esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In recent years, using sound as a source of information in environmental monitoring systems has become increasingly important. Thanks to the development of Internet of Things (IoT) and artificial intelligence (AI) technologies, it has become possible to create distributed, intelligent acoustic systems used in medicine, industry, cities, and the natural environment. The article presents an overview of modern methods of acquiring and analysing sound data, from MEMS sensors and microphones, signal processing, and feature extraction to machine learning algorithms. The analysis of many works shows how diverse the approach to acoustic analysis can be, depending on the purpose, context, and environmental constraints. Technical challenges, privacy issues, and possible directions for further development, such as integration with multimodal monitoring systems or edge processing, are also discussed. The article is cross-sectional and can be a starting point for further research on intelligent acoustic monitoring in systems based on AI and IoT.  
intelligent acoustic monitoring; artificial intelligence; internet of things; signal processing; edge computing 2020 ac acmcopyrightright: 20  
and device information in real time throughout the system. The main functions of AI systems include event prediction, automatic environmental response and process optimisation. The continuous learning capability of IoT systems becomes possible through AI integration, leading to enhanced algorithmic performance. The system achieves better precision and effectiveness in decision-making through data processing, leading to the creation of an intelligent environment that adapts to user needs through continuous learning [5; 6; 7].  
Developing intelligent acoustic systems for the IoT requires building distributed networks that combine microphones and sound sensors. The network extends its coverage across residential buildings, industrial production halls, cities, and forest areas. Each sensor operates as an information node, gathering environmental sound data. AI algorithms analyse and interpret the acoustic signals which the network acquires. Thanks to this, we can achieve automatic sound classification, which recognises the type of sound, identifies anomalies and unusual events, and determines the context and source of its occurrence. We can apply sound analysis in many areas, including security and cybersecurity, where the appropriate analysis of acoustic information, combined with a proper response, can save lives and property.  
The integration of AI and the IoT in acoustic systems opens up limitless possibilities. As technology advances, we can expect increasingly sophisticated systems to record sounds, understand them, and predict their behaviour. Figure 1 provides a symbolic overview of acoustic sensing and analysis in IoT ecosystems, including various sound sources and sensors, as well as processing and responsive systems.
## 3 Nanosized-Lipid-Based Drug Delivery Systems in Wound Healing
### Lipid Nanoparticles in Wound Healing  
Lipid nanoparticles have proven to be efficient topical carriers due to their increased biocompatibility and capacity to incorporate several natural and semi-synthetic agents, maximum drug incorporation and wide production capacity. Their occlusive characteristics, based on their lipid structures, minimize cutaneous water loss and increase skin hydration, making them proper carriers for damaged skin. Wound hydration is known as a key factor in accelerating the wound-healing process. Nonetheless, their occlusive effect depends on the lipids' melting point, the incorporated liquid lipids and the size of the formed nanoparticles [42; 136]. Small-sized particles and low melting points were associated with the maximum occlusion effect [137].  
Two generations of lipid nanoparticles are acclaimed, with the first generation being solid lipid nanoparticles (SLNs) and the second generation being nanostructured lipid carriers (NLCs).
## 2 Material and Methods  
### GPR System  
The GPR system consisted of an ultra-wideband stepped-frequency continuous-wave (SFCW) radar combined with a monostatic linearly-polarized double-ridged broadband horn antenna (BBHA 9120-F, Schwarzbeck Mess-Elektronik, Schnau, Germany). A vector network analyzer (VNA; ZVRE, Rohde & Schwarz, Munich, Germany) was used to set up the SFCW radar. The dimensions of the BBHA 9120-F are \(68\times 95\) cm\({}^{2}\) aperture area and 96 cm in height, with an isotropic gain range from 6-18 dBi and a nominal frequency range of 0.2-2 GHz. The relatively small 3-dB beamwidth of the antenna at 45\({}^{\circ}\)in both the E- and H-plane and a frequency \(f=1\) GHz make it suitable for using off ground. A high quality N-type 50-Ohm impedance coaxial cable of 2.5 m in length (Sucoflex 104 PEA, Huber+Suhner AG, Herisau, Switzerland) was used to connect the antenna to the reflection port of the VNA. An Open-Short-Match reference calibration kit was used to calibrate the VNA at the connection between the antenna feed point and the cable. An interface between the VNA and a field laptop was developed to automatically communicate with the GPR system and to save the time-lapse GPR measurements on a field laptop.
