esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion and Conclusions
It is worth mentioning that these microphone units (or cameras with multiple microphones) are inexpensive, portable and easily affordable, even with the significant disadvantage of poor sound source localization performance due to their small size. This study also suggests the possibility of using this type of portable and easily available microphone array in ecoacoustics, which can contribute to citizen science of ornithology [31] in addition to the recent development of bird song extraction apps based on deep learning techniques (e.g., BirdNet [32]). One of the problems in the application of these approaches is the low accuracy in detection of vocalizations overlapped with each other or with other environmental sound sources. The robot audition techniques can resolve this problem by separating sound sources by making use of spectogram information from multiple channels, as discussed in this paper. The future work includes practical comparisons of the efficiency of bird song localization and separation between the microphone arrays adopted in this study and such commercially available ones in order to further explore the applicability of robot audition techniques to ecoacoustic research. Conceptualization, K.H., H.O. and R.S. ; field experiment K.H. and H.O. ; formal analysis and writing, K.H. and R.S., supervision, S.M., T.A., K.N. and H.G.O., funding: R.S. and K.N. All authors have read and agreed to the published version of the manuscript. This research was funded by JSPS/MEXT KAKENHI: JP21K12058, JP20H00475, JP19 KK0260. The experimental procedures have been approved by the planning and evaluation committee in the Graduate School of Information Science, Nagoya University (GSI-H30-1). The experimental data are available upon request. **Acknowledgments:** We thank [PERSON] and [PERSON] for designing and conducting a speaker test. We also thank [PERSON] (Nagoya Univ.) for conducting a field recording in the experimental forest. **Conflicts of Interest:** The authors declare no conflict of interest.
# Operationalisation of Building Inspections and Repair: Systematisation-Based Approach
## 3 Results
### Most Common Diagnosis Methods to Prepare an Inspection
#### 3.1.2 Diagnosis Methods in Facade Elements/Claddings
[51] use a five-level scale from Level A (Very good) to Level E (Severe degradation). As for wall renders, the severity implicitly associated with the degradation levels for defects by [PERSON] et al. [52] may be used. Of the mentioned set of defects, these authors take all into account (\"A-A2 Surface moisture\", \"A-A3 Dirt and accumulation of debris\", \"A-B1 Biodeterioration/biological growth\", \"A-C1 Mapped cracking\", and \"A-C2 Oriented cracking on the current surface\"), which are mentioned as \"damp\", \"dirt\", microorganisms and algae\", and \"cracking\", respectively. Damp is referred to in Level C of degradation (light degradation), as well as dirt. Microorganisms may eventually be present in Level B (good condition) and in dark patches (microorganisms and algae) in Level C. Cracking may occur in all levels of degradation, depending on the crack width to classify the level of degradation: in Level A (best condition), if the width is up to 0.1 mm; in Level B, if the width is between 0.25 mm and 1.0 mm, and in localised areas; in Level C, if the width is from 1.0 mm to 2.0 mm; and in Levels D (broad degradation) and E (worst condition), if the cracks have or are wider than 2 mm. Considering these data, cracking may be the most severe defect in this set of defects in wall renders. [PERSON] et al. [52] use a five-level scale from Level A (Best condition) to Level E (Worst condition). In the case of ETICS, the severity implicitly associated with the degradation levels for defects by [PERSON] et al. [53] may be used. Of the mentioned set of defects, these authors take into account \"A-A2 Surface moisture\", \"A-A3 Dirt and accumulation of debris\", and \"A-B1 Biodeterioration/biological growth\" under the defect \"stains/colour changes\", and defects \"A-C1 Mapped cracking\" and \"A-C2 Oriented cracking on the current surface\", considered as \"loss of continuity/integrity defects\". None of these defects should be present in Level 0 (Unchanged). As for stains, in Level 1 (Good), only slight or few perceptible changes should be visible. In Level 2 (Slight degradation), they should be moderate or quite perceptible. In Level 3 (Moderate degradation), stains should be highly or strongly marked. In Level 4 (Generalised degradation), they should be very highly or quite strongly marked. As for cracking, in Level 1, cracks should be equal to or thinner than 0.2 mm and in a little or very little area of the cladding. In Level 2, they can be wider than 0.2 mm but only in a little or very little area or equal to or thinner than 0.2 mm in a moderate portion of the cladding. In Level 3, cracks can be wider than 0.2 mm but in a moderate portion of
