esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussions
### Limitations and Future Works
#### 4.5.4 Cost, Performance, and Security Comparison with Traditional Storage Solutions  
While blockchain-based storage solutions provide decentralization, immutability, and cryptographic security, they introduce challenges in terms of cost, performance, and scalability when compared to traditional centralized storage systems [102; 110]. A direct comparison is presented below to illustrate the trade-offs involved. Table 15 illustrates the comparison of traditional centralized storage and blockchain-based storage.  
These findings suggest that while blockchain-based storage solutions provide enhanced security and data integrity, they may not be the most cost-effective or high-performance alternative for all applications [97; 102; 103; 104; 105; 110].  
Furthermore, centralized storage systems, such as SQL-based databases and cloud storage providers, offer lower operational costs and higher transaction speeds [97; 103; 104]. However, these systems remain vulnerable to data breaches and lack the transparency of blockchain-based models [102].  
In contrast, blockchain storage solutions ensure data integrity and censorship resistance but at the cost of reduced efficiency and higher computational expenses. The integration of hybrid models, where blockchain is combined with traditional storage for critical data verification, is being explored as a potential solution to overcome these limitations [105].
# Clustering-Based Segmented Regression for Particulate Matter Sensor Calibration
## 3 Results
### Evaluation
#### 3.2.2 Clustering Algorithm  
In the clustering phase of the model, selecting different clustering algorithms to partition the data set will have an impact on the effect of subsequent regression training. In order to select an algorithm with low algorithm complexity and good clustering effect and then apply it in the model, the effects of using K-means, Mean-shift [28] and Fuzzy C-means [29; 30] as calibration models in the data partitioning phase on the model calibration performance are compared.  
As shown in Figure 10, the RMSE of the three groups of data when using K-means to partition the data set is lower than that of the other two algorithms. Compared with Fuzzy C-means, the RMSE of the three groups of data is reduced by 2%, 1.6%, and 1%, respectively; Compared with Mean-shift, the three groups of data decreased by 3.1%, 5.5% and 2.2% respectively. Among them, Mean-shift is vulnerable to noise interference, and the algorithm is affected by several data points with large offsets. Several similar data points with large offsets are regarded as a cluster, resulting in a small amount of data in the cluster, which cannot be fully trained and will cause large errors. The RMSE of Fuzzy C-means is also slightly higher than that of the K-means, so the subsequent experiments use the K-means algorithm to partition the data set.  
Figure 9: Taking RMSE as the evaluation index, the initial error of PM\({}_{2.5}\) concentration between the sensor and the standard station, the error after global linear regression and the error processed by our method are calculated.
# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
## 4 Experiments
### Performance of Image Restoration  
To evaluate the performance of the image restoration subnetwork, image restoration experiments are conducted on the LIVE, CSIQ, and TID2013 datasets. The average PSNR and average SSIM of the distorted and pseudo-reference images are used to assess the restoration performance. The performance of single-task and multitask image restoration networks is tested separately, and the experimental results are summarized in Table 8.  
From Table 8, it can be observed that the average PSNR and average SSIM of the multitask-generated reference images are higher than those of the single-task-generated reference images and distorted images on all three datasets. This suggests that the multitask image restoration network exhibits superior quality restoration performance, and the introduction of multitasking effectively enhances the quality of generated reference images.  
Figure 7 shows a comparison between single-task-generated pseudo-reference images and multitask-generated pseudo-reference images. From Figure 7, it can be visually observed that the multitask-generated pseudo-reference images exhibit significant advantages in terms of visual perceptual quality. Compared with the single-task-generated pseudo-reference images and distorted images, the image quality of the multitask-generated pseudo-reference images is closer to the reference image. This suggests that in the framework of multitask learning, the generation of reference images can better restore the visual quality and perceptual details of images, thereby improving the quality and usability of pseudo-reference images.
