoT-based ZigBee devices to monitor patient healthcare attributes and alert healthcare professionals for needed action. However, most of them still face unstable and frequent data interruption issues due to transmission service intrusions. Moreover, the medical data is publicly available using cloud services, and communicated through the smart devices to specialists for evaluation and disease diagnosis. Therefore, the applicable security is another key factor for any medical system. This work proposed an approach for reliable network supervision with the internet of secured medical things using ZigBee networks for a smart healthcare system (RNM-SC). It aims to improve data systems with manageable congestion through load-balanced devices. Moreover, it also increases security performance in the presence of anomalies and offers data routing using the bidirectional heuristics technique. In addition, it deals with more realistic algorithm to associate only authorized devices and avoid the chances of compromising data. In the end, the communication between cloud and network applications is also protected from hostile actions, and only certified end-users can access the data. The proposed approach was tested and analyzed in Network Simulator (NS-3), and, compared to existing solutions, demonstrated significant and reliable performance improvements in terms of network throughput by 12%, energy consumption by 17%, packet drop ratio by 37%, end-to-end delay by 18%, routing complexity by 37%, and tampered packets by 37%.  
reliable data transfer; internet of secured things; public health; mobility; ZigBee devices with regard to jurisdictional claims in published maps and institutional affiliations.
# Secrecy Enhancing Scheme for Spatial Modulation Using Antenna Selection and Artificial Noise
## 3 Secrecy-Enhancing Spatial Modulation Scheme  
### Antenna Selection and Insertion of AN  
In our proposed scheme, we consider a multiple-input single-output (MISO) system with \(N_{a}\) transmit antennas as shown in Figure 2. [PERSON] is equipped with \(N_{a}\) antennas, while [PERSON] and [PERSON] are equipped with only one antenna. In the proposed system, we assume that \(N_{a}\) is not necessarily a power of two, \(N_{t}\) is a power of two, so that the antenna index can represent digital information, and \(N_{a}>N_{t}\). Information symbol \(x_{m}\) is transmitted along with AN of \(\beta_{1}\
u\) by one of the \(N_{t}\) antennas selected by matrix \(\mathbf{T}_{k}\), and another AN of \(\beta_{2}\
u\) is transmitted by one of the remaining \((N_{a}-N_{t})\) antennas selected by matrix \(\mathbf{T}_{q}\), where \(\
u\) is the complex Gaussian AN with zero-mean and unit variance. In addition, \(\beta_{1}\) and \(\beta_{2}\) are coefficients, which are designed to cancel the AN at Bob. Accordingly, the signals received at Bob and Eve in the proposed scheme with antenna selection matrices \(\mathbf{T}_{k}\) and \(\mathbf{T}_{q}\) can be, respectively, presented by:  
\[y_{b}=\mathbf{h}\mathbf{T}_{k}\mathbf{e}_{j}(x_{m}+\beta_{1}\
u)+\mathbf{h} \mathbf{T}_{q}\beta_{2}\
u+n_{b}, \tag{4}\]  
and:  
\[y_{e}=\mathbf{g}\mathbf{T}_{k}\mathbf{e}_{j}(x_{m}+\beta_{1}\
u)+\mathbf{g} \mathbf{T}_{q}\beta_{2}\
u+n_{e}, \tag{5}\]  
where \(\mathbf{h}\in\mathbb{C}^{1\times N_{a}}\) and \(\mathbf{g}\in\mathbb{C}^{1\times N_{a}}\) are the row vectors representing the channel gains from Alice to Bob and Eve, respectively. \(\mathbf{T}_{k}\in\mathbb{C}^{N_{a}\times N_{t}}\) is the effective transmit antenna selection matrix for \(k\in\{1,2,\cdots,P\}\), which is constructed by selecting \(N_{t}\) columns from identity matrix \(\mathbf{I}_{N_{a}}\). \(P\) is the number of transmit antenna combinations, \((\frac{N_{a}}{N_{t}})\), and the sample space of all possible antenna combinations is represented as \(\mathbf{\Phi}=\{\Phi_{1},\cdots,\Phi_{k},\cdots,\Phi_{P}\}\), in which \(\Phi_{k}\) denotes the \(k\)-th combination of the effective transmit antenna set. At each time slot, [PERSON] selects one of the \(P\) combinations and shares it with [PERSON] through a low-speed forward link, as in other conventional schemes [15]. In addition, \(n_{b}\) and \(n_{e}\) are complex AWGN with a mean value of zero and variance of \(\sigma^{2}\). \(\mathbf{T}_{q}\in\mathbb{C}^{N_{a}\times 1}\) is a single column matrix to select another antenna transmitting AN for \(q\in\{1,2,\cdots,N_{a}-N_{t}\}\). \(\mathbf{T}_{q}\) is selected from submatrix \(\mathbf{I}^{\prime}_{N_{a}}\in\mathbb{C}^{N_{a}\times(N_{a}-N_{t})}\), which is composed of the remaining column vectors in \(\mathbf{I}_{N_{a}}\) after constructing \(\mathbf{T}_{k}\).  
Figure 2: System model of the proposed secrecy enhancement scheme for the spatial modulation (SM) system.
## 2 Materials and Methods
### Test Apparatus
#### 2.2.2 Vacuum Preloading Combined with PIV Technique Test Apparatus  
The test apparatus used for the vacuum preloading combined with the PIV technique is shown in Figure 4. It consists of a vacuum preloading model test box, a vacuum system, and a data acquisition system.  
The model test box is constructed from stainless steel with dimensions of 60 cm (length) \(\times\) 20 cm (width) \(\times\) 50 cm (height). A Perspex sheet is installed as a viewing window on the front side of the box. To ensure airtightness, a geomembrane and a sealing plate are positioned above the model box, with the geomembrane securely fixed between the box and the sealing plate using a flange connection. On the back side of the model box, channels for pore water pressure sensors are installed to facilitate connections with the pore water pressure gauge. A drainage channel is provided on the side of the model box for water discharge. In the center of the box, a PVD bracket is mounted to secure the prefabricated vertical drain (PVD), which is connected to the drainage channel through a geosynthetic cap and a dewatering pipe.  
The vacuum system consists of two SHZ-D circulating water vacuum pumps, a water-air separation flask, and an electronic scale. Based on engineering practice, the vacuum pressure is set to \(-\)85 kPa. To maintain a stable vacuum degree inside of the model box, the two vacuum pumps operate alternately, each running for 12 h. The water-air separation flask collects water discharged from the slurry during the vacuum preloading test. The electronic scale (HCT-U automatic weighing scale) records the weight of discharged water in real time.  
The data acquisition system includes an LED light source, a high-definition camera, and pore water pressure sensors. The LED light source provides uniform and bright illumination to ensure the clarity of captured images. The high-definition camera (MER-1810-21U3C-L which was procured from Daheng Co., Ltd., Beijing, China), with a resolution of 4912 \(\times\) 3684 pixels, captures high-resolution images for PIV calculations. The pore water pressure sensors continuously monitor real-time changes in pore water pressure during the vacuum preloading test.