esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 HSAB Principle Revisited
The HOMO-LUMO energy gaps in subsystems then reflect the substrate molecular hardnesses. The interaction between the reactant frontier MO of comparable orbital energies is predominantly covalent (chemically \"soft\") in character, while that between the subsystem MO of distinctly different energies becomes mostly ionic (chemically \"hard\"). Figure 4 summarizes the alternative relative positions of the donor (HOMO) levels of the basic reactant, relative to the acceptor (LUMO) levels of its acidic partner, for all admissible hardness combinations in the reactive systemR = A-B. In view of the proportionality relations between the energetic and information reactivity criteria, these relative MO energy levels also reflect the corresponding information potential and hardness descriptors of subsystems, including the in situ derivatives driving the information transfer between reactants. A magnitude of the ionic, CT-stabilization energy in A-B systems is then determined by the corresponding in situ populational derivatives in R,  
\[\Delta\varepsilon_{ion.}=\left.\right|E_{\mathrm{CT}}\left.\right|={\mu_{ \mathrm{CT}}}^{2}/(2\mu_{\mathrm{CT}})>0, \tag{65}\]  
where \(\mu_{\mathrm{CT}}\) and \(\eta_{\mathrm{CT}}\) stand for the effective chemical potential and hardness descriptors of R involving the FE of reactants. Since the donor/acceptor properties of reactants are already implied by their known relative acidic or basic character, one applies the biased estimate of the CT chemical potential. In FE approximation, the chemical potential difference \(\mu_{\mathrm{CT}}\) for the effective internal B\(\rightarrow\)A CT then reads (see Figure 4):  
\[\mu_{\mathrm{CT}}(\mathrm{B}\rightarrow\mathrm{A})={\mu_{\mathrm{A}}}^{(-)}- \mu_{\mathrm{B}}^{(+)}=\varepsilon_{\mathrm{A}}(\mathrm{LUMO})-\varepsilon_{ \mathrm{B}}(\mathrm{HOMO})\approx I_{\mathrm{B}}-A_{\mathrm{A}}>0. \tag{66}\]  
It determines the associated first-order energy change for the B\(\rightarrow\)A transfer of \(N_{\mathrm{CT}}\) electrons:  
\[\Delta E_{\mathrm{B}\rightarrow\mathrm{A}}(N_{\mathrm{CT}})=\mu_{\mathrm{CT} }(\mathrm{B}\rightarrow\mathrm{A})\ N_{\mathrm{CT}}>0. \tag{67}\]  
The CT chemical potential of Equation (66) thus combines the electron-removal potential of the basic reactant, i.e., its negative ionization potential \(I_{\mathrm{B}}=E(\mathrm{B}^{+1})-E(\mathrm{B}^{0})>0\),  
\[\mu_{\mathrm{B}}^{(+)}=\varepsilon_{\mathrm{B}}(\mathrm{HOMO})\approx-I_{ \mathrm{B}}, \tag{68}\]  
and the electron-insertion potential of the acidic substrate, i.e., its negative electron affinity  
\[ \begin{array}{l}A_{\mathrm{A}}=E(\mathrm{A}^{0})-E(\mathrm{A}^{-1})>0,\\ \mu_{\mathrm{A}}^{(-)}=\varepsilon_{\mathrm{A}}(\mathrm{LUMO})\approx-A_{ \mathrm{A}}.\end{array}  \tag{69}\]  
Figure 4: Schematic diagram of the in situ chemical potentials \(\mu_{\mathrm{CT}}(\mathrm{B}\rightarrow\mathrm{A})\), determining the effective internal charge transfer (CT) from basic (B) reactant to its acidic (A) partner in A–B complexes, for their alternative hard (H) and soft (S) combinations. The subsystem hardnesses reflect the HOMO-LUMO gaps in their orbital energies.
