esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Processing Static Analysis Alarms Based on Deep Learning
## 5 Experiments and Results
### Experimental Setup
#### 5.1.3 Training Configuration  
To extract context embeddings from the CodeBERT model, we passed the warning details and code snippets through the pre-trained CodeBERT model. The context embeddings were then obtained from the last hidden layer of the model. These context embeddings served as the input sequences for training the downstream classifiers. We experimented with different hyperparameters and training configurations to optimize the performance of our classifiers. Specifically, we employed the AdamW [33] optimizer and a linear learning rate scheduler to fine-tune the training process. All of the classifiers were trained for 20 epochs using a binary cross-entropy loss and the AdamW optimizer with a learning rate of \(1\times 10^{-3}\). Both the training and validation processes were performed on the same dataset with a batch size of 32. This allowed us to iteratively update the model parameters and evaluate the performance during the training phase. By tuning these hyperparameters and training configurations, we aimed to achieve the best possible performance for our classifiers in accurately classifying actionable alarms.
# Yeast Fermentation for Production of Neutral Distilled Spirits
## 2 Overview of Neutral Distilled Spirits Production
### Secondary Metabolic Products of Yeast  
The primary products of yeast fermentation are ethanol and carbon dioxide. Secondary yeast metabolites are minor compounds produced in much lower concentrations. Nevertheless, a large percentage of the flavour congeners in distilled spirits is contributed by the secondary metabolites produced by yeast during fermentation, as summarised in Figure 5.  
Figure 4: Summary of glycolysis and fermentation. Figure 5: Outline of metabolic inter-relationships in the synthesis of secondary yeast metabolites. As compared to a malt-based spirit, grain neutral spirits tend to be quite \"clean\" and contain relatively low levels of flavour congeners. Figure 3 and Table 3 present congener profiles to demonstrate the impact of yeast and distillation techniques on the presence of flavour congeners. The flavour (taste and aroma) characteristics of yeast-derived flavour congeners in distilled spirits are summarised in Table 7. The principal flavour congeners in distilled spirits include fatty acids and their esters (e.g., ethyl caprate, ethyl laurate), organic acids (e.g., succinin acid), higher alcohols (e.g., \(n\)-propanol, isoamylalcohol), aldehydes (e.g., acetaldehyde), and vicinal diketones (diacetyl). Additional information regarding the origins and identification of different flavour congeners in distilled spirits can be found in [9; 17; 19; 20; 26; 27; 28; 29]. Esters are especially important flavour congeners in alcohol distillates due to their fruity and floral characteristics. Ester production by yeast during fermentation is facilitated
## 5 Conclusions  
This study aims to improve the accuracy of UWB indoor positioning with significant NLOS conditions. We developed an experimental system, comprising a Leica MS60 high-precision RTS, four D-DWM-PG1.7 UWB base stations, and a moving target. The moving target is equipped with a coaxial reflective prism and a UWB tag. Based on the synchronous coordinates obtained by the RTS and the UWB system, the benchmark values of the UWB tag's positioning error are computed, of which, RMSE values are found to be 21.1 cm, 21.7 cm, and 89.2 cm in the \(x\), \(y\), and \(z\) components, respectively. NLOS conditions have severely degraded the UWB positioning accuracy, reducing it to less than one-third of the manufacturer's stated nominal value. Furthermore, distinct error distribution patterns have been observed across the three coordinate components.  
Two kinds of multilayer neural networks were designed and compared, i.e., a combined network and three separate networks. Both neural networks take three-dimensional coordinates as input values. However, the former outputs three coordinate components at a time, and the latter only fits one of the three coordinate components for each model. The results of employing benchmark errors as the training and verification datasets for neural networks showed that both networks had better performances than the traditional models. Specifically, they achieved an average reduction in RMSE of positioning errors by 61% and 72%, respectively. The separate networks perform better than the combined network since they are able to handle the error distribution pattern more precisely in each coordinate component. This customized strategy facilitates more accurate optimization and management of errors, leading to better modelling effects compared to a combined network configuration.  
