esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Influence of Slit Width on Frequency Response of Slitted Beams  
Optimizing the energy harvesting capabilities of slitted beams with PZT requires an understanding of how slit width affects the frequency response of the beams, the resonant frequencies, the resonance peak amplitudes, and the existence of anti-resonance dips, all of which are crucial elements in establishing the energy capture efficiency, as impacted by slit width. Our goal was to maximize the overall potential for energy harvesting by investigating dynamic response for slit widths of 2 mm, 4 mm, and 6 mm.  
Figure 8 illustrates the dynamic behaviour of a slitted beam with varying slit widths (2 mm, 4 mm, and 6 mm). As the slit width increases, the resonance peaks shift to lower frequencies due to the reduced effective stiffness of the beam, making it more flexible. In mechanical systems, lower stiffness corresponds to lower natural frequencies, explaining the leftward shift of the peaks. Additionally, the distance between the two resonance peaks decreases as the slit width increases, which is attributed to enhanced mode coupling. A wider slit introduces more interaction between the beam's vibrational modes, leading to closer resonance frequencies. This behaviour highlights how increasing slit width reduces stiffness and amplifies mode coupling, which are critical factors to consider in optimizing energy harvesting device designs using slitted beams.  
Furthermore, the anti-resonance frequency, where the response dips significantly, also shifts to lower values as the slit width increases. This shift is again due to the reduction in stiffness and the changes in mode coupling. The anti-resonance dip becomes more pronounced as the slit width increases because the stronger coupling between vibrational modes leads to more destructive interference at certain frequencies, resulting in a deeper and more noticeable reduction in output at the anti-resonance point. This behaviour emphasizes the need to carefully design slit dimensions when tuning the dynamic response for specific applications, such as energy harvesting or vibration control.  
Figure 7: Frequency response function of slitted beam with PZT layer and three different tip masses.
# A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud-Fog Computing
## 3 Problem Statement  
Recent advances in critical cloud-fog computing infrastructures as service-based dynamic environments have accelerated the deployment of intelligent devices to accomplish specific tasks and achieve market goals. Consider the example of applying IoT and sensory-based fog systems in vehicular networks. Some time-critical data, such as accident data, are of high importance to ambulance/police departments and must be sent to control systems equipped with powerful computing resources to take countermeasures. Delays in the processing of such data result in monetary and catastrophic effects, in which QoS requirements and agreements produce penalties reflective of such effects. Hence, the integration of such devices into cloud-fog computing environments constructs reliable networks that can further monitor, collect, analyze, and process data efficiently.  
Constraints on energy consumption, response time, and bandwidth requirements are inevitable challenges that have further attracted the attention of researchers. A fog computing environment increasingly produces a large volume of jobs waiting to receive cloud services, each of which consumes resources and energy to strictly meet SLA obligations. The growing volume of fog data potentially produces backlog bottlenecks that cause execution difficulties on cloud computing resources, which are structured with computational processing power to tackle complex fog workloads.  
Operation costs on fog clients and cloud service providers increase by increasing not only the cost of servicing fog jobs and SLA violation penalties but also the cost of the energy required to communicate and execute such fog demands. For instance, consider a fog node that requests a task to be serviced on a remote cloud within a specific tardiness limit. The longer the waiting and service times of the fog job in the cloud-fog environment, the higher the service cost and energy consumption required to meet the job's demand. Similarly, the performance metrics of the response and execution times incur high performance costs when their time values increase in the service environment.  
The research question arises when a scheduler formulates fog jobs for execution using the computing power of cloud resources so that QoS requirements are met while energy is concurrently saved. In this paper, the performance enhancement is focused on the side of the cloud computing environment, and thus the problem addressed is stated as follows:  
_Consider the case of fog nodes that deliver job workloads of various QoS expectations and energy demands to a cloud computing environment that comprises identical computing resources to service fog jobs. Each fog job is subject to SLA obligations that define constraints of service cost and execution energy. It is required to deploy and service fog jobs in the cloud computing environment such that energy is preserved and the cost of service is mitigated._
## 2 Problem Modeling
### Utility of the Solutions  
To quantify the goodness of the different network colorings, we have used the concept of utility, which is closely related to the perceived throughput and \(SINR\). According to [8], in a wireless network, the throughput equals a maximum value when the \(SINR\) is over a certain value \(SINR_{max}\) and monotonically decreases with the reduction of \(SINR\) until an insufficient value of \(SINR\), called \(SINR_{min}\), is reached, when the throughput falls to zero. We can consider the utility seen by node \(i\) (\(U_{i}\)) as a normalized throughput, so it can be defined as a value ranging from zero to one, with zero corresponding to the situations when there is a very low-quality reception and the devices cannot keep connected (throughput equals zero) and one corresponding to the case when the signal quality is excellent (throughput equals its maximum value). Figure 2 shows the relation between utility and \(SINR\), expressed in dB.  
