esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Sampling Methods for Learning from Imbalanced Data
#### 2.1.1 Oversampling Techniques  
Random oversampling tries to balance class distribution by randomly replicating minority class instances. However, several authors agree that this method can increase the likelihood of overfitting occuring, since it makes exact copies of existing instances [5].  
Synthetic Minority Over-sampling Technique (SMOTE), the most popular over-sampling method, was proposed by [PERSON] et al. [40]. Its main idea is to create new minority class examples by interpolating several minority class instances that lie together. SMOTE can avoid the over fitting problem [41]. However, its procedure is inherently dangerous since it blindly generalizes the minority class without regard to the majority class and this strategy is particularly problematic in the case of highly skewed class distributions since, in such cases, the minority class is very sparse with respect to the majority class, thus resulting in a greater chance of class mixture [42].  
Many improved oversampling algorithms attempt to retain SMOTE's advantages and reduce its shortcomings. MSMOTE (Modified SMOTE) [6] is a modified version of SMOTE. The main idea of this algorithm is to divide the instances of the minority class into three groups, safe, border and latent noise instances, by the calculation of distances among all examples. When MSMOTE generates new examples, the strategy to select the nearest neighbors is changed with respect to SMOTE and depends on the group previously assigned to the instance. For safe instances, the algorithm randomly selects a data point from the K nearest neighbors; for border instances, it only selects the nearest neighbor; finally, for latent noise instances, it does nothing. This method is effective to reduce the risk of introducing artificially mislabeled instances. Hence, it can lead to more accurate classification than SMOTE. [PERSON] et al. try to increase the effectiveness of SMOTE by dividing the data set into four groups: safe, borderline, rare and outliers [10]. In fact, it is another version of MSMOTE which considers a fourth group in the underlying instance categorisation: rare instances. Their results show that borderline examples are usually preprocessed. The preprocessing of outliers depends on whether the safe examples are representative enough within the core of the class: if the amount of safe examples is rather low, preprocessing outliers is usually a good alternative. Finally, the preprocessing of rare examples mainly depends on the amounts of safe examples and outliers.
# A Jamming-Resilient and Scalable Broadcasting Algorithm for Multiple Access Channel Networks
## 6 Jamming Model  
In this section, we describe the design of the network jamming model, which is called the randomized jammer. This entity, which is also known as a memoryless jammer, is defined based on the parameter \(j\in[0.0,1.0]\), which controls the jamming power. For any number of rounds \(\tau\), the channel is jammed for at most \(j*\tau\) rounds. This model specifies the jamming effect on the channel, which results in network disruption. Thus, it is not associated with the number of jamming entities but with the collective jamming power of these entities.  
This jamming model was chosen to assess the performance of the broadcast algorithms under both low and high jamming attacks. Furthermore, it can have more jamming power than intelligent jammers, which are typically designed for energy efficiency or to avoid detection. Therefore, this model can summarize the performance differences between the broadcast algorithms under jamming attacks with different levels of power and sophistication.  
When the jammer attacks the channel in a specific round, there are three possible scenarios. In the first scenario, no legitimate node is transmitting, which would result in hearing the packet that was transmitted by the attacker node. This packet is detected as nonessential by all nodes and is not counted towards the throughput. The second scenario takes place when a single legitimate node transmits a packet on the channel. In such a case, the packet transmitted by the attacker node causes a collision on the channel, which prevents the packet from being successfully transmitted. The third scenario occurs  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Environment** & \multicolumn{2}{c}{**KL-Divergence**} \\ \hline  & **RIM** & **LBIM** \\ \hline RAC 1 & 0.368829 1  & 0.693147 \\ \hline RAC 2 & 0.368829 & 0.693147 \\ \hline RAC 3 & 0.150489 & 0.222243 \\ \hline School’s Library 1 & 0.439448 & 0.058563 \\ \hline School’s Library 2 & 0.398518 & 0.057747 \\ \hline School’s Department 1 & 0.410021 & 0.155151 \\ \hline School’s Department 2 & 0.437304 & 0.153306 \\ \hline School’s Department 2 & 0.490143 & 0.33193 \\ \hline Coffe Shop & 0.01894 & 0.017405 \\ \hline Hotel 1 & 0.201748 & 0.09199 \\ \hline Hotel 2 & 0.263411 & 0.296524 \\ \hline Hotel 3 & 0.693147 & 0.11833 \\ \hline Hotel 4 & 0.004971 & 0.002968 \\ \hline Hotel 5 & 0.004933 & 0.007417 \\ \hline \hline \end{tabular}
\end{table}
Table 1: KL-divergence of the RIM and the LBIM for different network environments.  
