esa-sceva/satcom-chunk-collection huggingface dataset:
# An Automatic 3D Point Cloud Registration Method Based on Biological Vision
## 2 Cone Vertex Detection
### Shape Fitting  
Without loss of generality, \(\chi_{1}\) can be taken as an example. In this case, \(P_{i}^{1}=\left(x_{i}^{1},y_{i}^{1},z_{i}^{1}\right)(i=1,2,\ldots,\xi-1,\xi)\) represents the neighboring point cloud of \(\chi_{1}\), where \(\xi\) is the number of neighboring points. The quadratic form of the conic surface can be written as:  
\[a_{1}\left(x_{i}^{1}\right)^{2}+a_{2}\left(y_{i}^{1}\right)^{2}+a_{3}\left(z_ {i}^{1}\right)^{2}+a_{4}x_{i}^{1}y_{i}^{1}+a_{5}x_{i}^{1}z_{i}^{1}+a_{6}y_{i}^ {1}z_{i}^{1}+a_{7}x_{i}^{1}+a_{8}y_{i}^{1}+a_{9}z_{i}^{1}+a_{10}=0 \tag{15}\]  
Then, Equation (14) can be rewritten in matrix form, where  
\[E\varphi=0. \tag{16}\]  
Figure 4: The principles of cone vertex recognition. (**a**) Corner detection; (**b**) clustering of feature points; (**c**) data filtering; (**d**) rough localization.  
The matrices of \(E\) and \(\varphi\) are as follows:  
\[E=\left(\begin{array}{cccccccc}\left(x_{1}^{1}\right)^{2}&\left(y_{1}^{1} \right)^{2}&\left(z_{1}^{1}\right)^{2}&x_{1}^{1}y_{1}^{1}&x_{1}^{1}z_{1}^{1}&y_ {1}^{1}z_{1}^{1}&x_{1}^{1}&y_{1}^{1}&z_{1}^{1}&1\\ \left(x_{1}^{1}\right)^{2}&\left(y_{1}^{1}\right)^{2}&\left(z_{1}^{1}\right) ^{2}&x_{1}^{1}y_{1}^{1}&x_{1}^{1}z_{1}^{1}&y_{1}^{1}z_{1}^{1}&x_{1}^{1}&y_{1}^ {1}&z_{1}^{1}&1\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ \left(x_{1}^{1}\right)^{2}&\left(y_{1}^{1}\right)^{2}&\left(z_{1}^{1}\right) ^{2}&x_{1}^{1}y_{1}^{1}&x_{1}^{1}z_{1}^{1}&y_{1}^{1}z_{1}^{1}&x_{1}^{1}&y_{1} ^{1}&z_{1}^{1}&1\\ \left(x_{1}^{1}\right)^{2}&\left(y_{1}^{1}\right)^{2}&\left(z_{1}^{1}\right) ^{2}&x_{1}^{1}y_{1}^{1}&x_{1}^{1}z_{1}^{1}&y_{1}^{1}z_{1}^{1}&x_{1}^{1}&y_{1} ^{1}&z_{1}^{1}&1\\ \end{array}\right)\]  
\(E\) can be decomposed using a singular value decomposition (SVD)  
\[E=U_{E}D_{E}V_{E}^{\mathrm{T}}. \tag{17}\]  
Then, \(\varphi\) becomes the last column of \(V_{E}\). The quadratic form of the conic surface can be rewritten in matrix form as:  
\[\left(\begin{array}{cccccccc}x_{i}^{1}&y_{i}^{1}&z_{i}^{1}&1\end{array}\right) F\big{(}\begin{array}{cccccccc}x_{i}^{1}&y_{i}^{1}&z_{i}^{1}&1\end{array} \right)^{\mathrm{T}}=0, \tag{18}\]  
where  
\[F=\left(\begin{array}{cccccccc}a_{1}&a_{4}/2&a_{5}/2&a_{7}/2\\ a_{4}/2&a_{2}&a_{6}/2&a_{8}/2\\ a_{5}/2&a_{6}/2&a_{3}&a_{9}/2\\ a_{7}/2&a_{8}/2&a_{9}/2&a_{10}\end{array}\right)\]  
\(F\) can be decomposed using an SVD.  