# Reconstructing Polarization Multiplexing Terahertz Holographic Images with Transmissive Metasurface
## 3 Structure Design
Here, the cross-polarized and co-polarized transmissions are defined as the ratio of transmitted light power of the cross-polarized component and the co-polarized component to the total incident light power, respectively. At the frequency of 0.85 THz (corresponding to the wavelength of 282.55 \(\upmu\)m), the cross-polarized transmission reaches up to 98.9%, while the co-polarized transmittance is less than 0.1%, satisfying the condition of an HWP. Therefore, with the LCP light illuminating the metalens at the frequency of 0.85 THz, the transmitted light is converted into the RCP light carrying an additional phase \(\varphi_{\text{L}}\) and focused on its focal plane to reconstruct a patterned hologram. Similarly, with the RCP light illuminating the metalens at the frequency of 0.85 THz, the transmitted light is converted into the LCP light carrying an additional phase \(\varphi_{\text{R}}\) and focused on its focal plane to reconstruct another patterned hologram. Thus, we choose 0.85 THz as the working frequency in the following metalens design. Figure 2: _Cont._
# HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
## 2 Received Signal and Proposed HCTC Model
### Proposed HCTC Model
#### 2.2.1 Stage A--Convolutional Layer

Table 1: Configuration of the convolutional layer (stage A) of the proposed HCTC model.
# Matched-Filter Thermography
## 3 Matched Filtering
### Idea behind the Technique  
MF relies on orthogonality of harmonic functions and uses this property to detect replica(s) of a pre-known signal in noisy channels. The well-known cross-correlation (CC) technique is a special case of a MF, mathematically defined as:  
\[R_{xy}(\tau)\ =\ \int_{0}^{T}x(t)\ y(t+\tau)\ dt, \tag{1}\]  
where \(x(t)\) and \(y(t)\) are the pre-known signal (i.e., filter) and the noisy response containing replica(s) of the pre-known signal, respectively. Equation (1) is very similar to the mathematical definition of orthogonality of harmonic functions:  
\[\int\sin(\omega t+\alpha)\ \sin(\omega t+\beta)dt\ =\ \left\{\begin{array}{c}0,\ \alpha \
eq\beta\\ ||\sin\left(\omega t+\alpha\right)||,\ \alpha\ =\ \beta\end{array}\right.. \tag{2}\]  
Comparison of Equations (1) and (2) implies that MF operates based on incremental delaying of response with respect to the pre-known signal/filter and examining the orthogonality of the two waveforms at any incremental delay. As such, when the filter and its replica become in-phase at a certain imposed delay time (i.e., delayed response matches the filter), the output of Equation (1) becomes a large value, but, when the delayed response does not match the filter, the output becomes small due to the orthogonality property of harmonic functions. The stochastic noise in the response will have minimal contribution to the MF output due to its nonexistence in the pre-known/filter waveform. Figure 1 depicts several plots to highlight the basic operating principles of MF. Panel (a) shows a single cycle of a 1 Hz sinusoidal signal as the pre-known waveform/filter, while panel (b) is a simulated response that is composed of a delayed version (\(\tau~{}=~{}2\) s) of filter and stochastic noise (SNR = 10). As expected from mathematical representation of cross-correlation, the MF output, panel (c), contains a waveform with a single broad peak located at \(\tau~{}=~{}2\) s, indicating the delay between the filter and response. Note the minimal contribution of noise to the MF output and the significant improvement of SNR compared to the response waveform. Panels (e) and (f) depict a scenario where the response contains two delayed replicas of the filter (\(\tau_{1}~{}=~{}2\) s and \(\tau_{2}~{}=~{}2.5\) s) to highlight the motivation behind the concept of pulse compression in MF. In this scenario, the MF output is incapable of resolving the two responses, reliably, due to the superposition of the broad MF peaks of the individual responses.
