esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Skeleton-Based Action Recognition  
Skeleton-based action recognition has become a prominent approach for human action recognition due to its efficiency in capturing spatial and temporal features from skeleton data. Unlike pixel-based methods, skeleton-based models focus on the coordinates of key body joints, which makes them less susceptible to variations in appearance and lighting. Recently, deep learning methods, especially graph convolutional networks (GCNs) and Temporal Convolutional Networks (TCNs), have been widely adopted to model skeleton data for action recognition tasks [7; 8]. [PERSON] et al. proposed a Spatial Temporal Graph Convolutional Network (ST-GCN) which captures both spatial and temporal dependencies among joints, setting a foundational approach for skeleton-based action recognition [8]. [PERSON] et al. further extended this approach by incorporating directed graph neural networks to enhance the interpretability and performance of action recognition systems [7]. Another approach proposed by [PERSON] et al. [17] revisits the structure of ST-GCNs and uses optimization strategies that significantly improve recognition accuracy on standard datasets. Progressing from GCNs, CNN-based methods such as the one proposed by [PERSON] et al. have demonstrated efficacy in fusing temporal features through multi-scale convolution operations, which capture motion dynamics effectively [18]. Additionally, RNN-based architectures like those presented by [PERSON] et al. leverage Long Short-Term Memory (LSTM) networks to handle sequential skeleton data, proving effective in modeling temporal dependencies for complex actions [9]. Recent innovations also include Transformer architectures applied to skeleton-based recognition. [PERSON] et al. conducted a comprehensive review on the use of Transformers, emphasizing their ability to capture long-term dependencies more effectively than conventional methods [10]. Furthermore, [PERSON] et al. proposed an unsupervised clustering approach for skeleton-based action recognition, facilitating the model's adaptability to new datasets without extensive labeled data [19].  
These advancements collectively highlight the evolution and potential of deep learning techniques in skeleton-based action recognition. However, challenges such as handling noisy data and improving real-time efficiency remain areas for further exploration [20, 21].
## 3 Results
### MDR and Non-MDR Classification of Gram-Negative Organisms  
The distribution of multidrug-resistant (MDR) and non-MDR isolates fluctuates across pathogens and years-wise also. In 2023, _P. aeruginosa_ exhibited an MDR rate of 36.66% (228/622), whereas 63.34% (394/622) were classified as non-MDR. _K. pneumoniae_ had a higher proportion of MDR isolates at 57.20% (1199/2096), with non-MDR isolates comprising 42.80% (897/2096). _E. cloacae_ in 2023 was represented by a single non-MDR isolate. In contrast, _A. baumannii_ demonstrated a significant MDR burden, with 77.66% (73/94) of isolates identified as MDR and only 22.34% (21/94) non-MDR. In 2024, MDR rates declined for _P. aeruginosa_ to 26.89% (181/673), with a corresponding increase in non-MDR isolates to 73.11% (492/673). _K. pneumoniae_ showed an MDR rate of 50.60% (1020/2016), while non-MDR strains accounted for 49.40% (996/2016). _E. cloacae_ again showed only non-MDR isolates (3/3, 100%). _A. baumannii_ continued to exhibit a high MDR rate at 74.80% (92/123), with non-MDR isolates making up 25.20% (31/123) (Figure S2).
## 2 Corrosion Mechanisms and Simulation Schemes for Corroded Liner
### Simulation Scheme for Corroded Liners  
In previous work, we have carried out a series of experiments to investigate the mechanical behavior of typical containment liners under different degrees of corrosion [24]. Figure 2 presents an overview of tests conducted on corroded liners. The results indicate that the effective cross-section of the liner is reduced due to the generation of corrosion products, which results in a decrease in its ability to resist loads. Simultaneously, the non-uniform corrosion process leads to the formation of numerous corrosion pits on the liner (as shown in Figure 2a), which can easily create stress concentration areas on the liner. The localized damage effect caused by corrosion pits can accelerate the formation and extension of cracks in the uncorroded liner, resulting in a degradation of the mechanical properties of the liner. To accurately simulate the real corrosion conditions, it is imperative to explicitly consider both of these corrosion effects. Consequently, the simulation method for liner corrosion provided in this paper is as follows: Firstly, the thickness of corroded liners is thinned according to the severity of the corrosion. Subsequently, the corresponding stress-strain relationships for liners with different degrees of corrosion are applied to the corrosion-affected area. This comprehensive method ensures a detailed and realistic simulation of the corroded liner.
