esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 4 Proposed Method for Intrusion Detection and Prevention
### Prevention: MITRE ATT&CK  
Utilizing the MITRE ATT&CK framework, we establish a comprehensive database, referred to as the \"Cyber Threat Repository\", cataloging various attacks alongside associated tactics, assigned priorities, and corresponding mitigation strategies. As shown in Figure 7, upon detecting a potential attack, if it matches an entry within the repository, its priority level is promptly identified for subsequent actions. Attacks deemed high-priority trigger an immediate response, where the identified data traffic is swiftly intercepted and blocked by the \"Security Enforcement Gateway\", while simultaneous notifications are dispatched to relevant stakeholders. Conversely, low-priority incidents prompt the redirection of associated data traffic to a designated repository known as the \"Incident Log Database\", facilitating subsequent analysis and prompting alerts to cybersecurity personnel for targeted mitigation measures in accordance with the repository's guidance.  
The suggested model can be implemented by placing it between the firewall and the network router. This allows for it to predict potential attacks within the network after being trained on actual data sourced from the network. The data are collected, prepared, and  
Figure 7: MITRE ATT&CK response process.  
utilized for training the model. The trained model has the capability to analyze the data flowing through the network in real time.
## 5 Multi-Parameter Analysis of Angular and Linear Deviations
### Multi-Parameter Analysis of Angular Deviations
Using for the limit angles of each prism, the notations are  
\[\epsilon_{1I}=arcsin1/n_{1}\text{ and }\epsilon_{2I}=arcsin1/n_{2}, \tag{40}\]  
respectively, and by applying the above procedure for each scanner, one has the following:  
* From Figure 2a, for scanner (a) _ab-ab_, the conditions to have a beam emerging from the scanner are \[\theta_{1}<\epsilon_{1I}\text{ and }\theta_{2}<\epsilon_{2I}-arcsin\frac{ sin[arcsin(n_{1}sin\theta_{1})-\theta_{1}]}{n_{2}},\] (41) from which, for identical prisms and for small angles, \[\theta<n/\left(n-1+\sqrt{n^{2}-1}\right)=\theta_{a}^{lim}.\] (42)
* From Figure 2b, for scanner (b) _ab-ba_, the conditions to have a beam emerging from the scanner are \[\theta_{1}<\epsilon_{1I}\text{ and }n_{2}sin(\theta_{2}-\epsilon_{2I})-\theta_{2}< \theta_{1}-arcsin(n_{1}sin\theta_{1}),\] (43) from which, for identical prisms and for small angles, \[\theta<n/2(n-1)=\theta_{b}^{lim}.\] (44)
* From Figure 2c, for scanner (c) _ba-ba_, the conditions to have a beam emerging from the scanner are \[\theta_{1}-arcsin\frac{sin\theta_{1}}{n_{1}}<\epsilon_{1I}\text{ and }n_{2}sin(\epsilon_{2I}-\theta_{2})> sin\bigg{\{}arcsin\left[\theta_{1}-arcsin\frac{sin\theta_{1}}{n_{1}} \right]-\theta_{2}\bigg{\}},\] (45) from which, for identical prisms and for small angles, the condition (44) is valid in this case, as well, therefore \(\theta_{c}^{lim}=\theta_{b}^{lim}\). However, this is clearly not true for the exact limit angles that refer to the non-linear curves of the maximum angular deviations in Figure 9a. * From Figure 2d, for scanner (d) _ba-ab_, the conditions to have a beam emerging from the scanner are \[\theta_{1}-arcsin\frac{sin\theta_{1}}{n_{1}}<\epsilon_{1I}\text{ and }n_{2}sin(\epsilon_{2I}-\theta_{2})>n_{1}sin\theta_{1}-arcsin\frac{ sin\theta_{1}}{n_{1}},\] (46) from which, for identical prisms and for small angles, Figure 9:0 (47)  
While the stress in this analysis was put on the variation of angular deviations, another approach, entirely focused on the limit angles of Risley prisms, was done in [50]. The parameter the deviations depend upon is the refractive index of the prisms. Therefore, the second part of this study of maximum angular deviations is presented in Figure 10, where the angles \(D_{j}^{max},j=a,\,b,\,c,\,d\) are shown as functions of the angle of the prism \(\theta_{1}=\theta_{2}=\theta\), as well, but for two values of the refractive index of the two prisms (when \(n_{1}=n_{2}\)): for \(n=1.517\) (as in Figure 9) and for \(n^{\prime}=1.7\). One can see that the maximum angular deviation, therefore the FOV, is slightly increased when using Risley prisms with a higher refractive index, although the angle prism limit \(\theta_{d}^{lim}\) that can be chosen is smaller for a higher \(n\). The latter aspect is in good agreement with Equation (47). Values determined graphically for the limit angles, for both considered refractive indexes, are extracted from Figure 10 in Figure 9:1
# Mechanical Fault Prognosis through Spectral Analysis of Vibration Signals
## 5 Conclusions  
Modern industrial equipment has improved to a great extent for production efficiency and labor cost, and are moving towards higher precision. High-precision equipment will bring a relatively high abnormality rate. Through experimental tests, the proposed test platform is convenient for users to view the real-time data and graphs. Furthermore, users can also log into the proposed test platform with \"VNC viewer\" tool software to view real-time monitoring information of the long-running object under test remotely. The test platform is implemented in software, except for the necessary hardware. Therefore, better performance can be obtained by updating the algorithm. By using 5G networks, the raw data of vibration signals are uploaded to the server, which provides a huge amount of data for machine learning and artificial intelligence. The proposed test platform has a wide application value.  