## 5 Discussion and Future Challenges
* Applications of 5G and B5G network technologies in the SG system: The B5G (beyond 5G) means the next generation of communication technology that has a peak transmission speed dozens of times faster than 5G, and it can solve the energy consumption of 5G and improve coverage by applying low orbit satellites. Smart factories are formed by connecting various devices through the IoT. Therefore, it is important to carry out intelligent energy management [110]. 5G network technologies provide the industrial IoT with better communication quality and smart energy management [111], and it also solves the problem of communication latency in the manufacturing process [112]. Therefore, a line of the future challenge is to investigate how to further optimize the SG system integrated with 5G networks through AI technologies [113]. * Next-generational smart manufacturing: The emergence of Industry 4.0 has initial-ized the fourth industrial evolution. It has driven the development of smart manufacturing processes, including the wide introduction of human-machine systems, communication networks, big data analysis, and so on. It has brought efficient and effective manufacturing models, but some challenges still exist, e.g., circular economy, energy demand management, and net-zero emissions. Therefore, the development of next-generation intelligent manufacturing will increasingly emphasize the human-centered concept. Although the SG effectively has integrated energy into smart manufacturing and had a positive impact on reducing operating costs [114], when smart manufacturing systems are evolving to the next generation, the next-generation smart grid (NGSG) that can reduce nonlinear effects needs to be continuously developed [115]. At the same time, it is also a challenge to effectively integrate the concept of human-oriented to achieve more efficient smart factories [116].
## 2 Few-Mode Fiber Channel Model
Non-unitary modal coupling due to fiber misalignments at a splice point is given by an \(M\times M\) coupling matrix \(C_{k}\), with entries computed using an overlap integral of the electrical fields of propagating modes at fiber cross sections as in [6]:  
\[c_{i,j}=\iint\limits_{A}E_{i}^{(k-1)}(x,y)E_{j}^{(k)*}(x+\Delta x,y+\Delta y)dA \tag{3}\]  
where \(E_{i}^{(k-1)}(x,y)\) is the normalized complex amplitude of the of the \(i\)th mode before the splice and \(E_{j}^{(k)*}(x+\Delta x,y+\Delta y)\) is the normalized complex amplitude of the \(j\)th mode after the splice. \(\Delta x\) and \(\Delta y\) are fiber misalignments assumed to be independent Gaussian distributed in \(x\) and \(y\) directions with zero mean and standard deviation (std) \(\sigma_{x,y}\). In our channel model, a non-unitary coupling occurs between modes at each fiber misalignment, hence the effects of modal coupling and MDL cannot be separated. In [18], a more appropriate description of linear modal coupling of spliced FMFs was given, however MDL was not taken into account. In our simulations, we consider FMFs made of \(K=300\) concatenations of misaligned fiber sections. To emulate different MDL levels, we vary \(\sigma_{x,y}\) as a percentage of the fiber core radius \(r_{c}\). The accumulated effect from all misalignments results in a end-to-end MDL defined as the ratio in decibels (dB) of the maximum to the minimum eigenvalues of \(HH^{\dagger}\). \[\mathrm{MDL}(\mathrm{dB})=10\log_{10}\left(\frac{\lambda_{max}}{\lambda_{min}}\right) \tag{4}\]
## 3 ALS-KF Based Visual Object Tracking Algorithm
### Object Tracking Using Kalman Filter  
Once the objects are detected via the background subtraction, the next step is to track the detected object using the Kalman filter. Denote the object \(O_{i}\) is represented by its state \(x_{i}\), which includes its position and velocity. In the visual object tracking task, the Kalman filter is used to predict the state of each object in the next frame, given its current state and the observations (detected positions). The Kalman filter is a recursive algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone. It includes the prediction and update steps: The prediction step projects forward the current state and error covariance estimates to obtain the a priori estimates for the next time step; the update step incorporates a new measurement into the a priori estimate to obtain an improved a posteriori estimate.  
