esa-sceva/satcom-chunk-collection huggingface dataset:
# Zirconium-Containing Metal-Organic Frameworks (MOFs) as Catalysts for Biomass Conversion
## 8 Conclusions  
In this work, we have reviewed illustrative examples that demonstrate the great potential of Zr\({}^{4+}\) MOFs UiO-66 and MOF-808 as heterogeneous catalysts for the conversion of biomass-derived compounds into valuable chemicals. Despite the many similarities existing between these two Zr-MOFs, such as the presence of Zr\({}_{6}\)O\({}_{4}\)(OH)\({}_{4}\) oxoclusters, their chemical properties are very different in terms of Bronsted/Lewis characteristics, and they depend upon (i) the number of missing linker point defects (in UiO-66) and (ii) the hydration state of the solid at the beginning of the reaction. By adjusting these two important parameters, we have shown that it is possible to shift easily from a Bronsted acid to a Lewis acid, which adds a new dimension in the design and engineering of MOFs for catalytic applications. Thus, depending on the reaction that we want to address and the nature of the acid sites required to catalyze it, it is preferable to use one or the other catalyst.  
In this review, we have limited ourselves to consider only example reactions in which UiO-66 and MOF-808 compounds are used for the conversion of biomass compounds. However, it is worth recalling that these materials have been successfully used as heterogeneous catalysts for many other reactions, including the synthesis of fine chemicals [11]. Moreover, the reported examples are not just limited to Zr-containing compounds, since these materials can be readily obtained with other tetravalent ions such as Hf, Ce, or Ti [16; 55; 86; 87]. This provides additional tools for finely tuning the catalytic properties of the solid, such as the hardness/softness of the Lewis acid sites, Bronsted acid strength, or their redox properties. To this, it has to be added the possibility to modify the organic linkers to include additional functional groups that can participate directly in the catalytic reaction or they can modify the acid strength of the metal ions through electronic effects [52]. Altogether, the possibilities to change the chemical composition and properties are almost endless, which convert these MOFs into excellent platforms for evaluating structure-composition-activity relationships.  
Funding: Financial support from the Spanish Ministry of Science and Innovation (PID2020-112590 GB-C21, PID2023-146114 NB-C21, and CEX2021-001230-S grants funded by MCIN/AEI/10.13039/501100011033) is gratefully acknowledged. This study forms part of the Advanced Materials Program and was supported by MICIN with funding from European Union NextGeneration (PRTR-C17.11) and by the Generalitat Valenciana (MFA/2022/003).  
The authors declare no conflicts of interest.
# SDNPS: A Load-Balanced Topic-Based Publish/Subscribe System in Software-Defined Networking
## 1 Introduction
Finally, we calculate minimum overlay networks per topic and extract multicast routing paths in light of the well-known shortest path algorithm. Our design makes a better tradeoff between global load balancing of links and the minimum cost forwarding for per-topic events. The contributions of this paper are summed up in the following. * In SDNPS, we organize all of the topics into a topic tree in terms of their natural language semantics. On account of the subscription coverage relationship among topics at different levels of the topic tree, we present a topic-connected overlay constructing algorithm, which solves the specified multicasting problem among publishers and subscribers. This algorithm achieves minimum forwarding costs for every topic, as well as load balancing for the whole network. By means of incrementally generating and storing overlays, the time complexity and the space complexity of this algorithm are both significantly reduced. * SDNPS can filter and forward events directly on SDN-configurable switches with the aid of dexterously mapping topics to binary identifiers and embedding these identifiers into packet headers as matching fields. This helps to reduce end-to-end latency. We devise traffic engineering in SDNPS, which performs routing computation based on traffic prediction. It also dynamically adjusts routing in the presence of physical topology changing induced by traffic bursting and link/switch failures, as well as subscription topology changing brought by new (un)subscription events. The remainder of this paper is structured as follows. In Section 2, the related work is reviewed and discussed. Section 3 presents the system architecture; other corresponding aspects, including topic management, topology management and strategy management, are also described. Section 4 presents the problem statement and model for the minimal cost topic-connected overlay problem (in short MCTCO), then a heuristic algorithm for this problem is described in detail. Section 5 exhibits the performance evaluation, and conclusion remarks are given in Section 6.
