esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Technical Ear Training Game and Its Effect on Critical Listening Skills
## 4 Evaluation: Training Benefit in Speech Understanding
### Results  
We analyzed the data with two spatial-location conditions: Collocated vs. Separated condition. The Collocated condition was where both focal and background speeches were reproduced from the same, one location; the Separated condition was where focal and background speeches were reproduced from different locations (as illustrated in the previous Figure 7). In addition, the experiment adapted two tasks: the \"Focal\" task asked listeners to identify the focal-cued information (color and number) while the \"Background\" task asked them to identify the background-cued information. The results showed that all three listener groups (Reference, Control, and Treatment) did not make any significant difference in the \"Focal\" task, for both collocated and separated conditions.  
However, in the \"Background\" task, the Collocated condition caused a significant difference between the reference and treatment group (F(2, 79) = 6.35, \(p\) = 0.003). This result indicates that the given training (long-term and audiomotor training) helped the treatment group to better perform SiN in the hardest and thus most demanding task (Collocated condition of the \"Background\" task). The Separated conditions (both \"Focal\" and \"Background\") appear easy for both the Treatment and Control groups due to spatial masking release, resulting in no significant difference (please refer to the following section for more details). This experiment did not account for different masker (background) levels. The authors plan to conduct follow-up experiments and investigate the potential influence of various masker levels.
## 2 Methodologies
### Performance Indicators  
In order to evaluate the performance of each model in terms of accuracy, various statistical metrics were adopted, such as root mean square error (RMSE), the mean absolute percentage error (MAPE), coefficient of determination (\(\mathbb{R}^{2}\)), mean absolute error (MAE), and coefficient of variation of root mean square error (CVRMSE) that can be expressed as in the following equations:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method and Seasonality** & \(\alpha\) & \(\gamma\) & \(\delta^{i}\) \\ \hline HWSS—yearly & 0.048 & 0.023 & \(\delta^{y}=0.3\) \\ \hline HWDS—weekly, yearly & 0.039 & 0.029 & \(\delta^{y}=0.32\) \\ \hline HWTS—daily, weekly, yearly & 0.042 & 0.025 & \(\delta^{y}=0.28\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Multi-seasonality Holt-Winter parameters.  
\[\text{RMSE}=\frac{\sqrt{\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}}}{n} \tag{12}\]  
\[\text{CVRMSE}=\frac{\sqrt{\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}}}{\hat{y}} \tag{13}\]  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\abs{y_{i}-\hat{y}_{i}} \tag{14}\]  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\abs{\frac{\hat{y}_{i}-y_{i}}{y_{i}}} \times 100\% \tag{15}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\hat {y})^{2}} \tag{16}\]  
where \(y\), \(\hat{y}\), and \(\hat{y}\) represent the measured, predicted, and averaged values respectively.
# A Secure Protocol against Selfish and Pollution Attacker Misbehavior in Clustered WSNs
## 3 Model Establishment
### Threat Model and Security Objectives  
In the threat model, maliciousness is manifested in two ways. The first is when the malicious node refuses to cooperate in the routing process, i.e., it is a non-cooperative node that discards data packets once receiving them. The second is when the malicious node alters data packets before forwarding them or even injects a new packet with the same expected format. This type of node is known as a misbehaving node (\(MN\)). Since the next hop from source node in the path to the \(BS\) is always a \(CH\) node, the \(MN\), in our proposition, is a \(CH\) node. The adversary is modeled as a coalition of \(CH\) nodes varying from 1 to \(K\) (Figure 3). They can proceed to:  
1. reject packets received from source nodes, and stop them from reaching the \(BS\). This attack, considered as a form of \(DoS\) attack, causes loss of packets and degrades the performance of the network;
2. modify packets received from source nodes or inject fake ones into the network. This is known as a pollution attack where the \(BS\) receives polluted packets that cannot be separated from the original ones in a conventional standard routing.  
