esa-sceva/satcom-chunk-collection huggingface dataset:
# Isocyanide Multicomponent Reactions on Solid Phase: State of the Art and Future Application
## 4 Ugi Four-Component Reaction (U-4 CR)
#### Isocyanide Resin  
The first synthesis was carried out using acrylamide-containing resins with various chemical structures, crosslinks, and topographies (cf. Section 2.2) with the aim to synthesize peptides through ligation of a C-terminal fragment with an N-terminal fragment (Scheme 26) [29]. C-protected amino acid/peptide **123** (as an amine component) and \(N\)-protected amino acid/peptide **124** (as an acid component) were coupled in the presence of an aldehyde and resin-bound isocyanide at room temperature for 72 h. The use of H-Leu-OEt, Boc-Gly-Ala-OH, and 1-methyl-3-formylindole as amine, acid, and aldehyde components afforded tripeptides in approximately 0.5 mmol/g yield. More hindered amino acids, namely, Val-OMe and Fmoc-Leu-OH, yielded dipeptides in amounts of 0.24 mmol/g. Reactions using dipeptides and tripeptides Ala-Val-Leu-OEt with either Cbz-Gly-Phe-OH, Cbz-Gly-Ile-OH, or Boc-Leu-Ala-Gly-OH instead of protected amino acids proceeded less satisfactorily. The yields of the resulting peptides in those cases were approximately 0.05-0.07 mmol/g. The reaction was monitored by IR spectroscopy (by means of comparing the disappearance of the isocyano absorption (\(\sim\)2170 cm\({}^{-1}\)) and the emergence of the ester carbonyl absorption (\(\sim\)1700-1750 cm\({}^{-1}\)) with the intensity of the amide carbonyl moiety absorption (\(\sim\)1650 cm\({}^{-1}\))). This use of U-4 CC for chemical ligation has only recently attracted much attention [16].  
\(\alpha\),\(\alpha\)-Dialkylglycine, a highly hindered C-tetrasubstituted amino acid, was produced by U-4 CR [49]. The reaction utilized isocyanide resin either without a linker or equipped with a linker (cf. Scheme 7). The Ugi reaction was carried out with \(p\)-methoxybenzylamine **129** (Scheme 27), three different ketones **130**, and phenylacetic acid **131** by using isocyanide resins **40** and **43**. The experiment utilizing the resin without any spacer failed to release the target product. In contrast, the Ugi reaction with isocyanide resin equipped with the spacer was successful with all the used ketones (**130**, R = CH\({}_{2}\)CH\({}_{3}\), (CH\({}_{2}\))\({}_{2}\)CH\({}_{3}\), and -(CH\({}_{2}\))\({}_{5}\)-). The presence of the spacer seemed to be required.  
In addition to the synthesis on dipeptides by using Rink resin (cf. Scheme 25), the paper also studied the tandem Petasis/Ugi reaction utilizing universal Rink isocyanide resin **57** (Scheme 28) [58]. The Petasis reactions were carried out in the solution phase and afforded trisubstituted \(\alpha\)-amino acid **119**, which was subjected to U-4 CC.  
**Scheme 28. U-4 CR involving Petasis product leading to highly substituted dipeptides.**
## 4 Microscopic Tests of Alkali-Activated Grout Materials  
### FTIR Analysis  
The FTIR tests conducted on Shenzhen's in situ soil, alkali-activated composites, and alkali-activated grout materials focus on phase transformation analysis. The obtained FTIR spectra, covering the range of 400 cm\({}^{-1}\) to 4000 cm\({}^{-1}\), are illustrated in Figure 14.  
Figure 12: Setting time of copolymer grouting materials with different slaked lime contents.  
Figure 13: Setting time of copolymer grouting materials with different water-to-solid ratios.  
