esa-sceva/satcom-chunk-collection huggingface dataset:
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 3 PQC-Based PKE Protocols for Using the PQC Algorithm
### PQC Standalone PKE Protocol  
The KEM is often used in conjunction with a PKE scheme to securely exchange encryption keys, which can then be used to encrypt data using the PKE scheme [6]. Therefore, in this paper, we select PKE as the cryptographic scheme for using PQC algorithms in various applications. In the improved PQC standalone PKE protocol shown in Algorithm 1, both the client (C) and the server (S) generate two pairs of keys using post-quantum cryptographic algorithms. These include a public/private (secret) key pair for the KEM (\(pkk_{c}\) and \(skk_{c}\) for the client, and \(pkk_{s}\) and \(skk_{s}\) for the server), and a public/private (secret) key pair for the signature (\(pks_{c}\) and \(sks_{c}\) for the client, and \(pks_{s}\) and \(sks_{s}\) for the server). The client begins by signing its public KEM key (\(pkk_{c}\)) with its private signature key (\(sks_{c}\)) to create a digital certificate (\(cert[pkk_{c}]\)). This certificate, along with the client's public signature key (\(pks_{c}\)), is sent to the server to establish authenticity (Algorithm 1, lines 3-7). Upon receiving the certificate and public signature key from the client, the server verifies the authenticity of the certificate using the client's public signature key (\(pks_{c}\)). If the verification is successful, it confirms that the client's public KEM key (\(pkk_{c}\)) has not been tampered with. The server then performs encapsulation on the client's public KEM key (\(pkk_{c}\)) to generate a shared secret (\(s_{c}\)) and a ciphertext (\(ct_{c}\)). The server also signs its public KEM key (\(pkk_{s}\)) with its private signature key (\(sks_{s}\)) to create its digital certificate (\(cert[pkk_{s}]\)). The server sends this certificate, along with its public signature key (\(pks_{s}\)) and the ciphertext (\(ct_{c}\)), back to the client (Algorithm 1, lines 8-18). The client receives the server's certificate, public signature key, and ciphertext. It verifies the authenticity of the server's certificate using the server's public signature key (\(pks_{s}\)). If this verification succeeds, it confirms that the server's public KEM key (\(pkk_{s}\)) is legitimate. The client then decapsulates the received ciphertext (\(ct_{c}\)) using its private KEM key (\(skk_{c}\)) to derive a shared secret (\(ss_{c}^{\prime}\)). Next, the client performs encapsulation on the server's public KEM key (\(pkk_{s}\)) to generate another shared secret (\(ss_{s}\)) and a ciphertext (\(ct_{s}\)), which it sends back to the server (Algorithm 1, lines 19-30).
## 4 Results
### Classifier Assessment  
We demonstrated an automated age prediction based on RRI data using a deep learning approach. In addition, the HRV model, which is considered a conventional feature-based method for comparison with the proposed RRI model, was developed for age prediction. The HRV features used in this study clearly showed age dependence, supporting the potential of the HRV model for age prediction (Table 5). We demonstrated a model that combined RRI and HRV data, and its performance was compared to the RRI and HRV models to test whether the prediction was improved by combining the two data types. We compared the performance of models implemented with and without ADASYN oversampling, which addresses the class imbalance. We also compared two different loss functions, classification and hybrid loss functions, to investigate whether learning the ordinal relationship among the age classes would improve the performance. Therefore, four different combinations of methods were implemented: classification loss without ADASYN, classification loss with ADASYN, hybrid loss without ADASYN, and hybrid loss with ADASYN. Five different performance metrics, BAC, QWK, accuracy, precision, and F1, were used to consider the issues of class imbalance and ordinal classification. Table 6 and Figure 5 show the prediction performance based on data type and method. The bold entries in Table 6 indicate the best performance for each evaluation metric. The RRI model using hybrid loss with ADASYN achieved the highest BAC (44.8%) and F1-score (0.446). The same highest BAC was also achieved by the HRV model using classification loss with ADASYN. The highest QWK (0.59) was obtained by the combined model using hybrid loss with ADASYN. The highest accuracy (70.1%) and precision (0.496) were realized by the combined model using the hybrid loss without ADASYN. We noted that the RRI model using hybrid loss with ADASYN outperformed both the HRV and combined models using the same method in terms of BAC, precision, and F1-score. We further analyze the prediction results using confusion matrices in the following section to investigate the effect of class imbalance on the evaluation metrics.