Conceptualization, Y.W. and D.Z.; methodology, D.Z.; software, D.Z. and M.L.; validation, Z.L. and Y.Z.; formal analysis, Z.L. and M.L.; investigation, M.L. and Y.Z.; resources, Y.W. and T.F.; data curation, Y.Z. and M.L.; writing--original draft preparation, Y.W. and T.F.; writing--review and editing, D.Z.; visualization, M.L. and Y.W.; supervision, Z.L.; project administration, D.Z.; funding acquisition, D.Z. and Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the China Postdoctoral Science Foundation (2021 MD703896), Yunnan Fundamental Research Projects (grant NO. 202301 AU070101), the Fundamental Research Funds for the Central Universities (2042023 kf0002), and the Hubei Provincial Natural Science Foundation of China (2022 CFB090).  
Figure 11: Histogram for raw (untrained) and separate (trained) networks derived from UWB positioning errors.  
Data Availability Statement: Data are contained within the article.  
Author [PERSON] was employed by the company Joint Laboratory of Power Remote Sensing Technology, Electric Power Research Institute, Yunnan Power Grid Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges
## 4 WSN Systems in Different Applications: Aerospace, Bridge Structures, and Wind Turbines
### WSN in Wind Turbines
#### 4.3.3 Wireless Technology Options  
Energy suppliers have mostly used two WAN/cellular technologies: second-generation 2G and third-generation 3G. In the past, many energy suppliers used 3G services, but recently many carriers have been looking towards higher-bandwidth 4G networks to support SCADA systems. This concerns mainly applications that transmit a large amount of data. The choice of data services includes 4G networks: Wideband Code Division Multiple Access (W-CDMA) and High- Speed Downlink Packet Access (HSDPA). Due to the fact that newer HSDPA technologies are acquired at comparable cost to W-CDMA, they have become the preferred solution nowadays. [89].  
4G cellular technology plays an important role in SCADA communications and in power grid Long-Term Evolution (LTE) and fourth-generation, especially at meter aggregation points. The more utilities realize connecting an extremely large number of individual meters, the greater capacity and lower latency of 4G and LTE technologies are offered with a convincing connectivity solution. The ration of wireless technology development implies a fast development to be expected [89].  
Obviously, the stage of broadband 5G implementation is a challenge at present. However, one should remember that revolutionary 6G has already been announced. There are also really innovative technologies maturing very quickly as LoRa [89].  
LoRa employs license-free sub-gigahertz radio frequency bands as 433 MHz, 868 MHz (Europe), 915 MHz (Australia and North America) and 923 MHz (Asia). It allows long-range transmissions (over 10 km in open areas) with rather low power demand [90]. This technology covers the physical layer, while the other technologies and protocols including LoRaWAN (Long Range Wide Area Network) cover the upper layers [89].  
In January 2018, new LoRa chipsets appeared, characterized by much lower power consumption, offering enhanced transmission power, and reduction of size compared with the previous generation [91]. Further improvements of this technology should be expected in the near future.
# Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform
## 3 LEO/INS Alignment Architecture
### Observability Analysis and IMU Rotation Simulation
\begin{table}
\begin{tabular}{c c c c} \hline  & \(x\)**-axis (\(y\))** & \(y\)**-axis (\(\theta\))** & \(z\)**-axis (\(\psi\))** \\ \hline Interval 1 (initial position) & \(0^{\circ}\) & \(0^{\circ}\) & \(0^{\circ}\) \\ \hline Interval 2 (1 st rotation) & \(0^{\circ}\) & \(0^{\circ}\) & \(\pm 180^{\circ}\) \\ \hline Interval 3 (2 nd rotation) & \(0^{\circ}\) & \(\pm 180^{\circ}\) & \(0^{\circ}\) \\ \hline \end{tabular}
\end{table} 
Table 1: IMU rotation sequences. Figure 3: 3D demonstration of IMU rotation sequences in three steps. In a \(2\pi\) cycle, the elements of \(b_{g}^{b}\) and \(b_{a}^{b}\), which have a single sine or cosine function, are removed. The rotation sequence could be repeated several times during the real experiments. This motion could be implemented by simulation or using a rotatory platform connected to the IMU [38].
# Dynamic Malware Analysis Based on API Sequence Semantic Fusion
## 5 Conclusions  
To address the problem that the dynamic feature set is too large to discover the key hidden information, we propose a dynamic malware detection model, Mal-ASSF, based on the fusion of sequential features and semantic features of API call sequences. In Mal-ASSF, the API functions are represented by dimensionality reduction through word embedding. The sequential behavioral features of different length segments are captured by multiple convolution kernels. The information of the function API is depicted by a triplet of action, type, category, and other information.  
