esa-sceva/satcom-chunk-collection huggingface dataset:
# A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller
## 4 HMM-R Detection Scheme
### Detection Features and Performance Indices
#### 4.2.2 Performance Indices  
The performance indices are mainly divided into the true positive rate, false positive rate, and false negative rate. The true positive is the probability that the normal traffic is regarded as the normal and the abnormal flow as the abnormal; it will be convenient for defending and tracing if the flow type is to be detected correctly. The false positive refers to the probability of taking the normal traffic as the abnormal, and it may cause the normal traffic to be cleaned or defended. The false negative is the probability that the abnormal traffic is regarded as the normal, which may cause some anomalies to be missed, and result in the reduction of network performance.  
\[RP=\frac{TP+TN}{TN+FN+TP+FP}.FA=\frac{FP}{TN+FP},DR=\frac{FN}{TP+FN} \tag{6}\]  
Figure 1: Architecture of HMM-R detection algorithm.  
In Formula (6), \(RP\) indicates the true positive rate, \(FA\) indicates the false positive rate, and \(DR\) indicates the false negative rate, where \(TP\) means attack traffic is classified as an attack, \(TN\) indicates a normal traffic is classified as a normal, \(FP\) means a normal traffic is classified as an abnormal, and \(FN\) indicates that an attack traffic is classified as normal.  
For a detection model, if its \(RP\) is higher and its \(FA\) and \(DR\) are lower, the performance of the model is better.
## 6 Mechanosensing in Microgravity
### Yap and Taz  
Similar to \(\beta\)-catenin, yes-associated protein (YAP) and WW domain-containing transcription regulator protein 1 (WWTR1/TAZ) are transcriptional co-activators belonging to the Hippo pathway. YAP and TAZ are shuttled to the nucleus following the mechanical stimulation of the cells, and YAP/TAZ persistence in the nucleus is related to their phosphorylation on specific serine residues operated by LATS1/2 [121]. Furthermore, several studies have demonstrated that YAP/TAZ can be sequestered to the adherent junctions through the cadherin-catenin system and by the activity of ZO-1. YAP and TAZ moreover localize to the nucleus thanks to the interaction with SMAD creating a YAP/TAZ/SMAD complex. In addition, YAP can also shuttle to the nucleus by interacting with ErbB-4 [122].  
In cardiac adult cells, the expression of YAP is dysregulated in microgravity conditions. In fact, in normogravity conditions, the Hippo pathway is active while YAP is phosphorylated and barely expressed. On the contrary, in microgravity conditions, the Hippo pathway is inactive and YAP is not phosphorylated and highly expressed [123].  
In bone mesenchymal stem cells (BMSCs), simulated microgravity caused a F-actin depolymerization that, in turn, prevented nuclear accumulation of TAZ. Interestingly, the nuclear translocation of TAZ could be prevented by actin stabilizers hasplakinolide or lysophosphatidic acid [124].
## 4 Results and Discussion  
This study utilizes a mixed-mode method that uses both quantitative and qualitative questions to elicit information regarding online learning experience from the students' perspective. The quantitative information was derived using Likert-scale questions developed to specifically track the students' online learning experience.  
Figure 1 provides the findings of the students' online learning experience. The quantitative results as shown in Figure 1 reveals that students in the selected private HEIs had a positive learning experience, which reflects that the academic continuity plans implemented by these universities were rather effective. To check on the robustness of the quantitative findings of this study, we thematically analyzed the qualitative feedback given by the same set of students to analyze the possibility of deeper insights that may emerge from the thematic analysis that would be much needed by the universities to implement improvements in the business and academic continuity plans for any future disruptive crises that could unexpectedly happen.  
The students' qualitative feedback was analyzed to examine the effectiveness of HEIs' academic continuity plans in the form of online lessons during the Covid-19 pandemic. A pattern of commonalities was based on the broad-based, thematic analysis of the students' reflective journals. The pattern of commonalities gave rise to a total of four emerging constructs, which comprised of Academic Continuity, Learning Experiences, Academic's Abilities, Attainment of Soft Skills or Graduate Capabilities, Perceived Benefits of Technology, and Technological Drawbacks of Technology.
