esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Objectives: To evaluate the retreatment efficacy of premixed bioceramic sealer using a reciprocating system, comparing single-cone and carrier-based obturation techniques. Materials and Methods: Twenty-three monoradicular teeth with oval canals were divided into two groups: NeoSealer Flo with single cone (SC) and NeoSealer Flo with Guttafusion (GF). Retreatment was performed using Reciproc Blue (RB) with a crown-down technique. X-ray and CBCT images were taken to measure the remnant areas and volumes. Results: Apical patency was achieved in all the samples. The remnants were mostly distributed in the middle third in the GF group, while an equal distribution in both the middle and apical thirds was observed in the SC group. The GF group had a lower remnant area and volume after RB 50.05, respectively (0.18 \(\pm\) 0.33 mm\({}^{2}\) vs. 0.39 \(\pm\) 0.80 mm\({}^{2}\) and 0.36 \(\pm\) 0.59 mm\({}^{3}\) vs. 0.51 \(\pm\) 1.16 mm\({}^{3}\)). The use of RB 50.05 led to an additional reduction in the remnant areas in the SC (14.89%) and GF (69.49%) groups, while in terms of the volume, the reductions were 20.63% and 36.36%. Conclusions: Reciprocating instruments are effective in removing remnants from root canals filled with both single-cone and carrier-based techniques. The blooming effect in CBCT imaging suggests further in vivo studies for validation.  
premixed bioceramic sealer; CaSi sealers; single-cone technique; CBCT; blooming effect accepted: 22 July 2024
## 2 Method
### Crushing Theory  
A brief discussion is necessary to explain the characteristics and experimental parameters about the energy absorption of structures for crashworthiness applications. The load-deflection diagrams characterize the energy absorption capacity of crashworthy structures and can be divided into three phases: pre-crushing, progressive crushing and densification. These stages can be seen in Figure 1, which shows the typical load (\(P\)) vs displacement (\(\delta\)) behavior of energy-absorbing structures. The amount of energy absorption is the least in the pre-crushing stage, which is mostly characterized by elastic deformation [38]. Following PRE is the progressive crushing zone, as the material deforms further. The vast portion of energy is absorbed during this phase, in which permanent deformation and fracture occur. Thus, a longer progressive crushing zone suggests a higher energy absorption. The last phase is the densification phase in which the structure is fully compressed until it behaves like a rigid body and produces a higher resistance that can rise to over the testing system capacity [39]. For this reason, absorbed energy during the densification is excluded from the energy absorption calculations and all crushing behavior indicators used in this work cover the region of pre-crushing and progressive crushing zones as suggested in [3].  
An important indicator of material energy absorption is the specific energy absorption (SEA) [39]. SEA is defined as the absorbed energy (AE) per unit mass of the crushed specimen, and indicated by the area under the load-displacement curve:  
\[\text{SEA (J/kg)}=\frac{\text{energy absorbed (AE)}}{\text{mass of absorbent structure (m)}}=\frac{\int_{0}^{max}P(\delta)d\delta}{m} \tag{1}\]  
where \(P\), \(\delta\) and \(m\) represent the load, displacement and specimen mass, respectively.  
Another indicator of material crushing performance is the crushing force efficiency (_CFE_) [39], which is also known as the load uniformity ratio. The lower values of CFE  
Figure 1: Energy absorption stages explained on load–displacement curve.  
