esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
In order to assess the validity and reliability of the new proposed test, measurements were conducted twice, with a 72-h gap between the two. For the dominant leg, the internal consistency (\(\alpha\) Cronbach) was 0.992, and the ICC was 0.984 (\(p\) < 0.001). For the non-dominant leg, the internal consistency was 0.994, and the ICC was 0.978 (\(p\) < 0.001).  
For the dominant leg, Figure 3 presents the Bland-Altman plot [mean = \(-\)0.354, 95% CI (\(-\)3.577, 2.868)]. Only one point was outside the CI, thereby enhancing the validity and the reliability of the new test for the dominant leg.  
For the non-dominant leg, Figure 4 presents the Bland-Altman plot [mean = \(-\)1.268, 95% CI (\(-\)3.959, 1.423)]. Again, only one point was outside the CI, thereby enhancing the validity and reliability of the new test for the non-dominant leg. In addition, test/retest correlations were calculated, indicating a very high correlation for both the dominant and non-dominant leg [r = 0.985 (\(p\) < 0.001); r = 0.988 (\(p\) < 0.001), respectively]. Moreover,  
Figure 3: Bland-Altman plot—U1D, U2D.  
differences between U1D and U2D mean scores, examined through \(t\)-tests, were not found to be significant [t\({}_{21}\) = \(-\)0.101, \(p\) = 0.323], while differences between U1 ND and U2 ND were found to be significant [t\({}_{21}\) = \(-\)4.331, \(p\) < 0.001].  
Table 1 presents mean \(\pm\) SD of the new and standardized explosive power tests conducted in this study. The highest scores achieved in the novel test were U1D = 53.90 cm and U2 ND = 45.50 cm. Table 2 presents strong correlations between the results of the novel test and the standardized tests. The results indicate a high magnitude of correlations (Hopkins) for the new test, with all standardized tests being high (0.5 < r < 0.7), very high (0.7 < r < 0.9), and nearly perfect (r > 0.9). Correlations between U1D/U1 ND and both horizontal tests (5/10 m sprint) were high; correlations between U1D/U1 ND and all CMJ vertical tests were very high. Finally, especially high correlations were seen between the U1D/U1 ND scores and the BP test (r > 0.9) (r = 0.956 and r = 0.933, respectively).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Basketball Players (N = 22)** & **M \(\pm\) SD** \\ \hline
5 m Sprint (s) & 1.08 \(\pm\) 0.07 \\
10 m Sprint (s) & 1.84 \(\pm\) 0.09 \\ BP (m) & 13.2 \(\pm\) 1.73 \\ CMJf (cm) & 43.8 \(\pm\) 8.6 \\ CMJf (cm) & 35.8 \(\pm\) 7.6 \\ CMJDF (cm) & 24.40 \(\pm\) 5.45 \\ CMJDWH (cm) & 19.90\(\pm\) 4.20 \\ CMJNDf (cm) & 23.20 \(\pm\) 5.51 \\ CMJNDWH (cm) & 19.72 \(\pm\) 4.72 \\ U1D (cm) & 38.21 \(\pm\) 9.00 \\ U2D (cm) & 38.56 \(\pm\) 9.41 \\ U1 ND (cm) & 31.55 \(\pm\) 8.95 \\ U2 ND (cm) & 32.82 \(\pm\) 8.73 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of Lower Limb Explosive Power Tests.  
Figure 4: Bland-Altman plot—U1 ND, U2 ND.