The proposed solution guarantees that with a coalition of \(MN\)s of a size in the range of a threshold \(K=p-t-1\), the adversary fails to stop the successful reception of any message from a source node at the \(BS\). The proposed solution is also protected against eavesdropping attacks as in [15]. These occur when an adversary has unauthorized access to useful data transmitted by any link in the network. The adversary, in this case, can be internal to the network as any relay node except the source nodes and the \(BS\); or external to the network with the ability to wiretap inter-cluster or intra-cluster links.  
Our proposition deals with the following security objectives:  
* Availability: ensuring that messages transmitted by a source node reach the \(BS\), thus providing survivability of the \(W\)SN services.  
Figure 3: The network and adversary model.  
* Integrity: ensuring the reception of the correct original messages of source nodes when there are attempts by hackers to modify data en route to the \(BS\).
* Detection and isolation: identifying adversarial behavior sources, and isolating adversary nodes from the routing to prevent any damage to the network.
* Confidentiality: ensuring secrecy of the wireless communication links to avoid eavesdropping.
## 5 Simulation Outcomes and Analysis
### Influence of Communication Radius  
The five algorithms mentioned above test in the presence of varying communication radii. The total number of nodes is 100, with the proportion of anchor nodes being 20%. R varies in the range of 15 to 40 m. In Figure 11, two categories of errors for each algorithm are compared.  
Inside the communication radius of these five groups, we can analyze them as follows: CMWN-DV-Hop (a = 2) and DV-Hop take the same communication radius of 25 m for minimum localization error. The three algorithms consisting of BFO-GSO-DV-Hop, which achieve the same minimum positioning error of R, are all 20 m. Meanwhile, with the increase in segmentation factor, the localization error of the CMWN-DV-Hop algorithm shows an overall decreasing trend. In particular, among these five communication radii, CMWN-DV-Hop always has the minimum localization error value when the communication radius is greater than or equal to 30 m, regardless of the segmentation factor. By observing Figure 11b, the vertical coordinates of the five algorithms tested decrease as R increases. Overall, the average orientation error does not differ significantly for CMWN-DV-Hop (a = 2) compared to the other three. Alternatively, there is a significant decrease compared to DV-Hop. Furthermore, the average orientation error of CMWN-DV-Hop (a = 4, 6, 10) is the least when R is greater than or equal to 25 m. When the communication radius is varied, CMWN-DV-Hop (a = 2, 4, 6, 10) has the maximum reduction in the average localization error by 36.6216%, 64.5825%, 74.9304%, and 81.8575%, respectively, compared with DV-Hop. Meanwhile, CMWN-DV-Hop demonstrates the best locating  
Figure 10: Comparison of various localization algorithms with different anchor node ratio: localization error.  
capabilities when a \(\geq\) 4 and R \(\geq\) 25 m. In particular, CMWN-DV-Hop (a = 10) is reduced by a maximum of 75.2018%, 74.2070%, and 73.5611% compared to the other three proposed novel algorithms.
###### Abstract
This automated process saves time, is less prone to human error and may require less domain expertise of a specialized domain expert. This paper investigates the use of deep learning in the context of fault detection of rolling bearing elements and builds on our earlier research [11], exploring the usage of one-dimensional CNNs for classifying multivariate signals based on data derived from rotating machines. In particular, we propose several architectures that are built on the wide-kernel framework developed by [10]. These models tend to handle high frequency sensor data particularly well, while using a somewhat \"shallow\" deep learning structure; therefore, these models are easily trainable, can be scaled when the dimensionality of the data increases and are applicable in real-time settings. Due to these reasons, such architectures are then also applicable in different contexts and application domains. We exemplarily investigate those approaches using two datasets, where we demonstrate their efficacy for classifying multivariate signals. Our contributions are summarized as follows:  
1. We investigate the performance of wide-kernel CNNs [10] in several settings and adaptations designed to process multivariate time series derived from various experiments. For this, we demonstrate how high performance in multi-class signal classification tasks can be achieved. 2. We propose a method for implementing an adaptive wide-kernel in the first convolutional layer that is able to transform any form of sequential sensor data without any dimensionality reduction; therefore, abiding the principles of a wide-kernel convolutional layer as proposed in [10; 11]. We implement this in two models. 3. We evaluate our model options thoroughly on multiple datasets in deviating contexts in order to show their generalizability, both with and without large amounts of training data. 4. Finally, we illustrate the impact of model settings and architecture adaptations on model performance, resulting in a streamlined model on both the performance side, as well as computational efficiency. The rest of the paper is structured as follows: We discuss related work in Section 2, where we also briefly summarize the necessary background on deep learning. This is followed by the introduction of the proposed models in Section 3. Next, we describe the experiments and findings in depth in Section 4. Finally, Section 5 concludes with a discussion and summary and also outlines some promising future prospects.
