esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 4 Experimental Results and Analysis
### Comparison of Significant Differences  
In this section, a comparison of significant differences was made to validate the proposed method. The proposed method was compared to each of the traditional feature selection method. Because of the reason that the experimental results were obtained based on small samples, the \(t\)-test was applied to test whether the mean difference between the two methods was significant. According to the \(t\)-test, the assumption that judges whether the expectations of two compared methods were equal was made:  
\[H_{0}:\ \mu_{1}=\mu_{2},\ \ H_{1}:\ \mu_{1}\
eq\mu_{2}.\]  
If \(p\)-value < 0.05, the assumption will be accepted and it means the there is a significant difference between the two methods. If \(p\)-value > 0.05, the assumption will not be accepted and it means that there is not a significant difference between the two methods.  
Table 8 shows that different \(p\)-values were calculated under the \(t\)-test. From Table 8, it is can be seen that under comparisons between each traditional method and the proposed method, the \(p\)-values of proposed method are all smaller than 0.05.  
A conclusion can be drawn that the proposed method can achieve the highest diagnosis accuracy with fewer features and has a significant improvement in accuracy performance, compared with other 3 feature selection methods.
# Lightweight Attention Pyramid Network for Object Detection and Instance Segmentation
## 4 Results and Discussion
### Dataset and Metrics  
The MS COCO dataset is one of the most challenging datasets for object detection task due to data complexity. It consists of 115 k images for training and 5 k images for validation (new split of 2017). In total, 20 k images were used in the test-dev and 20 k images were used as a test-challenge. Ground-truth labels of both test-challenge and test-dev are not publicly available. The MS COCO dataset has 80 classes with bounding box annotation. We trained our models on the train-2017 subset and report results on the val-2017 subset for ablation study. We also report results for the test-dev for comparison with the single model result.  
In addition, the 2018 WAD dataset comes from the 2018 CVPR workshop on autonomous driving, which was sponsored by Baidu Inc. This dataset consists of approximately 35 K images for training and 5 K images for validation. In total, 2 K test images were used in the test challenge. The dataset includes seven classes with pixel-wise instance mask annotation. Ground-truth labels of the test challenge are not publicly available. We trained our models on the training dataset and report our results for the test-dev for comparison.  
We followed the standard evaluation metrics, i.e., _IoU_, _AP_\({}_[EMAIL_ADDRESS]}\), _AP_\({}_[EMAIL_ADDRESS]}\), _AP_\({}_{S}\), _APM_, and _APL_. The last three measure performance corresponding to objects with different scales. Since our framework is general to both object detection and instance segmentation, we also report the result for instance segmentation.  
Below is the definition of _IoU_:  
\[IoU=\frac{area(B_{p}\cap B_{GT})}{area(B_{p}\cup B_{GT})}, \tag{5}\]  
where \(B_{p}\) is the prediction region from the detection/segmentation network and \(B_{GT}\) is the ground-truth body region. We used the mean IoU calculated from all test images.  
The _AP_ is defined as follows:  
\[AP=\frac{1}{N}\sum\limits^{all}\frac{tp}{(tp+fp)}, \tag{6}\]  
where \(tp\) means the true positive, \(fp\) means the false positive, and \(N\) means the number of the detection/segmentation results. _AP_ in Equation (6) represents the averaged value of all categories. Traditionally, this is called \"mean average precision\" (_mAP_). In the MS COCO dataset, it makes no distinction between _AP_ and _mAP_. Specifically, the MS COCO dataset uses 10 _IoU_ thresholds of 0.50:0.05:0.95. This is a break from tradition, where _AP_ is computed at a single IoU of 0.5 (which corresponds to our metric _AP50_).
# Cross-Cycled Uplink Resource Allocation over NB-IoT
## 4 Performance Evaluation
### Simulation Results
Figure 8 shows the effect of different NPRACH cycles using 24 subcarriers on the subframe consumption under different \(R_{max}\) and \(G\) values with 2000 UEs. When the NPRACH cycle is longer, we have more NPUSCH subframes. Therefore, the three methods can diminish the consumed subframes further. We can observe that the change in NPRACH cycles has a lower impact on the subframe consumption under our proposed algorithm than under _LAURA_ and _ES_. This is because the proposed algorithm considers NPRACHs to adjust the unit type and modulation scheme and efficiently uses the subframes of the next NP. Therefore, the proposed algorithm can have more choices of unit types and modulation schemes for each UE. By contrast, although _LAURA_ also considers NPRACHs to select the used unit type and modulation scheme for each UE, it only considers using the subframes of the current NP, which limits the selection of unit types and modulation schemes. The simulation results justify our motivation that cross-cycled resource allocation is important for achieving massive connections and for increasing resource utilization, especially for the UEs with better channel qualities.