## 5 Numerical Results
Further, the difference between cloudy and rainy conditions is around 30-50 dB depending on the frequency and altitude. Here, the additional attenuation is mainly due to both rain and blockage by clouds specific to rainy weather conditions. By analyzing the presented data further, one may observe that the aircraft altitude, expectedly, provides a linear impact on the Rx power. Similar observations are provided in Figure 6c,e. In general, as one may observe, rainy conditions produces the worst propagation environment for ground-to-aircraft communications in the THz frequency band. Figure 5: The difference between the proposed model accounting for both atmospheric absorption and attenuation by clouds and the standard free-space propagation model. Figure 6: _Cont._  
Figure 6: The received power in the equator Earth zone. (**a**) Rx power as a function of frequency; (**b**) Rx power as a function of altitude; (**c**) Rx power as a function of Tx gain; (**d**) Rx power as a function of Rx gain; (**e**) Rx power as a function of Tx power. Figure 7: _Cont_. We now proceed to assess the effect of different Earth zones, north, equator, and middle part, all in sunny conditions, illustrated in Figure 7. Recall that in these conditions, there are almost no clouds in the equator and north zones, while the impact of clouds in the middle zone is rather limited. This is reflected in the presented illustrations, where the equator is characterized by the best possible propagation conditions. However, the north zone is just 1-3 dB lower in terms of overall impairments. The middle zone, on the other hand, is characterized by much worse conditions, with an additional 15-20 dB of attenuation. These impairments are mainly caused by cloud blockage. We also note that the path loss represented in Figures 6e and 7e is independent of the Tx power and these figures represent the required sensitivity level of Rx needed to receive the data correctly.
# Dual-Neighborhood Tabu Search for Computing Stable Extensions in Abstract Argumentation Frameworks
## 6 Discussion and Analysis
### The Importance of Tabu Strategy  
In this section, we test the impact of the tabu strategy on the algorithm.  
The algorithm version that does not execute tabu actions is named DNTS-NT.  
We use the same 50 instances as Section 5.2.1. The results in Table 10 show that both the DNTS and DNTS-NT algorithms produce fast results for small-size instances. For larger instances with 2000, 3000, and 4000 arguments, both algorithms present similar solution times in most instances. However, for complex instances that take longer to solve, particularly those with 4000 arguments, the DNTS algorithm significantly outperforms the DNTS-NT algorithm. This advantage is mainly attributed to its use of the tabu search mechanism.  
We use the same 200 instances as Section 5.2.2. The results in Table 11 show that, when processing instances with 3000 and 4000 arguments, both the DNTS and DNTS-NT algorithms encounter instances they cannot solve. However, the DNTS algorithm still successfully solves a higher number of instances compared with DNTS-NT.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**DNTS**} & \multicolumn{3}{c}{**DINTS-NT**} \\ \hline
**Group** & **Instance** & **Best** & **Worst** & **Average** & **Best** & **Worst** & **Average** \\ \hline \multirow{9}{*}{vertex\_500} & v500\_1 & 0.12 & 0.12 & 0.12 & 0.07 & 0.07 & 0.07 \\  & v500\_2 & 0.12 & 0.12 & 0.12 & 0.12 & 0.12 & 0.12 \\  & v500\_3 & 0.07 & 0.12 & 0.09 & 0.07 & 0.12 & 0.08 \\  & v500\_4 & 0.07 & 0.07 & 0.07 & 0.07 & 0.07 & 0.07 \\ \cline{1-1}  & v500\_5 & 0.12 & 0.12 & 0.12 & 0.07 & 0.12 & 0.11 \\ \cline{1-1}  & v500\_6 & 0.12 & 0.12 & 0.12 & 0.07 & 0.07 & 0.07 \\ \cline{1-1}  & v500\_7 & 0.07 & 0.12 & 0.11 & 0.07 & 0.12 & 0.09 \\ \cline{1-1}  & v500\_8 & 0.07 & 0.07 & 0.08 & 0.07 & 0.07 & 0.07 \\ \cline{1-1}  & v500\_9 & 0.07 & 0.12 & 0.08 & 0.07 & 0.07 & 0.07 \\ \cline{1-1}  & v500\_10 & 0.07 & 0.12 & 0.09 & 0.07 & 0.12 & 0.08 \\ \hline \multirow{9}{*}{vertex\_1000} & v1000\_1 & 0.42 & 1.04 & 0.55 & 0.38 & 4.49 & 1.23 \\  & v1000\_2 & 0.42 & 2.66 & 0.88 & 0.42 & 0.48 & 0.46 \\ \cline{1-1}  & v1000\_3 & 0.42 & 0.53 & 0.46 & 0.48 & 0.53 & 0.52 \\ \cline{1-1}  & v1000\_4 & 0.32 & 0.53 & 0.4 & 0.32 & 1.19 & 0.52 \\ \cline{1-1}  & v1000\_5 & 0.53 & 6.12 & 1.98 & 0.63 & 1.85 & 1.26 \\ \cline{1-1}  & v1000\_6 & 0.37 & 0.43 & 0.40 & 0.43 & 0.53 & 0.47 \\ \cline{1-1}  & v1000\_7 & 0.42 & 0.58 & 0.49 & 0.43 & 0.48 & 0.45 \\ \cline{1-1}  & v1000\_8 & 0.83 & 6.06 & 2.24 & 0.83 & 1.44 & 1.06 \\ \cline{1-1}  & v1000\_9 & 0.32 & 1.14 & 0.53 & 0.32 & 0.68 & 0.39 \\ \cline{1-1}  & v1000\_10 & 0.37 & 1.65 & 0.66 & 0.37 & 0.43 & 0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of tabu strategy.  