## 3 Materials and Methods  
### Datasets  
This study incorporated 2018 and 2019 MODIS GPP and MODIS burned area data, obtained using the cloud-based geospatial processing platform GEE. MODIS cumulative eight-day composite GPP products (MOD17A2H) with a 500 m resolution have been used to estimate the plant productivity for both years. The MOD17A2 product derived from the MODIS sensor provides the accumulated value of GPP based on the concept of the efficiency of solar radiation used by vegetation [12]. Similarly, MODIS Terra and Aqua, combining burned area monthly data products (MCD64A1 Version 6) with a 500 m resolution, were used to map the burned areas for both years.
# Federated Learning for Secure In-Vehicle Communication
## 4 Experimental Setup
### Data Preprocessing  
The INV-IDC dataset was collected and labeled but requires cleaning and normalization. To clean the data, missing values are replaced with 256. Additionally, the hexadecimal values of the IDs and data payload are converted into decimal values ranging from 0 to 256. The min-max normalization technique is employed to maintain the relationships within the original data [47]. This simple and effective method scales the data within a specified range, typically between 0 and 1 [47].  
SMOTE (Synthetic Minority Oversampling Technique) is an efficient approach for tackling unbalanced datasets within machine learning by creating artificial samples for the less represented classes [48]. The imbalanced-learn library in Python offers a user-friendly implementation of SMOTE, enabling users to achieve balance in datasets by oversampling the minority class. This strategy enhances model performance by establishing a fairer class distribution, avoiding the simple replication of current samples, and hence maintaining the dataset's variety [48]. This process ensures that the dataset is properly normalized and prepared for subsequent modeling tasks.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 4 Comparison of the Prestress Loss with Steel Strands
### Results for Steel Strands
#### 4.2.3 Total Prestress Loss of Steel Strands  
The total prestress loss of steel strands at different distribution angles and slab widths is summarized in Table 13.  
As shown in Table 13, given the slab width ranging from 3.0 m to 12.75 m and the distribution angle from 20\({}^{\circ}\) to 45\({}^{\circ}\), the total prestress loss of steel strands was from 21.8% to 31.8%, with the lowest and the highest prestress loss being 21.8% and 31.8%, respectively. For a given distribution angle of steel strands, the total prestress loss reduced first, and then underwent a small increase as the slab width increased. The smaller the distribution angle, the greater the decrease rate.
# A Study on Data Profiling: Focusing on Attribute Value Quality Index
## 1 Introduction  
In the era of the Fourth Industrial Revolution, more and more people and things are connected to the internet, and big data are being produced explosively [1]. In order for industries to improve the competitiveness of products and services, big data are explosively being produced and utilized, and the government also encourages the creation of new job openings throughout the open and private sectors, as well as the utilization of public data [2]. The amount of digital information produced and circulated globally is expected to be 90 zettabytes in 2020 [1], and digital information in Korea is expected to increase by an annual average of 57% [3; 4]. Digital information can be classified into unstructured data, such as image and voice, and structured data. Unstructured data account for an overwhelming proportion of 92% of the total amount of information, and structured data account for 8% [3]. Companies are focusing on enhancing the competitiveness of products and services using artificial intelligence (AI) technology, and machine learning, which is a core technology of artificial intelligence (AI), is a way for computers to acquire high-level characteristics through self-learning data. Securing high-quality big data is becoming an important asset to enhance corporate competitiveness. Also, with the advancement of data technology, the value of data utilization becomes higher; thus, systematic quality control is required. Until now, the quality control of data concentrated on structured data, with a very weak demonstration for unstructured data. Therefore, it can be said that a systematic study is needed to calculate the quality index of structured and unstructured data.  