## 4 Conclusions  
In this paper, we have presented an in-depth investigation of the performance of F-DMAS beamforming in the context of MLT imaging; this study is in fact not limited to the classic F-DMAS algorithm case, but it includes a thorough analysis of the image quality achievable with this beamformer when considering different maximum-lag values--and thus using the so called short-lag F-DMAS formulation--in different MLT imaging configurations.  
The short-lag F-DMAS formulation provided new insights on the relation between the performance of the F-DMAS algorithm and the SC of backscattered signals. Simulation results show  
Figure 9: Cross-section of the anechoic cyst in Figure 8, for DAS, F-DMAS (standard formulation with maximum lag = 63), short-lag F-DMAS with maximum lag = 10 (SL F-DMAS (10)) and maximum lag = 40 (SL F-DMAS (40)).  
that the SC trend over lags and its total value are influenced by the number and angular spacing of simultaneous TX beams. This in turn influences the CNR, CR, sSNR, and resolution of F-DMAS images: higher maximum-lag values improve contrast, while lower values improve resolution and sSNR, which makes the quality of images change with the SC trend.  
As a future development of this work, experimental tests will be carried out to assess the impact of noise on the trend of spatial correlation and on the image-quality parameters of interest considered here.  
This work is supported by the University of Pavia, under the Blue Sky Research project MULTIWAVE. The authors would like to thank [PERSON] (University Roma Tre) for all constructive discussions on the analysis of spatial coherence trends in MLT, and [PERSON] (University of Florence) and [PERSON] (University of Pavia) for their insightful comments that helped improve the paper.  
[PERSON] conceived the study, designed the simulation tests, and worked on the processing of data; [PERSON] contributed to the design of the study and to simulation tests; [PERSON] wrote the paper; [PERSON] revised the manuscript. Both authors read and approved the submitted version of the paper.  
The authors declare no conflict of interest.
# The Low-Loss Spot Size Converter for Alignment with Cleaved Single Mode Fiber
## 2 Design of Materials and Methods
### The 2-D Inversely-tapered Waveguide
By using the radiofrequency PECVD technique, the refractive index of the deposited SiO\({}_{2}\) can be controlled with extremely high precision [24], which could meet the requirement of depositing two SiO\({}_{2}\) layers with a refractive index difference of 0.002. We also discussed the effect of increasing the refractive index difference on the coupling loss of the device. The refractive index of Cladding 2 is set to \(n\), and in the paper, \(n\) is fixed at 1.458. The refractive index of Cladding 1 is set to \(n+\Delta n\), \(\Delta n\) represents the refractive index difference between Cladding 1 and Cladding 2. The MFD of the light source is set to 8 \(\upmu\)m, and the mode is TE mode. The coupling loss variation of the full SSC has been simulated when \(\Delta n\) varies between 0.002 and 0.017. According to Figure 7b, the coupling loss of the SSC increases with the increase in \(\Delta n\). The coupling loss of the SSC can be maintained at less than 0.98 dB when \(\Delta n\) is less than 0.015, which demonstrates an acceptable fabrication tolerance for the device.
# Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space
## 3 Experiments and Results  
### Databases and Evaluation Methodology  
We evaluated the performance of the proposed algorithm on six benchmark IQA databases: LIVE [28], TID2013 [39], CSIQ [29], LIVE multiply distorted database I (MD1) [40], LIVE multiply distorted database II (MD2) [40], and WIQCD [30]. LIVE database includes 29 reference images and 779 distorted images of five distortion types. TID2013 database consists of 25 pristine images and 3000 distorted images with 24 distortion types. CSIQ database contains 30 reference images and 866 distorted images with six distortion types. MD1 database includes 225 images distorted by blur and JPEG. MD2 database includes 225 images distorted by blur and noise. WIQCD database consists of 1169 widely diverse authentic distorted images. It is worth mentioning that images in WIQCD database are directly obtained by using a lot of highly different smart phones and tablets, and the distorted images are totally different from each other because they are authentically distorted images acquired from typical real scenes.  
Three commonly used performance metrics, i.e., the Spearman rank-order correlation coefficient (SROCC), the PLCC, and the root mean square error (RMSE), were employed to evaluate the competing BIQA algorithms. A better BIQA algorithm is expected to have lower RMSE value and higher values of SROCC and PLCC.