# Instance-Level Weighted Contrast Learning for Text Classification
## 4 Experiment
### Overall Results  
The following experiments were conducted on three datasets for the four baselines as well as for our proposed model, and we measured the model effect on four common metrics of classification: accuracy (ACC), precision (P), recall (R), and F1.  
\[ACC=\frac{Correct\ Predictions}{Total\ Samples} \tag{7}\]  
\[P=\frac{TP}{TP+FP} \tag{8}\]  
\[R=\frac{TP}{TP+FN} \tag{9}\]  
\[F1=2\times\frac{P\times R}{P+R} \tag{10}\]  
The experimental results are shown in Tables 3-5:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Food**} \\ \hline  & **ACC** & **Precision** & **Recall** & **F1** \\ CRNN & 0.8054 & 0.8396 & 0.6104 & 0.6255 \\ AEDA & 0.757 & 0.3785 & 0.500 & 0.4308 \\ LFCN & 0.757 & 0.3785 & 0.500 & 0.4308 \\ Ours & **0.8514** & **0.8612** & **0.7201** & **0.8596** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiments on the food dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Takeout**} \\ \hline  & **ACC** & **Precision** & **Recall** & **F1** \\ CRNN & 0.7518 & 0.7253 & 0.6897 & 0.6999 \\ AEDA & 0.6658 & 0.3329 & 0.500 & 0.3996 \\ LFCN & 0.6658 & 0.3329 & 0.500 & 0.3396 \\ Ours & **0.8471** & **0.8567** & **0.7936** & **0.8135** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiments on the takeout dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Film**} \\ \hline  & **ACC** & **Precision** & **Recall** & **F1** \\ CRNN & 0.6718 & 0.3796 & 0.4124 & 0.3782 \\ AEDA & 0.4735 & 0.1183 & 0.2500 & 0.1606 \\ LFCN & 0.4735 & 0.1183 & 0.2500 & 0.1606 \\ Ours & **0.7185** & **0.6058** & **0.5742** & **0.5515** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experiments on the film dataset.  
Although AEDA is a data enhancement technique, it does not significantly improve the results of these experiments. In the LFCN model, integrating BERT with CNN does not provide a significant advantage in these specific tasks. The proposed model, which performs best across all metrics, with the highest accuracy and competitive precision, recall, and F1 scores, demonstrates its effectiveness in text categorization tasks. The model's effectiveness is attributed to the proposed data enhancement technique, which, on one hand, better utilizes the existing data. On the other hand, the weighting of instances helps to filter out invalid data more effectively, leading to improved model training.
# Recent Advances in Protein Caging Tools for Protein Photoactivation
## 3 Site-Specific Protein Caging
Then, the caged C-terminal peptide is linked to the C-terminal end of the truncated protein through native chemical ligation [58]. A signal transduction protein with multiple caged phosphorylated serine residues was reported to be prepared by this approach [55; 56]. Based on the high degree of certainty and freedom of peptide synthesis, the semisynthetic approaches allow for the precise introduction of multiple photodegradable protecting groups and the expansion of the range of amino acids that can be protected. However, the caging position is limited to the terminal sequence of proteins, leading to limitation of applicable proteins. Recently, a total chemical synthesis-based approach was also reported to overcome this limitation of the semisynthetic protein caging [59], but it requires chemical reactions that are too specialized for anyone to immediately utilize. In ligand-directed caging approaches, a ligand molecule that selectively binds to the active site of a protein assists site-specific modification with the caging group [60, 61]. In a pioneering study of this approach, the substrate peptide with a thiol-reactive moiety through a photolceavable linker was utilized for active-site specific caging [60]. This synthesized peptide was recognized with enzyme and reacted with the cysteine residue selectively at the active site. Based on this approach, a protein kinase was caged in vitro and introduced into living cells by microinjection, leading to light-induced activation of phosphorylation for cellular morphological change [60]. Similarly, a small substrate analogue with photodegradable ability was reported to specifically incorporated into a serine residue at the active site of enzyme, and the protected activity of enzyme was regenerated by light [61]. [PERSON] et al. have presented a number of sophisticated reports in ligand-directed chemistry for in situ protein labeling [62, 63, 64]. For site-specific caging, a photolceavable linker was inserted between the ligand for proteins of interest and a special reactive group, and after mixing with proteins, this caging reagent could be selectively attached to amino acid residue surrounding the active site through a ligand-directed proximity effect on the reaction (Figure 2D) [65]. By employing this method, an enzyme and a receptor protein were efficiently caged with the reagents including specific ligand, respectively, and demonstrated to be uncaged by light exposure. Thus, ligand-directed approaches can achieve site-specific caging of intact proteins of interest simply by mixing with the specific caging reagents. However, the application is limited to enzymes and binding proteins. Moreover, ligands with an appropriate dissociation constant, which must meet both the requirements for the selective binding in the caging step and the rapid releasing in the photoactivation step, are needed. Such demands of this approach may be issues in terms of versatility.
