esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Ad Hoc Routing: Configuration, Scenarios, and Results  
In essence, an ad hoc network has a flat typological structure with dynamic interconnection of nodes, where each node can communicate with as many nodes as possible in the network through the intermediate or relay nodes, to efficiently forward the information to the desired destination. The flow of information in the network from one endpoint to another can be achieved either by the use of flooding or routing techniques. By implementing routing techniques, a message can hop multiple nodes to ultimately reach its destination. To ensure that there is always a path between the source and destination, the network must reconfigure itself periodically through the use of some routing techniques in order to counter any broken links. An ad hoc network comes with its own set of challenges--since the nodes tend to be mobile, routing algorithms are required to answer issues raised by the mobility of nodes. Luckily in the implemented TestBed, the nodes are OBUs, mounted in vehicles. Since vehicles or nodes are not random in their movement, they, in fact, follow a predefined path, such as a road. Hence, routing can be managed relatively easily.  
To test the designed application and the implemented TestBed, it is necessary to consider a routing protocol to configure the TestBed. For this purpose, a routing protocol Better Approach To Mobile Ad hoc Networking (BATMAN). It is a multi-hop mobile ad hoc routing protocol, and was developed by the German Freifunk community. It is a non-commercial open grassroots initiative to support free computer networks in the German region, through which a term known as Collective Intelligence emerged in early 2007. The German Freifunk community has also started to tinker with the idea of routing data on Ethernet Layer (Layer 2) of the TCP/IP Model, instead of Network Layer (Layer 3). This approach provides a virtual interface where packets get transported on their own without maintaining or manipulating any routing tables. This concept increases transparency and takes the burden of routing table management off the shelf. To differentiate this new approach from BATMAN protocol which are already available, they put an ADV at the end, to denote that it is \"advanced\" [34].
## 4 Results  
### Comparison of Mudstone Liquid/Plastic Limit Modification  
#### 4.1.1 Determination of the In Situ Mudstone Liquid/Plastic Limit  
To investigate the effect of electrochemical modification on the liquid and plastic limit of mudstone, the relevant parameters of mudstone before modification were first determined experimentally. The tests were carried out using the falling cone method with cone depths of 17 mm and 2 mm at the liquid and plastic limits, respectively.  
First, prepare three samples with different water contents, corresponding to the mudstone's flow state, plasticity state, and intermediate state, as shown in Figure 3a. The actual moisture content for these three states is calculated using the drying method after determining the liquid and plastic limits. The experimental process and results are shown in Figure 3.  
Figure 3b indicates that the difference in water content at a cone depth of 2 mm is \(21.591\%-19.867\%=1.72\%\), which is less than 2%, indicating that the results of the test are reliable. The plastic limit water content of the mudstone before modification was calculated to be 20.745% and the liquid limit water content to be 35.266%.
## 4 Experiment and Analysis
### Evaluation Indicators  
To better evaluate the performance of different models on the same dataset, the following metric is considered to be introduced to evaluate the models: accuracy, which calculates the number of correctly predicted samples as a proportion of the total number of samples, as shown in Equation (4). Precision is the probability that, given a positive label, how many of them are true positives, i.e., the ratio of correctly predicted positive samples to the total number of predicted positive samples, as shown in Equation (5). The recall is the accuracy of predicting positive sample instances, i.e., the ratio of correctly predicted positive samples to the total number of actual positive samples, as shown in Equation (6). the F1 score integrates _Precision_ and _Recall_ to reconcile both, as shown in Equation (7).  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}, \tag{4}\]  
\[Precision=\frac{TP}{TP+FP}, \tag{5}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{6}\]  
\[F_{1}=\frac{2\times Precision\times Recall}{Precision+Recall}, \tag{7}\]  
In the network training process of deep learning, the loss function calculates the gap between the actual value and the predicted value. The one used in this paper is the cross-entropy loss function with the following equation.  
\[L=-\sum_{i=1}^{n}p(x_{i})\log q(x_{i}) \tag{8}\]  
where \(p(x_{i})\) is the expected probability of the input and \(q(x_{i})\) is the actual probability of the input. The smaller the \(L\) obtained from both calculations, the smaller the difference between the actual value and the predicted value, and the more accurate the prediction is.