In summary, while the DNTS and DNTS-NT algorithms perform similarly in small or simple instances, the DNTS algorithm exhibits significant advantages in more complex and time-consuming instances. This is due to its effective use of the forbidden search mechanism, notably reducing the time required to find a solution.
# Effect of Surface Micro-Hardness Change in Multistep
## 3 Experimental Results and Analysis
### Friction Coefficient Curve  
According to the change of friction coefficient with time, this paper divided the friction coefficient curve into three parts: 0-\(t_{1}\)s, run-in friction phase; \(t_{1}\)s-\(t_{2}\)s, stable friction phase; after \(t_{2}\)s, intense friction phase. In order to further study the friction and wear characteristics of titanium alloy machined surface, this paper focuses on the length of the stable friction phase, \(\Delta t\), and \(\Delta t=t_{2}-t_{1}\). According to the \(\Delta t\), the wear resistance of multistep machined surface was investigated.  
Figure 8: Effect of roughing cutting parameters on the standard deviation of roughing surface layer and finishing surface micro-hardness. (**a**) \(f=0.25\) mm/r, \(a_{p}=1.0\) mm. (**b**) \(v=50\) m/min, \(f=0.25\) mm/r.  
Figure 9 shows the friction coefficient curve of machined surface as time under different cutting conditions. It can be seen from Figure 9 that the stable friction phase times \(\Delta t\)_a_, \(\Delta t\)_b_, \(\Delta t\)_d_, and \(\Delta t\)_f_ show no apparent differences, and the stable friction phase time \(\Delta t\) was about 500 s. However, for \(\Delta t\)c and \(\Delta t\)_e_, the stable friction phase time \(\Delta t\) were significantly lower than 500 s, especially \(\Delta t\)_e_, the stable friction phase time \(\Delta t\) was about 300 s. The main reason for the significant difference about the stable friction phase time \(\Delta t\) of the machined surface is that, due to the influence of the previous step (especially roughing), it could be found from Figure 8 that there was a difference in the machined surface micro-hardness of the localized area after finishing (indicated by standard deviation). The larger the standard deviation was, the greater the change of micro-hardness in the localized area of the machined surface, the greater the plasticity difference of the material, and the more easily the machined surface subjected to alternating contact stress was peeled off, making it that much easier to reach the intense friction phase, thereby leading to the shorter the frictional phase time \(\Delta t\).
###### Abstract  
tunneling exploration for rock mass classification is a common practice in tunneling projects. This study proposes a data-driven approach that allows for rock mass classification. Two machine learning (ML) classification models, namely random forest (RF) and extremely randomized tree (ERT), are employed to classify the rock mass conditions encountered in the Pahang-Selangor Raw Water Tunnel in Malaysia using tunnel boring machine (TBM) operating parameters. Due to imbalance of rock classes distribution, an oversampling technique was used to obtain a balanced training dataset for unbiased learning of the ML models. A five-fold cross-validation approach was used to tune the model hyperparameters and validation-set approach was used for the model evaluation. ERT achieved an overall accuracy of 95%, while RF achieved 94% accuracy, in rightly classifying rock mass conditions. The result shows that the proposed approach has the potential to identify and correctly classify ground conditions of a TBM, which allows for early problem detection and on-the-fly support system selection based on the identified ground condition. This study, which is part of an ongoing effort towards developing reliable models that could be incorporated into TBMs, shows the potential of data-driven approaches for on-the-fly classification of ground conditions ahead of a TBM and could allow for the early detection of potential construction problems.  
