esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 5 Analyzing the Challenges and Advantages of Protocols in Underwater Communication
### Routing Layer Protocols
#### 5.2.2 Routing Protocols without Localization Techniques  
DBR [42] was developed in 2008. In it, depth-based sensor nodes are used to send the data from bottom to top. The throughput rate is high for this approach. Its disadvantage is high power consumption. Q-ERP [43] was developed in 2017. The major advantages of this protocol are the high packet delivery rate, power efficiency, and absence of delays in data delivery. Adaptive Mobility of Courier nodes in the Threshold-optimized DBR Protocol (AMCTD) [44] was developed in 2013. The main advantage of this protocol is its improvements in the lifetime of the underwater network. The information-carrying based routing protocol (ICRP) [45] was developed in 2007. The main advantages of this protocol are its scalability and power efficiency. Low data delivery rates among its major disadvantages. The multi-layered routing protocol (MRP) [46] was developed in 2014. In this protocol, two phases are used: 1. a layering phase: In this phase the layers are formed towards the super node; and 2. A data forwarding phase: The data is forwarded through this formed layer.
# Novel Algorithm for Multi-Time Data Implantation in a Special Cyber-Manufacturing Architecture
## 5 Analysis of Proposed Method
### Manufacturing Architecture
#### 5.1.2 Manufacturing Architecture: Signaling Unit  
The signaling unit is developed to provide one of five notifications. The first four notifications are called in-control signals. The last one is referred to as an out-control signal. According to the demand of the users, the manufacturer can increase the number of in- and out-control signals. The signaling unit applies a mathematical function \(f\) to generate a number from 1, 2, 3, 4, i.e., \(f=1\) or \(f=2\) or \(f=3\) or \(f=4\). Here, we have not given any fixed  
Figure 4: Hardware architecture: Processing unit collects information from sensors. The processing unit then extracts the DNA sequence from the main memory. Then retrieve the historical and other data from the DNA sequence. The processing unit compares the sensor’s current data with the older one and signals accordingly. Updating the historical data with sensed information, the processing unit again implants that in the original DNA sequence. Finally, the processing unit sends back the stego DNA sequence to the main memory.  
equation as a part of that function. Rather, it is left to the manufacturer so that they can set their own function and fitted parameters according to this proposal or their modified one. The signaling unit may generate one of the four notifications automatically based on a function's generated value \(f\). When the signaling unit generates \(f=1\), the cyber device creates a gentle notification. That means that there is no problem in the sensed data. \(f=2\) indicates some minor changes in the health statistics. The device signals accordingly. Again, \(f=3\) indicates major changes in the health conditions. The person demands extra care. If the signaling unit measures \(f=4\), the person is in danger. They should consult a doctor immediately. The signaling unit then automatically calls a designated doctor. Notification 5 is not generated by the auto system of the signaling unit. This is done after receiving a request from a doctor. That request automatically triggers the signaling unit to set \(f=5\). When the device realizes a value of \(f=5\), it sends the report of the last-sensed and the extracted historical data to the doctor. A list of those notifications is shown in Figure 5.
## 2 Materials and Methods
### Alizarin Red S Staining  
DPSCs were seeded in a 24-well cell culture plate (SPL Life Science, Seongnam, Korea) at a density of 1.0 \(\times\) 10\({}^{4}\) cells/well for the 2D group, and a Corning(r) Ultra-Low Attachment 96-well plate (Corning) at a density of 2.0 \(\times\) 10\({}^{4}\) cells/well for the ULA group. DPSCs were cultured with an osteogenic medium containing \(\alpha\)-MEM with 10% FBS, gentamycin, dexamethasone (10 nM), L-ascorbic acid (100 \(\upmu\)M), and \(\beta\)-glycerophosphate (10 mM). The negative control group was cultured in a growth medium (control_GM), and the positive control group was cultured in an osteogenic medium (control_OM). Cells were incubated for 3 weeks, with their respective media being freshly replaced every 2-3 d. After 21 days, the medium was removed, and the cells were washed with PBS. DPSCs were fixed in 70% ethanol and stained with 300 \(\upmu\)L of 2% alizarin red S staining reagent (LIFELINE Cell Tech, Frederick, MD, USA). After removing the staining reagent, a photograph was taken as it was before and qualitatively examined.
