esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Discussions
### Specific Results  
According to our general planning, we start with identifying the KPI. For this specific problem, the corresponding alert is missing, but the \"Remote Collector Down\" KPI has a typical behavior shown in Figure 6. It started to oscillate, going up and down rather frequently, indicating a problem. The same behavior of the same KPI has been detected for a series of customers with totally independent cloud environments. We apply min-max smoothing with a 6 h time interval before applying more intelligent solutions. Figure 7 confirms the oscillating behavior of the KPI after the smoothing. It reduces the number of metric values from 7530 to 208.  
We separately analyze connections between the KPI and other indicative metrics for CaSA, collector, and Cloud Proxy. Since our research focuses on interpretable ML strategies, we apply the classification rule-induction system RIPPER (see the related literature [24,25]), which reveals rules containing the names of important metrics with some thresholds combined in conditions. The labeling of datasets is performed via the values of KPI (labels = 0, 1), where label = 1 indicates the \"down\" status of the cloud proxy. We detected 44 down conditions from 208 available data points.  
Applying RIPPER to the CaSA dataset exposed the following rule (Rule 1):  
\[\text{(CaSA API Calls Total Requests }\leq 0.1)\text{ and}\] \[\text{(CaSA Garbage Collector Aggregated Coll. Time }\geq 0.03)\to\text{ KPI }=\text{Down }(23/1).\]  
The fraction at the end of the rule characterizes the importance. The number 23 shows how often the rule has been fired, and the denominator indicates the number of misclassifications. The coverage of the rule is  
\[22/44\times 100\%=0.5\%\]  
and the accuracy is  
\[22/23\times 100\%=96\%\]  
Figure 6: The oscillating behavior of the “Remote Collector Down” KPI.  
Figure 7: “Remote Collector Down” KPI metric after the min–max smoothing.  
Applying RIPPER to the collector dataset detects the second rule (Rule 2):  
(Collector To Controller Lookup Resource Elapsed Time Summary \(\geq\) 0.002)  
\(\rightarrow\) KPI \(=\) Down (42/5)  
This rule has 84% coverage and 88% confidence.  
Applying RIPPER to the Cloud Proxy dataset returns the third rule (Rule 3):  
\[(\text{Net TCP Listen}\ =\ 0)\ \rightarrow\text{KPI}\ =\ \text{Down}\ (47/9)\]  
which has 86% coverage and 80% accuracy.  
These rules and the KPI's specific behavior can be stored as the knowledge that can be used to identify and quickly resolve similar issues. Aria Ops engineers have verified Rules 1-3, which, in combination, indicate firewall problems.
# Wireless Accelerometer Architecture for Bridge SHM: From Sensor Design to System Deployment
## 7 Conclusions  
This study demonstrates the development and validation of an MQTT-enabled, solar-powered wireless accelerometer network tailored for the SHM of bridges. By addressing key challenges such as power autonomy, data synchronization, and sensor accuracy, the system offers a robust and sustainable alternative to traditional wired solutions. Laboratory tests highlighted the energy efficiency and precision of the proposed sensor nodes, while field deployment on an actual bridge confirmed their practical applicability and reliability.  
The integration of advanced MEMS accelerometers, GNSS synchronization, and energy-efficient data transmission protocols underpins the system's ability to deliver accurate and real-time structural insights. The successful field validation illustrates its potential to revolutionize SHM, offering a scalable, cost-effective, and environmentally friendly solution for long-term infrastructure monitoring. Furthermore, the deployment successfully demonstrated the application of the SSICOV algorithm to extract natural frequencies, damping ratios, and mode shapes of the monitored structure. This validation underscores the system's capability to not only gather high-quality data but also support advanced analytical techniques for in-depth structural assessments. This approach is particularly suited for bridges in high-traffic areas, where minimizing downtime during installation is critical. The successful installation not only highlights the benefits of wireless accelerometers but also sets a precedent for future applications in civil infrastructure monitoring.  
Conceptualization, F.M.B., A.P. and L.R.; methodology, F.M.B., A.P. and F.C.-D.; software, F.M.B.; validation, L.R., A.P., L.B. and F.C.-D.; formal analysis, S.C., L.B. and M.B.; investigation, A.P. and F.M.B.; resources, L.R., L.B., S.C. and M.B.; data curation, A.P. and L.R.;writing--original draft preparation, F.M.B. and A.P.; writing--review and editing, F.M.B. and A.P.; visualization, L.R. and L.B.; supervision, M.B., S.C. and F.C.-D.; project administration, L.R., F.M.B. and L.B.; funding acquisition, L.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Data are unavailable due to privacy restrictions.  