Figure 14 shows that the absorption peak at 3620 cm\({}^{-1}\) corresponds to the stretching and bending vibrations of the H-O bond in molecular water, indicating the presence of chemically bound water in the alkali-activated grout materials [46; 47]. Additionally, two distinct absorption peaks are observed at approximately 1480 cm\({}^{-1}\) and 850 cm\({}^{-1}\), which are attributed to the out-of-plane bending and antisymmetric stretching of the O-C-O bond in CO\({}_{3}\)\({}^{2-}\)[48]. Furthermore, the presence of a prominent absorption peak between 950 cm\({}^{-1}\) and 1050 cm\({}^{-1}\), which is mainly attributed to the asymmetric stretching vibrations of the Si-O-X bond (where X can be Si or Al), indicates that polymerization reactions have occurred within the mixture. This finding is consistent with previous research.  
The study found that the inclusion of slag and fly ash in the reaction enhanced the absorption peaks, indicating that they accelerated the polymerization reaction rate. This acceleration is attributed to the smaller bond angle and force constant of Al-O bonds. As the substitution of Al by Si increases, the proportion of Si-O-Si bonds rises, leading to a higher wave number. The absorption peaks between 450 cm\({}^{-1}\) and 490 cm\({}^{-1}\) are due to the in-plane bending vibration of Si-O-Si in the silicate tetrahedra of the hardener. In summary, the absorption peaks suggest that the final composition of the samples includes both unreacted slag and fly ash powder (crystalline phase) and reaction products (such as N-A-S-H and C-A-S-H), which consist of SiO\({}_{4}\) and AlO\({}_{4}\) tetrahedra [49].
# An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation
## 5 Conclusions  
In distribution systems, variations of loads bring serious challenges to system operation. Moreover, the temporal variations of different types of loads are not the same, thus it is essential to conduct dynamic reactive power optimization for a certain time period instead of one time stamp. This paper proposed a strategy which segments the predicted integrated load curve first, and then conducts optimization using GA with adjustment times of reactive compensation devices as one objective.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case No.**} & **Total Losses (kWh)** & **Top Changes (Times)** & **Capacitor** & **Computation** \\  & **(kWh)** & **(Times)** & **Changes (Times)** & **Time (s)** \\ \hline Case 5 & 2403.1 & 8 & 3 & 1774.08 \\ Case 6 & 2350.1 & 6 & 1 & 818.81 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Network losses, device adjustment times and time consumed by cases 5 and 6.  
Figure 9: Comparisons of network losses (**a**) and tap position changes of one voltage regulator by cases 5 (**b**) and case 6 (**c**).  
Through case studies for a modified IEEE 34-bus system and three load curves extracted from field measurements, experimental results evidenced that, compared with optimization without load curve segmentation, the proposed strategy is able to produce satisfactory optimization results in terms of network losses and device adjustment times; additionally, it is able to reduce computation burden effectively at the same time. In the future, efforts will be made to improve sequence power flow calculation, which is an important procedure involved in dynamic reactive power optimization.  
Conceptualization, Y.L. and Z.D.; methodology, Y.L. and T.W.; software, Y.L.; validation, Y.L.; formal analysis, Y.L.; investigation, Y.L.; resources, Y.L.; data curation, Y.L. and T.W.; writing--original draft preparation, Y.L. and Z.D.; writing--review and editing, Y.L.; visualization, Y.L.; supervision, Y.L.; project administration, Y.L. and T.W.; funding acquisition, Z.D and T.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by State Grid Corporation of China SGCC Scientific and Technological Project-Research on the key technologies of power quality interference source hosting capacity assessment and active control device detection in distribution network.  
The authors declare no conflict of interest.
# A Novel Fragmented Approach for Securing Medical Health Records in Multimodal Medical Images
## 3 Materials and Methods  
In this paper, we propose a secure steganographic technique that involves distributing ciphertext across multiple digital images. Figure 1 illustrates the system model of the proposed technique for securely sharing medical image data along with patients' prescriptions for research purposes. Initially, the plaintext (patient information and/or prescription) is encrypted using 128 bits key based advanced encryption standard algorithm (AES128) to generate the ciphertext. The latter is then divided into segments, each of which is embedded into a distinct image chosen from a predetermined set of the patient's medical images. The selection of images for embedding is performed in a random fashion, a strategy that is crucial to enhancing data security. This random distribution has the advantage of obscuring the presence of the steganographic content, making it difficult to detect without specific knowledge of the selection process and the associated key. During the data recovery phase, the segments are extracted from the sequence of images, reassembled, and eventually decrypted to retrieve the original plaintext. This approach ensures that the data confidentiality and integrity are sustained from the point of encryption to decryption, making it suitable for sensitive communication scenarios. Additionally, this method would not affect the image quality, so images are still useful for diagnosis using AI techniques or manual diagnosis methods.
# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
## 2 Related Work  
### Anchor-Free Detection  
Balls in sports videos belong to the category of small objects, and the tracking results of small objects largely depend on the quality of the detector. Therefore, we discuss small object detection models in this section. In object detection, anchor boxes are multiple bounding boxes with different sizes and aspect ratios generated in the center of each image pixel. The current mainstream object detection algorithms are divided into anchor-based and anchor-free. Although the object detection algorithm based on the anchor frame mechanism can use a dynamic anchor frame size setting mechanism to improve a particular small object detection performance, compared with larger anchor boxes, the detector selects fewer small object anchor boxes, resulting in an imbalance of positive and negative samples, which is detrimental to small object detection. Secondly, the anchor box introduces too many hyperparameters, such as the number of anchor boxes, size, aspect ratio, etc. The research idea of the anchor-free mechanism is to change the original operation of using anchor boxes to select objects to position objects based on key points. CornerNet [19] turns the detection of the object box into a pair of key points, namely the upper left corner and the lower right corner, thus eliminating the trouble of designing the anchor. In addition, corner pooling helps CNNs better locate corner locations. ExtremeNet [20] also turns object detection into a key point estimation problem, including four extreme points and one center point of the object, and these five geometrically calibrated points form an object box. FSAF [21] is based on the online feature selection capability of the feature pyramid structure (FPN). It can dynamically allocate each instance to the most suitable feature layer during training, work with anchored module branches during inference, and output prediction results in a parallel manner. CenterNet [22] defines the object as a single point, that is, the center point of an object box. The detector uses key point estimation to find the center point and regress other object characteristics, such as size, 3D position, orientation, and posture. Anchor-free detection effectively solves the difficulty of small object detection caused by the predetermined fixed size and aspect ratio of the anchor frame. It has been applied in remote sensing images [23], face detection [24], surveillance pedestrian detection [25], and tennis ball detection in sports video [26]. We improve the ball detection and tracking model based on anchor-free CenterNet. CenterNet uses three key points, namely the upper left corner point, the center point, and the lower right corner point. A pair of detected corner points and embeddings are used to detect potential object frames; then, the detected center key point determines the final frame position. The acquisition of center points can enhance the network's discrimination ability and reduce redundant and wrong object frames.
## 3 Data and Methodology
### Research Methods
#### 3.2.2 Convolutional Neural Networks (CNN)  
CNN is the most popular type of neural network that has achieved great success in computer vision tasks, including image classification and object detection [28; 29; 30; 31] ([PERSON] and [PERSON] 2016; [PERSON] et al. 2016; [PERSON] et al. 2022, [PERSON] et al. 2022). It also achieved excellent results for remote sensing image classification [32; 33; 34; 35] ([PERSON] et al. 2017; [PERSON] et al. 2021; [PERSON] et al. 2022, [PERSON] et al. 2022). CNN was applied to solve remote sensing problems utilizing aerial photographs, multispectral images, and hyperspectral images. CNNs combine three basic architectural ideas, including local receptive fields, sharing weights, and subsampling, to achieve properties of the shift, scale, and distortion invariant, which are very important for image feature extraction. In general, local receptive fields provide each neuron within a specified connected convolutional layer with the only nearby region belonging to the previous layer, which assists the network in deriving the basic visual characteristics. Furthermore, the concept of sharing weights refers to the stability of the convolutional kernel's weight during the process of generating a feature map at a specified layer. Consequently, the number of trained parameters in CNNs will significantly decrease in comparison to the ANN method. Finally, the process of subsampling decreases the feature map's resolution that integrates with a convolution operator to obtain translation invariance.  