## 4 LPWAN Technologies
### Sigfox  
Sigfox [48] is a LPWAN technology that employs ultra-narrowband (UNB) modulation techniques. The modulation technique allows the receiver to only listen in a tiny slice of the spectrum, in order  
Figure 1: Structural architecture of a LoRaWAN [47].  
to minimize noise interference. They have the capacity to achieve a range between 10-50 km with minimal noise interference. The standard deploys differential binary phase-shift keying (DBPSK) and the Gaussian frequency shift keying which allows connectivity on the ISM frequency band. Sigfox operates on 902 MHz and 868 MHz in USA and Europe respectively [49]. Initial Sigfox release was only one directional communication system; however, the recent release supports a bidirectional communication. Sigfox communication standards support up to 140 uplink messages a day, each of which can carry a payload of 12 octets at a data rate of up to 100 bits per second. Figure 2 depicts the architecture of a typical Sigfox standard. Similar to LoRaWAN, Sigfox also deploy ALOHA principle for channel access and the architectural framework of a typical Sigfox network also consist of various end nodes, Sigfox gateways, Sigfox cloud and Application server. The network is based on one-hop star topology. The end nodes can be connected to gateway using star topology to relay messages to the gateways. The individual gateway forwards the received data to the Sigfox cloud using secure IP connections. The cloud is responsible for data management and data processing before sending it to the application server for further processing [50].
# Water Retention Curve of Biocemented Sands Using MIP Results
## 3 Materials and Methods
### Tests Performed
#### 3.4.4. Mercury Intrusion Porosimetry Tests  
Mercury intrusion porosimetry (MIP) tests were performed in cubic pieces extracted from the samples (1 \(\times\) 1 \(\times\) 1 cm\({}^{3}\)) after being air-dried in the laboratory environment for 48 h. These tests were performed to find the pore size distributions of the untreated soil (with the water content at preparation) and after the biocementation treatment. The equipment used was AutoPore IV 9500. The low pressures applied were from approximately 3.6 to 155 kPa, and the high pressures were from approximately 0.17 to 227 MPa, with 78 s intrusion time. The contact angle adopted for the mercury was 140\({}^{\circ}\).
###### Abstract  
The seismic damage of underground structures has been extensively investigated, and it has been demonstrated that underground structures located at weak interlayer sites are more prone to damage. In this study, a two-story two-span rectangular frame subway station structure is analyzed. A two-dimensional soil-underground structure model is developed using the large-scale finite element analysis software ABAQUS. The equivalent linear soil-underground structure dynamic time-history analysis method is employed to examine the seismic response of underground structures at weak interlayer sites. Variations in the thickness and shear wave velocity of the weak interlayer soil are analyzed. The seismic mitigation effects of split columns and prototype columns in underground structures at weak interlayer sites are systematically compared. The findings indicate that the relative displacement and internal force of key structural components significantly increase when the weak interlayer intersects the underground structure. Furthermore, as the thickness of the interlayer increases, the displacement and internal force also escalate. When the thickness of the weak interlayer remains constant and the shear wave velocity decreases, the relative displacement and internal force of the key structural components gradually intensify. Replacing ordinary columns with split columns substantially reduces the internal force of the middle column, providing an effective seismic mitigation measure for underground structures.  
underground structures; weak interlayers; split column; subway station; seismic design Article
## 4 Implementation
### User Study
#### 4.3.2 Study 1  
The participants were welcomed and thanked for their interest in joining the study. They were told about the device and informed that they could abort the study at any time. The participants were instructed to take hold of the DFF device, and the experimental procedure was provided with clear and straightforward instructions. The participants were instructed to correctly identify the direction of the force concentrated by the DFF device while blindfolded. The study was conducted in a controlled lab setting with an experiment mediator. The combinations of parameters influencing force concentration leveraged three rotation angle intervals (i.e., \(\pm 30^{\circ}\); \(\pm 60^{\circ}\); \(\pm 90^{\circ}\)) and four target directions (i.e., up, down, forward, backward). In total, 36 combinations (three tests per direction per interval) were used. The participants audibly spoke the rendered direction when DFF performed each combination. Then, the direction data was recorded by the experiment mediator.  
