esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
## 3 Materials and Methods
### Vision Transformer (ViT) Classifier  
The final stage of the model is a Vision Transformer (ViT) that serves as the classifier. The ViT takes the set of node features output by the GNN and processes them with a self-attention-based transformer encoder to produce a classification. In practice, the GNN output (the graph node features) is converted into a sequence of vectors suitable for transformer input. This involves flattening the graph node feature matrix into a sequence of length \(N\) (where \(N\) is the number of graph nodes, e.g., \(H\times W\) if using a grid graph). Each of these \(N\) feature vectors acts as a token or patch embedding for the transformer [47; 48]. Before data is fed into the transformer, a learned positional encoding is added to each token. Unlike the CNN feature map, which has an implicit positional structure by its index, and the graph, which has an inherent structure, the transformer self-attention mechanism lacks an inherent sense of position. If the graph is a regular grid, 2D positional encodings can be flattened to align with the sequence. For simplicity, one can also flatten in row-major order and use a 1D positional embedding of length \(N\). This positional information helps the ViT distinguish spatial relationships, allowing it to differentiate. We adopt a Vision Transformer encoder for classification, which consists of multiple layers of multi-head self-attention and feedforward networks with skip connections. A special classification token (CLS token) is added to the sequence, a learned embedding vector that does not correspond to a specific image patch but serves as an aggregate representation. The sequence length entering the transformer becomes \(N+1\) (the \(N\) node features plus the CLS token). The self-attention mechanism of the transformer enables each token to attend to the content of the features of each other, allowing the model to focus on the relevant regions for classification.  
For instance, if combinations of distant regions are important for a class, the self-attention layers capture these interactions, something neither the CNN nor the local GNN neighborhood fully captures. Multi-head attention enables the model to attend to multiple patterns or relationships in parallel, with each attention head learning a distinct attention map. The output of each attention layer is processed through a feedforward network and normalization, following the standard transformer architecture. During forward propagation, self-attention computes attention weights between every pair of tokens based on the products of their projected feature vectors. This results in a globally aware feature update, where each token receives a weighted sum of all other tokens' features. Consequently, the transformer output features encode global dependencies, with the representation of the CLS token at the final layer serving as an aggregated image descriptor that has attended to various important regions of the image, and CLS token output is then fed to a classification head.
###### Abstract  
To apply deep learning to estimate the three-dimensional interaction position of a Cherenkov detector, an experimental measurement of the true depth of interaction is needed. This requires significant time and effort. Therefore, in this study, we propose a direct annihilation position classification method based on deep learning using paired Cherenkov detectors. The proposed method does not explicitly estimate the interaction position or time-of-flight information and instead directly estimates the annihilation position from the raw data of photon information measured by paired Cherenkov detectors. We validated the feasibility of the proposed method using Monte Carlo simulation data of point sources. A total of 125 point sources were arranged three-dimensionally with 5 mm intervals, and two Cherenkov detectors were placed face-to-face, 50 mm apart. The Cherenkov detector consisted of a monolithic PbF\({}_{2}\) crystal with a size of \(40\times 40\times 10\) mm\({}^{3}\) and a photodetector with a single photon time resolution (SPTR) of 0 to 100 picosecond (ps) and readout pitch of 0 to 10 mm. The proposed method obtained a classification accuracy of 80% and spatial resolution with a root mean square error of less than 1.5 mm when the SPTR was 10 ps and the readout pitch was 3 mm.  
Keywords:Cherenkov radiation (Cherenkov); positron emission tomography (PET); deep learning +
Footnote †: journal: Applied Sciences
# Expansive Soil Stabilization with Time, Cement, and Silica Fume
###### Abstract  
The type of soil known as expansive soil is capable of changing its volume through swelling and contracting. These types of soils are mostly composed of montmorillonite, a mineral with the capacity to absorb water, which causes the soil to heave by increasing its volume. Due to their capacity to contract or expand in response to seasonal fluctuations in the water content, these expansive soils might prove to be a significant risk to engineering structures. Many studies have dealt with swelling soils and investigated the behavior of these soils, as well as their improvement. In this study, three percentages of lime, cement, and silica fume (5, 7, 9%) are used to stabilize the expansive soil, and the work is divided into two sections: the first is using a consolidation test to record the free swell and swell pressure for the untreated and treated soils; in the second part, the grouting technique is utilized as a process that can be applied in the field to maintain the improvement in the bearing capacity. It is concluded that the soil stabilized with different percentages of lime, cement, and silica fume exhibits a decrease in both free swell and swelling pressure by approximately 65% and 76%, respectively, as compared with untreated soil. The soil grouted with silica fume increases the bearing capacity of footings resting on the grouted soil by approximately 64% to 82% for the soil treated with 5% and 9% silica fume, respectively, as compared with untreated soil.  