## 5 Simulation
### Precise Target Detection  
In order to validate the correctness of the proposed SMI-LU method using LU decomposition and the MIMO radar system, another simulation was conducted. The key assumption made in this simulation was the presence of an environment characterized by inhomogeneous interference, so the frequently used model of inhomogeneous forest-covered terrain, referred to in the MATLAB environment as the gamma model, was adopted as the clutter.  
Figure 7: Performance comparison of STAP algorithms.  
Figure 9 shows the values of the received signals by the MIMO radar antenna array as a function of the range after transmitting the first pulse. At this stage, the received signals form a data cube, which has not yet been processed by the newly developed STAP algorithm. As a result, the radar against a background of strong clutter is unable to indicate the location of a target. As can easily be seen, the radar erroneously indicates that the target is at a distance of 1000 m from the radar.  
Figure 8: Plot of SINR at the output of the STAP processor.  
Figure 9: Values of received signals by the MIMO radar antenna array as a function of range after transmitting the first pulse before STAP.  
Figure 10 shows the values of the received signals by a MIMO radar antenna array as a function of range after transmitting the first pulse. However, this time, the raw data were subjected to STAP by implementing the proposed SMI-LU algorithm in the MATLAB environment. As can easily be seen, the radar correctly indicates that the target is at a distance of about 1900 m from the radar in a straight line.  
The execution of the latest simulation was guided by two goals. The first goal was to fully implement the proposed SMI-LU algorithm and environment along with a flying platform with radar designed to detect the target, as well as eliminate non-uniform clutter. The second goal was to recreate as real an environment as close to the real one as possible. This was due to the lack of research conducted practically. The obtained precise target data lead us to believe that the proposed STAP algorithm can be successfully applied in practical implementations.
## 2 Materials and Methods
### Statistical Analysis  
Data analysis was conducted with the Statistical Package for the Social Science (SPSS) Version 21 for Mac OS. Normal distribution of the SR data was verified using the Shapiro-Wilk test. Inter-examiner reliability of SR calculation was assessed by calculating the mean of the measurements with the upper and lower limits (95% CI) in terms of examiner experience and taking the mean difference between examiners (DBE = SR scored by the experienced examiner \(-\) SR scored by the novice examiner). Variability of the measurements (SoM% = standard deviation of the error \(\times\) 100) and validity was also assessed, taking the examiner experience into account by calculating the mean error (E = known SR \(-\) SR obtained by the examiner = 1 \(-\) SR obtained by the examiner), the mean absolute error (AE = absolute value of E), the mean error of the measurements (MEM = (mean AE of the experienced examiner + mean AE of the novice examiner)/2), the mean percent error (PE% = AE/mean error of the measurements \(\times\)100) and the mean coefficient of variation  
Figure 2: Strain ratio (SR) calculation of lateral and central areas. Central area 1 (reference) was compared with Central area 2 (target). Lateral area 1 (reference) was compared with Lateral area 2 (target).  
(CV% = Standard Deviation/mean). All the analyses were performed for both SRs calculated in the lateral areas and center of the images. Student's _t_-test for independent samples was utilized to determine examiners, location areas and reference differences. All tests were two-tailed and _p_-values < 0.05 considered significant.
## 5 Determination of Intermediate Cable Force Formula
### Derivation of Intermediate Cable Force Formula
\[H_{1}+H_{2}=T_{1}cos\alpha_{1} \tag{9}\]  
\[R_{1}+R_{2}+T_{1}sin\alpha_{1}=D_{2}+G_{1} \tag{10}\]  
The moment of the hinge support at point A is obtained as  
\[D_{2}\cdot L_{D2}+G_{1}\cdot L_{G1}=T_{1}sin\alpha_{1}\cdot L_{T1}+R_{2}\cdot L _{R1} \tag{11}\]  
\[T_{1}=\frac{D_{2}\cdot L_{D2}+G_{1}\cdot L_{G1}-R_{2}\cdot L_{R1}}{sin\alpha_ {1}\cdot L_{T1}} \tag{12}\]  
Substituting Equation (12) into Equations (9) and (10) yields  
\[H_{1}=\frac{D_{2}\cdot L_{D2}+G_{1}\cdot L_{G1}-R_{2}\cdot L_{R1}}{tan\alpha_ {1}\cdot L_{T1}}-H_{2} \tag{13}\]  
\[R_{1}=\frac{D_{2}\cdot(L_{T1}-L_{D2})+G_{1}\cdot(L_{T1}-L_{G1})+R_{2}\cdot(L _{R1}-L_{T1})}{L_{T1}} \tag{14}\]  
According to the above formulas, the intermediate cable forces of the three beam segments can be obtained, as shown in Equations (4), (8), and (12). Using these formulas, the specific tension values of the cables during construction can be determined, avoiding the use of artificial assumptions and empirical coefficients to determine the intermediate cable forces, ensuring the stability and safety of the structure from construction to completion.
