esa-sceva/satcom-chunk-collection huggingface dataset:
# A Voting-Based Star Identification Algorithm Using a Partitioned Star Catalog
## 6 Conclusions  
Aiming at the problem wherein traditional methods cannot meet the current demand for star sensors with high identification speed and a high identification rate, a voting-based star identification algorithm using a partitioned star catalog is proposed. Compared to conventional methods, the proposed algorithm significantly simplifies the navigation feature library and narrows the retrieval region during star identification, markedly enhancing identification speed while effectively reducing the probability of redundant and false matching. A simulation experiment and nighttime stragazing experiment verify that the proposed algorithm can exhibit high robustness against position errors, magnitude errors, and false stars. Additionally, when compared with other subgraph isomorphism algorithms, the average identification rate and average identification time of the proposed algorithm are 99.760% and 8.861 milliseconds, respectively, presenting a clear advantage over other methods and meeting the current requirement for star sensors with a high star identification rate and high identification speed. The proposed algorithm could be applied in many fields that use star sensors for astronomical navigation, such as in the navy, aviation, and aerospace, and it is especially effective under lost-in-space conditions. Take the famous satellite network plan for instance. When thousands of satellites are launched into space, the satellites are lost in space and it is time consuming to obtain the attitude of each satellite as well as establish the attitude relationships among them. The proposed algorithm  
\begin{table}
\begin{tabular}{c c c} \hline
**Algorithm** & **Average Identification Rate (\%)** & **Average Identification Time (ms)** \\ \hline Proposed & 99.760 & 8.861 \\ [PERSON] [7] & 98.953 & 33.855 \\ [PERSON] [10] & 99.285 & 19.498 \\ [PERSON] [11] & 99.141 & 20.879 \\ [PERSON] [12] & 99.423 & 23.064 \\ \hline \end{tabular}
\end{table}
Table 3: The performance of star identification algorithms.  
would help to improve the attitude acquisition speed and accuracy of the satellites, and ultimately contribute to the chain establishment efficiency of the whole satellite network.  
Conceptualization, X.H.; methodology, X.H.; software, X.H.; validation, X.H., J.W. and Z.L.; formal analysis, X.H., Z.L. and Z.M.; investigation, X.H. and J.W.; resources, L.Z. and J.H.; data curation, X.H.; writing--original draft preparation, [PERSON] and J.W.; writing--review and editing, [PERSON] and J.H.; visualization, X.H.; supervision, [PERSON] and J.H.; project administration, [PERSON] and [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Changchun Scientific and Technological Development Program (19 SS008).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 4 Disparity Computation and Subpixel Refinement  
The Winner-take-all [8] (WTA) strategy is used for determining the disparity of each point as follows:  
\[d_{\text{int}}(x_{m},y_{m},N)=\underset{d_{\text{min}}\leq d\leq d_{\text{ max}}}{\text{argmin}}[C(x_{m},y_{m},d,N)] \tag{11}\]where \(d_{\text{int}}\) (\(x_{m},y_{m},N\)) denotes the integer disparity of point \(m\) when the number of speckle stereo image pairs is \(N\), \(d_{\text{min}}\) and \(d_{\text{max}}\) represent the maximum and minimum disparity, respectively.  
Afterwards, the subpixel disparity of each point is computed by the interpolation function based on data histogram proposed by [29] as follows:  
\[d_{\text{sub}}(x_{m},y_{m},N)=\left\{\begin{array}{ll}d_{\text{int}}(x_{m},y _{m},N)-0.5+\frac{x^{2}+x}{4},&\text{if }leftDif\leq rightDif\\ d_{\text{int}}(x_{m},y_{m},N)+0.5-\frac{x^{2}+x}{4},&\text{otherwise}\end{array}\right. \tag{12}\]  
The \(x\) in Equation (12) is defined as:  
\[x=\left\{\begin{array}{ll}\frac{leftDif}{rightDif},&\text{if }leftDif\leq rightDif\\ \frac{rightDif}{leftDif},&\text{otherwise}\end{array}\right. \tag{13}\]  
\[leftDif=C(x_{m},y_{m},d_{\text{int}}(x_{m},y_{m},N)-1,N)-C(x_{m},y_{m},d_{ \text{int}}(x_{m},y_{m},N),N) \tag{14}\]  
\[rightDif=C(x_{m},y_{m},d_{\text{int}}(x_{m},y_{m},N)+1,N)-C(x_{m},y_{m},d_{ \text{int}}(x_{m},y_{m},N),N) \tag{15}\]  
After simple post-processing (hole filling and surface smoothing), the final disparity map for 3D reconstruction is obtained.
# Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks
## 3 Adaptive Downward/Upward Protocol
### Dynamic Next-Hop Table  
In RRD+, all neighbors with lower Ranks form a set that we call a parent set. In a data-collection process, before sending a packet, a node needs to select a next-hop from its parents set. In ADUP, the ID of this next-hop is included into data packets and sent to the sink. Instead of storing the entire addresses of next-hop nodes in data packets, we only use 1 byte to store the ID of the next-hop node of the source node. Due to the fact that the upper limit value of 1 byte is 255, the maximum number of nodes in the network cannot exceed 255. Figure 2 shows the fields of upward data packets. When the sink receives data packets, it builds a next-hop table as shown in Figure 3. The first column of this table stands for the ID of nodes, except the sink, in the network. We consider that there are \(n\) nodes and one sink in the topology. We define the ID of each node as \(ID_{i}\in\{ID_{0},ID_{1},ID_{2},...,ID_{n}\}\), where \(ID_{0}\) stands for the ID of sink. The second column stands for the ID of best next-hop for each node referred to as \(N(ID_{i})\). Note that \(N(X)\in\{ID_{0},ID_{1},ID_{2},...,ID_{n}\}\) and \(X\in\{ID_{1},ID_{2},...,ID_{n}\}\).  
RRD+ updates the Rank value of each node according to link quality and movement direction. Nodes in a parents set are automatically removed or added based on the variation of Rank values. Thus, the next-hop of each node also dynamically changes according to movement. Due to the fact that each node periodically sends packets to the sink, the next-hop table periodically adapts to mobility.
## 4 Analysis Model
### Analysis Setting
#### 5.1.3 Control Parameters of the TMD  
The control parameters of the TMD are as follows. The control mass block ratio (\(\mu\)), frequency ratio (\(\gamma_{\mathrm{f}}\)) and damper ratio (\(\xi_{\mathrm{a}}\)) have the same definitions as those of the ISAMD. To compare the influence of the shock absorption ratio by the detuning effect, the frequency ratio (\(\gamma_{\mathrm{f}}\)) of the control mass block of the TMD was changed. The range of variation of this frequency ratio was around 0.8145-1.2277. The detuning frequency (\(\Gamma\)) is the ratio of the difference of the frequency ratio of the control mass block and the optimal frequency ratio of the TMD to the optimal frequency ratio of the TMD:  
\[\Gamma=\frac{\gamma_{\mathrm{f}}-\gamma_{\mathrm{f,opt}}}{\gamma_{\mathrm{f,opt }}}\times 100\% \tag{8}\]  
The control parameters of the TMD are listed in detail in Table 1 and were based on the formula of the optimal control parameters suggested by [PERSON] et al. [48] to estimate the optimal frequency ratio and the damping ratio, 0.9694 and 0.07035, respectively. Therefore, the optimal frequency of the control mass block was 6.283 rad./s.  