# An Intrusion Detection System for RPL-Based IoT Networks
## 5 Evaluation
### Attack Detection Accuracy
#### 5.1.1 Flooding Attacks Detection
After this, the central IDS indicates in the log that it received an attack from node 12 at 02:21.501 (alert sent from the IDS detector), and that it also detected the attack at 02:22.526. In addition, when the central IDS sends a message to retrieve information from the IDS detectors at minute 3, an alert is sent back to central IDS, indicating a DAO insider attack at 03:01.001. In addition to the aforementioned detection simulations against flooding attacks, more simulations have been performed by using different topologies and changing the number of nodes in the simulation. All the simulations have succeed in detecting the attacks as the alert is only raised if the thresholds (\(T_{DIO}\), \(T_{DIS}\), and \(T_{DAO}\)) are exceeded, which clearly indicates an attack. To conclude the flooding attack detection testing, one last experiment was performed. It consisted in a simulation with one central IDS, two IDS detectors, one Hello flood attacker, one DIS attacker, one DAO insider attacker, and seven dummy nodes. The topology is shown in Figure 9. Our logs show that the detection was performed at around minute 2. It can be observed that at minute 2, three attacks were detected. More precisely, the Hello flood attack was detected from IDS detector node 9 at 02:04.621, while the DIS attack was detected from IDS detector node 7 at 02:01.812. Some seconds later, the central IDS node  
Figure 6: Hello flood attack log showing detection. Figure 7: DIS attack log showing detection. 1 detected the DAO insider attack at 02:22.129. With this simulation, one can see that the IDS design was working perfectly with more than one IDS detector. In fact, those are the scenarios where the hybrid placement is completely useful, and thanks to the distributed topology in this case, all the attacks were correctly detected.
## 2 Study Area and Datasets
### Datasets
#### 2.2.2 Reference GT Data  
The proposed method relies on supervised classification, which requires training data of high reliability. Multiple field surveys were conducted in the study area between June 2022 and September 2022 to collect reliable GT data for model validation and calibration. This period coincides with the peak growth period of autumn and spring products in the study site. The distribution of GT samples and some images during field surveys can be seen in Figure 2. Field visits were done so that the ground data have appropriate distribution in the study site. During the field surveys, 315 polygons were recorded, which were related to various classes such as 'Wheat', 'Corn', 'Beet', 'Onion', 'Alfalfa', 'Garden', and 'Other'. The 'Other' class encompasses bare soil, urban areas, and water bodies, while the 'Garden' class comprises apple gardens mostly. For each polygon, the corner coordinates of each field were recorded using a handheld GPS device (Garmin eTrex 20x) with a spatial accuracy of less than 5 m. To prevent the mixed pixels effect, the corner pixels were recorded with at least a 30 m distance from the surrounding landcover classes. Table 2 indicates the number of sample points (in a 30-m resolution) in each class derived from field surveys. Figure 2c also illustrates the NDVI behavior of randomly selected samples from each target class derived from the monthly medians of L8/9 (as mentioned in Section 3.1). The reference dataset was divided into two sections, 70% as training and 30% as validation. Training data were used for model calibration, while validation data were used in the accuracy assessment with no inference in the training phase.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Crop Name** & **Training Set** & **Validation Set** & **Total** \\ \hline Wheat & 589 & 253 & 842 \\ Corn & 336 & 144 & 480 \\ Beet & 413 & 177 & 630 \\ Onion & 210 & 90 & 300 \\ Alfafla & 676 & 290 & 966 \\ Garden & 911 & 390 & 1301 \\ Other & 415 & 178 & 593 \\ Total & 3550 & 1522 & 5072 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of sample points per class.
