esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Then, the data clustering is used to differentiate the faulty lines. Various existing clustering algorithms are selectively used based on crisp [13], fuzzy [14], and possibility methods [15; 16]. The hierarchy clustering algorithm is an unsupervised machine learning algorithm which builds a hierarchy of clusters based on group similarities without any knowledge about the pre-defined groups [17; 18]. The main advantage of the hierarchical clustering over partial clustering is that a hierarchy dendrogram can be drawn to find the appropriate number of clusters in a dataset. This paper extends the contribution of [19] in terms of the theoretical algorithm of fault detection and differentiation. In [19], we investigated the faulty line detection in a multi-core cable by using K-means clustering-based TFDR. When a fault occurred, results showed that other normal lines were affected at similar points in the multi-core cable. However, only the identification of the faulty line within the multi-core cable had been considered in the study. Moreover, classifying the impedance discontinuity points from the TFDR results in the multi-core cable have not been considered in [19] which is important for applications to the multi-core cable diagnosis. On the other hand, the applicability of the proposed algorithm in this paper is verified through additional experiments using various types of multi-core cables. To achieve this goal, we extracted input features of the TFDR results and learned each machine learning algorithm for diagnostic purposes. The detection and clustering results obtained from the experimental fault scenarios reveal that clustering TFDR results can be used for fault locating faulty line identification among a pool of normal lines in a multi-core cable. The following sections describe the TFDR, ANNs, and clustering algorithms that are used to detect and analyze the fault in multi-core cables. In Section 3, we present cable fault scenarios and our experimental setup, and, in Section 4, we discuss the obtained experimental results. Finally, we conclude the paper in Section 5.
## 3 Results and Discussion
### Principal Component Analysis  
PCA was carried out to discriminate against the presence of SDPP in the test samples. The results of the PCA application to the electronic nose data and all full-spectral data are shown in Figure 4. The first two principal components (PC) accounted for 95.64% of the variance in the electronic nose data,  
Figure 4: The T\({}^{2}\) contributions of (**a**) each electronic nose sensor and (**b**) the electronic nose raw data for sample 233.  
with PC1 and PC2 explaining 89.06% and 6.58% of the variance, respectively (Figure 5a). The majority of the variance in the NIRS data was captured by the first two PCs, accounting for 99.56%, with PC1 accounting for 85.37% of the variance and PC2 accounting for 14.19% of the variance (Figure 5b). The rest of the components did not provide further useful information for determining the presence of SDPP [45]. According to Figure 5a, the separation between samples with different concentrations was not substantial. There was an overlap between the \"0%\" samples and the other samples. As seen in Figure 5b, although the separation of different samples was not very clear, there was no overlap between the \"0%\" samples and the other samples. In addition, the clustering quality of the samples was better than that from the electronic nose test. The results indicated that the volatile components of SDPP and WPC might be similar, although the chemical compositions of the materials are different.