expansive soil; lime; silica fume; stabilization; grouting +
Footnote †: journal: Applied Sciences
###### Abstract  
The Orbita hyperspectral satellite (OHS) is the first hyperspectral satellite with surface coating technology for sensors in the world. It includes 32 bands from visible to near-infrared wavelengths. However, technology such as the fabricating process of complementary metal-oxide-semiconductor (CMOS) sensors makes the image contain a lot of random and unsystematic stripe noise, which is so bad that it seriously affects visual interpretation, object recognition and the application of the OHS data. Although a large number of stripe removal algorithms have been proposed, very few of them take into account the characteristics of OHS sensors and analyze the causes of OHS data noise. In this paper, we propose a destriping algorithm for OHS data. Firstly, we use both the adaptive moment matching method and multi-level unidirectional total variation method to remove stripes. Then a model based on piecewise linear least squares fitting is proposed to restore the vertical details lost in the first step. Moreover, we further utilize the spectral information of the OHS image, and extend our 2-D destriping method to the 3-D case. Results demonstrate that the proposed method provides the optimal destriping result on both qualitative and quantitative assessments. Moreover, the experimental results show that our method is superior to the existing single-band and multispectral destriping methods. Also, we further use the algorithm to the stripe noise removal of other real remote sensing images, and excellent image quality is obtained, which proves the universality of the algorithm.  
adaptive moment matching; orbita hyperspectral satellite; relative radiation correction; stripe noise; unidirectional total variational 2019 11 2098 123 July 2019; Accepted: 2 September 2019; Published: 9 September 2019
# Detecting and Solving Tube Entanglement in Bin Picking Operations
## 3 Bin Picking Algorithm
### Grasp Planning
#### 3.4.1 Grasp Synthesis  
The grasping point is always defined to be along one of tube's cylinder axis. The gripper is always set to be completely vertical, and thus only the yaw is adjusted so that the planes defined by the contact points between the fingertips and the tube's surface are always normal to the surface's normal vector. The approach vector is also always set to be vertical.  
For each trajectory, the grasping points are created by traversing the tube's cylinders and creating points at a constant distance between them and at a minimum distance from the cylinders' bases. Figure 5 shows examples of the grasping points generated for two sets of tubes. The grasping points are represented by a white sphere. In Figure 4(b), the points are only computed for the non-occluded tubes, which are colored in green. The yellow tubes are weakly-occluded, while the red ones are strongly-occluded.  
Figure 4: Examples of sets of simulated tubes in Gazebo.  
Three tests are performed to verify if a grasp is viable. Non-viable grasps are rejected, and not taken into account for the grasp evaluation.  
Firstly, to determine if the gripper has a risk of colliding with either another tube or the bin's boundaries when executing a grasp, a simplified model of the parallel jaw gripper as two oriented bounding boxes with a gap between them slightly larger than a tube's radius is used. The bounding boxes are positioned and oriented according to the gripper's pose as it is about to grasp the tube. The raw point cloud (i.e., without any filters applied to it) is used and the grasp is rejected if the number of points within the combined volume of both bounding boxes surpasses a certain threshold.  
Secondly, to prevent the gripper from overstepping its working area, it is checked if, for each waypoint of the trajectory, the point obtained by adding the waypoint's translation to the grasping point lies inside a predefined bounding volume. If any of these points is outside of this zone, then the grasp is rejected.  
Finally, grasps associated with an escape trajectory (and thus a weakly-occluded tube) are rejected if the grasping point lies in the section of the virtual tube that is expected to pass under the occluding tube. This prevents the gripper from colliding and dragging the occluding tube when following the trajectory. In Figure 3, escape trajectories that move the occluded tube to the upper-left corner can only have grasps whose points belong to the three cylinders on the left section.
# Accuracy of Guided Endodontics in Posterior Teeth
## 1 Introduction  
The aim of any root canal treatment is to cure or prevent apical periodontitis [1]. This is mainly achieved by disinfecting the root canal system [2, 3].  
In teeth with severe pulp canal calcification (PCC), endodontic access cavity preparation and identification of the root canal orifice can be very difficult, time-consuming and prone to a higher risk of iatrogenic damage (e.g., tooth structure loss and root perforation) [4]. An increased loss of enamel and dentin may result in an increased risk of fractures, ultimately worsening the prognosis of the tooth [4, 5]. Guided endodontics (GE) is an innovative method that uses 3D imaging (cone beam computed tomography, CBCT) and surface scans for virtual preoperative planning of minimally invasive access cavity preparation. A bur is guided through a template sleeve system to the planned position, similarly to the technique of guided implantology [6]. Guided endodontics enables the operator to locate the root canal orifice even in teeth with severe PCC [7, 8], and is less invasive than conventional endodontic techniques [9].  
