esa-sceva/satcom-chunk-collection huggingface dataset:
# Trend Forecasting in Swimming World Records and in the Age of World Record Holders
###### Abstract  
This study aimed to forecast trends in swimming world records (WRs) and in the age of record holders. A total of 566 individual freestyle WRs (290 for males and 276 for females) were retrieved from open access websites. The frequency of observations in WRs in each decade and event was computed for males and females. The swimmers' chronological age was converted into decimal age at the time of breaking the world record. ARIMA forecasting models and exponential smoothing techniques were used to examine historical trends and predict future observations. The WRs improved over time, and there was a nuanced pattern in the age of world record holders. While certain events (50 m and 100 m) showed swimmers achieving records at older ages, others (e.g., 200 m, 400 m, 800 m, and 1500 m) displayed variations. Forecasting shows a continuing improvement in WRs in the upcoming years, with the age of male world record holders stabilizing in shorter events and decreasing in longer distance ones, while for females, general stabilization should be expected for the majority of competitive events.  
swimmers; performance; decades; prediction; freestyle Article
## 3 Evaluation of Experimental Results
### Micrographic Examinations
Another method to reduce the risk of cracking in the deposited layer is based on inducing compressive stresses in the deposited layer by welding on a pre-tensioned surface. Figure 7 confirms the high capacity of the deposited metal for work hardening. On one hand, fragmentation of the dendritic structure is observed, while on the other hand, the appearance of twins and \(\epsilon\)-martensite is noted. Additionally, the precipitation phenomena of fine carbide particles intensify, exhibiting a uniform distribution in the deposited metal zone, (DM), Figure 5, particularly near its interface with the base metal (BM), Figure 4, and oriented towards the direction of \(\epsilon\)-martensite or twins, in other regions of the DM. These microstructural observations are in full agreement with the results of other research studies [18; 19; 20; 21; 22; 23; 24]. In the microstructure of work hardened deposited metal, alongside the base matrix, which is austenite, a certain proportion of martensite \(\epsilon\) is observed. This phase (white needles) is located along the slip lines and especially at their intersections. The \(\epsilon\) phase is a known microstructure from the Fe-Mn binary alloy system, for concentrations of 12...30% Mn, and it can transform through long-term heat treatment or cold deformation into the \(\alpha\) phase [18; 19; 23; 24].
###### Abstract  
We consider the direction of arrival (DOA) estimation of the frequency hopping (FH) signal. The frequency hopping (FH) signal has been widely used for communication to control UAVs. Since the frequency of the FH signal is continuously changing, a mismatch may occur between the actual frequency of the received signal and the nominal frequency of the array manifold. In this paper, the azimuth and elevation estimation error in DOA estimation due to frequency mismatch are analytically derived. It is shown that the azimuth error is equal to zero and that elevation error depends on true elevation angle of the incident signal, rather than the true azimuth angle of the incident signal. The elevation error is also dependent on the actual frequency and the nominal frequency.  
frequency hopping; analytic error; interferometer; performance analysis; direction of arrival (DOA) estimation +
Footnote †: journal: Applied Sciences
## 4 Conclusions  
A centrifugal spinning method was used to prepare a PU/PAN lithium-ion battery diaphragm by blending with different ratios of PAN. The properties of the PU/PAN lithium-ion battery diaphragms were characterized in this study. The results showed that the fiber films obtained at the blending ratios of 18% PU/PAN = 8:2 and 18% PU/PAN = 7:3 had a good 3D mesh structure. When 18% PU:PAN = 7:3, the PU/PAN composite fiber film had the most uniform diameter distribution, the porosity was 83.9%, the liquid absorption rate was 493%, and the thermal stability also displayed a corresponding increase with the increasing PAN content. When 18% PU:PAN = 7:3, the ionic conductivity of the fiber diaphragm increased to 1.79 mS/cm and the electrochemical stability window increased to 5.2 V, with a higher ionic conductivity and wider electrochemical stability window than the commercial Celgard 2400 diaphragm. In lithium-ion batteries assembled with LiFePO\({}_{4}\) as the cathode material, after 50 cycles at 0.2 C rate, the discharge specific capacity of the PU/PAN lithium-ion battery diaphragm could still be maintained at 147.1 mAh/g, with a capacity retention rate of 95.8%. Additionally, a high discharge specific capacity was maintained under different multiplier conditions, with an excellent charge/discharge cycle stability and multiplier performance. The application of PU and PAN blending can improve the performance of a single PU matrix, and the resulting membrane can be used as the preferred material for high performance lithium-ion battery separators.  
