esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Bio-Inspired Energy-Efficient Routing Protocols (Bio-Inspired E2 RPs)
#### 5.2.8 Cooperative Reliability-Based Energy-Efficient Routing Protocols (CO-RE2 RPs)  
Cooperative reliability-based E2 RPs (CO-RE2 RPs) based on reliability, which allows reliable data packet transmission from Tx anchor node to sensor Rx node, when considering the challenging underwater environment. Repeater nodes are used in these protocols to transmit data packets from the Tx anchor node to the sensor Rx node. The requirements of the network application heavily influence the choice of repeater nodes. This routing algorithm assists in boosting the network throughput by establishing a reliable connection between the sensor Rx node and the Tx anchor node. The same data packet is always received twice or more by the sensor Rx nodes, one from the Tx anchor node and the other from the repeater node. The sensor Rx node combines the data packets before extracting the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Protocol/** & **Position of a** & **Need of** & **Change for New** & **Energy** & **Advantages** & **Disadvantages** \\
**Year** & **Node** & **Localization** & **Cluster head** & **Efficiency** & **Contrud data packets \\ \hline E2 RU-CA/ & & & & & & \\
2015 & Layer-based & No & No & High & Reduce energy & End-to-end delay of the network is high \\ E2 GRCP/ & Random and & & & & & & \\
2016 & grid cube-based & Yes & Yes & Medium & Finds the shortest & Overhead control \\ E2 LRP/ & & & & & & & \\
2018 & Layer-based & No & Yes & Low & Balancing the load & routing and network \\ E2 ACA/ & & & & & & & \\
2018 & Layer-based & Yes & No & Low & Load of first layer & Control data packets \\
2018 & Layer-based & No & No & High & Load of first layer & exchange is high \\  & & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: A thorough analysis of cluster-based energy-efficient routing protocols ((C-bE2 RPs).  
necessary information. This technique makes sure that another connection can assist with the successful delivery of data packets along those network channels that are impacted by unstable connections. However, these routing techniques do not address duplicate data packet transmissions, which improves the network's end-to-end latency. Figure 13, depicts the cooperative routing protocol for UWSNs.
# Deep Learning-Driven Public Opinion Analysis on the Weibo Topic about AI Art
## 2 Materials and Methods
### Experiments  
#### 2.3.1 Data Collection and Preprocessing  
To verify the effectiveness of the proposed sentiment analysis model, we conducted an evaluation using the public Weibo sentiment dataset, weibo2018 [23]. For the collection of data from Weibo posts related to the \"AI Art\" Weibo topic, a Weibo crawler was employed to gather and categorize Weibo posts between August 2022 and August 2023, using the keyword \"AI Art\". A total of 21,200 Weibo posts were collected.  
According to the criteria established by weibo2018, the collected Weibo posts were manually labeled into positive and negative sentiment categories. Posts exhibiting positive sentiments demonstrated the endorsement of AI Art and acknowledged the potential advancements in this field, while posts reflecting negative sentiment opposed AI Art and raised worries and questions regarding its progress. For the training procedure of the proposed model, each dataset was divided into a training set and a test set according to the ratio 8:2. The statistical features of the three datasets are described in Table 1.  
To further describe the features of the two datasets and explain the labeling metrics of the AI Art topic dataset, samples of positive and negative posts are selected and shown in Table 2.  
The public dataset weibo2018 comprises Weibo posts spanning from 2017 to 2018, covering diverse topics, primarily emphasizing text sentiment annotations, and is suitable to evaluate the overall performance of models. The AI Art topic dataset comprises Weibo posts discussing AI Art from August 2022 (the open sourcing of Stable Diffusion) to August 2023, demonstrating a high level of timeliness. The labeling process of the AI Art topic dataset comprehensively analyzes attitudes towards the potential of AI Art, the applications of AI Art, and the overall sentiment expressed in the Weibo posts, with the aim to emphasize the central themes of discussions among Weibo users.  
For text data preprocessing, this article first uses regular expression matching to remove the hyperlinks, symbols, and numbers from the original Weibo posts. To segment the texts into word lists, the word tokenizer provided by HanLP [24] is employed, followed by the removal of stop words based on the stop word list.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & **Weibo2018** & **Topic Dataset** \\ \hline Average length & 80 & 75 \\ Positive samples & 5842 & 3500 \\ Negative samples & 4658 & 3500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical features of the public datasets.