## 3 Proposed Method
### Information Diffusion Process  
According to the analysis and formulas above, we can employ our model to simulate the process of information diffusion. At the beginning, we randomly assign a different state to each seed node. According to \(p^{s}_{ij}\), each time when a node accepts information, it has a single chance to transmit the information to its inactive neighbors. In the process, if a node decides to accept the information according to the probability of \(p^{a}_{ij}\), it will be a spreader, and simultaneously, its state is updated. Any node has a single chance to spread a piece of accepted information. When the spreader's attraction to the information fadesaway, it then becomes stifled. Finally, when the information popularity fades to \(R(t)\cong 0\), the diffusion process ends:  
\[R(t)=\sum_{i=1}^{|V|}M_{i}^{t}\cdot d_{i} \tag{11}\]  
where \(R(t)\) indicates the accumulative influence of all the individuals considering their authority and \(d_{i}\) is the connection degree of the node \(v_{i}\). The information popularity is a metric, which illustrates the evolution of the diffusion in view of the impact of each user. Algorithm 1 summarizes our proposed model.  
```
0: An OSN \(G(V,E,C)\), initial state of all nodes \(S_{1}\), the Information value \(W\), the queue of seed nodes \(Q_{seed}\)
0: The state of all nodes at the end \(S_{t}\)
1:while\(R(t)>0\)do
2:for\(v_{i}\in Q_{seed}\)do
3:for\(v_{inactive}\in inactive\) nodes among \(v_{i}\) neighborsdo
4:\(v_{i}\leftrightarrow v_{inactive}\) // \(v_{i}\) sends information with \(p_{ij}^{s}\), \(v_{inactive}\) accepts with \(p_{ij}^{s}\)
5:if\((v_{inactive}\) is activated)
6:\(v_{inactive}\to Q_{seed}\) // add \(v_{inactive}\) in queue
7:endif
8:endif
9:poll \((v_{i})\) // remove \(v_{i}\) from the queue
10:endfor
11: update \(S_{t}\)
12:endwhile
```  
**Algorithm 1** Information diffusion based on IDBESW model
# Electronic Beam Steering Metamaterial Antenna with Dual-Tuned Mode of Liquid Crystal Material
## 3 Design, Simulation and Analysis of Dual-Tuned CRLH Metamaterial Antenna Based on LC
### Theoretical Analysis of CRLH Structure with Dual-Tuned Mode
In addition, due to the addition of variable left-handed inductance in the dual-tuned LC metamaterial antenna, the change rate of the phase constant of the left-handed transmission line also increases. At the same time, the phase constant of the composite left-handed transmission line is expended, which is the second term on the right of the equal sign. Hence, the beam scanning range of the metamaterial antenna is amplified. To sum up, in theory, the dual-tuned CRLH structure can improve the change rate of the phase constant both of the left-handed and the right-handed transmission. The dual-tuned CRLH unit cell model is simulated and analyzed by CST electromagnetic simulation software. DC bias voltages \(V_{\text{DC1}}\) and \(V_{\text{DC2}}\) are loaded on two LC layers, respectively, leading to four different extreme states of LC, as shown in Figure 2. The simulation results of the CRLH unit cell are shown in Figures 4 and 5 under the four different states. When no bias voltage is loaded and the LC layers are aligned, the balanced frequency is 14.4 GHz, where the dispersion is zero. While the LC1 is loaded with the saturation bias voltage, and the LC2 is aligned, the left-handed capacitance is decreased, giving rise to the variation of the balanced frequency to 14.65 GHz. Then, if the LC1 is aligned and the LC2 is loaded with the saturation bias voltage, the right-handed capacitance is enlarged and the left-handed inductance is diminished, the balanced frequency is shifted to 14.78 GHz. When the two LC layers are loaded with the saturation bias voltage, both the left-handed capacitance and the left-handed inductance are reduced, while the right-handed capacitance is enhanced, corresponding to the movement of the balanced frequency from 14.78 GHz to 15.1 GHz. The dispersion curves of \(\beta d\) via frequency in Figure 4 are compared with numerical calculations of parameters in the equivalent circuit, which is represented by discrete points. The theoretical calculation and simulation results are in good agreement, thus demonstrating the correctness of the anterior theoretical analysis. Moreover, the results illustrate that the CRLH unit cell keeps balance states as the bias voltage changes all along. Figure 4: \(\beta d\) of the CRLH unit cell under dual-tuned mode of LC. Figure 5: Magnitude of S\({}_{11}\) of the CRLH unit cell under dual-tuned mode of LC.
# Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm
## 2 System Description
### Fractional PID Control  
Fractional order control is one of the main applications of fractional calculus, and uses fractional orders in the controller parameters. The fractional order PID (FOPID) controller commonly denoted as the \(PI^{\alpha}D^{\beta}\) controller, is an extension of the linear PID controller. Here \(\alpha\) and \(\beta\) are the fractional orders of the integrator and differentiator, respectively, and can vary from 0 to 2 [14; 45]. The FOPID controller can be expressed as:  
\[C(s)=K_{p}+K_{i}s^{-\alpha}+K_{d}s^{\beta} \tag{8}\]  
If the values of \(\alpha\) and \(\beta\) are made equal to one, the linear PID controller is obtained. Similarly, choosing \((\alpha,\beta)\) = {(1,0),(0,1)} gives the PI and PD controller, respectively. It can be easily seen that the FOPID is a generalized version of the conventional PID. By varying the values of \(\alpha\) and \(\beta\) in the range (0,2), fractional PI, PD, PID controllers are obtained. The FOPID controller has five parameters to tune \([K_{p},K_{d},K_{i},\alpha,\beta]\), and hence can meet five specifications [15]. The general block diagram for a FOPID controller is shown in Figure 2[46].
## 2 State-of-the-Art
### Criteria for Structural Analysis of WT  
From existing literature reviews and interviews, including [17; 18; 19; 20], multiple features for structural analysis of wind turbines are extracted and subsequently condensed in criteria. A holistic SW tool for structural health monitoring of offshore WF should be:  
* Graphical, as structural integrity is related to spatial distribution and location of occurrence.
* Scaleable and modular. WFs are modular and scaleable by nature, consisting of one, up to several hundreds, of WTs. Modular--to include other, non-corrosion wind-turbine failure modes (blade monitoring, gearbox monitoring, inverter monitoring,...).
* SCADA compatible, in order to leverage the vast amount of data already captured at the wind turbine (WT) and WF level.
* Web-based and secure, in order to allow data-analysis by experts, independent of the WF location and shielded from external tampering or unauthorised access.
* Maintenance planning-inclusive, as the data-based insights can trigger condition-based maintenance maintenance (CBM) or predictive maintenance (PdM) scheduling decisions, actively reducing OPEX.  
These identified criteria are subsequently used to evaluate existing SW solutions in the market.
# Analyzing Demand with Respect to Offer of Mobility
## 2 Data Source and Requirements Analysis
Likewise, in the afternoon, service providers (e.g., offices, stores, industries, schools, shopping malls, touristic places). * **commuter flows in the city** which can be obtained using different data sources (e.g., Wi-Fi, network cellular, mobile applications, PAXCounter);
* **commuter flows accessing the city**. For example, in cities (e.g., Venezia, Roma, Firenze) that the presence of tourists cannot be neglected compared to others (e.g., workers, students), data that come from cellular [39] or Wi-Fi [38] networks, can help to analyze different aspects (e.g., the number of tourists that are daily present in the city, the duration of their stay, their origin and destination in long term/distance). * **deployed transportation services** which can be achieved using different methods (e.g., counting the commuters onboard, waiting at stops/stations, in multimodal hubs, exiting from railway stations over time). Therefore, the proposed model and tool have to provide _enough flexibility_ to take into account those data source in modelling the service offer and the mobility demand. However, the analysis of the service offer with reference to the mobility demand requires processing a considerable volume of data which is a time-consuming process and could take several hours or even days. In order to provide the analysis of different scenarios it is necessary to have a fast model and to produce results in short time, and to provide them in a visual analytic tool.
