esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussion
### Experiments with CPNRA
#### 4.2.1 Job Input Rate Control  
To study the effect of job generation rate, we worked as follows:  
1. We generated a random number of users, from 50-2000, and a set of requests for each user. We run two sets of simulations. In every experiment, we used different total amounts of each available resource (CPU, Memory, Disk), so that in some cases the resources available were enough to satisfy all user requests, while in other cases, they were not. For example, in one experiment, the total number of resources was (1 K, 1024, 10,000), that is (1024 CPUs, 1 Tb memory, 100 Tb disk) while for next this number could be double or half and so forth.
2. We set the value of \(\mu\) equal to 30 jobs per second, thus, the system's input rate was at most \(\lambda=30\) jobs per second.
3. We kept tracing the system's state at regular time intervals \(h\) and recorded the percentage of resources consumed between consecutive time intervals, thus, we computed the \(b_{i}\) values. Every time a job \(i\) leaves a queue, the system's state changes. For example, if a job leaves the DSQ, it means that it has consumed \(F\) units of the dominant resource, changing the system's state from \(\mathbf{K}=K_{1},K_{2},\ldots K_{m}\) to \(\mathbf{K}^{\prime}=K_{1}-F,K_{2},\ldots K_{m}\). On the other hand, when multiple jobs enters a queue, the acceptable job input may be regulated accordingly, based on the model presented in Section 2.  
After running sets of simulations for different user numbers (from 50 to 2000), we averaged the percentage of resources consumed during all the recorded time intervals, for different recorded values of \(\lambda_{i}\). The results are shown in Figure 7a,b. For Figure 7a, the total numbers of resources were (1 K, 1024, 10,000) and the number of users was between 50 and 1000, while for Figure 5b, the total number of resources were double (2K, 2048, 20,000) and the number of users was from 1000 to 2000. When the number of users was relatively small (50-200), an average value of input rate \(\lambda_{i}=15\) was enough to exhaust almost 100% of the resources during the successive intervals. A larger number of users increases the competition for resources, thus the fair allocation policy is obliged to deliver far less resources than the requested to each job. As the job service time was considered to be constant, a larger input rate of \(\lambda_{i}=26.5\,\mathrm{jobs/s}\) (among all queues) was necessary to exhaust the resources requested over the time intervals when the number of users was 1000. This is more obvious in the second set of experiments, where we doubled the number of users and the available resources. Notice that in order to exhaust the resources, average input rates close to the maximum were required (see Figure 7b), from 28-30 jobs/s.
# Performance Evaluation of China's First Ocean Dynamic Environment Satellite Constellation
## 3 Results  
### Observational Effectiveness Assessment of the HY-2B/C/D Satellite Constellation  
The study analyzed the observation efficiency of HY-2B/C/D satellites, in comparison with six other satellite systems, including Sentinel-3(A, B), Jason-3, Sentinel-6A, Cryosat-2, and Saral. The comparison was conducted using nine satellites' altimetry data, and the observation efficiency was to search for the minimum radius of at least one observation point within a circle at any point on Earth within a 14-day period. The study then plotted the variations of that radius in an observation period of 1 to 14 days, which represented the observational effectiveness of China's first dynamic environment satellite constellation.  
The study analyzed the observation efficiency separately for the six satellites that are not Chinese, as well as the total nine satellites. The study aimed to illustrate the observation efficiency of the HY-2B/C/D constellation, which has achieved all-day, all-weather monitoring capabilities, including SSH, SWH, and other ocean dynamic environmental elements.  
As can be seen in Figure 2, the minimum radius to find the observation point on the global ocean surface shrank from 471.12 km to 90.33 km in the 14-day cycle, where the  
Figure 1: Schematic diagram for determining the location of intersections.  
HY-2C/D cycle was 10 days, and the HY-2C/D were repeat tracked from 11 to 14 days. The observation efficiency of the first nine days was slightly lower compared for the six non-Chinese satellites. However, from the 9 th day onward, the observation efficiency of the HY-2B/C/D network was higher than the six other satellites' observational effectiveness. Furthermore, it was determined from the overall observation efficiency of the nine satellites that the observation efficiency on the second day was quite obvious, and the minimum radius to find the observation point within 14 days was 59.52 km. Overall, the observation efficiency level of China's first dynamic environment satellite constellation was high. Therefore, the network has the ability to provide services for the safety of marine vessel navigation, marine disaster prevention and mitigation, and marine resource investigations.
