esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Consequently, when fine-tuning the classifier for downstream tasks, the encoder fails to rectify the misconceptions embedded within its representations, and the learned representations may not capture the underlying features of the raw data. The existence and impact of the DAB problem are discussed in the fourth section. To address the influence of the DAB, we propose the DAB-aware contrastive learning framework (DABaCLT) that aims to recognize and minimize the impact of DAB on downstream tasks. The framework consists of two streams: the DAB features stream (DABFS) and the raw features stream (RFS). The DABFS extracts augmented temporal and contextual features, while the RFS helps identify and mitigate the DAB issue present in the learned representations from DABFS. The DABFS incorporates a transformation inconsistency augmentation (TIA) module with a tempo-scale strategy for data augmentation. In the RFS, features from raw data are also extracted and used in the contrasting module alongside augmented data to create negative and positive pairs for DAB awareness. To minimize the DAB in the learned representations, we design DAB-minimizing temporal and contextual contrasting modules (DABa-TC and DABa-CC) that operate in higher dimensions and leverage the assistance of RFS to extract temporal and contextual features. This work is the first to address the DAB problem and propose a method to minimize it in learned representations. In summary, the main contributions of this work are the following:  
* We introduce the concept of the data augmentation bias problem (DAB) and present the DABaCLT framework to recognize and minimize DAB during the learning of time series representations. The RFS is developed as a criterion for perceiving DAB and is effectively combined with the DABFS. * We propose DAB-minimizing contrastive loss (DABMinLoss) in the contrasting module to reduce the DAB in extracted temporal and contextual features. The complete DAB-MinLoss consists of five parts implemented in the DABa-TC and DABa-CC modules. * Extensive experiments demonstrate the superiority of DABaCLT over previous works. Compared to self-supervised learning, DABaCLT achieves significant improvements ranging from 0.19% to 22.95% in sleep staging classification, 2.96% to 5.05% in human activity recognition, and 1.00% to 2.46% in epilepsy seizure prediction. The results also compete favorably with supervised methods.
## 4 Investigating Parameters' Impact on Beacon Rate Adaptation
### The Impact of the Selected Parameters  
We investigated the impact of the five aforementioned influencing factors. The preliminary results of our investigation were reported in [50; 51; 52]. This led us to establish in this work, and through extensive simulation experiments, that specific sets of parameters were best suited to adapt the beaconing rate for specific environments. Set 1 comprises the SINR, the count of neighboring nodes, and mobility. Set 2 is constituted of mobility, the CBT, and the PDR. Set 3 encompasses the SINR, the number of neighboring nodes, mobility, the CBT, and the PDR.  
We ran thousands of simulations and obtained consistent results that confirmed the effect of these parameters in different scenarios. We ended up with the following observations (Table 1). In nondense networks, we had two scenarios. The first scenario was a nondense clustered network environment. In that scenario (scenario 1), we obtained good performance when we adapted the transmission rate based on Set 1's parameters. The second scenario was a nondense uniformly distributed network environment. In that scenario (scenario 2), we obtained good performance when we adapted the transmission rate based on Set 2's parameters. In the third scenario, we found that in the case of dense networks, regardless of the type of environment (uniformly distributed or clustered), we obtained good performance when we adapted the transmission rate based on Set 3's parameters. These findings appeal to intuition. For instance, in nondense networks in urban environments, Set 1's parameters did not work very well, since a small value of the SINR does not always mean a vehicle is far away; it could be close, but there are some impeding objects such as buildings in between the two vehicles. From the above observations, it is clear that there is no one solution that fits all and every scenario requires different parameters to be considered. This strongly motivates the design of a protocol that adapts to the different scenarios.