## 2 Methods  
### Data Acquisition  
In this study, a mechanism model is employed to simulate the degradation processes of the PEM fuel cell with the DEA mode and anode recirculation mode [21]. The quasi-two-dimensional transient model fully considers the transport process of the electrode and channels, including gas and liquid transport, water phase change, heat transfer, nitrogen crossover and electrochemical reactions. Evolutions of performance change simulated by the model are utilized as the data. The parameters of the simulated PEM fuel cell are listed in Table 1. The governing equations of the employed model are briefly introduced as follows, and more details of transport coefficient and source term could found in Ref. [21]. One-dimensional mass transport along the channel is calculated. For the DEA mode, the liquid water and nitrogen accumulation in the anode channel must be involved. Nitrogen will be accumulated at the dead end of the anode channel firstly, and meanwhile, the nitrogen concentration gradient along the channel is also increasing over time. Although convection is generally dominated for gas species transport in channels, the species diffusion in the dead-ended channel still cannot be ignored. Gas species concentration (hydrogen, oxygen, water vapor and nitrogen) and liquid fraction in channels are calculated:  
\[\left(u_{\text{in}}^{k-1,[n]}-u_{\text{out}}^{k,[n]}\right)A_{\text{in}}\epsilon _{\text{g}}=\sum_{i}\phi_{i,\text{CH}}^{[n]}A_{\text{in}}+\left(\sum_{i}\phi_{ i,\text{CH-GDL}}^{[n]}+S_{i,\text{CH}}\right)\frac{A_{\text{act}}}{N} \tag{1}\]  
\[\frac{\left(c_{i}^{k,[n]}-c_{i}^{k-1,[n]}\right)A_{\text{in}}L/N}{\text{d}t}= \left(j_{\text{i,in}}^{[n]}-j_{\text{i,out}}^{[n]}+\phi_{i,\text{CH}}^{[n]} \right)A_{\text{in}}-\left(\phi_{i,\text{CH-GDL}}^{[n]}+S_{i,\text{CH}}\right) A_{\text{act}}/N \tag{2}\]  
\[\frac{\rho_{\text{lq}}}{M_{\text{w}}}\frac{\left(s_{\text{CH}}^{k,[n]}-s_{ \text{CH}}^{k-1,[n]}\right)d}{\text{d}t}=j_{\text{lq,in}}^{[n]}-j_{\text{lq, out}}^{[n]}-\phi_{\text{lq,CH-GDL}}^{[n]}-S_{\text{lq,CH}} \tag{3}\]  
where \(u\) (m\(\cdot\)s\({}^{-1}\)) is the velocity; \(A\) (m\({}^{2}\)) is the area; \(c\) (mol\(\cdot\)m\({}^{-3}\)) is the molar concentration; \(\phi\) (mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)) is the diffusion flux; \(S\) (mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)) is the source term; \(N\) is the number
## 3 VLC System Model
At the receiver side, the light wave is detected through photodiodes, which are then demodulated, and the original binary data is recovered.
# Weighted \(z\)-Distance-Based Clustering and Its Application to Time-Series Data
## 4 Experimental Results
### Comparisons with Other Methods  
Recently, evolutionary algorithms, e.g., simulated annealing and differential evolution, have been proposed to perform clustering [71]. The evolutionary algorithms can perform clustering using either a fixed or variable number of clusters and find the clustering that is optimal with respect to a certain validity index. Either a population of solutions or only one solution can be used. The single-solution-based evolutionary algorithms have smaller evaluation count but their solution quality is usually not as good as those that are population-based.  