## 3 Results and Discussions
### Damage Identification Based on Curvature Mode Difference  
Structural damage induces both global and local stiffness degradation. As Equation (8) demonstrates, decreased local stiffness increases the local curvature modes. Abrupt changes in nodal stiffness are detected before and after damage, which leads to significant modifications in the corresponding curvature modes. The CMD effectively captures these damage-induced local features, making it more adept at detecting local damage than directly using frequency or modal displacements. Moreover, the first-order curvature mode adequately reflects the structure's vibration characteristics and is easier to obtain. Therefore, the FCMD was used in this study as a damage indicator to explore its application in identifying structural sealant damage.  
The FCMD values of the glass panel unit are presented in Figures 7 and 8. Figure 7 shows distinct FCMD wave peaks across Conditions 2 to 5 at the damaged locations. The peak values and distribution ranges remain consistent for equivalent damage extent, maintaining an average value of 0.22 (averaging the peak values of Conditions 2-5). In undamaged locations, the FCMD fluctuations are attributed to minor deformations resulting from impact-induced vibrations in the glass panel. However, these FCMD variations are not indicative of structural damage. Figure 8 demonstrates that under Conditions 6 and 7, which involve more extensive structural sealant damage, elevated FCMD wave peaks are still detected within the affected regions, with recorded maximum values of 0.226 (Condition 6) and 0.272 (Condition 7)--significantly exceeding the 0.22 baseline. Through systematic analysis of FCMD wave peak ranges, the location and length of structural sealant damage can be reliably identified.
# Dynamic Line Rating--An Effective Method to Increase the Safety of Power Lines
## 4 Conclusions  
Dynamic line rating is a promising and cost-effective way of increasing the transfer capacity of the existing infrastructure. However, in the international literature it is not well established or well described how this method can directly affect high voltage lines' safety. In this paper, we present examples of power lines in the vicinity of which the electric field distribution exceeds the general public's limit value. This cannot be accepted from legal or safety points of view. To handle this phenomenon and ensure the level of safety, several methods are listed in the paper. To provide a cost-effective and efficient solution, a new expert system based on DLR has been analyzed. By applying this process, the safety level can increase to the acceptable level fulfilling the legal criteria. Via an example, it was shown that this expert system results in more economical results than the conventional static line rating with the line's full utilization. In some cases, even higher ampacity can be reached than the transfer limit in the operating conditions at present. To sum up, this paper shows that dynamic line rating as a part of an expert system could directly benefit safety and security for the power lines, and has other advantageous attributes in addition to increased transfer capacity.  
Conceptualization, L.R. and B.N.; methodology, L.R. and B.N.; software, L.R.; formal analysis, L.R. and B.N.; investigation, L.R. and B.N.; resources, L.R. and B.N; data curation, L.R. and B.N.; writing--original draft preparation, L.R.; writing--review and editing, B.N.; visualization, L.R.; supervision, B.N.; project administration, B.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by FARCROSS project of the European Union's Horizon 2020 research and innovation programme under grant agreement No 864274.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, [LR], upon reasonable request.  
This work has been developed in the High Voltage Laboratory of Budapest University of Technology and Economics within the boundaries of FARCROSS GA No 864274 project funded by Horizon2020. The project aims to connect major stakeholders of the energy value chain and demonstrate integrated hardware and software solutions that will facilitate the \"unlocking\" of the resources for the cross-border electricity flows and regional cooperation.  
