esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Discussion  
### Simulation Configuration  
In this paper, simulations were performed using four satellite systems: GPS, GLOASS, GALILEO, and BDS. Performance figures were analyzed for satellite visibility, GDOP, link-budget (\(C/N_{0}\)), URE, and navigation error, respectively, according to time and longitude.  
Scenarios and parameter settings for the simulation performed are given in Table 1. The beam-widths of the main lobe and first side lobe of all systems were set identically, and the receiver simulation part computed the range error and navigation error above received power threshold of 20 dB-Hz. Among the results, the figures of visibility, GDOP, and \(C/N_{0}\) were computed according to all of the GEO longitudes with the average simulation values for 24 h. Especially, URE and navigation errors were analyzed at regions corresponding to the free space above the Korean Peninsula and the opposite side. The results of the two regions represent the difference in the impact of the GEO and IGSO satellites of BDS.
## 5 Process of Optimal Foil Width Determination and Results  
The optimal size of the foil is the one at which the step voltage is below the permissible limit value but not significantly greater than the required size. It can only be determined by a larger number of FEM models for different _fw_ values. Since despite the parametric definition of _fw_ (shown in Section 4) this is a computationally expensive as well as time-consuming process, we keep the number of FEM calculations to a minimum.  
Figure 11: TP2 (**a**) pole, grounding, and probes; (**b**) pole partially covered with insulating coating, grounding, probes, and protective foil _fw_ = 2 m.  
Figure 10: TP1 (**a**) pole, grounding, and probes; (**b**) pole partially covered with insulating coating, grounding, probes, and protective foil _fw_ = 6 m.  
Since _fw_ is the distance of the foil from the parts under voltage in the event of failure (pole, grounding, and probes), it can be concluded that the maximum value of the step voltage depends on the following:  
* The soil structure;
* The magnitude of the fault current that causes the potential on the grounded parts;
* It does not depend on the geometry of the problem, because _fw_ stands for the distance from the grounded parts, regardless of their configuration.  
On the basis of what is written, it can be concluded that a function can be determined with an appropriate number of parameters that can be applied generally to the case under consideration.  
The idea of the procedure for determining the optimal _fw_ is as follows:  
* Three FEM calculations are performed and the maximum step voltage (\(U_{\text{Smax}}\)) for each is written depending on the _fw_ used. It is advantageous to specify \(U_{\text{Smax}}\) (_fw\({}_{1}\)_ small value), \(U_{\text{Smax}}\) (_fw\({}_{3}\)_ large value), and \(U_{\text{Smax}}\) (_fw\({}_{2}\)_ value between small and large).
* Based on \(U_{\text{Smax}}\)(_fw\({}_{1}\)_), \(U_{\text{Smax}}\)(_fw\({}_{2}\)_), and \(U_{\text{Smax}}\)(_fw\({}_{3}\)_), three parameters of the function \(U_{\text{Smax}}=\) F(_fw_) can be defined (three parameters, because we have three known values of \(U_{\text{Smax}}\) for three _fw_). The rest of the article shows the determination of the corresponding function F based on the choice between five functions.
* From the written function \(U_{\text{Smax}}=\) F(_fw_), _fw_ is determined by taking into account that \(U_{\text{Smax}}\) is equal to the limit value of the step voltage.  
TP1 is used to determine \(U_{\text{Smax}}=\) F(_fw_) and to show the process of determining the optimal _fw_. TP2, which has a completely different soil structure, is used to verify the correctness of the selected function F.