## 2 System Architecture
### Analog Component
#### 2.2.1 RF Frontend Modules
The measurement test was performed using an LO of 12 GHz and RF signal of 28 GHz. Marker M1 is at the desired IF frequency, 4 GHz (\(28-24\) GHz) and M3 is at the LO frequency signal (12 GHz). The downconverter accomplished a conversion gain of 4 dB, too close to the expected 3 dB. As for the P1 dB at the RF input, \(-7\) dBm was measured, a value well above the expected \(-13\) dBm. The third order intersection point is 4.4 dBm, and the intermodulation ratio is 39.2 dB. As in the upconverter, the results suggest a shift in the central frequency of the mixer, since the results at 25 GHz are better than at 29 GHz. In any case, at the frequency of interest, the results are quite satisfactory and close to what was intended. Figure 6: Measurements of upconverter module: (**a**) RF output spectrum (M1@20 GHz, M2@24 GHz, M3@28 GHz; (**b**) upconverter conversion gain at different RF frequencies. Figure 7: Downconverter designed module: (**a**) block diagram; (**b**) fabricated prototype board.
# A Machine Learning Approach to Algorithm Selection for Exact Computation of Treewidth
## 3 Methodology
### Reflections on the Choice of Machine Learning Model  
First, we note that in addition to Random Forests and decision trees the machine learning literature encompasses a wide range of other classifiers which could be used to select the best algorithm. It is not the purpose of this article to undertake comparative analysis of all these different classifiers in order to identify the _best_ classifier, although this is something that future work could certainly explore. Rather, we wish to demonstrate that, armed with a sensible choice of classifier, algorithm selection can already outperform the original algorithms in isolation. Random Forests are renowned for their predictive power (see e.g., the discussions in References [37; 45]), motivating our choice in this case. For completeness we did also try a Support Vector Machine (SVM) model [46], which attempts to classify instances through the use of separating hyperplanes but due to very weak performance in preliminary experiments we did not explore it further.  
Second, we remark that our current model--selecting the fastest algorithm--is only one of many possibilities for estimating performance. For example, one could try a _regression_-based approach whereby a mathematical model is trained that estimates the actual running time of an algorithm on a given input graph. Then, the (estimated) running times of the three algorithms could be computed and compared, allowing us a more detailed insight into the relative performance of the algorithms. Such an approach certainly has its merits and would also obviate the need for _moderate difficulty_ filtering, which we describe in the next section. However, we defer this to future work, since our simple classification model already proves to be highly effective, even with the aforementioned filtering technicality. Also, there are quite some complexities and choices involved in modelling and learning the running times of sophisticated algorithms, which at this stage would over-complicate the exposition and introduce an extra layer of parameter estimation.  
We return to both these points in the future work section.
# Kinetics and Thermodynamics of Efficient Phosphorus Removal by a Composite Fiber
## 2 Materials and Methods
### Static Adsorption and Phosphorus Removal Experiment of ZSFB Composite Fiber
#### 2.1.5 Influencing Factors  
Experimental influencing factors, such as the dosage of the composite fiber, pH value, type of anion in solution and ammonium concentration were considered.  
* Dosage: 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5 and 5.0 g of ZSFB composite fibers were placed respectively in a 250 mL conical flask, and 100 mL KH\({}_{2}\)PO\({}_{4}\) solution was added with a concentration of 10 mg/L.
* pH level: three groups of KH\({}_{2}\)PO\({}_{4}\) solution with a concentration of 10 mg/L were prepared, and the pH level of the solution of each group was adjusted to 4, 7, and 10 by adding HCl and NaOH solution. Then, 100 mL of three different pH level KH\({}_{2}\)PO\({}_{4}\) solutions were placed into 250 mL conical flasks, and 1 g of the composite fiber was added to each group.