## 1 Introduction
The first report of the longitudinal growth of metal silicides in nanowires and the formation of flat and abrupt interfaces to silicon was given by [PERSON] et al. [16] by the example of NiSi\({}_{2}\)-Si nanowire hetero-structures. [PERSON] et al. [45] published a detailed review of several processes to produce transition metal silicide nanowires. The desired silicide phase formation is facilitated by proper control of annealing parameters [46] in an axial hetero-structure. These hetero-structures have been used for FET applications and exploration of innovative device concepts [8; 16; 47; 48]. The silicide diffusion and phase formation in SiNWs has been described in various publications. [PERSON] et al. [29] demonstrated that the longitudinal growth of silicide in SiNWs is a function of the radius or the cross-sectional area of the nanowire and the produced silicide phase. The nature of the Si-silicide interface also influences the growth and the phase of silicide. The most common Ni-silicide phases in SiNWs are Ni\({}_{2}\)Si, NiSi, and NiSi\({}_{2}\). The phase of silicide can be controlled by a proper choice of Ni quantity and quality [27; 39], conditions of the reaction [30], strain in the nanowire [28; 49], and crystal orientation of nanowires [26]. In \(<\)112\(>\) nanowires, the hexagonal \(\theta\)-Ni\({}_{2}\)Si is formed at 300 \({}^{\circ}\)C, whereas this phase is not observed till 800 \({}^{\circ}\)C in bulk structures [47]. In \(<\)111\(>\) nanowires, first the epitaxial NiSi\({}_{2}\) is formed and it withstands temperatures upto 700 \({}^{\circ}\)C. The low-resistive NiSi is formed at 700 \({}^{\circ}\)C [26]. The thermal history of the SiNW influences the silicidation rate. [PERSON] et al. [50] presented a comparative illustration of silicidation rate for SiNWs fabricated by two different processes:The \"doping first\" process, where the annealing for dopant activation is carried out before the NW patterning; and the \"patterning first\" process, where NWs are patterned first and then subjected to further heat treatment for oxidation and dopant activation. They concluded that the silicidation rate in \"patterning first\" approach nanowires is higher than that for the \"doping first\" approach, as the oxidation induced strain in the \"patterning first\" approach is altered and distributed by dopant activation annealing in the later process step. Here we present the results of our investigations on the Ni silicidation of top-down fabricated SiNWs. In the next section, details of the SiNW fabrication and the analysis techniques are given. The initial investigations were carried out on Si thin film structures to identify the process window for SiNW silicidation. In the third section, the results obtained from scanning electron microscopy (SEM) and transmission electron microscopy (TEM) are presented and compared to simulations. Results of electrical characterization of back-gated RFETs are also presented.
# Location Based Indoor and Outdoor Lightweight Activity Recognition System
## 3 Outdoor Activity Recognition System
### Semantic Location Extraction
#### 3.2.1 Semantic Location Extraction Process  
Therefore, once the candidates to point of interests have been selected as it has been explained in Section 3.1, the semantic location extraction process starts. First, a reverse geocoding request is made to Nominatim's API with coordinates of the POI's centroid. However, it only returns the name of the place if the exact coordinates of the circle that represents the place are given (see Figure 12 to see the size of the circle); otherwise, it will return the address of the coordinates. If so, Overpass is used to request the nearby elements that we have considered useful for this task: amenities, shops, buildings, public transport stations, etc. An example of the data that is requested can be seen in the following figure.  
Then, the nearby elements are sorted according to the distance between POI's cluster centroid and the node of each of the retrieved elements, to later choose the nearest one. However, this approach has an issue, each of the places are modeled as a single node or point with a very small surface that does not match their real size. Therefore, it may occur that in some cases where the user has been inside a shop, the extracted centroid is not exactly in the node of the shop or has other places within a similar distance.  
In order to address this issue, we have developed a building partitioning method that distributes the surface of each of the buildings in smaller portions based on their shape, size and number of nodes. Consequently, the retrieved data is processed in order to transform the circle representing the place into an estimation of the geographical space or surface that the place could occupy as it can be seen in Figure 13. Then, the distances between the centroid and the surface of each of the elements are compared.  
Figure 12: Example of the data that can be retrieved from Overpass API.  
Figure 13: Example of how an area of Bilbao (Spain) would look after being partitioned.
## 5 Numerical Model Validation  
### Comparison of Load-Slip Curves  
From the comparison between the simulation curve and the measured curve in Figure 11, it can be seen that the line type and trend of the curve in the elastic bond stage and the elastic-plastic slip stage are consistent with the test results, and the load and displacement values corresponding to the elastic ultimate points of the curve are basically consistent with the test results. The quantitative analysis of the load-slip curve of the simulated specimen is shown in Table 5. The ratio of the test to the simulated value of the ultimate load value of each specimen in the elastic bond stage in the simulation curve is close to 1, indicating that the numerical simulation results can truly reflect the elastic ultimate strength of the specimen.
