esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Combination of DFT Spreading and PTS  
As shown in Figure 4, the PTS operation is performed separately at the FBMC in-phase and quadrature parts. After multiplying the real and imaginary parts with the phase patterns (8) and (9), the output sequence is divided into V adjacent sub-blocks, which are then zero-padded to create vectors of length N. The vectors are oversampled with (L-1)N zeros to achieve a better PAPR representation, that mimics the continuous-time FBMC/OQAM signal. L represents the oversampling factor and it is set to L \(\geq\) 4. The optimization process is performed consecutively for each part as follows:  
1. In the simulations, we selected W = {\(-\)1,1} = 2 as a phase factor because a higher value than W = 2 complicates the calculations [9]; therefore, the total number of candidates is 2\({}^{V}\).
2. After creating the PTS sequences, the output is copied K times and filtered with the PPN filter.
3. The PAPR of the filtered sequences is calculated, and the PAPR equations for each part are as follows: \[\mathrm{PAPR_{r}}=\frac{\max\limits_{(\mathrm{m}-1)\mathrm{T}+1\leq\mathrm{L} \leq\mathrm{L}\leq\mathrm{L}\leq\mathrm{L}}\left|\mathrm{real}(\mathrm{s}( \mathrm{t}))\right|^{2}}{\mathrm{E}\left[\left|\mathrm{s}(\mathrm{t})\right|^{ 2}\right]}\] (13)  
Figure 3: PTS (partial transmit sequence) scheme at the transmitter for OFDM (orthogonal frequency division multiplexing).  
\[\text{PAPR}_{\text{i}}=\frac{\max\limits_{(\text{m}-1/2)\text{T}+1\leq\text{t}\leq \left(\text{k}+\text{m}+3/2\right)\text{T}}\left|\text{imag}(\text{s}(\text{t}) )\right|^{2}}{\text{E}\left[\left|\text{s}(\text{t})\right|^{2}\right]} \tag{14}\]  
where \(\text{PAPR}_{\text{r}}\) represents the PAPR of the real part during the symbol duration \((\text{m}-1)\text{T}+1\leq\text{t}\leq(\text{k}+\text{m}+1)\text{T}\), and \(\text{PAPR}_{\text{i}}\) represents the PAPR of the imaginary part with symbol duration \((\text{m}-1/2)\text{T}+1\leq\text{t}\leq(\text{k}+\text{m}+3/2)\text{T}\).  
It can be clearly observed from Equations (13) and (14) that while calculating the PAPR, the effect of the previous overlapped parts is also considered.  
4. The selection of the optimum phase factors is as follows:  
\[\left\{\widetilde{\text{b}^{\text{T},}},\widetilde{\text{b}^{\text{T},}}, \ldots\widetilde{\text{b}^{\text{V},}}\right\}=\text{arg}\min\limits_{[\text {b}^{\text{T},}\,,\,\text{m},\,\text{b}^{\text{V}}](\text{m}-1)\text{T}+1\leq \text{t}\leq(\text{k}+\text{m}+1)\text{T}}\left|\sum\limits_{\
u=1}^{\text{ V}}\text{s}_{\text{m}}^{\
u,\text{t}}\text{b}^{\
u,\text{r}}\right|^{2} \tag{15}\]  
\[\left\{\widetilde{\text{b}^{\text{T},}},\widetilde{\text{b}^{\text{T},}}, \ldots\widetilde{\text{b}^{\text{V},}}\right\}=\text{arg}\min\limits_{[\text {b}^{\text{T},}\,,\,\text{m},\,\text{b}^{\text{V}}](\text{m}-1/2)\text{T}+1 \leq\text{t}\leq(\text{k}+\text{m}+3/2)\text{T}}\left|\sum\limits_{\
u=1}^{ \text{V}}\text{s}_{\text{m}}^{\
u,\text{t}}\text{b}^{\
u,\text{i}}\right|^{2} \tag{16}\]
5. After choosing the candidate with the least PAPR, the real and imaginary parts are combined after staggering the later by T/2.
