esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The ARDU Denoising Model
### Input Layer of the ARDU Model  
In the time-frequency domain, the seismic signal with noise can be expressed as:  
\[Y(t,f)=X(t,f)+N(t,f) \tag{1}\]  
In Formula (1), \(Y(t,f)\) represents the seismic signal with noise, \(X(t,f)\) represents the clean seismic signal, and \(N(t,f)\) represents noise.  
The input layer of the ARDU model consists of a convolution layer with a 3 \(\times\) 3 convolution kernel, a rectified linear unit (ReLU) activation function, and batch normalization (BN), whose main function is to perform preliminary signal feature extraction of the seismic signal with noise, \(Y(t,f)\). The ReLU activation function is shown in Formula (2), and its function image is shown in Figure 2. Here, \(x\) represents the seismic signal features extracted from the convolution layer. The ReLU activation function is simple in form, it only needs to judge whether the input is greater than zero, and its calculation speed is fast. It can mitigate the gradient-vanishing problem in the deep network and enable the deeper network to be better trained, thus fully maximizing the performance of the network. The main purpose  
Figure 1: ARDU model structure diagram.  
of batch normalization is to transform the input data into a normal distribution, mitigate gradient vanishing, and speed up network training.  
\[f(x)=\max(0,x)=\left\{\begin{array}{cc}x&x\geq 0\\ 0&x<0\end{array}\right. \tag{2}\]
# Optimization Design of RV Reducer Crankshaft Bearing
## 4 Optimization Example
### Optimization Design of Crankshaft Bearing of RV-20E

Table 2: Comparison of the design variables and fatigue life of RV-20E. Figure 8: Diagram of RV-20E crankshaft bearing: (**a**) Structure of the bearing before optimization; (**b**) Structure of the bearing after optimization. The optimization roller profile of crankshaft bearing of RV 20E is shown in Figure 9. The roller profile is partially crowned. The crown drop at roller end is 10 \(\upmu\)m. The ratio of crowning length to effective contact length is 0.7. The flat region increases the bearing capacity of the roller-raceway. The crown drop eliminates edge stress concentration. The contact stress on the roller that bears the maximum load is shown in Figure 10. The contact stress on the contact region was calculated using the non-Hertzian contact method. Before optimization, the contact stress in the middle of the roller was approximately 1400 MPa. An extremely large edge stress of 2190 MPa occurred at the end of roller. After optimization, the roller profile suitable for the work condition is obtained. The stress concentration at roller end is eliminated. The maximum contact stress of the roller is only 1245 MPa. As the roller diameter is larger, the contact stress between the raceway and roller decreases overall, and the stress in the middle of the roller decreases to 1150 MPa.
## 4 Fusion Algorithms  
While the enhanced A* algorithm excels at devising optimal paths that encompass only the starting point, ending point, and key nodes, its limitations become evident when it comes to circumventing temporary static obstacles and dynamic obstacles during AGV operation. On the other hand, the DWA algorithm demonstrates commendable proficiency in local obstacle avoidance. However, its single-target point nature renders it prone to getting ensnared in local optimal paths. The synergy between these two algorithms capitalizes on their strengths to address these shortcomings. By integrating the improved A* algorithm's path planning capabilities with the local obstacle avoidance prowess of the DWA algorithm, an effective solution can be achieved that navigates both static and dynamic obstacles, all the while steering clear of local optima. This amalgamation contributes to a more robust and adaptable path-planning approach for AGVs. Therefore, this study combines the improved A* and DWA algorithms. The flow chart of the fusion algorithm is shown in Figure 5.  
In the flowchart, an obstacle weight coefficient is first added to the evaluation function of the traditional A* algorithm. The search approach is adaptively selected based on the positions of obstacles. Next, an optimization process is applied to extract key points and remove redundant turning points. Finally, an improved A* algorithm is used to plan a global static path that includes only the starting point, key points, and destination point. Moving forward, each key waypoint assumes the role of a localized target point within the DWA algorithm. Furthermore, the initial azimuth of the AGV is meticulously adjusted, leveraging insights drawn from the first key node. The DWA algorithm undertakes speed sampling and trajectory simulation for the AGV. Through this process, a spectrum of potential motion trajectories is examined. Subsequently, an optimal set of speeds is meticulously selected through an optimized evaluation function. The AGV's operation is then expertly regulated at this selected speed. Ultimately, the improved A* algorithm and DWA algorithm synergize to empower the AGV. This amalgamation not only enables the AGV to progress along the globally optimal path but also equips it to adeptly navigate around newly introduced static and dynamic obstacles in real-time. The culmination of these efforts ensures the AGV's seamless and successful arrival at the designated target point.