Major companies around the world are focusing on securing artificial intelligence (AI) technology, which is a core technology of the Fourth Industrial Revolution, to enhance competitiveness. Global information technology (IT) companies such as Google, Facebook, Microsoft, and IBM are releasing AI algorithms. The volume of digital information is rapidly increasing worldwide, data technology is being developed further, and the value of using stereotyped and unstructured data is getting higher. Companies are focusing on securing artificial intelligence to enhance their competitiveness. Machine learning, which is a core technology of artificial intelligence technology, requires high-quality data in a way that enables computers to acquire high-level characteristics through self-learning of data. Big data that artificial intelligence can learn are exploding, but research on data profiling for securing reliable and high-quality data is very lacking. In particular, it is very important to develop a data quality index for evaluating data quality in digital information [3]. In this paper, we propose a research model for calculating the quality index of fixed and unstructured data, and we derive attributes for data quality diagnosis, calculate the data attribute value quality index, and calculate the data value quality index.
# Investigation of In Situ and Ex Situ Passivation of Pyrophoric Uranium-Niobium Alloy Powder
## 3 Results and Discussion
### Optical Analysis and Color
Variation due to multiple uranium oxidation states (e.g., UO\({}_{2}\) vs. U\({}_{3}\)O\({}_{8}\)) was also initially considered, as high temperatures during in situ passivation could favor the formation of higher-oxidation-state uranium oxides [40; 41]. However, this was not observed in PXRD, with only UO\({}_{2}\) detected (Figure S2), likely due to the limited oxygen content available during passivation. Therefore, oxide thickness variations rather than chemical or  
Figure 3: Optical microscopy of individual spheres showing change in color from passivation: (**a**) unpassivated powder is initially silver colored; (**b**) 5 SLPM in situ-passivated spheres are multicolored; (**c**) ex situ-passivated spheres are a more homogeneous light brown color. compositional discrepancies are the primary cause for the observed color range in the in situ-passivated powder. For the color range between particles to result from different oxide thicknesses, there must be some variability in the local conditions within the plasma chamber. As evidenced by the visibly flickering intensity of the plasma (Video S2), there are temperature variations within the plasma. It might be expected that particles that reach higher temperatures will oxidize to a greater extent due to the temperature dependence of the oxidation rate of uranium regardless of the impact of niobium on oxide growth [42; 43; 44; 45]. However, if temperature variation alone dictated the degree to which the powder particles oxidize, increasing the amount of passivation gas would not result in a higher oxygen content in the powder, which contradicts the experimental observations (Figure 2). Any variation in oxygen concentration in the plasma chamber could also affect oxide layer growth. The passivation gas is introduced into the chamber below the plasma through 12 small holes around the circumference. As the powder falls through the center and the oxygen is introduced circumferentially, an oxygen concentration gradient likely appears, causing some spheres to passivate to a greater extent than others. Additionally, since the powder is not fed into the spheroidizer at a perfectly consistent rate, nonuniformity in the spatial arrangement of powder particles can also result in competition for oxygen between more closely packed particles, leading to lesser passivation than isolated particles with an abundance of surrounding oxygen. Unfortunately, due to material availability limitations, these parameters could not be independently investigated for their specific effects. Consequently, the complex gas mixing and competition for oxygen during spheroidization could result in a wide distribution of oxide thicknesses within a batch, evident in the rainbow coloration of in situ-passivated powder, in contrast to the uniform color of ex situ-passivated and unpassivated powder.
## 4 Experimental Evaluations
### Roughness Inversion and Water-Level Forecasting  
As there were no measured bed-roughness values in practice, we compared our inversed roughness values established from the river and floodplain model by the Federal Emergency Management Agency [23]. Figure 11 shows that most river sections had bed roughness close to 0.035, and Section 2 had a much lower value of around 0.03. Compared to the estimation results from the Federal Emergency Management Agency [23], our inversed roughness values were slightly lower, particularly at Sections 3 and 6.  
