esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### CAD Modeling  
The abovementioned 3D model of the femur was then imported into Solidworks 2020 (Dassault Systemes, Velizy-Villacoublay, France) to prepare the bone as prescribed by the surgical procedure and to identify the medullary canal midline that was used to design the patient-specific stems [2; 3]. Several methodologies could be used to identify the medullary canal surface and midline [37]. In this work, firstly, the CAD model of the residual femur was modified by removing the callus at the distal part (Figure 2A). Then, the centroids of eighteen transverse sections, 10 mm apart, as shown in Figure 2B, were calculated and then interpolated with a spline to create the medullary canal midline curve. Moreover, the interpolated 3D spline was projected on the sagittal plane (Figure 2C) of the femur to obtain a planar representation of the medullary canal midline curve (Figure 2D). These curves were used to prepare two different versions of the bone, simulating the surgical procedure performed for the lower limb amputation treatment [2; 38; 39], reaming along the 3D spline and its planar projection, with a diameter of 19 mm.  
Two implants were created by extruding a circular section, with a diameter of 19.1 mm, along the 3D and 2D medullary canal midline curves, obtaining the patient-specific design (PSD) and the planar patient-specific design (PPSD) stems, respectively. The PSD and the two curves used to obtain the implants are presented in Figure 3.  
Figure 1: CT scan of the region of interest that was used during the segmentation (**A**), and the reconstructed CAD model (**B**) with the steps of the workflow (**C**).  
A 0.1 mm interference resulted between the bone and the implant, a typical value reported in the literature [6; 20; 25]. In addition, the same procedure of bone preparation and stem design was also performed by following a straight segment orthogonal to the cut plane, which represents a standard implant with a straight design (SD). The implants were designed with a length of 140 mm, as used for commercially available transfemoral prostheses [12; 40]. The abutment, commonly used in these implants to connect an intramedullary component to the external prosthetic limb, was added at the distal part of the prostheses, where the load will be applied. In the end, three bone-implant assemblies were obtained by matching the PSD, PPSD, and SD stems and the residual bone, respectively (Figure 4).  
Figure 3: The CAD model of the PSD with the medullary canal highlighted in black in the longitudinal (**A**) and frontal (**B**) view and the two curves (**C**), the medullary canal midline (black) and its projection on the sagittal plane (blue) used to design the PPSD.  
Figure 2: The original CAD model of the femur with the callus and the cutting plane selected to remove it (**A**), the cross-sections adopted to identify the centroids (**B**), the sagittal plane of the femur (**C**), the 3D medullary canal midline (black), and its projection on the sagittal plane (white) (**D**).
## 2 Materials and Methods
### Data Used and Preprocessing
Based on the national meteorological drought classification standard, SPEI is categorized into the following five levels: No drought (SPEI > 0); Mild drought (\(-1\) < SPEI \(\leq 0\)); Moderate drought (\(-1.5<\) SPEI \(\leq-1\)); Severe drought (\(-2<\) SPEI \(\leq-1.5\)); and Extreme drought (SPEI \(\leq-2\)). For image processing, we used ArcGIS 10.8.1 for the data analysis and visualization.
## 3 Results
### MFO Results  
The resulting angles and their theoretical THD are shown in Table 1.  
As previously mentioned, the MFO algorithm underwent a series of iterations totaling up to 100. These iterations were performed to optimize the objective function outlined in Equations (16) and (17). Remarkably, in the context of the five-level CHBMIL configuration, it was found that a mere ten iterations sufficed to attain and sustain a commendable THD level of 15.29%, as demonstrated in Figure 5.  
Meanwhile, the algorithm exhibited impressive performance when dealing with the more complex seven-level CHBMIL configuration. It reached and maintained a remarkably low THD of 10.43% at the 33 rd iteration, showcasing its efficiency and effectiveness, as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Angle** & **Five-Level CHBMIL1** & **Seven-Level CHBMIL1** \\ \hline \(\theta_{1}\) & 13.40\({}^{\circ}\) & 8.69\({}^{\circ}\) \\ \(\theta_{2}\) & 41.91\({}^{\circ}\) & 27.89\({}^{\circ}\) \\ \(\theta_{3}\) & - & 49.81\({}^{\circ}\) \\ \(\%\)THD & 15.29\% & 10.43\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Angles obtained using the MFO algorithm.  
Figure 5: THD vs. iterations for the five-level CHBMIL.  
Figure 6: THD vs. iterations for the seven-level CHBMIL.  