Meanwhile, the preparation method of PU/PAN nanofiber diaphragm based on centrifugal spinning method proposed in this paper is characterized by high preparation efficiency and suitable for industrial production; further optimization of co-blending formula and process parameters for different polymer materials and different nanofiber membrane performance requirements is a direction worthy of in-depth research in the future, which is of great significance for industrial production of nanofiber membrane.  
Methodology, L.C.; Formal analysis, Y.W.; Writing--original draft, T.L.; Writing--review & editing, S.M. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Battery multiplier performance of commercial Celgard 2400 and PU/PAN fiber diaphragms.  
**Funding:** This work was supported by The scientific research plan of the national high-end textile equipment research base (111 HTE2022002) and The National Science Foundation of China of China (No. 51175385).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
Various ML algorithms, e.g., linear discriminant analysis (LDA), artificial neural network (ANN), and support vector machine (SVM), are being explored for the detection, classification, and characterization of neuroimaging data, e.g., EEG [31]. For instance, [PERSON] et al. classified single-trial movement intentions associated with different hand grasp types using the EEG spectra as input features to an LDA model [33]. [PERSON] et al. distinguished correct/error feedbacks during hand and foot movements by feeding the EEG waveform features into an ANN model [34]. Limited classification accuracies were obtained in both studies: 41-86% [33; 34]. This may be attributed to insufficiencies in feature mapping by simple linear transformation of the LDA, leading to inefficient construction of the optimal decision function (classification boundary) for multichannel EEG [35; 36]. Although ANN-based models offer nonlinear feature mapping abilities during classifications, overfitting often occurs when there are several hyperparameters, e.g., numbers of hidden layers and nodes, to be determined during network optimization [36]. In contrast to the ANN, SVM-based models reduce the disadvantages of overfitting of the classification results with the help of kernel functions [37]. SVM with kernel functions effectively minimize model complexities via implicitly realizing nonlinear transformations of the feature spaces without explicit mathematical expressions, so that only specific hyperparameters related to the kernel functions of the SVM need to be optimized during model development [38]. In the SVM-ML models, several kernel functions are commonly used, namely linear, polynomial, and radial basis function (RBF)kernels. For example, [PERSON] et al. extracted the spectrum features from subject-related EEG frequency bands and channels, and the SVM with linear kernel was applied to each subject's EEG-based motor imagery classification [39]. [PERSON] et al. investigated the classification performance of SVM with a polynomial kernel in multiclass motor imagery EEG [40]. [PERSON] et al. used SVM with RBF kernel to classify the EEGs of imagined hand movements [41]. These studies reported a classification range of 67-92.8% [39; 40; 41]. Among the practical applications of the kernel functions mentioned above, the RBF kernel is a common choice in SVM-ML models because of its better performance on the nonlinearities in feature mapping capabilities with less hyperparameters compared to the other two types of kernel functions [42; 43]. Automatic evaluations of neuroimaging data by SVM-ML techniques have not been fully explored in literature, e.g., fine tactile sensation. [PERSON] et al. extracted the powers of the alpha and gamma bands as features representing EEG during touch with different objects, i.e., fabric, glass, and paper [44]. However, they only evaluated the tactile perception of unimpaired persons and obtained limited classification performance (68.1%) with the LDA model [44]. The purpose of this study was to automatically evaluate and assess post-stroke impairments in fine tactile sensation using a new EEG-based SVM-ML model.
