esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, establishing stable observation equations for long-term monitoring is challenging due to the algorithm's inclusion of intricate mathematical principles, multi-dimensional state observation handling, and iterative updates. This complexity becomes particularly pronounced when dealing with high-dimensional state spaces or frequent data updates, resulting in escalated computational demands, and calculating measurement noise covariance and state noise covariance adds to the algorithm's complexity, leading to excessive overhead. Therefore, this paper proposes a method of human pose angle extraction using an accelerometer and Euler angle calculation to recognize 19 kinds of human physical activities. Utilizing a sliding window technique, the method removes high-frequency noise to mitigate the impact of integration drift. Simultaneously, it introduces a module known as GAM to emphasize the temporal characteristics of sensor data, further counteracting integration drift. By adaptively adjusting model weights, it learns feature representations of the sensor data, reducing complex mathematical computations and enhancing recognition accuracy [6]. The contributions of the paper:  
1. A human feature extraction model is introduced to convert multi-dimensional sensor data into one-dimensional features;
2. An approach for feature group division and classifier network construction is proposed to improve group correlation analysis and human action recognition accuracy;
3. The impact of the K-nearest neighbors algorithm (KNN) and sliding window size on evaluation results is examined;
4. The study investigates the influence of GAM, transformer block, and classifier block on the experimental accuracy. The subsequent sections of this paper are organized as follows: The second section delineates recent advancements in the realm of human behavior recognition. Moving forward, the third section elucidates the transformation of Euler angles within human posture, alongside the methodology of integrating GAM-MLP information. Progressing further, the fourth section details the groundwork undertaken for the forthcoming experiments. In the subsequent fifth section, we present a comprehensive compilation of experimental findings, meticulously analyzed. Lastly, the sixth section encapsulates the entirety of the article through a concise summary, while also delving into an insightful analysis of potential avenues for future research opportunities.
# A Review on Wind Turbine Deterministic Power Curve Models +
## 3 Polynomial Models
### N-Degree Models  
Several tests have been carried out with polynomials of a degree higher than 3. Trying to fit the curve to one continuous polynomial function (NM) provides successful results in the case of a degree equal to 6 [12] and 9 [14]. Therefore, the model can be expressed as shown in Equations (14) and (15), respectively:  
\[P=a_{01}+a_{11}\cdot v+a_{21}\cdot v^{2}+a_{31}\cdot v^{3}+a_{41}\cdot v^{4}+a_{5 1}\cdot v^{5}+a_{61}\cdot v^{6} \tag{14}\]  
\[P=a_{01}+a_{11}\cdot v+a_{21}\cdot v^{2}+a_{31}\cdot v^{3}+a_{41}\cdot v^{4}+a_{5 1}\cdot v^{5}+a_{61}\cdot v^{6}+a_{71}\cdot v^{7}+a_{81}\cdot v^{8}+a_{91}\cdot v ^{9} \tag{15}\]  
where there is no relationship between the parameters of the model and those of the WT. However, this model does not fit properly for all types of WTs.  
Other options consist of the generalized n-degree models, which deal with values of \(n_{j}\) equal to or lower than \(N\), for all intervals. An example of this is the one based on three intervals according to Equation (16) [12].  
\[P=\left\{\begin{array}{cl}a_{01}&v_{1}\leq v<v_{2}\\ a_{02}+a_{N2}\cdot v^{N}&v_{2}\leq v<v_{3}\\ a_{03}&v_{3}\leq v<v_{4}\end{array}\right. \tag{16}\]  
Table 5 shows the proper assignation of parameters.  
However, this model is rarely used due to its performance in the whole second interval, thus making it not suitable for any value of \(N\).