# Hybrid Crow Search Algorithm-LSTM System for Enhanced Stock Price Forecasting
## 4 Results
### Comparisons of Result Obtained by Varying Time Steps  
To determine the optimal time step, an analysis was conducted by varying the time steps of each of the five stocks to 3, 4, 5, 7, 10, and 30 days. The performance was evaluated using RMSE, MSE, and MAE. As smaller values for all three indicators indicated that the predicted values aligned better with the actual ones, we aimed to minimize them. The results are listed in Table 5, The bold numbers represent the best values, which indicates that Stocks 2330, 2454, and 1216 with a time-step of 3 days, Stock 0050 with a time-step of 4 days, and Stock 2882 with a time-step of 30 days exhibited the best results. Additionally, although Stocks 0050 and 2882 did not produce the best results with a time-step of 3 days, the results were comparable. Considering the RMSE value, Stock 0050 exhibited a difference of only 0.005 between the time steps of 4 and 3 days, whereas Stock 2882 indicated a difference of only 0.032 between the time steps of 30 and 3 days. Here, we can see that these stocks show slightly better performance as the number of days reduces. Although 2882 performs the best with the 30-day period, it appears to be an outlier. We can clarify their trend by considering the average performance (time-step performance). Based on a comprehensive evaluation of the average performance with different time steps, a time step of 3 days was selected as the optimal time step.
# 3D U-Net for Skull Stripping in Brain MRI
## 1 Introduction
Despite the immense variety of algorithms and techniques published on skull stripping, there is no consensus among the research community regarding the best method, due to obscure brain boundaries, low-contrast brain MRIs, and the absence of an intensity standardization. In this paper, we propose the use of 3D U-Net [44], a deep network architecture designed for semantic segmentation, for skull-stripping in brain MRIs. 3D-UNet has been recently proposed and widely used for volumetric segmentation. As 2D-UNet cannot fully utilize 3D spatial information, the resulting image for the third axis is not good if the 2D-UNet is used for a 3D problem [44]. Therefore, 3D-UNet is more appropriate for skull stripping than 2D-UNet. The architecture of 3D-UNet comprises a contracting path, an expanding path, and precise localization for the good use of features in multiscale. Thus, it integrates localization and context information. 3D-UNet also uses concatenation to combine the features at high resolution with the up-sampled output to obtain local characteristics. We describe how to implement the 3D-UNet for skull-stripping in brain MRI, and how to get sufficient data for training. Then, we applied it to real publicly available brain MRI datasets and compared the brain extraction results with the three state-of-the-art widely used skull-stripping methods. Two are from classical approaches (BSE and ROBEX) and one is a CNN-based method ([PERSON]'s method) for evaluation.
## 3 Results and Discussion
### Morphological Analysis
Series _Atramentosa_ is characterized by moderately fast-growing colonies, brown reverse color on CYA and YES, velvety colony textures, predominantly terverticillate conidiophores with smooth-walled globose to subglobose or (broadly) ellipsoidal conidia and good growth on CREA, without acid production [9; 11; 29]. Nonetheless, morphological and phenotypic variation for this species has been reported in the pass [30]. Accordingly, the isolates studied exhibited light brown reverse colors on CYA [9; 29]. However, on YES, the studied isolates are reverse dull white, while per the _P. mexicanum_ type description, they are commonly dull yellow/olive [9; 29]. Moreover, the studied isolates presented somewhat relatively smaller conidia sizes (3-4 \(\times\) 3-3.5 um in _P. mexicanum_ type). In addition, the studied isolates differ from the typical displayed _P. mexicanum_ characteristics by the strong production of hyaline exudates on DG-18 and the presence of chlamdospores. So far, _Penicillium mexicanum_ has been detected in house dust, air and intertidal zones (mudflats and sands) [29; 30; 59; 60], with the results obtained during the course of this work pointing (to the best of our knowledge) for its first detection in Portugal and in soil samples from mining areas. Substrates such as mines, mining areas and acid mine drainage (AMD) ecosystems have been poorly studied so far, at least from a mycological perspective. They require additional focus, since underexplored substrates and extreme environments have been recently highlighted to contain unexpected high degrees of fungal diversity [61; 62]. _Penicillia_ ability to withstand extreme environments is linked to their halophilic/halotolerant, osmophilic/osmotolerant and xerophilic/zerotolerant characteristics [62]. When considering gold mines, arsenic resistance has also been highlighted as a key metabolic feature contributing towards _Penicillium_ successful survival at these sites [4]. This is a result of arsenic being frequently associated with gold extraction, being a common metalloid polluting gold mining areas. Further studies focusing on _Penicillia_ and related genera from mining soils and related substrates are crucial as these fungi can also hold additional biotechnological potential (see below), considering their wide range of applications on areas such as the bioremediation of soils, tailings and water; biomining, bioleaching and the biosynthesis of gold nanoparticles (e.g., [13; 14; 15; 16; 17; 18; 19]). These bioprocesses are not restricted to Bacteria or Archaea, and some extremophile fungi can also play an important role in these interactions with minerals and mining ores.