## 3 Hydroxyapatite Particles in Scaffold Manufacturing and Cell Signaling
### Three-Dimensional Printed HA-Based Scaffolds  
Recently, additive manufacturing has gained a lot of attention in many industries, including the tissue engineering area [51; 52]. Additive manufacturing offers the possibility of obtaining scaffolds that fit perfectly at the defect site, due to its ability to digitally recreate the damaged tissue before creating the scaffold. Even if there are multiple techniques that could be applied for creating tissue scaffolds, in the case of ceramic-based ones there are only two categories which are suitable for manufacturing: laser-based techniques and extrusion-based techniques. Intensive research has been made in order to manufacture HA-based structures able to induce osteogenesis and angiogenesis [53]. For example, [PERSON] et al. stated that 3D-printed HA scaffolds present low strength and brittleness. In order to overcome this issue, they created a HA scaffold coated with polycaprolactone (used in order to increase the compressive strength) loaded with BMP-2 (bone morphogenic protein). BMP-2 is used in order to intensify osteoinduction and to accelerate bone healing. The samples were subjected to in vivo testing in rabbit calvarial defects and showed an improvement in terms of bone regeneration at 8 weeks after implantation when compared to uncoated scaffolds. However, the authors concluded that the use of BMP-2 is quite expensive for this type of clinical applications, since a large dose is needed for an effective response [54]. On the other hand, in a study conducted by [PERSON] et al., a BMP-2 related peptide known as P28 was integrated in nano-HA scaffolds and showed higher adhesion rates and suitable proliferation percentages in cases of MC3T3-E1 cell lines when compared to controls (pure nanoHA) [55]. [PERSON] et al. stated in a comprehensive review on this topic that multiple HA-based composite scaffolds obtained via 3D printing processes were investigated over the years but most of them were based on polymeric matrices reinforced with HA particles of different sizes ranging from nanometers to micrometers [53]. Even if the results are promising for a broad range of polymers (e.g., chitosan, gelatin, silk, polycaprolactone, or collagen) from both physicochemical and biological points of view, the use of HA as a main and primary compound in 3D-printed scaffolds remains a challenge because of its mechanical properties which make it difficult to be processed by the available equipment. Moreover, the manufacturing of such products at an industrial scale is difficult especially due to the high costs involved in the design, production, and further in vitro, in vivo, and clinical set up testing.
## 4 Numerical Examples
### A Slanted Single-Edge Crack Plate
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(c\)/\(L\) & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** \\ \hline \multirow{2}{*}{Present} & \(K_{l}(\text{MPa}\cdot\text{m}^{1/2})\) & 7.494 & 6.968 & 7.046 & 7.083 & 7.102 & 7.168 \\  & \(K_{l}/\sigma\sqrt{\pi a}\) & 2.990 & 2.780 & 2.811 & 2.826 & 2.833 & 2.860 \\ \hline \multirow{2}{*}{[PERSON] et al. [7]} & \(K_{l}(\text{MPa}\cdot\text{m}^{1/2})\) & 7.445 & 6.931 & 7.009 & 7.046 & 7.064 & 7.129 \\  & \(K_{l}/\sigma\sqrt{\pi a}\) & 2.970 & 2.765 & 2.796 & 2.811 & 2.818 & 2.844 \\ \hline \multirow{2}{*}{[PERSON] et al. [8]} & \(K_{l}(\text{MPa}\cdot\text{m}^{1/2})\) & 7.490 & 6.971 & 7.049 & 7.086 & 7.104 & 7.171 \\  & \(K_{l}/\sigma\sqrt{\pi a}\) & 2.988 & 2.781 & 2.812 & 2.827 & 2.834 & 2.861 \\ \hline \multirow{2}{*}{Present} & \(K_{ll}(\text{MPa}\cdot\text{m}^{1/2})\) & \(-0.397\) & \(-0.338\) & \(-0.120\) & \(-0.030\) & \(-0.042\) & \(-0.153\) \\  & \(K_{ll}/\sigma\sqrt{\pi a}\) & \(-0.158\) & \(-0.135\) & \(-0.048\) & \(-0.012\) & \(-0.017\) & \(-Figure 8: SIFs for different crack spacing (\(L/W=3\)): **(a)** SIFs \(K_{I}\); **(b)** SIFs \(K_{II}\). Figure 6: SIFs for different crack spacings (\(L/W=1\)): **(a)** SIFs \(K_{I}\); **(b)** SIFs \(K_{II}\). Figure 7: SIFs for different crack spacing (\(L/W=2\)): **(a)** SIFs \(K_{I}\); **(b)** SIFs \(K_{II}\). The errors of energy norm of the P-FEM solutions and relative errors of \(S\!IF\!s\) comparison with [PERSON] et al. (2020) for the case \(L/W=3\), \(c/L=0.3\) are listed in Table 8, the others are similar.