# Analysis of Wind Speed Characteristics Along a High-Speed Railway
## 5 Detailed Analysis of Predictability Performance and Model Validation  
We further investigated the impact of wind speed predictability on forecasting outcomes. The wind speed in February was predicted for each station using different methods. Initially, the Bartlett test was employed to assess the predictability of wind speed. Subsequently, the widely used models--TCN [41], LSTM [42], informer [43], and MRIformer [44]--were applied to predict the 5 min average wind speed at four high-speed rail stations. While good prediction results can already be achieved using the historical wind speed data from a single station, incorporating spatial wind speed information from multiple stations enhances the accuracy of the predictions [45]. To evaluate this, we introduced a control group that used wind speed data from the nearest WS station as an auxiliary feature input to the forecast model. The wind speed prediction process for this part is illustrated in Figure 12. The wind speed prediction is represented by the following formula:  
\[f(\hat{x}_{t+1})=\textit{predictor}(x_{t};\theta), \tag{6}\]  
where \(f(\hat{x}_{t+1})\) is the predicted wind speed at time \(t+1\), \(x_{t}\) is the feature vector at time \(t\), _predictor_ is the function that represents the prediction model, and \(\theta\) represents the model parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Station** & **3 s** & **6 s** & **12 s** & **30 s** & **60 s** & **120 s** & **180 s** & **240 s** & **300 s** & **600 s** & **1200 s** & **1800 s** \\ \hline K1005 & 1670 & 1590 & 1270 & 1020 & 840 & 730 & 300 & 192 & 60 & 0 & 0 & 0 \\ K1066 & 4070 & 3710 & 3090 & 2790 & 2500 & 1220 & 1032 & 960 & 890 & 0 & 0 & 0 \\ K1245 & 1390 & 1330 & 1130 & 1090 & 1060 & 990 & 828 & 768 & 700 & 0 & 0 & 0 \\ K1256 & 4410 & 4400 & 4390 & 4340 & 4290 & 4000 & 4028 & 3992 & 3990 & 3560 & 2660 & 2030 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Duration of historical wind speeds available for forecasting (min).  
Figure 11: The result of predictability: (**a**) predictability of K1005 at different time scales; (**b**) [PERSON]’s test bar chart for predictability of K1005 at different time scales.
## 5 Experimental Setup  
### Datasets  
We validate the effectiveness of the proposed method on different sizes of datasets for music genre classification and auto-tagging. All dataset splits are available on the link [22]. The details of each dataset are as follows. The numbers in the parenthesis indicate the split of training, validation and test sets.  
* GTZAN [23]: 930 songs (443/197/290) (This is a fault-filtered split designed to avoid the repetition of artists across the training, validation and test sets [24]), genre classification (10 genres).
* MagnaTAgaTune (MTAT) [25]: 21,105 songs (15,244/1529/4332), auto-tagging (50 tags)
* Million Song Dataset with Tagtraum genre annotations (TAGTRAINUM): 189,189 songs (141,372/10,000/37,817) (This is a stratified split with 80% training data of the CD2C version [26]), genre classification (15 genres)
* Million Song Dataset with LastFM tag annotations (MSD) [27]: 241,889 songs (201,680/11,774/28,435), auto-tagging (50 tags)  
We primarily examined the proposed model on MTAT and then verified the effectiveness of our model on MSD which is much larger than MTAT (MTAT contains 170 h long audio and MSD contains 1955 h long audio in total). We filtered out the tags and used most frequently labeled 50 tags in both datasets, following the previous work [8,19,20]. Also, all songs in the two datasets were trimmed to 29.1 s long. For transfer learning experiments, the model is first trained with the largest dataset, MSD, and the pre-trained networks are transferred to other three datasets. The evaluation is conducted with area under receiver operating characteristic (AUC) for auto-tagging datasets and accuracy for genre classification datasets.
## 4 Methodology
Selecting an appropriate model architecture is key to balancing predictive power with computational efficiency, ensuring the model can handle the HVAC system's unique characteristics. In step 6, the chosen model is trained on historical data gathered from the HVAC system. During this process, the model learns patterns and relationships within the data, fine-tuning its parameters to improve its predictive accuracy. Hyperparameters are carefully adjusted to optimize the model's performance without overfitting. Training the model on a large and representative dataset allows it to generalize well to new data, making it robust for future predictions. This step may also involve regularization techniques to prevent overfitting, enhancing the model's generalizability. Finally, in step 7, the trained model is validated using a separate test dataset, distinct from the training data, to evaluate its real-world predictive performance. Validation metrics such as the coefficient of determination (R2) and the mean absolute percentage error (MAPE) are used to assess the model's accuracy and reliability. This step provides insights into the model's strengths and potential limitations, ensuring it meets the required performance standards before deployment. The proposed methodology focuses on a recursive model structure, where the model uses its past outputs as inputs for future predictions as seen in Figure 2. This approach inherently involves feeding the forecast values back into the model at each time instant, creating a sequence of predictions over time. Consequently, error accumulation is expected, as small inaccuracies in early predictions can propagate and amplify throughout the forecasting process. Additionally, any external variables or factors not forecast by the model must be known at each time instant to ensure accurate predictions and maintain consistency within the recursive framework.