address the severe load fluctuations in the progressive crushing zone and/or relatively high peak load, which is an undesirable behavior. CFE is the ratio of the average crushing load (_MCF_, the mean load during the collapse) to peak load (_PCF_, highest load experienced during a crushing event):  
\[CFE=\frac{MCF}{PCF} \tag{2}\]  
Based on the discussions above, the onset of densification should be identified. For this purpose, we propose an energy efficiency parameter, \(\eta(\delta)\):  
\[\eta(\delta)=\frac{1}{P(\delta)}\int_{0}^{\delta}P(\delta)d\delta \tag{3}\]  
By using the energy efficiency in Equation (3), a representative onset deflection of densification, \(d\), can be numerically and consistently defined by obtaining the energy efficiency-deflection plot and selecting the highest point [40], as expressed in Equation (4):  
\[\frac{d\eta(\delta)}{d\delta}\biggm{|}_{\delta=d}=0. \tag{4}\]
# MIMO Radar Imaging Method with Non-Orthogonal Waveforms Based on Deep Learning
## 4 Experimental Results  
This part first trains the proposed imaging network by generating simulation data, and then verifies the effect of the proposed imaging method by using two sets of test data. The simulation experiment adopts a MIMO linear array with 4 transmitters and 20 receivers. The interval between transmitting and receiving array elements is 3 m and 60 m, respectively, the radar center frequency is 10 GHz, and the transmitting waveform is 4 polyphase coded signals with 450 symbols. The signal bandwidth is 300 MHz. The time duration is 1.5 \(\upmu\)s. The range scope is set as 50 m, which corresponds to the time duration 0.333 \(\upmu\)s. The sampling interval in frequency is 0.545 MHz and the frequency scope is set as 768 MHz, which corresponds to a time sampling interval of 1.3 ns. Therefore, the number of frequencies is 768 \(\div\) 0.545 \(\approx\) 1410. The autocorrelation sidelobe of the four signals is low, but the cross-correlation sidelobe is high. This means that the waveform does not meet the ideal orthogonality. We set the target 5 km away from the center of the array, and the corresponding range and azimuth theoretical imaging resolutions are 0.5 m and 0.625 m, respectively. The training samples are generated by the method in Section 3.2. The number of scatterers of each target is uniformly and randomly generated in the range of 0-500, and all of them are in the range of 50 m \(\times\) 50 m. The scattering coefficient of the scatterer is generated by the standard complex Gaussian distribution. Each scatterer is set as the distributed scattering center or local scattering center with equal probability, and the length of the distributed scattering center is a uniform random number in the range of 0-2 m. We set the size of the ideal target image to 256 \(\times\) 256. A total of 40,000 training sample pairs of MIMO radar echo and target image are generated to form a training data set for training the MIMO-LI-Net shown in Figure 2. The training platform is an Intel Core i9-9900 CPU and NVIDIA gefore RTX 2080 Ti GPU, which has the memory size of 11 GHz. Under the Pytorch framework, the Adam optimizer is used for training, and the learning rate is set to 0.0001. There are 40,000 training samples. The batch size is set as 32, which occupies 4.8 GHz GPU memory. One training epoch takes around 13 min. In the training, we set the number of training epochs as 100. Thus, the total training time is around 21.67 h. For the imaging network after training, in the following, we will verify the trained imaging network by building test data.
# The Dichotomy of Neural Networks and Cryptography: War and Peace
## 4 Peace: Coexistence and Alliance
### Coexistence
#### 4.1.2 Cryptographic Technology Adapted to Neural Computing  
Cryptosystems are being adapted to neural computing. They are trying to encrypt data in a way that the encrypted data can be efficiently processed by NNs. Homomorphicencryption is the most common attempt made towards achieving this goal. While classification can be performed on normal encryption data, researchers have proposed some systems which use specially designed encryption schemes which obfuscate the data in a way that better allows processing. Homomorphic encryption is a form of encryption that allows a user to perform valid computations with the ciphertext. Upon decryption of the ciphertext the plaintext will have the same computations applied.  
In order to maintain privacy in the current world filled with powerful data mining techniques and massive networks of data gathering sensor it is becoming increasingly obvious that Fully Homomorphic Encryption (FHE) will be needed for end-to-end encryption [75].  
As this field has been growing rapidly we have seen many changes, as recently as 2018 it was shown that current homomorphic systems were too slow for large amounts of data and NNs were not yet secure enough to work with encrypted data [76]. The same year it was thought a possible solution would be to perform the homomorphic encryptions as a part of a cloud environment using multiple parties [60].  
