esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Implementation and Experiment
### Experimental Result
#### 5.3.3 Evaluation  
We also applied the proposed method to the other 10 faults in Table 1. As a result, we could find the cause of the fault in the fault candidate that presented for all the faults. The experimental results are summarized in Table 4. Of these, the actual fault was highlighted in the shadow cell at which it was found. As a result, faults were found in R1 and R2 in 6 cases out of 10. We could find the cause of the actual fault within a frame distance of 0.875, on average, at the fault-occurrence point that we proposed. That is, if the frame indicated by the fault-occurrence point and an additional frame are  
Figure 12: Finding the fault-occurrence point of fault index #1.  
Figure 13: Finding the fault-occurrence point of fault index #12.  
further checked, the cause of the fault can be found. The localization ratio is obtained by dividing the number of addresses up to the rank where the fault was found by the total number of updated addresses. For example, fault index #9 is \((2+4)/132\) with a 4.55% localization. As a result, our proposed method could find the point where the fault was updated by checking two frames on the average for 80% fault in the experiment to find the injected fault. The fault was found by checking only 3.28% of the total memory addresses updated.  
On the other hand, our proposed method does not suggest when is the fault-occurrence point in the case of Node 2. The proposed method uses the update pattern to present the point of occurrence of the abnormal pattern as the point of occurrence of the fault. Therefore, we confirmed the limit at which the fault-occurrence point cannot be determined when the updated patterns of the memory are normally maintained and updated to the wrong value. However, this limitation can be overcome. As shown in Table 4, all the detected sequences are presented as fault candidates for Rank R3, so we can find the cause of the fault by localization similar to the previous study.  
As a result, the proposed method can reduce the fault candidates strongly by presenting the point of occurrence for 80% of faults and can suggest a fault candidate similar to the previous study, even when the point of occurrence cannot be observed. When we test the same fault, the proposed method is localized to 3.28%, which is 57% of the memory compared to the previous study. We apply the proposed method to ECUs based on OSEK/VDX. This means that our proposed method can find faults that occur in the HiL test environment using the normal memory-updated features and the updated patterns without debugging tools or source code. However, because the proposed method only targets the update, not the value of the memory, it is limited when there is an error in the value while maintaining the normal pattern. In addition, because it focuses on updating, it is difficult to apply analog signals with frequent changes in values.
# ICRSSD: Identification and Classification for Railway Structured Sensitive Data
## 2 Methods
### Classification Based on Clustering and Thresholding
#### 2.3.2 Threshold-Based Static Classification  
As described in Section 2.3.1, the sensitivity level of each attribute is obtained through a combination of clustering-based dynamic classification and expert-driven rule-based adjustment. To determine the overall sensitivity of a data table, we compute a weighted aggregate sensitivity value by considering the sensitivity levels of all its constituent attributes. Based on this aggregate value, the data table is then assigned a corresponding sensitivity level using a predefined static threshold. Moreover, for system-level classification, a weighted aggregation of table-level sensitivity scores is used to derive the overall system sensitivity, enabling static classification at the system level.  
This classification takes into account sensitivities at the attribute, table, and system levels, enabling more accurate and structured protection strategies for sensitive data across varying granularities.  
Based on Equation (9), we calculate the sensitivity of each data table and classify the tables according to a predefined threshold. Similarly, the classification for systems involves calculating the weighted sensitivity of each system based on the associated data tables, followed by system classification using static thresholds.  
The sensitivity weighting formula is as follows:  
\[S(R)=\sum_{i=1}^{n}W_{i}*P(level_{i}) \tag{9}\]  
where \(P(level_{i})\) denotes the percentage of data with a sensitivity level of \(i\) and \(W_{i}\) represents the corresponding weight of data with sensitivity level \(i\).
# NetSel-RF: A Model for Network Selection Based on Multi-Criteria and Supervised Learning
## 3 NetSel-RF Model
### Motivation  
Figure 1 shows a user with a mobile device (_dev_1) moving through the hall. This _dev_1 needs to be always connected to the best network. As mentioned above, traditional handover approaches consider only RSSI for making decisions [26]; however, his approach produces frequent disconnection and inefficient seamless handovers, leading to handover operation failures [6].  
Several approaches have addressed these problems by using multiple criteria considering the handover decision parameters related to QoS and QoE [18]. Nevertheless, current approaches based on multi-criteria can perform incorrect network selection by skipping one or more criteria related to network, devices, users, or applications parameters. In this paper, we argue that a supervised learning model based on multiple criteria allows the network selection to be optimized by considering the parameters of networks, devices, users, and applications. In this way, such a model can learn the best network to satisfy QoS and QoE. Additionally, supervised learning allows the new network to be selected proactively; the devices perform a handover before the network link quality degrades.