\[F=U_{F}D_{F}V_{F}^{\mathrm{T}}. \tag{19}\]  
The homogeneous coordinates of the cone vertex are represented by the last column of \(V_{F}\) (see proof in Appendix A) and notated as \(\left[\begin{array}{cccccccc}v_{1}&v_{2}&v_{3}&v_{4}\end{array}\right]^{ \mathrm{T}}\). Thus, the \(\chi_{1}^{\prime}\) coordinates of the cone vertex are as follows:  
\[\chi_{1}^{\prime}=\left(\frac{v_{1}}{v_{4}},\frac{v_{2}}{v_{4}},\frac{v_{3}}{v_ {4}}\right). \tag{20}\]  
In the same way, all coordinates of cone vertices \(\chi_{1}^{\prime}=(x_{i},y_{i},z_{i})(i=1,2,\ldots,\rho-1,\rho)\) can be obtained.
## 3 Proposed Method
### Loss Function
The weighting factor \(\alpha\) can be dynamically adjusted based on real-time channel conditions, i.e., increase \(\alpha\) to prioritize ranging accuracy in high-Doppler-shift scenarios and reduce \(\alpha\) to optimize the system in low-SNR environments. Then, the framework essentially finds the optimal constellation \(\epsilon^{*}=\arg\min_{\mathcal{C}}Loss\), where \(\mathcal{C}\) denotes all possible constellation mappings.
## 5 Privacy Risk Assessment: Advancements within the PRUDEnce Framework
### Relationship between Entropy, Feature Frequency, and Re-Identification Risk
#### 5.1.3 Complexity Analysis  
Reducing the number of instances to evaluate will reduce the computational complexity. By reducing the number of instances, we decrease the number of matching operations, leading to lower time and resource requirements.  
To assess the risk, we simulated an attack by calculating all possible k-combinations of information that an attacker might possess. For each combination of k points, we assumed that the attacker utilized all these points to carry out the attack. This resulted in a high computational complexity of \(O\Big{(}\binom{len}{k}\times N\Big{)}\) because the framework created \(\binom{len}{k}\) different configurations of background knowledge and, for each configuration, it performed \(N\) matching operations using the matching function. We supposed we could reduce the number of combinations. In that case, we would also reduce the number of matching operations, decreasing the value of \(N\) and lowering the overall complexity.
# Shear Wave Velocity Estimation Based on Deep-Q Network
## 4 Geoacoustic Inversion Framework Based on DQN
### Agent and Action Space  
Action space consists of all the potential actions that may be selected by the agent during each iteration. For instance, the action space of GA consists of reproduction, crossover, and mutation [6]. The configuration of the agent for the DQN framework is listed as follows:  
* Agent state: during each iteration, the agent updates the agent state based on the feedback from the environment. The agent state is formulated as \[\mathbf{S}_{\text{agent}}{}^{i}=[\mathbf{B}^{i},\mathbf{m}^{i},\mathbf{m}^{i}_{\text{mean}}, \mathbf{m}^{i}_{\text{std}}]\] (9)where \(B\) refers to the search bounds of the parameters, \(m\) is a set of parameters with the lowest misfit value among the \(k\)-selected sets. \(m_{\text{mean}}\) and \(m_{\text{std}}\) are mean and standard deviation values of the parameters, respectively, whose misfit values are the first 30% lowest values among the \(k\)-selected sets.
* Action space: the agent has two actions for sampling from the parameter space. Each action consists of a sampling operation and an update rule.
* Action 0 samples with the uniform distribution from the search bounds \(B\) and iteratively searches the solution by compressing \(B\). More specifically, during each iteration, the agent conducts two steps: 1. At beginning of the \(i\)th iteration, the search bounds are compressed as follows: \[B^{i}=m^{i}\times\frac{i}{i_{max}}+B^{i-1}\times(1-\frac{i}{i_{max}})\] (10) 2. After updating the search bounds, \(k\) sets of parameters are sampled with a uniform distribution from the updated search bounds \(B^{i}\). The selected parameters are fed into the environment and the corresponding feedback is received by the agent. The agent state is updated accordingly.