The CNN architecture employed for the assessment of the integration of OBIA-CNN methods is shown in Figure 3, which is composed of 7 layers, including the input layer as well as the output layer. The image layer is used as input to the two-dimensional convolutional layers in order to learn the feature map. The extracted features are subjected to the next step, called the 2D max-pooling layer, which aims to repress unwanted information to optimize the computational efficiency of the framework. The extracted features from the learning process are then transformed into an individual feature vector based on the flattened layer. After that, a dense (fully connected) layer is used to learn the contextual information from the features and help the classification layer. The classification is performed with a softmax layer, and the output predictions are used to classify the given image.  
Figure 3: CNN architecture used in integrated OBIA-deep learning frameworks.
## 4 Discussion
### Smartphone and Gait
#### 4.2.3 Effect of Smartphone Use on Gait Parameters  
Four studies have explored the effects of smartphone use on gait parameters, specifically focusing on step length and width [7; 25; 34; 43]. Notably, a comparison of two trials--walking without a phone at a comfortable velocity and walking with a phone at a similar velocity--demonstrated a reduction of approximately 4 cm in both right and left step lengths, alongside an increase in step width of approximately 0.93 cm [34]. Furthermore, participants who engaged in texting while walking exhibited a 0.2 m decrease in step length relative to those walking without smartphone use [25]. These findings indicate that alterations in gait kinematics, exacerbated by reduced arm swing due to screen focus, likely contribute to modifications in step length and width.  
Research on diminished gait parameters has shown that changes in stride length, cadence, and double support time culminated in an approximate 33.9% reduction in walking speed compared to conditions without smartphone use. This decline in speed and stability is associated with decreased attentiveness, thereby potentially heightening fall risk [43]. Prior studies have similarly reported reduced foot clearance and compromised gait stability during smartphone use while walking [7]. Collectively, these findings suggest that negative alterations in gait parameters may decrease stability, mimic patterns observed in older populations, slow movement, impair attention, and subsequently elevate fall risks. Accordingly, exercising caution while using smartphones during ambulation is strongly recommended.
## 2 Related Work
### Face Parsing Techniques  
Face parsing is a fine-grained subtask of semantic segmentation that aims to decompose facial images into multiple semantic components, including eyes, eyebrows, nose, mouth, hairline, ears, and so forth. Compared with general semantic segmentation, face parsing involves more complex structural relationships and shape deformations, demanding higher spatial parsing accuracy and semantic modeling capabilities. Early approaches were mainly based on shape priors and handcrafted features, such as Active Shape Models (ASMs) and Active Appearance Models (AAMs). However, these techniques performed poorly under variations in expression, lighting, and pose. With the advancement of deep learning, CNN-based face parsing methods have become mainstream. Representative works include extended applications of FCNs, Interlinked CNN (ICNN) [17], and EHANet [18], which achieved more stable and efficient feature extraction and semantic prediction under end-to-end frameworks.  
Recently, Transformer-based architectures have also been introduced into facial parsing tasks. Architectures such as ViT [19] and Swin Transformer [20] leverage global attention mechanisms to improve semantic consistency. Representative models include Parsing-R-CNN [21] and HRFormer [22]. However, Transformer models tend to be computationally expensive, rely heavily on large-scale pretraining, and still face difficulties in balancing fine boundary detail preservation with high-level semantic completeness.