Since CBCT was introduced into dentistry, it has been widely used for diagnostics and treatment planning [10]. It is extremely important to adhere to the ALARA principle (as low as reasonably achievable) in order to keep the radiation exposure for patients as low as possible [11]. A CBCT in endodontics may be considered when the conventional radiograph is insufficient for diagnosis, and the additional information from the CBCTis likely to facilitate diagnosis and further treatment. The current guidelines explicitly state that implementation of a CBCT may be justified for the planning of GE in teeth with PCC [12].  
Guided endodontics is already being implemented in the clinical setting. However, it is mainly used in anterior teeth [8; 13], and only a few publications have provided details regarding the accuracy of GE in posterior teeth [14; 15]. Therefore, the aim of this study was to determine the general accuracy of template-guided endodontics for access cavity preparation in posterior teeth, and to evaluate variables that might affect the accuracy of access cavity preparation, such as the type of tooth (molar vs. premolar), location of the posterior teeth (maximla vs. mandible) and the number of root canals per tooth.
## 7 Conclusions  
This paper focuses on the analysis and research of security issues arising in UAV communication. We propose a security protocol suitable for UAV communication and use the key node to achieve secure communication between UAVs using multiple key encryption combined with hash functions and digital signature technology to achieve multiple identity authentication between both, effectively improving the security of the protocol. Through formal analysis of the proposed protocol, the verification results did not find any vulnerability, proving that the protocol has high security; finally, the security analysis and performance evaluation of the protocol were conducted, and the results showed that the protocol has the advantages of anonymity, integrity, and perfect forward secrecy.  
However, the following issues with this protocol still need further investigation: privacy protection is particularly important for drone communication, and identity privacy is not considered in the textual scheme. Although pseudonyms are used to hide the real identity, there is still the possibility of associativity. How to achieve privacy protection of user identities in a simpler way is still an issue that needs to be addressed. Drone privacy protection is particularly important, and identity privacy is not considered in the textual scheme. Although it is possible to use pseudonyms in a way that to hide the real identity, there is still the possibility of associativity. How to implement a simpler way to protect the privacy of the user's identity is still an issue that needs to be addressed.  
Methodology, L.L. and X.L.; Software, L.L.; Formal analysis, L.L.; Writing--original draft, L.L. and X.L.; Writing--review & editing, Y.W. and L.T.; Supervision, Y.W. and L.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Identification of Putative VOC Markers
#### 3.7.1 Butyric vs Normal Samples from Industry A  
In industry A, the comparison was F282 and F283 (butyric) vs F284, F285, F286, and F309 (normal fermentation). The following VOCs had normalised \(V_{j}^{*}\) above the 1.96 cut-off limit: methyl butanoate (V13), ethyl butanoate (V18), and butanoic acid (V62) (Figure 9A). Then, they showed significant high dissimilarity (high content) between the spoiled and normal samples.  
One can notice the identification of butanoic acid, having a sharp, cheesy, and dairy-like odor (Perflavory database, www.perflavory.com accessed on 19 April 2021), and its methyl and ethyl esters (fruity notes) in the butyric samples. They could be considered distinctive of this spoilage, as also recognised by other methodologies. On the contrary, the VOCs which characterised the normal process in industry A (low presence in butyric samples vs the others from the same company) were: methyl 2-methylbutanoate (V14), 2-pentanol (V28), 2-ethyl-l-hexanol (V54), and dimethyl sulfoxide (V60).
