esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions
**Fixed Experimental Conditions**  
The salt concentrations, pH, and temperature used here were chosen for consistency and reproducibility. However, bacterial cells often experience a wider range of pH values and ionic strengths. It remains to be seen how Omp-Pst2 and OmpF would perform under those varying conditions [15; 91; 99; 104; 105]. **Lack of Structural and Computational Insights**  
Although I discuss how differences in pore size, charge distribution, and lining residues may explain why Omp-Pst2 carries more ammonium while OmpF is more selective, no detailed structural modeling or molecular dynamics simulations were performed. As a result,  
(3) **Structural and Computational Studies**  
High-resolution structures (e.g., from X-ray crystallography or cryo-EM) and computer simulations could reveal exactly how specific amino acids and pore geometry determine both flux and selectivity [50; 71; 107; 113; 114; 108]. (4) **Antimicrobial Testing**  
Small molecules or peptides designed to block Omp-Pst2 could be screened for their ability to inhibit growth under nitrogen-limited conditions. Similarly, exploring drug conjugates that exploit these porins as \"entry points\" might open new paths for delivering antibiotics more effectively [48; 102; 103]. By addressing these limitations and pursuing these follow-up studies, a more complete picture will emerge of how outer membrane porins contribute to bacterial survival, nutrient acquisition, and potential vulnerabilities that can be targeted in antimicrobial therapy. This research received no external funding. The raw data supporting the conclusions of this article will be made available by the authors on request. I would like to sincerely thank [PERSON] for generously providing access to laboratory facilities, instruments, and materials, which were essential for carrying out this research. The author declares no conflict of interest.
# A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method
## 3 Reverse Shortest Path Tree Based MJN Selection Method
### Construction of RSPT
#### 3.1.3. The Generation of RSPT  
To reduce the number of probe messages, we construct a reverse shortest path tree(RSPT) with the DR as the root. Once DR establishes RSPT, DR can know all of the leaf nodes. Then, DR will periodically send probe requests to the leaf nodes. When nodes receive the probe request, they will know that they are the leaf nodes of the RSPT, and then reply to the DR with probe messages.  
When the DR receives a probe message, it can extract a node sequence \(Seq\) from it. The DR then checks whether \(Seq\) is included in the existing RSPT \(C\). If \(Seq\) is included in \(C\), it is discarded. Otherwise, \(Seq\) is added to \(C\), and any sequence in \(C\) that is contained by \(Seq\) is removed. \(C\) is a set of sequences, where \(C\), including \(Seq\), means that \(Seq\) is a subsequence of any sequence in \(C\).  
Once the DR receives the probe messages of all nodes, the first node of all sequences in \(C\) are leaf nodes in the reverse path tree. The algorithm is shown in Algorithm 1.  
```
\(C\leftarrow\emptyset\) for each new vector \(S\)do  InsertLeaf(\(S\), \(C\)) endfor return\(C\) procedureInsertLeaf(S,C) \(flag\gets 0\) for each \(T\) in \(C\)do if\(S\) is in \(T\)then \(flag\gets 1\) break elseif\(T\) is in \(S\)then  delete \(T\) from \(C\) if\(flag==0\)then  insert \(S\) in \(C\) \(flag\gets 1\) endif endif endfor if\(flag==0\)then  insert \(S\) in \(C\) endif endprocedure
```  
**Algorithm 1** The RSPT Generation Algorithm.
# Opportunities for Early Detection and Prediction of
### Recent ICS Ransomware Attacks  
There have been several recent ransomware attacks against ICS, including SCADA. In 2021, two major ransomware attacks targeted critical infrastructure in the United States [12]. In May, Colonial Pipeline, which supplies gasoline and jet fuel to much of the East Coast [12], was hit by a cyberattack that disrupted its operations for several days. The incident resulted in fuel shortages, panic buying, and price increases in multiple states. Then, in June, JBS, the world's largest meat supplier, was targeted by a similar attack that forced the company to shut down its plants in the US and Australia. The attackers used a variant of the Ryuk ransomware to encrypt the company's systems and demanded a ransom of several million dollars. These attacks caused significant financial losses for both companies, with Colonial Pipeline reportedly paying a ransom of $4.4 million to the attackers and JBS paying $11 million. In October 2021, a ransomware attack on the Czech Republic's largest power company, CEZ, resulted in the shutdown of several power plants and the disruption of the electricity supply to thousands of customers. The attackers used the Winni malware to gain access to the company's systems and then deployed the RansomExx ransomware. In December 2020, the Cybersecurity and Infrastructure Security Agency (CISA) issued an emergency directive regarding a ransomware attack on a natural gas compression facility; the attackers used a variant of the TrickBot malware. These examples demonstrate that ransomware attacks on ICS can have serious consequences and can cause significant disruption to critical infrastructure. These incidents highlighted the need for increased cybersecurity measures and sparked discussions about the vulnerability of critical infrastructure to cyberattacks. The attacks also demonstrated the potential for significant economic and social disruptions as a result of these types of attacks. Therefore, it is important for organizations that operate industrial systems to take steps to protect their systems from these types of attacks, by implementing robust cybersecurity measures and regularly updating and patching their systems.