###### Abstract  
In this study, the innovative dip-coating technique treated titanium (IDCT-Ti) implant with tetrapeptide Gly-Arg-Gly-Asp (GRGD) coating was investigated for its potential to enhance osseoeintegration. The L929 fibroblast cells were cultured in different concentrations of the GRGD (1%, 2%, and 5%). The cell viability was assessed through 3-{4,5-dimethylthiazol-2,5-diphenyltetrazolzolium bromide (MTT) assay and live/dead staining. The surface topography and nano-indentation were analyzed by atomic force microscopy. The hemocompatibility was evaluated via field-emission scanning electron microscopy, while contact angle analysis was detected by a goniometer. Radiograph evaluation was determined by panoramic imaging. It was found that the cell growth increased and had a survival rate of more than 70% in 1% GRGD. The mortality of L929 increased with the higher concentration of GRGD. The IDCT-Ti coated with 1% GRGD showed a nano-surface with a Young's modulus that was similar to human cortical bone, and it displayed greater red blood cell accumulations with abundant fibrin formation. As regards the wettability, the IDCT-Ti coated with 1% GRGD was lower than the SLA (sandblasted, large-grit, and acid-etched) treated implant. The X-ray image exhibited no bone loss around the implant at six months after placement. As a result, this study suggests that the IDCT-Ti implant, coated with 1% GRGD, has a tremendous likeliness to enhance osseoeintegration.  
tetrapeptide Gly-Arg-Gly-Asp; osseoeintegration; surface modification; dental implant 40.1
# Boundary Matching and Interior Connectivity-Based Cluster Validity Analysis
###### Abstract  
The evaluation of clustering results plays an important role in clustering analysis. However, the existing validity indices are limited to a specific clustering algorithm, clustering parameter, and assumption in practice. In this paper, we propose a novel validity index to solve the above problems based on two complementary measures: boundary points matching and interior points connectivity. Firstly, when any clustering algorithm is performed on a dataset, we extract all boundary points for the dataset and its partitioned clusters using a nonparametric metric. The measure of boundary points matching is computed. Secondly, the interior points connectivity of both the dataset and all the partitioned clusters are measured. The proposed validity index can evaluate different clustering results on the dataset obtained from different clustering algorithms, which cannot be evaluated by the existing validity indices at all. Experimental results demonstrate that the proposed validity index can evaluate clustering results obtained by using an arbitrary clustering algorithm and find the optimal clustering parameters.  
clustering evaluation; clustering algorithm; cluster validity index; boundary point; interior point +
Footnote †: journal: Applied Sciences
## 3 Methodology
### Methodology and Autoencoder Formulation
#### 3.3.1 Background  
Autoencoder is a specific deep learning architecture and, more precisely, a specific type of feedforward neural network, where both the input and output data are the same size. With the help of its layers, this network compresses the given input data to a lower-dimension code and then reconstructs the output based on this representation. Autoencoder architecture consists of 3 components: the encoder, the bottleneck and the decoder. As we mentioned above, the encoder is responsible for compressing the input into a coded representation. This representation is called bottleneck and 'it's the layer where the input data has lower compression. Finally, in the decoding phase of the autoencoder, the model learns how to reconstruct the compressed data from the bottleneck layer so that the output has the exact dimensions as the input. There are many autoencoders, such as feedforward or LSTM networks. The type of encoder we will build is a fully convolutional autoencoder.  
Modelling data that consists of images requires a particular approach in the world of neural networks. Autoencoders constitute a particular stream of neural networks whose input possesses the same dimension as the output. Since our input data is images of the eye retina, it makes sense to use Convolutional Neural Network (convnet) as the encoder and the decoder, respectively. The autoencoders used for images are large convolutional  
Figure 4: The training process: (**a**) Original image; (**b**) Random cropped patches with the corresponding cropped manual segmentation for the Supervised training; (**c**) Snapshot of the proposed autoencoder; (**d**) Classification of the image pixels.  
autoencoders due to their significantly better performance. We see a considerable loss of information when we are stacking our data. Instead of stacking the data, convolutional autoencoders preserve the dimensions of the input images and extract information gently and with the help of a layer called Convolutional. In convolutional autoencoders, the encoding part consists of hidden layers. The decoder has the same layers as the encoder but is mirrored. So, the encoder and the decoder are symmetrical with each other. This is not mandatory but is usually how we build our networks. We need to configure four parameters before continuing with the training phase. These are the number of nodes at the bottleneck layer (the smaller the number, the bigger the compression is), the number of hidden layers (it depends on how \"deep\" we want our network to be), the number of nodes in every dense layer as well as the loss function too. Below, we explain the types of layers that we used in our proposed structure.
