esa-sceva/satcom-chunk-collection huggingface dataset:
Although the post-modification strategy have found success in this regard [26; 27], the procedure is tedious and often low yielding. Therefore, exploration of new functional monomers for RPP is highly required. Imine is an important chemistry structure, which has been studied for many years [28]. The substituent groups on carbon atom affect the stability of the imine structure, and the electrophilic structures can stabilize imine in aqueous solution. Compared with aliphatic Schiff base, the aromatic Schiff base is more stable [29]. Of imine derivatives, hydrazones are very useful in controllable drug release because of their slow hydrolysis property in weak acidic aqueous solution [30]. Oximes are the most stable imine structure, which have been widely used in bioconjugation [31; 32; 33]. Recently, it is found that the nucleophilic catalyst could accelerate the reaction between alkoxyamine and ketone/aldehyde groups [34; 35; 36; 37]. This finding implies that alkoxyamine-functionalized nanomaterials have great potentials for use in identification of aldehyde-containing biomolecules, for example, glycopeptides and glycoproteins. Up to date, some papers report the post-modification preparation of alkoxyamine-functionalized polymers [32; 35; 38], but they do not concern with the problem of functional group density. In order to directly prepare the alkoxyamine polymers or polymer microspheres, \(N\)-hoc-protected alkoxyamine monomers have been synthesized [33; 39], but the deprotection condition is not fit to prepare hybrid materials. [PERSON] and co-workers prepared a phthalamide-protected alkoxyamine monomer, and the well-defined polymer was synthesized via RAFT polymerization [40]. However, it is evident that these monomers are not suitable to polymerize in RPP. Within the context, we designed a new alkoxyamine-based monomer to directly prepare alkoxyamine-functionalized polymer microspheres with magnetite nanoclusters in core by the RPP route. Phthalamide-protected \(N\)-aminooxy methyl acrylamide (NAMAm-\(p\)) was synthesized as monomer [41], and it was subjected to the RPP for well controlling the particle size and functional group density. Finally, the tailor-made microspheres were applied in enrichment of glycoproteins and glycopeptides.
## 5 Improvement for Fault Reconstruction  
To separate the sensor fault from the external output, (26) and (29) can be changed to (40) and (41) separately.  
\[\overline{\overline{F}}_{i}=T\overline{F}_{i}=\left[\begin{array}{c}0\\ \overline{C}_{i}P_{i}^{-1}\overline{C}_{i}^{T}D_{i}^{T}\end{array}\right]= \left[\begin{array}{c}\overline{\overline{F}}_{i}\\ \overline{\overline{F}}_{i2}\end{array}\right] \tag{40}\]\[\overline{\overline{H}_{i}}=T\overline{H_{i}}=\left[\begin{array}{c}0\\ \overline{C}_{i}P_{i}^{-1}\overline{C}_{i}^{T}K_{i}^{T}\end{array}\right]= \left[\begin{array}{c}\overline{\overline{H}_{i}}\\ \overline{H}_{i2}\end{array}\right] \tag{41}\]  
**Hypothesis 6.**_There exists a nonsingular matrix \(\mathbb{R}\), which satisfies_  
\[\mathbb{R}\left[\begin{array}{cc}\overline{\overline{H}}_{i2}&\overline{ \overline{F}}_{i2}\end{array}\right]=\left(\begin{array}{cc}\overline{E}_{1 }&\overline{\overline{E}}_{2}\\ 0&\overline{\overline{E}}_{3}\end{array}\right) \tag{42}\]  
_where \(\overline{E}_{3}\) is a nonsingular matrix._  
**Lemma 2.**_Hypothesis (6) enables that the sensor fault reconstruction value can be separated from the external output, and makes accurate reconstruction of sensor faults possible. It is equivalent to the row operation which makes \(\left[\begin{array}{cc}\overline{\overline{H}}_{i2}&\overline{\overline{F }}_{i2}\end{array}\right]\) into the upper triangular matrix._  
According to (30) and (31), multiple (31) with nonsingular matrix \(\mathbb{R}\), we obtain the following:  