The authors declare no conflict of interest.
# SAR Image Aircraft Target Recognition Based on Improved YOLOv5
## 4 Experiments and Analysis
### Description of the Dataset  
The dataset was obtained using the GF-3 satellite [36]. The SAR images of the dataset contained multitemporal scenes of several common airports around the world. The resolution of the images was 1 m and there were 2000 images in total, including 6556 aircrafts.  
Figure 4: Aircraft targets in SAR images. The green and yellow boxes represent aircraft targets and complex backgrounds, respectively. (**a**) Scattering points of the aircraft targets. (**b**) Interference of the complex background.  
Figure 5: Structure of the improved YOLOv5.  
These images range in size from 600 \(\times\) 600 pixels to 2048 \(\times\) 2048 pixels. The dataset includes seven categories of aircraft (Boeing 787, A330, Boeing 737-800, A320/321, ARJ21, A220, and others), as shown in Figure 6. It can be seen from the picture that the scattering points of the aircraft targets were highly discrete, and there was a complex and cluttered background, which made it difficult to accurately recognize the aircraft target.  
Figure 7a shows the distribution of the size of the bounding boxes. The horizontal axis is the width of the bounding box, and the vertical axis is the height of the bounding box. Figure 7b shows the distribution of the bounding boxes' aspect ratios. The horizontal axis is the aspect ratio, and the vertical axis is the number of bounding boxes. The sizes and aspect ratios of the bounding boxes of different aircraft targets vary greatly, which further increases the difficulty in recognizing targets. In our experiment, the dataset was divided into the training set, validation set, and test set with proportions of 8:1:1.  
Figure 6: Examples of seven categories of aircraft.  
Figure 7: Distribution of the bounding boxes of the aircraft targets in our dataset. (**a**) Distribution of the sizes of the bounding boxes. (**b**) Distribution of the bounding boxes’ aspect ratios.
## 1 Introduction
In short, the principle of isotension (Figure 3) can be summarized as follows [28]: The initial strand is anchored to one end of the stay cable and tensioned to a calculated force which, due structural flexibility, must exceed the desired strand force when all strands of the stay cable are stressed. Its wedge remains disengaged from the anchor block but connects to a specialized anchoring device  
Figure 1: Pedestrian bridge over the Nišava river in Nis, Serbia. Figure 2: Mono-strand jack usage for tensioning of the parallel strand stay cable using the ‘strand-by-strand’ technique: (**a**) pedestrian bridge over the Nišava river in Nis, Serbia [26]; (**b**) Seohae Grand Bridge, South Korea [27]. equipped with a load cell for force monitoring. Subsequent strands undergo a similar installation process, tensioned using a mono-strand jack with an identical load cell as the initial strand. As each strand is tensioned, the force in the preceding strands decreases until load cell readings match, ensuring uniform tension distribution. Each newly tensioned strand is then anchored to the permanent anchor block. This process is repeated until all strands are tensioned to their design force. The required designed force in the cable and the corresponding deformation are determined within the structural analysis model of the bridge for a specific construction stage [29]. The other method of individual strand tensioning is multi-cycle tensioning, where each cycle involves tensioning the strands successively with the same force. The number of cycles depends on the intensity of the designed force in the strands and the adjustability of the cable's anchorage points. With the tensioning of each subsequent strand, there is a decrease in the forces in previously tensioned strands due to the shortening of the cable axis and the design tension force in the cable cannot be achieved in just one cycle. This necessitates performing the tensioning process in several cycles, until the forces in all strands are equalized. Although the initial design of the cable-stayed bridge over the Nisava River in Nis specified cable installation using the single-cycle tensioning method, the cables had to be tensioned using the multi-cycle tensioning method. Despite initial reluctance, the designer accepted this approach as the contractor lacked the equipment for tensioning the strands in a single cycle. In this study, algorithms are proposed for the computer-based calculation of strand-by-strand tensioning in a stay cable, discretized from the bridge's structural system, with corresponding boundary conditions at the anchorage blocks (Figures 4 and 5). The calculation is conducted based on three algorithms, where the first one (Figure 6) pertains to the single-cycle tensioning procedure (isotension), and the other two (Figures 7 and 8) relate to the multi-cycle tensioning of strands. The number of required tensioning cycles depends on both the mechanical and geometric properties of the stay cable, the magnitude of the final tensioning force, as well as the displaceability of the cable anchoring points [26].