## 3 Results And Discussion
### Workflow I Results  
In the case of workflow I, the CNN architecture with the classification output layer outperforms in terms of precision and f1 score considering the positive class reaching 0.82 and 0.73, respectively. The KNN and SVM are significantly behind with respect to the precision of 0.71 and 0.70, respectively, while CNN weighted regression yields better recall, however, being less precise than CNN classification.  
Conversely, SVM and DT are the worst-performing models in terms of recall in our experimental setup, achieving 0.52 and 0.55, respectively.  
In our data for workflow I, statistically significant difference (\(p=0.003\)) was found between CNN classification and CNN weighted regression, and with \(p=0.003\) between CNN classification and CNN extended weighted regression precision. Recall distribution of the CNN regression was statistically different from DT one (\(p=0.035\)). The f1 score, which is harmonic mean between precision and recall, was stated different between CNN classification and both CNN regression; in particular, with a \(p=0.006\), CNN classification score was higher than CNN weighted regression and with \(p=0.035\), higher than CNN extended weighted regression.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1 Score**} \\ \cline{3-8}  & & **Pos Class** & **Neg Class** & **Pos Class** & **Neg Class** & **Pos Class** & **Neg Class** \\ \hline DT & 0.96 \(\pm\) 0.01 & 0.62 \(\pm\) 0.16 & 0.98 \(\pm\) 0.01 & 0.55 \(\pm\) 0.03 & 0.98 \(\pm\) 0.01 & 0.57 \(\pm\)  
Figure 4: Workflow I. The boxplot of the precision, recall, and f1 score distributions on the test sets for the weld defective class among six classifiers conceived for workflow I with * representing statistically significant comparisons with \(p\leq 0.05\) and ** representing \(p\leq 0.01\).  
Figure 5: Workflow II. Boxplot of precision, recall, and f1 score distributions on test sets for weld defective class among three classifiers performing on ‘feature reduction’ setup with ** representing statistically significant comparisons with \(p\leq 0.01\).
# A Piezoelectric Energy Harvester with Bending-Torsion Vibration in Low-Speed Water
###### Abstract  
This paper presents a piezoelectric energy harvester using an eccentric cylinder undergoing bending-torsion vibration in low-speed water. It can harvest energy from water using vortex-induced vibration (VIV). A distributed parameter beam model with respect to the motion of the piezoelectric beam was established based on Euler-Bernoulli beam theory. The governing coupled equations of the harvester system were derived by [PERSON]'s equations. The optimal configurations and work conditions of harvesters were numerically analyzed according to the above mathematical models. Experiments were designed and performed to verify the numerical results. The numerical results were in good agreement with the experiment results, which verifies the validity of the mathematical models. The harvester with bending-torsion vibration generated an output power of 0.3978 mW, which is 1.99 times of that of the harvester with a solid-cylinder tip undergoing bending only.  
vortex-induced vibration; piezoelectric energy harvester; bending-torsion vibration; eccentric cylinder; low-velocity water +
Footnote †: journal: Applied Sciences
# A Guaranteed Approximation Algorithm for QoS Anypath Routing in WMNs
## 1 Introduction
So far, there is minimal literature on anypath routing involve propagation delay, energy consumption, minimum bandwidth, delay jitter, and other QoS constraints. Only the Decision version of Multiple Constrained Anypath Problem (DMCAP) was proposed for the first time, and then the Optimized version of Multiple Constrained Anypath Problem (OMCAP) was studied in the literature [16]. Thus, the previous research on anypath routing of multi-constrained QoS has mainly focused on finding an anypath routing method with one optimal QoS parameter or approximating all constraints from an approximate perspective in the ideal state. There has been a lot of research on the multi-constrained single-path routing problem with good performance [20; 21]. The previous research is all aimed at general multimedia service applications, but may not be suitable for specific wireless multimedia applications. As far as we know, there is no research on the guarantee of multi-constraint anypath routing under the premise of satisfying one QoS. Therefore, we need to develop a new QoS guarantee scheme for specific wireless multimedia application services. However, in wireless multimedia applications, one of the QoS parameters needs to be guaranteed while others need to be optimized. For example, real-time services such as voice and video have strict requirements on delay jitter, but also need to provide as large transmission bandwidth and as small transmission delay as possible. Thus, the Multiple Constrained Optimization Anypath Problem (MCOAP) is addressed in this paper. Different from previous studies, a Heuristic Multi-constrained Anypath Algorithm (HMAA) with a guaranteed constraint while approximating \(K-1(K\geq 3)\) QoS constraints, compatible with existing wireless routing protocols, is developed to provide a fast and efficient solution for WMNs applications. This paper has three major contributions, which are as follows. * The MCOAP model is proposed from an approximate point of view. In the case that most important service performance of WMNs is guaranteed, the model can also consider multiple QoS metrics in WMNs;
* The problem of MCOAP is formulated, then an approximation algorithm called HMAA, which approximates \(K-1(K\geq 3)\) QoS constraints with a specific constraint, is developed;* The theoretical properties of the proposed algorithm are analyzed. The analysis results show that the proposed algorithm achieves lower complexity and approximate optimal solution for WMNs. The rest is arranged as follows: the second section formally sets out the problem to be studied and the symbols to be used in subsequent sections. The third section presents the approximation algorithm and theoretical analysis of anypath routing under multiple QoS constraints. In Section 4, simulation results obtained from special networks are reported to verify the algorithm. Finally, the fifth section summarizes the paper.