**Conflicts of Interest:** [PERSON], [PERSON], [PERSON], and [PERSON] was employed by the company displaid s.r.l. societa benefit. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflicts of interest.
## 2 System Model of NR-ODBR-DCSK  
The main limitation of the DCSK system is the use of half of the bit duration for transmitting reference samples that do not carry information, resulting in lower data rates and reduced energy efficiency. Therefore, the development of new and improved DCSK communication systems is essential to provide higher data rates and lower BER. To address this challenge, this paper improves upon the NR-DCSK system and focuses on an NR-ODBR-DCSK system that aims to improve the energy efficiency and transmission rate of NR-DCSK. This section details the chaotic signal generator and several important elements of this system, including the signal frame structure, as well as the modulation and demodulation processes of the NR-ODBR-DCSK system.
# Failure Prediction of the Rotating Machinery Based on CEEMDAN-ApEn Feature and AR-UKF Model
## 2 Methodology
### Fault Feature Extraction Stage
#### 2.2.3 Approximate Entropy  
[PERSON] et al. proposed the concept of the approximate entropy (ApEn) to quantitatively describe the complexity of the signals [32]. The approximate entropy is less dependent on the amount of data, and its value is stable, and non-stationary. Today, ApEn is widely used in engineering applications. The larger the value of ApEn, the more complicated the given time series is.  
Denote \(U=\left\{u\left(i\right),i=1,2,...,N\right\}\) as the signals; \(m\) and \(r\) are the pattern dimension and the similarity tolerance, which are given in advance. The value of ApEn can be obtained by the following steps.  
(1) The sequence \(\left\{u\left(i\right)\right\}\) is sequentially composed into an \(m\)-dimensional vector \(P\left(i\right)\):  
\[\begin{array}{c}P\left(i\right)=\left[u\left(i\right),u\left(i+1\right),..., u\left(i+m-1\right)\right]\\ s.t.\;\;i=1,2,...,N-m+1\end{array}. \tag{12}\]  
(2) Calculate the distance between the vector \(P\left(i\right)\) and the remaining vectors \(P\left(j\right)\) for each \(i\).  
\[d\left[P\left(i\right),P\left(j\right)\right]=\max_{k}\left|u\left(i+k\right) -u\left(j+k\right)\right|. \tag{13}\]  
(3) Given a threshold \(r\left(r>0\right)\), for each value of \(i\), count the number \(n\) of \(d\left[P\left(i\right),P\left(j\right)\right]<r\) and calculate the ratio \(c_{i}^{m}\left(r\right)\) of \(n\) to the total number \(N-m+1\).  
\[c_{i}^{m}\left(r\right)=\frac{n}{N-m+1}. \tag{14}\]  
(4) Calculate the logarithm of \(c_{i}^{m}\left(r\right)\) and get it average value \(\phi^{m}\left(r\right)\):  
\[\phi^{m}\left(r\right)=\frac{1}{N-m+1}\sum_{i=1}^{N-m+1}\ln c_{i}^{m}\left(r\right). \tag{15}\]  
(5) Add the dimension to \(m+1\), and repeat steps (1)-(4) to get \(\phi^{m+1}\left(r\right)\).  
(6) The value of ApEn for this sequence is:  
\[E\left(m,r,N\right)=\lim_{N\rightarrow\infty}\left[\phi^{m+1}\left(r\right)- \phi^{m}\left(r\right)\right]. \tag{16}\]In actual calculation, \(N\) is a finite value; thus, the estimation value of ApEn estimate can be expressed as:  
\[E\left(m,r,N\right)=\phi^{m+1}\left(r\right)-\phi^{m}\left(r\right). \tag{17}\]  
Generally speaking, the values of parameters are set to \(m=2\) and \(r=0.1\sim 0.2 SD\left(u\right)\), where \(SD\) represents the standard deviation. By calculating the value of ApEn of the reconstructed signals \(U\), the proposed feature named CEEMDAN-ApEn is achieved, and it is used as the health indicator of the rotating machinery.
# Analytical Model and Feedback Predictor Optimization for Combined Early-HARQ and HARQ
## 2 System Model
### Performance Measures
As input parameters, the model gets the transition probabilities \(b_{i}\), \(i=\overline{1,3}\) estimated from the e-HARQ prediction and HARQ mechanisms, and tunes the parameters of e-HARQ prediction by solving the optimization problem further described in Section 3. For the purpose of consistency, we have estimated the complexity of the model's stationary distribution computation, which is of the order \(O(NT_{c}logT_{c})\). The given complexity change corresponds to the worst-case scenario, when the analytical model together with e-HARQ is not able to reduce the amount of retransmissions based on the found optimal parameters. However, it has to be noted that e-HARQ configuration based on the proposed analytical model can be performed only once prior to the actual transmission, which obviously does not bring complexity changes to the actual transmission process demonstrated in the structure diagram above.