With the angles obtained in Table 1, the harmonic spectrum that should theoretically be obtained in the output voltage of the CHBMIL was calculated in Matlab, both for the five- and seven-level (Figures 7 and 8). This calculation was performed by graphing the sum values of Equation (16) and adding the value of the fundamental.
###### Abstract  
The lithium-ion battery has become the primary energy source of many electronic devices. Accurately forecasting the remaining useful life (RUL) of a battery plays an essential role in ensuring reliable operatioin of an electronic system. This paper investigates the lithium-ion battery RUL prediction problem with capacity regeneration phenomena. We aim to reduce the accumulation of the prediction error by integrating different capacity degradation models and thereby improve the prediction accuracy of the long-term RUL. To describe the degradation process more accurately, we decoupled the degradation process into two types: capacity regeneration and normal degradation. Then, we modelled two kinds of degradation processes separately. In the prediction phase, we predicted the battery state of health (SOH) by using the relevance vector machine (RVM) and the gray model (GM) alternately, updated the training dataset according to the prediction results, and then updated the RVM and GM. The RVM and GM correct each other's prediction results constantly, which reduces the cumulative error of prediction and improves the prediction accuracy of the battery SOH. Experimental results with the National Aeronautics and Space Administration (NASA) battery dataset demonstrated that the proposed method can accurately establish the degradation model and achieve better performance for the RUL estimation as compared with the single RVM or GM methods.  
Keywords: lithium-ion battery; remaining useful life; hybrid approach; relevance vector machine; gray model; regeneration phenomena +
Footnote †: journal: Applied Physics Letters
## 4 The Proposed DC-CAAE Method
### The Basic Principles of the CAAE  
Set network traffic \(\mathbf{x}\) as the real picture data after processing, that is, the input of the CAAE model is multiple network traffic vectors. We can map network traffic \(\mathbf{x}\) to the feature vector based on encoder \(\mathbf{E}\), that is, \(E(\mathbf{x})=\mathbf{z}\), where the dimension of the network traffic features is 100. The CAAE is a neural network that learns valid features in an unsupervised learning method. The reconfiguration of generator \(G\) forces the encoder to learn basic potential features, that is, \(\mathbf{x}^{\prime}=G(\mathbf{z})\). The core appeal of our method is to always maintain the extracted features to convey the characteristics of the original data as much as possible. Therefore, in this process we want \(\mathbf{x}^{\prime}\) to be as similar as possible to \(\mathbf{x}\), which can be expressed by minimizing the loss function:  
\[L_{BCE}=-(\mathbf{x}^{\prime}\log\mathbf{x}+(1-\mathbf{x}^{\prime})\log{(1-\mathbf{x})}), \tag{4}\]  
At the same time, \(\mathbf{z}\) is constrained by a random Gaussian distribution. Assume that \(p(\mu,\sigma)\) is an a priori distribution and \(\mathbf{z}^{\prime}\sim p(\mu,\sigma)\) is used to describe a random Gaussian process base on \(p(\mu,\sigma)\), where \(\mu\) is the mean and \(\sigma\) is the variance. When the distribution of the training data is expressed as \(p_{data}(\mathbf{x})\), the specific training implementation on the encoder and discriminator can be achieved through the maximum-minimum objective function:  
\[\begin{split}\min_{\mathbf{z}}\max_{\mathbf{D}}V(\mathbf{D},\mathbf{E})& =E_{\mathbf{z}^{\prime}\sim p(\mu,\sigma)}(\ln{D(\mathbf{z}^{\prime})})\\ &+E_{z\sim p_{data}(\mathbf{x})}(\ln{(1-D(\mathbf{E}(\mathbf{x})))}),\end{split} \tag{5}\]  
where \(D(\cdot)\) is the output function of the discriminant model, the output is a real value ranging from 0 to 1, which is used to judge whether the data are false or not. The expression \(E_{\mathbf{z}^{\prime}\sim p(\mu,\sigma)}(\ln{D(\mathbf{z}^{\prime})})\) is to make the real data put into the calculated value output by the discriminant model \(D(\cdot)\) and the whole formula subvalue as large as possible. The expression \(E_{z\sim p_{data}(\mathbf{x})}(\ln{(1-D(\mathbf{E}(\mathbf{x})))})\) is to make the calculated value of the false data from the output of the discriminant model \(D(\cdot)\) as small as possible and the whole formula  
Figure 4: The framework of the proposed CAAE method.  
as large as possible. This integration is to make the objective function as large as possible, so the gradient can be improved according to the objective function during training.