The statistical features of vibration signals, such as peak value, peak-to-peak (p2p) value, root-mean-square (RMS), crest factor skewness, kurtosis, spectral kurtosis, impulse factor, shape factor, and clearance factor, are suggested to determine the health state of machinery. However, how to choose appropriate feature parameters to distinguish a healthy state from a faulty state is a key problem. Furthermore, how to differentiate among faulty states is an even more crucial problem. We will intend to work on a solution to these problem with the help of machine learning and artificial intelligence.  
Conceptualization and methodology, K.W.; software Z.-J.X.; writing--original draft preparation, K.W.; writing--review and editing, Z.-J.X. and K.-L.D.; project administration, Y.G.; funding acquisition, Z.-J.X. and Y.G. All authors have read and agreed to the published version of the manuscript.  
This work was funded in part by the Science and Technology Project Funds of Zhejiang Provincial Water Resources Department under Grant no. RC2162, and in part by the National Natural Science Foundation of China under Grant no. 62071212.  
Not applicable.  
Figure 6: Water pump room of a water supply company with seven large-scale pumps, including gearboxes and water pipes.
## 4 Discussion
Similarly, the comparable fracture resistance demonstrated by the BC sealer in group 6 specimens to the positive control group can be explained by its property to produce hydroxyapatite, which leads to increased chemical bonding of sealer to the canal dental walls [11; 27]. In addition, the presence of small \"nanoparticles\" and their ability to penetrate deeply into isthmuses, accessory canals and canal irregularities also justifies the higher fracture strength of TotalFill sealer [5; 28]. This finding is in accordance with the outcomes of several studies that proposed that BC-based sealers were able to increase the fracture resistance comparable to that of the intact tooth [29; 30]. On the other hand, it was also found that root filling with the lateral condensation technique and Total fill root canal sealer is able to increase the fracture strength of the specimens but is not comparable to the un-instrumented sound tooth. Similar results were noted in the study conducted by [PERSON] and [PERSON] [31]. This finding can be explained by the fact that the finger spreader used for the lateral condensation technique generates stress on the canal wall that may weaken the tooth resulting in less fracture resistance [32]. Spreader design and applied forces are suggested as the contributing factors to the appearance of vertical root fractures during lateral compaction [31]. Moreover, the comparable fracture resistance among all the groups, which were sealed using TotalFill sealer and AH Plus sealer, further suggested that the difference in fracture load may be due to the difference in methodologies used [33]. [PERSON] et al. and [PERSON]. in their studies revealed that difference in fracture strength among different sealer groups was due to variation in the technique opted for obturation [11; 16]. The present in vitro study presented some inherent limitations. The diameter of the root was not standardized in the present study, which has a potential influence on the fracture resistance of the tooth. Moreover, the impact of canal shape cannot be overlooked as more tapered canals results in more dentin removal from the canal resulting in weakening of the specimens. Furthermore, the amount of dentinal tubules present in each specimen also influences the outcomes of root fracture resistance. As the present study was an in vitro experiment, more clinical-based studies should be conducted to validate the findings of the present study for clinical applications.