\[r_{f}=\frac{f_{a}}{f_{p}}=\left(\frac{a}{1+\mu}\right)^{b};\ a=1.0-\frac{\xi_{p }}{4};\ b=1.35e^{32\xi_{p}};\ \xi_{a}=0.46\mu^{0.48} \tag{9}\]
## 2 Distributed Energy Storage Aggregation Model Based on Inner Approximation of Minkowski Sum
### Day-Ahead Optimal Scheduling Model
#### 3.1.1 Objective Function
The formula for calculating the system's carbon emissions is expressed as follows:  
\[E_{\text{P}}=\sum_{t=1}^{T}\left(\sum_{i=1}^{N_{\text{G}}}\delta_{Gi}P_{ \text{G},i,t}+\delta_{Bi}P_{\text{buy},t}\right) \tag{30}\]  
where \(E_{\text{P}}\) is the total system carbon emissions in a dispatch cycle; \(\delta_{Gi}\) is the carbon intensity of GT unit \(i\); \(\delta_{Bi}\) is the carbon intensity of purchased electricity. The carbon emission cost is calculated based on the system's carbon emission allowance and actual carbon emissions as follows:  
\[f_{\rm CO2}=\left\{ \begin{array}{ll}\mu(E_{\rm P}-E_{\rm L})&E_{\rm P}\leq E_{ \rm L}+d\\ \mu d+(1+k)\mu(E_{\rm P}-E_{\rm L}-d)&E_{\rm L}+d\leq E_{\rm P}\leq E_{\rm L}+2d \\ (2+k)\mu d+(1+2k)\mu(E_{\rm P}-E_{\rm L}-2d)&E_{\rm P}>E_{\rm L}+2d\end{array} \right. \tag{31}\]  
where \(f_{\rm CO2}\) is the cost of carbon emissions of the system; \(\mu\) is the carbon trading price; \(d\) is the length of the carbon emissions interval; \(k\) is the growth rate of carbon emissions for every 1-step increase in the carbon trading price. It should be noted that when \(E_{\rm P}\leq E_{\rm L}\), \(f_{\rm CO2}\) is negative, which means that the actual carbon emission of the system is smaller than the system's quota, and the excess carbon emission quota can be traded in the carbon trading market at the initial carbon emission price, so that the system can obtain carbon benefits.
# Recent Development of Mid-Infrared Supercontinuum Generation in Fluoroindate Glass Fibers
## 2 Fluoroindate Fibers--Design and Material Properties
It is obvious that the entire process, including the purity of the starting materials, glass synthesis, preform preparation, and fiber drawing conditions (such as control of the heating zone length, extrusion temperature, and chemo-mechanical treatment), must be optimized to achieve  
Figure 2: Attenuation spectra of selected single-mode fluoroindate fibers (data provided by manufacturers [93; 94]). low fiber losses [57]. There is scope for improvement because the achieved attenuation level is much lower than the theoretical level. The dispersion of InF\({}_{3}\) fibers is similar to that of ZBLANs. The material dispersion of InF\({}_{3}\) fibers crosses zero at ~1.8 \(\upmu\)m [90,96] and monotonically increases with the wavelength; exemplary dispersion curves are shown in Figure 3. It should be noted that the material dispersion of each glass composition is inherent and cannot be changed. However, the total fiber dispersion also depends on the waveguide dispersion, implying that with suitable fiber engineering techniques, such as manipulation of the fiber core diameter and NA, it can be flattened or the ZDW can be shifted to shorter or longer wavelengths. Another issue addressed in fluoroindate fibers is their mechanical strength, which is much lower than that of silica fibers [89]. Their fragility results from extrinsic defects, such as microcrystals, microbubbles, and core-cladding interface imperfections [97]. However, in recent years, the strength of all fluoride fibers has been significantly improved, allowing the user to stripe, cleave, and even fusion-splice them [42]. Furthermore, to enhance their strength, the fibers can be coated with Kevlar or stainless-steel jackets and connected with different types of connectors (e.g., FC/PC, FC/APC, and SMA-905). Furthermore, InF\({}_{3}\) glass fibers have enhanced stability in atmospheric moisture compared with ZBLANs, owing to the lack of the NaF component. Thus, they satisfy the requirements of many industrial applications. Excellent reviews of InF\({}_{3}\) glasses are presented in refs. [90,92,96,98,99], whereas more information about InF\({}_{3}\) fibers can be found in refs. [56,57,81,83,89,97,100].