## 2 MIMO-WCDMA Orientation
In a realistic mobile environment, uplink or downlink transmission power per node is updated periodically using SINR measurements. In this work, however, as we are mainly interested in capacity limitations stemming from the deployment of MIMO architecture, transmission powers are calculated after linear matrix inversion on the matrices that are derived from the deployment of the equivalent SINR criterion on all nodes. Note that Equation (8) can be alternately written by \[p_{\text{TS},\mu}=\frac{p_{n}}{Tt_{n}}\left\{\left|\sum\limits_{i=1}^{L}\sum \limits_{ \begin{subarray}{c}t^{\prime}=1\\ t^{\prime}\
eq 1\end{subarray} }^{L}\left(\mathbf{r}_{\mu}\mathbf{H}_{\
u}\mathbf{w}_{n} \right)\left(\mathbf{\rho}_{n,\mu,j-r_{1}}+\mathbf{\rho}_{n,\mu,j-r_{1}} \right)\right|^{2}+2\text{Re}\left\{\sum\limits_{i=1}^{L}\left(\mathbf{r}_{\mu }\mathbf{H}_{\
u}\mathbf{w}_{n}\right)\mathbf{i}^{L}_{i=1}\sum\limits_{ \begin{subarray}{c}t^{\prime}=1\\ t^{\prime}\
eq 1\end{subarray} }^{L}\left(\mathbf{r}_{\mu}\mathbf{H}_{\
u}\mathbf{w}_{n} \right)\left(\mathbf{\rho}_{n,\mu,j-r_{1}}+\mathbf{\rho}_{n,\mu,j-r_{1}} \right)\right\}\right\}\right\} \tag{13}\]  
Hence, a linear form of equations can be derived for the \(N\times 1\) power vector matrix:  
\[\mathbf{Ap}=\mathbf{B} \tag{14}\]  
In Equation (14), Matrices \(\mathbf{A}\) (\(n\times n\)) and \(\mathbf{B}\) (\(n\times 1\)) are given by  
\[ \begin{split}&\mathbf{A}(n,n^{\prime})=-\frac{SINR_{n}}{TL_{\
u}} \left(\left|\sum\limits_{i=1}^{L}\sum\limits_{\begin{subarray}{c}t^{\prime}=1 \\ t^{\prime}\
eq 1\end{subarray}}^{L}\left(\mathbf{r}_{\mu}\mathbf{H}_{\
u}\mathbf{w}_{n} \right)\left(\mathbf{\rho}_{n,\mu,j-r_{1}}+\mathbf{\rho}_{n,\mu,j-r_{1}} \right)\right|^{2}+2\text{Re}\left\{\sum\limits_{i=1}^{L}\left(\mathbf{r}_{\mu }\mathbf{H}_{\
u}\mathbf{w}_{n}\right)\mathbf{i}^{L}_{i=1}\sum\limits_{ \begin{subarray}{c}t^{\prime}=1\\ t^{\prime}\
eq 1\end{subarray}}^{L}\left(\mathbf{r}_{\mu}\mathbf{H}_{\
u}\mathbf{w}_{n} \right)\left(\mathbf{\rho}_{n,\mu,j-r_{1}}+\mathbf{\rho}_{n,\mu,j-r_{1}} \right)\right\}\right\}\\ &\mathbf{B}=-SINR_{n}\mathbf{1}(N)\mathbf{\lambda}_{0}\end{split}  \tag{15}\]  
where \(\mathbf{1}(N)\) is an \(N\times 1\) matrix of ones.
# A Review of Research on Secure Aggregation for Federated Learning
## 5 Challenges and Future Directions
### Adaptability  
Adaptability is one of the key directions for the continuous development of federated learning across diverse application scenarios. Research on adaptation needs to focus on constructing more efficient and personalized models in heterogeneous data environments. Current methods addressing the challenges of non-IID data, such as data sharing and data distillation, carry risks of increasing privacy leakage, with the extent of potential harm remaining uncertain. Therefore, defining and quantifying the degree of privacy leakage in heterogeneous data environments remains a crucial research direction.  
Federated learning demonstrates broad application prospects across various fields. Although studies on local optimization algorithms [144], personalized modeling [145], and data preprocessing [146] contribute to reducing communication costs and improving model generalization in specific scenarios, research on federated learning tailored to distinct application contexts is still limited and often overlooked. Designing systems suited to specific application scenarios, optimizing model deployment, and enhancing both training performance and the generalization ability of global models in these contexts are critical challenges requiring further investigation.  
In practical applications, federated learning clients may encounter constraints in computational power, bandwidth, and storage resources. For instance, in mobile device scenarios, low-power devices demand lightweight models and optimized communication schemes [147]. In resource-constrained environments, studying efficient model compression, pruning techniques, and communication protocol optimization is a key research priority.  