# Environmental Risk Source Analysis and Classification of Zones: Subway Construction
## 3 Risk Source Analysis of Subway Construction
### Super High-Rise Building Distribution  
The construction of subways can disturb the underground soil and disrupt the original stress balance of the soil, which can cause surface subsidence. Additionally, when subway tunnels pass through super high-rise buildings, the impact on the construction of the subway tunnel cannot be generalized with ordinary buildings due to the higher load of super high-rise buildings compared to other buildings [45; 46; 47; 48]. According to the \"Code for Design of Civil Buildings\" GB50352-2005, super high-rise buildings are defined as buildings with more than 40 stories and a height of more than 100 meters [49].  
In Urumqi's Tianshan District, which is the political, economic, cultural, and financial center of the city, there are several subway stations that have been opened, including Line 1 North Gate Station, South Gate Station, Erdaoqiao Station, Xinjiang University Station, and Santunbei Station. According to the \"Construction Environment Investigation Report of Urumqi Metro Line 1\", there are 127 super high-rise building sites in this area, making it the most densely populated area of super high-rise buildings in the study area. The dashed boxes b and c in Figure 4a represent the two subsidence areas of Line 1, which are the International Airport Station-Xuanrendun Station, with a maximum displacement of less than 5 mm/year (Figure 4b), and from Erdaoqiao to Nannmen Station, where the maximum displacement exceeds 10 mm/year (Figure 4c). Comparing the settlements of the two areas shows that the distribution of super high-rise buildings is the main factor inducing the deformation of subway construction in this section. Therefore, the distribution of super high-rise buildings is considered the second risk source of the subway construction environment.
# Experiment on Flexural Fatigue Performance of Precast Bridge Deck Joints with Loop Connections
## 5 Discussion  
In order to clarify the applicability of loop connection in different types of prefabricated structural joints, the specimens representing different types of deck joints were designed. At the same time, the degradation of flexural fatigue performance of deck joints with loop connections is investigated with pure flexural static loading after 200 \(\times\) 104 fatigue-loading cycles.  
In the static loading process, after different durations of fatigue-loading cycles, the load-deflection curve basically increases linearly, indicating that different types of loop connection joints are still in the elastic stage. After different durations of fatigue-loading cycles, the slope of the load-deflection curve did not change much; the stiffness of the loop connection joint also changes little. The stiffness of each loop connection joint does not change much under the action of fatigue load. The joint with loop connections has good fatigue performance.  
In addition, the strain changes of concrete and steel bars are almost negligible by comparing the static loading results before fatigue loading and after 200 \(\times\) 104 fatigue-loading cycles. It also indicated that the fatigue-loading cycles has little effect on the mechanical properties of the deck joint with loop connection.  
The failure modes of each loop connection joint were still similar to the typical failure characteristics of pure flexural specimens loaded only with static loading. In the pure flexural section, the crack height increases with the increase of fatigue-load times, and expands horizontally after reaching a certain height, forming T-type or Y-type cracks.
# Examination of the Quality of Particulate and Filtered
###### Abstract  
(1) Background: Autologous bone is supposed to contain vital cells that might improve the osseeintegration of dental implants. The aim of this study was to investigate particulate and filtered bone chips collected during oral surgery intervention with respect to their osteogenic potential and the extent of microbial contamination to evaluate its usefulness for jawbone reconstruction prior to implant placement. (2) Methods: Cortical and cortical-cancellous bone chip samples of 84 patients were collected. The stem cell character of outgrowing cells was characterized by expression of CD73, CD90 and CD105, followed by osteogenic differentiation. The degree of bacterial contamination was determined by Gram staining, catalase and oxidase tests and tests to evaluate the genera of the found bacteria (3) Results: Pre-surgical antibiotic treatment of the patients significantly increased viability of the collected bone chip cells. No significant difference in plasticity was observed between cells isolated from the cortical and cortical-cancellous bone chip samples. Thus, both types of bone tissue can be used for jawbone reconstruction. The osteogenic differentiation was independent of the quantity and quality of the detected microorganisms, which comprise the most common bacteria in the oral cavity. (4) Discussion: This study shows that the quality of bone chip-derived stem cells is independent of the donor site and the extent of present common microorganisms, highlighting autologous bone tissue, assessable without additional surgical intervention for the patient, as a useful material for dental implantology.  