when two or more legitimate nodes transmit a packet on the channel. The transmission of another packet by the attacker does not have any effect on the resulting feedback, which is a collision. Following this event, the jammer starts jamming the network as soon as the execution begins, based on the jamming rate \(j\).
# Underwater Image Super-Resolution via Dual-aware Integrated Network
## 3 Methods
### Multi-Scale Color Correction Block (MCCB)  
In underwater images, uneven decay of light propagation leads to low contrast and high color distortion leading to color asymmetry, with the degree of decay varying with wavelength. Conventional underwater processing methods usually adopt a global color compensation strategy and ignore the problem that different color channels are subject to different degrees of attenuation in underwater environments. Consequently, we propose an MCCB that differentially handles three color channels and effectively compensates for different attenuation conditions, avoiding the shortcomings of global processing.  
As illustrated in Figure 1, the input features were initially divided into three scales through the use of various convolution sizes dependent on the level of underwater light reduction. Since the attenuation of red light is the most significant, we use a \(1\times 1\) convolution, while green light and blue light utilize \(3\times 3\) and \(5\times 5\) convolution kernels, respectively. This is because different sizes of convolution kernels have different performance in terms of image details and a wide range of features. Let us take the first MCCB as an example, where the procedure could be written as follows:  
\[F_{R,1}=\sigma(f_{1\times 1}(F_{\text{d}-1})) \tag{6}\]  
\[F_{\text{G},3}=\sigma(f_{3\times 3}(F_{\text{d}-1})) \tag{7}\]  
\[F_{B,5}=\sigma(f_{5\times 5}(F_{\text{d}-1})) \tag{8}\]  
where \(f(\cdot)\) represents the convolution operation, subscripts represent convolution kernels, and \(\sigma(\cdot)\) represents the Relu activation function.  
Subsequently, we splice and fuse the feature information extracted from each of the three colors:  
\[F_{cat}=F_{R,1}\odot F_{G,3}\odot F_{B,5} \tag{9}\]  
where \(\odot\) denotes the channel connection and \(F_{cat}(\cdot)\) denotes the output after the channel connection. Also, to combine the spatial and channel attentional weights, we use a convolutional block attention module (CBAM) [13]. Finally, we fuse and extract more advanced feature information through the CBAM, which can be defined as follows:  
\[F_{MCCB}=H_{CBAM}(F_{cat}) \tag{10}\]  
where \(H_{CBAM}(\cdot)\) denotes the operation of the CBAM. \(F_{MCCB}\) is the output of the MCCB.  
The CBAM is an effective and efficient attention module that is widely used in many computer vision tasks. It integrates channel and spatial attention mechanisms to improve the model's ability to perceive different feature channels and spatial locations. Overall, the use of a CBAM module in underwater image processing can assist the neural network in concentrating on significant features, intensifying the details and contrast of the image and mitigating the lighting problem, thereby enhancing the quality and visualization of underwater images.
# Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery
## 2 Materials and Methods
### Data Sources  
#### 2.2.1 Satellite Data  
Nine Landsat 8 OLI images acquired from April to May 2015 were used in this study. The Landsat 8 satellite was launched by NASA on 11 February 2013. It carries two sensors, the OLI terrestrial imager and a thermal infrared sensor (TIRS). Landsat 8 images have a high spatial resolution, rapid revisit time, and easy data acquisition. The L1T data product has been radiometrically and geometrically corrected. It includes a metadata file (.txt), 11 single-band files (.tif), and band quality assessment (BQA) information. The spectral bands and resolutions of the Landsat-8 OLI data are listed shown in Table 1. The path/row numbers of the nine images were 118/29, 119/27, 119/28, 119/29, 120/27, 120/28, 120/29, 121/27, and 121/28, and the cloud cover was less than 2% for all images. The images were rectified to the Universal Transverse Mercator (UTM) coordinate system using the World Geodetic System (WGS) 1984 datum in the north UTM Zone 36. The multi-spectral bands were fused with the panchromatic band. Radiometric and atmospheric corrections were performed using the Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) algorithm in the ENVI 5.1 to remove atmospheric artifacts produced by molecules and aerosols. The images were geometrically corrected (accuracy: \(\pm\)0.5 pixels).