We observed that the DFF's inertia of weight dragged the force to the course of target direction when we accelerated/decelerated motor velocity. We termed this \"dragged direction\" and used it as an evaluation measure of directional force concentration with the target direction. The result of the first study indicated that there were directions that could be better sensed than others. The result showed that the 83.8% (SD = 0.104) of the participants felt the directional force generated by DFF (Figure 11). The most effective direction was backward (closest to participants). 44.4% of the participants correctly identified the target direction, whereas the 47.2% answer correctly identified the dragged direction. The next most effective direction was upward (target direction = 32.4%; dragged direction = 57.4%). Next was the forward direction (target direction = 35.2%; dragged direction = 43.5%), and the downward direction (target direction = 23.7%; dragged direction = 50.9%). The reason that the downward direction scored worst was that the weight of the device made the gripping hand force feel like an upward force, resulting in relatively insensitivity to the weight change. In addition, in terms of the upward direction, the participants responded that they felt the forward direction more than the upward direction, the target direction. This may have resulted in similar results from the downward direction. However, the forward and backward directions showed higher corrected answer rates in the target direction than the upward and downward directions. In response to changes to the rotation angle interval, the corrected answer rate about the dragged direction was higher at the shorter interval (\(\pm 30^{\circ}\), 52.1%) than the longer intervals (\(\pm 60^{\circ}\), 48.6%; \(\pm 90^{\circ}\), 48.6%). In the shorter rotation angle interval, both acceleration and deceleration occurred rapidly. Therefore, it resulted in a strong inertial force.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 4 Combination of Multisource Data for Road Extraction
### Combination of LiDAR with Other Data for Road Extraction  
Aerial imaging cameras are often accompanied by LiDAR sensors in airborne mapping systems. Thus, it is common to fuse LiDAR point clouds and optical aerial imagery. In addition, optical satellite data are used as well. In particular, additional colour information is useful for single-channel LiDAR. Segmentation of the input imagery is often performed to enable object-based fusion of the datasets.  
[PERSON] [245] proposed a method to improve the classification of urban areas by fusing high-resolution satellite images (WorldView-2) and ALS data. Special attention was paid to the elevated roads, which were first detected in LiDAR ground points. Then, buildings were detected, and supervised SVM classification was performed on areas without elevated roads or buildings. [PERSON] [246] proposed a road extraction framework based on the fusion of ALS point clouds and aerial imagery; in this framework, pseudo scan lines were created from the fused data, and a rule-based edge-clustering algorithm was used to extract the road segments. [PERSON] classified road regions using an ANN by integrating aerial RGB (where R is red, G is green and B is blue) images, laser intensity and height images. Compared to the use of optical data alone, incorporating the laser intensity data helped to overcome the road obstructions caused by shadows and trees, and the height information helped in separating the aboveground objects from the ground objects. [PERSON] [235] proposed an object-based method for road centreline extraction from aerial images and ALS DSMs; using this method, a completeness and correctness of over 90% was obtained for two of the test data sets, and a completeness and correctness of over 80% was obtained for a third large site. Further developments were proposed for curved roads.
# Link Prediction Based on Data Augmentation and Metric Learning Knowledge Graph Embedding
## 2 Related Work
### Translational Distance Models  
The TransE model proposed by [22] represents a seminal work in translational distance models. It operates on the assumption that if a triple \((h,r,t)\) exists, the vector representations of the head and tail entities, denoted as \(h\) and \(t\), along with the relation \(r\), should approximately fulfill the constraint \(h+r\approx t\). This simple constraint yields a straightforward and effective modeling approach. However, it can sometimes result in the convergence of representations for semantically different entities in vector space. After being trained by TransE, entities like (Shanghai, located in China) and (Beijing, located in China) may exhibit vector representations \(\mathbf{v}_{\text{Shanghai}}\approx\mathbf{v}_{\text{Beijing}}\), even though these entities should not possess such a high similarity.  
To overcome challenges, TransH, introduced by [9], utilizes hyperplanes. Independently projecting the head and tail entities onto the relation-associated hyperplane precedes the transformation process. In contrast, RotatE, proposed by [23], utilizes rotation operations in a complicated space, departing from operations of summing in Euclidean space. This enhances the model's capability to handle symmetry relations. PairRE, proposed by [10], leverages pairwise relationships to model complex relations effectively.  