## 3 Beamformer Design  
Here, we propose two schemes for the alternating optimization of \(\mathbf{w}\) and \(\mathbf{\Phi}\), maximizing the metric in (3). We begin by recalling the SDR-based optimal beamforming for the multicasting system, which corresponds to the optimization of \(\mathbf{w}\) with a fixed \(\mathbf{\Phi}\) in (3). Defining  
\[\mathbf{\tilde{g}}_{k}^{T}\doteq[\mathbf{g}_{k}^{T}+\mathbf{h}_{k}^{T} \mathbf{\Phi}\mathbf{H}_{0}]\]  
and \(\mathbf{Q}_{k}=\mathbf{\tilde{g}}_{k}^{*}\mathbf{\tilde{g}}_{k}^{T}\), (3) turns into  
\[(\mathbf{w})=\min\mathbf{Tr}[\mathbf{w}\mathbf{w}^{H}],\] \[\mathbf{Tr}[\mathbf{w}\mathbf{w}^{H}\mathbf{Q}_{k}P]\geq\gamma, \ \text{for}\ k\in\{1,\ldots,K\}, \tag{4}\]  
where the SDR-based solution has been well-known for many years [1; 2] with the introduction of semi-definite matrix \(\mathbf{X}\) in place of \(\mathbf{w}\mathbf{w}^{H}\) and following the rank-reduction process. Here, \(\gamma\) can be adjusted to make the magnitude of found \(\mathbf{w}\) to one. Since the SDR approaches of (4) require a demanding amount of computation, an alternative method with reduced complexity is proposed as the second scheme, which utilizes the least squares (LS) rule to equalize the gains of all user channels.  
Then, two algorithms to determine the IRS phase shifters are proposed such that we create two sets of algorithms for finding the AP pre-coder \(\mathbf{w}\) and for finding the IRS phase shift matrix \(\mathbf{\Phi}\), respectively. Overall, four optimizations can be conducted in an alternating manner between the SDR method or the LS method and Algorithm 2 or Algorithm 3 to be presented for the IRS phase adjustment. Here, the alternation starts either with the SDR method or with LS with a \(\mathbf{\Phi}\) whose diagonal elements have random phases. The SDR method and the LS method produce unit magnitude AP pre-coders \(\mathbf{w}\), while Algorithm 2 and Algorithm 3 output the IRS phase shift matrices \(\mathbf{\Phi}\), where each of two methods in a combination rely on the results of the other method in the same combination.
# Photonic Crystal Cavity with a Thin Low-Index Layer for Silicon-Compatible Nanolight Source
## 3 Dispersion Curves of Slot Slab Waveguide
The dispersion curve of an infinitely thick Si waveguide mode and light line are indicated by dotted black and blue lines, respectively; (**b**) Effective index of Si slab waveguide mode as a function of slab height (\(H\)) for the wavelength of 1550 nm. estimating the ratio of the entire electric field density and slot area density. This confinement factor represents the density of the fields in the slot area [18; 19]. \[\Gamma_{z}=\frac{\int_{\text{slot}}\lvert E\rvert^{2}dV}{\int\lvert E\rvert^{2} dV}=\frac{\int_{\text{slot}}\lvert E_{z}\rvert^{2}dV}{\int\lvert E_{z}\rvert^{2}dV} \tag{1}\]  
\[g_{\text{mod}}\approx\Gamma_{z}g_{i} \tag{2}\]  
where \(g_{\text{mod}}\) and \(g_{i}\) are the modal gain and bulk material gain, respectively. Figure 3d shows the confinement factor (black) and the normalized confinement factor (blue) as functions of horizontal slot thickness. The maximum confinement factor of 0.25 is found at the slot thickness 20 nm. However, the normalized confinement factor, which is defined as the ratio of the confinement factor and the slot thickness, is a maximum at a slot thickness of 10 nm. The normalized confinement factor means the averaged electric field intensity over the slot, where the confinement factors are normalized by the whole electric field. In general, because the rare-earth emitter is randomly distributed over the low-index layer, the normalized confinement factor can guarantee better performance as a light-emitting diode for the suggested structure. Depending on the applications, the structure that has a high confinement factor or normalized confinement factor can be chosen. In the rest of this paper, we use a horizontal slot thickness of 10 nm at a fixed rod height of 1000 nm for the high confinement of the cavity mode in the horizontal slot, which will be used as gain materials, including rare-earth dopants.