## 2 Fundamentals of Ion-Imprinted Polymers
### Components of the IIP
#### 2.3.1 Counter-Ions of Template  
Organic or inorganic salts generally are used as the source of the metal ion. Among the most counter-ions used are sulfate (SO42\({}^{-}\)), nitrate (NO3\({}^{-}\)), chloride (Cl\({}^{-}\)), iodide (I\({}^{-}\)), acetate (CH3 COO\({}^{-}\)), and perchlorate (ClO4\({}^{-}\)). [PERSON] and coworkers [36] assessed the effect of four anions (SO42\({}^{-}\), NO3\({}^{-}\), Cl\({}^{-}\), and CH3 COO\({}^{-}\)) on the polymerization process and the adsorption capacity of ion-imprinted polymers for Cu2\({}^{+}\) ions (CuSO4-IIP, Cu(NO3)2 IIP, CuCl2-IIP, Cu(CH3 COO)2-IIP). The authors discerned that the template and solution anions have a strong influence on the polymerization process, specifically, in the formation of cavities, as well as the adsorption process, respectively. They observed that due to the smaller ionic radius of Cl\({}^{-}\), compared to the other three anions (SO42\({}^{-}\), NO3\({}^{-}\) and CH3 COO\({}^{-}\)), the imprinted cavities formed in the polymer CuCl2-IIP do not fit with the size and shape of the SO42\({}^{-}\), NO3\({}^{-}\) and CH3 COO\({}^{-}\) anions. Thus, lower values of adsorption of Cu2\({}^{+}\) ions (mg g\({}^{-1}\)) were obtained when the solutions of CuSO4, Cu(NO3)2, and Cu(CH3 COO)2 and the polymer CuCl2-IIP were employed. However, as soon as the CuCl2 solution was used, higher values of adsorption of Cu2\({}^{+}\) ions (mg g\({}^{-1}\)) were obtained with the four polymers CuSO4-IIP, Cu(NO3)2-IIP, CuCl2-IIP, Cu(CH3 COO)2-IIP, which was associated with the smaller size of CuCl2.  
The same authors expected to obtain a higher capacity for adsorption of Cu2\({}^{+}\) ions by applying the polymer Cu(CH3 COO)2-IIP because the ionic radius of CH3 COO- is greater than that of SO42\({}^{-}\) and NO3\({}^{-}\). However, as CH3 COO\({}^{-}\) is an organic anion, it interferes in the complexation of Cu with the functional monomer, as well as the formation of the cavity.  
Even though lower adsorption values were observed with Cu(CH3 COO)2-IIP, this polymer showed greater selectivity than the polymers CuCl2-IIP \(>\) CuSO4-IIP \(>\) Cu(NO3)2-IIP, in the presence of Ni2\({}^{+}\) and Co2\({}^{+}\) ions. Regarding the CuSO4-IIP and Cu(NO3)2-IIP polymers, the higher adsorption capacity was observed using the first one, most likely due to the lower ionic strength and chemical stability of the anion NO3\({}^{-}\) during the synthesis of Cu(NO3)2-IIP.  
A briefly survey of literature shows that the most of works on imprinted polymers synthesis for the Cu2\({}^{+}\)[37; 38; 39; 40; 41; 42], Ag\({}^{+}\)[43], K\({}^{+}\)[44], Li\({}^{+}\)[45], Pb2\({}^{+}\)[46; 47], Zn2\({}^{+}\)[48; 49], Ni2\({}^{+}\)[50; 51; 52; 53], Hg2\({}^{+}\)[54] Cd2\({}^{+}\)[55; 56; 57], Co2\({}^{+}\)[58], U5\({}^{+}\)[59], and In3\({}^{+}\)[60] used nitrate salt.
# A Random Subcarrier-Selection Method Based on Index Modulation for Secure Transmission
### Benefits and Challenges  
The main benefits are summarized as follows:  
* A new random subcarrier-selection method was proposed to guarantee the randomness of the selected subcarriers. In contrast to the scheme proposed in [18], RSCS-IM avoids the randomization procedure while the selected subcarriers are more random.