## 4 Experiment Results and Discussion
### Experiment Results
#### 4.2.1 Experiment 1  
The proposed SFSDT model is implemented on both datasets including NSL-KDD and ISCX. The original feature number of these datasets are 41 and 15, respectively. This model visualizes how much accuracy and error scores obtained for each combination number of features in each dataset.  
In NSL-KDD dataset, the results of SFSDT model are plotted in Figure 9a,b. From observation, the proposed model achieved the highest accuracy 0.969017 at the number of combination features is \(k=12\). Besides, the minimum error score at 12 combined features is 0.00336. Therefore, the best feature subset is selected including 12 features. The corresponding to the list of selected feature subset is [_protocol_type_, _service_, _flag_, _src_bytes_, _logged_in_, _num_file_creations_, _is_guest_login_, _count_, _srv_count_, _dst_host_srv_diff_host_rate_, _dst_host_rerror_rate_, _dst_host_srv_reror_rate_].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date** & **Total of Record** & **No. Training** & **No. Testing** \\ \hline Saturday & 133,194 & 93,236 & 39,958 \\ Sunday & 275,528 & 192,870 & 82,658 \\ Monday & 171,380 & 119,966 & 51,414 \\ Tuesday & 192,041 & 134,429 & 57,612 \\ Wednesday & 182,968 & 128,078 & 54,890 \\ Thursday & 149,625 & 104,738 & 44,887 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of number of record for training and testing data.  
Similar to ISCX dataset, Figures 10 and 11 show the accuracy and error scores of the proposed model for each day dataset. For example, the best feature subset obtained at 3 combined features which are [_appName_, _totalDestinationBytes_, _source_] in Saturday data.  
Figure 10: _Cont._  
Figure 9: Visualization performance of SFSDT model on NSL-KDD dataset. (**a**) accuracy score & (**b**) error score.  
Figure 11: _Cont._  
Figure 10: Visualization accuracy and error scores of SFSDT model on each day of ISCX dataset. (**a**) & (**b**) Saturday data; (**c**) & (**d**) Sunday data; (**e**) & (**f**) Monday data.  
In summary, the results of the selected feature subsets detail on two datasets are shown in Table 2. **No.FF** is the number of features in the original dataset. **No.SF** is the number of the best-selected feature subset by SFSDT algorithm.
## 2 Problem Modeling
### Problem Definition  
To model the confrontation scenario, we assumed that both \(r\) and \(m\) can effectively perceive the spectrum environment and have a common understanding of the game rules. \(r\) needs to transmit signals on multiple channels with a number of \(n\), but faces interference from \(m\), so \(r\) needs to allocate power to the transmission channels to meet the standard of signal transmission (as defined in Equation (1)), and \(m\) allocates the interference power to the transmission channels in an attempt to prevent the signal transmission. Both \(r\) and \(m\) do not know the power allocation strategy adopted by the opponent before taking action, it is a difficult decision to make in this situation. This paper mainly constructs two confrontation scenarios:  
* The first is to assume that \(r\) and \(m\) are completely rational, and they perform power allocation on \(n\) channels with the same/different resource budgets; our goal is to solve the approximate Nash equilibrium of the BG model under a one-shot confrontation scenario;
* The second is to assume that \(m\) is an oblivious opponent that exists widely in the MAB problem [15], the opponent does not have the ability to remember the interaction history of the game played, and the power allocation strategy obeys a fixed distribution. There are multiple confrontations between \(r\) and \(m\). In each round of confrontation, the two parties perform power allocation on \(n\) channels. After a complete confrontation process is over, \(r\) obtains the losses of this confrontation, that is, the value (vector) of the channels successfully interfered with by \(m\). The goal of \(r\) is to achieve the smallest expected regret after multiple rounds of complete confrontation.
# Hyperspectral Super-Resolution Technique Using Histogram Matching and Endmember Optimization
## 3 Proposed Solution
### Overall Algorithm and Implementation  
The method proposed in Section 3.1 proceeds with the following procedure, as described in Table 1. The algorithm requires **H** and **I**, which are low-resolution HSI and high-resolution RGB images, respectively. Because **Z** will be reconstructed using endmember abundance with the same resolution  
Figure 1: Overall framework of the proposed solution for hyperspectral super-resolution when spatial information of an Red-Green-Blue (RGB) image and a hyperspectral image (HSI) is mismatched.  
as **I**, the resolution of **I** has to be an integer multiple of the resolution of **H** with upsampling rate **S**. Additionally, RGB camera sensitivity **C** is required to reconstruct an RGB image from **H**.  
