esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Virtual Soundwalk Approach for Assessing Sound Art Soundscape Interventions in Public Spaces
## 3 Results
### Results of the Qualitative Data Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Mean Value**} \\ \hline
**Location** & **Sound Source Type** & **1** & **2** & **Lower Limit** & **Difference** & **Upper Limit** & \(p\)**-Value** \\ \hline  & Traffic noise & 49.11 & 31.89 & 7.74 & 17.23 & 26.71 & **0.0004** \\  & Other noise & 45.03 & 33.13 & 2.43 & 11.90 & 21.37 & **0.0138** \\ A & Human sounds & 40.96 & 40.04 & \(-\)8.10 & 0.92 & 9.95 & 0.8408 \\  & Natural sounds & 35.69 & 44.20 & \(-\)18.13 & \(-\)8.51 & 1.12 & 0.0832 \\  & Designed sound sources & 35.10 & 44.78 & \(-\)18.95 & \(-\)9.67 & \(-\)0.39 & **0.0410** \\  & Acoustic effect & 39.51 & 41.49 & \(-\)11.93 & \(-\)1.97 & 7.98 & 0.6974 \\  & Traffic noise & 39.12 & 43.88 & \(-\)12.84 & \(-\)4.76 & 3.33 & 0.2489 \\  & Other noise & 43.10 & 37.90 & \(-\)4.59 & 5.20 & 14.99 & 0.2979 \\  & Human sounds & 50.81 & 31.43 & 9.95 & 19.39 & 28.82 & **0.0001** \\ B & Natural sounds & 38.10 & 44.90 & \(-\)16.22 & \(-\)6.80 & 2.61 & 0.1566 \\  & Designed sound sources & 43.23 & 38.83 & \(-\)5.45 & 4.40 & 14.24 & 0.3817 \\  & Acoustic effect & 38.21 & 44.79 & \(-\)16.50 & \(-\)6.59 & 3.33 & 0.1928 \\  & Traffic noise & 41.44 & 39.56 & \(-\)7.49 & 1.88 & 11.24 & 0.6948 \\  & Other noise & 46.20 & 31.99 & 4.86 & 14.21 & 23.56 & **0.0029** \\ C & Human sounds & 38.54 & 42.46 & \(-\)13.41 & \(-\)3.93 & 5.56 & 0.4174 \\  & Natural sounds & 46.05 & 33.79 & 2.80 & 12.26 & 21.71 & **0.0111** \\  & Designed sound sources & 37.25 & 43.75 & \(-\)15.62 & \(-\)6.50 & 2.62 & 0.1626 \\  & Acoustic effect & 26.95 & 54.05 & \(-\)36.99 & \(-\)27.10 & \(-\)17.21 & **0.0000** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical significance of change in soundscape descriptors between the measurement points
## 3 Results and Discussion
### Formation Mechanism of N-CQDs
Specifically, in the context of the hydrothermal approach, the mass yield of CQDs synthesized from pure compounds generally surpasses that derived from natural biomass mixtures; indeed, synthetic methodologies employing biomass mixtures generally produce yields below 40%. Only one study in the detection limit column was used to detect Fe (III). At present, CQDs prepared by various methods are mostly used in the field of fluorescence labeling. The detection limit of this study is within the same order of magnitude as those reported in other studies, but slightly higher. Regarding fluorescence performance in CQD synthesis, carbon quantum dots produced from biomass mixtures frequently exhibit multifunctionality due to self-doping effects, whereas pure compounds typically excel in terms of singular optical properties and quantum efficiency. Nevertheless, when considering environmental sustainability and cost-effectiveness, biomass mixtures align well with green chemistry principles [55; 56]; notably, their abundance and economic viability significantly exceed those associated with pure compounds requiring specialized extraction processes [57; 58]. To evaluate the mass yield of CQDs, as illustrated in Scheme 1a, two poplar blocks (about 12 g) were utilized as precursor materials and placed into a 100 mL Teflon-lined stainless-steel autoclave. Approximately 5.04 g of CQDs was acquired post-drying. After numerous tests conducted under identical conditions, the maximum mass production of CQDs was ultimately established at 42 wt%. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Serial Number** & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\
**Mass Yield/\%** & 37.4 & 40.2 & 43 & 50.6 & 39 & 41.8 & 43 & 42 & 42.8 & 42 \\ \hline \end{tabular}
\end{table} 
Table 1: The results of 10 repeated experiments under the same experimental conditions.