\[\mathbb{R}\dot{\overline{e}}_{yi}=\begin{array}{c}\mathbb{R}\overline{A}_{ 21i}e_{1i}+\mathbb{R}\overline{A}_{22i}e_{yi}+\\ \left(\begin{array}{cc}\overline{E}_{1}&\overline{E}_{2}\\ 0&\overline{E}_{3}\end{array}\right)\left(\begin{array}{c}d(t)\\ f_{i}(t)\end{array}\right)-\mathbb{R}\overline{C}_{i}P_{i}^{-1}\overline{C}_{i} ^{T}D_{i}^{T}v_{i}(t)\end{array} \tag{43}\]  
If the states arrive at the sliding mode surface in the finite time, which means \(\dot{\overline{e}}_{yi}=e_{yi}=0\), (43) can be changed to  
\[\mathbb{R}\overline{C}_{i}P_{i}^{-1}\overline{C}_{i}^{T}D_{i}^{T}v_{i}(t) \rightarrow\left(\begin{array}{cc}\overline{E}_{1}&\overline{\overline{E }}_{2}\\ 0&\overline{E}_{3}\end{array}\right)\left(\begin{array}{c}d(t)\\ f_{i}(t)\end{array}\right) \tag{44}\]  
Therefore, according to (44) and (38), robust estimation can be achieved, and the value of the sensor fault can be reconstructed as follows:  
\[\hat{f}_{i}(t)\rightarrow\rho_{i}\overline{E}_{3}^{-1}\mathbb{R}_{2} \overline{C}_{i}\overline{F}_{i}\frac{D_{i}\vec{e}_{yi}}{\|D_{i}\vec{e}_{yi} \|+\delta_{i}} \tag{45}\]
# Performance Evaluation of Carbon Black Nano-Particle Reinforced Asphalt Mixture
## 3 Results and Discussion
### Effect of CBNPs on Storage Stability of Asphalt Binder  
The difference in the softening point of the top and bottom sections of the test tube indicates the storage stability of CBNPs-modified bitumen. The results of the storage stability test are displayed in Table 6. Results indicate that difference of only 1 \({}^{\circ}\)C has been observed in case of 5% CBNPs content and 1.3 \({}^{\circ}\)C by incorporating 10% CBNPs content into asphalt binder. This indicates that 5% and 10% CBNPs content in asphalt binder meet the storage stability criteria i.e., the softening point difference should be less than 2.5 \({}^{\circ}\)C for good high-temperature storage stability. Thus, CBNPs-modified asphalt binder is a stable material and can be utilized in road construction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Softening Point (\({}^{\circ}\)C)**} & \multirow{2}{*}{**Base Asphalt Binder**} & \multicolumn{2}{c}{**CBNPs Content (\%)**} \\ \cline{3-4}  & & **5** & **10** \\ \hline
**Top Portion** & 49.5 & 52 & 54.4 \\
**Bottom Portion** & 49.9 & 53 & 55.7 \\
**Difference** & 0.4 & 1 & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Difference in softening point of CBNPs-modified bitumen.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Tests**} & \multirow{2}{*}{**Base Asphalt Binder**} & \multicolumn{2}{c}{**CBNPs Content (\%)**} \\ \cline{3-4}  & & **5** & **10** \\ \hline
**Penetration (1/10 of mm)** & 61 & 53 & 46 \\
**Softening Point (\({}^{\circ}\)C)** & 48 & 51.5 & 54 \\
**Ductility (cm)** & 100 & 75 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Conventional testing results.
## 2 Materials and Methods  
### Data Collection and Processing  
A total of 1484 samples collected every 30 min during January 2023 were used. The applied methodology involved using the load curve data (in Watts) from a residence to predict the load curve for future or intermediate periods. After forecasting, the estimated curve was accumulated to obtain the total consumption curve. The same procedure was applied to the actual load curve, enabling the calculation of error metrics between the predictions and the real data.  
The data used for testing was collected using a smart meter based on the ATM90E26 (Microchip, Sao Paulo, Brazil) front-end from Atmel, connected to an ESP8266 microcontroller responsible for cloud communication (Google Sheets) and continuous data storage. This device is configured to record data at 30-min intervals. The flowchart of the data collection and processing stage is shown in Figure 1.  