Given the current state of the object \(O_{i}\) at time \(t\) denoted as \(x_{i,t}\), and observation \(z_{t}\), we want to estimate the state \(x_{i,t+1}\) at time \(t+1\). The observation \(z_{t}\) can be seen as the detected position of the object from the background subtraction. The dynamic model of the system and the measurement model can be represented as:  
\[\begin{split} x_{k+1}&=Fx_{k}+Bu_{k}+w_{k}\\ z_{k}&=Hx_{k}+v_{k}\end{split} \tag{16}\]  
where \(x_{k}\) is the state vector, \(u_{k}\) is the control input, \(w_{k}\) is the process noise, \(z_{k}\) is the measurement, and \(v_{k}\) is the measurement noise. \(F\), \(B\), and \(H\) are system matrices.  
Based on the Kalman filter introduced in (3) and (4); the KF derivation for the object tracking problem is given below.  
Prediction:  
\[\begin{split}\hat{x}_{k|k-1}&=F\hat{x}_{k-1|k-1}+ Bu_{k}\\ P_{k|k-1}&=FP_{k-1|k-1}F^{T}+Q\end{split} \tag{17}\]  
Correction:  
\[\begin{split} K_{k}&=P_{k|k-1}H^{T}(HP_{k|k-1}H^{T}+R)^ {-1}\\ \hat{x}_{k|k}&=\hat{x}_{k|k-1}+K_{k}(z_{k}-H\hat{x}_ {k|k-1})\\ P_{k|k}&=(I-K_{k}H)P_{k|k-1}\end{split} \tag{18}\]
## 1 Introduction
### The Impact of Corrosion on Seismic Vulnerability  
As a result of the loss in structural capacity and the increased vulnerability to failure, seismic vulnerability is correspondingly heightened. The effect of corrosion on seismic vulnerability has been investigated in the literature. For instance, [PERSON] et al. [45] investigated the seismic vulnerability assessment of RC buildings. Their study found that corrosion-induced aging and soil-structure interaction (SSI) significantly increase the seismic vulnerability of RC structures over time. Additionally, [PERSON] et al. [46] developed a probabilistic framework for the seismic vulnerability analysis of corroded reinforced concrete frames. Their study highlighted that disregarding the progressive nature of damage and the changes in structural performance over time underestimates the probability of failure of corroded RC frames.  
Several studies have also investigated the effect of corrosion on bridges; [47; 48] found that corrosion significantly deteriorates the structural integrity of RC bridges by causing cross-sectional loss, ductility degradation, and the compressional buckling of steel rebar. A similar effect was also found in the case of prestressed concrete bridges, where the corrosion-induced degradation of tendons significantly compromises the flexural and shear capacity of bridge cross-sections [49]. [PERSON] et al. (2022) further demonstrated that moderate corrosion can significantly alter the seismic vulnerability of RC bridges over time, requiring more proactive maintenance and retrofitting strategies [50]. [PERSON] et al. [51] found that seismic safety margins could be reduced by up to 40% due to the corrosion-induced mass loss in steel bars. Furthermore, the cumulative effects of the corrosion and repeated earthquake shocks further degrade highway bridges over time, highlighting the necessity for proactive maintenance and comprehensive seismic assessments [52].  
Furthermore, the impact of corrosion was also observed in the case of shallow circular tunnels. [PERSON] et al. [53] examined the effects of soil-structure interaction (SSI) and lining corrosion on the seismic vulnerability of shallow circular tunnels, highlighting the significant impact of these factors on tunnel resilience. Their findings emphasize that corrosion must be considered together with the SSI to ensure the long-term durability and safety of tunnel structures.  