Threshold values for \(SINR\), which in turn define maximum and minimum utility values, have been defined from the values presented in [32]: \(SINR_{min}\) = 10 dB and \(SINR_{max}\) = 40 dB.  
Finally, the utility value for a specific network coloring is computed as the sum of the utility values for all nodes in the network, that is:  
\[U=\sum_{\forall i}U_{i} \tag{7}\]  
In a wireless sensor network, it is important to consider sensor energy consumption issues, as they will have a clear impact on sensor lifetime. However, a surveillance camera will consume the same energy independent of the frequency channel on which it is operating. For that reason, the different \(SINR\) values that the different strategies provide come from different values in the interferences (denominator in the \(SINR\) equation), not by different values in the signal (numerator in the \(SINR\) equation).
## 4 Current Status and Identified Needs  
The state-of-the-art in terms of Cal/Val infrastructure and methods for optical land imaging sensors and the current needs and gaps are reported and discussed within this chapter. The needs are identified and grouped per products level, i.e., starting from Level 1 and moving to Level 2 surface reflectances and bio-geophysical variables. This is in line with the ESA's bottom-up approach to Cal/Val, which starts with the verification of cross-sensors consistency at TOA level and continues along the processing chain up to the bio-geophysical variables. This approach ensures full traceability and detailed characterization of the various uncertainty contributions and their propagation through the different products levels.
## 4 Reading Musical Notes as an Image Recognition Task and Its Possible Solutions
### Problem Related to Reading Notes  
Considering the fact that musical notation is performed in the form of black characters on a white background, a sufficiently processed image will be a grayscale image or even a binary black and white image. Further, limiting the area of the recognized image to the height of one staff significantly reduces the amount of memory area occupied and thus, the computational complexity. In the image prepared in this way, the position of the musical notation marks can be distinguished using an algorithm that denotes the change in the arithmetic mean value of individual columns and rows describing the pixel positions.  
The isolation of objects in the image does not pose a major problem, whereas the identification of the symbol itself becomes problematic. This is due to the homogeneity of the images. Regardless of the position of the same note at different heights of the treble staff, the mathematical representation of the image has almost identical values.  
It was assumed that the neural network must identify single symbols of musical notation appearing at the network inputs. Thus, for network learning, images containing single symbols were prepared with distortions in the form of symbol deformation, as shown in Figure 3, and as output patterns, target symbols without interference. They are shown in Figure 4. For the purposes of this research, the patterns of symbols such as the treble clef and the whole note, half note, crotchet and eighth note were made in all positions of the scale. For each symbol, 37 images of distorted patterns were generated, and the final size of the patterns was set to 90 \(\times\) 240 pixels.  
Figure 3: Sample images of distorted musical notes [39].  
Figure 2: The methodology of musical notation classification task [source: authors].  
In the case of designing the classical image identification algorithm, only the model symbols of musical notation from Figure 4 were used.
## 4 Experiment
### Localizing Accuracy  
In this study, localizing accuracy \(e_{\text{accu}}\) is defined as the pixel distance from the detected point \(\mathbf{P}_{\text{det}}\) relative to the labeled point \(\mathbf{P}_{\text{lab}}\):  
\[e_{\text{accu}}=\min\{|\mathbf{P}_{\text{det}}-\mathbf{P}_{\text{lab}}|,d_{ \text{thre}}\}. \tag{14}\]  
The distance threshold \(e_{\text{accu}}\) serves as an upper limit to prevent a single outlier value from excessively influencing the average positioning accuracy. This metric plays a pivotal role in determining the overall accuracy of the robot's final assembly.  
