esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison of Different ML Model Performances  
#### 3.3.1 Comparison of Model Performance in NE1, NE2, and NE3 Sites  
With 70% of data in the NE1, NE2, and NE3 sites as the training set and the other 30% as the testing set, three other ML methods (i.e., SVM, CNN and ELM) also completed 1000 random runs. The parameters of the SVM model were determined by the grid search and cross-validation approaches [88] (\(c=16,g=1\)). The parameters of the CNN model were determined by the stochastic gradient descent with momentum (SGDM) algorithm (max epochs = 1000, mini batchsize = 1200, initial learn rate = 0.01, learn rate drop factor = 0.5). A 3 \(\times\) 1 convolution kernel was used in the CNN model. An appropriate activation function was the key factor to promote arithmetic speed for the ELM model, and a sigmoid activation function was adopted for calculation (number of hidden layer nodes = 50). In Table 3, the performance of all four ML methods is shown to be strong for estimating maize's daily GPP. The SVM was the best, and the evaluation metrics of NSE, RMSE, and CV were improved compared with that of RF. CNN had the weakest performance, but its accuracy was very close to other ML methods. It has also demonstrated that the performance of all models significantly improved when SLN or NMP was introduced, although various ML methods showed different sensitivity to them. As for RF and SVM, model performance was better when SLN and NMP were considered simultaneously. But for CNN and ELM, NMP had a stronger ability than SLN for model improvement. It was speculated that the data redundancy in the input variables of SLN and NMP with a fixed polynomial relationship led to this result. Combining Figure 7 and Table 3, the CNN and ELM methods might be more sensitive to data redundancy than the RF and SVM methods.  
Figure 7: Validation of RF in ROI site while trained in NE1, NE2, and NE3 sites. The red line is the fitting line between estimated GPP (GPPm) and ground-measured GPP (GPPg), and the dashed line is the 1:1 line.
## 3 LOADng Protocol Overview
### LOADng Functioning in Brief  
LOADng is a reactive routing protocol based on route discovery using route request and route reply messages. Thus, when a node wants to send a data message and the route to the destination is unknown, it should begin a new route discovery process. To this end, the node broadcasts a route request (RREQ) message to search for a route to the desired destination. Each node that receives an RREQ should perform message processing and consider the message to be forwarded. This process continues until the RREQ reaches the sought destination. The destination should then generate a route reply (RREP) message to answer the received RREQ. The RREP is forwarded in unicast to the RREQ originator, constructing a route between the two nodes interested in the message exchange. Finally, the RREP is received by the RREQ originator, which should begin to send data messages using the path created by the route discovery process.
## 4 Simulation Verification and Result Analysis
### Performance Testing under Multiple Operating Conditions
#### 4.2.2 Performance Verification at Various Speed
(2) Performance verification at 120% rated speed:  
When the speed is set at 1200 r/min, the speed and speed error curves under the control of VPDPI regulator and PI regulator are shown in Figures 24 and 25, respectively. Figures 26 and 27 record PMSM rotor position Angle information under the control of the two regulators and the error of tracking the rotor position Angle of the actual system, respectively. Moreover, the output torque and three-phase current of PMSM are shown in Figures 28 and 29, respectively. From the above experimental results, the control performance of the two regulators is similar to the above two working conditions. Figure 28: Three phase current response process under two control methods. (**a**) Current response under the proposed VPDPI control. (**b**) Current response under the PI control. Figure 26: The estimation of rotor position Angle using VPDPI. (**a**) Observed and actual rotor position Angle. (**b**) Rotor position Angle observation error. Figure 27: The estimation of rotor position Angle using PI. (**a**) Observed and actual rotor position Angle. (**b**) Rotor position Angle observation error. Figure 28: Three phase current response process under two control methods. (**a**) Current response under the proposed VPDPI control. (**b**) Current response under the PI control. Figure 29: Torque response process under two control methods. (**a**) Torque response under the proposed VPDPI control. (**b**) Torque response under the PI control. The maximum overshoot of the traditional PI regulator is 548 r/min, and the adjustment time is about 0.115 s. When the load mutation occurred, a dynamic landing of 272 r/min occurred. In terms of observation accuracy, the traditional PI observation error pulsation reaches 4.5 r/min. On the contrary, the proposed VPDPI regulator does not have overshoot, and the adjustment time is only 0.053 s. The dynamic landing under load mutation is only 32 r/min. Meanwhile, the steady-state observation error fluctuation is only 1.8 r/min. The position Angle observation error is more gentle, and the observation result is more stable. Therefore, the capability of VPDPI regulator to solve integral saturation in PMSM sensorless control system is proved again.