## 2 Materials and Methods
### Methods
#### 2.2.2 Spectral Indices: NDVI and NDWI  
The Normalized Difference Vegetation Index (NDVI) is widely used in detecting and quantifying green plant biomass through the reflectance of their leaves captured by remote sensors in the visible and near-infrared spectral bands. This index rescales the image to vary between \(1\) and \(-1\)[39]. When values are in the positive domain between \(0\) and \(1\), it indicates the presence of vegetation such as crops, shrubs, grasslands, and forests. Therefore, higher NDVI values indicate vigorous green vegetation cover [40; 41]. In contrast, negative values (\(0\) to \(-1\)) correspond to other land uses, such as rocks, water, and exposed soil, where vegetation is absent [42]. The index can be calculated using the following equation [41; 42; 43]:  
\[NDVI=\frac{NIR-RED}{NIR+RED} \tag{9}\]  
where \(NIR\) is the near-infrared spectral band, reflecting between \(0.85\)\(\upmu\)m and \(0.88\)\(\upmu\)m, and \(RED\) is the red visible band, reflecting between \(0.64\)\(\upmu\)m and \(0.67\)\(\upmu\)m. To perform this calculation, bands \(04\) (reflecting in red) and \(5\) (reflecting in near-infrared) of Landsat \(08\) were used.  
The NDWI (Normalized Difference Water Index), adopted the model by [PERSON], where its mathematical expression was defined by [43]:  
\[NDWI=\frac{(IVP-SWIR)}{(IVP+SWIR)} \tag{10}\]  
IVP is infrared ranging between \(0.841\)\(\upmu\)m and \(0.876\)\(\upmu\)m, and SWIR (Shortwave Infrared) is infrared medium red between \(1.628\)\(\upmu\)m and \(1.652\)\(\upmu\)m. We used bands \(05\) and \(06\) from the Landsat \(08\) satellite. Similar to \(NDVI\), \(NDWI\) ranges from \(-1\) to \(1\); however, we adopted the scale proposed by [44]: wet soil ranges from \(0.23\) to \(0.60\), water ranges from \(0.35\) to \(0.90\), and tree vegetation ranges from \(0.10\) to \(0.4\).
# Novel Extensions to Enhance Scalability and Reliability of the IEEE 802.15.4-DSME Protocol
## 5 Performance Assessment
### Scalability Assessment
To assess the scalability, a network with star topology was simulated using Algorithm 1 implemented from scratch in Python. Each node periodically transmits to the _PAN-Coordinator_ two kinds of real-time messages (\(f_{1}\) and \(f_{2}\)) with periods equal to \(4T_{SF}\) and \(8T_{SF}\), respectively. In this assessment no retransmissions and errors are considered, as the aim is to compare the scalability of the standard DSME protocol with and without the D-DSME extensions. The scalability is assessed increasing the number of nodes in the network. The number of GTSs (hence, the number of superframes) within the multi-superframe that are required to schedule all network flows without reaching the network saturation was calculated according to Algorithm 1. The analytical results are shown in Figure 4.  