## 4 Results
### 3-D Magnetotelluric Data Inversion and Results
The resistivity model obtained from MT data in the study area shows a heterogeneous highly conductive shallow subsurface (0-200 m), resting on a lower resistive zone. A significant resistive feature was identified that is widely distributed horizontally in the area, reaching depths of more than 3 km and resistivities over 500 \(\Omega\)m.  
The 3-D inversion of the MT data reveals some key resistivity features such as a shallow conductive zone, C1 (\(<\)10 \(\Omega\)m), seen at the hot spring site and extending to depths of more than 100 m, followed by a second zone of high resistivity (R1) beneath the C1 layer, as shown in Figures 8 and 9. Conductive structures (C4 and C5), which appear at the edges of the survey area, are not constrained by the measurement data and are, therefore, not discussed in this paper. Two resistivity cross-sections, one chosen to investigate the influence of the Chobe River and the other one cutting the regional faults in the region, were extracted from the 3-D model and discussed. Figure 7: Observed data (symbols) and model responses (lines) from some sites, which are displayed as apparent resistivity between 0.0001 s and 10 s. The red represents the XY component, and the blue is the YX component of the apparent resistivities and phases. Refer to Figure 2 for location of stations on the map. LOG10(X) = 10\({}^{\text{X}}\). Figure 8: The horizontal slices at different depths were obtained from the 3-D MT inversion in the study area. The black cross in the center of the maps represents the location of the hot spring. The black dots represent MT sites. It was created by the initial 100 \(\Omega\)m half space and is, therefore, artificial. The same reasoning applies to the structure at greater depths. LOG10(\(\mathcal{O}\)) = 10\({}^{\mathcal{X}}\).
## 5 Results and Discussions
### The Effect of Minimum Allowable Arrival Time Difference \(t_{occ}\)  
The first simulation set was established to evaluate the relation between the minimum allowable arrival time difference, \(t_{occ}\), and the solution's existence. The purpose of the simulations is to confirm the hypotheses. In such simulations, we used \(\alpha=1\) and \(\beta=1\) and evaporate rate \(\rho=0.95\). We applied five values of \(t_{occ}\), i.e., 10, 50, 100, 150, and 300 s.  
Tables 1 and 2 show the results of 10 trials for varying allowable minimum occupation time \(t_{occ}\) and varying evaporate constant \(\rho\), respectively. In each table, as shown in Column (1), for each occupation time, we established five trials. Column (1) reveals the values of \(t_{occ}\). Column (2) is the success rate of finding a complete solution. Columns (3) and (4) show the average and standard deviation of the maximum traveling times, respectively. Column (5) shows the minimum value of maximal traveling times.  
Figure 4: Graph with CONFIG 15-1 configuration (15 nodes).  
Figure 5: Graph with CONFIG 20-1 configuration (20 nodes).  
The application of \(t_{\rm occ}\) is evaluated for its effect on the percentage of successful trials from 10 trials, i.e., \(n_{\rm success}\), average traveling time of the slowest vehicle, \(T_{\rm avg}\), the standard deviation of \(T_{\rm avg}\), i.e., \(\sigma_{\rm T}\), and the minimum traveling time that is ever found from the ten trials, i.e., \(T_{\rm min}\). For this purpose, we use a statistical correlation technique. The correlations between \(t_{\rm occ}\) and \(n_{\rm success}\), \(T_{\rm avg}\), \(\sigma_{\rm T}\), and \(T_{\rm min}\) are -0.99, 0.83, 0.75, and 0.88, respectively. In can be concluded that there is a strong negative relationship between \(t_{\rm occ}\), = and \(n_{\rm success}\) and almost-strong positive relation between \(t_{\rm occ}\) and the other three variables. In addition, the tendency of failure can be identified by a parameter \(\sigma_{\rm T}/t_{\rm occ}\). We found from the simulation that \(\sigma_{\rm T}/t_{\rm occ}\) is directly proportional to the probability of success in finding a solution, i.e., \(n_{\rm success}\).
## 2 Materials and Methods
### OTS Infrastructure Construction
#### 2.3.1 DCS Configuration  
As illustrated in Figure 5, the DCS was designed to transmit the status of the facility in real time when instrumentation devices such as valves are operated.  
Figure 4: The roles and communication systems of the participants.  