## 2 Previous Works
### In-Process Welding Quality Control  
Using Machine learning techniques, weld quality can be monitored during the welding process. Various types of data can be gathered from the welding pool, such as the welding current, the arc spectrum, and Infrared images. Subsequently, this data can be used to create a dataset to train an ML-based model and afterward, the trained networks can identify possible defects that occurred in the welding process.  
[PERSON] et al., 2020 [2] used a convolutional neural network (CNN)-based model to predict the penetration of the GTAW process using the top-side image of the weld pool. Additionally, a pre-trained model based on the residual neural network (ResNet) was proposed to improve the predictions' accuracy. By using transfer learning, they reduced the training time. [PERSON] et al., 2019 [3] developed a framework based on a cooperative awareness of the arc spectrum, vision data, and electrical parameters to control the quality of the welding process in wire-arc additive manufacturing. They found that the self-emitted radiation coming from the weld pool mainly consists of near-infrared light. Thus, to capture a better-quality image, an 850 nm high-pass filter was utilized to block high-frequency arc interference. Different classifiers were used depending on the welding current and wire material. For the welding process features, they monitored welding speed, shielding gas flow rate, and weld defects by analyzing multi-source data from the weld pool. Finally, a K-Nearest Neighbor (KNN) classification algorithm was used to classify the weld features extracted from vision and spectral data. [PERSON] et al., 2020 [4] proposed a framework to detect defects in the fiber laser welding of stainless steel. By capturing the image of the keyhole, the features based on the shape and edges of the keyhole were extracted using grayscale projection distribution and the Poisson matting technique. Then, a Hidden Markov Model (HMM) was applied to correlate the geometry features of the keyhole and welding defects. To obtain desirable weld characteristics, the information provided by the vision-based model could be further utilized to adjust the significant welding variables. [PERSON] & [PERSON], 2019 [5] used a fuzzy logic-based controller to actively monitor and enhance the penetration quality of MIG welding in a thin aluminum sheet by manipulating the welding current. In a similar study conducted by [PERSON] et al., 2019 [6], a proportional-integral controller was proposed to provide feedback control over the penetration quality of the GTAW process by adjusting the welding current.
## 2 Materials and Methods
### Image Acquisition  
To avoid the inclusion of errors due to weather conditions, the UAV and GNSS field campaigns were conducted under optimal conditions. In the case of GNSS observations, atmospheric conditions, such as ionospheric and tropospheric disturbances, can introduce errors in satellite signal propagation, affecting positional accuracy [54]. To minimise these errors, data acquisition was performed under clear skies and stable atmospheric conditions. For UAV photogrammetry campaigns, weather factors like wind, the presence of clouds, or rain play crucial roles [55]. Adequate lighting is essential for capturing high-quality images; poor lighting conditions, such as overcast skies or low sun angles, can result in shadows and low-contrast images that hinder photogrammetric analysis. Wind can also significantly impact UAV operations; strong winds can destabilise the UAV, making it difficult to maintain a consistent flight path and potentially leading to blurred or misaligned images. Additionally, rain can adversely affect UAV flights in several ways: it can obscure camera lenses, causing blurry images; can compromise the UAV's electronic systems through moisture infiltration; and can create reflective surfaces on the ground that distort the data. These weather factors collectively influence the overall quality and accuracy of the data collected during UAV photogrammetry missions, making it necessary to choose optimal weather conditions for conducting these surveys.  
The images were acquired using a DJI (Da-Jiang Innovations Science and Technology Co., Ltd. Shenzhen, China) Inspire 2 multirotor UAV (Figure 2B) equipped with a Zenmuse X5S camera. This camera features a 4/3 sensor with dimensions of 17.3 \(\times\) 13 mm and a resolution of 5280 \(\times\) 3956 pixels, resulting in a pixel size of approximately 3.28 \(\times\) 3.28 \(\upmu\)m. The X5S is equipped with an electronic rolling shutter with speeds ranging from 8 s to 1/8000 s. The flight was planned using the DroneDeploy application [46], which divided the area into four blocks of 0.2 km\({}^{2}\) to optimise battery use. In addition, the 3D perimeter was activated in all the blocks in the flight plan. The DroneDeploy app offers an option to capture images with an angle of 65\({}^{\circ}\) (Figure 2F). The flight was conducted in a visual line of sight (VLOS) scenario using different flight heights with a maximum of 120 m, as specified by Spanish regulation [56], and a flight speed of 14.5 m\(\cdot\)s\({}^{-1}\).  