# Sensing and Device Neighborhood-Based Slot Assignment Approach for the Internet of Things
## 2 Literature Review
Additionally, an automatic repeat request approach was adopted to further strengthen its efficiency and reliability. However, it was applicable to a particular IoT hardware plate-form, and its applicability is questionable in other application domains. A token-enabled adaptive approach (preferably distributed) was presented to address one of the challenging issues--that is, hidden terminal scenarios--with mobile ad hoc networks (MANET) and IoTs [30]. However, the simultaneous transmission of packets, end-to-end delay, and one-point failure are some of the common issues with this approach. Similarly, a distributed TDMA-enabled approach was presented to resolve bandwidth wastage issue in resource constraint networks where various slots are assigned according to a pre-defined mature scheduling policy [31]. The priority of member devices was utilized to ensure a proper time slot allocation. However, concurrent communication among all devices was not guaranteed, as slots were assigned according to the priority, and low priority devices may starve for a particular resource. A naive multi-channel scheme, which was a hybrid of the TDMA and FDMA, was proposed to enable and ensure the establishment of proper communication sessions among various wearable devices in the Internet of Medical Things (IoMT) [13]. Energy efficiency, as well as time and space complexity, are tightly coupled issues with this approach. In addition to it, topological orders were utilized to develop an enhanced TDMA approach [14] where every device has prior knowledge about its waiting time for its assigned time slot. However, slot waiting time directly is proportional to the network density, i.e., the waiting time is comparatively longer if devices are densely deployed. Likewise, a distributed TDMA-oriented slots allocation mechanism was proposed by [PERSON] et al. [32] where slots were randomly assigned. However, synchronization is one of the common issues associated with this approach. [PERSON] et al. [33; 34] presented an enhanced version of the distributed TDMA scheduling scheme to improve latency in the IoTs networks. These approaches have neglected an important metric--that is, the device neighborhood--in developing TDMA approaches.
# A Robust Image Watermarking Scheme Based on SVD in the Spatial Domain
## 3 SVD-Based Watermarking and Its Impact on the Spatial Domain
### The Impact of SVD-Based Watermarking on the Pixel Values in the Spatial Domain
's conclusion to the proposed watermarking scheme. To verify the correctness of this conclusion, several experiments are conducted by utilizing two grayscale images: Lena and Barbara. The images are firstly divided into non-overlapping blocks with a size of \(m\,\times\,m\). Then, the SVD is performed for each image block, and \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\) is calculated. Figure 1 shows the frequency distribution histogram of the elements in \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\), where \(m=2\). From Figure 1a,b, it ismanifested that the matrix elements in \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\) are in the normal distribution approximately. Furthermore, most of the elements are gathered in \(0.5\,(1/2)\)[24]. To prove the universality of this conclusion, Figure 2 illustrates the frequency distribution histogram of \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\) for block sizes \(4\,\times\,4\) and \(8\,\times\,8\), respectively. It is shown that with the increase of block size, the occurrence frequency of the same elements is decreased. This is because the probability of the similar neighboring pixels in each block becomes much lower than that of the block with a small size. In spite of this, the elements in matrix \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\) are still in normal distribution approximately, and most of them are around \(0.25\,(1/4)\) and \(0.125\,(1/8)\) for the \(4\,\times\,4\) block and the \(8\,\times\,8\) block, respectively. In other words, most of the values in \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\) are closely related to the block size. According to this analysis, the matrix \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\) for each image block can be obtained approximately by Equation (10) in the spatial domain. In this way, the difference matrix \(\Delta\mathbf{A}\) caused by watermark embedding can be approximately calculated based on Equation (8). It is noted that the above analysis provides the possibility to achieve SVD-based watermarking in the spatial domain. Figure 1: Frequency distribution histogram of the values in \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\) for block size \(2\,\times\,2\): (**a**) Image Lena; (**b**) Image Barbara.
# Weak Signal Processing Methods Based on Improved HHT and Filtering Techniques for Steel Wire Rope
## 5 Discussion  
Although a shorter liftoff distance makes a better detection signal, it may cause impact, wear, and abrasion between the tested wire rope surface and inner wall of the detector, especially for online wire rope detection when they are operated under high-speed conditions. Therefore, long length defect detection is urgently needed especially in complicated application scenarios, as well as weak signal detection and compensation methods. Moreover, when the defect signals are not so weak under short liftoff distance testing conditions, higher SNR can still be obtained through our improved HHT and filtering techniques. Further, the new proposed HHT and filtering combined method are also capable of weak signal processing for other ferromagnetic objects including steel pipe, oil tank, steel rail other than wire rope under bigger liftoff distance, and complex application environment. However, the feasibility of this study for other types of wire rope defects such as LMA (including wire rope abrasion and corrosion) is still waiting to be validated. Further work may continue to focus on the weak signal recognition and further improvement of the SNR for more types of wire rope defect detection.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Signal**} & \multirow{2}{*}{**LP-WA**} & \multirow{2}{*}{**Median**} & \multirow{2}{*}{**Wiener**} & \multicolumn{4}{c}{**Proposed Method**} \\ \cline{5-8}  & & & & **HHT** & **LP** & **MA** \\ \hline S1 & 43.7742 & 45.9040 & 32.1887 & 1.8256 & 23.6263 & 80.6473 \\ S2 & 45.9942 & 45.9440 & 32.1642 & 0.8415 & 24.4783 & 90.3688 \\ S3 & 40.8502 & 45.6552 & 31.9675 & 1.1997 & 23.9081 & 79.1788 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SNR (dB) results by different signal processing techniques.