## 5 Effects of ABH Parameters and Damping Layer
### Effect of the Truncation  
Due to the limitations of the manufacturing process, the ideal ABH structure is difficult to implement in practice; thus, the actual acoustic black hole structure often has a truncation in the portion with a thickness close to zero. The existence of truncation in an ABH structure will seriously affect the aggregation effect of the flexural wave. The truncation thickness was set to 0.1 mm, 0.2 mm, 0.3 mm, and 0.4 mm, respectively, and the \(\varepsilon\) in Equation (1) was adjusted to ensure the diameter of ABH be 60 mm as the truncation thickness varying. Figure 14 shows the effect of the truncation thickness on the sound transmission loss of a circular plate embedded with 19 ABHs. It can be seen from Figure 14 that the TLs of the circular plate with a truncation thickness of 0.3 mm and 0.4 mm are substantially the same compared with that with a thickness of 0.5 mm in the range of 5 kHz and below, and they show a certain difference in the interval of 5 k\(\sim\)10 kHz. However, the above difference is significantly smaller than the difference between the circular plate with a truncation thickness of 0.1 mm, 0.2 mm and that with a truncation thickness of 0.5 mm. The ABH circular plates with truncation thicknesses of 0.1 mm and 0.2 mm have a TL reduction of 5.08 dB and 3.7 dB at the first-order resonance frequency, respectively. At most frequencies in the mass-controlled region, the circular plates with truncation thicknesses of 0.1 mm and 0.2 mm can significantly improve the sound transmission loss of the plate, and their TL values are very similar, among which their TL values are increased by 9.8 dB and 10.47 dB respectively at 1 kHz. At the coincidence frequency, the circular plate with a truncation thickness of 0.5 mm has the best sound insulation performance. While in the damping-controlled region, the reduction in the truncation thickness could increase the sound transmission loss at 8 kHz but does not increase the TL of the plate at other frequencies in this region.
## 8 Discussion
### Motivations
#### 8.3.1 Big Data and 6G  
In this section, we'll take a quick look at some of the outstanding questions. This also provides guidance for future advancements in research at the intersection of big data and 6G, as tackling these difficulties has the potential to provide significant breakthroughs.  
Caching and computing on a proactive basis: By utilising modern big data analytics methodologies, the cost of computing and caching for future generation networks can be decreased. As a result, resources are to be efficiently allocated and utilised, which will result in a better balance between caching and computing overhead. If the intermediate and final outcomes are not significant, they should not be stored because storing all of the information would be prohibitively expensive.  
Security and privacy: Big data analytics is used to unearth information that has been hidden within a large amount of data. Because of this, large-scale data analysis may result in security and privacy concerns. In order to ensure that data cannot be modified or altered during the storage, management, and processing stages, it is critical that information is properly encrypted. Furthermore, only authorised entities should be permitted access to the data, and access should be granted only through secure means. As a result, security and privacy problems are important considerations for such large-scale data analysis, and they should be addressed thoughtfully.  
Big Heterogeneous data: Large amounts of heterogeneous data: Large amounts of data from a variety of sources with varying data rates, mobility, and packet loss. Analysis of heterogeneous data in wireless networks is a difficult problem to solve. Spatial-temporal dynamics are brought about by heterogeneous data. As a result, for large-scale spatiotemporal data analysis in mobile networks, novel methodologies are necessary that are not commonly used.
## 2 Proposed EPP-Based Beam Scanning Approaches
### Test case (3): \(R=0.7778\)
#### Test case (5): \(R=0.5\)  
In this case, the cosine wave half period is chosen to be equal to \(H=7\)\(\lambda\), and the array length equals \(L_{h}=3.5\)\(\lambda\), hence \(R=0.5\). Accordingly, the EPPs of the array elements for \(A_{c}\) changing from positive to negative and \(A_{c}\) changing from negative to positive are shown in Figures 19a and 19b, respectively.  
Figure 17: Polar plots of the synthesized radiation patterns using the EPP techniques for 8-element ULAA at compression ratio \(R=0.7\) at: (**a**) \(A_{c}=0\), (**b**) \(A_{c}=0.25\)\(\lambda\), (**c**) \(A_{c}=0.35\)\(\lambda\), (**d**) \(A_{c}=0.39\)\(\lambda\), (**e**) \(A_{c}=0.5\)\(\lambda\), (**f**) \(A_{c}=0.75\)\(\lambda\), (**g**) \(A_{c}=\lambda\), (**h**) \(A_{c}=1.25\)\(\lambda\), (**i**) \(A_{c}=1.5\)\(\lambda\), (**j**) \(A_{c}=1.75\)\(\lambda\), and (**k**) \(A_{c}=2\)\(\lambda\) and changing from positive to negative.  
When the cosine wave amplitude is changing from positive to negative and its magnitude is changing from \(A_{c}=0\) to \(A_{c}=0.65\)\(\lambda\), the array main beam is scanned from \(0^{\circ}\)to \(10.83^{\circ}\) with a constant HPBW and the SLL is less than \(-10\) dB. However, as the cosine wave amplitude increases from \(0.65\)\(\lambda\) to \(2\)\(\lambda\), the scan angle is increased from \(10.83^{\circ}\) to \(31.25^{\circ}\), the SLL is increased by more than \(-10\) dB, and the HPBW is decreased by \(2.83^{\circ}\) as listed in Table 5. The synthesized radiation patterns using the EPP technique are shown in Figure 20.  