# Phase Balancing and Reactive Power Support Services for Microgrids
## 2 Microgrid Architecture
### Microgrid Central Controller (MGCC)  
The proposed MGCC is responsible, among others, for the Q and PB compensation at the MG's PCC as shown in Figure 1. A smart meter installed at the PCC sends its measurements to the MGCC using Modbus TCP/IP communication. It should be noted that the use of an independent local area network (LAN) is highly suggested for the communication between MGCC, smart meter and inverters in order to minimize cyber-security threats. The MGCC also requires the knowledge of the real-time availability of each PV or ESS for ancillary services provision in order to allocate the compensated currents. In addition, the same communication protocol is utilized to send to each inverter a sharing constant, which is used by the inverters for fair allocation between Q-PB provision according to the real-time loading conditions and the reference signals for reactive power and negative sequence current.
## 3 Results and Discussion
### Performance under Extremely Low Temperature Condition  
Figures 9-11 present the ambient temperature and air temperature is inside the container without PCM, with water as PCM, and with OP5E as PCM under extremely low temperature conditions, respectively. It was observed that the temperature-controlled time was about 1 h (62 min) by using the container without PCM. This time was extended to 10 h when using water as PCM and to 102 h when using OP5E as PCM. The air temperature in the container dropped swiftly to \(-6\)\({}^{\circ}\)C and rose to \(-1\)\(\sim\)\(-2\)\({}^{\circ}\)C by applying water. This phenomenon could be caused by the subcooling of water. The nucleation of water did not occur until the air temperature in the container decreased to \(-6\)\({}^{\circ}\)C. The air temperature increased when water began to freeze and amounts of heat were released during the liquid-solid phase transition. The temperature inside the container kept constant at \(-1\)\(\sim\)\(-2\)\({}^{\circ}\)C until the nucleation process ended. On the other hand, the air temperature in the container maintained at 4-5 \({}^{\circ}\)C for the first 60 h and then decreased slowly to 2 \({}^{\circ}\)C after 102 h when using OP5E as PCM. In all three cases, the measuring point, which firstly exceeded the minimum temperature of 2 \({}^{\circ}\)C, was also one of the points in the corner as in the case under extremely high temperature condition.  
Figure 10: The ambient temperature and air temperature inside the container with water as PCM under extremely low temperature condition.  
Figure 9: The ambient temperature and air temperature inside the container without PCM under extremely low temperature condition.