## 3 Results
### Mechanical Properties  
The tensile test results of the joints are shown in Figure 7. The tensile properties of the joints were significantly improved by introducing the additional shielding gas. The maximum tensile strengths of the samples 1-6 were 648 MPa, 776 MPa, 716 MPa, 775 MPa, 694 MPa and 773 MPa, respectively, which indicates that with the same welding current, the introduction of additional shielding gas increases the maximum tensile strength of the joint by 19.7%, 8.2% and 11.3% when the welding speed was 1.6, 1.8 and 2.0 m/min, respectively. The fracture cross-sections are shown in Figure 8. The fracture section of sample 1 shows a transgranular fracture along the cleavage plane because of the brittle fracture; the fracture section of Sample 2 shows a large number of irregular dimples, and the inclusion of second-phase particles can be observed at the bottom of the dimple, indicating that the joint fracture is ductile.  
Figure 5: Metallographic test results: (**a**) Metallographic image of HAZ in Sample 1, (**b**) Metallographic image of HAZ in Sample 2, (**c**) Metallographic image of HAZ in Sample 3, (**d**) Metallographic image of HAZ in Sample 4, (**e**) Metallographic image of HAZ in Sample 5, (**f**) Metallographic image of HAZ in Sample 6, (**g**) Metallographic image of fusion zone in Sample 1, (**h**) Metallographic image of fusion zone of in Sample 2, (**i**) Metallographic image of fusion zone in Sample 3, (**j**) Metallographic image of fusion zone in Sample 4, (**k**) Metallographic image of fusion zone in Sample 5, (**l**) Metallographic image of fusion zone in Sample 6.  
Figure 6: Rockwell hardness distribution in welded joints.  
The Charpy impact test results of the joint and base metal are shown in Table 4. It can be seen from Table 4 that the average absorbed energy of the base metal is 54 J, while the average absorbed energy of the joints without the additional shielding gas ranges from 12 to 18.2 J, amounting to approximately 22.2-33.7% of the absorbed energy of the base metal. After introducing the shielding gas, the absorbed energy of the joints ranges from 27 to 30 J, amounting to approximately 50-55.6% of the absorbed energy of the base metal. At the same weld current and weld speed, the joint with additional shielding gas has a higher absorbed energy, and hence, better toughness.  
Figure 8: Photographs of fractured tensile samples: (**a**) Sample 1, (**b**) Sample 2.  
Figure 7: Tensile properties of the base metal and welded joint: (**a**) Tensile curves, (**b**) Result of tensile tests.
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 3 Proposed Method
### Proposed Model
#### 3.4.1 Contextualizing U-Net  
The U-Net architecture, renowned for its prowess in image segmentation, serves as the foundational framework for this advanced model. Its inherent ability to capture hierarchical features through encoder-decoder structures forms the backbone of the proposed design. Recognizing the unique demands posed by satellite imagery, this model takes a deliberate step further to enhance its segmentation capabilities. Satellite imagery, characterized by vast spatial intricacies, necessitates a model that goes beyond conventional segmentation approaches. The proposed variant is meticulously tailored to accommodate and capitalize on the distinctive features of satellite images. The integration of separable convolutions and self-attention mechanisms represents a strategic choice aimed at addressing the challenges specific to this domain. The efficiency of separable convolutions becomes particularly pertinent in the context of processing expansive satellite image datasets. This technique, characterized by its parameter-efficient design, empowers the model to navigate the complexities of high-resolution imagery while maintaining computational tractability. Each layer strategically utilizes separable convolutions, optimizing feature extraction without compromising on model efficiency.