###### Abstract  
This paper proposes novel satellite-based wireless sensor networks (WSNs), which integrate the WSN with the cognitive satellite terrestrial network. Having the ability to provide seamless network access and alleviate the spectrum scarcity, cognitive satellite terrestrial networks are considered as a promising candidate for future wireless networks with emerging requirements of ubiquitous broadband applications and increasing demand for spectral resources. With the emerging environmental and energy cost concerns in communication systems, explicit concerns on energy efficient resource allocation in satellite networks have also recently received considerable attention. In this regard, this paper proposes energy-efficient optimal power allocation schemes in the cognitive satellite terrestrial networks for non-real-time and real-time applications, respectively, which maximize the energy efficiency (EE) of the cognitive satellite user while guaranteeing the interference at the primary terrestrial user below an acceptable level. Specifically, average interference power (AIP) constraint is employed to protect the communication quality of the primary terrestrial user while average transmit power (ATP) or peak transmit power (PTP) constraint is adopted to regulate the transmit power of the satellite user. Since the energy-efficient power allocation optimization problem belongs to the nonlinear concave fractional programming problem, we solve it by combining [PERSON]'s method with Lagrange duality method. Simulation results demonstrate that the fading severity of the terrestrial interference link is favorable to the satellite user who can achieve EE gain under the ATP constraint comparing to the PTP constraint.  
Keywords:wireless sensor network; cognitive satellite; power allocation; energy efficiency; fading channels; interference power constraint; transmit power constraint +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Amino Acid Composition of Hydrolysates  
LC-MS analysis of mussel by-product Protamex(r) hydrolysates showed them to contain most essential amino acids except tryptophan, which is not measured by the method used. The ratio of essential to nonessential amino acids varied from 0.68-0.96 and this ratio was 0.96 for sample KHN18, as shown in Table 2. At 0.96 the ratio was close to the ideal ratio of 1 and was better than plant sources like soy, pea and hemp protein [39] as well as marine-sourced salmon hydrolysate generated by other researchers [40]. Glutamate was the most abundant amino acid present, which is known to be responsible for the umami taste of food products, followed by aspartate and glycine, in the total amino acid fraction of the hydrolysate. GC-MS analysis of mussel by-product hydrolysates showed the presence of minor components such as benzaldehydes, which are known to be flavour compounds. Benzaldehyde is thought to be a product of the Maillard reaction, and was reported to be present in volatiles of other protein hydrolysates. Hydrolysates from other mussel varieties were explored previously for the development of flavour ingredients [41] and the presence of these compounds in the hydrolysates would favour their use for flavour ingredients. Certain amino acids are associated with specific tastes, such as L-histidine (essential amino acid), which is bitter; L-glutamate (non-essential amino acid), which imparts an umami flavour and L-threonine (essential amino acid), which gives a sweet taste [42]. Thus the relative abundance of these amino acids in the peptides dictates the final taste of the hydrolysate product. Further, taurine, a Sulphur-containing amino acid was found abundantly (0.86%-3.25% _w/w_) in the mussel hydrolysate samples, with maximum amount detected in KHM19 at 3.25% _w/w_ of hydrolysate. Taurine is considered a critical nutrient for humans and diet represents its main source. Among the several functions of taurine, it is known to be an anti-oxidant and also effective against cardiovascular disorders [43]. Figure 6: The percentage solubility of various mussel Protamex® hydrolysates across a pH range of 2 to 10. Six representative samples from the three sampling sites, Mulroy bay, Killary harbour and Addgroom, collected from November 2018 to July 2019, were used for analysis. Analysis was performed in triplicate (n = 3).
## 6 Conclusions  
This paper has described an automatic vehicle positioning data collection system for public transportation on roads. The system was developed to help transportation regulatory agencies perform their objectives related to the control and improvement of public transportation. The system works autonomously, acquiring position readings for the vehicle and storing and transferring these readings automatically to remote data banks held by the regulatory authorities. In addition, the system works transparently and without interfering with the remaining systems installed in the vehicle. To attain its objectives, the system only uses position information provided by a GPS, without requiring data from the automatic passenger counting systems or rate-of-use systems used by transportation companies. Wi-Fi technology is used to transfer the positioning data and for software updates. 3G/GPRS/GSM technology is used to communicate data related to information on the state of the different elements of the system. Due to its structure and requirements, the system can be used in any type of public transportation vehicle that operates on roads.  