###### Abstract  
Helmholtz resonators (HR) have been proven to have feasibility for sensor applications with good performance. However, for biosensor application, when the amount of test sample is limited, the operating frequency tends to be very high, which may bring challenges of resonance excitation and frequency measurement. In this paper, a modified HR was proposed for biosensor application at a lower frequency, by designing the neck of the HR as a type of an Archimedes spiral, which can increase the neck length as much as possible without occupying much space. The resonant frequency of the proposed HR was derived by treating it as an HR with a straight neck with the equivalent length. An analytical example was calculated and verified by acoustic finite element analysis, and the results clearly showed that the frequency decreases in comparison to that of the conventional HR. Based on the underlying theory, the measurement principle was proposed and the experiment setup was established using a 3D-printed structure. The experiment was done using test solutions with different glucose concentrations, whose results showed consistency with the analytical results and noticeable frequency increases with the glucose concentration. Prospective results of the proposed HR after miniaturization show an operating frequency around 3 kHz when the amount of test sample is 0.1 mL, which will facilitate use of the common acoustic power source and acoustic pressure sensors in the sensing system.  
Helmholtz resonator; analyte concentration; low frequency; biosensor application; glucose sensor +
Footnote †: journal: Applied Physics Letters  
_Applied Physics Letters_
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 4 Review of the Chuang-Lee's Scheme
### Login  
The login procedure is performed when a user \(U_{i}\) intends to access the service from vehicle sensor networks. The login procedure is described as follows:  
1. \(U_{i}\to OBU_{i}\): \(ID_{i}\) and \(PW_{i}\) are input to \(OBU_{i}\) by \(U_{i}\).
2. First, \(OBU_{i}\) verifies \(ID_{i}\). Then, it checks whether \(B_{i}=h(PW_{i})\bigoplus C_{i}\) holds. If so, \(OBU_{i}\) launches the general authentication procedure or the trust-extended authentication procedure. Otherwise, the login request will be rejected.
## 3 Design and Simulation of a Velocity Loop Controller Based on Discrete SMC
### Basic Theory of Discrete SMC  
#### 3.1.1 System Discrete Time Ideal Equation of State  
According to the identification model equation of state shown in Equation (7), the ideal state equation of the system can be expressed as follows:  
\[\dot{x}(t)=Ax(t)+Bu(t)+\zeta(t). \tag{8}\]  
For the digital control system, the sampling frequency is 1 KHz, and the discretized equation of state is expressed as:  
\[x(k+1)=A_{k}x(k)+B_{k}u(k)+\zeta(k), \tag{9}\]  
where \(A_{k}=A_{o}+\Delta A(k)\), \(B_{k}=B_{o}+\Delta B(k)\) are respectively represented as the real state matrix and output matrix of the system. \(A_{o}\), \(B_{o}\) are nominal parts of \(A_{k}\) and \(B_{k}\) respectively, which can be obtained by Matlab as follows:  
\[A_{o}=\left[\begin{array}{ccc}0.98&-12.87&-48.52\\ 0.001&0.99&-0.024\\ 0&0.001&1\end{array}\right]B_{o}=10^{3}\cdot\left[\begin{array}{c}0.992\\ 0.0005\\ 0\end{array}\right].\]  
The modeling error and the parameter variation \(\Delta A(k)\), \(\Delta B(k)\) are assumed to be differentiable with respect to temporal series \(k\), and \(\zeta(k)\) represents nonlinear factors of the system.  