## 4 Research Methodology
### Identification of FM Key Performance Indicators (KPIs)
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**No**} & \multicolumn{2}{l}{**User Aspect in Healthcare**} & \multirow{2}{*}{**O**} & \multirow{2}{*}{**T**} & \multirow{2}{*}{**S**} & \multirow{2}{*}{**No**} & \multicolumn{2}{l}{**User Aspect in Healthcare**} & \multirow{2}{*}{**O**} & \multirow{2}{*}{**T**} & \multirow{2}{*}{**S**} \\  & \multicolumn{2}{l}{**Facilities**} & & & & & & **Facilities** & & \\ \hline U1 & Indoor air quality & X & X & & U10 & Catering satisfaction & & X & X \\ U2 & Prediction of indoor air parameters & & & & & U11 & Security satisfaction & & X & X \\  & and indoor quality & & & & & & U11 & Security satisfaction & & X & X \\ U3 & Number of patient/worker & & & & & & & & \\  & complaints per year & & & & & & & & \\ U4 & Complaint intensity per floor area & & & & & & & & \\ U5 & Categorical and spatial breakdown & & & & & & & & \\  & of the complaints per area & & & & & & & & \\ U6 & Percentage patient/worker & & & & & & & & \\  & dissatisfied & & & & & & & & \\ U7 & Predictive mean vote (Thermal) & & X & X & U16 & impact on user & & & & \\  & & & & & & & & & productivity & \\ U8 & Predictive percentage dissatisfied & & & & & & & & \\  & (Thermal) & & X & X & U17 & Accessibility for disabled & & & & \\ U9 & Cleaning satisfaction & & X & X & U18 & Adjust treatment index & & X & X \\ \hline \multicolumn{10}{l}{Sources: [4,10,13,15,16,17,24,25,27,29,31,33,39,46,73,76,79,81,86,70,96,100,103,105,106,115,16,117,126,118]} \\ \\ \end{tabular}
\end{table} 
Table 2: FM KPIs for user aspect in healthcare facilities (O: operational, T: tactical, S: strategic). Maintenance and repair activities are important to maintain production or service processes, which are performed under facilities. Identified FM KPIs for maintenance and repair in healthcare facilities are given in Table 4.
# Machine Learning Enabled Performance Prediction Model for Massive-MIMO HetNet System
## 2 System Model
### Interference Analysis  
Using a matched filter beamformer, the transmit vector of the BS of \(m\)-th cell in \(k\)-th tier is as follows  
\[x_{(m)k}=\sum_{j=1}^{R_{k}}\frac{\left[g_{(m)k}^{(nj)k}\right]^{H}}{\|g_{(m)k }^{(mj)k}\|}s^{(mj)k}. \tag{3}\]  
Here, \(s^{(mj)k}\) is the information symbol intended for the \(j\)-th user in same cell. The signal received at the \(j\)-th associated user of the \(n\)-th cell in the \(l\)-th tier is as follows  
\[y^{(nj)l}=\sum_{k=1}^{K}\sum_{m=1}^{L_{k}}P_{k}g_{(m)k}^{(nj)k}x_{(m)k}+z \tag{4}\]  
Here, \(z\) is the additive Gaussian noise, \(z\sim\mathbb{C}\mathbb{N}\big{(}0,\sigma_{z}^{2}\big{)}\).  
\[y^{(nj)l}=\sum_{m=1}^{L_{l}}P_{l}g_{(m)l}^{(nj)l}x_{(m)l}+\sum_{k=1,k\
eq l}^{ K}\sum_{m=1}^{L_{k}}P_{k}g_{(m)k}^{(nj)k}x_{(m)k}+z. \tag{5}\]  
The expression of inter-tier interference is as follows  
\[I_{inter-tier}=\sum_{k=1,k\
eq l}^{K}\sum_{m=1}^{L_{k}}P_{k}g_{(m)k}^{(nj)k}x_{ (m)k}. \tag{6}\]  
Putting Equation (6) in Equation (5),  
\[y^{(nj)l}=P_{l}g_{(n)l}^{(nj)l}x_{(n)l}+\sum_{m=1,m\
eq n}^{L_{l}}P_{l}g_{(m) l}^{(nj)l}x_{(m)l}+I_{inter-tier}+z. \tag{7}\]The expression of inter-cell interference is as follows  
\[I_{inter-cell}=\sum\
olimits_{m=1,m\
eq n}^{L_{l}}P_{l}\mathcal{S}_{(m)l}^{(nj)l} \mathcal{I}_{(m)l}. \tag{8}\]  
Putting Equations (3) and (8) in Equation (7),  
\[y^{(nj)l}=P_{l}\mathcal{S}_{(nj)l}^{(nj)l}\sum\