Mal-ASSF has advantages in the semantic representation and key sequence recognition abilities of API call sequences. Compared with traditional machine learning methods such as KNN, SVM, DT, RF, and XGB, the detection performance of Mal-ASSF is improved by three to eight percentage points; compared with deep learning models such as TextCNN, CNN + LSTM, and TextCNN + LSTM, the performance of the Mal-ASSF model is also improved by two to five percentage points. However, through the ablation experiment, it can be concluded that the improvement of the BiLSTM module is not particularly obvious. Further studies can be conducted to obtain a better representation of semantic features using pre-trained models.  
Conceptualization, S.Z. and W.Y.; methodology, S.Z. and M.Z.; software, J.W. and M.Z.; validation, M.Z. and J.W.; formal analysis, J.W.; investigation, J.W.; resources, W.Y.; data curation, M.Z.; writing--original draft preparation, S.Z.; writing--review and editing, J.W.; visualization, M.Z.; supervision, W.Y.; project administration, W.Y.; funding acquisition, W.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program, grant number 2018 YFB1003800.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ablation Part** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline one-hot & 87.22\% & 87.63\% & 87.22\% & 0.8721 \\ nosemantic & 91.12\% & 91.21\% & 91.12\% & 0.9116 \\ nobilstm & 92.75\% & 92.55\% & 92.75\% & 0.9265 \\ no-attention & 92.13\% & 92.17\% & 92.13\% & 0.9215 \\ Mal-ASSF & 94.49\% & 94.01\% & 94.19\% & 0.9402 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of the results of ablation experiments.
# Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images
## 3 Ore Rock Fragmentation Calculation Method
### Ore Rock Fragmentation Calculation Based on Multi-Modal Calibration Mapping
#### 3.6.3 Pixel Size Calibration and Ore Rock Fragment Calculation  
On the plane projection of the point cloud for the mining and rock segmentation, the minimum bounding rectangle is drawn around the selected high-density rock point cloud \(P=\{P_{i}|1\leq i\leq 3\}\). The length \(l_{p_{i}}\) and width \(w_{p_{i}}\) of the rectangle are calculated based on the three-dimensional coordinates of the points. The rock block image \(C=\{C_{i}|1\leq i\leq 3\}\), corresponding to the high-density rock point cloud, is identified on the rock segmentation image. The minimum bounding rectangle is drawn around each rock image, and the length \(l_{c_{i}}\) and width \(w_{c_{i}}\) of the rectangle are computed in pixels as the basic unit. Subsequently, the pixel size calibration of the image is performed using the characteristic dimensions of the rock point cloud. The calibration coefficient \(\tau\) is determined as follows:  
\[\tau=\frac{1}{6}\sum_{i=1}^{3}\biggl{(}\frac{l_{p_{i}}}{l_{c_{i}}}+\frac{w_{p_ {i}}}{w_{c_{i}}}\biggr{)}, \tag{18}\]  
Figure 9: Segmentation result of the ore rock point cloud.  
where the unit of \(\tau\) is cm/pixel. Afterward, appropriate evaluation indicators for rock fragmentation are meticulously selected, and the computation and statistical analysis of rock fragmentations are carried out using the rock segmentation image.  
The proposed method for pixel size calibration reduces the need for specific markers to be placed within the ore rock, making it more practical for real-world applications. In comparison with existing research methods, this approach better aligns with the requirements of practical applications. Moreover, by selecting multiple high-density rock point clouds as dimension reference objects, the robustness of the pixel calibration coefficient is greatly enhanced, thereby improving the accuracy of rock fragmentation calculations.