## 3 Data Processing of PS-InSAR
However, it was noted that more consistent findings could be achieved from the C-band for this type of region, as shown in Figure 10. In the second region, the presence of residential and industrial structures leads to a greater number of reflecting objects compared to the predominantly rural first area. This situation led to the acquisition of more PSs located along the railway track. Upon further examination of the PSs in the third study region, it is evident that the coherence values are high, and the mean standard deviations are within an acceptable range. The analysis of the deformation maps in Figure 10 reveals that the first study area is indicated by a red circle, the second study area is depicted within a green rectangular box, and the third study area is situated near the Konya city center, denoted by an asterisk at the end of the line. However, there is a spot, located between 37.98\({}^{\circ}\) and 38\({}^{\circ}\) latitude and 32.6\({}^{\circ}\) longitude, detected through SARanalysis where no evidence of deformation has been found using the diagnostic train and the relevant maintenance department. This location is in close proximity to the railway and has been recognized as a problematic area due to the presence of deformation. This area is situated to the south of the line and mostly encompasses the Konya Organised Industrial Zone, as shown by a black arrow in Figure 10. This event once again highlights the significance and thoroughness of the deformation maps derived from InSAR analysis. The data acquired from the InSAR analysis will enable us to continuously monitor whether or not the scenario occurring around the line will have a direct impact on it. In summary, the second and third research regions yield more favorable outcomes, including improved coherence, increased number of PSs on the railway tracks and reduced standard deviation, compared to the first area, despite the findings being deemed satisfactory (Table 4).
# A Minimal GBT Model for Distortional-Twist Elastic Analysis of Box-Girder Bridges
## Appendix C Matrices in the GBT Equation (22)  
By performing integrations in Equation (21), with the modes given by Equations (13), (15), (17), and (19), the following expressions are found for the GBT matrices:  
* Matrix \(\mathbf{C}^{e}\): \[\mathbf{C}^{e}=EI_{\omega}\left(\begin{array}{cc}1&\frac{1}{b}\\ \frac{1}{b}&\frac{1}{b^{2}}\end{array}\right)\] (10) where \(\beta\) is the warping ratio (Equation (20)) and \(I_{\omega}=\frac{b^{2}b^{2}}{24}\frac{\left(ht_{1}-ht_{2}\right)^{2}\left(ht_ {1}+ht_{2}\right)}{\left(ht_{1}+ht_{2}\right)^{2}}=\beta^{2}\frac{b^{2}b^{2}}{2 4}\left(ht_{1}+ht_{2}\right)\) is the warping stiffness. As already observed, when \(\frac{h}{b}=\frac{t_{2}}{t_{1}}\), \(C_{11}^{e}=C_{21}^{e}=C_{12}^{e}=0\) while \(C_{22}^{e}\
eq 0\).
* Matrix \(\mathbf{C}^{f}\) : \[\begin{split} C_{11}^{f}&=\frac{E}{72}\left(b^{3}t _{1}^{3}+h^{3}t_{2}^{3}\right)\\ C_{12}^{f}&=C_{21}^{f}=-\frac{E}{360}\left(b^{3}t_{1}^{3}( -6+\alpha)+h^{3}t_{2}^{3}(6+\alpha)\right)\\ C_{22}^{f}&=\frac{E}{2520}\left(b^{3}t_{1}^{3}(51+2 \alpha(-9+\alpha))+h^{3}t_{2}^{3}(51+2\alpha(9+\alpha))\right)\end{split}\] (11) where \(\alpha\) (Equation (16)) is the joints' distortional rotation.
* Matrix \(\mathbf{D}^{s}\): \[\mathbf{D}^{s}=\left(\begin{array}{cc}GJ&0\\ 0&0\end{array}\right)\] (12) where \(J=\frac{4(bh)^{2}}{2\left(\frac{h}{t_{1}}+\frac{h}{2}\right)}\) is the Bredt torsional stiffness.