* Anion: 24 groups of KH\({}_{2}\)PO\({}_{4}\) solution with a concentration of 10 mg/L were prepared. By adding Na\({}_{2}\)SO\({}_{4}\), NaCl, NaNO\({}_{3}\) and Na\({}_{2}\)CO\({}_{3}\) solutions, the concentrations of anions (SO\({}_{4}\)\({}^{2-}\), Cl\({}^{-}\), NO\({}_{3}\)\({}^{-}\) and CO\({}_{3}\)\({}^{2-}\)) in solution were adjusted to 0, 2, 4, 6, 8 and 10 mmol/L, respectively. Then, 100 mL six-different-anion-concentration KH\({}_{2}\)PO\({}_{4}\) solutions were taken and placed in 250 mL conical flasks. For each group, 1 g of the composite fiber was added and put into a water bath thermostatic oscillator.
* Ammonium concentration: seven groups of KH\({}_{2}\)PO\({}_{4}\) solution with a concentration of 10 mg/L were prepared, and NH\({}_{4}\)Cl solution was added to each group to adjust the concentration of NH\({}_{4}\)\({}^{+}\) to 0, 100, 200, 300, 400, 500 and 600 mg/L. Then, 100 mL KH\({}_{2}\)PO\({}_{4}\) solution of each group was placed into a 250 mL conical flask, and 1 g of the composite fiber was added to each group.  
The conical flasks were placed in a water bath thermostatic oscillator for 48 h adsorption at 25 \({}^{\circ}\)C with a speed of 150 r/min. After adsorption, the solution was extracted with a syringe, and the concentration of phosphorus was measured by the ammonium molybdate spectrophotometric method [15]. The adsorption capacity and the removal rate were then calculated.
# Communication Transport Protocol Strategies for Rail Applications
## 6 Results
### Results on Rail Procedures
Figure 12: PR/MA over-limit outage vs. network packet losses for a delay of 100 ms.  
Figure 13: PR/MA over-limit percentage vs. network packet losses for 1 TCP bearer and 2 TCP bearers: considered delays are 0, 50, 100, 200 and 400 ms. Figure 14 reports the outage due to PR/MA over-limit when UDP is selected as transport protocol for one bearer (solid lines) and two bearers (dashed lines). Several overall network delays ranging from 0 ms to 400 ms were considered. As expected, UDP presents lower performance with respect to TCP due to lack of retransmissions. As an example, for UDP with one bearer the outage is lower than \(10^{-4}\) when the channel delay is negligible and for packet loss equal to no more than 5%. In contrast to the TCP cases in Figure 13, UDP is more sensitive to using more than one bearer, showing a good improvement in the over-limit performance in cases where two UDP bearers are used. In fact, \(P_{out}=10^{-4}\) is obtained for channel delays of 100 ms and a packet loss of 10%. Figure 15 reports the packet loss of the overall network as a function of the channel delay for several transport protocol strategies. Results in this figure are very useful to the Tunnel Manager in selecting the proper strategy based on the experienced packet losses as well as the measured delay in the network channels in order to respect the requirement \(\xi\) of the rail application with a given outage over-limit \(P_{out}<\phi_{0}\). In Figure 15, an over-limit threshold \(\phi_{0}=0.1\%\) was selected. This means that for \(\phi_{0}=0.1\%\), we have a couple of \(<\textit{packet loss},\textit{delay}>\) values, according to the Figures 12-14 for each transport protocol strategy. For example, in case of a delay of 200 ms, the Tunnel Manager can select UDP and activate two bearers if they experience a packet loss of 7% or can activate a second TCP bearer (thus having one TCP bearer and one UDP bearer) in case it experiences a packet loss of 15%. In case the wireless channel is particularly noisy (e.g., 20%), two TCP bearers should be considered in order to guarantee the over-limit outage of \(\phi_{0}=0.1\%\), even for a channel with 300 ms of delay. Figure 14: PR/MA over-limit percentage vs. network packet losses for one UDP bearer and two UDP bearers: considered delays are 0, 50, 100, 200 and 400 ms.