As can be seen in Table 3, with an upper limit of 30 pixels, the average pixel accuracy of the proposed algorithm is the best among all the tested algorithms. The results show that YOLOv5s and YOLOv5x performed better than SBM and NCC. As shown in Figure 14,  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Conf@30\%**} & \multicolumn{2}{c}{**Conf@50\%**} & \multicolumn{2}{c}{**Conf@70\%**} & \multicolumn{2}{c}{**Conf@90\%**} \\ \cline{2-9}
**Method** & **TPR** & **FPR** & **TPR** & **FPR** & **TPR** & **FPR** & **TPR** & **FPR** \\ \hline Proposed & 1.000 & 0.966 & 0.975 & 0.860 & 0.741 & 0.299 & 0.006 & 0.000 \\ SBM & 0.968 & 0.871 & 0.964 & 0.871 & 0.869 & 0.854 & 0.227 & 0.641 \\ NCC & 0.321 & 0.847 & 0.321 & 0.847 & 0.170 & 0.722 & 0.042 & 0.114 \\ YOLOv5s & 0.392 & 0.424 & 0.308 & 0.214 & 0.230 & 0.086 & 0.230 & 0.086 \\ YOLOv5x & 0.538 & 0.307 & 0.485 & 0.193 & 0.350 & 0.094 & 0.302 & 0.056 \\ \hline \hline \end{tabular}
\end{table}
Table 2: TPR and FPR of test methods.  
Figure 13: Recall, precision, and F1-Score of the 5 test methods.  
YOLOv5 methods exhibit inconsistencies in the size of the detection bounding box and frequently demonstrate irregular shifts in the detection position near ground truth, potentially leading to a certain degree of reduced accuracy.
## 4 Conclusions  
In summary, damage evolution and failure behavior of carbon fiber woven composites were monitored in real time by AE technology during the tensile tests. AE signals were accurate selected based on AE parameters such as count, energy, peak amplitude, and frequency. Moreover, a complementary method which combined multi-parameters analysis with SF is used to establish the relationship between peak frequency and failure mechanisms in composites. Some important conclusions are summarized as follows:  
1. Under tensile tests, the occurrence of some damage induces AE activity including an increase in accumulative AE energy and count. Specifically, the onset of critical damage is manifested as a high release rate of AE energy and counts.
2. Compared with the peak amplitude, the progressive frequency distribution shows noticeable variety with the applied load and exhibits a better classification feature for implementing mechanism-based analysis. In addition, at 90% of the failure load, the AE signals are mainly concentrated in the range of 200 to 300 kHz, indicating that fiber/matrix debonding plays a leading role in the later activity of fracture.
3. By comparison of the cluster results based on a K-means algorithm with the SF curve in AE energy, the relationship between mechanical damage and AE characteristic parameters was established. Matrix cracking and delamination correspond to low levels of frequency from 0 to 100 kHz; the intermediate frequency range from 150 to 300 kHz is associated with fiber/matrix debonding; the higher peak frequency from 350 to 600 kHz was attributed to fiber damage. In addition, the progression of damage motivates the drop in the SF curve. The ability of the composite material to store mechanical energy as shown by the SF curve is a good reflection of the degree and the evolution of damage.  
Conceptualization, W.Z.; Methodology, W.Z., P.-f.Z. and Y.-n.Z.; Software, Y.-n.Z.; Validation, W.Z., P.-f.Z. and Y.-n.Z.; Formal Analysis, P.-f.Z.; Investigation, W.Z., P.-f.Z. and Y.-n.Z.; Resources, W.Z. and Y.-n.Z.; Data Curation, W.Z. and Y.-n.Z.; Writing-Original Draft Preparation, W.Z., P.-f.Z. and Y.-n.Z.; Writing-Review & Editing, W.Z., P.-f.Z. and Y.-n.Z.  
This research was funded by [the National Natural Science Foundation of China] grant number [11502064], [the Natural Science Foundation of Hebei Province] grant number [E2016201019] and [the Key Projects of Hebei Education Department] grant number [ZD2016097].  
**Acknowledgments:** The authors gratefully acknowledge the financial support of the National Natural Science Foundation of China (grant no. 11502064), and the Natural Science Foundation of Hebei Province (grant no. Z2016201019), and the Key Projects of Hebei Education Department (grant no. ZZD2016097).  