## 2 Background Knowledge
### Emerging Technologies: Transforming ASD Detection
#### 2.2.2 IoT and Real-Time Data Monitoring  
IoT provides the infrastructure for interconnecting various devices, enabling real-time data collection and monitoring in naturalistic settings. IoT-powered wearable devices, smart home sensors, and environmental monitoring systems offer the potential to provide continuous, unobtrusive insights into an individual's behavior, communication patterns, and physiological responses. While IoT-enabled technologies could complement traditional assessments by delivering a more holistic understanding of ASD characteristics across different contexts, their effectiveness in reliably detecting ASD remains an area of active exploration of characteristics across different contexts [29].
# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 4 AIoT Applications in Aquaculture
### Smart Feeding Systems  
Smart feeding, which utilises AIoT applications to track fish feeding activity and improve feeding schedules, is one of the most significant AIoT applications in aquaculture. Techniques such as deep learning models (e.g., ResNet [100]) and gradient boosting machines (GBM) [101] analyse real-time data from computer vision and acoustic sensors to assess fish appetite appropriately and adjust the amount of food accordingly. These systems reduce food waste and improve feed conversion ratios (FCRs) as well as the growth rates of fish. However, their effectiveness varies across species and environmental conditions, requiring further refinement. Research by [102] employs bioenergetic and protein flux models to optimise gilthead seabream's feeding schedules, demonstrating that the fish's weight increased by 46.85% by adjusting feeding frequency and composition. Additionally,real-time acoustic monitoring, as explored in [103], detects variations in feeding intensity by analysing sound patterns, offering a non-intrusive way to assess hunger levels.
## 3 Analysis and Design of Key Components
### Driving Module for Simulating Coring Tests
#### 3.2.1 Structure and Working Principle of the Lifting Motion Mechanism  
In an actual field coring process, rotational and vertical downward coring movements must be completed. To maintain the core pressure after the core enters the core barrel, it must be pulled into the core device, which provides similar environmental conditions. Because the rotary drive pump station can provide only rotary coring motion, it cannot provide the downward feeding motion. Therefore, the lifting mechanism in the cabin was designed independently to realize the vertical downward coring motion and core lifting motion after drilling. The lifting mechanism in the cabin was mainly composed of a rotating shaft, rotating piston, core lifting mechanism, rotating piston joint, and corer joint, as shown in Figure 7.  
The working principle of the lifting motion mechanism is as follows: The rotation shaft is driven by the rotating pump station, and a spline connection is placed between the rotating piston and the rotating drive shaft, so the rotating shaft causes the piston to rotate. Additionally, the rotary piston is driven by the drilling pressure and moves downward in a straight line. Furthermore, power is transferred to the upper part of the corer by rotating the piston joint and the upper part of the corer, thus initiating the coring motion. After the coring motion is completed, the drilling pressure is reduced, and the piston is moved upward by the pressure difference; the corer joint and coring device form a threaded connection, and the rotating piston joint and coring device jointly adopt the form of a spline connection to release the spline. This part of the rotating piston connection moves upward, driving up the core-lifting mechanism to complete the coring motion.
## 5 Test Validation
### Excitation Test of Pipeline  
To clearly illustrate the outstanding vibration reduction performance of the CMS, an excitation test is carried out on the pipeline. The test flow chart is shown in Figure 18. A signal generator is used to produce the excitation signal, which is then amplified by a power amplifier and transmitted to an electrodynamic shaker. The shaker applies controlled vertical excitation to the midpoint of the pipeline. The dynamic response is acquired by a data acquisition system, which records both the acceleration signals from appropriately positioned sensors and the input force signal. All acquired data are then processed and subsequently analyzed using specialized data analysis software on a computer to extract relevant vibration characteristics.  
Establishing excitation conditions with limited white noise superimposed on harmonic excitation can simulate the excitation characteristics of actual pipelines to a certain extent. The harmonic excitation is set with an amplitude of 5.0 N, a frequency of 90 Hz, and a phase angle of 0\({}^{\circ}\). The white noise frequency band ranges from a lower limit of 70 Hz to an upper limit of 120 Hz, with an excitation power spectral density of 1.0 N\({}^{2}\)/(Hz). Generate normalized complex excitation signals in MATLAB 2020a and input them into the signal generator to drive the shaker to apply vertical excitation to the midpoint of the pipeline. The acceleration responses of the pipeline before and after installing the CMS are shown in Figure 19.  