## 3 Results and Discussion
### The Characteristics of Transport during Coking Coal Charging  
Through the observation and research of coal transporting phenomenon during the experiment process, we found that if the cylinder lifting speed was too fast and the spiral conveyor feeding was too slow, the coal was subjected mainly to gravity rather than extrusion during its falling, this would cause the coal to be transported in a dilute state within the cylinder, the transportation with dilute state at the bottom of the coal in Figure 8b-d. This method of transportation is not only easy to cause the escape of coal particles and increase the amount of dust in the coking chamber, but also makes the accumulation of coal uneven on both sides of the chamber, and then leads to an increase in the times of twwelling with leveling equipment. Finally, by matching the two speeds, the coal can be discharged through extrusion, and the discharge phenomenon was shown in Figure 8a. Under this state, the coal was transported to the carbonization chamber in a dense phase flow, this coal charging process with dense phase flow had a low dust and good accumulation effect.  
Figure 8: Coking coal discharge status at bottom of cylinder. (**a**) Dense phase flow phenomenon; (**b–d**) Dilute phase flow phenomenon.  
Figure 7: The true strain changes with time under different conditions. (**a**) The 310 austenitic chromium nickel stainless steel; (**b**) The 410 Ferritic stainless steel.  
Therefore, the lifting speed of the cylinder and the extrusion speed of the screw feeder needed to be adjusted and matched, so that the coal could finally be extruded.  
Figure 9a-d represent different moisture content of coal, respectively (3%, 5%, 7%, and 9%). The red region represents the distribution of the screw feeding speed required to achieve the condition of dense-phase continuous transport, the green area is screw feeding speed, in which distribution of the dense-phase continuous transport conditions is estimated. It was observed that the lower the moisture level of coal, the greater the value for screw feeding speed required to reach dense-phase continuous transport. The greater the value for the cylinder lifting speed, the higher the screw feeding speed required to meet the condition of dense continuous transport. For example, when the moisture content was 3%, the lifting speed of the cylinder was 0.02 m/s, the distribution range of screw feeding speed was [0.38, 0.50]; when the moisture content was 5%, the distribution range of the speed was [0.31, 0.50].  
Figure 9: The influence of moisture content of coking coal on transporting characteristics. (**a**) The moisture content is 3%; (**b**) The moisture content is 5%; (**c**) The moisture content is 7%; (**d**) The moisture content is 9%.
# A Machine Learning Approach to Algorithm Selection for Exact Computation of Treewidth
## 3 Methodology  
### Features  
Algorithm selection is normally based on a set of features that are extracted from problem instances under the assumption that those features contain signal for which algorithm to select. The choice of features is, of course, subjective. To enhance generality and avoid circularity, we deliberately avoided selecting features based on specific knowledge of how the treewidth algorithms operate. To that end, we extracted thirteen features from each of the graphs, listed below. These are very standard summary statistics of graphs, which are a subset of the features used by Reference [37] where algorithm selection was used to tackle the NP-hard graph colouring problem. More specifically, we selected these features because they are trivial to compute, ensuring that the feature extraction phase of our hybrid algorithm requires only a negligible amount of time to execute. The remaining features used by Reference [37] are more complex and time-consuming to compute, requiring non-trivial polynomial-time algorithms to approximate a number of other graph parameters, several of which are themselves NP-hard to compute. Compared to many machine learning frameworks, our selected subset is a fairly small set of features: it is more common to start with a very large set of features and then to progressively eliminate features that do not seem to contain signal. Nevertheless, as we shall see in due course, our parsimonious choice of features seems well-suited to the problem at hand.  