Processing the collected and stored data is a crucial step to ensure that the patterns used in the neural network training process and prediction tests are valid and reflective of real behavior. Devices with limited memory need to continuously save data to prevent loss, which is not always feasible depending on the installation location. For the developed prototype, data is collected via the AFE measurement and then sent to the cloud. The main issues associated with this process include temporal jitter between data transmissions and potential connection or power outages.  
Additionally, the load curve used in the prediction considers the average power over the day; therefore, the recorded instantaneous power is not suitable for prediction. To address this, a process involving interpolation and downsampling of accumulated consumption data (Wh), along with Unix timestamp information, was implemented to generate load curves based on the average power for the period, thus correcting inconsistencies associated with data collection.
# Application of USCCD on Girth Weld Defect Detection of Oil Pipelines
## 2 Setup and Execution of Pull Tests  
### Ultrasonic Measurement Tool  
The UltraScan(tm) CCD pipeline inspection tool consists of several vehicles connected by linkage towbars as shown in Figure 1.  
The electronics vehicle contains the ultrasound instrumentation units. Each of these units collects and processes data from ultrasound sensors acting as both transmitters and receivers. The ultrasound signals received are amplified, digitized and stored.  
The inspection itself is accommodated by a high-density ring of ultrasonic sensors on a specially designed highly flexible polyurethane sensor carrier which guides the sensors along the pipe wall at a constant distance and orientation to the pipe wall.  
The sensor carrier is designed such that the entire pipe circumference is redundantly inspected in a single run. For 32\" pipe diameters, 512 sensors are mounted on 16 skids that are used for the crack detection with 240 sensors inspecting in upstream and 240 in downstream direction. This configuration results in a distance between sensors in circumferential direction of approximately 10 mm which provides a sufficient overlap of neighboring ultrasonic sensor tracks. This design ensures that signal reflectors are detected redundantly and can be distinguished from possible geometrical indications. Additionally, each of the 16 skids features two perpendicular ultrasonic sensors to provide a measure of the spool's wall thickness and localize the position of indications found with respect to the typical pipeline features as reliably as possible.
# An Improved Reversible Image Transformation Using K-Means Clustering and Block Patching
## 2 Related Work  
The RIT method was designed for image privacy protection, in which secret information is the image itself. Although [PERSON] et al. [16] can embed an image into several other images for \"secretsharing\", the transmission and storage of multiple images cause practicability to be low. Therefore, it is very hard and important to hide one image inside other of the same size, which is called \"image transformation\". [PERSON] et al. [17] proposed the first image transformation method. They chose a target image similar to the secret image in an image database, and transformed each secret block to generate the transformed image by the map between secret blocks and target blocks; then, they embedded the map into the transformed image to obtain the final stego-image. However, the visual quality of stego-images in [PERSON] et al.'s method was not good because the AI is very large, and it needs extra time to select a target image in a database. In order to improve [PERSON] et al.'s method, [PERSON] et al. [18] transformed a secret image to a freely-selected target image and reduced the auxiliary information. But this only recovers a good estimation of the secret image because traditional color transformation methods are not reversible.  
Inspired by [PERSON] et al.'s and [PERSON] et al.'s methods, [PERSON] et al. [19] designed a reversible image transformation (RIT) method, in which they transformed a secret image to a freely-selected target image and obtained a stego-image similar to the target image by designing a reversible shift transformation. Before shifting image blocks, an effective clustering algorithm is used to pair secret and target blocks, which are able to not only to improve the visual quality of the transformed image, but also to reduce the auxiliary information for recording block indexes. Based on [PERSON] et al.'s method, [PERSON] et al. [20] transformed the original image into the encrypted image, which looks like the target image, and proposed the RDH-EI framework based on RIT. The RDH-EI method used by the cloud sever is not affected by the encryption and decryption algorithm, and thus, it is irrelevant to the sender or the receiver. Thus, it is a very meaningful task to improve the visual quality of stego-image by reducing the auxiliary information in the traditional RIT method.  