Federated learning must also adapt to the demands of multi-domain collaboration and heterogeneous platform integration. For example, in cross-domain data collaboration, achieving efficient and effective collaborative modeling of data across different domains [148] and enabling seamless deployment on diverse hardware platforms are essential directions for adaptability research.
# Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
## 4 Experiment and Results  
### Parameters  
Table 3 shows the prediction models applied to the network traces. The first model, LSTM, operated on traces without clustering. As the statistical characteristics of the traces listed in Table 2 are too diverse to be described by a single model, we trained the LSTM model separately on each trace (F1, F2, M1, and M2).  
The next three listed models, LSTM + KMeans, LSTM + OPTICS, and LSTM + AutoClass, applied LSTM to the traces clustered by the respective methods. The LSTM networks had two hidden layers compared to three layers for the universal LSTM model--we wanted to replace a single complex model with an ensemble of simpler ones. The models operated on three to five clusters depending on the employed clustering algorithm and the trace. To specify the optimal number of clusters, we employed the Silhouette index (SIL). The SIL measures the distance between feature vectors and the cluster's centroid compared to the other clusters' centroids. Its values range from \(-1\) to \(1\). The closer the SIL value to \(+1\), the better the clustering. Unlike KMeans, OPTICS allows for specifying indirectly the number of clusters by manipulating their input parameters, namely \(\epsilon\)-neighbourhood and _minPts_. AutoClass, as the name suggests, automatically adjusts the number of clusters to the data.  
The last model--MA(1) (a moving average)--represents a naive estimator \(x_{t}=x_{t-1}+\epsilon\), where \(\epsilon\) is white noise. We used the benchmark to check if the complex model could beat the simplest one.  
Figure 7: Multilayer LSTM. Hidden layers are stacked in such a way that the output of an LSTM hidden layer is provided to the subsequent LSTM hidden layer.  
While training the ANNs, we applied K-fold cross-validation. First, we divided our dataset into \(K\) equal-sized parts \(\{p_{1},p_{2},\dots,p_{K}\}\). Next, we kept one of the parts as the validation set and combined the remaining \(K-1\) parts to form the training set. We repeated this process \(K\) times, each time leaving another one of the parts out, until we had \(K\) different training sets and \(K\) different validation sets. For every set, we calculated the NRMSE (2), obtaining its range for every trace. The training parameters for the employed ANNs are listed in Table 4.
## 4 Analysis of the Thermal Performance and Temperature Field
### Influence of Speed and Load on Bearing Temperature  
In the process of rolling bearing rotation, bearing temperature is directly related to speed and load. In order to analyze the influence of speed and load on the temperature field of the ball bearing, the parametric analysis and calculation are carried out under the conditions of constant load/variable speed and constant speed/variable load, and the change in transient temperature is compared. The average temperature of the bearing is shown in Figure 11. It can be seen from the figure that, with the increase in rotating speed and load, the heat generated rises and the bearing temperature gradually goes up. Because the transient stability time is affected by both heat generation and the convective heat transfer coefficient, the shorter the heat generation is, the smaller the convective heat transfer coefficient is, and  
Figure 10: Temperature distribution on the centerline of the longitudinal section.  
Figure 9: Temperature distribution of overall ball bearing and cross section after steady state: (**a**) overall ball bearing; (**b**) cross section.  
the longer the stability time is; therefore, the transient temperature of the bearing at low speed takes a longer time to stabilize. Compared with the transient temperature of Figure 11a,b, the contribution of load to temperature rise is smaller. When the radial load increases from 0 to 2650 N, the average steady-state temperature merely goes up approximately 1 \({}^{\circ}\)C; therefore, it can be inferred that the bearing speed has a greater impact on the bearing temperature rise.
# A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research
## 1 Introduction
### Amino-Acids neurotransmitters
#### 1.1.3 Gamma-Aminobutyric Acid (GABA)  
GABA is the major inhibitory neurotransmitter in the central nervous system [15]. It is synthesized from glutamic acid [15].  
Studies have shown that GABA is excitatory in early development [38]. Indeed, it induces a depolarization instead of hyperpolarization in various area of the nervous system, including the neocortex, hippocampus, hypothalamus, cerebellum and spinal cord. This is due to the higher chloride concentration in neurons during the early development of the human body, leading to an outward instead of inward chloride flux [38]. Therefore, the role of GABA during the first years of youth is not the same as in adults. The change of expression of two categories of chloride transporters, the sodium potassium chloride co-transporters and the potassium chloride co-transporters mediate the change of GABA action from excitatory to inhibitory [38]. [PERSON] proposed that this GABA action switch promotes synapse formation [38]. He proposed the following three-stage mechanism [38]:  
* GABA initially enables an excitatory effect on neurons due to the expression of transporters.
