esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Cluster analysis was performed on a data sample of urban road traffic noise monitored in the city of Milan, formed by the hourly values of A-weighted continuous equivalent level L\({}_{\text{Aeqh}}\), describing the sound energy, and the intermittency ratio (IR), quantifying the noise events owing to vehicle pass-by.  
The obtained two clusters can be fruitfully applied to stratify road sampling by choosing a representative road sample in each cluster and, therefore, to optimize noise monitoring resources [31]. The approach of road traffic noise clustering can be integrated in the noise mapping process too, as already applied in the DYNAMAP project [40; 41], where a 24 h pattern representative of each cluster is assigned to non-monitored roads belonging to the cluster itself.  
The assignment of new roads to the clusters can be obtained by supervised algorithms, like k-NN, when the acoustic data are known and, much more often, by a classifier linked to the road noise, herewith the logarithm of day-time (06:00-22:00) traffic flow. The two data partitions obtained by k-means clustering and by the threshold x = 4.48, corresponding to an average day-time hourly traffic flow of 1900 vehicles/hour, are in a satisfactory accordance (classification matching 71.9%).  
Of course, the results are strongly dependent on the local situation and could not be generalized to other contexts. However, besides the above limitations, the described methodology could be fruitfully applied on road traffic noise data in other cities, allowing to compare different noise contexts.  
Conceptualization, G.B. and R.B.; methodology, G.B. and R.B.; software, G.B.; formal analysis, G.B.; investigation, G.B., R.B., and H.E.R.; data curation, C.C.; writing--original draft preparation, G.B.; writing--review and editing, R.B.; supervision, R.B. and G.Z.; funding acquisition, G.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 11: Comparison of the new data roads, \(\delta\) (**a**) and IR (**b**), with the centroids of the two clusters.
# Failure Modelling of CP800 Using Acoustic Emission Analysis
## 1 Introduction
With this method, the material failure is determined macroscopically on the specimen surface. However, the material usually starts to fail from the inside through microscopic cracks [12]. With ongoing deformation more microcracks form and merge until material decohesion occurs. Using acoustic measuring systems, it is possible to determine the failure initiation inside the specimen [13]. Hence, acoustic emission analyses offer great potential to improve failure characterisation. Therefore, in this paper the flow and the failure behaviour of the steel CP800 will be experimentally characterised. The failure behaviour tensile tests with butterfly specimen, which were developed at the IFUM, were performed for a wide range of stress states. Beside an optical measurement system, the tests were monitored in parallel with an acoustic measurement system as a novelty. The failure onset of the material was evaluated using three methods based on force, optical, and acoustic data. Using the three evaluation methods, the butterfly tests are numerically calculated and the JC failure model is parametrised for each evaluation method based on the simulations. The three failure models are subsequently compared to show the influence of the evaluation method on the course of each failure model. Hence, the paper combines the two different research areas of \"acoustic emission analysis\" and \"failure modelling\", which is unique so far.
# PCONet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
## 4 Experimental Evaluations
### Data Generation  
We firstly generate \(L\) channel realizations \(\left\{\mathbf{H}^{1},\ldots,\mathbf{H}^{L}\right\}\in\mathbb{C}^{L\times N_{l} \times N_{t}}\) for the i.i.d. Rayleigh channel or randomly sample \(L\) data frames from the iMaster NAIE platform [25] for the NAIE channel. Then, we calculate the noise variance and normalize the channel matrix. The precoding matrices \(\mathbf{F}^{l}\triangleq\left[\mathbf{F}_{1},\ldots,\mathbf{F}_{K}\right]\in \mathbb{C}^{N_{l}\times N_{s}},l=1,\ldots,L\) are generated by the channel matrices \(\mathbf{H}^{l}=\left[\mathbf{H}_{1},\ldots,\mathbf{H}_{K}\right],l=1,\ldots,L\) utilizing the Algorithm 1. Lastly, we sample \(L\) training data \(\mathcal{F}=\left\{\left(\mathbf{H}^{1},\mathbf{F}^{1}\right),\ldots\left( \mathbf{H}^{L},\mathbf{F}^{L}\right)\right\}\).  
At the training and evaluation stage for the networks, the precoding matrix \(\mathbf{F}_{k}\in\mathbb{C}^{N_{k,r}\times N_{k,s}}\) of the \(k\)-th UE is randomly picked from the set of \([\mathbf{F}^{1},\ldots,\mathbf{F}^{L}],l=1,\ldots,L\). The number of frames of the training, validation, and testing data for our proposed PCQNet are \(L_{1}\) = 100,000, \(L_{2}\) = 20,000, \(L_{3}\) = 10,000, respectively.