autologous bone graft; dental implant; microbial contamination; antibiotic prophylaxis; mesenchymal stem cells; cell viability; osteogenic potential +
Footnote †: journal: Medical Imaging and Vision
## 5 Results and Discussion
Furthermore, this modifies the normalised clipping levels to be \(k_{\text{hom}}=-0.78\), and \(k_{\text{t}_{\text{norm}}}=8.4\) for DCO-OFDM, while for PA DCO-OFDM \(k_{\text{b}_{\text{norm}}}=-0.81\), and \(k_{\text{t}_{\text{norm}}}=8.78\). The BER performance of this setup is shown in Figure 4c for both systems. In the third case (case 3), the normalised clipping levels are modified by reducing the bias current further to \(i_{\text{bias}}=8\) mA. This results in the increase in the lower-level clipping where \(k_{\text{b}_{\text{norm}}}=-0.61\), and \(k_{\text{t}_{\text{norm}}}=8.57\) for DCO-OFDM, while for PA DCO-OFDM, \(k_{\text{b}_{\text{norm}}}=-0.64\), and \(k_{\text{t}_{\text{norm}}}=8.95\), which are shown in Figure 4d. In all the clipping cases, the PA DCO-OFDM achieved a better BER performance than that of the conventional DCO-OFDM without the PAPR reduction, as shown in Figure 4b-d. PA DCO-OFDM requires less \(i_{\text{bias}}\) to convert the signal's negative peaks to positive, and hence, PA DCO-OFDM is more efficient in terms of optical power than the conventional DCO-OFDM. Furthermore, PA DCO-OFDM is expected to perform better under clipping conditions, as shown in Figure 4, by reducing the high peaks of the OFDM waveform which results in minimising the clipping distortion and the nonlinearity effect. As a result, system's SNR is maximised, and hence, a better BER performance of the system is observed. In addition, the PA DCO-OFDM allows the signal to have more swing within the linear dynamic range of the system transmitter and reduces the upper and/or lower level clipping effect. The PA performance improvement reaches its lowest when the clipping noise becomes dominant. The error floor observed in the results is caused by the clipping. And, in all cases, the error floor is lower with PAPR reduction technique  
Figure 4: Analytical simulation of BER for PA DCO-OFDM and conventional DCO-OFDM. (**a**) ideal case (zero clipping); (**b**) case 1 (moderate clipping); (**c**) case 2 (high clipping); (**d**) case 3 (severe clipping). implemented. The clipping noise becomes dominant and degrades the performance of both systems. Therefore, the limitation in this work is the clipping noise which causes the BER performance to approach an error floor for both systems.
# DOW-PR DOlphin and Whale Pods Routing Protocol for Underwater Wireless Sensor Networks (UWSNs)
## 5 Simulation Analysis
### Data Delivery Mechanism  
Data delivery mechanism is used to forward the generated or received packets to its destination. Every node can generate a packet. If the closest proximity sink is present in predefined maximum transmission power range of the source node, then it relays the packet to the sink and it is therefore successfully delivered to the destination. All the sinks are communicated/connected via high speed radio links. If the sink is not in the range, then the Algorithm 1 selects the best node for forwarding according to the fitness function (HH). The fitness function is set in such a way that the forwarding node has a higher weighting sum of depth difference of the two hops, average PFNs number, least suppressed node numbers and having a lower hops count value.  
Each node is assigned a unique HH value calculated through a fitness function in Equation (9). When the HH value is set differently for each node, then, according to that, value holding time from Equation (19) for each node is calculated. The holding time is set to a very low value from Equation (12) pertaining to the node having the highest (HH) value among all the nodes. Thus, the node having the lowest holding time will bear highest priority and be selected for retransmitting the packet. All other  
Figure 10: Hop count mechanism.  
nodes in its vicinity (range) will simply drop the packet by over hearing and therefore considerably reduce the energy consumption and the collision probability. Data delivery mechanism for Whale Pods is almost the same as Dolphin Pods, but only direction of data flow is different and it is based upon the minimum distance either from the surface sinks or embedded sink \(D_{EM}\). For instance, any node live in the region closer to the embedded sink (Figure 8) as compared to the surface sink will find next forwarder in the direction of the embedded sink. Consequently, Potential Forwarding Nodes (PFNs) will also be selected that lie in the hemisphere in the direction of the embedded sink \(D_{EM}\).
# A Scalogram-Based CNN Approach for Audio Classification in Construction Sites
## 8 Conclusions  
In this paper, we have investigated the effectiveness of a Convolutional Neural Network (CNN) fed by scalograms in the classification of audio signals acquired in real-world construction sites. Specifically, after splitting the recorded signals into smaller chunks, the scalogram (i.e., the squared magnitude of the Continuous Wavelet Transform) has been computed and used as input to a customized version of the well-known AlexNet. The customization takes into account the single channel of the scalogram input and the reduced number of output classes. Some experimental results and comparisons with other state-of-the-art approaches confirm the effectiveness of the proposed idea, showing an overall accuracy of 98.9%.  