Traditional TDMs, including TransE, come with their merits, such as straightforward operations, a reasonable parameter count, and effective learning. However, they often fall short in capturing the full semantic richness of learned vector representations. In our approach, we seek to augment the modeling capacity beyond what TransH offers by introducing paired additional mapping matrices. This augmentation allows for a more sophisticated representation of intricate relations while preserving the simplicity and efficiency found in TDMs.
###### Abstract  
Phase contrast (PC) magnetic resonance imaging (MRI) is a primary method used to quantify blood flow. Cerebral blood flow (CBF) is an important hemodynamic parameter to characterize cerebrovascular and neurological diseases. However, a critical step in CBF quantification using PC MRI is vessel segmentation, which is largely manual, and thus time-consuming and prone to inter-rater variability. Here, we present encoder-decoder deep learning models to automate segmentation of neck arteries to accurately quantify CBF. The PC-MRI data were collected from 46 Moyamoya (MM) patients and 107 healthy control (HC) participants. Three segmentation U-Net models (Standard, Nested, and Attention) were compared. The PC MRI images were taken before and 15 min after vasodilation. The models were assessed based on their ability to detect the internal carotid arteries (ICAs), external carotid arteries (ECAs), and vertebral arteries (VAs), using the Dice score coefficient (DSC) of overlap between manual and predicted segmentations and receiver operator characteristic (ROC) metric. Analysis of variance, Wilcoxon rank-sum test, and paired \(t\)-test were used for comparisons. The Standard U-NET, Attention U-Net, and Nest U-Net models achieved results of mean DSCs of 0.81 \(\pm\) 0.21, and 0.85 \(\pm\) 0.14, and 0.85 \(\pm\) 0.13, respectively. The ROC curves revealed high area under the curve scores for all methods (\(\geq\)0.95). While the Nested and Attention U-Net architectures accomplished reliable segmentation performance for HC and MM subsets, Standard U-Net did not perform as well in the subset of MM patients. Blood flow velocities calculated by the models were statistically comparable. In conclusion, optimized deep learning architectures can successfully segment neck arteries in PC MRI images and provide precise quantification of their blood flow.  
deep learning; phase contrast MRI; blood flow; Moyamoya 2023
# Recent Updates on the Synthesis of Bioactive Quinoxaline-Containing Sulfonamides
## 2 Synthesis and Biological Activities
### Synthesis of Quinoxaline-Based Benzene Sulfonamide Derivatives with Anti-Inflammatory Activity  
The property of a substance or drug that reduces inflammation or swelling is specified as anti-inflammatory. In other words, nonsteroidal anti-inflammatory drugs (NSAIDs) are drugs that help reduce inflammation, which often helps to relieve pain. NSAIDs can be very effective, and some of the NSAIDS are high-dose aspirin, ibuprofen (Advil, Motrin, Midol) and naproxen (Aleve, Naprosyn), etc.  
[PERSON] et al. synthesized novel quinoxaline sulfonamide derivatives and described their anti-inflammatory activity. The sulfonation of 2,3 diphenylquinoxaline **56** was carried out by the electrophilic agent **47** to produce quinoxaline sulfonyl chloride **57** at 76% yield that was further treated with aliphatic and aromatic substituted amines to obtain the final quinoxaline sulfonamides **105** at 59-85% yield (Scheme 11) [137].  
The synthesized quinoxaline sulfonamide derivatives were tested for their in vivo anti-inflammatory activity in rat paw edema. The compounds **106-108** (Figure 16) exhibited anti-inflammatory activity, showing inhibition of edema in the range 1.17-4.04% and a mean paw volume of 0.96-0.98 mL. Diclofenac sodium used as reference drug showed, instead, 15.15% inhibition of edema after 30 min with a mean paw volume of 0.85 mL (Table 10) [137].  
Figure 15: Substituted quinoxaline sulfonamide derivatives **103–104**.