## 2 Mathematical Model
### ORC Model  
The energy balance at the ORC cycle gives the following [24; 6; 10]:  
\[W_{ORC}=m_{ref}(h_{1}-h_{2}) \tag{5}\]  
\[Q_{WHB}=m_{ref}(h_{1}-h_{4}) \tag{6}\]  
\[Q_{COND}=m_{ref}(h_{2}-h_{3}) \tag{7}\]  
\[W_{P_{ORC}}=m_{ref}(h_{4}-h_{3}) \tag{8}\]  
\[Q_{Cc}=m_{ref}(h_{6}-h_{5}) \tag{9}\]  
\[Q_{regon}=m_{ref}(h_{7}-h_{6}) \tag{10}\]where \(h\) is the enthalpy  
\(h_{1}\): enthalpy at the outlet of the waste heat boiler (kj/Kg)  
\(h_{2}\): enthalpy at the exit of the vapor turbine (kj/Kg)  
\(h_{3}\): enthalpy at the condenser outlet (kj/kg)  
\(h_{4}\): enthalpy at ORC pump outlet (kj/kg)  
\(h_{5}\): enthalpy at inlet of cooling/freezing coil (kj/kg)  
\(h_{6}\): enthalpy at outlet of cooling/freezing coil (kj/kg)  
\(h_{7}\): enthalpy at the outlet of regenerator (kj/Kg)  
\(m_{\textit{ref}}\): refrigerant mass flow rate (kg/s)  
The ORC-PV hybrid system efficiency can be calculated as  
\[\eta_{ORC-h}=\frac{W_{ORC}+p(t)+Qcc-W_{P_{ORC}}}{Q_{in}} \tag{11}\]  
where  
\(W_{ORC}\): power produced by ORC (KW)  
\(p(t)\): PV solar output (kW) defined by Equation (4)  
\(Qcc\): cooling coil thermal capacity (kw) and defined by Equation (9)  
\(W_{P_{ORC}}\): pump power consumption defined by Equation (8)  
\(Q_{in}\): solar radiation (kw) and defined by Equation (1)
## 4 Discussion
### Detection  
By looking at the results achieved in both the laboratory and realistic datasets, it is quite evident that the covariate shift phenomenon significantly impacts the performance of the machine learning algorithm behind the passive BCI system, inducing decrements of even 20% in accuracy (\(p\)-value \(<\) 0.05), in the worst condition. In particular, the covariate shift phenomenon is reflected in a degradation of classification performance, quantified through accuracy, which becomes more pronounced with an increase in the shift. The phenomenon seems to be more prominent, as expected, to a variation in both recording and reference sensors' positioning, with respect to the only recording or only reference sensors' positioning. In fact, no statistical difference was found between the latter two configurations.  
The significant difference in accuracy values is also highlighted by [PERSON]'s D values, which, in both the laboratory and realistic datasets, consistently exceed 2 for each comparison between control configuration (or similar positioning) and test configuration.  
It is worth noting to highlight that the phenomenon of covariate shift persists regardless of the amount of new testing data, as depicted in Figures 9 and 13. This observation implies the persistent nature of data shifts over time, induced by the change in sensor positioning. Hence, the need to detect the occurrence of the shift as soon as possible.  
In this regard, the isolation forest method was able to significantly detect the covariate shift phenomenon, on average, using just the first 15 s of new coming data, allowing the possibility to alert the user to adjust the headset position or eventually to run correction algorithms able to properly correct the data distribution accordingly.
## 5 Security Analysis
### Case Scenario Security Analysis Simulation  
To prove the compelling of our CP-ABE enhancement solution against traffic analysis, we conducted a case study, as shown in Figure 5. In this experiment, we conducted a passive attack that attempted to learn the system's information without affecting the system's resources. We employed FileZilla Transfer Protocol (FTP) Client and Server Tools for transferring data between the owner and user, as shown in Figure 6. As an adversary in this experiment, the attacker had been designed to perform an unauthorized sniff and read the information through the access policy.  
In this experiment, the FileZilla Client had been installed on the DO computer. The file is transferred via FTP upon a successful connection between the DO s computer and the DU. The adversary \(\mathcal{A}\) exploited the communication between the DO and DU via the Wireshark packet analysis tool by sniffing the inter-communication and capture the data (in packet form).  
We experimented with two scenarios. The first scenario involved the DO sending the ciphertext \(\mathbf{CT}\) with access policy (\((\mathbf{A},\ \mathbf{\rho})\), \(\mathcal{T}\)) in a readable format to the DU, as shown in Figure 7. While Figure 8 discovers the second scenario where the packet of the ciphertext had been applied with the Policy Hiding Algorithm and sent to the DU. The packet shown in Figure 8 are in unreadable format \((HV)\).  
Based on these results, the study proved that the adversary \(\mathcal{A}\) unable to retrieve any information regarding the data because it is in unreadable (ciphertext) format. Additionally, the adversary \(\mathcal{A}\) could not learn anything because the policy is also generated in scrambled form. This experiment shows that our proposed scheme resists any malicious act to sniff the information during the data transmission.  