On the other hand, when the cosine wave amplitude is changing from negative to positive and its magnitude is changing from \(A_{c}=0\) to \(A_{c}=0.65\,\lambda\), the array main beam is scanned from \(0^{\circ}\) to \(-10.83^{\circ}\) with a constant HPBW and the SLL is less than \(-10\) dB. However, as the cosine wave amplitude increases from \(0.65\,\lambda\) to \(2\,\lambda\), the scan angle is increased from \(-10.83^{\circ}\) to \(-31.25^{\circ}\), the SLL is increased by more than \(-10\) dB, and the HPBW is decreased by \(2.83^{\circ}\) as shown in Figure 21. The achieved maximum scanning range is \(-31.25^{\circ}\)\(\leq\theta_{r}\leq 31.25^{\circ}\) around the broadside direction.
## 2 Materials and Methods
### Gene Expression Analysis  
A gene expression analysis was conducted according to methods in previous studies [29]. Briefly, total RNA was extracted from intestinal mucosa and liver with TRIzol reagent (Beyotime, Shanghai, China), and quantified with a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Total RNA was then reverse transcribed into cDNA with the SweScript RT I First Strand cDNA Synthesis Kit (Servicebio) according to the manufacturer's protocol. Gene expressions responsible for cholesterol synthesis (HMGCR and SREBPP-2 in liver), cholesterol absorption (NPC1L1 and LXR-\(\alpha\) in intestine), cholesterol uptake (LXR-\(\alpha\), LDLR, IDOL and PCSK9 in liver), cholesterol efflux (ABCG5, ABCG8 and ABCA1 in intestine), and bile acid synthesis (CYP7A1 and FXR in liver) were quantified with a three-step RT-qPCR method using SYBR Green mixture (Takara, Tokyo, Japan) and a Quant Studio(tm) 6 Flex Real-Time PCR system (Thermo Fisher Scientific). Sequences of all primers (Table S1) were designed according to previous studies and were synthesized by Sangon Biotech Co., Ltd. (Shanghai, China). The housekeeping gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as the internal control. Gene expression was quantified using the 2\({}^{-\Delta\Delta\text{CT}}\) method and expressed as a fold change normalized to the expression in CD control mice.
## 3 Topology Optimization Design of U-Frame of Space Electro-Optical Tracking and Pointing Turntable
### Model Geometry Reconstruction after Optimization  
Analyzing the U-shaped frame requires establishing both a static analysis step and a modal analysis step, with appropriate loads and boundary conditions applied. In the topology optimization process, an appropriate algorithm is selected, with the objective function and constraint conditions being established. TOSCA is subsequently employed to conduct the topology optimization analysis, yielding a conceptual model as depicted in Figure 8. After 59 iterations, the objective function converges, and the constraint function values meet their limit values. Figure 9 shows the convergence curves of the objective and constraint functions during the topology optimization process.  
Figure 8: U-frame topology optimization concept model.  
Figure 7: U-frame mesh model.  
From the optimized conceptual model, material distribution results are derived. Subsequently, unnecessary materials are removed, leading to the creation of the topology optimization conceptual model. This model is then further reconstructed to align with process requirements and manufacturing constraints. Ultimately, the final design structure of the U-shaped frame is obtained, as illustrated in Figure 10b.  
As observed from Figure 8, under primarily vertical loads, significant differences emerge between the topology-optimized conceptual model of the U-frame and the empirically designed U-frame structure, particularly in the orientation of side wall holes. This preference for lateral hole placement in the side wall offers valuable insights for engineers in future U-frame design endeavors.
## Materials and Methods
### Phosphorus Removal in Batch Adsorption Experiments  
These experiments were conducted for two concentrations that had occurred in the evaluated river (3.38 and 7.72 mg P/L) and for two concentrations above the typical phosphorus values.  
The adsorption study of phosphorus was carried out with different initial concentrations of phosphorus and a fixed dose of the adsorbent at a constant temperature (25 \({}^{\circ}\)C \(\pm\) 2) in a magnetic stirrer. The stock solution of potassium dihydrogenophosphate (KH\({}_{2}\)PO\({}_{4}\)) was prepared by dissolving it in DI to the concentration of 40 mg P/L. For adsorption isotherms, the tested mass of HAOPs was introduced into 100 mL of the examined solution with different phosphorus concentrations (28.7, 18.4, 7.72 and 3.38 mg P/L). The pH of the solution was maintained at 6.5. The solution was stirred for 60 min to ensure approximate equilibrium. At the end of the adsorption period, the solution was filtered through a 0.45 \(\upmu\)m membrane filter and then analyzed for phosphorus concentration.  