## 2 Materials and Methods
### Phytochemical Analysis  
Total flavonoid content (TFC) and total phenolic content (TPC) were measured using the method described by [PERSON] et al. [6]. To analyze TFC, 0.2 mL of the 70% ethyl alcohol extract and its fractions were combined with 0.1 mL of 10% aluminum nitrate (\(w/v\)), 0.1 mL of 1 M potassium acetate, and 4.6 mL of 80% ethanol. These reaction mixtures were allowed to stand at room temperature for 40 min, after which the absorbance was measured against a blank at 415 nm. For TPC analysis, 100 \(\mu\)L of each sample and 50 \(\mu\)L of Folin-Ciocalteureagent (2 N) was mixed and incubated for 5 min. Then, 0.3 mL of 20% Na2 CO3 was added to the mixture and incubated for 15 min. The resultant blue color was read at an absorbance of 725 nm. The TPC and TFC in each extract were quantified in terms of milligrams of gallic acid equivalents (ug GAE/mg of extract) and quercetin equivalents (ug QE/mg of extract), respectively. Furthermore, the total saponin content (TSC) was assessed using the vanillin-sulfuric acid assay, following the method outlined in a prior study [13]. Briefly, 10 mg of each extract were dissolved in 1 mL of ethyl acetate and mixed with 500 uL of A reagent [0.5% (\(v/v\)) p-anisaldehyde in ethyl acetate] and 500 uL of B reagent [50% (\(v/v\)) H2 SO4 in ethyl acetate]. After incubation at 60 \({}^{\circ}\)C for 10 min, the absorbance was measured at 430 nm. TSC was expressed as diosigenin equivalents (ug DE/mg of extract).  
The contents of lupenone and \(\beta\)-sitosterol were determined using high-performance liquid chromatography equipped with a diode array detector (Agilent Technologies, Waldbronn, Germany) and a Poroshell 120 EC-c18 column (4.6 \(\times\) 150 mm, 4 um), as described by [PERSON] et al. [14]. The contents of all these compounds were analyzed by comparing the retention times and the ultraviolet spectral data. The concentration of these compounds was calculated by comparing the peak areas of the samples with the calibration curve of each standard.
## 5 Adaptive Decision Threshold Design Method Based on Auxiliary Channel  
In the actual burst spread spectrum communication system, the noise intensity of the channel is constantly changing with time. Therefore, in signal detection, if the fixed threshold is used, it will be difficult to detect the signal correctly and accurately. Therefore, it is necessary to adaptively change the decision threshold according to the strength of the current background noise to maintain a high detection probability [28; 29]. At this time, the PN code acquisition of the wireless mobile communication system can be completed only by real-time estimation of the decision threshold according to the statistical characteristics of the test statistics \(Z\)[30; 31]. Based on the statistical characteristics \(Z\) of the test statistic, and the relationship between unit false probability, unit detection probability, acquisition success probability and decision threshold in (6), (7) and (11), an adaptive design method of a decision threshold based on the auxiliary channel is proposed.  
The block diagram of the acquisition algorithm based on adaptive threshold decisions is shown in Figure 10. The acquisition threshold \(V\)th is related to the normalized threshold \(\lambda\) and noise power \(P_{n}\).
## 3 Results
### Association of Pain Score Changes with OA Grade
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Spearman Correlation Coefficient**} & \multicolumn{2}{c}{**Spearman Correlation Coefficient**} \\  & **between OA Grade \& VAS Change** & **between OA Grade \& WOMAC Change** \\  & **r (\(p\) Value)** & & **r (\(p\) Value)** \\ \hline \multirow{2}{*}{**Treatment Group**} & **Pretreatment to** & **Pretreatment to** & **Pretreatment to** & **Pretreatment to** \\  & **3 mo f/u** & **12 mo f/u** & **3 mo f/u** & **12 mo f/u** \\ \hline
**HA** & \(-\)0.20 (0.015) \(\dagger\) & 0.50 (0.005) \(\dagger\) & \(-\)0.42 (\(<\)0.001) \(\dagger\) & 0.68 (\(<\)0.001) \(\dagger\) \\
**ACS** & \(-\)0.36 (\(<\)0.001) \(\dagger\) & 0.26 (0.069) & \(-\)0.44 (\(<\)0.001) \(\dagger\) & 0.17 (0.056) \\
**BMAC** & \(-\)0.27 (0.006) \(\dagger\) & \(-\)0.06 (0.78) & 0.06 (0.592) & 0.16 (0.459) \\
**Total** & \(-\)0.33 (\(<\)0.001) \(\dagger\) & 0.28 (0.004) \(\dagger\) & \(-\)0.39 (0.001) \(\dagger\) & 0.34 (\(<\)0.001) \(\dagger\) \\ \hline \end{tabular} OA = osteoarthritis; VAS = Visual Analogue Scale; WOMAC = Western Ontario and McMaster Universities Osteoarthritis Index; HA = hyaluronic acid; ACS = autologous conditioned serum; BMAC = bone marrow aspirate concentrate; mo = month; f/u = follow-up; \(\dagger\) statistically significant correlation.  