However, this too showed to be too slow, but brought to light possibilities of using GPU's to increase efficiency [60]. The authors of [77] have taken into consideration the space-efficiency problems with FHE functions and have begun work on a method named DOReN. Their method is used to instantly evaluate multiple quantized ReLU-activated neurons in the NN which are processing encrypted data. This considerably cuts down on the space needed to perform these neuron activations.  
Other research groups have tackled FHE's time efficiency problem by transforming all the operations into bit-wise functions and transforming the input encrypted data into binary format; this technique roughly equates to a 6.3 times increase in the speed of CNN's [78].  
More recent work has shown that the security of NNs lead to Fully Homomorphic Encryption over Torus (TFHE), which is a scheme that uses NN's to effectively evaluate encrypted input data [75]. TFHE along with the protection against backdoor attacks into NNs has provided NNs with security against some forms of malicious attacks [79].  
While optimizations of how we use FHE are still in production, some groups have already begun applying these systems to facial recognition and English to Arabic translation [79; 80].  
Although the translation time for even small words still has a massive run-time to provide adequate accuracy, facial recognition on ciphertext using CNN's provides real-time usage for high accuracy verification [79; 80].
## 2 State of the Art  
At present, there are no studies published on that matter. Several other studies were conducted in ITU-R, but they have not been published in any relevant journal or conference paper [10]. This is mainly related to the fact that in the past years, when cellular technologies were coming to different frequency bands, there were never any compatibility issues between cellular technologies and aeronautical/maritime mobile services. Therefore, this work provides a novel approach in evaluating sharing conditions between 5G and aeronautical and maritime mobile services, which includes defining the proper scenario conditions for the simulations as well as the methodology of Monte Carlo analysis, which takes into account 5G NR base station beam position changes and the movements of aircraft and vessels. This allows us to perform close-to-real interference analysis which would allow us to understand whether 5G might be deployed in the 4800-4990 frequency band.
## 4 Experimental Verification
### Verification of Generalization  
To verify the generalization of the model, we used dataset 2 mentioned in Section 3.1 to conduct experiments. The settings of data augmentation factor and hyperparameters remained unchanged. The comparison model still used three classic regression prediction models: LR [48], SVM [16], and AdaBoost [19]. The data obtained by the augmentation of the GRW-DBA method was used as the training set, and the source data were used as the test set. The differences between the predicted values and observed values given by the four comparison models are shown in Figure 11. The \(x\)-axis represents the observed value, and the \(y\)-axis represents the predicted value. The closer the predicted value given by the model is to the observed value, the closer the points in the graph are to the \(y=x\) line. It can be seen that in the new data distribution, the prediction method of GRW-DBA combined with the ANN used in this paper still gives the result closest to the observed value, which proves the generalization of the proposed method.  
Figure 10: Predicted value of the four comparative models.  
Figure 9: The prediction accuracy evaluation indexes of four comparison models: (**a**) results of the training stage; (**b**) results of the testing stage.
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 4 c-Cbba
### Single-Chain Strategy  
According to whether the two satellites establishing the inter-satellite link belongs to the same orbit, the inter-satellite link is divided into two types: intra-plane ISLs and inter-plane ISLs. Satellites in the same orbital plane have stable inter-satellite links due to their stable relative positions, i.e., they have fixed \"neighbors\" that do not change over time. On the contrary, as relative motion occurs between satellites in different orbital planes, the connectivity of the inter-plane ISLs is time-varying and less stable.  
As can be seen from Figure 3, when the constellation scale is large, each satellite has more than one neighbor, which is prone to information interaction redundancy during communication. For example, in Figure 7, satellites \(s_{2}\) and \(s_{3}\) are both neighbors of \(s_{1}\), and \(s_{2}\) and \(s_{3}\) are neighbors of each other. If satellites \(s_{1}\), \(s_{2}\), and \(s_{3}\) send information in sequence, the information transmitted in communication link \(s_{1}\to s_{2}\to s_{3}\) already contains the information transmitted in \(s_{1}\to s_{3}\). Therefore, such multi-span communication links can be deleted. Based on the consideration of the stability of the communication link, the operation of removing the redundant link is only performed on the intra-plane ISLs. This operation is called the Single-Chain Strategy (SCS).  