## 3 Results and Discussion
### Efficiency of Polar and Nonpolar LEDs in the \"Green Gap\"  
Using the theoretical RRC and ARC and experimental SRH coefficient \(A=7\times 10^{5}\) s\({}^{-1}\) being nearly constant in the spectral range of 440-520 nm (see Figure 1), the factor \(Q=B/(AC)^{1/2}\) and then \(IQE_{max}\) were calculated for the polar LED structure versus emission wavelength (Figure 4). The obtained curve fits the experimental points obtained from independent IQE evaluations very well (see [10] for summary of the data) at wavelengths shorter than \(\sim\)540 nm. However, at longer wavelengths, the experimental \(IQE_{max}\) declines much faster than it is predicted by the theory (dotted line in Figure 3 shows this trend). It looks like intensive defect formation starts to occur at about 540 nm, resulting in additional IQE reduction.  
In order to assess the roles of QCSE and carrier localization for the efficiency of LEDs emitting in the \"green gap\" spectral range, one can compare IQEs of polar and nonpolar LEDs, which is possible by using the theoretical RCs plotted in Figure 3. For this, however, it is necessary to properly account for the variation of the SRH coefficient \(A\) with the crystal orientation. The importance of such an account follows, in particular, from time-resolved measurements of carrier dynamics in polar and nonpolar InGaN QWs: the nonpolar QWs exhibit much shorter carrier life times than the polar ones [51].  
Unfortunately, detailed experimental information on the RCs of nonpolar LEDs and their wavelength dependence is not available in the literature. Therefore, the following approach has been applied to compare IQEs of LEDs at different crystal orientations. First, the SRH coefficient \(A\) was chosen for nonpolar LED in such a way as to provide the IQE value of 94.3% at 395 nm, equal to that of the polar LED. Then, the coefficient \(A=4.7\times 10^{6}\) s\({}^{-1}\) obtained in this manner was expanded over the whole spectral range, similar to the case of polar LEDs. Finally, \(Q\)-factor and \(IQE_{max}\) were calculated for nonpolar LEDs, using the estimated SRH coefficient and wavelength-dependent RRC and ARC from Figure 3.  
Despite the vanished built-in polarization field, \(IQE_{max}\) of nonpolar LEDs is found to decrease substantially with the emission wavelength, similar to the case of polar LEDs (Figure 4). Being in line with the experimental behavior reviewed in [29], this result points out the importance of carrier localization for the efficiency of nonpolar LEDs. Breakdown of theoretical IQE losses in the \"green gap\" shown in Figure 4 demonstrates that about 53% of the efficiency decline can be attributed to hole localization, whereas only 29% is related to QCSE; the remaining 18% of the IQE reduction corresponds to variation of the native InGaN parameters, \(E_{g}\), \(E_{p}\), and carrier effective masses, with wavelength. Since composition fluctuations are unavoidable in InGaN-based LEDs of any crystal orientation, the carrier localization seems to be the major mechanism limiting the device efficiency in the \"green gap\".
## 2 Vein Pattern Identification System
### Feature Extraction Process
#### 2.3.2 Local Dorsal Hand Vein Feature Extraction
Curvatures exceeding this threshold are considered as veins. The maximum point of curvature is equivalent to the center of the vein, identified as P\({}_{x}\) in Figure 11e. Points where the curvature crosses the threshold line determine the width of the vein detected in that vicinity, illustrated as W\({}_{x}\) in Figure 11e. Pixels lying within this region are labeled as vein portions, whereas those lying outside this region are classified as non-vein portion pixels. To improve the accuracy of vein detection, this algorithm is repeated for intensity profiles computed in four directions (Figure 9). The resulting images detected from all intensity profiles are then combined to yield the final vein image. Figure 12 demonstrates the vein image detection from four directional intensity profiles computed using the described algorithm. Figure 11: Vein extraction process: (**a**) intensity profile of vein image; (**b**) compute the curvature of the intensity; (**c**) curvature of the intensity; (**d**) compute peak detection and width detection; (**e**) peak and width detection’s position; (**f**) mapping function of (**a**) and (**e**); and (**g**) result of vein extraction. B. Minutia Extraction  
The goal of this feature extraction process is to obtain minutiae from the vein image. The interesting minutiae are the points of vein termination or bifurcation. Minutia extraction is performed by the following steps below. 1. Apply morphological closing to remove possible noise from the vein image, as shown in Figure 13a. 2. Apply morphological thinning to the result from step (i). 3. Apply morphological pruning to the result from step (ii) to remove existing spurs. 4. Apply vein ending and vein bifurcation pattern matching to locate the minutia. This minutia extraction process is illustrated in Figure 13. Example results of this procedure are shown in Figure 13e.