# A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
## 1 Introduction
Without the need for paired sonar and other source images, the Cycle Consistent Generative Adversarial Network (CycleGAN) model inherits the adversarial training idea of GAN and uses a dual training learning approach to achieve mapping between the source domain and the target domain [11]. Therefore, the CycleGAN model can achieve the style transfer task from optical remote sensing images to side-scan sonar images when facing small sample side-scan sonar datasets. [PERSON] et al. [12] proposed a method for generating pseudo-forward-looking sonar images using the CycleGAN model based on a dataset generated by sonar image modeling software and effectively generated realistic forward-looking sonar data. [PERSON] et al. [13] proposed an improved style transfer model based on the CycleGAN model, which uses optical images to generate sonar images and improves the accuracy of the target detection model after enhancing the real sonar data. Although many researchers have proven the effectiveness of using GANs in generating sonar images, relevant research is still lacking application in the field of target detection of side-scan sonar images. To improve the bottom detection accuracy of side-scan images with only small samples, this study combines up-to-date data augmentation and the target detection model. The main contributions of our proposed method are as follows:  
1. To improve the detection accuracy on side-scan images, the original YOLOv8 model is revised by adding an attention mechanism to the backbone and neck, replacingthe C2f module with the C2f-DCNv3 module, and updating the loss function using EIoU loss. 2. The combination of data augmentation using CycleGAN and the improved YOLOv8 model enhanced the detection accuracy and robustness in target detection of side-scan sonar images with only small samples. In the following section, the CycleGAN model is used to generate side-scan sonar images from optical remote sensing images to solve the small sample problem. Then, the original YOLOv8 model is revised to improve the detection accuracy for sonar image targets. In the experiments, the effectiveness of the data augmentation model and the improved YOLOv8 model is demonstrated, and the proposed method achieves better detection accuracy compared with the existing method.
## 2 The Proposed Method
### Monte Carlo Sampling  
The Monte Carlo method is a computational algorithm, the underlying concept of which is using randomness to solve problems that might be deterministic in principle. It is often used in optimization, numerical integration, and other related application.  
The fitting problem associated with using the cubic Bezier curve in this application can be formulated as:  
\[\underset{x\in X}{min}\{f(x):=E[F(x,\xi)]\} \tag{4}\]  
where \(F(x,\xi)\) is the objective function of two vectors \(x\in R^{n}\) and \(\xi\in R^{d}\). In this paper, \(x\) is a given point set, and \(\xi\) means a candidate sub curve \(\left\{P(\omega,t)_{m}^{s}\right\}\), the vector \(\omega\) is a group of randomly generated control points which can define a cubic Bezier curve, and \(x\) refers to a given sub points set \(\left\{CP_{m+j}^{s}\right\}\). The function \(F(x,\xi)\) is formulated as:  
\[F(x,\xi)=\sum\limits_{j=0}^{2L+1}|P(\omega,t)_{m}^{s}-CP_{m+j}^{s}|^{2} \tag{5}\]  
The goal of this method is to calculate the optimal fitting Bezier curve of the given point group through Monte Carlo sampling by using the least square method.  