The RIT method may be described with three steps: (1) feature extraction for image blocks matching; (2) reversible shift and rotate transformation; (3) secret image extraction. Details are introduced in Figure 1.
## 3 Distributed Residual Current Protection Utilizing Closed-Section Methods
### Adaptive Expansion of Closed Sections  
In instances where the residual current protection device malfunctions or is inactive, the closed section associated with the device no longer adheres to [PERSON]'s law, leading to the blocking of the corresponding closed-section protection. Consequently, adjustments must be made to the closed protective section to either safeguard the initial closed-section range or minimize the unprotected area. The schematic illustrating the self-adaptive expansion of the closed section is depicted in Figure 5.  
(1) Expansion of Closed Sections: In cases where one or more protection devices within the closed section upstream of a line exhibit abnormalities, the closed-section protection can be enlarged to encompass the original closed section within the protection scope. As illustrated in Figure 5, if the upstream measurement or protection device for line L21 malfunctions, rendering the current \(i_{721}\) unmeasurable, the residual current measurement at the rear of the line remains intact. To address this, the closed section CS2 extends its protection range by incorporating line L21 and merging it into a new CS2. This adjustment maintains the system's actual protected area as unchanged.  
(2) Unexpandable Closed Sections: When the protection device at the boundary (end of the line) in the power distribution system malfunctions, preventing residual current measurement at other points, and hindering continued protection, the original closed section must be reduced. However, efforts should be made to minimize the unprotected area. For instance, if the measurement device behind line L31 malfunctions, rendering the line unprotected, line L31 is excluded from the protection range of the initial closed section CS3 and designated as a new closed-section protection CS3.
## 3 Test Results and Analysis
### Bearing Capacity Analysis  
Compared with the unreinforced beams A0 and B0, the cracking load of the reinforced beams A1 and B1 was increased by 14% and 23%, respectively. The main reasons are as follows: (1) the section height of the reinforced beam increases by 5%; (2) the thin layer of epoxy mortar participates in the cooperative force of the original beam; (3) the thin layer of epoxy mortar effectively limits the concrete cracking in the tensile area.  
The yield load of the reinforcement beams A1 and B1 was increased by 32% and 40%, respectively. Under the same thickness of the thin layer of epoxy mortar, the increase in the yield load was related to the reinforcement rate of the original beam. The smaller the longitudinal reinforcement rate of the original beam, the greater the effect of the thin layer and the greater the increase in the yield load.  
The peak load of the reinforcement beams A1 and B1 increased by 18% and 17% respectively. The increase in the peak load was not obviously affected by the ratio of the longitudinal reinforcement of the original beam. The main reason is that the tensile steel of the A-series test beam has a yield step, and the increase in the tensile steel load of the A1 test beam mainly depends on strengthening the thin layer. While the reinforced bars of the B-series test beam have no yield step, the reinforcement of the B1 test beam after yielding is still caused by the joint action of the reinforced bars and the reinforced thin layer. The contribution degree of the reinforced thin layer to the peak load therefore decreases.
## 3 Results and Discussion
### Head Height  
In this study, the head height was defined by the height difference between the rivet head and the top sheet (Figure 4). The head height is related to the cosmetic appearance, the tightness of the joints, the vacancy between the bottom of rivet head and the top sheet, the damage of the rivet to the top sheet, and so on [5]. The head height is also related to the effective length of the rivet in the bottom sheet \(t_{\text{eff}}\), which is important for joint strength in the cross-tension test [8; 9] and the tensile-shear test [8].  
The \(p\)-value for the setting force is 0.0, so the effect of the setting force on the head height is definitely significant. This denotes that the difference of head height is obvious if the setting forces have a variation of 5 kN. By increasing the setting force, the head height decreases (Figure 5a). The setting force plays a predominant role in the piercing and flaring of SPR, and its fluctuation should be strictly managed. The \(p\)-values for the pre-clamping force and angular misalignment are relatively low, between 0.05 and 0.1. A low pre-clamping force of 1 kN caused the lowest head height, while no angular misalignment led to the lowest one. A low head height by a low pre-clamping force was originated from a large vacancy between the bottom of the rivet head and the top sheet (Figure 4). The angular misalignment induced asymmetry, increasing the head height. The effect of poor fit-up and edge offset distance on head height were not significant.  