Figure 13: Summary of frequency range for damage mechanisms.
## 3 Results
### Polyphenolic Profile Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{
\begin{tabular}{c} **Poly-Phenolic** \\ **Compound** \\ \end{tabular} } & \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**Methanol–Water**} & \multicolumn{2}{c}{**Ultrapure Water**} \\ \cline{4-6}  & & & **(1:1, \(v/v\))** & & \\ \cline{3-6}  & & & **Concentration** & **(mg/g)** & **Concentration** & **Concentration** \\ \hline
1. & Protocatechuic acid &
\begin{tabular}{c} Hydroxybenzoic acid \\ acid \\ \end{tabular} & 4.15 & 89.97 & 0.72 & 57.26 \\ \hline
2. & \(p\)-Hydroxybenzoic acid &
\begin{tabular}{c} Hydroxybenzoic acid \\ acid \\ \end{tabular} & nd & - & 0.22 & 17.49 \\ \hline
3. & Vanillic acid &
\begin{tabular}{c} Hydroxybenzoic acid \\ acid \\ \end{tabular} & nd & - & 0.16 & 13.42 \\ \hline
4. & Caffeic acid &
\begin{tabular}{c} Hydroxycinamic acid \\ acid \\ \end{tabular} & 0.19 & 4.16 & 0.05 & 4.08 \\ \hline
5. & Chlorogenic acid &
\begin{tabular}{c} Hydroxycinamic acid \\ acid \\ \end{tabular} & 0.09 & 1.98 & 0.05 & 4.35 \\ \hline
6. & \(p\)-Coumaric acid &
\begin{tabular}{c} Hydroxycinamic acid \\ acid \\ \end{tabular} & 0.05 & 1.29 & nd & - \\ \hline
7. & Rosmarinic acid &
\begin{tabular}{c} Hydroxycinamic acid \\ acid \\ \end{tabular} & 0.12 & 2.60 & 0.04 & 3.40 \\ \hline  & Total & 4.61 & 100 & 1.26 & 100 \\ \hline \end{tabular}
\end{table} 
Table 3: Phenolic compounds extracted from optimized horseradish root extracts. In both types of solvent used for extraction, the most abundant polyphenolic compound was protocatechic acid, representing 89.97% of the total percentage of polyphenols in the hydroalcoholic extract and 57.26% in the aqueous one. In the hydroalcoholic extract, it was followed by caffe acid (4.16%), while in the aqueous extract, by \(p\)-hydroxybenzoic acid (17.49%).
## 3 Combined Numerical Forecast Model and Machine Learning
### Building the Training and Validation Datasets  
The datasets utilized for ML training and validation comprise data generated by the WRF numerical model, serving as input features for the ML models, and the IMS observations at Kefar Nahum (32.88329, 35.57923) as labels. This measurement station is located very close to the coastline and was chosen because the buoy station suffers from frequent malfunctions. Because the high-resolution domain output frequency was 30 min and the low-resolution domain output frequency was once per three hours, we interpolated the coarse-resolution simulation to 30 min output resolution using linear interpolation. This adjustment was made to allow for a quality comparison between simulations and ML models. For the ML model inputs, we used the meridional (east-west) and zonal (north-south) 10 m wind vectors from the WRF output, denoted as U10 and V10, respectively. The data labels (observations) were taken from the IMS Kfar Nahum meteorological station, shown in Figure 3, by the IMS data repositories. This is the closest station to the Kineret sailing competition area, and it was chosen because it has the largest and most reliable dataset of the Kineret stations. The complex topographic area of this station is advantageous when trying to develop a local-scale forecasting method. The sampling rate at the IMS station was six samples h\({}^{-1}\). The U10/V10 and IMS observations from the Kfar Nahum were downscaled to model the output interval using a moving average. The observed data were randomly partitioned into two datasets for training and validation, with a ratio of 1:4 in favor of the training dataset. Because we employed classification ML models, we began by converting the IMS observations into five equally sized wind-interval classes. This resulted in extreme wind conditions being represented by only a few samples. In a later stage, we further refined the approach by converting the IMS observations into equal bins of classes, significantly improving predictability.