From a single satellite perspective, in the orbital plane, neighbors exist on both sides of the satellite. The single-chain strategy refers in particular to the fact that when there are multiple neighbors, the satellite selects only one neighbor for communication for each side. Assuming that the current satellite communication topology is shown in Figure 8a. Due to the fact that satellites are linked from an individual perspective, in order to ensure the connectivity of whole constellation, it can be specified that all satellites are linked according to the same rules, such as prioritizing the link with the nearest satellite. In this way, the communication topology is shown in Figure 8b. Considering the complexity of the communication environment, there may be situations where it is not possible to build a link due to special circumstances. In this case, the satellite can choose any neighbor to establish a link, as shown in Figure 8c.  
The premise of CBBA convergence is that the communication topology is connected during the scheduling period. For other specific constellation configurations, we can design reasonable pruning strategies based on the characteristics of the constellation to improve information exchange efficiency while ensuring that the communication link topologies are connected graphs.  
Figure 7: Redundant communication example, which shows the information transmission flow among three satellites. The information transmitted in link \(s_{1}\to s_{2}\to s_{3}\) contains the information transmitted in \(s_{1}\to s_{3}\), so the link \(s_{1}\to s_{3}\) is redundant.
# An Efficient and Secure Certificateless Aggregate Signature Scheme for Vehicular Ad hoc Networks
## 6 Performance Comparison  
This section compares the proposed scheme with relevant schemes in terms of security requirements, computational cost, and communication overhead.
## 3 Optical Design and Analysis  
There are two difficulties in the optical design of GeoFWHIS, one is to realize the full-spectrum imaging through integrated design. Since GeoFWHIS contains many optical  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specifications and Parameters** & \multicolumn{4}{c}{**Values**} \\ \hline Orbital altitude & \multicolumn{4}{c}{geostationary orbit (-36,000 km)} \\ Swath width/km & \multicolumn{4}{c}{400 \(\times\) 400} \\ FOV & \multicolumn{4}{c}{0.64\({}^{\circ}\times\) 0.64\({}^{\circ}\)} \\ Entrance pupil diameter/m & \multicolumn{4}{c}{3.2} \\ \cline{2-6} Band & B1 (UVIS) & B2 (VNIR) & B3 (SWIR) & B4 (MWIR) & B5 (LWIR) \\ \cline{2-6} Wavelength range/\(\mu\)m & 0.3–0.56 & 0.55–1.01 & 1–2.5 & 3–5 & 8–12.5 \\ Spatial resolution/m & 25 & 25 & 50 & 50 & 100 \\ Spectral resolution (FWHM)/nm & 4 & 5 & 12 & 50 & 200 \\ Spectral sampling distance/nm & 0.17 & 0.17 & 0.17 & 0.12 & 0.12 \\ MTF & 21.6 & 21.6 & 17.28 & 17.28 & 8.64 \\ Focal length/m & 6.75 & 6.75 & 5.4 & 5.4 & 2.7 \\ F number & 4096 \(\times\) 2048 & 4096 \(\times\) 2048 & 2048 \(\times\) 256 & 2048 \(\times\) 256 & 1024 \(\times\) 256 \\ Pixel size/\(\mu\)m & \(15\times 15\) & \(15\times 15\) & \(24\times 32\) & \(24\times 32\) & \(24\times 32\) \\ Pixels per spectral channel & 6 & 6 & 2 & 3 & 3 \\ Total length of slit/mm & 241.3 & 241.3 & 193 & 193 & 96.5 \\ Number of splicing spectrometers & 4 & 4 & 4 & 4 \\ Single slit length/mm & 61.44 & 61.44 & 49.152 & 49.152 & 24.576 \\ Slit width/\(\mu\)m & 15 & 15 & 24 & 24 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications and system parameters of GeoFWHIS.  