* IM is combined with subcarrier selection. By employing IM, the information was conveyed not only by MQAM modulation but also by the indices of the activated subcarriers, which improved the SE. Operating at the same SE, the BER performance was promoted by employing our scheme.
* The secure precise transmission via computer simulation was demonstrated and derived the closed-form expression of BER for the desired position. The theoretical outcomes were validated by simulation results as well.  
* The main challenges are summarized as follows: in order to achieve precise and secure wireless communication, the randomness of the selected subcarriers must be guaranteed; the system complexity must be reduced and SE must be improved.  
The rest of the paper can be summarized as follows. In Section 2, the system model is presented, including the conventional random subcarrier-selection method and the proposed random subcarrier-selection method based on IM. Performance analysis is depicted in Section 3. The computer simulation results are given in Sections 4 and 5 draws the conclusion.  
The notations used in this manuscript are shown in Table 1.
## 5 Using MEC to Augment the Computing Capabilities of Vehicles
### Resource Allocation  
Compared to cloud computing servers, MEC servers' storage and processing capabilities are more constrained. Managing scarce resources to meet changing resource demands, complex traffic situations, heterogeneous application features, and effective usage is a complex task [113]. The authors of [114] suggest a real-time strategy to balance energy consumption and task latency for vehicles. They use a MINLP problem to optimize computation offloading and resource allocation, employing bi-level optimization to break down the problem into two subproblems. Power and channel allocation is a lower-level challenge, and task offloading is an upper-level problem [114]. Additionally, a DRL offloading strategy is proposed to allow users to choose the best offloading options while taking into consideration the mobility of VUEs and the accessibility of cloud resources. Also, they integrate power distribution, channel distribution, and compute offloading in a multicell scenario to minimize the total latency and energy consumption of the system by the VUEs. Researchers, in [115], used the DRL to minimize system overhead as well. Based on the system's current state, they utilized an actor-centric approach to optimal decision making about controlling resource utilization and task migration. To use this algorithm, it is necessary to define a state and action table and a reward function. In comparison with other algorithms, the vehicle will perform more tasks. Processing tasks by themselves will result in a significant delay due to the vehicle's limited computing capabilities. The suggested algorithm takes into account the cooperation of the terminal, edge, and cloud, increases resource usage efficiency, and minimizes system delay. In ref. [116], the authors aim to boost the RSU's profit constantly in a highly dynamic environment by addressing
# Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton using Electromyography
## 5 Experimental Results
### Discussion  
Table 2 summarizes the results of three experiments and presents the root mean square error (RMSE) and the normalized RMSE (NRMSE) for each joint in each test. Considering only the scenarios with variable load, the average NRMSE equaled 3.24% for the elbow and 4.21% for the shoulder. Our solution has better precision than that obtained in related work presented in the literature. For instance, [PERSON] [34] presented an average error of 8.42%, while the authors of [35] achieved an average error of 7.10% for the elbow. In [33], the average error equaled 10.82% for the elbow and 10.34% for the shoulder. [PERSON] et al. [32] only reported the maximum error (12%), which was higher than the maximum error we obtained in our work. These data are summarized in Table 3.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Test** & **RMSE Elbow** & **RMSE Shoulder** & **NRMSE Elbow** & **NRMSE Shoulder** \\ \hline One degree—no load & 2.735\({}^{\circ}\) & - & 2.452\% & - \\ Two degrees—no load & 3.609\({}^{\circ}\) & 3.642\({}^{\circ}\) & 4.190\% & 4.433\% \\ Two degrees—load = 76 g & 2.531\({}^{\circ}\) & 3.339\({}^{\circ}\) & 3.164\% & 4.174\% \\ Two degrees—load = 121 g & 2.431\({}^{\circ}\) & 3.109\({}^{\circ}\) & 3.198\% & 4.091\% \\ Two degrees—load = 197 g & 2.658\({}^{\circ}\) & 3.464\({}^{\circ}\) & 3.365\% & 4.385\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Error obtained in the three experiments.  
Figure 14: Analysis of the normalized error with two degrees of freedom in tests with different loads.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Test** & [32] & [33] & [34] & [35] & **This Work** \\ \hline One degree & – & – & NRMSE = 8.42\% & NRMSE = 7.10\% & NRMSE = 2.45\% \\ Two degrees & Max. = 12\% & NRMSE = 10.82\% & – & – & Max. = 6.96\% \\ Two degrees & Max. = 12\% & NRMSE = 10.34\% & – & – & NRMSE = 4.19\% \\ Two degrees & Max. = 12\% & NRMSE = 10.34\% & – & – & Max. = 7.10\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with related works.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## The Proposed Scheme
### User Anonymity  
In our scheme, even if an attacker \(A\) has obtained the message \(\{H_{i},R_{c},L_{i}\}\) transmitted via the public channel, then \(A\) cannot obtain the true identity \(ID_{i}\) because \(H_{i}\), \(R_{c}\), and \(L_{i}\) are protected by random integer \(r_{c}\) and the server's secret key \(k_{s}\), which are unknown to \(A\). Therefore, our proposed scheme provides user anonymity.