# A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues
## 5 Existing Indexing Techniques
### Multidimensional Indexing Techniques
#### 5.1.2 Tree-Based Technique
[PERSON] et al. [152] presented a new method based on searching for the nearest neighborhood to accelerate the matching of corresponding faces for large-scale facial recognition systems. This method uses the k-means algorithm for clustering data and the Kd-tree structure for cluster storage. However, this technique presents, in addition to all the advantages, a problem linked to the complexity of the closing forms, which leads to an increase in the costs of insertion and search operations. Table 7 analyzes multidimensional indexing techniques based on data partitioning, taking into account dataset type, data dimension, indexing nature, and complexity as comparison metrics and Table 8 shows the advantages and disadvantages of these techniques, as well as their challenges. **Partitioning of Space:** In this category, indexing techniques are based on space partitioning into sub-spaces (or cells) where each sub-space contains a subset of data. Unlike indexing techniques based on data partitioning, this type of partitioning eliminates region intersections. Many existing approaches are proposed in the literature. Reference techniques include for example: Kd-tree [158], Quadtree [159; 160], Pyramid [161] and VA-file [162]. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Proposition** & **Refs** & **Dataset Type** & **Data Dimension** & **Indexing Nature** & \multicolumn{2}{c}{**Complexity (BigO)**} \\ \cline{3-5}  & & & & **Insertion/Deletion** & **Search** \\ \hline B-tree & [125] & & & \(O(\mu\log(n))\) & \(O(n\log(n))\) \\ \hline B-tree & [126] & & & \(O(\mu\log(n))\) & \(O(n\log(n))\) \\ \hline B-tree & [127] & & & \(O(2n\log(n))\) & \(O(n\log(n))\) \\ \hline UB-tree & [128] & & & \(O(\mu\log(n))\) & \(O(n\log(n))\) \\ \hline Palndex & [153] & & & \(O(\mu\log(n))\) & \(O(n\log(n))\) \\ \hline MLB+tree & [154] & Seismic data & & \(O(\mu\log(n))\) & \(O(n\log(n))\) \\ \hline SR-tree & [132] & Image feature vectors & & \(O(n\log_{n}(n))\) \\ \hline E-tree & [146] & Spatial & & \\ \hline ER+tree & [149] & OpinRank Review & & \\ \hline SUSHI & [151] & Color histogram and Synthetic data & & \\ \hline R-tree & [130] & & & \\ \hline R+tree & [137] & Geographical and Multi-media & & \\ \hline R+tree & [138] & & & \\ \hline Hilbert R-tree & [139] & Spatial & Multi-dimensional & \\ \hline SS-tree & [140] & Multi-media data & & \\ \hline BFM \& R-tree & [143] & Not mentioned & & \\ \hline DCC \& R-tree & [144] & Medical data & & \\ \hline X-tree & [131] & Spatial data and Synthetic data & & \\ \hline aX-tree & [155] & Spatial data & & \\ \hline X+tree & [156] & Spatial data & & \\ \hline R’Q-tree & [150] & Special data & & \\ \hline BB-tree & [157] & Synthetic data, Sensor data and Genomic & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Analysis of multidimensional indexing techniques based on data partitioning.
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Clustering Algorithms
#### 3.5.3 Density-Based Clustering Methods  
The spatial density of the data objects is used to find clusters in density-based clustering algorithms [149]. The goal of data partitioning density is to identify groups of dense data points that cluster together in Euclidean space. A cluster is defined as a densely linked component which grows in any direction to increase density. One advantage of density-based algorithms compared with the partition-based clustering approaches is that they can detect groups with more dense and natural forms. Furthermore, these approaches can find outliers in a dataset in a natural way [159]. The difference between the two types of clustering algorithms is shown in Figure 16.  
The standard method for density-based clustering type is DBSCAN [26]. It uses two parameters _Minpts_ and \(\in\) to determine the following rules:  
* The main data object (a data object which has more than MinPts neighbours in its neighbourhood).
* A neighbourhood of a data object \(x\) is denoted by (\(N\) (\(x\)) = \(y\in X\) | \(d\) (\(x\),\(y\)) \(<\)).
* The density of the accessible data objects shows that two data items, \(x\) and \(y\), can be reached via a set of core data objects.