## 5 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**MLP**} \\ \hline \multirow{2}{*}{**Number of Variables**} & \multicolumn{3}{c}{**Accuracy (100)**} & \multicolumn{3}{c}{**Accuracy (200)**} & \multicolumn{3}{c}{**Accuracy (300)**} \\ \cline{2-10}  & **KNN** & **Mode** & **MI** & **KNN** & **Mode** & **MI** & **KNN** & **Mode** & **MI** \\ \hline \multirow{2}{*}{\(10\)} & 58\% & 54\% & 51\% & 57.3\% & 56.1\% & 53.1\% & 60.9\% & 58.2\% & 64.4\% \\  & 67\% & 65\% & 62\% & 65.5\% & 68.6\% & 64.5\% & 68.8\% & 64\% & 62.5\% \\  & 84\% & 75\% & 87\% & 79.2\% & 71.7\% & 73.7\% & 72.5\% & 77.8\% & 78.5\% \\ \hline \multicolumn{10}{c}{**Naïve Bayes**} \\ \hline \multirow{2}{*}{ \begin{tabular}{c} Number of Variables \\ \end{tabular}  } & \multicolumn{3}{c}{Accuracy (100)} & \multicolumn{3}{c}{Accuracy (200)} & \multicolumn{3}{c}{Accuracy (300)} \\ \cline{2-10}  & KNN & Mode & MI & KNN & Mode & MI & KNN & Mode & MI \\ \hline \multirow{2}{*}{\(10\)} & 42\% & 45\% & 48\% & 48.2\% & 51.8\% & 42.2\% & 68.1\% & 62.5\% & 64.2\% \\  & 62\% & 69\% & 63\% & 65.2\% & 62.4\% & 64.6\% & 69.3\% & 65.6\% & 66.7\% \\  & 76\% & 74\% & 77\% & 79.5\% & 71.7\% & 72.6\% & 74.8\% & 71.8\% & 76.8\% \\ \hline \multicolumn{10}{c}{**SVM**} \\ \hline \multirow{2}{*}{\ \begin{tabular}{c} Number of Variables \\ \end{tabular}  } & \multicolumn{3}{c}{Accuracy (100)} & \multicolumn{3}{c}{Accuracy (200)} & \multicolumn{3}{c}{Accuracy (300)} \\ \cline{2-10}  & KNN & Mode & MI & KNN & Mode & MI & KNN & Mode & MI \\ \hline \multirow{2}{*}{\(10\)} & 55\% & 57\% & 51\% & 52.7\% & 57.4\% & 55.5\% & 62.3\% & 64.2\% & 62.6\% \\  & 65\% & 67\% & 59\% & 65.4\% & 68.2\% & 62.1\% & 64.2\% & 68.1\% & 67.6\% \\  & 77\% & 81\% & 82\% & 84.1\% & 82.7\% & 76.4\% & 72.5\% & 79.5\% & 72.8\% \\ \hline \multicolumn{10}{c}{**LSTM**} \\ \hline \multirow{2}{*}{ \begin{tabular}{c} Number of Variables \\ \end{tabular}  } & \multicolumn{3}{c}{Accuracy (100)} & \multicolumn{3}{c}{Accuracy (200)} & \multicolumn{3}{c}{Accuracy (300)} \\ \cline{2-10}  & KNN & Mode & MI & KNN & Mode & MI & KNN & Mode & MI \\ \hline \multirow{2}{*}{\(10\)} & 60\% & 62\% & 64\% & 60.5\% & 58.2\% & 57.4\% & 56.8\% & 54.5\% & 65.8\% \\  & 67\% & 68\% & 74\% & 72.8\% & 71.5\% & 78.5\% & 73.5\% & 75.4\% & 77.1\% \\  & 82\% & 84\% & 86\% & 80.9\% & 82.8\% & 87.8\% & 90.
# Leveraging the Internet of Things and Blockchain Technology in Supply Chain Management
## 5 Combining IoT with Blockchain Technology: Emerging Research Areas
### Quality  
Apart from providing a decentralized migration path for IoT data, Blockchain technology addresses the problem of data and information quality existing in other information technology platforms. It complements the need for maintaining consistent data provenance that describes where the data of interest originates, who owns the data and what transformations were made to the data [125]. In this way, the metadata posted on the Blockchain is protected from compromise and unauthorized disclosure. For instance, Blockchain technology is suitable for distributed cloud storage. Blending the technology into the cloud environment can lead to improved data provenance where cloud nodes record data over a distributed network with a fault-tolerant ledger and strong cryptography [126]. Hence, the 'block cloud'--a combined usage of the Blockchain and cloud computing--paves the way to self-sovereign data management and ownership by allowing private and contextual access to IoT data hosted across cloud layers. In empowering IoT, Blockchain technology could be easily merged with the application of big data in supply chain use cases which implies a shift for supply chains from being digitally enabled to digitally-led and process-centric [127]. However, several shortcomings still exist in this infrastructural integration. For instance, big data is increasingly encountering three critical challenges, namely control (governance and monitoring of the data structure when several parties are involved), data authenticity (reliability and trustworthiness), and data monetization (transfer of the ownership of data and exchange in a universal data marketplace) [128]. Blockchain technology can be used to exert more control over IoT-based supply chain data, ensure their integrity and immutability, and establish a marketplace for data provided by IoT devices [129]. Moreover, a Blockchain-based big data system can facilitate aggregation of IoT data coming from scattered sources across the supply chain, thus creating a more suitable data sharing ecosystem and conducive analytics for business insights and decision making. Hence, we propose:  
RP6: Blockchain technology positively impacts the quality and integrity of IoT solutions.