The adsorbed quantity \(Q_{e}\) was calculated according to:  
\[Q_{e}=(C_{o}-C_{e})\frac{V}{m} \tag{1}\]  
where \(C_{o}\) and \(C_{e}\) (mg/L) are the initial and equilibrium concentrations of phosphorus in the solution, respectively, \(V\) (L) is the volume of the solution, and \(m\) (g) is the mass of adsorbent.  
The pH effect on phosphorus adsorption was studied in the range from 2 to 11. The pH was adjusted with 0.1 M NaOH and 0.1 M HCl solutions. In the stock solution, the ionic strength was from 0.00014 mol/L to 0.00002 mol/L. The ions present in the stock solution are potassium ion \(K^{+}\) and dihydrogen phosphate ion \(H_{2}PO_{4}^{-}\). The ionic strength was adjusted by adding NaCl.  
The adsorption isotherms were evaluated according to the Langmuir and Freundlich models, represented by Equations (2) and (3), respectively:  
\[Q_{e}=\frac{Q_{m}K_{L}C_{e}}{1+K_{L}C_{e}} \tag{2}\]  
\[Q_{e}=K_{f}C_{e}{}^{1/n} \tag{3}\]  
where \(Q_{e}\) represents the mass of adsorbate adsorbed in mg per mass of adsorbent in g, \(C_{e}\) is the equilibrium concentration of adsorbate in mg/L, \(Q_{m}\) represents the maximum Langmuir adsorption capacity of the adsorbent in mg of adsorbate per g of adsorbent, and \(K_{L}\) is the Langmuir adsorption constant in L/mg. The magnitude of \(K_{L}\) in the Langmuir model indicates the degree of adsorbate-adsorbent affinity. The adsorption constants for the Freundlich model, \(K_{F}\) (L/mg)\({}^{1/n}\) and \(n\) (dimensionless), are determined empirically.  
The separation factor or equilibrium parameter (\(R_{L}\)) can be defined as:  
\[R_{L}=\frac{1}{1+bC_{0}} \tag{4}\]  
where \(b\) is the Langmuir constant and \(C_{0}\) is the highest initial concentration of phosphorus (mg/L). The value of \(R_{L}\) indicates the adsorption nature to be either unfavorable (\(R_{L}>1\)), linear (\(R_{L}=1\)), favorable (0 \(<R_{L}<1\)) or irreversible (\(R_{L}=0\)) [33].
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 3 Results
### U-Net Training and Test  
The U-Net tested here is configured with the values shown in Table 1.  
Training and validation were performed in Tensorflow Keras [43] considering three epochs and a dimension of the training batch equal to three. This has been conducted for four different values of initial filters: 16, 32, 64 and 128. This choice was made to see how performance in terms of accuracy and loss varies with the increasing network complexity in order to select the minimum required number of filters while achieving satisfactory performance. Every trained network reached accuracies higher than 99.80% after the first epoch and remained constant for training, validation and testing. The same behavior applies for the final losses which are not higher than 0.016. Moreover, no overfitting phenomenon was indicated.  
The training, validation and tests were performed on the following workstation:  
* CPU: AMD Ryzen Threadripper PRO 3975 WX 32 Cores 3.50 GHz
* RAM: 128 GB
* GPU: Nvidia Quadro RTX 5000.
## 4 Results and Discussion
### Effect of Area Replacement Ratio (Ar %) of the Granular Columns on the Factor of Safety (FoS)  
The current analysis examined the impact of varying the diameter of granular columns on the factor of safety (FoS). Seven diameters (50, 60, 70, 80, 90, and 100 cm) were selected, representing 5.815-11.63% area replacement ratio. Results showed that using low elastic modulus geogrid (E = 0.120 GPa) resulted in an average 13% increase in FoS across all embankment angles (as seen in Figure 8a). Using medium strength elastic modulus geogrid (E = 2.625 GPa) led to a 20% increase in FoS compared to the low elastic modulus geogrid (Figure 8b). Additionally, the contribution of encasement to FoS values decreased as the embankment angle increased (Figure 8c). Using high modulus geogrid had a very minor effect on FoS compared to medium strength elastic modulus. The findings suggest that increasing the area replacement ratio improves slope stability by increasing the shear resistance and dissipating energy generated by soil movement. This is achieved  
Figure 5: Soil—column interaction at the shear plane. (**a**) Normalized height vs. normalized lateral displacement. (**b**) Normalized shear strength vs. normalized soil movement in end bearing columns.  