## 2 Materials and Methods
### Quantitative Reverse Transcriptase-Polymerase Chain Reaction (qRT-PCR)  
Total RNA was extracted from liver tissue using TRIzol reagent (Life Technologies, Rockville, MD, USA) according to the manufacturer's protocol. cDNA was synthesized by reverse transcription using a Transcripto First-Strand cDNA Synthesis Kit (Life Technologies, Rockville, MD, USA). Gene expression levels were determined via the Universal Probe Library on a Light Cyler 96 system (Hoffmann La Roche, Basel, Switzerland). The qRT-PCR thermocycling conditions were as follows: initial denaturation at 95 \({}^{\circ}\)C for 30 s, 40 cycles at 92 \({}^{\circ}\)C for 5 s, and 60 \({}^{\circ}\)C for 20 s. Relative mRNA expression levels were calculated using the [PERSON] et al. method and normalized to _Gapdh_. Gene primers, purchased from Bioneer (Daejeon, Korea), were as follows: _Gk_, 5\({}^{\prime}\)-CACCAGTTCCTCACAGCTCA-3\({}^{\prime}\) (forward) and 5\({}^{\prime}\)-GAGCCATGTGTTCCTCAGT-3\({}^{\prime}\) (reverse); Glucose-6-phosphatase 5\({}^{\prime}\)-AACCCATGTAAAGACAGTTGACAG-3\({}^{\prime}\) (forward) and 5\({}^{\prime}\)-GGAGTCTTCTGCTAACTGGAAATG-3\({}^{\prime}\) (reverse); Phosphoenolpyruvate carboxykinase, 5\({}^{\prime}\)-ATCCCAACTCGAATTCTG-3\({}^{\prime}\) (forward) and 5\({}^{\prime}\)-CCCAGGCAGGTTCAATAAT-3\({}^{\prime}\) (reverse); _Gapdh_, 5\({}^{\prime}\)-AAGAGGGAGTGTCCCTTAC-3\({}^{\prime}\) (forward) and 5\({}^{\prime}\)-CCATTTGTCTACGGG
# What Lies Beneath One's Feet? Terrain Classification Using Inertial Data of Human Walk
## 2 Materials and Methods
### Data Collection  
Most modern digital devices such as smart phones are equipped with a range of sensors including cameras and inertial measurement units (IMUs). For the purpose of data collection, we used two Android-based smart phones. The smart phones have on-board 6D IMUs (MPU-6500-InvenSense, San Jose, CA, USA) which can measure tri-axial accelerations and tri-axial angular velocities. The technical specifications of both MPU-6500 are given in Table 4.The smart phones were tightly attached at the chest and lower back of the subject using elastic belts, as shown in Figure 2.  
A data recording application was developed to record 6D inertial data with the smart phones. The data were recorded at a sampling rate of 75 Hz.  
The standardized gait task consisted of a straight walk for a distance of 10 m from a starting point, turning around, and returning to the starting point. Subjects were asked to walk in their natural gait while wearing shoes and repeat the standardized gait task twice on all six surfaces. This resulted in 40 \(\times\) 6 = 240 m of walking by each subject on all terrains.
## 8 Conclusions and Future Work  
This study introduces an interpretable regression-based framework for predicting the severity stages of COPD using GLMs. The model leverages clinically relevant covariates, including age, gender, FEV\({}_{1}\), pack-year smoking history, six-minute walking distance, and comorbidities to yield a statistically grounded and transparent estimation of disease severity. The approach achieved a predictive accuracy of 86.7%, underscoring its potential utility as a clinically viable decision-support tool. The emphasis on model interpretability addresses a key barrier to the adoption of data-driven approaches in healthcare by aligning statistical outputs with clinical reasoning.  
In future work, several enhancements are planned to extend the applicability and robustness of the proposed framework. First, the model will undergo external validation using larger, multi-institutional datasets to assess generalizability across diverse patient populations. Second, advanced machine learning models such as Random Forests and XGBoost will be explored to capture nonlinear effects and potential higher-order interactions. These models will be augmented with explainable artificial intelligence (XAI) techniques, such as SHAP or LIME, to preserve transparency in clinical interpretation. Additionally, parametric studies will be conducted to systematically evaluate the sensitivity and robustness of model parameters, identify critical thresholds, and enhance personalized risk stratification. These analyses are expected to address the limitations of the current linear modeling framework and improve predictive accuracy by better capturing the complex, nonlinear dynamics of COPD progression.  
Third, and most crucially from a translational standpoint, a digital interface will be developed to facilitate the practical deployment of the model. This platform will be designed to support real-time input of patient data and immediate risk stratification, enabling point-of-care application by physicians. Such a tool has the potential to enhance early detection of disease progression, inform individualized treatment planning, and ultimately improve long-term patient outcomes in COPD management.  
Conceptualization, M.S.S.; Methodology, L.S.; Software, L.S.; Validation, L.S.; Formal analysis, L.S.; and H.A.; Resources, M.S.S.; Data curation, H.A.; Writing--original draft, L.S.; Writing--review & editing, H.A. and M.S.S.; Visualization, H.A.; Supervision, M.S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used to support the findings of this study is available upon request from the first author.  