## 2 Comparison of BRI with Other Global and Regional Infrastructure Initiatives
### Inclusivity and Regional Integration
Afrobarometer's national surveys, conducted across 34 African countries between 2019 and 2021, indicate that slightly more than half of the respondents favour greater local control over the use of external development funds and the management of human rights and socio-political issues within their countries ([PERSON], 2023). Moreover, the BRI's Digital Silk Road has given China a first-mover advantage in setting standards within the digital economy, such as in 5G, the IoT, and smart city development ([PERSON], 2022). This established influence could make it challenging for new initiatives to persuade countries to adopt alternative standards before proving their resilience and value. In sum, inclusivity remains a contested notion across both the BRI and its Western and allied counterparts. In the case of the BRI, while it is rhetorically framed as an open and cooperative initiative, limited local consultation in practice, the dominance of Chinese firms in infrastructure projects and insufficient environmental or social impact assessments have led to tensions over project legitimacy. However, the BRI's appeal also stems from its relatively flexible terms--fewer policy conditions, faster disbursement, and limited requirements to conform to international standards. This low-conditionality approach can be seen as a form of inclusiveness, particularly for countries that struggle to meet the strict environmental, governance, and human rights criteria imposed by other initiatives. Conversely, initiatives such as PGII/BDN, GG, FOIP/PQI, and 3 SI promote inclusiveness through transparent procurement, sustainability standards, and fair access under multilateral or regional frameworks. These features aim to reduce corruption, encourage stakeholder participation, promote green energy and ensure benefits are more evenly distributed. Yet, from the perspective of developing countries, the need to conform to predefined norms and standards may be perceived as intrusive or sovereignty-limiting. Thus, despite their intentions, these initiatives may inadvertently exclude some countries that lack the institutional capacity to comply with such frameworks and hinder the mobilisation of critical infrastructure investments.
## 3 Results and Discussion  
### Proximate Composition  
Table 2 shows the proximate composition of the 16 nutraceutical powders, where 62.5% of the samples had a moisture content between 4 and 8%. The crude protein content showed very large variances. Fruit powders were generally low in protein, except for the goji berry powder, which had a crude protein content of 9.3%. [PERSON] and co-workers obtained a crude protein content of 13.2% for a goji berry powder [33], which is very similar to our result considering that they used the general N:P conversion of 6.25, while a specific N:P factor of 4.05 was used for the calculation by our research team to obtain a more accurate result. Spirulina and chlorella powders contained outstanding amounts of protein, with percentages of 55.3% and 45.5%, respectively. These products are considered sustainable nutrient sources and have attracted the interest of environmentally friendly and health-conscious consumers. However, future research is needed on the cultivation, harvesting, and processing methods, as well as on the food safety aspects and sensory acceptance of microalgae-based foods [34]. Duckweed powder, another promising alternative nutrient source, contained 17.7% crude protein, shortly followed by spinach, moringa, and wheat grass powders. Based on the current European Union legislation [35], the claim \"rich in protein\" can be indicated on the label of spirulina, chlorella, and duckweed powders because a least 20% of their energy content derives from protein, while spinach, moringa, and wheat grass powders can be labeled \"protein sources\" since at least 12% of their energy content derives from proteins (considering an energy content of 4 kcal/g for carbohydrates and proteins and an energy content of 9 kcal/g for fats, assuming that all carbohydrates are digestible). The total carbohydrate content of most samples was estimated to be above 60%, except for spirulina, chlorella, spinach, and moringa. The crude fat content of the examined nutraceuticalals ranged between 0.2% and 12.4%. Sea buckthorn contained the highest amount of fats followed by goji berry. The reason for this observation may be that  
these products contained not only the pulp but also the seeds of the fruits (see Table 1). Sea buckthorn is known to be a rich source of oil, which is industrially extracted and marketed. According to a recent study, sea buckthorn contains mainly linoleic acid, palmitoic acid, palmitic acid, oleic acid, linoleic acid, and stearic acid [36]. On the other hand, acerola, beetroot, duckweed, spirulina, and ginger powders contained fats below 1%. Regarding the ash content, three samples need to be highlighted: spinach, duckweed, and morning powders contained 16.6, 12.6, and 10.3% of ash, respectively, in accordance with literature data [37; 38; 39].