The recommendations of specific studies [57; 58; 59; 60] were followed for the flight planning, adding cross strips on the edges of each block.  
Table 2 contains the parameters used in the configuration of the blocks, including the forward and side overlap, height flight, area of each block, GSD, and number of images. Therefore, it can be seen that the forward overlap was programmed at 80% and the lateral overlap at 60%, following the recommendations of the Pix4D photogrammetric software. The cross strips were shared in the adjacent blocks with a height of 110 m to combine the images with different heights.
## 2 Materials and Methods
### Sampling  
The start point samples of spinach were taken just before the transplantation of spinach seedlings to the aquononics system (5 May). The start point samples of fish were taken at the time of fish stocking (4 May). The endpoint samples were taken after six weeks on the day of the harvest of fish and spinach on (14-15 June). For the measurement of change in spinach biomass, 15 seedlings were sampled in the beginning, while at the end of the experiment, 20 plants were sampled from each DWC. The length and dry weight of the shoots and roots were recorded separately. The plants were dried at 60 \({}^{\circ}\)C for 72 h. For the GSM and MIB analyses, six fresh spinach seedlings were taken at the start, and three fresh spinach plants at the end from each tray, shoots, and roots were separated, cut into small pieces, and mixed into one homogeneous sample, i.e., one sample for each tray. The dry matter content of spinach was determined by the ISO 638:2008 standard method. The final samples from spinach shoots from each DWC were also analyzed for macronutrients (N, P, K, Ca, Mg, S) and micronutrients (Fe, Cu, Mn, Zn, B) at Eurofins Agroscience Services, Mikkeli, Finland. B, Ca, Cu, Fe, K, Mg, Mn, P, and Zn were measured with an ICP-OES method as reported by Eurofins. Nitrogen was determined with Kjeldahl-method while sulfur with ICP-OES method. Limits of detection (LOD) and limits of quantification (LOQ) for each nutrient are given in Supplementary Table S1.  
For estimating the fish growth, the fish were weighed in the beginning (in batches) and at the end (individually) of the experiment. For the measurement of lipid content and off-flavors (GSM and MIB) in the fish muscle, three randomly selected individuals were sampled in the beginning. At the end, three individuals were sampled from each fish tank, i.e., nine fish per treatment. The sampled fish were killed with a sharp blow on the head, gutted, and filleted. From the lateral part of the fillet [29], 500 mg of muscle was taken from each fish, and the three samples from each tank were pooled. Water samples (500 mL) were taken from each DWC, each fish tank, and tap water at the beginning and the end of the experiment for the analysis of the off-flavor compounds (GSM and MIB) and anions (chloride, phosphate, sulfate, and nitrate, and nitrite). All samples were stored at \(-\)20 \({}^{\circ}\)C before the analyses.
# The Role of Electron Trajectories in XUV-Initiated High-Harmonic Generation
## 2 Experimental Results  
### Experimental Setup  
XUV-initiated HHG is produced in a collinear apparatus with two stages (see Figure 1b). First, we generate an XUV attosecond pulse train (APT) by focusing 787 nm laser pulses from a Ti:sapphire amplifier system (KMLabs Red Dragon) into a gas cell filled with xenon. The pulse duration (intensity duration full-width at half maximum (FWHM)) is about 25 fs. At the cell position, we reach a peak intensity of about \(9\times 10^{13}\) W cm\({}^{-2}\). The resulting low-divergence XUV beam is spatially separated from the copropagating IR beam by a thin aluminum filter (200 nm thickness). A two-segment mirror (focal length 600 mm, Ultrafast Innovations) is used to refocus the beams, where the inner segment is coated for reflecting the XUV beam at the spectral range of harmonics H11-H17. A piezoelectric actuator controls the position of the inner segment, providing an accurate delay control between the XUV beam and the IR beam. Both beams are refocused into a gas cell filled with helium (20 Torr) to produce XUV-initiated HHG. The IR intensity at the helium gas cell is \((7.8\pm 1.9)\times 10^{13}\) W cm\({}^{-2}\), which is insufficient for HHG without the assistance of the APT. Once both beams overlap temporally and spatially, we observe the generation of new harmonics above the helium ionization threshold.