# Integrated Radar and Communications Waveform Design Based on Multi-Symbol OFDM
## 5 Simulation Results  
In this section, we simulate and verify the effectiveness of the proposed algorithm. In our simulations, \(\mathcal{R}\) is randomly selected and \(R_{N}=50\%\) is set, i.e., half of the entire bandwidth used for radar detection is used to transmit communication data. Unless otherwise specified, the randomly generated input data is modulated by 4 QAM. The parameter settings are shown in Table 1.  
To examine the effectiveness of the proposed algorithm from both the obtained PMEPR and PSLR performances, \(10^{5}\) initial waveforms were randomly generated. In the following, we first study the effectiveness of the proposed algorithm with a fixed weight \(\alpha=5\), and then analyze the impact of weight \(\alpha\) on the performance of PMEPR and PSLR, since \(\alpha\) can change the proportion of PMEPR and PSLR in the objective function.
# A Strategic Pathway from Cell to Pack-Level Battery Lifetime Model Development
## 5 Pack-Level Electro-Thermal Modeling
### 3D Thermal Model Implementation  
It is well-known that temperature plays a crucial role in batteries' performance, and the model parameters change nonlinearly at various thermal conditions. Therefore, a 3D thermal model that can predict the surface temperature of the batteries as a function of internal power loss, can improve the accuracy of the 1D electrical model [31] for sensitive designs such as cooling/heating system optimizations, and lifetime predictions. Model order reduction techniques can significantly eliminate this problem while maintaining the accuracy of the 3D thermal model [47]. In this section, the implementation of a 3D tripleadjacent module in the FIRE M platform is described to simulate the heat transfer effect in cell-to-cell and cell-to-environment levels, and then this 3D model order is reduced to 1D to be coupled with the electrical model for online coupling during pack-level simulations in CRUISE M platform.  
As shown in Figure 6, the implementation of this model is summarized in four major steps. The first step consists of designing a proper CAD geometry including every highlighted detail of the three battery cells adjacent to each other. The casing or shell covers the inner parts of the cells. The following step is assigning a domain to each part of the CAD. Each cell has been divided into seven separate domains including the Aluminum casing on six sides and an inner cell part (fill). The advantages of such a multi-domain approach are that the temperature distribution of each can be analyzed separately and there will be control over where to apply the heat input. The third step is conducting a proper and neat meshing process on the model for which AVL FIRETM M provides a polygonal meshing approach with the flexibility of size and boundary layers adjustments. The quantity and the size of mesh elements define the simulation accuracy and solver precision. On the other hand, a higher number of mesh elements causes a much more computational workload on the CPU. Therefore, a trade-off shall be made between these two factors to achieve the most accurate and efficient simulation. Once the meshing is done, the solver shall be prepared, and every simulation parameter is tuned. The battery power loss under various current profiles is imported from the 1D electrical model as volumetric heat input and it is applied to the inner (fill) domain of the three batteries. After simulation as shown in Figure 6d, post-processing of the results is performed to extract and demonstrate the model outcome. Such a 3D thermal modeling approach not only provides accurate temperature values but also gives a temperature distribution over the entire geometry which makes it helpful for optimal cooling/heating system designs.
## 4 Results
### Ground Verification Experiment
In order to improve the cone-sweep imaging mode, we next designed and simulated the dual-camera variable speed OCPSI mode. The results showed that the overlapping area of this mode was significantly reduced, and the exposure time was increased by at least two times, but the effective width decreased. The single-camera constant speed OCPSI mode had the larger coverage area and the simpler control strategy. The dual-camera variable speed OCPSI mode had less overlapping area and better image quality. Finally, we designed the ground experiment equipment of the OCPSI mode and carried out the ground experiments of the OCPSI mode, the push-broom mode and the whisk-broom mode, and compared them. The results showed that, under the same conditions, the OCPSI mode can achieve a larger width on a single track. However, after analysis, the SNR of the OPCSI mode was smaller, and the image quality would be worse. Due to the unique motion imaging method, the camera pendulum scanning velocity would be very fast, which would put forward higher attitude control requirements for the satellite platform.
# Considering Uncertainties of Key Performance Indicators in Wind Turbine Operation
## 2 Key Performance Indicators  
In operational management of WTs, KPIs are used to evaluate and categorize the performance of WTs with regard to (1.) Predetermined target values, which are in many cases part of contractual agreements, (2.) comparable WTs (e.g., of the same WF), or (3.) the long-term trend of the WT itself. In any case, KPIs are always compared to a reference, and significant deviations usually trigger actions [18; 19].  