## 3 Result and Discussion  
Detailed performance analyses are performed in this section through solving the real, unavoidable and hybrid thermodynamic cycles. The computing procedure is compiled in MATLAB and the fluid properties are resorted to the REFPROP database and subroutines. The flow chart of the calculation procedure is illustrated in Figure 2. Design parameters of the CCES-based CCHP system are shown in Table 2 and the three set of assumptions given for the real, unavoidable, and ideal cycles are summarized in Table 3. It is noted here that ambient temperature is chosen as the reference temperature for the exergy analysis in this work. The system exergy efficiency \(\varepsilon_{tot}\) at the real cycle is 56.25% while at the unavoidable cycle it is 74.79%. This demonstrates that the system efficiency can be enhanced largely, attracting interest in the CCHP system based on TC-CCES concept. In addition, the exergy efficiency is almost equivalent with the CCHP system based on CAES (\(\varepsilon_{tot}=56.48\)[18]). However, a large advantage of the system based on TC-CCES is its much higher exergy density (the ratio of total output exergy to the total volume of tanks [18]), 7.07 times of the value for CAES-based CCHP system. This authenticates the presented CCHP system as a promising option for satisfying the diversified need of users.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Unit** & **Value** \\ \hline Ambient temperature & K & 298.15 \\ Ambient pressure & MPa & 0.1 \\ Pressure of HST & MPa & 16 \\ \(\Delta p_{TV2}\) & MPa & 4 \\ Inlet pressure of C1 & MPa & 0.8 \\ Rated output power & MW & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design parameters of the developed CCHP system.
## 2 Characteristics of Strong Earthquake Signal  
To design the PLLs of a GNSS receiver, used for strong earthquake monitoring, we shall find out the motion characteristics of strong earthquake signal. Taking the 2011 M9.0 Honshu earthquake in Japan as an example, its acceleration sequences in three axes were recorded by the MYG004 station during the earthquake period. Figure 1 shows the acceleration sequence in the north-south (N-S) direction. It can be seen that the earthquake lasted more than 250 s, and the maximum acceleration in the N-S direction exceeds 2 g, which is a typical strong earthquake wave. The maximum acceleration will provide the benchmark for performance testing of PLLs.  
The power spectral density of acceleration data is useful for understanding the range of frequencies in strong earthquake signals [24]. The acceleration sequence is processed using the earthquake wave processing software SeismoSignal [25]. The power spectral density is shown in Figure 2. It reflects the power distribution on each frequency of the earthquake signals. The majority of the acceleration in the N-S direction is in the range of 0 to 10 Hz.  
Since the earthquake has a large amplitude and high frequency acceleration, the GNSS receiver's design must be targeted to improve the carrier phase accuracy. Based on the characteristics of a strong  
Figure 1: Acceleration sequence from 2011 Honshu earthquake.  
earthquake signal, typical sine wave motion was set on the simulator, and a shake table was used to simulate strong earthquakes in our research.
## 2 Background of Sensors and Sensor Networks in the Ag-IoT
### Agricultural Internet of Things  
The world recognizes the IoT as the third wave of the world's information industry after computers, the Internet, and mobile communication networks. It can realize a comprehensive network of people and people, people and things, and things and things. As agriculture enters the 4.0 era, the current planting and breeding industry are showing the characteristics of scale and refinement through the integration of agriculture with emerging technologies such as the IoT, big data, artificial intelligence, etc., via various sensors to collect information, such as temperature, humidity, nitrate content in the soil, conductivity, and PH [37]. Monitoring and controlling essential factors that affect crop growth and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Transmission** & **Power** & **Delay** & **Advantage** & **Disadvantage** \\ \hline Bluetooth [32] & \textless{}10 m & Low & \textless{}1 s & Low cost & Incompatible protocols between different devices \\ \hline Wi-Fi [33] & \textless{}50 m & \(\mathrm{High}\) & \textless{}1 s & Easy fault location & The transmission process is unstable \\ \hline ZigBee [34] & \textless{}10-100 m & Low & \textless{}1 s & High security & High cost \\ \hline LoRa [35] & \textless{}10 km & Low & \textless{}1 s & The networking mode is flexible and can connect multiple nodes & Users need to form their own network \\ \hline NB-IoT [36] & \textless{}25 km & Low & \textless{}10 s & Wide coverage & Low data transfer \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of data transmission technologies commonly used in agricultural IoT.  
yield can significantly reduce economic losses through the timely discovery of risks in the breeding process. The continuous emergence of intelligent agricultural equipment will promote the automation and intellectualization of agriculture. As shown in Figure 2, IoT technology is currently widely used in agriculture, including precision agriculture, livestock monitoring, smart greenhouses, fisheries management, and weather tracking [38; 39].