[PERSON] and [PERSON] [72] propose a heuristic for data-clustering problems, abbreviated as FCM. It comprises two parts, a greedy algorithm and a single-solution-based heuristic. The first part selects the data points that can act as the centroids of well-separated clusters. The second part performs clustering with the objective of optimizing a cluster validity index. The proposed heuristic consists of five main components: (1) genes; (2) fitness of genes; (3) selection; (4) mutation operation; and (5) diversification. The objective functions used in the proposed heuristic are the Calinski-Harabasz index and Dunn index. [PERSON] et al. [73] propose a clustering algorithm, called ICFSKM, for clustering large dynamic data in industrial IoT. Two cluster operations, cluster creating and cluster merging, are defined to integrate the current pattern into the previous one for the final clustering result. Also, k-mediods is used for modifying the clustering centers according to the new arriving objects. Table 12 shows a DI comparison between SCC-I and HDC, and Table 13 shows a NMI comparison between SCC-I and ICFSKM, for some datasets. In these tables, the datasets Balance scale, Banknote authentication, Landsat satellite, Pen-based digits, Waveform-5000, and Wine are also selected from the UCI repository [61]. The results for HDC and ICFSKM are copied directly from [65; 66]. We can see that SCC-I is comparable to HDC and ICFSKM. Note that HDC and ICFSKM are goal-oriented. For example, the objective functions used in HDC include the Dunn index. Therefore, DI values can be optimized by HDC intentionally. Furthermore, the objective function is usually computationally intensive and the evolutionary algorithms are considered to be slow.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Robustness Against Common Attack Vectors
#### 5.3.6 Known-Plaintext Attacks  
If an attacker somehow knows some of the plaintext and tries to use this to break the encryption, the AES-256 and PQC KEMs are designed to be resistant to known-plaintext attacks.
## 3 GNC System with LOS-Based Model  
### AUV Guidance System  
The yawing motion control uses an LOS vector from the AUV position to the next waypoint, similar to [16]. This study focuses on a 2D covering algorithm, so the motion control system holds a constant depth for the heave motion in the LOS-based, path-following algorithm. The LOS-based guidance control determines the necessary course angle \(\psi_{\rm d}\) using the velocity-path relative angle \(\chi_{\rm r}\) and the path-tangential angle \(\chi_{\rm p}\), where the lookahead-based steering is defined by  
\[\psi_{\rm d}=\chi_{\rm p}+\chi_{\rm r}-\beta, \tag{13}\]  
where the sideslip angle (drift) \(\beta\) is neglected to simplify the steering law [16]. The path-tangential angle \(\chi_{\rm p}\) and the velocity-path relative angle \(\chi_{\rm r}\) are defined as  
\[\chi_{\rm p}=\text{atan2}(E_{k+1}-E_{k},N_{k+1}-N_{k}), \tag{14}\]  
\[\chi_{\rm r}(e)=\arctan(-K_{\rm p}e-K_{\rm I}\int_{0}^{t}e(\tau)d\tau), \tag{15}\]  
where \((N_{k},E_{k})\) and \((N_{k+1},E_{k+1})\) are the positions of the reached and following waypoint, respectively, \(K_{\rm P}>0\) represents the proportional gain, and \(K_{\rm I}>0\) the integral gain. Finally, the cross-track error (normal to path) \(e(t)\) is defined as  
\[e(t)=-[N_{\rm AUV}(t)-N_{k}]\sin(\chi_{\rm p})+[E_{\rm AUV}(t)-E_{k}]\cos( \chi_{\rm p}). \tag{16}\]  
The switching mechanism of the guidance system is defined as a combination of a sphere of acceptance [37] and reaches a predefined minimum yaw angle error. This cascade switching mechanism permits a continuous path operation to properly occur in all the waypoints at the path-following algorithm. The sphere of acceptance is defined as  
\[[N_{k+1}-N_{\rm AUV}(t)]^{2}+[E_{k+1}-E_{\rm AUV}(t)]^{2}+[D_{k+1}-D_{\rm AUV }(t)]^{2}\leq R_{k+1}^{2}. \tag{17}\]  
where if the time AUV position \((N_{\rm AUV}(t),E_{\rm AUV}(t),D_{\rm AUV}(t))\) satisfies Equation (17), the course angle \(\psi_{\rm d}\) needs to be selected. Then, the next waypoint \((N_{k+1},E_{k+1},D_{k+1})\) is chosen as a lookahead point whether the AUV's yaw angle error is less than a predefined minimum angle.  