**Assumption 1**.: _The uncertainties \(\Delta A(k)\), \(\Delta B(k)\), and \(\zeta(k)\) are bounded and satisfy the \"matching\" condition that \(\Delta A(k)\), \(\Delta B(k)\), and \(\zeta(k)\)\(\varepsilon\)\(\mathrm{spant}\)\(\{B_{k}\}\)._  
Therefore, \(\Delta A(k)\), \(\Delta B(k)\), and \(\zeta(k)\) must be able to be unified as nonlinear factors for theoretical research [34; 35; 36; 37; 38], as follows:  
\[\varepsilon(k)=\Delta A(k)x(k)+\Delta B(k)u(k)+\zeta(k), \tag{10}\]  
where \(\zeta(k)\) satisfies \(\|\zeta(k)\|\)\(<\)\(\zeta_{max}\) with \(\|\cdot\|_{\infty}\) being the vector infinity norm and \(\zeta_{max}\) being an unknown constant.  
Therefore, the discrete equation of state shown in Equation (9) can be rewritten as:  
\[x(k+1)=A_{k}x(k)+B_{k}u(k)+\varepsilon(k). \tag{11}\]
## 5 Conclusions  
This study analyzed the influence of the surface roughness on ultrasonic nonlinearity parameter measurements. For this purpose, Al6061-T6 and SUS304 specimens with different surface roughness on both sides were prepared (ranging from 0.5 to 3 \(\upmu\)m); additionally, Al6061-T6 specimens with one rough surface and one smooth surface were prepared. Then, the absolute and relative ultrasonic nonlinearity measurement experiments were conducted using the through-transmission technique.  
When both surfaces of the specimen were rough, the absolute nonlinearity parameter showed no noticeable change as the surface roughness increased when compared to the relative nonlinearity parameter. This was because the influence of the surface roughness was compensated by the calibration function obtained from calibration during absolute nonlinear measurements. These results were similar for the Al6061-T6 and SUS304 specimens. Therefore, the variation in the nonlinearity parameter with roughness is less related to the material properties. Furthermore, a rough reception surface affected the relative nonlinearity parameter more than a rough transmission surface.  
Therefore, it is desirable to measure the nonlinearity parameter after polishing the material surface to reduce the roughness to approximately Ra \(<\) 1.5 \(\upmu\)m to ensure the reliability of the nonlinearity parameter measurements using a contact transducer. For rough surfaces, the absolute nonlinearity parameter measurement is more accurate than the relative nonlinearity parameter measurement. These results can be used to improve the reliability of ultrasonic nonlinearity parameter measurements on rough surfaces formed because of oxidation and corrosion.  
Conceptualization, J.K., H.-P.H., and K.-Y.J.; methodology, J.K., and H.-P.H.; software, J.K., and H.-P.H.; validation, J.K., H.-P.H., and K.-Y.J. formal analysis, J.K., and H.-P.H.; investigation, J.K., and H.-P.H.; resources, J.K., and H.-P.H.; data curation, J.K., and H.-P.H.; writing--original draft preparation, J.K.; writing--review and editing, J.K., K.-M.K., and K.-Y.; visualization, J.K., and H.-P.H.; supervision, K.-Y.J.; project administration, K.-M.K., and K.-Y.J.; funding acquisition, K.-M.K., and K.-Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Nuclear Power Research and Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2013M2A2A9043241), and the Ministry of Education (NRF-2017R1D1A1B03027828).  
The authors declare no conflict of interest.
# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 7 The Role of AI in Cancer Genomics
### AI Approaches and Applications: Overview  
AI includes various technologies designed to simulate human cognitive processes, allowing machines to perform tasks that typically require human intelligence. Fundamentally, AI involves various approaches, such as machine learning, deep learning, and natural language processing, each serving different functions and applications [74].  
ML is a subset of AI that allows systems to learn from data without being explicitly programmed [74]. ML algorithms identify patterns and relationships in large datasets, making predictions or decisions based on new data. These algorithms can be divided into supervised learning, where the system is trained on labeled data, and unsupervised learning, where the system identifies patterns on its own. One example of ML in practice is clustering algorithms, which group data into categories based on similarities, a technique used in diverse fields, like marketing and finance.  
DL, a more advanced form of ML, uses artificial neural networks with many layers of processing (hence the term \"deep\") to analyze complex data [74]. DL excels in tasks like image and speech recognition, where it can process vast amounts of unstructured data, such as images or audio files. Convolutional neural networks (CNNs) are one of the most common types of DL models, used for tasks like facial recognition, while Recurrent Neural Networks (RNNs) are used for sequential data analysis, such as language translation or speech-to-text systems.  