## 2 Crack Length Measurement with the DC-Potential Drop Method
### Investigation of Ferromagnetic Alloys-The Villari Effect
In the case of the ferromagnetic sample made from C45E, the increase in the potential difference is much steeper. This increase cannot be explained by the contact of the crack flanks alone, it is rather an increasing potential difference due to the Villari effect. [PERSON] and [PERSON] [37] observed the same effect in their investigations. This increase in the potential difference can be attributed to the stresses at the crack tip increasing with the length of the crack, and the magnetic properties that depend on the crack length [39]. The Villari effect can cause problems in testing ferromagnetic materials when the maximum potential within a cycle is used to calculate the crack length. If, for example, a test according to ASTM E647 [6] is carried out, the load level is reduced to reach the threshold value. Since the potential change induced by the Villary effect increases with the crack length and the loading level, the influence of this effect on the measured potential changes during the experiment. This error can be avoided if the potential at the maximum force is used to calculate the crack length. Since this value is not available by the control electronics and can only be extracted from the hysteresis loops, it is advisable to use the mean value of the potential. However, it must be noted that, as mentioned in Section 2.3, measurement errors may occur due to contacts of electrically conductive fracture surfaces. Despite the problems presented here, which must always be taken into account when evaluating the results, the DCPDM is the most suitable and preferred method for crack length measurement in electrically conductive materials due to its high accuracy and ease of automation. In order to reduce or even avoid measurement errors, the special features of the examined material must be considered. The potential probe hysteresis should be used for this purpose, as these are the best way to identify the peculiarities that occur during the measurement.
## 2 Materials and Methods
### Oxide Surface Characterization  
Thin-film X-ray diffraction (XRD, XDS 2000, Scintag, Franklin, MA, USA) was utilized to determine the crystalline phases present within the anodized oxides. Representative disc specimens (n = 4) from each oxide group were rotated 1\({}^{\circ}\) away from a copper X-ray source (0.154 nm Cu-K\({}_{\alpha}\)) to enhance the X-ray interaction volume. XRD scans were conducted over a two-theta angle range from 20\({}^{\circ}\) to 90\({}^{\circ}\). Optical imaging (Keyence, Osaka, Japan, VHX-1000) and scanning electron microscopy (SEM, Zeiss, Jena, Germany, Supra 40) were utilized to characterize the surface topography of each oxide surface. A 12 kV accelerating voltage was used for SEM imaging at 5000\(\times\) to capture the micron-level surface topography, while a lower 9 kV accelerating voltage was utilized at 75,000\(\times\) to mitigate the intensified surface charging effects exhibited by the anodized oxides at higher magnifications. A laser confocal microscope (LCM, Keyence, Osaka, Japan, VK-X3000) was used to measure the oxide surface roughness values from triplicate areas of four specimens of each anodized oxide (n = 4). Gwyddion software was used to calculate each oxide group's average roughness (R\({}_{\text{a}}\)) values and peak-to-valley roughness (R\({}_{\text{z}}\)) values from the 3D profiles. Energy-dispersive X-ray spectroscopy (EDS, EDAX, Mahwah, NJ, USA, APEX EDS Software Suite) was used to collect surface chemistry spectra from each oxide group using an SEM accelerating voltage of 12 kV and a magnification of 500\(\times\). Representative EDS spectra were collected from triplicate areas of each oxide. The average surface concentrations of each element and the surface Ca/P ratios were calculated for each oxide group. Attenuated total reflectance Fourier transform infrared spectroscopy (ATR FTIR) was also performed on each oxide group to assess any molecular differences. For these analyses, a Spectrum 100 FTIR device (PerkinElmer, Waltham, MA, USA) was used over the range of 650-4000 cm\({}^{-1}\) at a spectral resolution of 100 cm\({}^{-1}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Solution Component** & **Citric Acid (M)** & \
\begin{tabular}{c} **Magnesium** \\ **Phosphate (M)** \\ \end{tabular} &
\begin{tabular}{c} **Calcium Acetate** \\ **(M)** \\ \end{tabular} \\ \hline
500 mL Distilled water & 0.5 & 0.15 & 0.275 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Anodization electrolyte composition.