A high deviation to the reference reflects a high need for intervention and leads to a higher prioritization in the work of an operational manager. This usually means a more detailed review of the performance of the WT and the operational data to identify the underlying causes. At this point, one has to ask what deviation to the reference value can be considered to be healthy or can be explained due to the uncertainty of the KPI itself, and thus either should not trigger any action or lower its priority. To answer this question is one of the main scopes of the present work.  
As a single KPI is usually not sufficient to rate the performance of a WT, in most cases, a set of different metrics is used. The selected KPIs are calculated on a regular base. Monthly, quarterly, half-yearly, and yearly periods are common resolutions for KPIs and are normally synchronized with reporting periods [18; 19]. According to [PERSON] et al. [7] and [PERSON] et al. [6], KPIs for O&M of WTs belong to one of the following five different groups.  
* Performance KPIs
* Maintenance KPIs* Reliability KPIs
* Health, Safety and the Environment (HSE) KPIs
* Finance KPIs  
Financial and HSE KPIs are not wind-specific, and are thus excluded from further evaluation. Furthermore, the calculation of HSE, reliability, and some maintenance KPIs is solely based on distinct events instead of measurements. As the idea to provide uncertainties for theses KPIs is misleading, they are excluded as well. According to [PERSON] et al. [20], standardization of the collected data and a review of documentation processes provides much more utility in this case. To select a total of five KPIs for further assessment out of the remaining KPIs, the prioritization of KPIs, as provided by [6], is used. The selected KPIs are briefly described and defined in Sections 2.1-2.4 based on [6].
# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
## 3 Traditional Feature-Based Mosaic Process for SAR Images
### Homography Matrix Calculation  
The transformation of the pixel coordinates, which is called the homography matrix, was calculated based on the coordinates of matched keypoints [34; 35]:  
\[\mathbf{H}=\left[\begin{array}{ccc}a_{x}&b_{x}&c_{x}\\ b_{y}&a_{y}&c_{y}\\ 0&0&1\end{array}\right] \tag{1}\]  
where \(a_{x}\) and \(a_{y}\) stand for the scaling degree of rows and columns, respectively, \(b_{x}\) and \(b_{y}\) reflect the rotation of the image rows and columns, respectively, and \(c_{x}\) and \(c_{y}\) represent the translation of rows and columns, respectively. The homography matrix, which directly affects the accuracy of image projection, should be filtered by the RANSAC algorithm because using mismatched keypoints for the calculation is possible [36; 37]. Four pairs of matched keypoints were selected randomly to calculate a homography matrix. Then, all matched keypoints were projected based on the homography matrix, and their Euclidean distances between coordinates (threshold) were recorded. The number of the correctly matched keypoints, whose threshold was less than one, were also recorded. After iterating the above steps multiple times, the accurate homography matrix with the most correctly matched keypoints could be screened. The threshold was one pixel in this paper. The number of iterations was 2000. Such parameters of the RANSAC algorithm can result in a higher accuracy of homography matrix calculation and were independent from the images to be stitched. In addition, the RANSAC algorithm used in this paper was very efficient and took time in seconds.
# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
## The Proposed Protocol
### Attacker Model  
In this section, the attacker model of the proposed protocol is introduced. The RPL network topology comprises one root node, multiple normal nodes, and some malicious nodes that are rank attacks and version number attacks. We are assuming that the root node cannot be exposed, and its ID is encrypted and cannot be violated [13]. The proposed protocol is safe from insider attacks using Elliptic Curve Cryptography (ECC) [34]. In RPL, the version number and rank are carried in a DIO message, and the version number is used as an indicator for the global repair operation. The DODAG root node is the only node that can change the version number. All the nodes in the RPL network topology begin exchanging control messages to rebuild the network topology after the root nodes change the version number.  
While sending the DIO packet, malicious nodes attach their rank and version in the DIO packet. Subsequently, the attacker can exhaust the restricted drain and the limited resources of all the nodes in the RPL network, and this can lead to detrimental impacts on the network performance. The malicious nodes start their attacks by broadcasting a fake rank and version number during the cycle RPL trickle time. The version attacker is the one that changes the version number of nodes by incrementing their nodes, and a rank attacker is the one that falsely proposes the rank value to be chosen as a parent node. The nodes can spread their version and rank in the DODAG. While receiving the DIO packets from the malicious nodes (including rank and version), the current node changes their rank and version. Hence, they cannot determine the path to reach the root node.