The authors declare no conflict of interest.
## 4 Discussion
### Confirmatory Tests
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Respenses**} & \multirow{2}{*}{**Condition**} & \multicolumn{4}{c}{**Validation Test**} & **Initial Run** & **Difference** \\ \hline \multirow{2}{*}{Surface roughness (\(\upmu\)m)} & Best & 32 & \begin{tabular}{c} 35.12 \\ (0.45 f\({}_{\mathrm{z}}\), 25 V\({}_{\mathrm{c}}\), 60 a\({}_{\mathrm{p}}\)) \\ \end{tabular} & 8.5\% \\ \cline{2-5}  & Worst & 96.5 & \begin{tabular}{c} 73.11 \\ (0.65 f\({}_{\mathrm{z}}\), 75 V\({}_{\mathrm{c}}\), 60 a\({}_{\mathrm{p}}\)) \\ \end{tabular} & 24.2\% \\ \hline \multirow{2}{*}{Burr width (\(\upmu\)m)} & Best & 154.47 & \begin{tabular}{c} 159.81 \\ (0.45 f\({}_{\mathrm{z}}\), 25 V\({}_{\mathrm{c}}\), 60 a\({}_{\mathrm{p}}\)) \\ \end{tabular} & 3.4\% \\ \cline{2-5}  & Worst & 680.51 & \begin{tabular}{c} 580.75 \\ (0.25 f\({}_{\mathrm{z}}\), 25 V\({}_{\mathrm{c}}\), 30 a\({}_{\mathrm{p}}\)) \\ \end{tabular} & 14.6\% \\ \hline \multirow{2}{*}{Burr length (\(\upmu\)m)} & Best & 102.7 & \begin{tabular}{c} 198.45 \\ (0.75 f\({}_{\mathrm{z}}\), 65 V\({}_{\mathrm{c}}\), 60 a\({}_{\mathrm{p}}\)) \\ \end{tabular} & 48.24\% \\ \cline{2-5}  & Worst & 696.55 & \begin{tabular}{c} 582.09 \\ (0.25 f\({}_{\mathrm{z}}\), 50 V\({}_{\mathrm{c}}\), 60 a\({}_{\mathrm{p}}\)) \\ \end{tabular} & 16.3\% \\ \hline \multirow{2}{*}{Tool wear (\(\upmu\)m)} & Best & 7.95 & \
\begin{tabular}{c} 8.20 \\ (0.45 f\({}_{\mathrm{z}}\), 25 V\({}_{\mathrm{c}}\), 60 a\({}_{\mathrm{p}}\)) \\ \end{tabular} & 4\% \\ \cline{2-5}  & Worst & Already examined &
\begin{tabular}{c} 24.00 \\ (0.25 f\({}_{\mathrm{z}}\), 75 V\({}_{\mathrm{c}}\), 90 a\({}_{\mathrm{p}}\)) \\ \end{tabular} & -* \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Confirmatory test results. Figure 14: Burr width at \(\rm f_{z}\) = 0.65 (\(\rm\mu m/tooth\)), \(\rm V_{c}\) = 75 (\(\rm m/min\)), \(\rm a_{p}\) = 30 \(\rm\mu m\) (best confirmatory).
# RALR: Random Amplify Learning Rates for Training Neural Networks
## 5 Experiment
### RALR's Effectiveness on Other Techniques  
To verify that RALR is complementary to existing popular data augmentation and regularization techniques, we do some experiments with ResNet-56 on the CIFAR100 dataset. We compare our proposed method with four state-of-the-art methods: Cutout, Mixup, CutMix, and Label Smoothing.  
As seen in Table 7, we observe that if we combine the techniques above with RALR methods, the classification performances are outperformed than when we use these methods alone.  