The D-DSME scenario requires a lower number of slots (dark lines in Figure 4) compared with the standard DSME (light lines in Figure 4). This entails a higher scalability even in terms of the number of flows that can be supported. In fact, in the case of 25 nodes (in Figure 4), the number of available GTSs with 4 superframe is equal to 52. Using the standard DSME protocol 50 out of 52 GTSs are required. Conversely, using the D-DSME only 38 out of 52 GTSs are required, thus there are 14 GTSs that can be assigned to additional flows (12 GTSs more than the standard DSME protocol). In both scenarios, when there are more than 39 nodes for the DSME and 40 nodes for the D-DSME, the network reaches saturation (i.e., the number of required GTSs exceeds the number of available GTSs, as it was explained at the beginning of this subsection. Hence, in the worst-case, i.e., before reaching the network saturation, the D-DSME scalability performance are the same than the standard DSME ones. The results show that when the multi-superframe period is lower than the period of the flows \(f_{2}\), in this scenario, the D-DSME provides a higher scalability than the standard DSME protocol. In this scenario, with \(n_{SF}=4\), the improvement of the number of nodes that can be supported is 30% higher than in the standard DSME protocol. D-DSME, compared to the standard DSME protocol, reduces the number of required GTSs, with the consequence of a higher number of available GTSs, thus providing two advantages. First, with a fixed number of superframes within the multi-superframe, the D-DSME provides more room for new periodic flows. Second, as the number of superframes within the multi-superframe can be reduced to provide a shorter multi-superframe, the CAP frequency increases and the average delays for aperiodic non real-time messages are shortened.
## 2 Explanation Methods
### Lime  
LIME stands for Local Interpretable Model-agnostic Explanations. It was developed by [PERSON] et al. [12]. This method is local and model-agnostic because it can explain local individual prediction for any kind of ML model.  
The goal of the explanation is to comprehend why the black-box model made a certain prediction. LIME explains the model prediction locally by investigating what occurs to the prediction when the input data instance is varied (perturbed). To this end, LIME generates new samples that consist of perturbed instances, and the black box is used to find corresponding predictions of these new samples. LIME then trains a simple explainable model, which gains weight by the vicinity of the perturbed instances to the interesting instance. The simple explainable model can be chosen from the explainable model's space; for instance, this model can be Least Absolute Shrinkage and Selection Operator (LASSO) [21]. LASSO uses \(L_{1}\) regularization to avoid problem of overfitting in ML models and improve the prediction accuracy and interpretability. Mathematically, LIME explanation could be expressed as  
\[\text{Expl}(x)=\underset{g\in G}{\text{argmin}}\mathcal{L}(f,g,\pi_{x})+\Omega (g), \tag{1}\]  
where \(g\) is the explainable model for instance \(x\) that minimizes loss \(\mathcal{L}\). \(G\) is the group of available explainable models, for instance, all possible linear regression models. \(\mathcal{L}\) estimates how the explanation is close to the prediction of the intended black-box model \(f\). \(\Omega(g)\) is a penalty term to measure the complexity of explanation result. The vicinity measure, \(\pi_{x}\), determines how the neighborhood we consider for the explanation around instance \(x\) is large.
# Research on Fracture Mechanism and Stability of Slope with Tensile Cracks
## 4 Results Analysis
### The Effects of Parameters on the Stability of Slope
#### 4.2.3 The Effects of Crack Depth and Internal Friction Angle on the Safety Factor  
The safety factor is calculated under the different values of soil internal friction angle \(\varphi\) from \(10^{\circ}\) to \(40^{\circ}\), illustrated in Figure 11. As to be expected, the slope is more stable for higher values of the internal friction angle. It is shown in Figure 11 that the higher values of soil internal friction angle, the greater safety factor of the slope. The results of two cases also show that with the increasing internal friction angle, the safety factor gradually increases after a relatively significant decrease, especially on the higher values of friction angle. This indicates that the safety factor is highly sensitive to change in internal friction angle values. Hence, the internal friction angle plays an important role in slope instability subjected to tensile crack. As for an explanation in case 2, when \(\varphi\) increases from \(10^{\circ}\) to \(40^{\circ}\), the value of safety factor increases by 110.56% (from 1.421 to 2.992). For the same reason, in case 1, the safety factor also increases by 129.65% (from 1.150 to 2.641). It is noteworthy that the higher values of internal friction angle, the more visible the optimal crack depth effects on the safety factor. This is due to the calculation method of crack depth. When the internal friction angle is very small, the slip surface is mainly composed of the shear surface, so the tensile crack has only a minor effect on the safety factor under this condition. For \(\varphi=10^{\circ}\), the curves of safety factor of the case 1 and case 2 decrease linearly.  
Figure 11: Relationship between safety factor and crack depth under different friction angle of soil.