\end{table} 
Table 6: Correlation between grade of OA and change in VAS and WOMAC score over the follow-up period. Figure 3: Forest plot showing VAS and WOMAC score changes in patients with different OA grades for the overall study cohort. Mean values of score change \(\pm\) SEM are presented. OA = osteoarthritis; VAS = Visual Analogue Scale; WOMAC = Western Ontario and McMaster Universities Osteoarthritis Index; mo = months; f/u = follow-up.
## 2 Architecture of the Microgrid-Based Base Transceiver Station (MG-Based BTS)
### Conventional Base Transceiver Station  
Figure 1 shows the architecture of a conventional grid-connected BTS without renewable energy generation nor local controller.  
This BTS layout can be applied to any BTS regardless of the type [9]. As seen in Figure 1, the BTS consists of a grid-connected power supply system which integrates a rectifier, the BESS unit, the Base Band unit (BB), the Radio Frequency (RF) unit, the Power Amplifier (PA) and different AC loads, such as the cooling system and lighting. The DC output of the rectifier is connected to: (i) the BESS, which acts as a backup source to facilitate continuous operation in case of a power outage; (ii) the transmission/reception equipment (RF, PA and BB). The grid-connected power supply system manages the charge of the BESS.  
A power modeling approach for conventional BTSs can be found in [32], in which, at the time of publication, it was estimated that, in a full-load scenario, the power demand by BTSs would be reduced by 50% and 20%, respectively, from 2014 to 2020. With this power estimation in mind, the BESS size is determined as a function of the required autonomy, i.e., the amount of time the BESS can power the BTS uninterruptedly to ensure 100% operability in case of a power outage.  
Figure 1: Conventional BTS architecture.
# Cooperative Guidance Law for the Mother-Cabin of the Anti-UAV Cluster Mother-Son Missile
## 2 Description and Analysis of Cooperative Guidance of Multiple Mother-Cabins
### Analysis of the Mother-Cabin Guidance  
The guidance phase of the mother-cabin refers to the phase in which the mother-cabin flies to the \"release point\" according to the constraint requirements. The mother-cabin guidance is a key link in the whole interception process. The result of the mother-cabin guidance is related to whether the mother-cabin seeker can track the targets stably and release the son-interceptors effectively. The mother-cabin guidance should meet two requirements:  
First, the impact process between the son-interceptors and the targets will produce complex infrared features and more target-points in the air, which will interfere with the subsequent identification and interception of the UAV targets by the mother-cabin and son-interceptors. Therefore, it is hoped that multiple mother-cabins will reach the \"release point\" at the same time and release the son-interceptors at the same speed to achieve simultaneous interception of the target.  
Second, considering the detection of the target by the mother-cabin, the LOS of the mother-cabin to the \"release point\" must converge to zero before reaching the \"release point\". This ensures that the carried composite seeker has been aligned with the cluster before reaching the \"release point\" so as to achieve the track of the target as quickly as possible.  
Based on the above two constraints, this paper designs a multi-missile cooperative guidance law that satisfies the constraints of impact time, speed, and zero LOS. In the design of the guidance law, this paper draws on consistency theory and sliding mode control theory, which are currently more widely used in state-of-the-art methods. Because of the weak maneuverability of UAV cluster, it is relatively easy to achieve the interception. In view of this, we try to obtain a simpler controller structure, smooth controller output, and smooth trajectory while still satisfying the guidance constraints.  