tunneling machine (TBM); rock classification; Japanese highway classification system (JH System); random forest (RF); extremely randomized trees (ERT)  
1
# Pathological Gait Classification Using Early and Late Fusion of Foot Pressure and Skeleton Data
## 2 Related Works
### Sensor-Based Approaches  
Wearable sensors are widely employed in studying the impact and significance of descriptive statistical factors on osteopenia and sarcopenia using artificial intelligence (AI) [17]. ML techniques have been explored to address natural variations in gaits among different subjects using wearable sensors, with an accuracy rate of 81% [18]. Furthermore, a DL approach based on long short-term memory (LSTM) has been proposed for the automatic detection of initial contact (IC) and toe-off (TO) using foot-marker kinematics [19]. Another DL-based study has been reported on the accuracy of various neural networks in modeling lower body joint angles in the sagittal plane, utilizing kinematic records from a single inertial measurement unit (IMU) attached to the foot [20]. This model demonstrates superior performance compared to ML approaches. Additionally, a paper [21] delves into gait classification based on CNN using interferometric radar.  
In [22], a smart insole equipped with various sensor arrays is discussed for gait classification based on DL. The results encompass seven types of gaits: walking, fast walking, running, stair climbing, stair descending, hill climbing, and hill descending. The method achieves a high classification rate of 90%. In [23], two models are presented, one hardware-based, consisting of multiple sensor modules (MSM), and another software-based, composed of a biomedical and inertial sensor algorithm, along with the leg health classification net (LCNet) model. These models achieve an impressive accuracy of 94.41%. Ref. [24] introduces a DL-based model that conducts a comprehensive examination of ground reaction force (GRF) patterns to detect normal gait and gait abnormalities. Another model, described in [25], employs five classifiers (K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Stochastic Gradient Descent (SGD)) for the electromyography (EMG) dataset. The results demonstrate 99% accuracy using KNN and RF, whereas the DT classifier achieves 97% accuracy. In [26], a model is discussed for classifying numerous anatomical regions and their combinations using a vast and highly unbalanced dataset. Furthermore, ref. [27] presents a model that employs [PERSON]'s additive explanations to select important parameters with the Support VectorMachine (SVM), RF, and multilayer perceptron. The highest accuracy of 95% was achieved using an SVM classifier.  
These sensor-based approaches sometimes achieve high accuracy in gait classification. However, they demand specialized hardware sensors and possess limitations in their general applicability, particularly for gait classification in cases of sarcopenia.
# Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter
## 4 Numerical Simulations
### Fault Parameter Estimation for Linear System
#### 4.1.1 Normal Parameter Estimation  
Figure 4a shows the results of parameter estimation using the standard KF and the AFKF for the estimation of normal parameters with the initial values \(\hat{\theta}_{0}=\left[{\begin{array}{*{20}{c}}{\hat{\varepsilon}_{0}}&{ \hat{\omega}_{n0}}\end{array}}\right]^{T}=\left[{\begin{array}{*{20}{c}}{0.1 }&{9}\end{array}}\right]^{T}\). In the simulation, to estimate the innovation covariance, the window size is \(M=2\) or 6. Both methods estimate the true values of parameters. As shown in Figure 4a, the estimation performance of AFKF depends on the window length. The filter converges to the true value quickly with a short length of the window, while the convergence rate becomes slow with a long length of the window. Figure 4b shows the results of parameter estimation using the proposed AFFKF. The performance of the proposed method is compared with the existing AFKF [8]. As shown in Figure 4b, the proposed method has an advantage in the convergence rate, especially for estimating \(\omega_{n}\). Table 2 shows the comparison of performance indices for four different filters: the standard KF, the AFKF with the window length of 2, the AFKF with the window length of 6, and the proposed AFFKF. From Table 2, the proposed method, AFFKF, shows the best performance in estimating the normal parameters.  
Figure 4: \(Cont.\)
## 2 Materials and Methods
### Clock and IFCB Estimation  
Finally, the overall flow chart for real-time BDS-3 clock and IFCB estimation is illustrated in Figure 1. The whole processing consists of two main procedures, i.e., data preprocessing and parameter estimation. The raw observations are firstly corrected for biases and errors. In the data preprocessing, the phase cycle slips are detected with the MW (Melbourne-Wubbena) and GF (Geometry-Free) combination. Then, with cleaned raw observations, the parameter estimation with various models developed above, including the IF model, the UC model and the GFIF model, are conducted to obtain the real-time clock and IFCB solutions. To reduce the side effect of remaining bad observations after data preprocessing, the post-fit residuals are edited with predefined empirical thresholds. The corresponding observation noises for linear combinations can be mapped from that of raw observations following the law of error propagation. Obviously, linear correlations exist between the satellite and receiver clock/IFCB parameters. To remove the intrinsic rank deficiencies, clock and IFCB datums, such as the constellation zero-mean constraint, should be introduced.  