## 2 Materials and Methods
### Procedure  
Participants performed a standardized warm-up, followed by two to ten sprints, depending on the participant (41 sprints in total). They performed 30 m sprints on a soccer pitch (along the midfield line), with 3 min recovery between each sprint. No specific signal was given for the start. They started from a standardized standing position and had to reach their maximum speed as quickly as possible. This 30 m distance allowed all subjects to reach their maximum speed, as the data showed that all the subjects reached a speed plateau within 30 m. The weather conditions were good, with no wind. The measurements were taken in the afternoon, and the subjects wore their own football shoes.  
During these sprints, measurements were taken using two GPS (K-Sport(r)) devices named \"GPS 1\" and \"GPS 2\", respectively, with a sampling frequency of 50 Hz and connection to four satellite constellation systems (Galileo, GPS, BeiDou and Glonass), and using a radar (Stalker(r) Pro II Sports Radar Gun, Plano, TX, USA) with a sampling frequency of 46.875 Hz. This device was placed on a tripod (one meter above the ground) approximately 5 m behind the participants. The two GPS devices were placed on the upper back of the participants with enough space between them to ensure that the systems did not interfere with each other [14]. On average for both GPS, horizontal dilution of precision was 0.83 \(\pm\) 0.11, and the average number of satellites connected during the session was 16, indicating a very good signal quality [15].  
Speed and time data measured by radar and GPS were analyzed in an Excel spreadsheet based on [PERSON] et al.'s calculation method [3; 4; 5]. Processing using this Excel spreadsheet allowed us to compute the variables required to compare the two GPS units and the two measurement devices (GPS and radar). Figure 1 shows a schematic diagram of raw data processing.  
To test the reliability between the two GPS units, the measurements from each device were compared. The GPS and the radar measurements were then compared to check the concurrent validity of the GPS device for computing a force-velocity profile. Finally, to test inter-trial reliability, the first two trials were compared for each participant. The data compared were the main input (V\({}_{\text{max}}\) and \(\tau\)) and output variables (P\({}_{\text{max}}\), F\({}_{0}\) and V\({}_{0}\)) of the model. The measured data were all processed by the same person, with the same calculation process. For each sprint, the raw running speed data were recorded via radar and GPS, and then adjusted with an exponential equation [3; 4; 5]. Means and standard deviations were calculated for all variables (V\({}_{\text{max}}\), \(\tau\), F\({}_{0}\), V\({}_{0}\), P\({}_{\text{max}}\)).
## 4 Discussion
In fact, one of the limitations of this work is related to the difference in IFM between the cell load and that of bone fragments, i.e., the bone callus. The IFM of bone callus is not limited to the axial displacement, there are three other movements which include: transverse, angular, and axial rotation (torsional shear) [18]. In addition, the natural weight-bearing loads include bending and shear (transverse and torsional). Actually, the natural loading is very complex and difficult to simulate. Moreover, it would require considerable resources to validate an experimental and/or numerical model that were able to predict important parameters. Nevertheless, because the generality of studies concerning the fracture healing process has been focus on the compression load [28; 35; 38; 41; 42] and the main goal of this work was to investigate the viability of the LRS fixator in the dynamic compression mode on different configurations, we were interested in relative rather than absolute results. Another limitation of this work is associated with the use of a nylon bar instead of a realistic geometry of the tibia. Nowadays, there is a great number of published works wherein realistic CAD geometries of femur and tibia have been generated using the computed tomography (CT) imaging technique [43; 44; 45; 46]. Despite the excellent quality of these results, they are subject-specific finite element studies and, because strain variability for cadaveric specimens is larger than 100% [47], their use in test specimens concerning the external fixator configurations would require a sample of several hundred specimens. Moreover, as in any other FE model, reliable subject-specific numerical models need to ensure that modeling assumptions reflect the in vivo environment. Hence, considering the low availability of donors and the large variability of human anthropometry and material properties, it seems that synthetic bones could be an attractive alternative to this study [22]. Nevertheless, it would be interesting to develop subject-specific image-based FE models for all healing phases and try to establish a relationship between the experimental and numerical displacements of the free clamp, in order to obtain comparative results that could contribute to the evaluation of the consolidation phase and help decide when the clamp can be unlocked. Yet, these results should not be used in a quantitative way, but to compare the patient outcome between two different moments of consolidation phase. In addition, this procedure can be used on other patients and avoid exposure to additional X-ray radiation for consolidation level information. The findings of this work cannot be applied blindly to address issues concerning the external fixation of fractured tibia, for instance the sensitivity values of load share ratios to the distance between the external fixator body and the bone. In fact, this ratio is affected not only by the distance between mentioned axes but also by the nylon stiffness, and therefore different stiffness values in real bones are expected. Nevertheless, the trends identified in this work could remain valid on real bones.