**Remark 1**.: _Considering the simplicity of the analysis process, this paper discusses the guidance law of the mother-cabin in the two-dimensional plane. If it is in the three-dimensional plane, the \"zero LOS constraint\" mentioned above should be described as follows: the mother-cabin should be at the same height as the cluster, and the direction of speed should be the direction of the line connecting the \"release point\" and the cluster threat \"center of gravity\". The expression of other constraints remains unchanged. The \"release point\" changes with the movement of the cluster, which can be regarded as a maneuvering target for the guidance of the mother-cabin. For the sake of readability, the \"release point\" will be collectively referred to as the target, and the mother-cabin will be collectively referred to as the missile in the following text._
## 4 Experiments
### Experiment Analysis
#### 4.3.3 Performance Comparison with Respect to Hyper-Parameters  
The dropout layer can avoid overfitting by randomly dropping incoming and outgoing connections in the hidden layer based on the dropout rate during the training process. We will discuss the effect of the dropout rate on model performance. We take the dropout rate as (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7), and the performance is the best when the dropout rate is 0.5, as shown in Figure 11a. If the dropout rate is too small, the training will be overfitting. If the dropout rate is too large, it may drop useful features and leave useless features, resulting in insufficient training.  
We also discuss the effect of embedding size on model performance, and the results are shown in Figure 11b. We set the embedding size to (8, 16, 32, 64, 128, 256, 512). As the embedding size increases from 8 to 512, the performance of the model first becomes better and then becomes worse. The performance is best when the embedding size is 128. If the embedding size is too small, the embedding representation will be rough, and the fitting ability will be insufficient. If the embedding size is too large, the embedding representation will become sparse, resulting in performance degradation.
## 2 2D Wave Field Reproduction in 3D Space Using a Single Circular Array
### Wave Field Reproduction Error with Respect to \(r_{ref}\)  
The wave field \(e(\textbf{x},k)\) of the wave field reproduction error is expressed as follows:  
\[e(\textbf{x},k)=10\log_{10}\frac{\left|S_{D}(\textbf{x},k)-S_{s}(\textbf{x},k) \right|^{2}}{\left|S_{D}(\textbf{x},k)\right|^{2}}, \tag{13}\]  
Considering the audible area, the region-of-interest (ROI) was defined as the inner part of the circle with a radius equal to \(R_{mic}-0.1\) m for calculating the average wave field reproduction error and its variance. The average reproduction error was expressed as the mean of all \(\textbf{x}\) of \(e(\textbf{x},k)\) within the ROI. We assumed that the sound source existed at [5 m, 0 m], [\(-3\) m, 3 m] and emitted a periodic signal at 200 Hz. Referring to Equations (1) and (4), 11 monopoles and microphones were placed equiangularly. Figure 3 shows the wave field reproduction results according to \(r_{ref}\) when \(R_{s}\) was assumed to be 2 m, and \(R_{mic}\) was set to 0.65, 0.7, 0.75, and 0.8 m.  
As shown in Figure 3a, the reproduction error varied according to \(r_{ref}\). Interestingly, when \(r_{ref}\) was equal to \(R_{mic}\), it yielded good performance outcomes for all \(R_{mic}\) radii. Unlike the 2.5D and 2D wave field reproductions in 3D and 2D spaces, respectively, wave field reproduction based on 2D spatial information in 3D space did not generate a reference circle, as shown in Figure 4c, except when \(r_{ref}=R_{mic}\). When \(R_{mic}=0.8\) m and \(r_{ref}=R_{mic}\), the desired wave fields, reproduced wave fields, and wave field reproduction error are shown in Figure 4a,b,d, respectively. Unlike 2.5D wave field reproduction, the wave field reproduction error inside the microphone array was not uniform, as shown in Figure 4d, because only 2D information was used to reproduce the 3D sound field. The variance for all points inside the ROI is plotted in Figure 3b to observe the wave field reproduction uniformity. Nonuniformity is undesirable in terms of a wave field reproduction that provides an immersive experience. In global ANC, where the only concern is sound pressure, the importance of nonuniformity can be reduced. Therefore, we can use a single circular array for global ANC and expect Figure 3a to indicate noise reduction when the \"anti\" wave field of the noise source is reproduced by the circular array with respect to \(r_{ref}\).  
Figure 4: (**a**) Desired wave field. (**b**) Wave field reproduction based on the use of a circular array. Wave field reproduction error at (**c**) \(r_{ref}=0.4\) m and (**d**) \(r_{ref}=R_{mic}\).  
Figure 3: Wave field reproduction error and variances with respect to \(r_{ref}\). (**a**) Average wave field reproduction error. (**b**) Variance.