Figure 1: Layout block diagram of GeoFWHIS.  
modules, and the specifications are different between each optical module. In the design process, all optical modules should meet the requirements of pupil matching and field matching. Therefore, in addition to ensuring the imaging quality, it is also necessary to ensure the pupil quality of the optical system, so as to facilitate the splicing and integration of each module. Another difficulty is designing long-slit compact spectrometers suitable for splicing. Generally, the spectrometer will become larger with the increase in its slit length, which makes it hard to splice. How to realize a long slit while keeping a compact structure is the primary problem to be solved in the spectrometer of GeoFWHIS. This section gives the design results and performance of each part of the system.
# Visual Description Augmented Integration Network for Multimodal Entity and Relation Extraction
## 3 Results and Discussion
### Ablation Study
#### 3.4.2 Analysis of the Effectiveness of Visual Description Augmented Integration Network  
In this research, we present three approaches to demonstrate the efficiency of VDAIN. Specifically, the first method, \"Direct\", aligns each layer of visual features from ViT and semantic features generated by image description directly with the corresponding layers in BERT. The second method, \"Concatenate\", concatenates all visual features as well as the semantic features generated by image description and passes them through a linear layer. The third method, \"Random\", randomly fuses each layer of visual features with the corresponding layers in BERT. Through these three methods, we can observe the effectiveness and flexibility of VDAIN in handling multimodal data.  
As illustrated in Table 6, we note that the F1 scores on the three datasets, when utilizing the VDAIN, are 1.05%, 1.91%, and 3.22% higher than the \"Direct\" method. It is also 0.88%, 1.87%, and 3.79% higher than the \"Concatenate\" method, and 1.42%, 1.99%, and 2.98% higher than the \"Random\" method. Therefore, the visual description augmented integration network is a core component in achieving outstanding performance. Specifically, the visual description-enhanced fusion mechanism can adaptively merge various image features with the semantic features generated by image descriptions and match them with each layer of the BERT model. The strategy helps improve the performance of multimodal data processing and reduce the modality gap, further enhancing the understanding of images and key information.
# A Survey: Network Feature Measurement Based on Machine Learning
## 3 Network Measurement Based on ML Method
### Packet Loss  
The term \"packet loss\" refers to the failure of one or more packets' data to travel through a network to their intended location. Numerous factors contribute to this, including packet loss due to channel blocking at the early layer and signal attenuation brought on by multipath fading on the network. The quality of multimedia applications that depend on delay is largely determined by packet loss [66]. Some applications in scientific networks demand frequent, large-volume data transfers with precise network performance specifications; as a result, transmission becomes extremely sensitive to performance deterioration events [22]. Keeping track of packet loss on a link enhances network administration and service [67]. Thus, to enable researchers or network operators to reduce packet loss through various host or stream reallocation technologies, advanced technologies are needed. Due to this requirement, researchers started to forecast packet loss using ML techniques.  
To forecast the packet loss rate, [PERSON] et al. [22] employed a decision tree and a logical regression model. To present decisions and potential outcomes, a decision tree was employed as a tool. To conduct predictive analysis and characterize the data, logical regression was utilized. The decision tree approach, in comparison, has a stronger predictive impact. For the IoT's packet loss prediction, [PERSON] et al. [65] suggested a multi-step advance prediction time series based on a feedback neural network. However, because packet loss and queuing delay are closely related and because most researchers still focus on queuing delay, there is little research on the packet loss rate. Instead, the prediction accuracy of the neural network learning process is estimated by the mean square error, maximum likelihood error,and minimum mean square error. The issue of unusual packet loss instances is still open. The chosen method places restrictions on the flexibility of the output and input parameters, and data delivery without retransmission is unpredictable. Changes in the distribution of the input characteristics had a major impact on the forecast period's accuracy as well.