## 3 Results
### Helicity-Dependent Domain Wall Motion (HD-DWM)
#### 3.2.2 Helicity-Dependent Domain Wall Motion with Magnetic Circular Dichroism  
In order to check whether the MCD is consistent with experimental observations, we also evaluated the HD-DWM processes when instead of the IFE, only the MCD is considered. Similar to previous Section 3.2.1. We worked in the range of MCD absorption rates and fluences which does not lead to the switching but is close to the transition frontier. For instance, for \(F~{}=~{}0.35\) J/m\({}^{2}\) and MCD = 10%, a similar behavior to the one of the linear polarization presented in the previous section is expected. Nevertheless, the existence of the MCD implies a different rate of absorption of energy for up and down domains and therefore, an increase of the temperature gradient leading to higher forces pushing the DW to the hot region [35]. Figure 8 shows different final magnetic configurations for the two possible circular polarizations and laser positions (see top and central panels in Figure 8a,b for a laser beam at the left and the right side of the DW, respectively). The case of linearly polarized laser pulses is shown for comparison (bottom snapshots in Figure 8). These results indicate that the HD-DWM is negligible for both circular polarizations and relative laser-DW locations. Note that the DW displacement it is comparable with the one obtained by the linear polarization. Although we plotted one combination of fluence and MCD value in Figure 8, none of the different trials in the no-switching region resulted in appreciable HD-DWM. Although we cannot completely exclude some influence of the MCD in real experiments, these results (Figure 7 for IFE and Figure 8 for MCD) show that the IFE is the dominant phenomena involved HD-DWM observations. A video with some of the results already included in Figures 7 and 8, and showing the DW dynamics in the IFE and MCD scenarios for two laser spot locations and helicities is provided in the supplementary material for comparison (video S2).  
Figure 7: Initial and final snapshots of a down-up DW after 100 laser pulses of 200 ps of duration. The magneto-optical effective field due to the IFE has maximum amplitude of \(B_{MO,MAX}~{}=~{}10\) T, and its direction, either up or down, is given by the circular polarization (\(\sigma^{-}\) and \(\sigma^{+}\) for top and central graphs). The fluence is \(F~{}=~{}0.3\) J/m\({}^{2}\). The results for linear polarization (\(B_{MO,MAX}~{}=~{}0\) ) are shown in bottom graphs. The red dotted circles indicate the location of the laser beam. The in-plane size is \(1.5~{}\upmu\)m \(\times\) 1.5 \(\upmu\)m, and the center of laser was displaced 192 nm to the left (**a**) or to the right (**b**) of the DW.
## 4 Conclusions  
The conclusions of the performed research can be summarized as follows:  
* The density, MOR, and MOE of the bonded elements are comparable to the values obtained for the solid wood samples from the outer area;
* Generally, the MOR and MOE values obtained for the glued samples were smaller than for the tangentially loaded solid wood samples, but slightly higher (or comparable) to the radially loaded ones;
* With average values MOR = 82 N/mm\({}^{2}\) and MOE = 12,300... 12,800 N/mm\({}^{2}\), the bonded elements can be considered as suitable for CLT products, where usually the minimum required MOR is 24 N/mm\({}^{2}\) and the average MOE is 11,000 N/mm\({}^{2}\) (according to CEN EN 338) [19].  
The proposed solution enables increasing the lumber yield from tapered logs with minimum changes to the existing technology.  
Conceptualization, M.B. and M.C.; methodology, M.C., M.P. and M.I.; software, B.B.; formal analysis, M.P. and M.B.; investigation, M.P.; writing--original draft preparation, M.B.; writing--review and editing, M.C.; visualization, M.I.; supervision, M.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline \multirow{2}{*}{**Log Number**} & \multirow{2}{*}{**Loading Direction**} & \multicolumn{2}{c}{**MOE (N/mm\({}^{2}\))**} \\ \cline{3-4}  & & **Solid Wood** & **Glued** \\ \hline \multirow{2}{*}{\#1} & Tangential & 13,116 (12) (1102.66) A * & 12,828 (24) (635.36) A \\ \cline{2-4}  & Radial & 12,290 (12) (848.76) A & 12,828 (24) (635.36) B \\ \hline \multirow{2}{*}{\#2} & Tangential & 12,751 (12) (1477.61) A & 12,306 (29) (1004.02) A \\ \cline{2-4}  & Radial & 12,022 (12) (1255.13) A & 12,306 (29) (1004.02) A \\ \hline \multirow{2}{*}{\#1 + \#2} & Tangential & 12,934 (24) (1288.62) A & 12,542 (53) (888.78) A \\ \cline{2-4}  & Radial & 12,156 (24) (1056.72) A & 12,542 (53) (888.78) A \\ \hline \hline \end{tabular}  
* The values in the first and second parentheses in Tables 6 and 7 are number of replications and standard deviation, respectively. Two means in each row not followed by a common letter are significantly different from one another at the 5% significance level [18].  