# Automatic False Alarm Detection Based on XAI and Reliability Analysis
## 3 Materials and Methods
### Reliability Analysis  
Indicators are used to examine the parts that AI can predict incorrectly. SSSF is an indicator that can be interpreted intuitively from a human point of view, and here, the part that AI can predict incorrectly by representing the minute differences in data from an AI point of view is examined.  
The SHAP value of a single feature provides information about how the feature value contributes to the prediction made by the AI model. The overall properties of the data can be inferred through complex features that represent data rather than a single feature. The features represented here are the important features that were previously selected. The sum of the SHAP value of the selected feature (SSSF) gathers the label tendency of each feature to reveal the overall label tendency of the data.  
After we calculate the SSSF of the dataset, we classify the data by label and look at the SSSF distribution to understand the properties of the label. For data predicted to be malware, the SHAP value of the selected features will generally be high, and the SSSF will also naturally be high. For the data predicted by AI as normal, the SHAP value of the selected features will generally be low, and the SSSF will of course also come out low. It is possible to evaluate the reliability of the judgment by looking at the degree of difference from this expectation, and it is possible to interpret from a human point of view based on SHAP.  
SSSF is an indicator of what AI judges incorrectly from a human point of view, whereas the reliability score is an indicator of what AI judges incorrectly by adding interpretable indicators that are intuitive from an AI point of view. The reliability score is calculated by measuring the degree of the anomaly of SSSF and the outlier score. Figure 4 briefly explains how the reliability score for the test data is calculated. After calculating the SSSF and outlier score of test data, which are new input data, the distance between the SSSF and outlier score of the training dataset is measured and used as a reliability score. It is calculated in the same way as the existing outlier score, but it is measured based on two-dimensional data consisting of SSSF and an outlier score and not based on all of the feature values. A reliability score that uses the outlier score along with the SSSF analyzes the general trend of the training dataset and means a degree of difference.  
Figure 3: Summary plot designed to display an information-dense summary of how the top features in a dataset impact the model’s output.
## 2 TCP Specimen
### Compressive Strength  
For each type of concrete (NWM, and LWM), six standard cylinders were cast with the TCPs and then placed into a fog room for maintenance. After 28 days, they were taken out for compressive strength testing with a universal testing machine (Figure 3a). The loading rate was 0.10 mm/min with displacement control, which complied with the specification of ASTM C617 (2015) [18] and ASTM C39 (2018) [19]. With this method, the post-peak branch of the stress-strain curve could be captured. The stress-strain relationship from the cylinders was observed (Figure 3a). In the test, the typical fracture pattern of the cylinder was columnar vertical cracking through both ends, and no well-formed cones were found in each cylinder (Figure 3b,c). The rupture progress in the NWM cylinder was much slower than that of the LWM. There were slight forewarnings, such as the appearance of the development of cracks in the surface of the cylinder. The stress-strain curves clearly exhibited the damage mode of the two types of cylinders (Figure 4). The NWM cylinders achieved peak stress at 40.0 to 50.0 N/mm\({}^{2}\), then decreased slowly after that. However, the resulting compressive stress of the LWM cylinders dropped suddenly after higher peak values (approximately 60.0 N/mm\({}^{2}\)), showing that they were more brittle than the NWM cylinders because of the use of SRA and SP. The experimental compressive strengths of the two materials were 39.1 and 58.8 N/mm\({}^{2}\). The compression strength for LWM was about 20% higher than the observed values for NWM. It should be mentioned that there were no OFs embedded in the cylinders because the primary objective of the tests was to acquire the compressive strength of the NWM and LWM mortars.  
Figure 2: Completed translucent concrete panels (TCPs) (VR = volumetric ratio): (**a**) VR of 1.5% (NWM); (**b**) VR of 3.0% (NWM); (**c**) VR of 6.0% (NWM); (**d**) VR of 1.5% (LWM); (**e**) VR of 3.0% (LWM); (**f**) VR of 6.0% (LWM).