Figure 1: Flow chart for real-time BDS-3 clock and IFCB estimation.
# Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study
## 2 Related Work
### Machine Learning and Blockchains  
Given all the excitement around deep learning and blockchains over the past few years, it is not surprising that researchers and business developers are exploring both disruptive technologies to understand how AI can help blockchains and vice versa and how both paradigms may converge in the future. One such example is the emergence of AI marketplaces, such as SingularityNET ([[https://singularitynet.io](https://singularitynet.io)]([https://singularitynet.io](https://singularitynet.io))), Effectai (https://effectai), and Cerebrum ([[https://cerebrum.world](https://cerebrum.world)]([https://cerebrum.world](https://cerebrum.world))), where blockchains are used to run AI algorithms in a decentralized manner.  
With the hype around cryptocurrencies and the development of decentralized applications on public blockchains, a straightforward example is the use of machine learning for automated trading of cryptocurrencies [22; 23]. [PERSON] et al. [24] used machine learning to detect Ponzi schemes on the blockchain using various features of smart contracts and user accounts. With their XGBoostclassification model, they were able to estimate more than 400 Ponzi schemes running on Ethereum.  
More related to our work is the anomaly detection research done by [PERSON] et al. [25]. They presented an IoT solution for monitoring interdependent components. It uses online machine learning for anomaly detection optimized for interpretability, combined with the public Ethereum blockchain to guarantee data integrity. [PERSON] et al. [26] focused on intrusion detection systems and collaborative intrusion detection networks. Their motivation for adopting blockchain technology was that the latter can protect the integrity of data storage and ensure process transparency. They reviewed the intersection of blockchains and intrusion-detection systems and identified open challenges. DeepChain by [PERSON] et al. [27] specifically focuses on federated learning and privacy-preserving deep learning. They addressed a threat scenario including malicious participants that may damage the correctness of training, a concern also relevant in our work. DeepChain gives mistrustful parties incentives to participate in privacy-preserving learning and share gradients and update parameters correctly. DeepChain addresses privacy requirements which are beyond the scope of our work. As a consequence of these additional requirements, their solution relies on the availability of trusted execution environments--like the Intel Software Guard Extensions (SGX)enclaves--and expensive cryptographic functions--such as homomorphic encryption--to compute model updates. As a result, their federated learning experiments on a single workstation node using the MNISTdataset demonstrated a rather significant computational complexity, which makes the proposed solution not practical for our use case.
## 4 Data Analyses and Results
### Bivariate Analyses
Additionally, most Polish students felt punishment is needed for harmful acts of virtual relationship violence (81.2%), and most Americans also felt this way (75.3%), but only marginally. While the former relationship was expected, the latter was not. Using a layered analysis, follow-up comparisons among these groups revealed a statistically significant difference among groups (\(\chi^{2}=18.13\), df = 1, \(p<0.001\)). Polish women had the highest rates of believing punishment was warranted (86.0%), followed by American women (81.5%), followed by Polish men (71.4%), followed by American men (59.4%). If the majority of females and Americans reported experiencing socially interactive intimate victimization, and if the majority of males and Poles reported not experiencing such victimization, then the results from attitudes on punishment are interesting. They could indicate that females are pressing for punishment at higher rates than males because they are more likely to have been hurt by socially interactive violence. Alternatively, they might signal that men are more tolerant of covert violence than women in both countries, possibly due to gender role socialization. Findings may also be the result of American men being more likely than Polish males to receive actual punishment for offenses against intimate partners [31], making them fear the possibility of punishment if they ever engaged in such offenses.
## 4 Conclusions, Limitations and Future Research
### Limitations  
Despite the robustness of the findings, several limitations should be acknowledged:  
* _Simplified wind representation._ The simulations assumed a time-invariant and spatially uniform wind field over the lagoon, whereas in reality, wind characteristics are unsteady and may vary locally, influencing surge dynamics in a more complex manner.
* _Neglect of additional hydrological inputs._ The study focused primarily on wind-driven surges, without considering the potential contribution of river inflows, precipitation, or ice cover effects, which may also influence water levels.
* _Static sea level assumption._ The boundary conditions did not incorporate a projected long-term sea level rise due to climate change, which could exacerbate future flood hazards.