Since the OTS does not deal with chemicals directly, a sensor was installed in the facility to create a virtual environment, so that users could feel as if the chemicals were actually present. In addition, a programmable logic controller (PLC) module used for automatic control and monitoring was installed in the pilot plant to transmit information between the control room and the worksite. It was configured to transmit and receive data by converting analog signals to digital signals, and it was programmed to transmit and receive signals to and from a computer in the control room and an AR head-mounted display (HMD) in real time. A local network was then configured to exchange signals with each module, and the corresponding values were stored on a server PC.  
The DCS was used to transmit the scenario information to the PLC installed in the pilot plant at the beginning of the chemical accident scenario. It was configured to visualize the status of the facility as per the scenario through the instrumentation device attached to the facility. In addition, the system enabled the trainees to confirm the status of the facility through the GUI located in the control room.
## 5 Nanoformulations for Wound Healing
### Electrospun Nanofibers Loaded with Phytochemicals
Finally, electrospun wound dressings based on PLGA, poly(methyl methacrylate) (PMMA), collagen, and glycine and loaded with _Syzygium cunini_ leaf extract were developed via electrospinning and examined for their in vivo wound healing potential. The nanofibers demonstrated strong antimicrobial properties against _S. aureus_, _C. albicans_, _C. glabrata_, and _B.s cereus_, as well as _S. paratyphi_ and _E. coli_, while in vivo models showed an improved wound healing process [100]. At last, [PERSON] et al. fabricated electrospun dressings (with sizes ranging from micro to nano diameters) composed of PLGA and loaded with propolisis (Figure 6). The biodegradable mats released propolisis in a controlled manner, and when they were applied on skin burns, they promoted burn wound healing in a desirable way [101]. Table 1 summarizes the electrospun nanofibrous matrices, the materials were developed from, and the extracts. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Polymers Used for the Wound Dressings** & **Pytochemical/Plant/Extract** & **In Vivo Models** & **Ref.** \\ \hline PLLA/PVA/CS & _Hypericum perforatum_ L. & No & [76] \\ PVA & Flaxseed extract (_Linum usitatsissimum_) & No & [14] \\ Chitosan (CS) & Baicalein (_Scutellaria bicalciensis Georgia_) & No & [85] \\ PVA/CS & Royal Jelly & No & [86] \\ PVA/CS & _Thymus vulgaris_, _Zingther officinal_ & Yes & [87] \\ CS/PVA-PCL bilayer & Pomegranate flower extract & Yes & [88] \\ PVA/Alginic Acid (ALG) & _Lawsonia intermis_, _Scrophularia striata_ & Yes & [102] \\ PVA/DA-functionalized Alginate (Alg-DA) & Chlorogenic acid & Yes & [89] \\ PCL/PVA-CS & _Tridax procumbens_ L. extract & Yes & [90] \\ PCL-Silk Sericin/CS-Alginate Hydrogel & 10-Hydroxycaconio acid (10-HDA) & Yes & [91] \\ Cellulose (Cel)/Pectin (Pec)/Soy Protein Isolate (SPI) & Pomegranate peel extract & Yes & [92] \\ PCL/Collagen & _Malva sylvestris_ extract & Yes & [93] \\ PCL & Curcumin (_Curcum longa_), Berberine & Yes & [94] \\ PCL-CS & chloride & Yes & [95] \\ PCL/Cellulose Acetate (CA) & Oryzanol (_Oryza sativa_) & Yes & [96] \\ PCL/Collagen & _Rhamtus principales_ leaf extract & Yes & [97] \\ Pectin Polysaccharides (PP)/CS/PVA & Propolis extract & Yes & [98] \\ Sodium Alginate (SA)/Gelatin (Gel) with PAN top layer & Dihydromyricetin (_Hovenia dulcis_, & \\ layer & _Ampelopsis grossedentata_) & Yes & [98] \\ PLGA/PMMA/Collagen/Glycine & _Matricaria chamomilla_ L. extract, silver & Yes & [99] \\ PLGA & sulfadiazine (AgSD) & Yes & [100] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Applications of electrospun nanofibers loaded with phytochemicals. Figure 6: In vivo burn wound healing using PLGA 85/15 and 5% propolisis dressings to burn wounds before (**A**) and after 21 days of treatment (**B**); PLGA 85/15 and 10% propolisis dressings application before (**C**) and after 21 days of treatment (**D**). Reproduced under open access Creative Common CC BY License MDPI 2020 [100].