olimits_{t=1}^{R_{l}}\frac{ \left[\mathcal{S}_{(n)l}^{(nt)l}\right]^{H}}{\|\mathcal{S}_{(n)l}^{(nt)l}\|}+I _{inter-cell}+I_{inter-tier}+z. \tag{9}\]  
The intra-cell interference is as follows  
\[I_{intra-cell}=P_{l}\mathcal{S}_{(nj)l}^{(nj)l}\sum\
olimits_{t=1,t\
eq j}^{ R_{l}}\frac{\left[\mathcal{S}_{(nj)l}^{(nt)l}\right]^{H}}{\|\mathcal{S}_{(n)l}^{(nt)l}\|} \mathcal{S}^{(nj)l} \tag{10}\]  
Hence, the received signal,  
\[y^{(nj)l}=P_{l}\|\mathcal{S}_{(nj)l}^{(nj)l}\|\mathcal{s}^{(nj)l}+I_{intra- cell}+I_{inter-cell}+I_{inter-tier}+z. \tag{11}\]  
The first term of the r.h.s. of Equation (11) is the weighted value of the intended signal. The signal power to interference plus noise power ratio (SINR) experienced at the receiver is given by the following:  
\[SINR=\frac{\left(P_{l}\|\mathcal{S}_{(nj)l}^{(nj)l}\|\mathcal{s}^{(nj)l}\right) ^{2}}{I_{total}^{2}+\sigma_{z}^{2}}. \tag{12}\]  
Here, \(I_{total}=I_{intra-cell}+I_{inter-cell}+I_{inter-tier}\).
## 4 Parametric Study
### Effect of Acceleration Amplification Factor \(f_{\rm a}\)  
Figure 8 shows the effect of the acceleration amplification factor \(f_{\rm a}\) on the dynamic earth pressure coefficients \(K_{\rm a}\), the failure wedge angles \(\alpha_{\rm a}\), and the normalized dynamic earth pressure distribution of the rigid retaining wall filled with cohesionless backfills (\(c/\gamma H=0\)) and cohesion backfills (\(c/\gamma H=0.05\)), with \(\varphi=30^{\circ}\), \(\delta=\varphi/2\), \(\theta=20^{\circ}\), \(i=8^{\circ}\), and \(k_{\rm v}=0\).  
It can be observed in Figure 8a that the dynamic active earth pressure coefficient \(K_{\rm a}\) increased with the increase in the acceleration amplification factor \(f_{\rm a}\). The larger the horizontal acceleration coefficients \(k_{\rm h}\), the stronger the effect of the amplification factor \(f_{\rm a}\) on \(K_{\rm a}\) was. When the \(c/\gamma H\) of a backfill increases from 0 to 0.05, \(K_{\rm a}\) decreases significantly overall. For example, \(K_{\rm a}\) decreases from 0.541 to 0.424 when \(k_{\rm h}=0\), which is a decrease of 21.6%. The differences in the active earth pressure coefficient with different \(f_{\rm a}\) values tend to increase as \(k_{\rm h}\) increases, as shown in Figure 8b.  
In Figure 8c, the failure wedge angle \(\alpha_{\rm a}\) in the active condition is decreased as \(f_{\rm a}\) changes from 1.0 to 1.8. The failure wedge angles increase in active conditions when the \(c/\gamma H\) of a backfill increases from 0 to 0.05. The differences in the failure wedge angle with different \(f_{\rm a}\) values tend to increase as \(k_{\rm h}\) increases.  
Figure 8d shows the typical normalized dynamic active earth pressure distribution for different values of \(f_{\rm a}\) with \(k_{\rm h}=0.2\) and \(k_{\rm v}=0\). The critical tension depth \(z_{\rm c}\) is defined as the distance from the acting point of zero active earth pressure to the top of the wall. In the case where \(c/\gamma H=0\), the critical tension depth is 0. The dynamic active earth pressure at the bottom of the wall increases from 0.739 to 0.899 as \(f_{\rm a}\) increases from 1.0 to 1.8, corresponding to an approximate 21.7% increase. When \(c/\gamma H\) increases from 0 to 0.05,the critical tension depth increases significantly. For \(c/\gamma H=0.05\), the normalized critical depth \(z_{\rm c}/H\) shows a slight increase with rising \(f_{\rm a}\), with an average value of 0.117.