# Antibiotic Resistance in the Farming Environment
## 5 Determinants of Antibiotic Resistance in Farming
Interestingly, the occurrence of antibiotic resistance genes in the aquatic environment showed a stronger correlation with vanadium concentration than with antibiotic concentration [91]. One factor influencing the selection of antibiotic-resistant microorganisms may be their interactions with other substances used in agricultural and farming practices. It has been observed that a strain of _E. coli_ subjected to simultaneous exposure to pesticides and ampicillin at sub-inhibitory concentrations also showed cross-resistance to such antibiotics as tetracycline, chloramphenicol, and ciprofloxacin [92]. [PERSON] et al. [93] observed that bacteria belonging to genera _Escherichia_ and _Salmonella_ exposed simultaneously to selected commercial herbicides and antibiotics of different classes showed a significant increase in antibiotic resistance. The presence of herbicides caused an increase in minimum inhibitory concentration (MIC) for some antibiotics, although the opposite effect was observed for other pharmaceuticals. However, there is no doubt that additional environmental factors related to agricultural practice can indirectly influence the formation of antibiotic resistance. A source of long-term exposure in the agricultural environment to both antibiotics and antibiotic-resistant microorganisms may be the use of reclaimed wastewater for crop irrigation [94; 95]. Irrigation of fields with reclaimed wastewater (treated sewage effluent) may be an important factor in the spread of antibiotic resistance genes in the environment [96]. Reclaimed water can contain numerous drug-resistant bacteria and drug resistance genes, including those to aminoglycos, sulfonamides, tetracycline, \(\beta\)-lactam antibiotics, chloramphenicol, fluoroquinolones, macrolides, rifampicin, and trimethoprim [97]. [PERSON] et al. [98] in a study conducted under batch microcosm conditions showed the presence of sulfonamide resistance genes in soil subjected to repeated irrigation with reclaimed water. However, there are also studies that do not support the thesis that the use of irrigation with reclaimed water has an impact on the spread of antibiotic resistance agents [99] or suggest that the possible level of risk associated with this is not high [24].
## 3 Results and Discussion
### Comparisons of DI-, HS- and HS-TWA SPME Methods toward Water Analysis
Further, DLs fall within a similar range of those reported elsewhere [21]. In comparison to the NL government monitored total THM values for these two ponds, the data obtained by all three methods (in Table 1) corresponds closely to their Total THM values, given that fluctuations with natural condition changes can occur. Further, there may be other THMs that have not been detected. In such cases, UV detection may be better and more universal for total THM and haloalkane concentration assessments. However, specific THM speciation cannot be determined by UV methods and such information is critical to gain knowledge about potential chemical reactivity and insight to individual reaction pathways, which SPME methods can provide. In comparing HS- and DI- SPME sampling methods, optimized under 10% salt added salt conditions (in Table 2), the DI- method gives slightly lower LODs. However, when sample matrices are complex or contaminated, direct sampling methods, like DI-SPME, can be prone to contamination or fiber breakage. Under such conditions, alternate HS- or TWA- SPME sampling methods could be utilized. In this research, the drinking water pond samples and their calibration matrices were clean and free of debris. HS- and TWA- methods gave similar results. However, TWA sampling is simpler to prepare. Further, it is much easier to carry multiple fibers away from a water body in the case of TWA-on-site sampling than to remove multiple volumes of water. This is an especially important consideration when contemplating measurements of water areas that are remote or where it is a physically difficult to carry multiple water samples out for analysis. Further TWA gives a more accurate assessment over time of a dynamic water system than does spot or grab sampling and hence TWA monitoring data would achieve more reliable background information about the water body.
## 3 Results and Discussion  
### Surface Morphology and Structure  
FESEM  
The surface morphology observed through the FESEM of various nanocoated paramamid fabric structures are presented in Figure 4(a1-d3). As illustrated in Figure 4(a1-d3), the neat para-aramid plain fabrics (Figure 4(d1)) coated with n-B4C, MWCNTs, and GNPs reveal a consistent nanoparticle distribution across the surfaces of the multifilaments (Figure 4(a1), Figure 4(b1), Figure 4(c1), respectively). Upon closer inspection of the warp tow, the filament surfaces exhibit complete nanoparticle coverage (Figure 4(a3,b3,c3)) compared to the neat filament surfaces (Figure 4(d2,d3)), and the high-magnification images further confirm a homogeneous dispersion along the filament surface. Notably, a minimal agglomeration of B4C particles as bulk masses, MWCNTs as entangled tubes, and GNPs as dimple-like formations are observed (Figure 4(a4,b4,c4)).
## 3 Results and Discussion
### YI Value Prediction  
In comparing the _YI_ value data between the experimental results and the prediction values, the second-degree model had a better fit for prediction than the zero-degree and first-degree models. This result was confirmed by the higher mean of the coefficient of determination (Figure 9a-c). The mean of the coefficient of determination, or R2, was between 0.8817 and 0.9675, which is satisfactory (Figure 9c). Hence, the response of these models showed an increasing trend of the _YI_ values as the drying time progressed.