For every \(\left\{CP_{m+j}^{s}\right\}\), each candidate sub curve can be generated using the random control points vector \(\omega^{\xi},\xi=1,2\ldots N\) where \(N\) is the total candidate number (or sample number, and the proof of minimum sample number can be found in next section). A candidate curve can be expressed as the following:  
\[P_{\xi}(\omega^{\xi},t)_{m}^{s}=\sum\limits_{i=0}^{3}P_{i}(\omega^{\xi})_{m}B _{i,3}(t)=\begin{bmatrix}t^{3}&t^{2}&t&1\end{bmatrix}\begin{bmatrix}-1&3&-3&1 \\ 3&-6&3&0\\ -3&3&0&0\\ 1&0&0&0\end{bmatrix}\begin{bmatrix}\omega^{\xi}_{0m}\\ \omega^{\xi}_{1m}\\ \omega^{\xi}_{2m}\\ \omega^{\xi}_{3m}\end{bmatrix} \tag{6}\]  
The four control points \(\omega^{\xi}_{0m^{\prime}},\omega^{\xi}_{1m^{\prime}},\omega^{\xi}_{2m}\) and \(\omega^{\xi}_{3m}\) are random variables, and they have different distribution areas:  
\[\begin{split}\omega^{\xi}_{0m}&\in\left[CP_{m-L}^{s}-T_{0},CP_{m -L}^{s}+T_{0}\right]\\ \omega^{\xi}_{1m}&\in\left[T_{min}-T_{0},T_{max}+T_{0}\right]\\ \omega^{\xi}_{2m}&\in\left[T_{min}-T_{0},T_{max}+T_{0}\right]\\ \omega^{\xi}_{3m}&\in\left[CP_{m+L}^{s}-T_{0},CP_{m+L}^{s}+T_{0}\right] \end{split} \tag{7}\]  
Figure 6: Cubic Bezier Curve.  
where \(T_{0}\) is a tolerance interval threshold of the certain range, and \(T_{min}\), \(T_{max}\) are described as:  
\[\begin{array}{l}T_{min}=\min\Big{\{}C^{s}_{m+j}\Big{\}}-L\leq j\leq L\\ T_{max}=\max\Big{\{}C^{s}_{m+j}\Big{\}}-L\leq j\leq L\end{array} \tag{8}\]  
Figure 7 explained Equation (7) in a more detailed way. \(P_{0}\) is the start control point and \(P_{3}\) is the end control point, and they are expected to appear within a relatively small region centered in the related point of the given points (\(C^{s}_{m-L}\) and \(C^{s}_{m+L}\)), while \(P_{1}\) and \(P_{2}\) will have the chance to be in any position of the whole region defined by \(\Big{\{}C^{s}_{m+j}\Big{\}}\).
## 3 Auto-Adaptive Multi-Objective Task Selection Approach
### Path Utility  
Path utility measures the relative effort needed for a robot to reach a task from its current location. The _path_ utility function \(\Psi_{i}:T\times S^{m\times n}\rightarrow[0,1]\) is defined as follows:  
\[\Psi_{i}(T_{j},E_{known})=2\left(\frac{\overline{\Delta}-\Delta_{i}(T_{j})}{ \overline{\Delta}}\right)^{\gamma}-1 \tag{8}\]  
s.t.  
\[1\leq i\leq M=|R|,1\leq j\leq N=|T|\]  
where:  
\[\overline{\Delta}=\overline{d}-\underline{d}\] \[\overline{d}=\max\left\|X_{i},T_{j}\right\|_{sp},\forall j\] \[\underline{d}=\min\left\|X_{i},T_{j}\right\|_{sp},\forall j\] \[\Delta_{i}(T_{j})=\left\|X_{i},T_{j}\right\|_{sp}-\underline{d}\] \[\left\|X_{i},T_{j}\right\|_{sp}=\min\begin{subarray}{c}wp_{k} \in E_{known}\forall k\in\{1..\varepsilon\}\\wp_{1}=X_{i},wp_{p}=T_{j}\end{subarray}\sum_{k=1}^{e-1}\left\|wp_{k}-wp_{ k+1}\right\|_{2}\]  
Given the current environment knowledge \(E_{known}\), the function \(\Psi_{i}\) estimates the path utility obtained by a robot \(R_{i}\) in case of selecting the task \(T_{j}\). The parameter \(\gamma\) works as a shaping factor that could be used to tune the relation between distance and utility. The ordered sequence of waypoints \(wp_{k}\) represents the shortest path between the robot configuration \(X_{i}\) and the target \(T_{j}\). All segments \((wp_{k},wp_{k+1})\) are safe given that they are always built regarding only the collision-free pathways present in the known region \(E_{known}\). The wavefront propagation method proposed by [43] is employed to determine the waypoint sequence. The shape and behaviour of the \(\Psi\) function are depicted in Figure 5.  
Figure 5: Path utility function behaviour. There are several tasks in the scene (blue circles). The closest is located 6 m away from the robot while the furthest is 36 m far away. The closest and furthest tasks always return 1.0 and 0.0, respectively.