Figure 17: Test results of the FRF of the pipeline. (**a**) Point A1. (**b**) Point A2.  
It is evident that under the applied excitation, the bare pipeline produces significant vibrations in the broadband range of 70-120 Hz. At the same time, two prominent vibration line spectra also appear. The line spectrum at 82.5 Hz is caused by the mode resonance of the pipeline, while the line spectrum at 90 Hz is caused by harmonic excitation. After installing the CMS, the broadband acceleration response and multi-frequency line spectrum of the pipeline are significantly reduced.  
Calculate the average acceleration at two measurement points and the broadband vibration level. It is crucial to note that data at 90 Hz should be excluded when calculating the broadband vibration level to avoid prominent vibration line spectra having a significant impact. The results indicate that compared with the bare pipeline, the installation of the CMS reduced the acceleration amplitude of the pipeline by 34.7 dB at 82.5 Hz, 16.0 dB at 90 Hz, and 19.8 dB in the broadband vibration level across the 70 Hz to 120 Hz range.
# Development and Simulation of Motion Control System for Small Satellites Formation
## 2 Satellite Formation Modeling  
In satellite formation flying, the relationship of the positions between satellites is very important. Hence, the rotating reference coordinate is used: a chief satellite is centrally located and circles the Earth. Note that it is assumed that the main satellite is located at the origin of the coordinate system because it provides a reference point for the formation. The local-vertical local-horizontal (LVLH) coordinate system [1] is used to describe the relative motion in the formation. The coordinate systems are illustrated in Figure 1. In this figure, \(\vec{r}_{\rm c}\) is the position vector of the master satellite, \(\vec{r}\) is the position vector of the slave satellite, and \(\vec{\rho}\) is the relative position vector between the master and slave satellites. A general non-linear equation of relative motion is expressed as in [1]:  
\[ \begin{bmatrix}\ddot{x}-2\dot{\theta}\dot{y}-\ddot{\theta}y-\dot{\theta}^{2}x+ \frac{\mu}{\gamma}x+\frac{\mu}{\gamma}r_{\rm c}-\frac{\mu}{r_{\rm c}^{2}}\\ \ddot{y}+2\dot{\theta}x+\ddot{\theta}x-\dot{\theta}^{2}y+\frac{\mu}{\gamma}y \\ \dot{z}+\frac{\mu}{\gamma}z\end{bmatrix} = \begin{bmatrix}a_{\rm x}\\ a_{\rm y}\\ a_{\rm z}\end{bmatrix}  \tag{1}\]  
\[\ddot{r}_{\rm c}=r_{\rm c}\dot{\theta}^{2}-\frac{\mu}{r_{\rm c}^{2}},\quad \ddot{\theta}=-\frac{2\dot{r}_{\rm c}\dot{\theta}}{r_{\rm c}} \tag{2}\]  
where \(x\), \(y\) and \(z\) are state variables to describe the relative position vector, \(\vec{\rho}\), in the \(x,y\) and \(z\) axes, respectively, and \(a_{\rm x}\), \(a_{\rm y}\) and \(a_{\rm z}\) are the orbital perturbation terms, such as the aspherical geopotential perturbation, thrust, air drag, and solar radiation pressure. \(a_{j}=(f_{j}+d_{j})/m_{\rm f}\) for all \(j\in\{x,y,z\}\) with \(m_{\rm f}\) denoting the mass of the deputy spacecraft and \(f_{j}\) denoting the control input applied by the deputy spacecraft, \(d_{j}\) is for the perturbation term, \(\theta\) is latitude angle of the chief, \(r_{\rm c}\) is the radius of the chief orbit and \(\mu\) is a gravitational parameter. Finally, \(\gamma\) is defined as  
\[\gamma\equiv|\vec{r}_{\rm c}+\vec{\rho}|^{3}=\Big{(}(r_{\rm c}+x)^{2}+y^{2}+z ^{2}\Big{)}^{3/2}. \tag{3}\]For controller design, it is desirable to rewrite system (1) in the following state-space form  
\[\dot{\mathbf{x}}=\mathbf{A}(\mathbf{x},t)\mathbf{x}+\mathbf{B}\mathbf{u}, \tag{4}\]  
where the state variables are the relative positions and relative velocities of the deputy satellite with regard to the chief satellite  
\[\mathbf{x}=\left[ \begin{array}{cccccccc}x&y&z&\dot{x}&\dot{y}&\dot{z}\end{array}  \right]^{\mathrm{T}},\quad\mathbf{u}=\left[ \begin{array}{cccccccc}f_{x}&f_{ y}&f_{z}\end{array} \right]^{\mathrm{T}}\]  
It is assumed in this paper that all the system states are measured. Therefore, the full state feedback control can be implemented. Let us rewrite the term of \(\left(\dfrac{\mu}{\gamma}r_{\mathrm{c}}-\dfrac{\mu}{r_{\mathrm{c}}^{2}}\right)\) in (1) to preserve the non-linearity as much as possible and to avoid a singularity [30].