* Action 1 samples with the Gaussian distribution and iteratively searches the solution by updating \(m_{\text{mean}}\) and \(m_{\text{std}}\) of the selected \(k\) sets. More specifically, during each iteration, the agent conducts the following steps: 1. At the beginning of the \(i\)th iteration, \(k\) sets of parameters are sampled with a Gaussian distribution defined by \([m_{\text{mean}}^{i-1},m_{\text{std}}^{i-1}]\). Note that the operation of sampling needs to be repeated when any of the selected parameters exceed the original search bounds or the bounds of \([0.5B_{low}^{i},1.5B_{up}^{i}]\), where the subscripts \(low\) and \(up\) refer to the lower and upper bounds of \(B^{i}\), respectively. 2. The selected parameters are fed into the environment and the corresponding feedback is received by the agent. The agent state, except for the search bounds \(B\), will be updated accordingly. 3. In the received feedback, if \(abs(\frac{max(E^{i})-min(E^{i})}{max(E^{i})+min(E^{i})})<=e_{\text{convergence}}\), the expansion is activated for jumping out of the local minimum. The expansion is conducted as follows: 1. \(m_{\text{std}}^{i}\)=10\(m_{\text{std}}^{i}\) 2. \(k=2k\) 3. Repeating steps 1 and 2.
## 5 Conclusions  
The effectiveness of the GPU acceleration processing method proposed in this paper was verified through experiments on feature point extraction and matching in GF-3 satellite SAR images. Thus, the following conclusions can be drawn:  
1. After single-node GPU matching, the feature point extraction and matching of GF-3 satellite SAR images improved by about 96%, verifying the effectiveness of the optimization strategy implemented by the SAR-SIFT algorithm based on GPU mapping.
2. After multi-GPU nodes processing, the feature point extraction and matching of GF-3 satellite SAR images improved by about 99.75%, verifying the effectiveness of the multi-GPU nodes parallelization strategy.  
The proposed method greatly improved the performance of the GPU-based image-matching technique. However, the limitation of GPU memory will restrict the migration of the proposed method on portable devices. Therefore, the lightweight technology will be conducted in the future.  
Conceptualization, L.D.; Methodology, L.D., N.J. and T.Z.; Software, L.D., N.J. and F.L.; Validation, F.L. and H.Y.; Formal analysis, L.D. and N.J.; Resources, H.Y.; Writing--original draft preparation, L.D.; Writing--review & editing, L.D., N.J., T.Z. and F.L.; Visualization, F.L; Supervision, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 5 Discussion  
By using steel pipes of different diameters and thicknesses, more significant experimental signals were obtained and analyzed. The magnetic permeability perturbation of axial cracks in the internal wall of a steel pipe is determined by the internal diameter of the pipe, the wall thickness of the pipe, the magnetization state of the pipe, and the size of the defect. The increase in wall thickness and the decrease in pipe diameter will also affect the detection performance of this method. Even when in the same magnetization state and for defects with the same depth, different diameters will bring about differences in signal amplitude. For internal axial cracks with greater burial depths, the measurement of the weak MPP signal requires a probe with higher sensitivity. For the differential probe used in this method, both the external surface defects and internal surface defects can be detected, making it difficult to distinguish between the external and internal surface defects according to the signals. In the inspection process, as the thickness of the steel pipe increases, the difficulty of magnetization increases, and the volume of the magnetizer increases. Future work will focus on the optimization of the probe, the lightening of the magnetizer, and the differentiation of the MPPT signals between internal and external cracks.
## 3 Results And Discussion
### Workflow III Results  
Workflow III enhances DT performance regarding precision and f1 score, lowering the recall and flattening the performance of KNN, which yields best recall value.  
In the feature selection (i.e., third) workflow, all classifiers do not yield statistically significant differences. Regarding precision, the [PERSON] analysis gives \(p=0.449\), for recall \(p=0.472\), and for f1 score \(p=0.074\).