1. **number of nodes:**\(v\)
2. **number of edges:**\(e\)
3.4. **ratio:**\(\frac{e}{e^{\prime}}\)\(\frac{e}{v}\)
5. **density:**\(\frac{2e}{v(e-1)}\)
6-13. **degree statistics:**\(\min\), max, mean, median, \(Q_{0.25}\), \(Q_{0.75}\), variation coefficient, entropy.  
For brevity, the features \(Q_{0.25}\), \(Q_{0.75}\) and variation coefficient will be referred to as **q1** (first quartile), **q3** (third quartile) and **variation** in the rest of this paper. We note that there are some mathematical correlations in the above features, particularly between \(v/e\) and \(e/v\). We keep both to align with Reference [37], where both features were also used but also because we wish to defer the question of feature correlations and significance to our post-facto feature analysis later in the article. There we observe that leaving out exactly one of \(e/v\) and \(v/e\) in any case does not _improve_ performance. Other correlations in the features are more indirect and occur via more complex mathematical transformations. There is no guarantee that the classifiers we use can easily 'learn' these complex correlations, which justifies inclusion. It is also useful to keep all 13 features to illustrate that careful pre-processingof features, which can be quite a complex process, is not strictly necessary to obtain a powerful hybrid algorithm.
## 2 Related Works  
### Omnidirectional and Directional Chargers  
Previous studies assumed that omnidirectional chargers have the same charging capacity at the same distance [7, 8]. When the distance increases, the received power decays, and when the threshold distance is finally reached, the received power becomes zero. The charging range of an omnidirectional charger is defined as the center of the charging range. The coverage area of the directional charger is a sector [7, 8]. Devices from different manufacturers may have different ranges of charging angles. Due to the somewhat steady power coverage and low cost, directional chargers are frequently used in WRSNs.  
As the mathematical formulation of a charger's charging power, previous researchers defined the charging formula as follows (1) [9, 10, 11]:  
\[W(dist(a_{i},s_{i}))\ =\ \begin{cases}\frac{\alpha}{(dist(a_{i},s_{i})+\beta)},&dist(a_{i},s_{i})\leq r\\ 0,&dist(a_{i},s_{i})>r\end{cases}. \tag{1}\]  
In Equation (1), \(dist(a_{i}s_{i})\) is the Euclidean distance between charger \(a_{i}\) and sensor \(s_{i}\alpha\ =\ \frac{\xi Wg_{a}G_{a}}{C_{W}}(\frac{\lambda}{4\pi})^{2}\), \(\lambda\) is the wavelength, and \(\xi\) is the utility of the rectifier. \(G_{a}\) and \(G_{s}\) are the antenna gains of the charger and the sensor, respectively. \(W_{0}\) is the charger power and \(\beta\) is the compensation parameter for short-distance transmission and \(C_{W}\) in \(\alpha\) is polarization mismatch loss [9]. When the range is larger than the charging radius r, the charging power is set to 0.
## 4 Model Development
### Robot-to-Camera Inverse Kinematic Model  
Inverse kinematics determines the joint angles required to achieve a given camera pose relative to the inertial frame. The camera pose in the inertial frame can be expressed as a 4 \(\times\) 4 matrix:  
\[Pose^{O}=\begin{bmatrix}n_{x}&s_{x}&a_{x}&d_{x}\\ n_{y}&s_{y}&a_{y}&d_{y}\\ n_{z}&s_{z}&a_{z}&d_{z}\\ 0&0&0&1\end{bmatrix} \tag{26}\]  
Here, the vectors \(\left[n_{x},n_{y},n_{z}\right]^{T}\), \(\left[s_{x},s_{y},s_{z}\right]^{T}\) and \(\left[a_{x},a_{y},a_{z}\right]^{T}\) represent the camera's directional vectors for Yaw, Pitch, and Roll, respectively, in the base frame {C}. Additionally, the vector \(\left[d_{x},d_{y},d_{z}\right]^{T}\) denotes the absolute position of the camera center in the base frame {C}.  
The camera pose in the camera frame {C} is straightforward as it can be expressed as another 4 \(\times\) 4 matrix:  
\[Pose^{C}=\begin{bmatrix}1&0&0&0\\ 0&1&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix} \tag{27}\]  
The nonlinear inverse kinematics problem involves solving for the joint angles \(q\) that satisfy the equation:  
\[Pose^{C}=T_{E}^{C}.T_{0}^{\xi}(q).Pose^{O} \tag{28}\]  
where \(T_{E}^{C}\) is the transformation from the end-effector frame to the camera frame, and \(T_{0}^{\xi}(q)\) represents the transformation from the base frame to the end-effector frame, which is a function of the joint angles \(q\).  