NLP is another essential area of AI that focuses on the interaction between computers and human language [75]. NLP techniques enable machines to understand, interpret, and generate human language, allowing for applications like chatbots, feeling analysis, and automatic translation. NLP models can also assist in extracting useful information from large bodies of text, as seen in applications ranging from legal document analysis to customer service.  
Together, these AI approaches form a powerful toolkit that is transforming industries from healthcare to finance, allowing for automation, enhanced decision making, and improved efficiency in managing complex tasks. In oncology, for example, these technologies are being applied to interpret genomic data, predict disease progression, and optimize treatment plans, but they have broad applications across various domains, including robotics, autonomous vehicles, and personalized recommendations in e-commerce.
# Adaptation in Gait to Body-Weight Unloading
## 4 Discussion
### Temporal Gait Measures  
The results indicate that there were no significant changes in any of the temporal gait measures during and after unloaded walking, except for swing time, which was significantly increased during unloaded walking. The increase in swing time during unloaded walking corroborates the findings from previous studies [11; 18; 24; 46; 47]. This observation of increased swing time with body-weight unloading is in line with the predictions of the ballistic pendulum model of walking, which states that the motion of the swing leg is like that of a pendulum of which the oscillation period is inversely related to gravity. Additionally, the setup of LBPP by itself might also have contributed to this increase in swing time. Specifically, since the lower body is supported in this type of setup, there is less of a threat to postural instability, due to which one can afford to spend a longer time in the swing phase.  
The lack of significant changes in temporal parameters following adaptation to unloading could be due to the constraints imposed on the walking performance by the speed and the dynamics of the treadmill and particularly due to the lack of stride to stride variability inherently associated with treadmill walking [48]. Thus, potentially testing the aforeeffects of adaptation to unloading during over-ground walking as opposed to treadmill walking could provide additional insights into the adaptive process.  
Although not statistically significant, there was a trend towards increased stance time accompanied by a decrease in swing time immediately after unloaded walking (at T\({}_{1}\)) similar to that reported in another similar study [30]. These changes in stance and swing time after unloading have been proposedas a control strategy adopted by the motor control system to maintain prolonged foot contact with the ground and, hence, to maintain stable balance by increasing the stance time while also decreasing the time spent during the unstable swing phase to prevent the risk of falls following adaptation to unloaded walking. However, since the lower body was securely attached to the antigravity chamber in the current study, there was no risk of falling. Thus, similar trends in the adaptive changes of stance and swing times between the two studies indicate that these changes might have been a direct result of modulation of somatosensory information resulting from changes in body-load sensing mechanisms and not that due to postural instability.
# ICRSSD: Identification and Classification for Railway Structured Sensitive Data
## 3 Experiment
### Experimental Design and Result Analysis
#### 3.2.2 Experimental Validation of the Effectiveness of Fine-Grained Division  
To validate the effectiveness of fine-grained division, we conducted comparative experiments using the dataset described in Table 4. The experiments compare the identification accuracy achieved without division versus that achieved after applying fine-grained data division. Both experiments employed a clustering-based dynamic classification approach and the voting strategy.  
Table 8 presents a comparison between the experimental results with fine-grained division of structured data and those without division. As shown, performance improves across all systems after fine-grained division, except for System 4. On average, the proposed method resulted in a 2.05% improvement in accuracy across all systems. The most notable improvement was observed in System 2, where accuracy increased by 4.08%. These results demonstrate our fine-grained division strategy lead to an improvement in the accuracy  
Figure 6: Comparison of different methods.  
of structured sensitive data identification and classification. In System 4, both methods achieve the same level of accuracy, indicating that they correctly identify the same number of sensitive attributes. Upon analyzing the original data, we found that, since our method is non-incremental, it replaces rather than builds upon the results of information entropy. Although it is capable of identifying some semi-canonical attributes that information entropy alone cannot identify, it simultaneously results in the loss of attributes that were originally identified correctly by the entropy-based method. The offsetting effects of the two factors result in the overall accuracy remaining unchanged.