# Target Tracking Based on a Nonsingular Fast Terminal Sliding Mode Guidance Law by Fixed-Wing UAV
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} UAV & Unmanned Aerial Vehicle \\ SMC & Sliding Model Control \\ NFTSM & Nonsingular Fast Terminal Sliding Mode \\ TSM & Terminal Sliding Mode \\ EKF & Extended Kalman Filter \\ \(x_{a},y_{a}\) & UAV horizontal position \\ \(v_{a},\psi_{a}\) & UAV airspeed and heading angle \\ \(v_{g},\chi_{a}\) & UAV groundspeed and course angle \\ \(x_{t},y_{t},v_{t},\psi_{t}\) & The motion state of target \\ \(L\) & Distance from target to UAV \\ \(R_{d}\) & Desired separation distance \\ \(r\) & Distance error \\ \(\theta\) & Azimuth angle of UAV in target-frame \\ \(\lambda\) & Auxiliary parameter about azimuth angle \\ \(\bar{\psi}_{a},\bar{\psi}_{t},\bar{\psi}_{w}\) & Auxiliary heading angle of UAV, target and wind \\ \(\bar{\psi}_{a}^{d}\) & Steady value of UAV \\ \(\bar{\psi}_{a}\) & Heading angle error \\ \(s\) & Sliding manifold \\ \end{tabular}  
\(\ell\) & Auxiliary parameter about distance error \(\alpha_{2}\), \(\alpha_{3}\),\(\beta_{2}\), \(\beta_{2}\), \(\eta\) & NFTSM manifold parameters \(k_{1}\), \(k_{2}\), \(k_{3}\) & NFTSM fast reaching law parameters \(\omega_{\psi}\) & UAV command
# Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images
## 1 Introduction
Furthermore, labeling is more challenging with medical data. In this study, we propose a framework for automated 3D segmentation of the mandible and maxilla using deep learning. We aim not only to accurately delineate the mandible and maxilla from CT, but also to improve the compatibility of multicenter data so that the model performs well on new domain data. To this end, we employed four multi-center datasets acquired by various conditions, with one used to train the models, and three used to evaluate the performance of the segmentation and the data compatibility. For the neural network, we applied residual connections [39] to U-Net, as it has been empirically and theoretically determined that the generalization is improved in residual networks compared with non-residual networks [40; 41].
# A Review of Hydrogen Sensors for ECLSS: Fundamentals, Recent Advances, and Challenges
## 6 Comparison and Development Trend of Each Sensor
Fiber-optic sensors have the lowest sensitivity to temperature and humidity, and are more suitable for hydrogen detection in harsh environments, such as high temperature/high humidity, intrinsically safe, resistant to electromagnetic interference, and easy to realize distributed measurement; however, they have long response times, low lower detection \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Device**} & **Sensitive** & \multirow{2}{*}{**[H\({}_{2}\)] Range (ppm)**} & \multicolumn{2}{c}{**Minimal Detection Limit (ppm)**} & \multirow{2}{*}{**Response Time (s)**} & \multirow{2}{*}{**References**} \\  & **Materials** & & & & \\  & & & & & \\ \hline Catalytic combustion & TiO\({}_{2}\)/Pd/Pt & 5000–50,000 & / & 10 & [87] \\ Catalytic combustion & Pt & 20–20,000 & / & 0.36 & [88] \\ Thermoelectricity & Pt & 500–30,000 & \textless{}500 & 60 & [89] \\ Thermoelectricity & Pt & 500–10,000 & 250 & 40 & [90] \\ Semiconductor Type & Pd/GaO\({}_{\text{X}}\)/GaN & 5–10,000 & 5 & 22 & [91] \\ Semiconductor Type & Pt/NiO\({}_{2}\) & 30–1000 & 30 & 10 & [92] \\ Fiber-optic microscope & Pd & 10,000–100,000 & / & \textless{}5 & [93] \\ Fiber-optic microscope & Pt/WO\({}_{3}\) & 0–40,000 & / & 5 & [94] \\ Fiber-optic grating & 110 nm Pd/WO\({}_{3}\) & 0–90,000 & / & \textless{}90 & [95] \\ Fiber-optic grating & 110 nm Pd/Ni & 0–40,000 & / & 240–300 & [96] \\ Fiber-optic interference & M-Z Pt/WO\({}_{3}\) & 0–40,000 & / & 120 & [97] \\ Fiber-optic interference & Sagnac Pt/WO\({}_{3}\) & 0–10,000 & / & 60 & [98] \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of various hydrogen sensors. limits, and complicated signal processing at the later stage, as well as a lack of a mature signal demodulation and detection system.