Specifically, we select the hole size of \(8\times 8\) pixels for the CIFAR-100 dataset when training on the full datasets for Cutout, which is the best parameters adapted in [11]. For Mixup, we tested the hyper-parameter \(\alpha=1.0\). \(prob=0.5\) and \(\alpha=1.0\) are adapted in CutMix. We use \(smoothing=0.1\) for Label Smoothing technique.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Optimizer** & **+RALR** & **Acc.** \\ \hline \multirow{4}{*}{SGD} & \(\times\) & 67.48 \(\pm\) 0.35 \\  & \(\surd\) & **69.99 \(\pm\) 0.15** \\ \cline{2-3}  & \(\times\) & 74.53 \(\pm\) 0.41 \\  & \(\surd\) & **75.87 \(\pm\) 0.07** \\ \hline \multirow{2}{*}{Adam} & \(\times\) & 72.55 \(\pm\) 0.06 \\  & \(\surd\) & **73.55 \(\pm\) 0.09** \\ \hline \multirow{2}{*}{Adagrad} & \(\times\) & 64.56 \(\pm\) 0.80 \\  & \(\surd\) & **65.01 \(\pm\) 0.86** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Validation accuracy (%) over different optimizer based on ResNet-110 trained on CIFAR-100.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Tokenization**} & \multirow{2}{*}{**Cased**} & \multicolumn{2}{c}{**BLEU**} \\ \cline{3-4}  & & **Baseline** & **+RALR** \\ \hline
13a & Yes & 25.1 & **25.5** \\  & No & 25.6 & **25.9** \\ \hline \multirow{2}{*}{International} & Yes & 25.9 & **26.1** \\  & No & 26.3 & **26.7** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison results of WMT 2014.
# A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM
## 4 Particle Swarm Optimization-Support Vector Machine (PSO-SVM)
### Support Vector Machine (SVM)
As a result, the first one in Equation (29) is modified as:  
\[\min\sum\limits_{i=1}^{n}\beta_{i}-\frac{1}{2}\sum\limits_{i=1}^{n}\sum \limits_{j=1}^{n}y_{i}y_{j}\beta_{i}\beta_{j}K(\mathbf{V_{i}},\mathbf{V_{j}}) \tag{30}\]  
Assuming that the obtained optimal solution of the above equation is \(\beta_{i}^{*}\), then the optimal coefficients \(\mathbf{w}^{*}\) and \(b^{*}\) of the hyper-plane are calculated as:  
\[\left\{ \begin{array}{c}\mathbf{w}^{*}=\sum\limits_{i=1}^{n}\beta_{i}^{*}y_{i} \theta(\mathbf{V_{i}})\\ b^{*}=-\frac{1}{2}\mathbf{w}^{*T}[\theta(\mathbf{V_{h}})+\theta(\mathbf{V_{l}})]\end{array} \right. \tag{31}\]  
where \(\theta(\mathbf{V_{h}})\) and \(\theta(\mathbf{V_{l}})\) are a pair of support vectors belonging to different classes. Hence, the corresponding decision function \(f(\mathbf{V})\) is expressed as follows [38]:  
\[ \begin{array}{rl}f(\mathbf{V})&=\text{sgn}\bigg{(}\sum\limits_{i=1}^{n}\beta_{i }^{*}y_{i}\theta(\mathbf{V_{i}})^{T}\theta(\mathbf{V})+b^{*}\bigg{)}\\ &=\text{sgn}\bigg{(}\sum\limits_{i=1}^{n}\beta_{i}^{*}y_{i}K(\mathbf{V_{i}},\mathbf{V })+b^{*}\bigg{)}\end{array}  \tag{32}\]  
whose output is the result of fire image detection. In addition, as kernel function is the key technology of SVM, its selection will affect the learning ability and generalization ability of the model directly. At present, there have been several kernel functions available: polynomial kernel, sigmoid kernel, linear kernel, and radial basis function (RBF) kernel. Among them, the RBF kernel has the advantage of low computational complexity. Therefore, it is adopted as the kernel function for SVM in this paper and denoted by:  
\[K(\mathbf{x},\mathbf{x}^{\prime})=\exp\left(-\frac{\left\|\mathbf{x}-\mathbf{x}^{\prime} \right\|^{2}}{\tau}\right) \tag{33}\]  
where \(\tau\) is the kernel parameter to measure the width of RBF and \(\tau>0\); \(\mathbf{x}\), \(\mathbf{x}^{\prime}\in\mathbf{R^{d}}\).