Figure 6: Soil—column interaction at the shear plane. (**a**) Normalized height vs. normalized lateral displacement. (**b**) Normalized shear strength vs. normalized soil movement in embedment ratio L/D = 10.  
by increasing the sheared area, which reduces the distribution of shear forces and strains along the failure line [20; 25; 35]. [PERSON] and [PERSON] [11] found that increasing the area replacement ratio significantly improved the bearing capacity of encased floating stone columns, especially when the area replacement ratio exceeded 0.25. They also observed that using a geogrid encasement significantly reduced lateral displacement compared to using ordinary stone columns.
# FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders
## 6 Conclusions and Future Work  
In this paper, a new approach of scan-matching and low-cost mobile robot localization is presented using a low-cost LRF sensor. The missing data caused by the out-of-measurement range are processed as features for estimating the rotation angle under certain typical scenes. FFT-based matching is applied to determine the rough pose. An effective implementation of an FFT-ICP-based method, in an attempt to reduce the computational cost, is proposed. The experiments show the proposed method has an advantage in computational cost over our previous methods. The proposed method is also more robust than traditional methods in long-distance matching, which suggests its potential for loop-closing detection in SLAM.  
Figure 11: Localization results using ICP (**a**) and our FFT-ICP2 method (**b**) under different speeds.  
Figure 12: The generated maps using NDT (**a**) and the proposed FFT-ICP2 method (**b**). The red stars represent the real locations of the robot.  
The drawback of this method is its detection range, because we only considered the obstacles near to the robot for matching. In wide scenarios, the current method may not work well. For the missing data, we only considered that they are caused by the out-of-measurement range. However, for low-cost LRF sensors, missing data can also be caused by on-total reflection or absorption of the laser. Scenes with a lot of glass are still a big challenge for all the laser-based localization and mapping approaches.  
In the future, we will enhance the algorithm by scaling the scan image adaptively and developing quick loop-closing modules based on the image for the SLAM framework so that the algorithm can adopt to big scenes. We will also put forward a missing-data analysis to make full use of the information contained within the missing data.  
Conceptualization, W.X. and Y.O.; Data curation, L.Y.; Funding acquisition, Y.O.; Investigation, Y.O.; Methodology, G.J.; Software, G.L.; Supervision, Y.O.; Validation, L.Y.; Writing--original draft, G.J.  
This work was supported by the National Natural Science Foundation of China (Grants No. U1613210), and the Shenzhen Fundamental Research Program (JCYJ20170413165528221).  
The authors declare no conflict of interest.
# FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks
## 3 Methodology
### The Routing Procedure of FL-SDUAN  
The routing algorithm of FL-SDUAN consists of three phases: node clustering, route calculation, and data transmission.  
(1) Node clustering phase  
The main steps of node clustering include four actions, i.e., temporary head (TH) election, cluster construction, CH election, and DN joins to the corresponding cluster. The clustering is triggered based on the trigger-cluster message sent by a controller. The DN decides whether it is a TH by comparing its random number with the TH threshold. When the TH is determined, clusters are formed and centered on the TH. The nodes in the cluster select the CH by comparing their respective communication capabilities. If the CH is elected, the DN signs the CH, and the clustering process is completed. In a cycle, there is only one CH in a cluster and each node belongs to only one cluster. Then, the DN sends  
Figure 1: The FL-SDUAN model.  
a cluster-statistics message to the controller. The message includes cluster ID, remaining energy, resource utilization, node static data, node ID, timestamp, and rounds. The format of the cluster-statistics message is shown in Figure 2.  
Route calculation phase  
In FL-SDUAN, the controller receives cluster-statistics messages and stores them in the corresponding node state table. At this time, the controller can candidate CHs. If CHs are selected, they are periodically registered to corresponding controllers. Therefore, controllers have a global view of the network in real time. Then, they use cluster-statistics messages to calculate path weights between CHs based on fuzzy logic and construct routing paths. As shown in Figure 3, the yellow nodes are the CHs elected by each cluster, while the white nodes are the data nodes. Routing decisions are calculated and passed by the controller. The routing paths are then implemented by multi-hop relay communication through the corresponding CHs.  
Data transmission phase  
In data transmission, each DN sends data only to the corresponding CH, and multiple CHs transmit data to the controller through relay communication. The controller calculates an optimal path for the node requesting the route, and the data is forwarded along this path. The FL-SDUAN routing procedure is shown in Figure 4.