## 3 Fuzzy-Logic Topology Control (FTC)
### WSNs Topology Control Using Fuzzy-Logic
#### 3.1.2 FTC Learning Data Set  
Provided a training data set from Equation (1), the fuzzy controller can be obtained through learning algorithms, such as neuro-adaptive learning algorithm. With the help of the Matlab \"adaptive neuro-fuzzy inference system\" (ANFIS) tool, FTC controller is not difficult to get. In ANFIS, the membership function parameters are automatically tuned through a backpropagation algorithm individually or in combination with a least square method. Once the data training is completed, the fuzzy inference system is available by simply using the function \"evalfis\" provided by Matlab. Hence, we concentrate on how to generate the training data set. As illustrated in Figure 1 and Equation (1), the inputs are \(ND_{ref}\) and \(Prob\), and the output is \(CR\). Given \(\rho\), \(ND_{ref}\in\{k_{1},k_{2},\cdots k_{m}\}\) and \(CR\in\{r_{1},r_{2},\cdots,r_{j}\}\), \(Prob=f(ND_{ref},CR)\) can be calculated from Equation (1). The training data set \(\mathbf{T}\) is a \(s\times 3\) matrix in the form of [\(ND_{ref}\), \(Prob\), \(CR\)], where \(s=m\cdot j\). For instance, one item in the training data set could be [3, 0.9, 25]. It means that the CR is set to 25m if the probability that \(ND_{ref}\geq 3\) is 0.9.
## 3 Models and Methods  
Our proposed method is a physically-based method based on the 1-D radiative transfer modeling of GLAS waveforms, which can fully utilize the forest canopy structure information contained in GLAS lidar data compared with commonly used empirical models. To assess how the new method can potentially improve on existing VFP and LAI products, we apply three strategies for algorithm assessments. The workflow of our method is shown in Figure 2. The details of the used model and method are introduced in Sections 3.1 and 3.2.
# Comparative Analysis of MoCA and DigiMoCA Test Results: A Pilot Study
## 2 Materials and Methods
### Statistical Analysis
These negative correlations suggest that, as scores on the GDS-15 scale increase, indicating relevant depressive symptoms, scores on these cognitive measures tend to decrease. Furthermore, a significant positive correlation was observed between the GDS-15 and [PERSON] and Brody's, indicating that as depressive symptoms increase, functional dependency also increases, as assessed using the Lawton and Brody scale. **Response time**: correlates negatively with the CDT and MoCA and more strongly with the visuospatial, attention, language, abstraction, and memory MoCA dimensions, and positively with age. Additionally, it also correlates with the DigiMoCA's global score and with the DigiMoCA's factor 3. Overall, these correlations indicate that the cognitive task performance tends to decrease as the average response time increases, particularly in tasks related to visuospatial ability, attention, language, abstraction, and memory. Additionally, the CDT and MoCA total scores also exhibit negative correlations with the response time. On the other hand, as age increases, so do the execution times. **Age**: correlates negatively with the CDT, total MoCA score, and all its dimensions: visuospatial, attention, language, identification, abstraction, memory, and orientation. [PERSON] and [PERSON]'s scores correlate positively with age. Additionally, correlations were found with the DigiMoCA's total score and its factors 2 and 3. As age increases, there is a tendency toward a cognitive and functional performance decline, as evidenced by negative correlations with various cognitive and functional measures. Additionally, positive correlations with age were found in the Lawton scale, indicating a higher level of functional dependence with increasing age. Similarly, correlations with the DigiMoCA and its factors suggest age-related decline in the cognitive assessment performance by means of the digital instrument. **Educational level**: positively correlates with the CDT; MoCA's visuospatial, identification, attention, language, and abstraction; [PERSON] and [PERSON]'s score; MoCA's total score; and DigiMoCA's factor 2. A higher educational level is associated with better performance in the cognitive MoCA dimensions mentioned, and in factors 1 and 2 of the DigiMoCA. On the other hand, residence correlates negatively with factor 1 (r = \(-\)0.296, \(p\) = 0.025). This correlation suggests that individuals who live alone tend to have higher scores on factor 1 compared to those who live with others.