To illustrate the utility of the system, we have presented a case study of its application. This practical application consists of the verification and identification of the locations at which the vehicles stop systematically along a route. This functionality allows the regulatory agency to verify that the locations where stops occur are those planned and agreed upon by transportation companies, verify the frequency of use of the stops used by the vehicles to adequately define the stops in the transportation network, and identify points that are not stops but at which the vehicles stop systematically due to traffic signals or the topology of the road on which the vehicles circulate. This identification would allow regulatory agencies improve the representation of the transportation network and design better routes. To perform this identification, K-means, the classic technique of pattern recognition, was used.
## 3 Methodology
### Model Explainability via SHAP  
In addition to tree-based models, we performed a global feature importance analysis for the logistic regression classifier using SHAP (SHapley Additive exPlanations). SHAPvalues quantify the impact of each feature on model predictions, providing an interpretable visualization of how individual attributes influence the classification outcome. We used the LinearExplainer variant, which is optimized for linear models, and calculated SHAP values on the training set. The resulting summary plot enables a direct comparison between the factors driving predictions in logistic regression and other model types.
## 4 Results
### Communication between Stations  
To transport the phones between the stations, a robot that has a Lidar sensor model SICK-S300 generates the mapping and takes the best route. This mapping can be visualized in the Virtual environment and helps the operator to know the possible routes, and thanks to the continuous data received, they can plan new routes based on the conditions that can be simulated or mapped before executing the trajectory. First, the creation of a new map is carried out; by default, Robotino Factory places a brief environment in space and the user configures the environment by moving the Robotino, where it is necessary to move in all possible directions for the Lidar sensor to start performing the mapping of the physical environment and all the objects that are around it; in this way the workspace is established. It is important to consider that the minimum and maximum height must be considered when mapping. Furthermore, the system implemented in the MES allows for obstacle avoidance as shown in Figure 10.  
When performing the trajectory analysis with the obstacle avoidance system of the conventional MES vs. the MES with the DT, it was observed that there is an improvement in route times, whereby several tests were considered by placing obstacles in the trajectory. This is because, in the conventional MES, there is a fixed distance that is programmed when an obstacle is detected. In the new system, this distance can be configured through tests carried out in the digital environment, and depending on the characteristics of the obstacle and based on the data obtained in real time, the avoidance distance can be configured. The results of the tests carried out are seen in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Scenario with Several** \\ **Obstacles** \\ \end{tabular} & \begin{tabular}{c} **Mean Time (s) MES** \\ \end{tabular} & \
\begin{tabular}{c} **Mean Time (s)** \\ **DT-MES** \\ \end{tabular} &
\begin{tabular}{c} **Reduction in Time (s)** \\ \end{tabular} \\ \hline
0 & 25.3 & 19.6 & 5.7 & 22.5 \\
1 & 37.5 & 28.1 & 9.4 & 25 \\
2 & 46.1 & 35.2 & 10.9 & 23.6 \\
3 & 59.8 & 47.8 & 12 & 20 \\
4 & 75.3 & 62.1 & 13.2 & 17.5 \\
5 & 87.2 & 71.4 & 15.8 & 18.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis of obstacle avoidance times for the MES vs. the DT-MES.  
Figure 10: (**a**) Digital mapping and (**b**) the physical environment for obstacle avoidance.  
As seen in the previous table, there is an average time reduction of 21.1% of the proposed system; this is because thanks to the data received by the DT, it can simulate trajectories and configure the parameters for evasion based on the characteristics of the obstacles presented, which represents a significant advantage and therefore an increase in production. The statistical distributions for the percentage of time reduction can be observed in the histogram of Figure 11, where it can be observed that the time reduction is almost similar in all cases regardless of the number of obstacles that are presented, this is due to the operation performed by the DT.
# Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services
## 5 Use Cases  
Mobile data collection has received significant attention in recent years as described in the related works (Section 2). This is because it is important for several use-cases as discussed in the survey [27] including traffic prediction, enhancing routing, traffic classification, resource management, root cause analysis and QoS/QoE management.  
We are interested here in three main use cases. The first one is the measurements analysis in heterogeneous environments. By heterogeneous environments, we mean the use of different user's terminals and the consideration of three application services in addition to the application of two mobility test modes.  
The second use case presents the root cause analysis (RCA) of poor quality identified in some sectors for a given service. The idea is to address the performance of the connections that seem to be \"poor\" from the system's point of view (that has the status \(FAILED\) or \(TIMEOUT\)) or from the user's point of view (when the MOS score is 1 or 2). Notice here that, when the user application succeeds at connecting to and obtaining a response from a server (i.e., system view status \"OK\"), the service quality could be poor for the user (e.g., low bitrate).  
The third and last use case consists of studying the impact of radio parameters on the video metrics and user QoE using the test cycles (sessions) dataset. The objective is to explore the possibility to predict the overall video quality with ML techniques using radio parameters including RSRP, RSRQ and SSNR.
## 3 DePaaS: Architecture and Design
### Advanced Algorithms  
Software Defect Prediction is a novel idea for the industry to follow and implement. DePaaS implements several advanced algorithms to help end users to choose the best of SDP models, most appropriate feature sets for training SDP models, and optimum values for model tuning parameters.
* _Recommending Models_: The knowledge base of software defect prediction recognizes about eightty SDP models. End users might find it overwhelming to choose the best SDP model out of this large number of choices. As a means of help, these models could be ranked based on Friedman test scores of SDP model performance. However, an advanced algorithm that recommends the most suitable search based, or hybridized SDP models based on the parameters of the project on hand could be very useful. DePaaS implements such an algorithm.
* _Recommending Features and Feature Sets_: End users need to select a set of features (called feature sets) to train the chosen SDP model. As there are about eighty features that can be extracted from software code and defect datasets, choosing the most optimum set would be a difficult task for DePaaS users. Therefore, DePaaS provides an advanced algorithm to recommend features sets. [PERSON] et al. (2019) (1996) established that [PERSON]'s OO metrics show to be better predictors than the best set of \"traditional\" code metrics. Hence, they can be offered as the default feature set to train the SDP model. Other feature sets such as [PERSON] metrics Henderson and [PERSON] (2018), [PERSON]'s metrics [PERSON] (2018) and QMOOD metrics suite [PERSON] et al. (2019) could also be offered by the recommendation algorithm.
* _Recommending Model tunable parameters_: Every SDP model implements a learning and/or searching algorithm. Performance of such algorithms could be influenced by tuning several parameters. Such parameters vary with the chosen SDP models. For example--common tunable parameters of GP SDP models include mutation probability (Mp), crossover probability (Cp), size of population (PS), number of generations (NG) and distance function (DS) [PERSON] et al. (2019). Other parameters include number of rules, number of labels, convergence platform width, etc. [PERSON] et al. (2019). For machine learning based SDP models, tunable parameters include confidence, number of leaf instances, maximum depth, number of labels, feature adjustment rate, number of nodes between prune, maximum number of nearest neighbour and probability of growth and pruning [PERSON] et al. (2019). An advanced algorithm would recommend the most optimum value for these tunable parameters.  
Thus, the advanced algorithms of DePaaS could successfully recommend SDP models, feature sets and tunable parameters of SDP models. Such algorithms would reduce the complexity of the SDP process. End users of DePaaS could accept recommendations of these algorithms initially and could take independent decisions as they gain experience with the SDP process.