## 4 Results and Discussion
### Thermogravimetric Analysis (TGA/DTG)
[18], the ML in the temperature range RT--220 \({}^{\circ}\)C reflects the hygroscopic properties of mortar by affecting the heat of desorption and vaporization, as well as the amount and form of water availability at different relative humidity levels. The high sensitivity of muscovite to milling results in the rearrangement and activation of Al-OH bonds, as well as the diffusion of hydroxyls from the inner part of the mineral to its surface due to the mechanochemical activation treatment. This process leads to surface aluminum enrichment and a reduction in binding energies of both Si and Al elements, enhancing the muscovite reactivity [50]. The hygroscopic properties of muscovite in mortar are determined by the amount of hygroscopic water evolved between room temperature (RT) and 100 \({}^{\circ}\)C. A measured moisture loss of around 6% in this temperature range indicates that the samples do not exhibit significant hygroscopic properties. This lack of hygroscopic behavior can have implications for the performance and durability of the mortar, as materials with a high hygroscopicity may be more susceptible to moisture-related degradation [64]. The thermal reactions and effects that take place between 220-600 \({}^{\circ}\)C, with a total ML range of 0.14-4.16%, are related to the decomposition and combustion of organic matter. At these temperatures, organic matter undergoes thermal decomposition, breaking down into simpler compounds such as carbon dioxide, water, and other by-products. Combustion reactions can also occur, especially if there is sufficient oxygen present, leading to the oxidation of the organic matter and the production of carbon dioxide and water as the main products [65]. Taking into account that ML derived from organic components and hydraulic phase decomposition was observed in the 220-600 \({}^{\circ}\)C temperature range [3; 5; 13; 51; 52], the behavior to the decomposition of organic materials under different temperature conditions can be deduced. Moreover, it should be mentioned that the hydraulic component formation could be provided from chemically active phases such as clays, ceramic fragments, or volcanic ash [52]. The dehydroxylation of phyllosilicates in the temperature range of 600-720 \({}^{\circ}\)C can lead to structural changes and the breakdown of the layer structure of the minerals. This process may result in the formation of chemically active phases as the dehydroxylated phases no longer resemble a phyllosilicate. The presence of chemically active phases can impact the specific heat, crystal phase transition, surface functional groups, and co-ordination of silicon and aluminum in the minerals [59; 60]. Under this context, the parameter loss on ignition (LOI) in the natural clay seems to be a supplementary index. This value is quite different: 1.07 \(\pm\) 0.01 (%) for P1, 12.9 \(\pm\) 0.08 (%) for P2, and 5.55 \(\pm\) 0.034% for natural clay. A higher LOI may indicate residual organic materials present in RTW, the presence of carbonates, and/or the subsequent dehydration of clay minerals [30].
# A Case Study on User Evaluation of Scientific Publication Summarization by Japanese Students
## 6 Discussion
### Limitation Concerns for Future Directions  
Here, we discuss some concerns of this study that can be tackled in future work. This study utilized automatic text summarization and machine translation techniques to generate a Japanese summary from a scientific paper written in English. The evaluation by the subjects could be affected by the qualities of the two methods, but it does not remain easy to distinguish these factors. For future work, it will be important to handle two factors separately. One possible solution is to prepare manually written summaries in English as well as in Japanese. Preparing manually written summaries in English and Japanese enables a comparison of manually generated summaries in Japanese written by a native Japanese speaker and translated from English into Japanese. It is also possible to compare manually and automatically generated summaries in English, which was not considered in this study.  
Furthermore, in our experiments, we did not separate the benefit of translation into Japanese and linguistic error caused by machine translation. To investigate the advantage of translation into Japanese, evaluating summaries translated into Japanese without any linguistic errors caused by the translation is necessary. For further study, it will be interesting to ask a professional translator to translate summaries into Japanese.  
Another concern is that we used CV papers only. Papers in other disciplinary areas might have different characteristics. Therefore, using papers in different fields such as humanity or social science will be an important research topic.  
Finally, evaluations in both experiments 1 and 2 are highly subjective. In experiment 1, we asked the subjects to judge which summary was better or not. Each subject could evaluate summaries with their criteria, which is highly subjective. However, we can identify what characteristics the subjects think are necessary for a good summary. One future direction will be interviewing each subject so that we can deeply understand their evaluation criteria. In experiment 2, the subjects were asked to judge whether they would like to read the original paper. However, each subject has their preference on the topic, which could affect the answers. We designed this experiment to realize the users' behavior under the practical scenario of finding relevant papers by reading summaries. A detailed analysis of an individual case is meaningful, including the relationship between subjects' preferences and scores in experiment 2. Additionally, though we shuffled the order of summaries for each subject, there is no guarantee that reading different orders by the same subject does not affect the result. Comparison among subjects who read different orders but have similar preferences will be also important to consider the bias.
## 3 Proposed Framework  
Our proposed SMBF framework is composed of four modules: Redundant Information, Message Benefit, Malicious Node Verification (MNV) and Malicious Data Verification (MDV) as shown in Figure 1. SMBF consists of the steps which are given below:  
* Step 1) Vehicle A wants to share a safety message with Vehicle B
* Step 2) SMBF sends message to redundant information for verification
* Step 3) On the basis of the reply, SMBF decides to forward or discard the message.