As demonstrated by the literature, corrosion significantly increases the seismic vulnerability of CI by deteriorating the structural integrity of RC elements. This degradation impacts the overall structural capacity of CI, making the CI systems more vulnerable to damage during seismic events.  
Furthermore, corrosion is a time-dependent process [54; 55], meaning the impact of corrosion on structural capacity constantly increases as the RC elements age. This highlights the importance of ongoing, time-dependent assessment and the monitoring of RC elements to obtain a comprehensive understanding of the seismic risk to CI.
# Fog vs. Cloud Computing: Should I Stay or Should I Go? +
## 5 Conclusions
\begin{table}
\begin{tabular}{l l c c c c c c c c c c} \hline \hline
**Benchmark** & **Dataset** & \(\mathbf{t}\) **(m)** & \(p\) **(\%)** & \(p\) **(\%)** & \(n\) & \(f\) **(Est.)** & \(f\) **(Real)** & **Slope (Est.)** & **Slope (Real)** & **Slope Error (\%)** \\ \hline \multirow{8}{*}{MinMax \([-15,15]\)} & HRelHumidity & 0.023170 & 0.001384 & 361 & 361 & 1.0000 & 1.0000 & \(-0.5244\) & \(-0.5244\) & \(-0.0000\) \\  & HVisability & 0.021135 & 0.001262 & 396 & 0 & 0.0000 & 0.0000 & \(-1.2089\) & \(-1.2089\) & \(-0.0000\) \\  & HWBTempC & 0.021667 & 0.001294 & 386 & 217 & 0.5622 & 0.2784 & \(-0.7091\) & \(-0.8878\) & \(-20.1222\) \\  & HVindSpeed & 0.021299 & 0.001272 & 393 & 7 & 0.0178 & 0.1393 & \(-1.1745\) & \(-1.0278\) & \(+14.2665\) \\  & Synthetic & 0.021848 & 0.01035 & 383 & 209 & 0.5457 & 0.5536 & \(-0.7139\) & \(-0.7099\) & \(+0.5616\) \\ \hline \multirow{8}{*}{MinMax \([-5,5]\)} & HRelHumidity & 0.023174 & 0.001384 & 361 & 361 & 1.0000 & 1.0000 & \(-0.5244\) & \(-0.5244\) & \(-0.0000\) \\  & HVisability & 0.022708 & 0.001356 & 368 & 304 & 0.8261 & 0.8336 & \(-0.5831\) & \(-0.5806\) & \(+0.4365\) \\  & HVBtempC & 0.022264 & 0.001329 & 376 & 376 & 1.0000 & 0.6911 & \(-0.5346\) & \(-0.6403\) & \(+0.1527\) \\  & HVindSpeed & 0.022567 & 0.001348 & 370 & 200 & 0.7568 & 0.8013 & \(-0.6097\) & \(-0.5936\) & \(+2.7142\) \\  & Synthetic & 0.022150 & 0.001323 & 377 & 312 & 0.8276 & 0.8299 & \(-0.5901\) & \(-0.5894\) & \(+0.1345\) \\ \hline \multirow{8}{*}{Outlier 16} & HRelHumidity & 0.024622 & 0.001470 & 340 & 10 & 0.0294 & 0.0236 & \(-1.0070\) & \(-1.0128\) & \(-0.5816\) \\  & HVisability & 0.02958 & 0.001371 & 364 & 43 & 0.1181 & 0.0911 & \(-0.9836\) & \(-1.0105\) & \(-2.6690\) \\  & HVBtempC & 0.024901 & 0.014787 & 336 & 5 & 0.0149 & 0.0222 & \(-1.0106\) & \(-1.0032\) & \(+0.7445\) \\  & HVindSpeed & 0.024746 & 0.001478 & 338 & 10 & 0.0296 & 0.0267 & \(-1.0019\) & \(-1.0048\) & \(-0.2919\) \\  & Synthetic & 0.025497 & 0.001523 & 328 & 1 & 0.0030 & 0.0076 & \(-0.9990\) & \(-0.9945\) & \(+0.4576\) \\ \hline \multirow{8}{*}{Outlier 256} & HRelHumidity & 0.089120 & 0.0035232 & 93 & 0 & 0.0000 & 0.0051 & \(-0.2867\) & \(-0.2863\) & \(+0.1470\) \\  & HVisability & 0.098941 & 0.005524 & 10 & 55 & 0.3929 & 0.1112 & \(-0.3675\) & \(-0.4100\) & \(-10.3520\) \\ \cline{1-1}  & HVindSpeed & 0.088124 & 0.005263 & 95 & 0 & 0.0000 & 0.0124 & \(-0.2899\) & \(-0.2889\) & \(+0.3601\) \\ \cline{1-1}  & HVindSpeed & 0.084443 & 0.005043 & 99 & 0 & 0.0000 & 0.0160 & \(-0.3026\) & \(-0.3011\) & \(+0.4848\) \\ \cline{1-1}  & Synthetic & 0.090580 & 0.005410 & 92 & 1 & 0.0109 & 0.0012 & \(-0.2812\) & \(-0.2820\) & \(-0.2709\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: NodeMCU energy consumption results in cloud-prone cases.
## 2 Related Work  
Given the advantages stated in the Section 1, many researchers and enterprises have begun researching the use of blockchain for cross-domain authentication and generating reliable cross-domain authentication without needing trusted third parties.  