Figure 6: Model-validation using parameter estimation for the Girona500: (**a**) Heave motion (thruster setpoints equal to \(-0.75\)), (**b**) Heave motion (thruster setpoints equal to \(-1.00\)).
## 7 Discussion  
The results of this study shed light on the effectiveness of the proposed learning-based energy-efficient device grouping mechanism for massive machine-type communications (mMTCs) in beyond 5G (B5G) networks. By leveraging reinforcement learning techniques, the algorithm optimizes device grouping decisions and intra-group access scheduling, aiming to reduce miss rates, energy consumption, and access delays in contention-based random access channel (RACH) frameworks.  
Our analysis revealed that the RL-based model successfully improved energy efficiency, reduced access delays, and enhanced the success rate of mMTC devices within B5G networks. Considering key factors such as application arrival times, time constraints, and device heterogeneity, the algorithm provided a comprehensive solution for optimizing device grouping in large-scale MTC deployments, mirroring a real-world scenario. Comparisons with existing strategies demonstrated the algorithm's superiority in addressing key challenges in mMTC environments.  
However, it is essential to acknowledge the computational complexity associated with the reinforcement learning model, which may pose implementation challenges in highly dynamic network settings. Additionally, the algorithm's generalizability across diverse network configurations and communication scenarios requires further investigation to ensure robust performance in varied environments. Future research efforts should mitigate these limitations and enhance the algorithm's adaptability to evolving network conditions.
## 2 Methodology
### Modeling Method of the Communication Effect  
While communicating in the ducting layer, electromagnetic waves can be transmitted to a distance that is beyond line-of-sight [8; 10]. However, the spatial and temporal distribution of meteorological parameters is uneven, resulting in a variation of communication effects under this mechanism [29]. Therefore, the Parabolic Equation Toolbox (PETOOL) [30] with parabolic equation (PE) method [31] was used to simulate the propagation loss under microwave channels.  
For a microwave communication system, the path loss budget \(A\) can be expressed as:  
\[A=P_{t}+G_{t}+G_{r}-S-10\log 10(B) \tag{7}\]  
where \(P_{t}\) is the transmitting power; \(G_{t}\) and \(G_{r}\) are transmitting and receiving antenna gain, respectively; \(S\) is the noise level of the receiver; and \(B\) is the receiver bandwidth in Hz. The typical values of \(P_{t}\), \(G_{t}\), \(G_{r}\), and \(S\) in a communication system were selected as 40 dBm/10 W, 30 dBi, 30 dBi [32], and -150 dBm (1 Hz) [33], respectively. Thus, the path loss budget \(A\) could be set as 250 dB at 1 Hz, or 160 dB at 1 GHz, for the receiver to demodulate the signal.