# Experimental Evaluation of an IoT-Based Platform for Maritime Transport Services
###### Abstract  
In recent years, the adoption of innovative technologies in maritime transport and logistics systems has become a key aspect towards their development and growth, especially due to the complex and heterogeneous nature of the maritime environment. On the other hand, Internet of Things (IoT) solutions are gaining importance in the shipping industry thanks to the huge number of distributed cameras and sensors in modern ships, cargoes and sea ports, which can be exploited to improve safety, costs and productivity. This paper presents an experimental evaluation of a maritime platform, which enables a wide range of 5G-based services in the context of logistics and maritime transportation. Its core is a Narrow Band (NB)-IoT framework used to run massive IoT services on top of a hybrid terrestrial-satellite network and feed a OneM2M platform with significant data on maritime transport to develop high-level and value-added logistic applications on top. Among the many different services that could be provided by the maritime platform, we focus on the cargo-ship container tracking use case through the Global Tracking System, which allows for continuous container monitoring all over the seas in a port-to-port service scenario. The results of the experimental tests illustrate the capacity of the platform in managing the high number of messages transmitted by the container tracking devices (i.e., more than 3000) and its efficiency in limiting the average maximum latency and packet loss below 5.5 s and 0.9%, respectively.  
IoT; 5G; satellite networks; maritime transportation +
Footnote †: journal:  
0023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results
### Estimation of the Genus  
The RF of the genus was calculated for control and treatment samples and is displayed (Figures 3B and 4B). The genus includes _Staphylococcus_, _Escherichia-Shigella_, _Cutibacterium_, _Lactobacillus_, _Splingomonas_, _Enhydrobacter_, _Microococcus_, _Corynebacterium_, _Bacillus_, _Gemmatta_, _Stentrophomonas_, _Kocuria_, and _Streptococcus_ were detected in the control group (Figure 3B). The genus such as _Orthochatrum_, _Microoccus_, _Enhydrobacterium_, _Cutibacterium_, _Xanthomonas_, _Corynebacterium_, _Staphylococcus_, _Mycobacterium_, _Splingomonas_, _Rosemonas_, _Paracoccus_, _Finegoldia_, _Recuria_, _Microbacterium_, _Chrysebacterium_, _Haenophilus_, _Acinetobacter_, _Allordinizobium_, _Pla1_lineage_, _Streptomyces_, _JG30-KF-CM45_, _Pseudomonas_, _Neochlamydia_, _WD2101_, _Treponema_, _Stentrophomonas_, _Pseudogracilibacillus_, _Bacillus_, _Ruminiclostridium_, _WPS-2_, _67-14_, _Lysinibacillus_, _Paenibacillus_, _Aquisphaera_, _Bryobacter_, _IMCC26256_, _Nitrospira_, _Mesorhizobium_, _TK10_, _Legionella_, _SA-28_, _SC-1-84_, and _Anaeromyobacter_ genus was detected in the treatment group (Figure 4B).  
Non-parametric statistical analysis was performed to predict the changes in the detected genus in the control and treatment samples. The Wilcoxon matched-paired signed-ranks test was implemented with the statistical significance of \(p\leq 0.05\). The statistical outcomes indicated no significant differences between the Cpre and Cpost samples. _Cutibacterium_ (\(p\)-value = 0.0431), _Corynebacterium_ (\(p\)-value = 0.0431), and _Acinetobacter_ (\(p\)-value = 0.0431) showed significant changes in the Tpost samples compared to the Tpre samples (Table S3).  
Streptomyces, JG30-KF-CM45, Neochlamydia, WD2101, Treponema, Pseudogracilibacillus, Ruminiclostridium, 67-14, Lysinibacillus, Aquisphaera, Bryobacter, IMCC26256, Nitrospira, Mesorhizobium, TK10, Legionella, _SA-28_, _SC-1-84_, and _Anaeromyxobacter_ were detected only in the Tpost samples (Figure 4B and Table S4).
## 2 Related Work
### Research on Speech Emotion Recognition  
Recent research has focused on improving SER using deep learning models. Particularly, a log mel-spectrogram and a mel-frequency cepstral coefficient (MFCC) are the most frequently used techniques in the field of emotion recognition based on voice pitch, loudness, and prosodic information. A log mel-spectrogram is used to analyze speech containing frequency characteristics that vary depending on time [7]. For instance, [PERSON] et al. [8] demonstrated increased accuracy in a SER model through the combination of a log mel-spectrogram and a transfer learning technique, while [PERSON] et al. [9] exhibited enhanced performance by employing a log frequency spectrogram with a convolutional neural network (CNN). The MFCC method captures distinctive sound features [7]. For example, [PERSON] et al. [10] introduced the RoutingConvNet model based on bidirectional MFCC, which offers a lightweight design despite showing only marginal performance gains in SER. In addition, [PERSON] et al. [11] reported that a CNN-based SER model applying a log mel-spectrogram exhibits improved performance compared to one applying an MFCC.