## 3 Data Bias  
As discussed earlier, one major cause of biased results of ML models is the fact that the data used for training those models are also biased. Data bias is mostly related to the way that data are selected and collected, as well as the nature of the data. As a result, three categories of data bias can be identified, namely reporting bias, selection bias and cognitive bias.  
Apart from approaches that exist for dealing with specific categories of data bias which will be analyzed in the following subsections, there are some more scenario-agnostic techniques that can be applied in order to reduce data bias. These techniques mostly refer to imbalanced datasets (i.e., one label occurs more often in the data than the other(s)) and aim to reduce the bias in the datasets by under-sampling or over-sampling [16].  
In under-sampling, the number of data instances that belong to the majority class is reduced, whilst in over-sampling, the number of data instances that belong to the minority class is increased [17]. A common approach for performing under-sampling includes the application of a clustering algorithm to cluster the imbalanced dataset and then remove instances of the majority class, based on the cluster to which every instance belongs [18]. Other techniques that can be applied are random under-sampling and Tomek links under-sampling. In random under-sampling, as its name suggests, a random number  
Figure 2: Selection process of relative studies.  
of observations to keep is selected [19]. In Tomek links under-sampling, data instances that \"overlap\" each other are identified, thus removing instances of the majority class that belong to the class \"boundaries\" [20].  
With regard to over-sampling, there exist two main approaches, namely the synthetic minority over-sampling technique (i.e., SMOTE) and adaptive synthetic sampling (i.e., ADASYN). SMOTE first identifies the minority class instances then selects the nearest neighbor and generates a synthetic data sample by joining the minority class instance and the corresponding nearest neighbor [21]. As for the ADASYN, it is similar to SMOTE; however, it generates a different number of samples based on the distribution of the minority class, thus also improving learning performance [22]. What is more, there exist approaches that perform over-sampling on multi-class datasets such as Mahalanobis distance-based over-sampling (i.e., MDO) and adaptive Mahalanobis distance-based over-sampling (i.e., AMDO) that are appropriate for numeric and mixed data, respectively [23].
# 5G-AKA-FS: A 5G Authentication and Key Agreement Protocol for Forward Secrecy
## 4 A 5G-AKA Protocol for Forward Secrecy  
In this section, we propose a 5G-AKA protocol for forward secrecy (for short, 5G-AKA-FS) that is compatible with the current 3 GPP standards [3], and the proposed protocol is shown in Figure 2. Before executing the 5G-AKA-FS protocol, UE holds \((k,PK_{HN},SUPI,SQN_{UE})\) secretly and HN stores \((k,sk_{HN},ID_{HN},SQN_{HN})\) secretly. Also, SN stores \(ID_{SN}\). We denote by \(\mathsf{H_{SHA-256}}\) the SHA-256 cryptographic hash function.
## 1 Introduction and Related Work
This significant reduction in complexity makes it an excellent choice for cryptographic techniques that operate within limited spatial constraints. Overall, this innovative construction presents an efficient solution for conducting Montgomery multiplication in GF(\(2^{k}\)) within resource-constrained cryptographic scenarios. This paper is organized into distinct sections, each contributing to the overall understanding of the research. In Section 2, a concise elucidation of the adopted Montgomery multiplication algorithm within the context of \(GF(2^{k})\) is presented [20; 21]. Section 3 delves into the exploration and analysis of DGs. Section 4 delves into an intricate examination of the bit-serial systolic array multiplier structure, encompassing its comprehensive design and insightful implementation considerations. To comprehensively evaluate the proposed approach, Section 5 conducts a thorough analysis of the recommended multiplier layout, meticulously assessing the spatial and temporal complexity of the observed effective bit-serial architectures. Finally, Section 6 serves as a fitting conclusion to the paper, concisely summarizing the key findings and significant contributions of this study.