## 2 Background
### Related Work
One set of them is feature selection (FS), which is broadly used to clean up the noisy, redundant, and irrelevant data [20]. The use of FS can improve the accuracy, efficiency, applicability, and understandability of a learning process. For this reason, many methods of automatic FS have been developed. In FS, a subset of features is selected from the original set of features based on feature redundancy and relevance. We can classify feature subsets as four types according to [21], as follows: (1) noisy and irrelevant; (2) redundant and weakly relevant; (3) weakly relevant and non-redundant; (4) strongly relevant. Some of the popular approaches to classify the features in these types are filter methods, wrapper methods, and embedded methods. Filter methods analyze the usefulness of each feature by using relevance techniques, mainly from hypothesis tests or mutual information estimations [22], and they work independently from the used classifier. Wrapper methods, such as forward feature selection and backward feature selection [23], solve ML problems to assess the relevance of each feature in the input space [24]. They use a ranking procedure that allows us to remove low-scoring features. These methods are found to be fast, scalable, computationally simple, and independent of the classifier. Finally, embedded methods, such as recursive feature elimination (RFE) [25], aim to increase their efficiency by combining the FS procedure with training a subsequent learning machine. Many of the embedded methods impose regularization on the solution. Recent works have proposed a novel FS method, known as IVI [13], which is capable of identifying the informative, the redundant, and the noisy variables. It transforms the input-variable space distribution into a coefficient-feature space by using existing linear classifiers or efficient weight generators, and it has been shown to provide improved performance and interpretability compared with classical methods and with the RFE criterion.
# Medical Augmented-Reality Visualizer for Surgical Training and Education in Medicine
## 3 Results
### Angular Accuracy of Phantom Tracking  
To assess the accuracy of angular position estimation for the phantom, we employed a dedicated hardware configuration. Specifically, the phantom was placed on a stack comprising a tilting table and a rotary table. The phantom was positioned flat and attached to the table surface. The starting orientation of the phantom was estimated based on the flat arrangement of its bottom part, such that it was tangent at two points to a vertical line projected by the PrM.  
Each axis of rotation was independently validated in Table 1a-c. Angular tracking about the \(X\) and \(Y\) axes was conducted with a \(2^{\circ}\) resolution, corresponding to the angular spatial resolution of the prerendered positions in the MDB. The average error was \(1.38^{\circ}\) (maximum \(3.0^{\circ}\)) about the \(X\) axis, and \(1.92^{\circ}\) (maximum \(4.0^{\circ}\)) about the \(Y\) axis. We believe that the accuracy of the silicon mold is the most important factor that influences the matching procedure, because the crucial contour detection operation is applied to local changes in shape (Figure 13). The detection of rotation resolution about the \(Z\)-axis is limited by the resolution of the PhTM camera.  
Figure 12: User’s viewpoint and projector remapping of internal structures in the liver phantom.
## 4 Target Model and Frequency Offset Extraction Method
### Summary of Detection Methods  
In summary, the overall process of using 5G signals for rotating target detection can be obtained as shown in Figure 12. When a 5G base station is used for measurement, it is necessary to obtain the configuration parameters of the CSI-RS signal. Firstly, demodulate the 5G signal obtained by the receiving antenna to obtain the resource grid, and then extract the CSI-RS signal based on the CSI-RS configuration parameter to obtain the channel response. The next step is to suppress random phase noise and zero frequency components in the channel response. The signal after interference suppression is subjected to time-frequency analysis, and the target frequency offset is extracted from the results of the time-frequency analysis. After smoothing the frequency offset curve, the rotating target speed is obtained through FFT. The key to the method proposed in this paper is to understand the parameters of CSI-RS signals and obtain the parameters of 5G base stations. If the configuration parameters of 5G base stations cannot be directly queried, other methods need to be found to obtain them.  
Figure 12: Experimental flowchart.  
To make this method more widely used, it is necessary to develop applications that can obtain configuration information of any 5G base station and perform signal demodulation in order to further integrate it with the signal processing method in this paper.