## 4 Results and Discussion
### Parameters Analysis
#### 4.2.2 Case 2--Linearly Increasing Suction  
This section presents the safety factor of the tunnel face under the condition of linearly increasing matric suction. Figure 8a,b illustrate the trends in how the \(FS\) is affected by C/D and D across various linear increase coefficients. Figure 8c,d delineate the impacts of variations in \(\rho\) and \(\varphi\) on the \(FS\).  
It is evident that an increase in \(\rho\) can significantly enhance the stability of the tunnel cross-sections. As illustrated in Figure 8a, the \(FS\) increases as C/D rises up until C/D surpasses a certain threshold. Under conditions of smaller \(\rho\), the critical C/D value at which the \(FS\) transitions to a stable state is relatively low. From Figure 8b, it is evident that variations in D do not affect the \(FS\). Referring to Figure 8c, it can be observed that when C/D is small, the \(FS\) linearly increases with \(\rho\), whereas when C/D is large, the pattern of the \(FS\) variation with respect to \(\rho\) begins to exhibit nonlinearity. This suggests that when C/D is large, the impact of \(\rho\) on the \(FS\) becomes more pronounced. In Figure 8d, an increase in \(\varphi\) can bolster the \(FS\) until \(\rho\) and \(\varphi\) attain specific larger values.  
Figure 7: The factor of safety (\(FS\)) for tunnel faces under uniformly distributed matric suction.
## 3 Results
### Exposure Time to Hazards According to Musculoskeletal Pain  
#### 3.2.1 Hazard Exposure Related to Back Pain  
Figure 2 and Table 3 present the \(t\)-test results comparing daily hazard exposure between workers with back pain and those without. Among physical hazards, a significant difference was observed in the average daily exposure time between complainers and non-complainers only in the low-temperature environment (\(t=-3.155\), \(p=0.002\)). Specifically, the low-temperature exposure time for those complaining of back pain was 1.417 (1.882/1.328) times longer than that of those who did not complain. Among the biochemical hazards, there were significant differences in daily exposure times between groups, specifically regarding chemical contact (\(t=-2.199\), \(p=0.029\)) and tobacco smoke (\(t=-2.086\), \(p=0.038\)). Workers complaining of back pain were exposed to chemical contact 1.103 (0.923/0.837) times longer and to tobacco smoke 1.052 times longer than those who did not complain. In terms of ergonomic hazards, there was a significant difference between the groups regarding the daily exposure time to awkward posture (\(t=-4.546\), \(p<0.001\)) and handling heavy loads (\(t=-4.326\), \(p<0.001\)). Complainers experienced awkward posture 1.377 (3.371/2.448) times longer and handled heavy loads 1.380 times longer than non-complainers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multirow{2}{*}{**Hazard**} & \multicolumn{2}{c}{**Exposure Time**} & \multicolumn{2}{c}{**Exposure Time Distribution**} \\ \cline{3-8}  & & **Mean * & **SD** & **\textless{}2 h** & **2-4 h** & \textgreater{}4 h \\ \hline \multirow{4}{*}{\begin{tabular}{c} Physical \\ hazards \\ \end{tabular} } & Vibration & 2.432 & 2.314 & 58.2\% & 14.9\% & 27.0\% \\  & Noise & 1.778 & 1.927 & 73.0\% & 12.1\% & 14.9\% \\  & High temperature & 1.432 & 1.472 & 81.3\% & 9.1\% & 9.6\% \\  & Low temperature & 1.519 & 1.779 & 83.2\% & 5.8\% & 11.1\% \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Biochemical \\ hazards \\ \end{tabular} } & Vapor & 0.880 & 0.374 & 98.2\% & 1.1\% & 0.7\% \\  & Chemical contact & 0.866 & 0.355 & 99.1\% & 0.5\% & 0.4\% \\  & Tobacco smoke & 0.847 & 0.218 & 99.5\% & 0.5\% & 0.0\% \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Ergonomics hazards \\ \end{tabular} } & Awkward posture & 2.766 & 2.308 & 45.3\% & 22.3\% & 32.4\% \\  & Handling heavy loads & 2.513 & 2.102 & 43.8\% & 32.8\% & 23.4\% \\ \cline{1-1}  & Standing posture & 4.404 & 2.402 & 16.7\% & 21.2\% & 62.1\% \\ \cline{1-1}  & Reetitive motion & 4.367 & 2.463 & 21.6\% & 17.0\% & 61.4\% \\ \hline \hline \end{tabular} Note.* hours per day, SD = standard deviation.  
\end{table}
Table 2: Distribution and mean of hazard exposure time.  
Figure 1: Mean of hazard exposure time (hours per day); red = physical hazards, blue = biochemical hazards, green = ergonomics hazards; error bars = standard deviation.