# A Reversed-Phase HPLC Method for Determination of Osteopontin in Infant Formula
## 3 Results and Discussion
### Method Validation
#### 3.2.3 Precision and Accuracy  
The precision was evaluated from the retention times after injecting a homogenous sample (1 mg/L) for 10 times [16]. The precision under repeated conditions on the same day (intra-day; repeatability) and on a different day (inter-day; intermediate precision) was measured and reported in Table 2. The precision RSD for both intra-day (0.161%) and inter-day (0.148%) variations were \(<\)1% which further assures the validity of this method [19; 20].  
The accuracy refers to the closeness of the measured value to the actual value for the sample. In this work, accuracy was evaluated on the basis of percentage recovery of OPN after spiking IMF with low, medium and high levels of OPN, and the results are summarized in Table 3. The results show the accuracy of the method within the recovery range of 99-102% (with RSDs less than 3%), which fits within the generally accepted range of 95-105% and therefore could be considered as high level of accuracy [16; 17; 19; 20]. However, there is no distinct and official benchmark for percentage recovery to be achieved in a quantitative analysis. The AOAC guidelines suggest that the lower the concentration of analyte in the sample, the bigger the tolerance [13]. The guidelines further indicate the recovery rate as 92-105%, 90-108% and 85-110% for analyte at concentrations of 1, 0.1 and 0.01%, respectively. The recovery rate (99-102%) obtained in this study (Table 3) therefore was in agreement with the AOAC guidelines.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Spiked Amount (mg/L)** & **Recovery (\%)** & **RSD (\%)** \\ \hline
1.25 & 100.48 \(\pm\) 1.62 & 1.62 \\
2.5 & 99.63 \(\pm\) 2.73 & 2.74 \\
5 & 102.39 \(\pm\) 2.17 & 2.12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy (recovery) of OPN from IMF spiked with three concentration levels of OPN.  
Figure 2: Calibration curve obtained from OPN standards following the method developed in this study. Error bars represent the standard deviation of duplicates.
## 3 Results and Discussion
### Energy-Harvesting Performance of The Downstream Harvester
Figure 8: Performance of the downstream harvester at the spacing ratio \(L/D\) = 3.3: (**a**) Time history of the output voltage at the water speed of 0.306 m/s; (**b**) Fast Fourier Transform (FFT) analysis of the output voltage at the water speed of 0.306 m/s; (**c**) Comparison between the frequency of vortex shedding from the upstream harvester and that of the downstream harvester; (**d**) Output power of the downstream harvester as a function of the water speed. To further investigate the impacts of the water speed and the spacing ratio on the performance of the downstream harvester, the variations of the output power with the water speed and the spacing ratio are illuminated in Figure 10. It can be found that, on the one hand, the effect of the spacing ratio on the performance of the downstream harvester is negligible in the low water speed range, but remarkable in the high water speed range. On the other hand, no matter how the spacing ratio changes, the effect of the water speed on the output power is always there.
## 3 Proposed Method
### Multi-Criteria Decision Making Technique  
Though the selection of a route to destination in WMN based on a combination of multiplicative metrics has been proven to be NP-complete [30], MCDM techniques, such as the AHP and pruning proposed in [23], have been used to get around the NP-completeness. AHP is a robust and flexible multi-criteria decision analysis methodology of measurement and widely known in the field of decision-making when different qualitative and quantitative criteria must be applied [31]. This methodology is already being used in a number of applications in the field of telecommunications [22; 23], petroleum pipeline network [32], project management [33] and health services. By making minor changes to the original AHP methodology, it was used in [23] to decide the best route for multimedia applications to a given destination. This approach is adapted to explore the possibility of providing good quality routes and QoS support for different traffic types characterized by various requirements in the NAN. These changes include pruning, which is applied before the first AHP step to eliminate the paths exceeding the desired threshold, thereby reducing AHP's computations, and the use of weight assignment between zero and one; this is to better reflect the importance of one metric over another. Of all of the existing MCDM techniques that have been applied for decision-making in communication systems, the AHP is regarded as the best because it has support for multi-dimensional criteria. It has also been applied in OLSR and showed better routes compared to other metrics.