In future work, we will investigate the effect of choosing different types of wavelet functions and the idea of early data fusion, i.e., by joining the scalograms with other bi-dimensional representations, such as the spectrogram or similar ones, and providing this augmented representation as the input to a CNN.  
Figure 8: The 256 feature maps of the fifth and last convolutional layer of the trained architecture. Maps have been normalized in \([0,1]\) for visualization: lighter colors are close to 1, while darker colors are close to 0.  
**Author Contributions:** Conceptualization, M.S.; methodology, R.P. and Y.-C.L.; software, M.S. and R.P.; validation, M.S. and Y.-C.L.; formal analysis, Y.-C.L. and M.S.; investigation, M.S., Y.-C.L. and R.P.; data curation, Y.-C.L.; writing--original draft preparation, M.S.; writing--review and editing, Y.-C.L., R.P. and M.S.; visualization, M.S.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially funded by Sapienza University of Rome grant numbers RM12117A39E2E9A7 and RM122180 FB3 CA3F2.  
**Institutional Review Board Statement:** Not applicable  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Defect Detection in Solid Timber Panels Using Air-Coupled Ultrasonic Imaging Techniques
## 1 Introduction
[22] suggested that resonators made of composite materials comprising piezoelectric ceramics and polymer could improve conversion efficiency and reduce energy losses. They all focused on high-performance ultrasonic transducers. Studies on high-performance ultrasonic transducers use composite materials, which significantly increases the cost of testing systems. The focus of this paper is reducing the cost of industrial quality assurance of engineered wood products by improving signal processing. [PERSON] et al. [23] pursued research on the applications of the ACU technology to wood, fiber-reinforced plastics, \"smart\" structures, and fiber-reinforced ceramics, which is the enlightenment of applying the ACU method for defect detection of timber panels. In this study, ACU measurements on timber panels for defect detection in engineered wood products are reported. To satisfy the trade-off of cost and efficiency for commercial applications, commercially available piezo-ceramic ACU transducers were adopted, and a detection testbed was designed. Through-transmission mode was used in this testbed. The composite images were drawn using the C-scan technology to determine and detect existence and identify their specific types and locations in timber panels. The results showed that the testbed was capable of detecting and visualizing most of the defects in the sample timber panels within a short time.
## 3 Result and Discussion  
### Possible Reaction Mechanism of Green Synthesis CuO Nanoparticles  
The phytochemicals present in plant materials are rich in secondary metabolites with different functional groups, such as -C-O-H, -C-O-, -C-N-C-, -C=C-, -C=O- etc., which have a significant role in the formation, nucleation, accumulation, and stabilization of the nanomaterials. Different plant parts, such as roots, stems, leaves, seeds, fruits, peel, or flowers, can be used in the biosynthesis of nanoparticles (NPs). The cactus pear commonly known as Prickly Pear belongs to Angiosperm Cactaceae family. The cactus pear (Opuntia ficus indica) is the source of several phytochemicals like flavonoids, saponins, phenylpropanoids, alkaloids, Rutin, essential oils, and steroids. In biosynthesis, the plant extracts are mixed with metal precursor salts at ordinary temperature, which is easy, environmentally friendly, cost-effective, and non-toxic [17; 27; 34].  
In this reaction mechanism, we hypothesized the organic molecules present in plant extracts with poly-hydroxyl functional groups as a phytochemical agent for CuO NPs formation as displayed in Figure 2. In this biosynthesis of CuO NPs, the phytochemicals in the cactus extracts activate the copper (Cu) metal in the copper nitrate-trihydrate (Cu(NO\({}_{3}\))\({}_{2}\cdot\)3H\({}_{2}\)O) into Cu\({}^{2+}\) ion in a mixture solution, leading to the formation of complex structures with phytochemicals of the plant extracts or [Phyto-Cu-Phyto]\({}^{2+}\) complex structures. The formed complex structure is converted into Cu(OH)\({}_{2}\) and byproduct, and then to CuO after being dried in a vacuum oven temperature of 100 \({}^{\circ}\)C. Finally, the obtained amorphous CuO stabilized and capped into CuO nanopowders by annealing at 420 \({}^{\circ}\)C [17; 27; 34; 35].  
Figure 2: Proposed reaction mechanism of CuO NPs via Cactus extracts.