Existing blockchain-based cross-domain authentication schemes can be broadly categorized into three types: authentication based on blockchain and Public Key Infrastructure (PKI), authentication based on blockchain and IBA, and authentication based on blockchain and Certificateless Public Key Cryptography (CL-PKC).
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques  
In this section, we provide a comprehensive review of the existing localization/positioning algorithms and techniques, classified according to the signal measurements employed (i.e., time-based, angle-based, channel-based), the localization methods (i.e., range-based, range-free, ML/AI), and the tracking methods (i.e., DR, Filters, SLAM). The complete classification is shown in Figure 1.
## 6 Results and Discussion
### Clustering of Segmental Floor  
#### 6.1.1 Clustering Based on Morphology Similarity  
Figure 8 shows the variation of the dendrogram and validity measure index during clustering based on morphology similarity. With the evolution of the clustering process, the neighbor floor series were agglomerated gradually into the same cluster, as shown in Figure 8a. The DBI increased with the decreasing of number of clusters. When the clusters were agglomerated to 36, the DBI reached the maximum. After that, it began to decrease rapidly. The DI decreased slowly until the number of clusters decreased to about 100. And then it increased slowly until the number of clusters decreased to about 5. It increased sharply when the clusters further agglomerated. The CS increased slowly when the number of clusters decreased until the cluster number reached about 120. After that, the CS remained constant at about 0.35 until the cluster number reached 7, and then it increased sharply.  
Figure 8: The segmental floor dendrogram (**a**) and variation of validity measure index (**b**) of clustering based on morphology similarity.  
Figure 7: The segmental floor located between 0 and 2100 m in face-advancing direction.  
Taking into account the dendrogram and the validity measure index, the number of clusters is determined as 7, as shown in Figure 8a. The morphology of the determined cluster centers is exhibited in Figure 9. According to the intuitive overall morphology, the clusters can be classified into two categories. One category includes the clusters No.1-4, and another includes the clusters No.5-7. This means that these clusters would be agglomerated into two clusters if the clustering process took a step further. This coincides with the dendrogram (Figure 8a). For each category, some observable morphology differences between the clusters are shown in Figure 9.  
Table 1 lists the position of the cluster centers and cover zone of each cluster. Each cluster center is the representative morphology of the cluster zone, and can be regarded as the generating line (generatrix) of the cluster zone. The floor in this cover zone can be re-constructed by stretching the generating line with a predefined angle along \(X\)-axis. The predefined angle can be determined by the cluster centers based on coplanarity.