# Faster and Better Nested Dissection Orders for Customizable Contraction Hierarchies
## 2 Materials and Methods
### Preliminaries  
An undirected graph \(G=(V,E)\) consists of a set of _nodes_\(V\) and a set of _edges_\(E\subseteq\binom{V}{2}\). A directed graph \(G=(V,A)\) has directed _arcs_\(A\subseteq V\times V\) instead of undirected edges. It is _symmetric_ if for every arc \((x,y)\in A\), the reverse arc \((y,x)\) is in \(A\). For ease of notation, we do not distinguish between undirected and symmetric graphs in this paper, and we use them interchangeably, whichever better suits the description. Let \(n:=|V|\) denote the number of nodes and let \(m:=|E|\) denote the number of edges of an undirected graph. All graphs in this paper contain neither self-loops \((x,x)\) nor multiedges. \(H=(V^{\prime},A^{\prime})\) is a subgraph of \(G\) if \(V^{\prime}\subseteq V\) and \(A^{\prime}\subseteq A\). The subgraph induced by a node set \(U\subseteq V\) is defined as \(G[U]:=(U,\{(u,v)\in A\cap(U\times U)\})\), the graph with nodes \(U\) and all arcs of \(G\) with endpoints in \(U\). The degree \(\deg(x)=|\{(x,y)\in A\}|\) is the number of _outgoing_ arcs of \(x\). A path is a sequence of edges such that consecutive edges overlap in a node. A graph is called \(k\)-connected if there are \(k\) node-disjoint paths between every pair of nodes. The \(k\)-connected components of a graph are the node-induced subgraphs, which are inclusion-maximal regarding \(k\)-connectivity: 1-connected components are called connected components, while 2-connected components are called biconnected components.
# A Review on Nanocomposite Materials for
## 4 Cathode
### Layered Oxides, LiMO\({}_{2}\) (\(M\) = Co, Mn, Ni and Their Mixtures)
Adapted with permission from [54], © American Chemical Society, 2009.  
diffraction (XRD) and cyclic voltammetry (CV) experiments [60]. The thermal stability of Li\({}_{\text{x}}\)Ni\({}_{1-\text{x}}\)O\({}_{2}\) is also of great concern and it is function of Li content (i.e., x), the smaller the Li content (i.e., x \(<\) 0.5) the less thermally stable the LiNiO\({}_{2}\). In addition to the problems associated with the capacity fading, overcharging the LiNiO\({}_{2}\) also poses a safety risk; however, thermal stability of LiNiO\({}_{2}\) can be improved by Mg doping and coating [61; 33]. LiNiO\({}_{2}\) can also be prepared using a single source precursor (i.e., Li(H\({}_{2}\)O)Ni(N\({}_{2}\)H\({}_{3}\)CO\({}_{2}\))\({}_{3}\)]-0.5H\({}_{2}\)O) which is a desired synthetic route to prepare this cathode material in large scale [62]. LiMnO\({}_{2}\) is another layered metal oxide which is isostructural to LiCoO\({}_{2}\); similar to LiNiO\({}_{2}\), LiMnO\({}_{2}\) is a low cost (i.e., Co is $23.99/kg, Ni is $8.94/kg and Mn is $2.01/kg as of 9 May 2016 [63]) and environmentally friendly cathode material compared to LiCoO\({}_{2}\). LiMnO\({}_{2}\) irreversibly transforms from layered structure into spinel structure upon cycling and its capacity fades quickly [64; 65]. The Al\({}_{2}\)O\({}_{3}\) and CoO coatings improve the cycle life of LiMnO\({}_{2}\) by impeding Mn dissolution and lattice instability due to Jahn-Teller distortion [66; 67]. Another strategy to lower the cost of LiCoO\({}_{2}\) is to partially or fully substitute Co with Ni, Mn and Al such as LiNi\({}_{1/3}\)Mn\({}_{1/3}\)Co\({}_{1/3}\)O\({}_{2}\) (NMC), LiNi\({}_{0.8}\)Co\({}_{0.15}\)Al\({}_{0.05}\)O\({}_{2}\) (NCA), LiNi\({}_{\text{x}}\)Co\({}_{1-\text{x}}\) (0 \(<\) x \(<\) 1) and Li(Ni\({}_{\text{x}}\)Mn\({}_{1-\text{x}}\))O\({}_{2}\) (0 \(<\) x \(<\) 1) [24; 46; 68; 69; 70; 71; 72; 73]. These composite cathodes derived from LiCoO\({}_{2}\) have also higher specific capacity than the LiCoO\({}_{2}\), and are extensively used in portable electronics.