## 3 Methodology
### Cfs  
Correlation feature selection (CFS) is an essential step of the preprocessing phase in the process of classification and prediction. Attribute selection, variable selection, feature construction, and feature extraction are the different names assigned to feature selection algorithms. They are mainly used for data reduction by eliminating unrelated and redundant data. Feature selection enhances the characteristics of the data and raises the accuracy of classification algorithms by decreasing the data volume and processing time [30]. There are many feature selection algorithms, such as principal component analysis (PCA), singular value decomposition (SVD), CFS, etc. The most effective feature selection algorithm with our data is the CFS. It is suitable for our data parameters, which are numerical and textual data. In addition, CFS can improve the classification accuracy and efficiency by removing redundant features.
# Efficient Inorganic Stabilization Materials for Chromium and Arsenic Pollution in Water and Soil
## 2 Inorganic Stabilization Materials
### Graphene  
Graphene was identified as a two-dimensional material composed of single-layer carbon atoms arranged in the form of sp\({}^{2}\) hybridization [52]. Its two-dimensional structure was found to endow the material with a series of unique physicochemical properties, including a large specific surface area and stability. Graphene with a specific surface area of 1300-1400 m\({}^{2}\)/g was synthesized by [PERSON] et al. [53], which greatly improved its adsorption capacity. Graphene was considered relatively stable in most chemical environments to resist acid and alkali [54]. [PERSON] et al. [55] investigated the arsenic removal ability of graphene oxide iron nanomaterials (GFEN), achieving a 95% removal rate for As(V) and a 99% removal rate for As(III) within the pH range of 3-9. The removal capacities of As(III) and As(V) were found to be 306 mg/g and 431 mg/g, respectively. Polyethyleneamine (PEI) was decorated onto graphene oxide (GO) by [PERSON] et al. [56]. The adsorption performance of GO and PEI was improved after cross-linking; GO and PEI are connected together through covalent bonds, hydrogen bonds, and electrostatic interactions. As the molecular weight of PEI increases, its adsorption capacity for Cr(VI) gradually increases. The best adsorption performance is achieved using a molecular weight of 70K, with an adsorption capacity of 436.2 mg/g (Figure 7a). XPS shows partial reduction of Cr(VI) adsorbed onto the material (Figure 7b).
# Non-Local Means Hole Repair Algorithm Based on Adaptive Block
## 3 Experimental Results and Analysis  
The algorithm presented in this study is implemented on a system with the following specifications: Windows 10 operating system, 2.80 GHz Intel(R) Core(TM) i7-7700 HQ CPU, and 16 GB of memory. The experiments are conducted using Visual Studio 2019 and OpenCV 4.6.0. To evaluate the performance of the proposed depth image optimization algorithm, employ the NLM algorithm, the Multiple Edge Converge Inpainting Algorithm (MEC) [10], and the AB-NLM algorithm as comparative benchmarks.  
The experimental phase involves the utilization of fifty sets of depth and grayscale images sourced from the Middlebury dataset [23], along with one hundred and twenty-six self-captured sets of depth and grayscale images acquired through Orbbec's imaging system. The chosen scenes for evaluation consist of five intricate scenarios from the Middlebury dataset, namely \"Art\", \"Teddy\", \"Moebius\", \"Recycle\", and \"Jadeplant\". Additionally, five self-captured scenes from the Orbbec dataset, identified as \"Bag\", \"Case\", \"Corner1\", \"Corner2\", and \"Miscellany\", constitute the designated test set employed for the assessment of visual performance. The depth image dataset are now partitioned into a training set comprising 70% (123 images), a validation set consisting of 15% (26 images), and a test set comprising the remaining 15% (27 images). The partitioning process are randomized, ensuring the maintenance of data balance between the training and test sets.  