* Step 4) Redundant Messages are discarded
* Step 5) New Information is sent to Message Benefit
* Step 6) Relevance value is sent to SMBF
* Step 7) Request to MNV for malicious node verification
* Step 8) Receive Reply from MNV and decide to forward or discard the message
* Step 9) If the node is malicious, data is discarded
* Step 10) Request is sent to MDV to verify the malicious data
* Step 11) Receive Reply from MDV and decide to forward or discard the message
* Step 12) If the data is malicious, it is discarded
* Step 13) If the node and data are not malicious then it is forwarded to Vehicles  
Figure 1: Secure Multimedia Broadcast Framework (SMBF).  
**Redundant Information:** Every node maintains a table of Message IDs of currently received messages. We assume that the Message ID is unique and on its basis we detect the redundant messages.  
**Message Benefit:** We calculate the priority of each message. Safety Messages get higher priority than any other messages.  
**Malicious Node Verification:** We detect the malicious nodes on the basis of signal strength.  
**Malicious Data Verification:** We detect the malicious data on the basis of existing messages from neighbors and also on the basis of the position of nodes.
## 1 Introduction
Meta-heuristic searching algorithms are categorized under several categories such as discrete vs. continuous [19], single objective versus multi-objective [20], fixed length versus variable length [21], and evolutionary versus swarm [22]. One appealing application of meta-heuristic algorithms is the field of feature selections which aims at obtaining the best candidate number of features out of high possible selections and combinations. Considering that iris coding is a type of feature selection and recoding, using meta-heuristic searching optimization for this application has a high potential in increasing the performance in terms of recognition, noise filtering, memory reduction and computational efficiency. A limitation of the previously developed meta-heuristic algorithms is their assumption of fixed length or number of features. This is because the knowledge of the true number of relevant features is not available for all datasets  
This article includes several contributions, they can be stated as follows  
1. To the best of our knowledge, it is the first article that presents the problem of iris recognition from a feature selection perspective. Mainly, it presents a variable length of the number of features to be selected for optimizing the iris recognition performance instead of traditional methods that use a fixed-length number of features. This provides two benefits, namely, reducing the size of feature space needed for training and prediction which means memory reduction, and improving the accuracy considering the non-relevant features are removed. 2. It uses existing variable length black hole optimization for the purpose of feature selection in the context of iris recognition. This algorithm is selected due to the competing performance it provides compared with other similar methods such as variable length particle swarm optimization VLPSO [21]. It compares the performance of variable length black hole optimization for feature selection in iris recognition using two feature spaces, namely, Gabor and LBP and using two classifiers, namely, support vector machine SVM and logistic regression model which shows the superiority of VLBHO over VLPSO. The goal of this article is to adapt recently developed work by us in the field of iris coding and feature selection. Particularly, we use variable length black hole optimization for coding and selecting the best features in order to obtain reduced and relevant feature space. The remaining of the article is organized as follows. In Section 2, we present the background of iris coding. Next, the literature survey is given in Section 3. Afterwards, the methodology is presented in Section 4. Next, the experimental works and evaluation are given in Section 5. Lastly, the summary and conclusion are presented in Section 6.
# Urban Free-Space Optical Network Optimization
## 1 Introduction
### Motivation  
This work focuses on the challenges of creating urban FSO networks. The suggested adaptive solution is designed as a cost-effective and best-effort algorithm to improve existing urban networks. The need for high-speed cost-effective communication in urban regions is ever-growing as the average communication demands of 4G and 5G users are increasing rapidly. In particular, during the COVID-19 lockdown, the usage of video calls and video-on-demand services increased dramatically [7,8], pushing the overall data throughput to new demand peaks. Motivated by the need for high-speed, cost-effective, and adaptive urban networks, and based on the growing deployment cost of fiber-optic infrastructure, we propose a free-space laser communication network-optimization algorithm. The purpose of such a network is to thicken the existing urban network, which has dynamic needs for internet communication. One example of such dynamic need is the case of a crowded event in which many people upload their photos to social networks. The dynamic nature of urban networks also can be expressed in a daily context, such as when the demand for internet migrates from business centers during the daytime to residential areas at night. The suggested solution allows a flexible and robust optimization of existing urban networks by adding a thin layer of FSO communication.