Figure 5: _Cont._  
Figure 5: Main effects for control parameters. (**a**) Head height; (**b**) bottom thickness; (**c**) interlock; (**d**) tensile-shear strength.
# Flexible Phased Antenna Arrays: A Review
## 4 Major Challenges
### Electrical Performance Limitations  
Achieving consistent electrical performance, including low insertion loss, wide bandwidth, and stable impedance matching, is a core challenge in FPA designs. Flexible substrates have different dielectric properties compared to traditional rigid materials, which lead to variations in signal propagation speed, impedance, and power loss. Flexible materials such as polyimide and Kaptor(r) exhibit higher dielectric losses compared to rigid substrates. This was evident in the design of PAs printed by IP which encountered a significantly performance-degrading insertion loss of 8.17 dB [38]. The utilization of CNT-TFTs as switching elements for phase shifters introduced a high insertion loss reducing overall system performance. Similarly, in the design of self-adapting FPA, gain degradation (loss of 0.6-1.8 dB) due to phase shifter insertion loss was found [36]. The impact of insertion loss was minimized in different studies by optimizing the design parameters. For example, curved microstrip lines exhibited only a 0.3 dB increase in insertion loss compared to planar configurations, indicating negligible performance degradation [64]. Moreover, a highly efficient PA system with insertion loss below 0.1 dB was demonstrated through direct on-package fabrication, eliminating traditional connectors and thus shortening the signal path [42]. Bandwidth is also an important electrical performance parameter. Some applications require wide-band operation which is often constrained by coupling effects and impedance mismatches, especially on flexible platforms. In an attempt to enhance the bandwidth, stacked-patch design and inverted-F feed structures were utilized to ensure impedance matching and bandwidth enhancement. This solution delivered the widest bandwidth (17.5%) among active bendable PA antennas while maintaining scanning up to 60\({}^{\circ}\)[72]. Among other methodologies to achieve a wider bandwidth, an important technique is the use of proper feeding mechanism. In the design of a flexible Ka-band active PA on LCP substrate to support SATCOM applications, a novel feeding mechanism of proximity-fed patch antennas leveraged the proximity effect to achieve a wider bandwidth [67]. With the integration of LCP layers, bandwidth limitations emerge as a major challenge in such applications that is mitigated by using proximity feed design [55]. An interesting field of research regards the use of AME platforms to achieve precise multilayered structures while maintaining full custom design. Impedance matching in FPA is another critical parameter. In [61], authors designed periodic rectangular microstrip patches with an inset feed. The design reduced
## 4 Applications of Multimodal Learning for Fault Detection Diagnosis
### Applications in Manufacturing Systems  
Maintaining high operational efficiency while minimizing equipment downtime is crucial for productivity and cost-effectiveness in the manufacturing sector. Fault detection and diagnosis (FDD) systems play a key role by identifying issues early, enabling timely maintenance and reducing the risk of unexpected failures. Recent advances in machine learning (ML) and deep learning (DL) have significantly enhanced the effectiveness of FDD systems, making them indispensable in modern manufacturing environments.  
A major application of FDD in manufacturing is the condition monitoring of critical machinery, such as motors, conveyors, and pumps. These components are essential for continuous production, and any malfunction can result in costly delays. [PERSON] et al. [46] proposed the use of convolutional neural networks (CNNs) for motor fault detection. By analyzing both vibration and acoustic sensor data collected from motors, the model effectively identified faults such as imbalance, misalignment, and bearing damage. This enabled early interventions and the implementation of predictive maintenance strategies.  
FDD is also essential for monitoring robotic arms and automated production lines, which are frequently used in operations such as assembly, welding, and material handling. Failures in these systems can interrupt workflows and jeopardize product quality. [PERSON] et al. [47] employed recurrent neural networks (RNNs) to detect anomalies in the behavior of robotic arms. Their approach examined time-series data from joint angle sensors, force sensors, and torque measurements to identify unusual motion patterns that indicate mechanical degradation or sensor errors (See Table 2).