* Glutamategic synapses are formed after the GABAergic neurons.
* GABA and glutamate excitatory action enables oscillation of intracellular calcium that modulates synapse formation.  
[PERSON] et al. also showed that the excitatory action of GABA plays a major role in the morphological maturation of cortical neurons [39].
# Deceptive Jamming Detection for SAR Based on Cross-Track Interferometry
###### Abstract  
Deceptive jamming against synthetic aperture radar (SAR) can create false targets or deceptive scenes in the image effectively. Based on the difference in interferometric phase between the target and deceptive jamming signals, a novel method for detecting deceptive jamming using cross-track interferometry is proposed, where the echoes with deceptive jamming are received by two SAR antennas simultaneously and the false targets are identified through SAR interferometry. Since the derived false phase is close to a constant in interferogram, it is extracted through phase filtering and frequency detection. Finally, the false targets in the SAR image are obtained according to the detected false part in the interferogram. The effectiveness of the proposed method is validated by simulation results based on the TanDEM-X system.  
deceptive jamming detection; synthetic aperture radar; electronic counter-countermeasure; synthetic aperture radar interferometry +
Footnote †: journal:  
0000-0002-4891-9800][PERSON] 0000-0002-0002-333-0180][PERSON] 0000-0002-0002-333-0180][PERSON] 0000-0002-0001-8002-333-0180][PERSON] 0000-0002-3333-0180][PERSON] 0000-0002-3333-0180]Received: 17 April 2018; Accepted: 23 May 2018; Published: 13 July 2018
## 4 Vibration Displacement of the Atomizing Sheet
### Vibration Displacement of the Atomizing Sheet in the Existing Theory
#### 4.2.3 Vibration Displacement Experiment on the Atomizing Sheet  
The vibration displacement and vibration modes of the atomizing sheet at different driving frequencies were measured using a three-dimensional Doppler laser vibrator (LV-FS01, SOPTOP, Hangzhou, China). The experimental setup is shown in Figure 18. The entire disc is divided into 41 concentric circles, represented by \(r_{k}\), where \(k=1,2,\cdots,41\). Each concentric circle is evenly divided into six sectors, and the polar angle is denoted by \(\theta_{l}\), where \(l=1,2,\cdots,6\). Measurements are mainly made at the intersection of the concentric circles and sector radius (denoted by \(P_{k,l}\)) and at the central position \(O\). Therefore, the vibration displacement at these 247 locations was measured experimentally. The driving frequency of each position was swept from 0 kHz to 200 kHz, and the effective value of voltage was set to 80 V.  
When only the nodal circle exists in the vibration mode, the maximum value of vibration displacement of the thin plate is located at its center position \(O\). What is studied in Reference [27] is the variation in the maximum vibration displacement of the thin plate with the resonant frequencies when the vibration modes only have nodal circles. Therefore, the vibration displacement measurement results at point \(O\) under different driving frequencies are shown for comparison in Figure 19.  
As shown in Figure 19, the vibration displacement achieves peaks at 20, 56.6, 103.5, and 170 kHz, as indicated by the blue circles. To explore the vibration modes of the atomizing sheet at these resonant frequencies, the distribution of the vibration displacement of the atomizing sheet was measured.  
Figure 18: Experimental setup of the vibration displacement measurement: (**a**) Experiment setup main view; (**b**) Top view of the atomizer; (**c**) The location distribution of the scanning points of atomizing sheet.  
When the driving frequency is 20 kHz, the vibration displacement of the atomizing sheet at point \(O\) reaches the maximum, which is 3.7903 \(\upmu\)m, as shown in Figure 20. Figure 20 shows only the vibration displacement at point \(O\) and several scanning points at the 30 th concentric circle.  
The vibration displacement at other resonant frequencies is also measured. The distribution of vibration displacement corresponding to different resonant frequencies is shown in Figure 21.  
Figure 19: Vibrational displacement of pole \(O\) at different driving frequencies.  
Figure 20: Vibration displacement of some scanning points at 20 kHz.  
According to the displacement distribution, the atomizing sheet has B00, B10, B12, B20, and B30 vibration modes at 20, 26.3, 56.6, 103.5, and 170 kHz, respectively.  
On the basis of the analysis in Figures 19 and 21, the maximum displacement of the atomizing sheet initially decreases and then increases slowly with the increase in the number of nodal circles. This finding is consistent with the simulation results obtained using MATLAB software.