## 2 Research Background
### Understanding of the Digital Twin
#### 2.2.2 Application along the Asset Life Cycle
With regard to increasing digitalization and the advancement of Industry 4.0 as well as the _industrial IoT (IIoT)_, the interoperability of DTs, i.e., the ability to work together almost seamlessly, is becoming increasingly important [15]. This aggregation of DTIs is known as _digital twin aggregation_ (DTA). It can be a computer construct that has an independent data structure and queries the DTIs either ad-hoc or proactively. In a visionary picture, a DTA of the German rail transport, including all its relevant assets, could proactively and continuously utilize data of different DTIs to enable a fully digital and optimized operation of the whole system [9] (see Section 5.2.3 and Figure 2).
# Intelligent Network Service Optimization in the Context of 5G/NFV
## 3 MAPE Framework Architecture  
In this section we present architectural decisions and the components that compose the proposed solution, following the MAPE paradigm in use cases regarding IoT-enabled CNAs that run on central or fog-level cloud infrastructures and require automated and intelligent orchestration of services. The proposed architecture integrates with the appropriate tools that allow for the quality control of the MAPE services and the infrastructure resources focusing on QoS provisioning capabilities, enabled by monitoring certain related metrics that are collected from both the underlying infrastructure and the running VM-based or container-based services.  
Notwithstanding the obstacles, the state of the art is mature enough to integrate the MAPE framework into use cases regarding IoT-enabled CNAs that run on central or fog-level cloud infrastructures and require automated and intelligent orchestration of services.  
From this viewpoint, MAPE principles can be applied in many applications in the IoT domain that are sensitive to deviations in QoS levels. These types of applications could include e-health, autonomous ground and aerial vehicles, ecosystems providing streaming video from disaster sites, crops, etc. [31]. The increasing number of self-driving vehicles will eventually produce staggering amounts of data. Consequently, the network infrastructure should demonstrate quick and precise reflexes, in order to transparently handle the countless events triggered and optimally adapt the underlying network and computing resources to ambiguous issues and circumstances. A versatile and robust framework such as MAPE, with integrated learning mechanisms, would enable the close-to-real-time identification of state changes in the monitored vehicular ecosystem. Followingthe gathering of the desirable performance metrics from moving or stationary sources and sensors, the planning and execution service would commence targeted reallocations of the available computational and networking resources. These tailored alterations would aim to improve not only the overall road safety but also parameters such as the efficiency of the highway energy budget and traffic orchestration, after each iteration.  
MAPE is responsible for gathering performance metrics from the running VNFs and application services, as well as the status of assigned resources in both computation and networking terms. These metrics are necessary for the process of optimization of network resources and for QoS provisioning. The proposed MAPE-based design shown in Figure 1 is composed of the following subcomponents that realize the MAPE concept: the monitoring service, the analysis service, and the planning and execution service. The roles and functionalities of these subcomponents are explained in the following paragraphs.
## 3 Numerical Simulations
### The Measurement Point Arrangement Determined by SVM
When the reflection boundary exists, the number of measurement points turns out to be 234 (axial: 18; circumferential: 13). The prediction results are shown in Figure 6. It could be seen that, with a tiny amount of measurement points, the sound prediction result in the half-space could also be accurate enough on the whole prediction plane. When the reflection boundary exists, the quantity of measurement points may change with the modal variation. Figure 5: The sound prediction errors of ESM on the prediction plane in the free field at the frequency of 71 Hz (270 uniform arranged measurement points determined by SVM). (**a**) \(p_{E}\) and (**b**) \(\varphi_{E}\). Figure 6: The sound prediction errors of ESM on the prediction plane when \(h_{U}=0.2\) m at the frequency of 71 Hz (234 uniform arranged measurement points determined by SVM). (**a**) \(p_{E}\) and (**b**) \(\varphi_{E}\). Thus, it could be seen that, if the detail of vibration modes could be obtained in advance, SVM could be an effective guidance to the design of the measurement system. The above simulations confirmed the possibility of sound prediction with a small-scale measurement system. However, it is difficult to acquire the exact detail of SVM in engineering via vibration measurement. Instead, the vibration modes could only be analyzed with numerical methods. Thus, it is often computationally costly to design the measurement system. Moreover, when the size of the structure is quite large or the excitation is complex, the details of structural vibration could be very complex, which means that an enormous quantity of measurement points are in need according to the theory of SVM [7]. Therefore, the method based on SVM could hardly be regarded as the optimal strategy of the measurement point arrangement.