## 4 Dynamic Simulation of the Tooth Breakage Fault
The RMS of the fault gears are larger than the RMS of the healthy gear under any rotational speed. The healthy gear, the half tooth breakage fault gear with \(p=0.2\) and the whole tooth breakage fault are chosen for vibration comparative analysis. The carrier rotational speed chosen here is 237 rpm. Figure 7 shows the waveform of these three acceleration signals. Compared with the signal under the healthy state in Figure 7a, the effect of the half tooth breakage fault on the vibration is slight. However, when the whole tooth is broken, strong impacts can be observed, and the peak value of the signal has increased nearly forty-fold. The spectrum of the vibration signals is shown in Figure 8. It can be found that there is an outstanding component at about 2632 Hz; this frequency equals to \(7f_{m}\) and is near the fourth rotational natural frequency of 2662.8 Hz, so this is a typical harmonic resonance phenomenon. There is a sideband at the high frequency region with the frequency interval of \(f_{m}\), which is caused by modulation. The spectrum of the half tooth breakage fault is close to the spectrum of the healthy gear; thus, it is hard to recognize the tooth breakage fault only only with spectrum analysis when the breakage size is \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \(C\) & \(\bar{\theta}_{cZ}\) & \(f_{c}\) & \(f_{s}\) & \(f_{m}\) & \(f_{p}\) \\  & (**rpm**) & (**rpm**) & (**Hz**) & (**Hz**) & (**Hz**) & (**Hz**) \\ \hline
1 & 77 & 76.4 & 1.27 & 5.80 & 122.2 & 3.5 \\
2 & 117 & 116.1 & 1.93 & 8.81 & 185.7 & 5.3 \\
3 & 157 & 155.7 & 2.60 & 11.8 & 249.17 & 7.12 \\
4 & 197 & 195.4 & 3.26 & 14.8 & 312.7 & 8.93 \\
5 & 237 & 135.1 & 3.92 & 17.9 & 376.1 & 10.8 \\
6 & 277 & 274.8 & 4.58 & 20.9 & 439.6 & 12.6 \\
7 & 317 & 314.4 & 5.24 & 23.9 & 503.1 & 14.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulated rotational speed conditions. small. However, as shown in Figure 8c, new sidebands caused by the fault become very obvious when the whole tooth is broken. Due to the existence of resonance and modulation in the vibration signals, demodulation is necessary in gear tooth breakage fault feature extraction. Figure 9 shows the envelope spectrum of the three signals at the low frequency region. From Figure 9a, one can see that the mesh impact is the main excitation source when the gearbox is healthy. From Figure 9b,c, it is clear to find that, when a planet gear tooth breakage fault occurs, the sum- and difference-frequency of the rotational frequency between carrier \(f_{c}\) and the fault characteristic frequency \(f_{p}\) will occur in the envelope spectrum. This is a useful indicator for the fault detection. Figure 6: The changes of root mean square (RMS) of the simulated ring vertical acceleration versus the (a) breakage size and (b) rotational speed. Figure 7: Waveform of the simulated ring acceleration of the (a) healthy gear \(p=0\), (b) half tooth breakage fault gear \(p=0.2\) and (c) the whole tooth breakage fault \(p=1\).
## 3 Results
### Application Domains
#### 3.2.5 Health Conditions Monitoring  
Four articles [73; 77; 84; 94] described solutions aiming to monitor the health conditions of older adults when moving in the urban spaces.  
[PERSON] et al. [94] proposed a wearable biosensor (i.e., a smart watch) and hotspot analysis-based framework to continuously monitor older adults' interactions with the built environment aiming to support interventions to minimize stressful interactions. To test the proposed framework, stress hotspots were detected based on the data related to 30 older adults, which were collected during two weeks of their daily trips. The detected stress hotspots were then investigated by site inspections and interviews with subjects. According to the authors, the proposed sensing framework strengthens the smart city paradigm and can be a basis for optimizing interventions to improve the mobility of older adults.  
Through highlighting the importance of integrating social resources into the core of what a smart city is, the ideas presented in [73; 77], which took part in the same project (City4 Age), were related to the use technologies for the prevention of mild cognitive impairment and frailty in older adults.  
Open data services available in the smart city technological infrastructure together with data collected by wearables, smart phones and sensors were used to track the users within the city, including visited points of interest and some activities performed, such as visiting a family member, and their usage of public transportation (e.g., buses or railway trains). The authors claimed that this tracking is useful to build a comprehensive and predictive picture of older adults' wellbeing and health conditions, aiming to sustain better health outcomes and deliver early interventions to anticipate needs [73; 77].  
[PERSON] et al. [77] reported the cocreation framework used for the Madrid City4 Age pilot, which involved informal interviews with representatives of public transport organizations and a manager from a private older adults' care service, as well as structured interviews with a manager of day centers and home care services and the people responsible for social services of a municipality of fifty thousand inhabitants. In turn, [73] described the architecture supporting an innovative approach for interfacing open data from smart cities (e.g., public transportation or weather conditions) with data acquired (e.g., walking or enter home) via multiple sensors, namely GPS, Bluetooth Low Energy (BLE) beacons, smartphones, and smart wristbands.  
A similar solution was presented in article [84], which described a smartphone-based prototype for remote and non-invasive older adults' monitoring. In the described implementation, a smartwatch with a heart rate monitor and a tri-axial accelerometer was used to determine if a dangerous situation is occurring (e.g., abrupt changes of the heart rate or a sudden acceleration, followed by a state of quiet that might be a sign of fall or fainting).