# A Traceable and Verifiable Tobacco Products Logistics System with GPS and RFID Technologies
## 2 Preliminary
### Threat Model  
According to Table 1, we sorted and reviewed the past research, and found some research gaps. Therefore, we sorted out the threat patterns that need to be overcome. The threats are generally due to system security vulnerabilities, which may cause the system to be attacked illegally by an external malicious person, or may cause the system to crash and leak data. As a result, the tobacco products logistics system will suffer from potential risks. The related risks are defined as follows:  
1. Data integrity issues: All data stored with the system must be integrated. The system must ensure that the data will not be modified by anyone during the transmission and storing process.
2. Decentralized database: The blockchain center can be known as the decentralized database, with multiple agencies maintaining the same ledger or data in different locations. Once the data are verified and added to the blockchain, the block is chained with a timestamp and the previous block hash value; every modification with the blockchain needs to be verified. Therefore, it is hard to change the data in the blockchain center, and the decentralization characteristics can achieve data transparency and reliability.
3. Decentralized authentication: The authentication inherits the decentralized database's characteristics. This is more secure than the general database that is set up with the central server architecture.
4. Message repudiation issues: To ensure the undeniable transmission of the message sent by the sender, a signature mechanism needs to be implemented to prove the message is signed by the sender.
5. Message transmission issues: The system must be ensured that the message will not be intercepted and altered during transmission.
6. Tobacco product counterfeiting issues: The counterfeiting of tobacco products can harm the health of the smoker, they never know the legal origin and quality of  
Figure 1: Hyperledger Fabric blockchain network [28].  
the products. Furthermore, the counterfeiting products will also cause the original manufacturer to lose their reputation.
7. Tobacco product tracking issues: These issues are linked to counterfeit issues. The government needs a complete tracking system to manage the logistics status of tobacco products.
8. Fair arbitration: Every system that is managed or used by a human with multiple parties cannot avoid dispute. To avoid disputing the legality of tobacco products, fair arbitration should be considered to clarify smuggled tobacco products.
9. Known attacks: 1. Man-in-the-middle attack: The sender needs to communicate to the receiver, the attacker intercepts the message in the middle, then the attacker is able to obtain the message from the sender and resend the same message to the receiver. Therefore, the message will be exposed, because the attack can easily obtain the content of the message in the middle. 2. Replay attack: The attacker sniffs the message sent by the sender, so the attackers can replay the message.
# Automatic Understanding and Mapping of Regions in Cities Using Google Street View Images
## 4 Methodology
### Lexical Labeling Using CNNs  
#### 4.4.1 Semantic CNN Descriptor  
In this work, we are interested in the use of lexical annotations to automatically generate categories valid for semantic localization. This involves labeling each Street View capture, namely an RGB image, with a set of lexical terms. This process consists of using previously trained deep learning models, CNNs in our case, to assign a probability value to each lexical label that the model is able to recognize. This set of labels is formulated as in Equation (1):  
\[\mathcal{L}=\{l_{1},\ldots,l_{|\mathcal{L}|}\}\;\textit{where}\;\sum_{i=0}^{| \mathcal{L}|}p_{I}(l_{i})=1. \tag{1}\]  
where \(\mathcal{L}\) is the set of predefined lexical labels of a CNN model, and \(p_{I}(l_{i})\) is the probability of describing an input image \(I\) using the \(i\)-th label in \(\mathcal{L}\).  