\end{table}
Table 7: Summary of mean values of modulus of elasticity (MOE).
# Measuring Method of Node Importance of Urban Rail Network Based on H Index
## 3 Method for Calculating the Importance of Urban Rail Network Nodes Based on \(h\)-Index
### Relevance Analysis of Node Passenger Flow in Urban Rail Network
#### 3.2.1 Node Correlation Coefficient of Urban Rail Network  
The correlation coefficient is used to describe the degree of correlation among the stations in the urban rail network. The calculation formula is as follows:  
(1) Establishing the original passenger flow matrix between stations.  
\[Q=\left[q(1),q(2),\cdots q(t)\cdots q(N)\right]\in R^{m\times N} \tag{3}\]  
\[q(t)=\left[q_{1}(t),\cdots q_{m}(N)\right]^{T}\in R^{m} \tag{4}\]  
Among them, \(m\) is the number of stations, and \(N\) is the length of the data sample.  
(2) Normalization of data. Data are standardized using the following formula:  
\[X=\left[x(1),x(2),\cdots x(t)\cdots x(N)\right]\in R^{m\times N} \tag{5}\]  
\[x_{i}(t)=\frac{q_{i}(t)-e(q_{i})}{\delta(q_{i})}\forall 1\leq i\leq m,1\leq t\leq N \tag{6}\]  
Among them, \(e(q_{i})=\frac{1}{N}\sum_{i=1}^{N}q_{i}(t)\) represents the sample mean and \(\delta^{2}(q_{i})=\frac{1}{N-1}\sum_{i=1}^{N}[q_{i}(t)-e(q_{i})]^{2}\) is the sample variance. Standardized passenger flow data mean is 0, variance is 1.  
(3) Correlation matrix.  
\[R(X)=\frac{1}{N-1}XX^{T}=\frac{1}{N-1}\sum_{i=1}^{N}x(t)x^{T}(t)=\left(r_{ij} \right)_{m\times m} \tag{7}\]  
Among them, \(r_{ij}=\frac{1}{N-1}\sum_{k=1}^{N}x_{ki}x_{kj}\) is the correlation coefficient between node \(i\) and \(j\) of urban rail network.
## 1 Introduction
[19] proposed the ES-SCL-Flip decoding algorithm, which enhances the error correction performance of the conventional SCL decoder while minimizing decoding complexity, making it an effective solution for improving Polar Code decoding, particularly in 5G applications. [PERSON] et al. [20] introduced the LS-SCLF decoding algorithm, which improves the error correction performance of Polar Codes with a slight increase in decoding complexity by employing a new flip-bit metric, a perturbation parameter, and a layered search strategy, achieving a good balance between performance and complexity. [PERSON] et al. [21] proposed the Pre-configured Error Pattern Ordered Statistical Decoding (PEPOSD) algorithm, which optimizes the decoding order by pre-generating the error patterns and combining them with the CRC test, thus improving the performance of short CRC-Polar Codes while significantly reducing the decoding complexity, showing a better balance of performance and complexity than the traditional methods. [PERSON] et al. [22] proposed an improved SC-Creeper decoding algorithm, which effectively improves the efficiency and performance of Polar Code decoding by combining the threshold control and the cost function, especially in the case of large code lengths, outperforming the traditional Fano decoding algorithm and approaching the CA-SCL-8 decoder, demonstrating better computational efficiency and performance balance. Table 1 is a comparison of various schemes. In this paper, we introduce a novel error correction coding scheme, which is an enhanced version of the error correction scheme for CRC codes and Polar Codes. This scheme is optimized through Successive Cancellation Decoding algorithms, with the aim of enhancing the error detection and correction capabilities during data transmission. Our main contributions are as follows:  
1. We propose a novel error correction scheme that improves the data error correction performance. 2. We introduce a new sequence interleaving technique to enhance the decoding reliability of Polar Codes. 3. The proposed scheme efficiently identifies and corrects errors without requiring additional retransmission by employing dynamic error pattern analysis. 4. Experimental results demonstrate the effectiveness of the proposed scheme in reducing BER and improving decoding efficiency. This research not only enhances the reliability of data transmission but also offers substantial technological support for 6G and other high-speed communication technologies, with the potential for widespread application across various critical domains.