## 3 Dynamic-Static Conversion Relationship
### Model Optimization for Different Rock Types  
For this study, data from 40 various types of rock samples from the article are analyzed [37]. These data are classified based on rock types, and the correlation between dynamic and static parameters is studied, as shown in Tables 4-6. By fitting linear or nonlinear correlation to the data in Tables 4-6, the fitting methods of sedimentary rocks, igneous rocks, and metamorphic rocks through different curves are shown in Figures 8-10. These curves fit the data very well. In addition, the various types of rocks' dynamic and static elastic moduli are fitted in logarithmic coordinates, as shown in Figures 11-13. The R\({}^{2}\) values of linear regression, power-law regression, and nonlinear logarithmic regression between static and dynamic elastic moduli of various types of rocks are shown in Figure 14. Based on rock types, the most significant correlation that exists between static and dynamic elastic moduli for igneous and metamorphic rocks is a power-law correlation, with a value of (igneous R\({}^{2}\) = 0.97, metamorphic R\({}^{2}\) = 0.93); for sedimentary rocks, it is a linear and nonlinear logarithmic correlation, with a value of (R\({}^{2}\) = 0.91). However, in his study, [PERSON] [7] found that the optimal correlation between igneous and metamorphic rocks is linear, with a value of (R\({}^{2}\) = 0.82).  
Figure 11: Log–coordinate fitting relationship for dynamic and static elastic moduli of sedimentary rocks.  
Figure 10: Relationship between different curve fits of dynamic and static elastic moduli of metamorphic rocks.  
Figure 9: Relationship between different curve fits of dynamic and static elastic moduli of igneous rocks.  
Figure 14: R\({}^{2}\) for linear correlation, power-law correlation, and nonlinear logarithmic correlation between static and dynamic for various rock types.  
Figure 12: Dynamic and static elastic moduli log–coordinate fitting relationships for igneous rocks.  
Figure 13: Dynamic and static elastic moduli log–coordinate fitting relationships for metamorphic rocks.  
In summary, data from 40 various types of rock specimens were analyzed and compared. We believe that based on rock types, for igneous and metamorphic rocks, the optimal relationship between static and dynamic moduli of elasticity is a power-law correlation, with a value of (igneous R\({}^{2}\) = 0.97, metamorphic R\({}^{2}\) = 0.93); for sedimentary rocks are linear and nonlinear logarithmic correlation, which is (R\({}^{2}\) = 0.91). However, there is no obvious relationship between dynamic and static Poisson's ratios.
# Improved Low-Depth SHA3 Quantum Circuit for Fault-Tolerant Quantum Computers
## 2. Preliminaries
### Secure Hash Algorithm (SHA) 3
#### 2.2.2 Quantum Pre-Image Attack  
In the worst case, it will take \(N\) searches to find specific data in \(N\) unsorted datasets. On quantum computers, [PERSON]'s algorithm allows one to find specific data in \(\sqrt{N}\) searches. [PERSON]'s algorithm speeds up pre-image attacks on hash functions as it can quickly search \(N\) data fields to find an input that outputs a specific hash value in the hash function. Therefore, the computational complexity \(O(N)\) for a brute-force attack in a classic computer is reduced to the computational complexity \(O(\sqrt{N})\) in a quantum computer. [PERSON]'s algorithm for a pre-image attack is divided into the Oracle and Diffusion operators, as shown in Figure 3. This attack is a known plaintext attack (KPA) that proceeds when the plaintext-ciphertext pairs of the block cipher are known. The Oracle function includes the hash function \(f_{g}(x)=y\) and the inverse operation \(f_{g}^{\dagger}(x)=y\). If the result of \(f_{g}(x)\) is \(y\), then \(x=1\) in Oracle, and the measurement probability for the state is increased through the diffusion operator \(U_{s}=2|s\rangle\langle s|-I\). It is known that the state of the correct qubit can be found in about \(\lfloor\frac{\pi}{4}\sqrt{N}\rfloor\) iterations of [PERSON]'s algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Hash Length** & **Block Size** & **Capacity** & **Pre-Image** \\ \hline SHA3-224 & 224 & 1152 & 448 & 224 \\ SHA3-256 & 256 & 1088 & 512 & 256 \\ SHA3-384 & 384 & 832 & 768 & 384 \\ SHA3-512 & 512 & 576 & 1024 & 512 \\ SHAKE128 & \(d\) & 1344 & 256 & \(\geq\)min(\(d\),128) \\ SHAKE256 & \(d\) & 1088 & 512 & \(\geq\)min(\(d\),256) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters and pre-image resistance for SHA3 hash function family.  
Figure 3: [PERSON]’s algorithm with \(f_{g}:\{0,1\}^{n}\leftarrow\{0,1\}^{n}\) in oracle. \(\dagger\) indicates reversible gates.