The proposed method begins by constructing a low-resolution RGB image **L** from **H**, using **C**. The camera sensitivity for each RGB channel is multiplied by the spectral information of **H** of each pixel, and normalized into an 8-bit-precision RGB image **L**, for further histogram equalization with **I**. Then, the histogram of **I** is matched with that of **L**, so that **I** has the same color distribution as **L**. The histogram equalization is performed using a MATLAB built-in function [30]. The next step is to find the initial values, **E\({}^{(0)}\)** and **A\({}^{(0)}\)**, to optimize **Z**, which consists of the endmember vector **E** and the per-pixel abundance vector **A**. Simplex identification via split augmented Lagrangian (SISAL) [31] initializes endmember **E** and sparse unmixing by variable splitting and augmented Lagrangian (SUnSAL) [32] initializes **A\({}^{\prime}\)**, respectively. SISAL is an algorithm for unsupervised hyperspectral linear unmixing and finds the minimum volume simplex containing the hyperspectral vectors, by augmented Lagrangian optimizations. SUnSAL is an eigen decomposition-based hyperspectral unmixing algorithm. The MATLAB code for SISAL and SUnSAL is available at the author's webpage [33]. The low-resolution per-pixel abundance **A\({}^{\prime}\)** is upsampled with **S** and will be used as the initial point of the low-resolution step. The optimization is performed with a projected gradient method for 7a. The equation for the projected gradient method is:  
\[\textbf{U}^{q} = \textbf{E}^{q-1}-\frac{1}{c}\Big{(}\textbf{E}^{q-1}\textbf{A}- \textbf{H}\Big{)}\textbf{A}^{\intercal} \tag{8a}\] \[\textbf{E}^{q}=\text{prox}_{\textbf{E}}(\textbf{U}^{q}) \tag{8b}\]  
where \(\text{c}=\gamma_{2}\parallel\textbf{A}\textbf{A}^{\intercal}\parallel_{\text {F}}\) is a nonzero constant and \(\text{prox}_{\textbf{E}}\) is a proximal operator that is constrained to 7b. The optimization procedure is repeated for \(\text{q}=1,\ 2,\ \ldots\) until convergence, or until the pre-determined error rate, calculated by an error metric, is reached.
## 2 Provision of Energy and Macronutrients in Pediatric Patients on PN  
### Energy  
Generally, energy supply should meet the requirements to cover resting energy expenditure, plus those for physical activity, growth, diet-induced thermogenesis, and the correction of pre-existing malnutrition when present [3; 10]. ESPGHAN/ESPEN/ESPR guidelines recommend calculating total parenteral energy requirements of stable patients from the resting energy expenditure, which may be either measured by indirect calorimetry or estimated from [PERSON]'s formula based on gender, age, body weight, and length [11]. Energy intake must cover energy expenditure for physical activity and growth (including catch-up growth) and must be adjusted according to age and disease states [3].  
In clinical practice, PN intake must be adapted to the degree of intestinal insufficiency in pediatric patients. It may be estimated by the level of PN required for normal or catch-up growth. PN dependency index (PNDI) is defined as the ratio of non-protein-energy intake (NPEI) provided by PN for achieving optimal growth to the resting energy expenditure (REE). REE is calculated by using the [PERSON] formulas. The PNDI has been validated from several papers [12; 13; 14; 15]. In clinical practice, the PNDI corresponds to the caloric requirements from PN to achieve normal growth. It reflects the degree of intestinal insufficiency more accurately than other criteria, such as the ratio of parenteral versus enteral intakes, or the addition of PN and enteral caloric intake when compared to recommended dietary allowances. The PNDI for estimating the PN requirements for optimal growth considers the final nutritional efficiency. Based on the PNDI expressed as a percentage, it can be considered high when \(>\)120%, and even higher for achieving catch-up growth, and low when \(<\)50% [12; 13; 14; 15].  