The formulas for computing each joint angle are derived from the geometric parameters of the robot. The results of the inverse kinematics calculations for the ABB IRB 4600 elbow manipulator [33], used for simulations in this paper, are summarized in Appendix A.
# Satellite-Derived Bathymetry for Selected Shallow Maltee Coastal Zones
## 4 Results  
### Ghajn Tuffieha and Golden Bay Embayments  
Three images of the regions of interest were selected under cloud-free conditions obtained on 9 July 2020, 20 August 2020, and 22 February 2021 for the Golden Bay embayment and on 10 July 2020, 21 August 2020, and 22 February 2021 for the Ghajn Tuffieha embayment. The in situ data points collected during the same month were coupled together to create three datasets for July 2020, August 2020, and February 2021.
## 4 Conclusions  
The present research states the NIFGS implementation to amputate cations crystal violet dye from aqueous solution and textile industrial effluent. The observations made admit that NIFGS is an efficient, cost-effective and eco-friendly adsorbent. The experimental equilibrium (\(q_{c}\)) values of 184.00 mg/g at pH value 2 and 82.00 mg/g at almost a neutral pH is quite encouraging to commercialize the process. Modelling analysis suggests the transfer of the dye from a bulk solution using NIFGS. The results categorise the process as physisorption compatible with the Langmuir isotherm. Adsorption followed the pseudo-second order model and reported as thermodynamically favourable. The process was observed to be spontaneous and feasible which commemorates the endothermic nature. The value of \(p<0.001\) projects the significance of pH and the contact duration for efficient CV adsorption onto an adsorbent surface. The dye-modified NIFGS has potential as a reinforcing material for the fabrication of composites using plastic waste.  
In brief, the study has relevance to three industrial sectors, namely: textile industries, nutraceutical industries and plastic industries. The industrial production has a linear model to create value in making the product and completes the life cycle with disposal after use. This model seriously affects the environment and ecology due to the unprecedented problem of resource depletion. To address the issue of resource depletion there is a need from the industries to shift from virgin inputs to recycled and/or reused products. This study is a step towards this goal.  
Conceptualization, S.N.T., M.E.M.S. and M.C.S.; Methodology, S.N.T., M.C.S. and B.A.K.; Investigation, S.N.T. and M.E.M.S.; Resources, S.N.T., M.C. and B.A.K.; Writing--original draft preparation, M.R.S. and M.S.G.; Writing--review and editing, I.B.K.; Review and editing, M.I.H.S., M.A.A. and Z.S.; Project Administration, M.R.S. and M.E.M.S.; Funding, M.A.E. and C.A.S.; Research Support, A.S.K.; Support, Review and Editing, A.E. All authors have read and agreed to the published version of the manuscript.  
King Khalid University, Saudi Arabia, Research Group Program under grant no. RGP2/108/42. Taif University researchers supporting project number (TURSP-2020/123), Taif University, Taif, Saudi Arabia.  
Not Applicable.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University, Saudi Arabia for funding this work through General Research Group Program under Grant No: RGP. 2/108/42. This work was supported by Taif University researchers supporting project number (TURSP-2020/123), Taif University, Taif, Saudi Arabia.  
The authors declare no conflict of interest.
# The Bivariate Empirical Mode Decomposition and Its Contribution to Grinding Chatter Detection
## 3 Application of a Feature Vector Extraction Method for Simulating Chatter Signals  
In order to evaluate whether BEMD and Hilbert transform can be a reliable solution for detecting chatter, an experimental program based on BEMD and Hilbert transform was designed (Figure 1). The process consists of the following steps: (1) signal acquisition and pre-processing, such as signal denoising and reconstruction; (2) applying the BEMD and extraction criterion of true IMFs to obtain all true IMFs; (3) applying the Hilbert transform to true IMFs to obtain Hilbert and marginal spectrum distributions; (4) calculating the real-time variance and instantaneous energy of IMFs, and then superposing and normalizing the feature vectors, respectively; and (5) predicting grinding chatter by contrasting the changes of the feature vectors.