## Materials and Methods
### Characterization  
After thermal processing, the produced HESO-samples were analyzed by means of a number of complementary techniques. A Phenom Pro-X scanning electron microscope, equipped with an energy-dispersive X-ray (EDX) spectrometer, was used to investigate the texture and morphology of HESO-samples by means of scanning electron microscopy (SEM). High-resolution transmission electron microscopy (HRTEM), high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM), selected-area electron diffraction (SAED) and energy dispersive X-ray spectroscopy (EDX) elemental mappings were carried out on a FEI Talos F200S scanning/transmission electron microscope, operated at 200 kV.  
The crystalline phases of the HESO-samples were identified by X-ray diffraction (XRD) and micro-Raman spectroscopy (MRS). A Bruker D2 Phaser diffractometer, equipped with a Ni \(\beta\)-filtered Cu-K\(\alpha\) radiation source, was used to record XRD patterns, whereas JCPDS database of reference compounds allowed for the diffraction-peak identification. A NT-MDT NTEGRA Spectra SPM spectrometer, equipped with MS3504i 350 mm monochromator and ANDOR Idus CCD, was used to measure Raman scattering excited by a solid-state laser operating at 532 nm. Raman measurements were carried out in reflection mode using a Mitutoyo high numerical aperture 100\(\times\) objective to excite and collect the scattered signal from the sample surface. The acquired signal was dispersed by an optical grating (600 lines/mm) and, finally, detected by the cooled CCD. The use of a very low laser power (250 uW at the sample surface) prevented local heating of the samples and annealing effects.
## 6 Factors Affecting the CTE of Concrete
### Aging  
Fresh concrete has high CTE values because it contains a lot of unbound water in the liquid phase, which has a CTE that is roughly seven times higher than that of mature concrete. Concrete exhibits solid material behavior later, as its microstructure begins to form, leading to a significantly lower and more stable CTE [53]. The study carried out to understand the effect of aging on CTE found that aging can significantly impact CTE, particularly within the first 8-10 h following placement, which is likely due to a combination of temperature and hydration effects, thereafter stabilizing at a rather constant value. Over time, age effects on CTE were found to be minimal [75]. The amount of variance brought about by varying curing time is comparable to that brought about by using various mixes for a specific curing period.  
However, [PERSON] [76] suggests that concrete age has no effect on CTE readings for up to three weeks, and [PERSON] et al. [77] also concluded that saturated concrete specimens showed no discernible variation between the 28-day and 90-day moist-curing times. Similarly, the study was carried out to measure the typical CTE values of pavements made of Portland cement concrete (PCC) using different aggregates used in Louisiana, and aging  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Aggregate Type**} & \multicolumn{2}{c}{**Coefficient of Thermal Expansion (CTE) \(10^{-6}/^{\circ}\)C**} \\ \cline{2-3}  & **Aggregate** & **Concrete** \\ \hline Granite & 7–9 & 8.50 \\ \hline Basalt & 6–8 & 7.80 \\ \hline Limestone & 6 & 7.80 \\ \hline Dolomite & 7–10 & 8.92 \\ \hline Sandstone & 11–12 & 9.58 \\ \hline Quartz & 11–13 & 9.34 \\ \hline Marble & 4–7 & \\ \hline Siliceous Limestone & 7–9 & \\ \hline Siliceous River Gravel & 11–13 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: CTE values of different aggregate types and concrete by aggregate type [4; 73].  
was found to have no statistically significant impact on CTE, with measured CTE at different ages (3, 5, 7, 14, 28, 60, and 90 days) fluctuating within 0.3 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C [78]. We can infer from the studies that aging has a variable and context-dependent impact on concrete CTE. Temperature and hydration influence CTE in the short term (the first 8-10 h), stabilizing at a more consistent value as the concrete ages particularly after the initial hydration period.