## 4 Numerical Results  
In this section, we compare the proposed RU-EPD scheme against the baseline C-EPD scheme using both simulated and experimental data. We first evaluate the pipe diameter estimation and robot pose estimation performance based on the simulation data. Then, we evaluate the performance with the real experimental data collected in 20\({}^{\prime\prime}\) pipelines.
## 6 Strengths, Limitations, and Future Research Directions
### Limitations
#### 6.2.2 Robustness  
CNNs may struggle to capture long-range contextual information, as they focus primarily on local feature extraction, which may limit the ability to understand global context, leading to reduced robustness in tasks that require global context, such as scene understanding, image captioning or fine-grained recognition.  
Both architectures can be prone to overfitting, especially when the training data is limited or noisy, which can lead to reduced robustness to input data outside the training distribution. Adversarial attacks can also pose a challenge to the robustness of both architectures. In particular, ViTs do not have an inherent spatial inductive bias like CNNs, which are specifically designed to exploit the spatial locality of images. This can make them more vulnerable to certain types of adversarial attacks that rely on spatial information, such as spatially transformed adversarial examples.
## 2 Materials and Methods  
The FID signal consists of both real and imaginary terms corresponding to the \(x\) and \(y\) components of rotating magnetization, which is perpendicular to the magnetization vector in the z-axis [20]. The FID is a detectable NMR signal that generates a process known as Larmor precession [21]. The complex notation is used to represent the FID signal due to its suitability for further mathematical operations.  
The algorithm consists of two main steps: preprocessing and modeling. Preprocessing steps include zero-filling, signal smoothing, and noise filtering [22], eddy current correction [23], water peak removal, phase correction, and baseline correction. The modeling steps include decomposition, clustering, peak matching, and quantification. Truncated HSVD is utilized to decompose the FID signal in time domain into single tone FIDs, and each tone generates a single peak in the frequency domain. K-means clustering with initialized centroids was employed to cluster the single peaks as the metabolites basis sets. The diagram of the proposed approach is shown in the Figure 1 and the details of each step are described in the following sections.
## 2 Optimisation Techniques in Reinforcement Learning
### Reinforcement Learning (RL)
#### 2.1.2 Hyperparameters in Deep Q-Learning  
Hyperparameters are manually set parameters that significantly influence the performance of machine learning algorithms. In DQN, the most important hyperparameters include the following:  
* Gamma (\(\gamma\)): The discount factor, \(\gamma\in[0,1]\), determines the importance of future rewards. A value close to 0 focuses on immediate rewards, while a value close to 1 emphasises long-term planning. However, \(\gamma=1\) may lead to divergence in environments with infinite episodes [14; 15; 16].
* Exploration Rate (\(\epsilon\)): The \(\epsilon\)-greedy strategy balances exploration and exploitation. The agent explores with probability \(\epsilon\) and exploits with probability \(1-\epsilon\). In practice, \(\epsilon\) is often decayed over time (\(\epsilon\)-soft) to encourage exploration early and exploitation later [14; 15; 17].
* Learning Rate (\(\alpha\)): Controls the impact of new information on Q-value updates. A low \(\alpha\) slows learning, while a high \(\alpha\) may lead to instability by excessively overriding past estimates [14; 15; 18].  
* Replay Buffer Size: Determines the number of past experiences stored for training. A small buffer speeds up training but may lead to highly correlated updates, while a large buffer improves generalisation but increases memory usage and slows learning [11; 19].
* Target Network Update Frequency: Controls how often the target network's weights are updated. Frequent updates improve adaptability but can destabilise training, while infrequent updates maintain stability at the cost of slower adaptation [14; 15; 16].
* Neural Network Architecture: Includes hyperparameters such as the number of layers, neurons, batch size, activation functions, and optimisers. These influence the Q-function approximation and overall training efficiency [14; 16].