# Self-Organizing and Scalable Routing Protocol (SOSRP) for Underwater Acoustic Sensor Networks
## 4 Performance Metrics and Analysis
### Analysis  
The simulation was tested against different variations in several network parameters and scenarios. Simulation parameters are discussed in Table 1, with some of them taken from References [19; 20; 21; 22]. Considering the different distances between neighboring nodes, the energy and propagation model were simulated to calculate the required power to transmit the packet from one node to another in order to conserve the energy (power control technique).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Notation** & **Parameters** & **Value** \\ \hline - & Simulation Rounds & 50 \\ - & Network Area (m) & \(200\times 200\times 200\) \\ - & Network Size & 50, 60, 70, 80, 90, 100 \\ - & Transmission Range (m) & 70, 80, 90, 100 \\ SNR & Signal Noise Ratio (dB) & 20 \\ NL\({}_{\text{GH}}\) & Noise level (Shallow Waters, dB re 1 uPa) & 70 \\ NL\({}_{\text{DP}}\) & Noise level (Deep Sea, dB re 1 uPa) & 50 \\ f & Frequency (kHz) & 20 \\ BW & Bandwidth (kHz) & 4 \\ E\({}_{\text{dec}}\) & Electronics Energy (nl) & 50 \\ E\({}_{\text{amp}}\) & Amplifier Energy (nl) & 0.0013 \\ E\({}_{\text{iLte}}\) & Idle State Energy (nl) & 30 \\ T & Temperature (Celsius) & 20 \\ S & Salinity (ppt) & 34 \\ Pdi & Data Packet Length (bytes) & 240 \\ PH & HELLO Packet (bytes) & 4 \\ PINFO Packet & 6 \\ PREQ & Request Packet (bytes) & 4 \\ R & Transmission rate (kbps) & 26.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
Two scenarios were considered for simulation: Optimal behavior and Pragmatic behavior. The Optimal behavior presents the flawless path selection and data delivery during the entire simulation time period. Whereas in Pragmatic behavior, a temporary failure is introduced with the probability of 0.2 to test and identify the fault-tolerance mechanism and its effects on the performance metrics selected.  
Each scenario was further implemented for both different and same topologies. In different topology, new locations were assigned to nodes with increasing network size, whereas in the same topology, new nodes were added in the existing network keeping intact the previous node locations. In each case, network size and transmission range values were swept to identify (i) the effects of adding new nodes to the network and (ii) the optimal range to use a multi-hop communication. The simulation runtime was 50 rounds; in each round every node sent a single INFO packet containing the sensed data to the sink node.
## 6 Evaluation and Discussion
### Security Analysis
#### 6.1.3 Man-in-the-Middle Attack  
In this attack scenario, the adversary (\(adv\)) tries to substitute the key value of a legitimate producer with its own in order to obtain a certificate. For instance, the \(adv\) intercepts the message: \(PV\hookrightarrow MS:si(/NDN-BDA/MS/sign/{VID/<key>}K_{MS})\) and then changes the values of \(/VID/<\)key>/ with its own. When it receives the request, the \(MS\) will detect that the \(Interest\)'s information has been altered since the signature value does not match the tampered \(Interest\)'s hash. The adversary (\(adv\)) may also try to inject fake information into the message: \(MS\hookrightarrow PV:si(/NDN-BDA/MS/sign/{VID/<key>}K_{MS})\) containing the generated certificate for the \(PV\). However, the \(PV\) will still be able to detect it when it authenticatesthe received _Data_ with the \(MS\)'s key \(K_{MS}\), thereby making NDN-BDA resilient against such attacks.