Excess energy intake may increase the risk of hyperglycemia, infections, and fat deposition (fat body mass and liver steatosis). Underfeeding may result in failure to thrive, poor development, and impaired immune responses, increasing the risk of morbidity and mortality in newborn infants, infants/toddlers, and children [3]. In acute critical illness, energy provision should be reduced compared to stable patients, while overfeeding (glucose) increases ventilatory workload and potentially prolongs the need for mechanical ventilation and the length of hospital stay [3; 16]. During the stable phase of critical illness, requirements are approximately 1.3 times the resting energy expenditure and further increase in the recovery phase for catch-up growth [3]. Therefore, energy provision must be carefully managed to ensure that energy needs are met but not exceeded.
## 5 Simulation and Discussion
### Reconstruction Performances under Different Temporal Compression Ratios
As Figure 8 demonstrates, the proposed algorithm performs significantly better than GAP + b and GAP-acc + TV in terms of PSNR when the compression ratio is less than 24. It is robust enough to be applied under available compression ratios for video CS. Figure 7: Comparison of reconstruction results of traffic datasets with compression ratio range from 4 to 24: (**a**) peak-to-peak signal-to-noise ratio (PSNR) for reconstructed frame of Traffic dataset under different reconstructed ratio; (**b**) reconstruction error for reconstructed frame of Traffic dataset under different reconstructed ratio. Figure 8: Comparison of reconstruction results of different datasets: (**a**) PSNR for each reconstructed frames of Traffic dataset (\(N_{f}=8\)); (**b**) PSNR for each reconstructed frames of Traffic blink dataset (\(N_{f}=22\)); (**c**) reconstruction error for each reconstructed frames of Traffic dataset (\(N_{f}=8\)); (**d**) reconstruction error for each reconstructed frames of Traffic blink dataset (\(N_{f}=22\)).
## 3 Experiments
### Experimental Results
#### 3.5.5 Visualization Results  
To more clearly illustrate the advantages of HoRFNet in BCHI classification, this study presents a set of representative examples: images that are misclassified by the backbone model but correctly identified by HoRFNet. These examples are shown in Figure 9. Upon inspection, these histopathological slide images often contain highly complex and subtly varying texture patterns, which challenge the feature extraction capability of the backbone model and may result in misclassification. In contrast, HoRFNet improves the ability to capture fine-grained information by introducing second-order statistical features and a receptive field enhancement module, enabling stronger discriminative power when processing complex images. This structural optimization significantly enhances recognition accuracy, further demonstrating the effectiveness of HoRFNet in BCHI classification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{5}{c}{**100\(\times\)**} \\ \cline{2-6}  & **Backbone** & **SeONet** & **RFNet** & **EfficientNet-Lite** & **HoRFNet** \\ \hline Parameter count (M) & 396.98 & 101.77 & 431.58 & 81.64 & 101.72 \\ Training time (min) & 64.00 & 39.00 & 71.00 & 198.00 & 41.00 \\ Inference speed (s) & 3.28 & 7.55 & 4.40 & 26.40 & 7.80 \\ FLOPs (G) & 3.45 & 3.50 & 3.54 & 6.80 & 3.57 \\ Accuracy (\%) & 91.20 & 95.84 & 94.56 & 92.96 & 98.56 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of computational efficiency and resource consumption of different models at 100\(\times\) magnification.  
Figure 9: Selected sample images from 40\(\times\), 100\(\times\), 200\(\times\), and 400\(\times\) datasets. The backbone model cannot effectively identify these slices of breast cancer histopathological image, in contrast, HoRFNet correctly classifies them. In the first row are tissue samples from benign cases, whereas the second row illustrates sections associated with malignant tumors.  
In addition, we provide visualizations of model attention regions under different magnification levels to further evaluate the discriminative capability of HoRFNet. As shown in Figure 10, HoRFNet (second row) consistently exhibits more concentrated high-response regions across all magnifications, clearly outperforming the dispersed activation maps produced by the Backbone (first row). At 40\(\times\) and 100\(\times\), HoRFNet effectively focuses on key tissue areas with pathological significance, whereas the Backbone shows substantial background interference and lacks precise region localization. At 200\(\times\) and 400\(\times\), HoRFNet demonstrates superior sensitivity to fine-grained structures, accurately capturing edges and detailed regions, while the Backbone displays blurred boundaries and unstable attention patterns. These results indicate that HoRFNet has stronger multi-scale feature extraction capabilities, maintaining stable and accurate discriminative performance across varying resolutions.