## 3 Ferry Node Ranking Clustering Algorithm (FNRCA)
### Description of the Algorithm  
The proposed algorithm is an extension based on our previously published work NRCA [7; 8; 9] with node ranking being based on the planned path of the ferry rather than the location of the BS. The following steps give a description of the algorithm and cluster heads' selection process:  
* The BS divides the sensing field into smaller partitions called clusters based on the assumed communication/sensing range of the nodes.
* The sensing field will then be divided into virtual square grids based on the specified sensing range. Each virtual grid will be of size \(r\times r\) where \(r\) is the sensing range. Multiple clusters fall within one or more virtual grids.
* Initially, a ferry checkpoint (virtual base station) is placed at the center of each virtual grid.
* Initially NRCA is used to choose CHs based on their location from the ferry's checkpoints.
* Nodes and cluster heads will associate themselves with the ferry's checkpoint based on their location within each virtual grid.
* Border line nodes and cluster heads will be associated with cluster heads and checkpoints closer to them based on distance respectively.
* After the initial phase, NRCA is applied on each virtual grid based on the position of the ferry's checkpoint and energy values of the associated nodes. Therefore, the energy consumed per virtual grid will be minimized. This is explained below in the subsequent sections.
* The ferry will be dispatched from the BS to visit all checkpoints and return back to the BS using a Hamilton cycle as will be shown later.
* At each checkpoint the ferry stops to collect gathered data from cluster heads associated with this checkpoint. Gathered data consists of sensed data and control information like nodes energy values and nodes GPS locations.
* Dissemination of data from cluster heads to the ferry is triggered by a control message communicated by the ferry to the cluster heads associated with each checkpoint. The time the ferry will stay for at each checkpoint is determined based on several parameters as will be shown later.
* In the subsequent rounds of dispatching the ferry, the BS choses the new locations of the checkpoints based on the collected information to minimize the energy of the overall sensing field as will be shown later. The BS will then determine the new path of the ferry using Hamilton cycle as was done in the initial phase.
* The energy model used for sensing and dissemination of data in our simulation is the same used by [33; 34; 35; 36; 37; 38].
## 3 Results
### Functional Outcomes, Patient Satisfaction and Complications  
The VAS, AOFAS, and FADI scores collected before the procedure and at 6, 12, 24 and 36 months were analyzed with Student's test. The analyses demonstrated a significant or marked improvement in all scores throughout the study period. The VAS and FADI scores showed significant differences with the baseline scores at all time points (all \(p<0.005\)). The AOFAS scores were also significantly different from the baseline score at 6, 12, 24, and 36 months (\(p=0.013\), \(p<0.005\), and \(p<0.005\), and \(p<0.005\), respectively). All these data are reported in Table 2.  
Pairwise comparisons were also performed, between 6 and 12 months, 12 and 24 months, and 24 and 36 months (Table 2). The differences in the VAS, AOFAS, and FADI scores between 6 and 12 months were not significant (\(p=0.760\), \(p=0.210\), and \(p=0.490\), respectively). In contrast, the differences between 12 and 24 months were significant for the AOFAS and FADI scores (\(p<0.005\) and \(p=0.006\), respectively), but not for the VAS score (\(p<0.090\)). Finally, all scores significantly declined between 24 and 36 months for the VAS, AOFAS, and FADI scores (\(p<0.005\), \(p=0.040\) and \(p=0.020\), respectively). The trends in the AOFAS and FADI scores during the 36 months of follow-up are shown in Figure 1.  
At 36 months, 81% of patients were very satisfied and 19% were satisfied (Table 2). None of the patients experienced complications related to the injection either during the procedure or in the follow-up period.