This label assignation generates a lexical-based descriptor for every image analyzed by the CNN model. The generated descriptor can then be defined as in Equation (2):  
\[d_{\text{CNN}}(I)=\left([pI(l_{1}),\ldots,pI(l_{|\mathcal{L}|})]\right) \tag{2}\]  
This produces an encoding that is similar to the bag of visual words [43], or the semantic manifold [44]. Based on this representation, each perception system of a mobile vehicle is expected to semantically describe its location at that specific moment [45]. Therefore, we can adopt lexical encoding to automatically categorize the locations of any outdoor environment from a semantic perspective. That is, two visually different images are assigned  
Figure 4: Example of images captured for every point on the map.  
to the same category if they represent similar lexical labels. To use a specific set of lexical descriptors, we need to select the pretrained CNN model.
# Effects of Adding Alkali Metals and Organic Cations to Cu-Based Perovskite Solar Cells
## 4 Conclusions  
In summary, the effects of Cu, Na, and EA co-addition to MAPbI\({}_{3}\) perovskite crystals were investigated with first-principles calculations and experimental devices. The calculations indicated that substitution of all the I with Br or Cl increased the energy gap and increased \(V_{\text{OC}}\). These additives to the perovskite precursor solution also increased the energy gap and \(V_{\text{OC}}\) in all the devices relative to those of MAPbI\({}_{3}\). When some of the Pb was replaced with Cu, a shallow band of Cu d-orbitals formed slightly above the valence band maximum, which decreased the energy gap and reduced hole mobility. When a small amount of Cu was added to actual devices, the conversion efficiency and the energy gap increased slightly, which indicated that Cu energy levels formed in the forbidden band and functioned as acceptor levels. The excitation of electrons from the I p-orbitals to the Cu d-orbitals generated carriers, increasing \(J_{\text{SC}}\). The alkali-metal energy bands formed above the conduction band minimum, which differed for the various alkali metals. Another possible reason for the increased \(J_{\text{SC}}\) was the reduced loss of generated carriers by excitations from the Cu d-orbitals and the I p-orbitals to the s-orbitals of the alkali metals. The total-energy calculations indicated that the substitution of Pb and MA with Cu and Na, respectively, destabilized the crystal structure. When MA was replaced with EA, the crystal structure was stabilized. Thus, the addition of EA to the Cu and Na systems increased structural stability. The actual device doped with Cu, Na, and EA exhibited higher durability than that device doped with only Cu and Na. Therefore, crystal stabilization by EA addition occurred, even when Cu and Na were also added. Furthermore, the durability of the devices was highly dependent on the stability of the perovskite crystals, which could be estimated by the calculated total energies. Overall, it is expected that the photoelectric conversion and durability of Cu-based perovskite solar cells could be enhanced by adding various halogens or introducing more-stable organic cations.  
Conceptualization, R.O. and T.O.; Methodology, R.O., T.O. and A.S.; Formal Analysis, R.O., T.O. and A.S.; Investigation, R.O. and A.S.; Resources, M.O., S.F., T.T. and T.H.; Data Curation, R.O. and T.O.; Writing--Original Draft Preparation, R.O. and T.O.; Writing--Review and Editing, R.O., T.O., A.S., M.O., S.F., T.T. and T.H.; Project Administration, T.O.; Funding Acquisition, T.O. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by Japan Society for the Promotion of Science as a Grant-in-Aid for Scientific Research (C) 21K04809.  
Not Applicable.  
Figure 9: (**a**) Changes of band structures for various alkali metals. (**b**) Enlarged view at the band gap.
## 2 Materials and Methods
### Inline Raman Spectroscopy and Data Processing  
To determine the acrylic acid concentration in the solution and thus the reaction conversion at any time during the reaction, the RamanRxn1-785 system was used (Kaiser Optical Systems, IO-1/2S-NIR probe, laser power 10.1 mW at the probe) to monitor the stretching vibration of \(\
u\)(C=C) at 1635 cm\({}^{-1}\). Spectra were collected every 32 s with an integration time of 30 s. The spectral data were processed using Spectragryph 1.2 software to apply SNV standardisation between 1850 and 350 cm\({}^{-1}\) and to integrate the C=C band between 1580 and 1660 cm\({}^{-1}\) using individual baselines. The linear decrease of the band with an increasing conversion can be observed during the polymerisation.