* Matrix \(\mathbf{D}^{f}\): \[\begin{split} D_{11}^{f}&=\frac{2}{3}G\left(bt_{1}^{3}+ht_{2}^{3} \right)\\ D_{12}^{f}&=D_{21}^{f}=\frac{2}{3}G\left(bt_{1}^{3}-ht_{2}^{3} \right)\\ D_{22}^{f}&=\frac{2}{15}G\left(bt_{1}^{3}(6+(-2+\alpha) \alpha)+ht_{2}^{3}(6+\alpha(2+\alpha))\right)\end{split}\] (13)
* Matrix \(\mathbf{B}^{f}\): \[\mathbf{B}^{f}=\left(\begin{array}{cc}0&0\\ 0&B_{22}^{f}\end{array}\right),\qquad B_{22}^{f}:=2\frac{E}{bh}\left(t_{1}^{3} h(\alpha-1)^{2}+t_{2}^{3}b(\alpha+1)^{2}\right)\] (14)
# Resource Management for Collaborative 5G-NR-V2X RSUs to Enhance V2I/N Link Reliability
## 4 User Association Scheme for Collaborative RSUs
### Relailibilty Evaluation Based on BS-Type, UE-Type, and Hybrid RSU
Figure 4c shows the result of the VUE average throughput, which is 1.528 Mbp/s when the vehicle receives data from the BS-type RSU only, and 2.5 Mbp/s when receiving data from the UE-type RSU. The average throughput using the hybrid RSU method, which is the proposed BS/UE-type RSU cooperation scheme, is 3.06 Mbp/s, which shows a 100% performance improvement over 1.532 Mbp/s when data transmits from the BS-type RSU. In addition, we confirm that the average throughput is increased by 0.56 Mbp/s and the performance is improved by about 22% compared to the case of receiving data from the UE-type RSU. This result confirms that the proposed hybrid RSU method improves the reliability of vehicle users by mitigating the same interference channel and enhancing the quality of the communication system. Additionally, in a 5G-NR V2X environment, it is observed that the shadow area is minimized by applying the hybrid RSU method, and the average processing capacity of the vehicles is maximized through collaborative access between BS- and UE-type RSUs.
# Real-Time, Deep Learning Based Wrong Direction Detection
## 3 Proposed System  
### Detection and Tracking  
We separate our task into three main steps: detection, tracking, and direction checking. YOLOv3 is an object detection method targeted at real-time video processing. The result of the YOLOv3 model extracts a vehicle's bounding box information in order to apply robust tracking algorithms and identify direction.  
First, we send a continuous frame to the pre-trained YOLOv3 model that returns us bounding boxes of vehicles. Our algorithm is based on centroid tracking, which is a multi-step process. Centroid tracking assumes that we are passing \((x,y)\)-coordinates in a set of bounding boxes for each detected object in each frame. Once we have the bounding box coordinates, we compute the \"centroid\" (i.e., the center \((x,y)\)) coordinates of the bounding boxes. Since these are the initial set of bounding boxes presented to our algorithm, we assign them unique IDs. Then, we calculate the cost value between the detected car and its prediction. If there are multiple detections, we need to assign each of them to a tracker. We use intersection over union (IOU) of the tracker bounding box and detection bounding box as a metric. We maximize the sum of the IOU assignment using the Hungarian algorithm [16]. The machine learning package scikit-learn has a build-in utility function that implements the Hungarian algorithm. Then we implement updated Kalman filter equations that are described by [PERSON] and [PERSON] [17] to predict multiple car objects from a video frame.  
1. Prediction phase Notations: \(x\)--state mean, \(P\)--state covariance, \(F\)--state transition matrix, \(Q\)--process covariance, \(B\)--control function (matrix), \(u\)--control input. \[\overline{x}=Fx+B*u\text{ Predicted (a priori) state estimate};\] (1) \[\overline{P}=FPF^{T}+Q\text{ Predicted (a priori) error covariance}.\] (2)
2. Update phase Notations: \(H\)--measurement function (matrix), \(z\)--measurement, \(R\)--noise covariance, \(y\)--residual, \(K\)--Kalman gain. \[y=z-H\overline{x},\text{ Innovation or measurement pre -- fit residual};\] (3) \[S_{k}=HPH^{T}+R,\text{ Innovation (or pre -- fit residual) covariance};\] (4) \[K=\overline{P}*H^{T}*S_{k}^{-1},\text{ Optimal Kalman gain};\] (5) \[x=\overline{x}+K*y,\text{ Updated (a posteriori) state estimate};\] (6) \[P=(I-KH)\overline{P},\text{ Updated (a posteriori) estimate covariance};\] (7) \[y=z-H*\hat{x},\text{ Measurement post -- fit residual}.\] (8)  
The formula for the updated (a posteriori) state estimate works each time for the measurement update pair. The process is repeated with the previous (a posteriori) estimates used to project or predict the new (a priori) estimates. An implementation of the updated Kalman filter algorithm can be found in **Algorithm 1**. Then we used OpenCV drawing libraries to draw tracking lines on each detected car. Figure 5 presents the full pipeline of our proposed method.  
Figure 5: Detecting and tracking pipeline. YOLOv3: you only live once version 3.  
```
```  
**Algorithm 1**Pseudo-code for Kalman filter tracker.