# Patients' Perceptions of Sound and Noise Dimensions in the Dental Clinic Soundscape
## 2 Methodology
### Statistical Analysis  
Descriptive statistics were employed to summarize demographic information and participant responses. Correlation analysis was conducted to explore relationships between sound preferences, anxiety levels, and demographic variables. Data collected from the survey were analyzed with the statistical package IBM SPSS v.28. [PERSON]'s alpha index was used to examine the reliability of the questionnaire. Absolute and relative frequencies were calculated to summarize demographic characteristics of the study sample. Quantitative variables were summarized with descriptive statistics (M, SD). Dummy variables were created for categorical variables to calculate Spearman correlation coefficients between demographics, music preferences, health status, noise disturbance from machines, patients, and staff and feelings of anxiety and nervousness because of the mobility in the clinic, people, and machines. Multiple linear regression analyses were used to detect significant predictors of health status and perceived noise disturbance from machines as well as noise disturbance from patients and staff [39]. Multiple linear regression was used in cross-sectional data, and all categorical variables were dummy coded. Also, the assumptions were met, considering that predictor variables were all dummy coded. More specifically, multicollinearity was assessed with VIF index, multivariate outliers were excluded based on the Mahalanobis distance, normality of residuals was assessed by producing the histogram of the regression standardized residuals, and homoscedasticity was assessed by the scatterplot of standardized predicted values with standardized residuals.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Questionnaire Parts** & **Questions** & **Number of Questions** & **Scales** \\ \hline Part I: Demographic information & Gender, age, educational level, clinic of attendance, duration of treatment & 5 & Nominal, multiple choice \\ \hline Part II: Acoustic health & Diagnosis of hearing loss, sensitivity to noise & 2 & Multiple-choice data \\ \hline Part III: Discomfort in dental setting & Irritation by crowded environment, irritation by noise generated by suction devices, handpieces, ultrasonic scaler data & 2, including 6 subquestions in total & 5-point Likert scale, multiple choice \\ \hline Part IV: Patient’s preferences & Preferences regarding audiovisual content and music in the waiting room, people’s behavior in the office data & 2, including 2 subquestions in total & Multiple choice \\ \hline Part V: Hobbies and habits & Hobbies, music devices, listening habits & 8, including 1 & Nominal, multiple choice \\ \hline Part VI: General health & General health, interest in the effect of sound on health & 2 & Multiple choice \\ \hline Part VII: Overall dental experience & Enhancement of dental experience, concerns regarding noise within the dental practice & 2 & Open-ended \\ \hline \end{tabular}
\end{table}
Table 1: Summary of the questions in the self-administated questionnaire.
# Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET
###### Abstract  
Shadows in drone images commonly appear in various shapes, sizes, and brightness levels, as the images capture a wide view of scenery under many conditions, such as varied flying height and weather. This property of drone images leads to a major problem when it comes to detecting shadow and causes the presence of noise in the predicted shadow mask. The purpose of this study is to improve shadow detection results by implementing post-processing methods related to automatic thresholding and binary mask refinement. The aim is to discuss how the selected automatic thresholding and two methods of binary mask refinement perform to increase the efficiency and accuracy of shadow detection. The selected automatic thresholding method is [PERSON]'s thresholding, and methods for binary mask refinement are morphological operation and dense CRF. The study shows that the proposed methods achieve an acceptable accuracy of 96.43%.  
shadow detection; deep learning; U-Net; aerial images; automatic thresholding; binary mask refinement; post-processing +
Footnote †: journal: _Future Internet_
# Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications
## 3 How EIT Works
### Inverse Problem  
In EIT, the approximation of the conductivity distribution from the calculated data is called the inverse problem. The inverse problem is an ill-conditioned problem, which means that a slight variation in the measured data causes a huge impact on image reconstruction. As a result, in terms of the existence of noise, the inverse problem is highly unstable in nature. In addition, for the isotropic conductivities, the solution must be unique and the information for boundary condition is required. Due to the anisotropic nature of human tissues, the implementation of the many electrodes create practical complications and, for this reason, it is not viable to reconstruct the conductivities exactly. Therefore, a stable reconstruction approach is required that can obtain an approximate solution. In EIT, the inverse problem is an ill-posed problem but through regularization by introducing prior information, and it is possible to transform it into a well-posed problem.  