In certain studies, metrics are computed as part of an objective analysis to assess the correspondence between reconstructed depth images and the unprocessed raw depth images. Nevertheless, owing to the existence of holes, the original depth images exhibit incompleteness. Metrics comparing the original and reconstructed depth images may introduce inaccuracies into the analysis and assessment of the efficacy of the restoration process. To ensure precise effectiveness evaluation, all depth images within the training set undergo manual augmentation, wherein holes are filled with ground truth depth values.  
The evaluation of the results is carried out both qualitatively and quantitatively to assess the effectiveness of the proposed method. The first set of experiments utilizes the Middlebury stereo matching dataset. The second set of experiments involves using grayscale and depth images captured by the Orbbec [24; 25] Astra S sensor to perform depth restoration in indoor scenes.  
Based on extensive experimentation, the threshold values for the proportion of non-hole points in \(A(x)\) and \(Q\) in \(B(x)\) are determined to be 0.7 and 0.5, respectively. The decay rate for the grayscale weight \(h\) is set to 2, and the decay rate for the distance weight \(\sigma\) is also set to 2. Additionally, the standard deviation of the Gaussian kernel, denoted as \(a\), is set to 2.
## 5 Distinguishing AI Agents in Autonomous Maritime Systems from Conventional AI Systems
### Human-AI Collaboration and Learning in Maritime Contexts
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **AI Agents in Maritime** & **Conventional AI Systems** \\ \hline Operational Environment [86] & Dynamic, vast, and unpredictable & Controlled and predictable environments \\ \hline Decision-Making & Real-time, autonomous navigational & Often batch processing or \\  & and safety decisions & supervised decision-making \\ \hline Sensor Integration [104] & Diverse maritime-specific sensors & Typically standard sensors \\  & (LIDAR, AIS, sonar) & for specific applications \\ \hline Latency Requirements & Extremely low latency & Varies, generally less stringent \\  & for immediate response & latency requirements \\ \hline Safety and Reliability [106] & High emphasis on fail-safes & Varies by application, generally \\  & and redundancy & lower safety stakes \\ \hline Scalability & Must handle fleet-wide operations & Often limited to specific use \\  & and varied vessel types & cases or environments \\ \hline Regulatory Compliance & Integrated with maritime regulations & May not be directly linked to \\  & for emissions, safety & specific regulatory frameworks \\ \hline Human-Machine Interaction [107] & Requires seamless integration & Varies, may have limited interaction needs \\  & with human oversight & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of AI agents vs. conventional AI systems. In the next section, we explore practical case scenarios where real-time AI agents are applied in specific maritime safety contexts, further illustrating their distinctive value and operational challenges.
# Numerical Simulation on Backfilling of Buried Pipes Using Controlled Low Strength Materials
###### Abstract  
The backfill quality of a pipeline has an important influence on pipeline operation. When loose backfill is used, the pipeline may be damaged after short term operation. In this study, the numerical simulation analysis of buried pipes was carried out under three conditions: loose backfill around the pipe, dense backfill, and controlled low strength materials (CLSM) backfill. The effects of narrow trench backfilling using CLSM on the force and deformation of pipelines were studied. The results showed that When CLSM was used for buried pipe backfilling, the pressure on the top of the pipe and on the side of the pipe was significantly reduced. When the surface pressure was 200 kPa, the radial displacement at the top of the pipe was only 0.6 mm. Compared with the dense backfill of the pipe, the radial displacement of the pipe top was reduced by 82.9%, which greatly reduced the deformation of the pipe. CLSM backfilling is a good way to protect the pipeline. The pressure is uniformly applied around the pipe, and the circumferential strain around the pipe is greatly reduced. Pipelines backfilling with CLSM for buried flexible pipes has good mechanical properties and it is expected to be applied to engineering practice.  
buried pipes; controlled low strength materials; numerical simulation; static load; flexible pipes; mechanical properties +
Footnote †: journal: Applied sciences