## 1 Introduction
As silica reacts with H\({}_{2}\)O, the silica surface becomes hydroxylated where the Si-OH groups can be formed on the silica surface [14]. In addition to pure water, hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) can also react with the silica surface. Recently, it was reported that the nanoscatch resistance, nanoharness, and elastic modulus of the FS surface modified with a lower concentration H\({}_{2}\)O\({}_{2}\) solution (5%) were weakened compared to those of pristine FS, and they were close to those of pristine FS when the concentration was higher (15%), which was attributed to the presence of a thin corroded layer on the surface [15]. This raises an important question about whether the surface hydroxylation and adsorbed water layer thickness may vary after treatment with various H\({}_{2}\)O\({}_{2}\) solutions. However, the modification of the FS surface properties by H\({}_{2}\)O\({}_{2}\) solutions, the further effects on the surface and subsurface damage behavior caused by indentation, and scratch testing have not been studied yet. In this study, the FS surfaces were treated with various concentrations of H\({}_{2}\)O\({}_{2}\) solution, and the degree of hydroxylation of the FS surface was revealed by the water contact angle, adhesion force, Raman, and sum frequency generation spectra measurements. The nano- and macromechanical properties of various FS surfaces were studied using nanoindentation and Vickers indentation, respectively. The nanowear behavior of various FS surfaces was studied with a diamond tip in humid air. The subsurface damage of various FS surfaces caused by indentation and scratching was revealed by sub-\(T_{\rm g}\) annealing treatments. The obtained results show that the surface hydroxylation and the change in adsorbed water film thickness can significantly affect the mechanical and mechanochemical properties of the FS surface, suggesting H\({}_{2}\)O\({}_{2}\) treatment could be a potential alternative method to modify the surface quality, and mechanical as well as mechanochemical properties, of FS optics used in high-energy laser systems. This research can be helpful for improving the surface quality and lifetime of FS surfaces for various applications.
# Rolling Bearing Fault Diagnosis Based on CEEMDAN and CNN-SVM
## 4 Experimental Validation
### Experimental Results and Analyses  
The change curves of the accuracy and loss function of the CNN-SVM model are shown in Figures 9 and 10, respectively. It can be seen from the figures that when the number of iterations reaches 20 times, the accuracy curves of the training and validation sets begin to converge and gradually tend to be stable. It can be concluded that the CNN-SVM model has a high degree of accuracy and stability on the fault diagnosis task, reflecting the powerful ability of CNN in feature extraction and the accuracy of SVM on the classification task.  
The CNN-SVM model test set fault diagnosis results are shown in Figure 11. From Figure 11, it can be concluded that six samples with true label 6 in the confusion matrix are incorrectly classified as label 5 and the final fault classification accuracy of 98.93% is obtained after calculation.  
Figure 10: Loss function of CNN-SVM model.  
Figure 9: Accuracy of CNN-SVM model.  
In order to reduce the error and enhance the credibility of the experimental results, the average of 10 experiments is taken as the final result in this paper. The fault diagnosis results of different models are shown in Table 3.  
From the data in the table, it can be learnt that the proposed method CEEMDAN+CNN-SVM has the best classification effect, with an average accuracy of 99.25%. Compared with single SVM and CNN, the average accuracy is 16.54% and 9.52% higher; compared with EMD+CNN and EEMD+CNN models, the fault diagnosis accuracy is improved by 4.14% and 2.38%, respectively; compared with CEEMDAN+CNN-Softmax model, the fault diagnosis accuracy is improved by 0.73%. It can be learnt that the method proposed in this paper can effectively reduce the noise of the original signal and has a high fault classification accuracy.