Methods for Solving the Inverse Problem  
There are many approaches to solve the inverse problem with many of these techniques which are adding a priori information through a method known as regularization. The image reconstruction algorithms are divided into two categories deterministic and stochastic. The stochastic method depends upon probabilistic statistical reasoning whereas; the deterministic method is further be categorized into non-linear and linear techniques.
## 2 Forward Dynamics and Human Exoskeleton Interaction Mechanism Interpretation of The Knee Joint
### [PERSON]'s Muscle Model Based Forward Dynamics of the Knee Joint
#### 2.1.3 Joint Motion Model  
Based on the muscle contraction dynamics and the musculoskeletal geometry, the joint motion model can be described as:  
\[\bar{\theta}I_{e}=\tau-\tau_{ex} \tag{7}\]  
where \(I_{e}\) is the moment of inertia of the knee joint, \(\tau_{ex}\) is a combination of the external torque and the calf gravity torque, the maximum value of which is denoted as \(\tau_{ex\_max}\). Their relation is as follows:  
\[\tau_{ex}=\tau_{ex\_max}sin\theta \tag{8}\]Given that in practical control processes both the angular velocity and acceleration are calculated in the discrete way through numerical methods, they are defined as follows when the sampling time is set as \(T_{s}\):  
\[\left\{\begin{array}{c}\dot{\partial}_{k}=\frac{1}{T_{s}}(\partial_{k+1}- \partial_{k})\\ \dot{\partial}_{k}=\frac{1}{T_{s}}\big{(}\dot{\partial}_{k+1}-\dot{\partial}_ {k}\big{)}\end{array}\right.\]  
Consequently, when combining (6), (7) and (8), the joint motion model can be expressed as follows:  
\[\frac{1}{T_{s}}\big{(}\dot{\partial}_{k+1}-\dot{\partial}_{k}\big{)}=\ddot{ \partial}_{k}=\frac{1}{I_{e}}(\tau-\tau_{ex})=\frac{1}{I_{e}}\big{[}F^{mt}(t )b_{1}-\tau_{ex\_max}sin\theta_{k}\big{]} \tag{9}\]  
Substituting the variables in (1)-(8) into (9), the forward dynamics model is acquired:  
\[\left\{\begin{array}{c}\dot{\theta}_{k+1}=\big{(}g_{1}\theta_{k}^{2}+g_{2} \theta_{k}+g_{3}\big{)}\mu(k)+g_{4}e^{8g_{5}\theta_{k}}-g_{6}sin\theta_{k}+ \dot{\theta}_{k}\\ \theta_{k+1}=\theta_{k}+\dot{\theta}_{k}T_{s}\end{array}\right. \tag{10}\]  
where the corresponding parameters are listed as follows:  
\[\left\{\begin{array}{c}g_{1}=\frac{T_{s}\tau_{e}^{m}b_{1}^{3}\eta_{2}}{I_{e }^{m}\cos\phi}\\ g_{2}=\frac{2T_{s}\tau_{e}^{m}b_{1}^{3}\dot{\theta}_{2}(0\to f)}{I_{e}^{m} \cos\phi}+\frac{T_{s}\tau_{e}^{m}b_{1}^{2}q_{1}}{I_{e}^{m}}\\ g_{3}=\frac{T_{s}}{I_{e}}F_{0}^{m}\cos\phi b_{1}\big{[}\frac{q_{2}}{I_{e}^{m} (0\to f)}^{2}\big{]}^{2}+\frac{q_{1}b_{1}}{\cos\phi}+q_{0}\Big{]}\\ g_{4}=\frac{T_{s}}{I_{e}}F_{0}^{m}\cos\phi b_{1}e^{\frac{100b_{1}-f}{0}\cos \phi}-15\\ g_{5}=\frac{10b_{1}}{\cos\phi}\\ g_{6}=\frac{T_{s}}{I_{e}}\tau_{ex\_max}\end{array}\right.\]  
Considering that all the variables on the right side of the above equations are constants, \(g_{i},~{}i=1,2,\ldots,6\), can also be treated as constants.