## 12 Simulation and Results
### Analysis of Localization Error and Energy Reduction  
In this scenario, the speed of the nodes plays a crucial role in affecting network topology. For this experiment, the number of nodes is fixed, starting at 500 and increasing to 700, while the maximum speed varies from 5 m/s to 25 m/s. The performance evaluation of metrics, for example, normalized Route REQUest (RREQ), Routing Overhead (RO), Packet Delivery Ratio (PDR), and Average End-to-End (AE2E) delays are detailed in Table 3.  
Figure 8(a) illustrates the localization error for all studied protocols, showing that as the network stability decreases with an increase in node speed, the PDR also declines. Despite this, the proposed SRAL protocol successfully restricts the maximum number of RREQs, ensuring that increased node density does not negatively impact performance. Furthermore, SRAL improves PDR through enhanced routing path stability. By reducing the number of RREQs, the likelihood of packet collisions decreases, ultimately enhancing PDR and minimizing localization error. Notably, SRAL achieves approximately 97% accuracy in reducing localization error, outperforming EEPDBR and RDBF, which show 2% higher localization errors as node density increases up to 500 nodes. In contrast, RDBF experiences a 21% reduction in PDR, while EEPDBR shows a 6% drop in PDR as network density and speed approach their limits. At a node count of around 400, which represents a mid-sized network, the localization errors for RDBF, EEPDBR, UDB, 3 DUL, UL, and RSMB are 21%, 27%, 19.6%, 17%, 15%, and 22.2%, respectively. In contrast, the proposed SRAL only increases by 15% with the error continuing to decrease as the network size grows.  
Figure 8: Comparative analysis with maximum speed of nodes for (**a**) AE2E delay; (**b**) Packet Delivery Ratio; (**c**) number of RREQ; and (**d**) number of RO.  
Figure 9(b) depicts the energy reduction (measured in Joule) for all benchmark techniques. The results show that all techniques are compatible with metaheuristic approaches when performed with hybridization. Among all baseline schemes, SRAL consumed the least energy at 72,000 J, compared to other techniques like RDBF, which consumed 90,000 J.
# Smart Infrastructure and Additive Manufacturing: Synergies, Advantages, and Limitations
## 2 3D Printing in Smart Infrastructure: An Overview
### Types of 3D Printing Technologies Relevant to Smart Infrastructure
To fully comprehend the impact of 3D printing on smart infrastructure, it is essential to compare the fundamental principles, advantages, disadvantages, and application scopes of various additive manufacturing technologies. Fused Deposition Modeling (FDM), for example, is widely used due to its affordability and ease of implementation, making it ideal for fabricating modular components and urban furniture. However, its layer-by-layer extrusion process limits resolution and mechanical performance. In contrast, Stereolithography (SLA) and Selective Laser Sintering (SLS) provide superior precision and material versatility, enabling the fabrication of intricate IoT-enabled devices and durable mechanical parts, respectively. Large-scale applications, such as concrete 3D printing, leverage extrusion-based techniques to construct entire buildings with minimal waste, yet scalability and material reinforcement remain key challenges. Moreover, Directed Energy Deposition (DED) is employed for in situ repair and reinforcement of infrastructure elements, offering significant longevity benefits but requiring complex operational setups. Understanding these trade-offs is critical for selecting the most appropriate 3D printing approach based on structural demands, environmental conditions, and economic constraints, ensuring the successful integration of additive manufacturing into smart infrastructure development. Also, large-scale 3D printing techniques utilizing concrete mixes are now applied to modern construction by enabling the automated production of entire building infrastructures with high efficiency and minimal waste [63]. These methods involve the layer-by-layer extrusion of specialized cementitious materials, eliminating the need for conventional form-work, thus, significantly reducing construction time. Featuring precise digital control, concrete 3D printing allows for the fabrication of structurally sound, customized architectural elements that were previously difficult or expensive to achieve. Additionally,  
Figure 2: Formlabs Form 2 SLA desktop 3D printer. it enhances sustainability by optimizing raw material use and incorporating recycled components [64; 65]. As cities grow larger and the demand for cost-effective, rapid, and resilient housing grows, concrete 3D printing stands out as an alternative to traditional manufacturing methods.