In order to verify the inversion accuracy, measured water-level data from 25 May to 17 June 2003 at the Bonners Ferry station were used to compare the model forecasting results of our model. Generally, the forecasting errors produced by this work approached zero with the elapsed time, while those produced by the Federal Emergency Management Agency [23] deviated from zero with the elapsed time, as illustrated in Figure 12. The forecasting water-level RMSE in this work was 0.041 m, which is 31% less than that from Federal Emergency Management Agency (i.e., RMSE = 0.060 m) as shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Date** & **18 August** & **19 August** & **20 August** & **21 August** & **22 August** & **23 August** & **24 August** & **25 August** & **26 August** \\  & **2003** & **2003** & **2003** & **2003** & **2003** & **2003** & **2003** & **2003** & **2003** \\ \hline Daily mean discharge (m\({}^{3}\)/s) & 532.4 & 535.2 & 529.6 & 532.4 & 470.1 & 410.6 & 410.6 & 407.8 & 410.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Observations of volume flow in Kootenai River.  
Figure 10: Information of the Kootenai river obtained from the United States Geological Survey. (**a**) Kootenai River; (**b**) map of Kootenai River.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Section Number** & **River Length/m** & **River Width/m** & **Average Slope** \\ \hline
1 & 1595 & 265–432 & \\
2 & 4634 & 173–319 & \\
3 & 10,327 & 188–343 & \\
4 & 1567 & 151–289 & \\
5 & 1543 & 106–197 & \\
6 & 4627 & 155–299 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Basic information of Kootenai River.  
Figure 11: Schematic diagram of Kootenai River.  
Figure 12: Inversed bed roughness at different river sections, and their comparison with the estimated values by [PERSON] and [PERSON].
# Research on Fractional-Order Control of Anchor Drilling Machine Optimized by Intelligent Algorithms
## 4 Mathematical Modeling and Simulation Test
### Performance Metric Function Evaluation  
The performance of control systems is typically evaluated using metrics such as Integral of Squared Error (ISE), Integral of Absolute Error (IAE), Integral of Time-Weighted Squared Error (ITSE), and Integral of Time-Weighted Absolute Error (ITAE) [23]. These metrics aim to minimize errors and improve the performance of the FOPID controller. The evaluation formulas for these metrics are as follows:  
\[ISE=\int_{0}^{\infty}e^{2}(t)dt,IAE=\int_{0}^{x}|e(t)|dt,ITSE=\int_{0}^{\infty }te^{2}(t)dt,ITAE=\int_{0}^{\infty}t|e(t)|dt \tag{5}\]  
This study compares four algorithms--WOA, GWO, PSO, and ILPSO--as benchmark optimization methods to assess the control system's performance [24; 25; 26]. These algorithms are evaluated based on performance metrics like ITAE, ISE, ITSE, and IAE, which aim to minimize accumulated errors over time.  
As shown in Figure 7, the WOA algorithm excels with time-weighted error functions such as ITAE and ITSE, showing rapid convergence in early iterations, though it performs less effectively with ISE and IAE. The GWO algorithm performs best under ITAE, with fast convergence and the lowest final fitness value, making it ideal for high dynamic response tasks, but is weaker with ISE. PSO shows the lowest fitness value for ITAE but converges slower; it performs well with IAE and ITSE, stabilizing quickly in early iterations. ILPSO achieves low final fitness values and fast convergence for both ITAE and IAE, offering a balanced global and local search, though it underperforms with ISE.  
Figure 6: Step response curves of the system with optimal PID, PI\({}^{\lambda}\)D\({}^{\mu}\), and PID\({}^{\mu}\) controllers. The black arrows highlight zoomed-in views (insets) of the response curves within the region demarcated by the black rectangular box, allowing for a more detailed examination of the system’s behavior during this interval.