Figure 5: Passive Attack Architecture.  
Figure 6: Filezilla Tool.
## 4 Accurate Recognition of Landslides Based on Semi-Supervised Generative Adversarial Network  
### Dataset Building  
In order to express the environmental characteristics of landslide development and the optical texture features of landslide occurrence, this paper proposes to construct three kinds of sample sets representing landslide characteristics, which are the landslide influence factor sample set, the Sentinel-2A optical remote sensing sample set, and the joint influencing factor and Sentinel-2A sample set. These three kinds of sample set form a comparative study for landslide identification and analysis.  
For each type of feature sample set, the sample is obtained in the same way. In order to achieve the purpose of pixel-level discrimination, this paper proposes a sliding cutting strategy to construct a single sample, and the production process is shown in Figure 4. The specific methods are as follows: first of all, 10 environmental image factor features, 12 band optical feature datasets, and historical landslide distribution maps are converted to the same resolution. Sentinel-2A imagery is used as the reference for resampling all image factors to a 10 m resolution. The selected feature data and the historical landslide distribution map are combined to create the original dataset, comprising pixel images with dimensions of 4314 rows and 7210 columns. A 13 \(\times\) 13 pixel window is used to slide across the image, covering every pixel in the image. This synchronized sliding window cutting for image factors, optical images, and historical landslide images ensures that all features correspond to the original landslide area, creating individual image samples. The labeled data sample set assigns labels based on the pixel value at the original landslide center point, with landslide points labeled as 1 and non-landslide points labeled as 0. The unlabeled sample set is obtained from non-landslide locations to enable the model to learn the characteristics of the actual environment. Among all the image samples generated through cutting, 89,160 landslide samples, an equal number of non-landslide samples, and an equal number of unlabeled data samples, are randomly selected. Finally, the landslide samples, non-landslide samples, and unlabeled samples form the dataset, which is then divided into a model training set and a test set in a 7:3 ratio. This ratio is widely adopted in practice and has been proven to be effective. The training set is used for the model to learn the characteristics of landslides and train the internal parameters of the model, while the test set is used to assess the final generalization error of the model and evaluate its robustness.
# An Earthquake Early Warning Method Based on Bayesian Inference
## 6 Discussion and Conclusions
Furthermore, combined with historical earthquake information, the Gutenberg-Richter relationship is employed as the initial prior probability, and the \(\tau_{c}\) and \(P_{d}\) of the first-three-second P- wave recorded in real time by the seismic station are used as the likelihood functions to predict the magnitude and PGV. The proposed method was compared with the traditional fitting approach [18], revealing the higher accuracy of the former. Finally, the \(M_{\parallel}4.3\) earthquake in the Mt. Fuji area and the \(M_{\parallel}5.5\) earthquake in the Ibaraki area of Japan were selected as examples to evaluate and discriminate the magnitude and distance of the earthquake and to analyze the under-prediction and false-prediction rates. Despite the substantial progress made using the classification model of earthquake types in this paper, it has some limitations. For example, the early warning threshold needs to account for the characteristics of the local geographical environment, which vary across regions. This is particularly important, as the threshold settings play a large role in the model's accuracy. Future research will focus on refining the model to further explore the available early warning indicators. Conceptualization and methodology, Z.L. and J.Y. ; software, F.H. and Y.Z. ; validation, F.H. and J.Y. ; resources and data curation, Z.L., J.Y., F.H. and Y.Z. ; writing--original draft preparation, Z.L. and J.Y. ; writing--review and editing, Z.L., J.Y., F.H. and Y.Z. ; supervision, Z.L. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by National Natural Science Foundation of China, grant number 41874019. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Earthquake data used in the study are available from Japan National Research Institute for Earth Science and Disaster Prevention: Available online: [[https://www.kyoshin.bosai.go.jp/kyoshin/quake/index_en.html](https://www.kyoshin.bosai.go.jp/kyoshin/quake/index_en.html)]([https://www.kyoshin.bosai.go.jp/kyoshin/quake/index_en.html](https://www.kyoshin.bosai.go.jp/kyoshin/quake/index_en.html)) (accessed on 6 July 2022). We would like to thank the editor and reviewers for their valuable comments. Conflicts of Interest: The authors declare no conflict of interest.