# Precise Point Positioning Using Triple GNSS Constellations in Various Modes
## 5 Results and Discussion
Figure 6 shows a summary of the standard deviations of the station coordinates in the East, North, and Up directions, respectively. As shown in Figure 6, the standard deviations of the GNSS BSSD post-processed PPP solutions are improved compared to the un-differenced post-processed PPP solutions. In addition, as the number of epochs, and consequently the number of measurements, increases the performance of the various models tends to be comparable. In order to assess the RT-PPP solution, all of the IGS RTS products are used to produce various real-time PPP solutions. Figure 5: Summary of convergence times for BSSD post-processed PPP solutions. Figure 6: Summary of positioning standard deviations in East, North, and Up directions of all BSSD post-processed PPP solutions. (**a**) East standard deviation; (**b**) North standard deviation; (**c**) Up standard deviation. Figure 7 summarizes the convergence times of the various RT-PPP solutions. As shown in Figure 7, the RT-PPP solution convergence time depends on the IGS RTS satellite orbit and clock corrections used. The IGS03 satellite corrections provided the best PPP solution, with a convergence time around 25 min. This is expected because the IGS03 precise satellite products corrections are based on multi-constellation GNSS solution. The other RTS-IGS products provided longer PPP solution convergence times. Figure 8 summarizes the convergence times of the RT-PPP solutions for the various test stations when the IGS03 satellite corrections are used. As can be seen, the convergence time for the RT-PPP solution is similar to the convergence time of the GPS-only RT-PPP solution. As mentioned earlier the RTS-IGS satellite clock corrections are not available for the BeiDou system. Similar to previous cases, the RT-PPP solution output is sampled every 10 min and the standard deviation of the estimated station coordinates is calculated for each sample. Figure 9 shows the standard deviations of the station coordinates in the East, North, and Up directions, respectively. As can be seen, the use of IGS03 satellite corrections improves the RT-PPP solution precision, in comparison with other real-time satellite correction products. Figure 8: A summary of the convergence time of RT-PPP solutions using IGS03 satellite clock correction. Figure 7: Summary of convergence times of RT-PPP solutions using different satellite clock products.
# The Application of an Impedance-Passivity Controller in Haptic Stability Analysis
## 5 Experimental Results
### Case I: Positive Damping  
The experiment results are shown in Figure 9, where \(K\) = 2000 N/m and \(B\) = 5 Ns/m inside the stable passive region. In Figure 9, the responses from top to bottom are the stylus position of the haptic device, the force feedback from VE, and the compensating force from controller, respectively. We set up the initial probe position to be at 55 mm. In addition, the surface position of virtual object was at 12 mm, and the starting instant was set at 1 s. When the tip of the visual surgical knife touches the surface of a visual object, the VE calculates the displacement and velocity of the virtual object and returns the force to the haptic device. The motor-driven mechanism plays a role like a human operator, providing an external force for haptic interaction. From Figure 4, since the parameter setting was already in the passive region, the haptic system achieved stable equilibrium after about 4 bounces with or without the controller.
###### Abstract  
Turbulence can cause effects such as light intensity fluctuations and phase fluctuations when a laser is transmitted in the atmosphere, which has serious impacts on a number of optical engineering application effects and on climate improvement. Therefore, accurately obtaining real-time turbulence intensity information using lidar-active remote sensing technology is of great significance. In this paper, based on residual turbulent scintillation theory, a Mie-scattering lidar method was developed to detect atmospheric turbulence intensity. By extracting light intensity fluctuation information from a Mie-scattering lidar return signal, the atmospheric refractive index structure constant, \(C_{n}^{2}\), representing the atmospheric turbulence intensity, could be obtained. Specifically, the scintillation effect on the detection path was analyzed, and the probability density distribution of the light intensity of the Mie-scattering lidar return signal was studied. It was verified that the probability density of logarithmic light intensity basically follows a normal distribution under weak fluctuation conditions. The \(C_{n}^{2}\) profile based on Kolmogorov turbulence theory was retrieved using a layered, iterative method through the scintillation index. The method for detecting Kolmogorov turbulence intensity was applied to the detection of the non-Kolmogorov turbulence intensity. Through detection using the scintillation index, the corresponding \(\widetilde{C}_{n}^{2}\) profile could be calculated. The detection of the \(\widetilde{C}_{n}^{2}\) and \(C_{n}^{2}\) profiles were compared with the Hufnagel-Valley (HV) night model in the Yinchuan area. The results show that the detection results are consistent with the overall change trend of the model. In general, it is feasible to detect a non-Kolmogorov turbulence profile using Mie-scattering lidar.  
non-Kolmogorov turbulence; Mie-scattering lidar; refractive index structure constant; probability density distribution; residual turbulent scintillation theory +
Footnote †: journal: _Entropy_  
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