# Examining Pipe-Borehole Wall Contact and Pullback Loads for Horizontal Directional Drilling
## 2 Analysis of Pipe-Borehole Wall Contact
### HDD Pipe-Borehole Wall Contact Model
#### 2.2.3 External Load-Displacement Relationship  
A relationship between the external load \(P\), maximum contact pressure \(p_{0}\), and the semi-chord length of contact \(l\) can be obtained by integrating the contact pressure distribution formula (Equation (13)):  
\[P=2\int_{0}^{l}p_{0}\left(1-\frac{x^{2}}{l^{2}}\right)^{n}\mathrm{d}x. \tag{18}\]  
Substituting Equation (14) into Equation (18) yields  
\[P=2 Bl\left[\left(\frac{PE^{*}}{\pi R}\right)^{1/2}+\Delta p\right], \tag{19}\]  
where  
\[B=\frac{\sqrt{\pi}\Gamma(n+1)}{2\Gamma(n+3/2)}, \tag{20}\]  
Figure 7: Distribution of the contact pressure.  
Here, \(\Gamma\) denotes the gamma function, particularly if \(n=1/2\), \(B=\pi/4\).  
The analysis of the FEM results reveals that for the HDD pipe-borehole wall contact problem, there are significant errors (see Figure 8) when using the Hertz model (Equation (5)) or Persson model (Equation (8)) to describe the geometric relationship between the semi-chord length \(l\) and the compressive displacement \(\delta\). Notably, both the FEM and Hertz model results showed a linear relationship. Therefore, the Hertz model was modified to express the geometric relationship of the HDD pipe-borehole wall contact as follows:  
\[l=k\sqrt{R\delta}, \tag{21}\]  
where \(k\) is a dimensionless coefficient fitted in Figure 8 with a value of 0.5663.  
Figure 8 shows the geometric relationship between the semi-chord length \(l\) and the compressive displacement \(\delta\). It can be seen that Equation (21) fits the FEM results well. Substituting Equation (21) into Equation (19), the relationship between the external load \(P\) and the compressive displacement \(\delta\) is obtained as follows:  
\[\delta=\frac{P^{2}}{4B^{2}k^{2}R\left[\left(\frac{PE^{*}}{\pi R}\right)^{1/2}+ \Delta p\right]^{2}}. \tag{22}\]  
This section establishes the HDD pipe-borehole wall contact model, which forms the basis for calculating pipe-soil friction from a contact perspective.
###### Abstract  
Underwater inspection images of hydraulic structures often suffer from haze, severe color distortion, low contrast, and blurred textures, impairing the accuracy of automated crack, spalling, and corrosion detection. However, many existing enhancement methods fail to preserve structural details and suppress noise in turbid environments. To address these limitations, we propose a compact image enhancement framework called Wavelet Fusion with Sobel-based Weighting (WWSF). This method first corrects global color and luminance distributions using multiscale Retinex and gamma mapping, followed by local contrast enhancement via CLAHE in the L channel of the CIELAB color space. Two preliminarily corrected images are decomposed using discrete wavelet transform (DWT); low-frequency bands are fused based on maximum energy, while high-frequency bands are adaptively weighted by Sobel edge energy to highlight structural features and suppress background noise. The enhanced image is reconstructed via inverse DWT. Experiments on real-world sluice gate datasets demonstrate that WWSF outperforms six state-of-the-art methods, achieving the highest scores on UIQM and AG while remaining competitive on entropy (EN). Moreover, the method retains strong robustness under high turbidity conditions (T \(\geq\) 35 NTU), producing sharper edges, more faithful color representation, and improved texture clarity. These results indicate that WWSF is an effective preprocessing tool for downstream tasks such as segmentation, defect classification, and condition assessment of hydraulic infrastructure in complex underwater environments.  
underwater image enhancement; wavelet fusion; Sobel weighting; hydraulic infrastructure inspection; defect assessment
# Parameter Optimization for Ship Antiroll Gyros
## 6 Conclusions  
Little work has been conducted for optimizing gyro parameters. In the present study, we designed a parameter optimization method for antiroll gyros of ships. The specific contributions of the present work are as follows:  
We established a joint dynamical equation of ships and antiroll gyros and analyzed the influence of gyro's damping coefficient \(C\), rotational momentum moment \(h_{0}\), and precession momentum moment \(J\) on roll reduction rate. Following this, we proposed a calculation method for the roll reduction rate. Then, taking the minimum rotor mass and highest roll reduction rate as the objective function, and the ship space, power drive, and material strength as the constraints, we established a gyro parameter optimization model. Finally, we used the PSOA and BFOA to solve the two-parameter optimization models.  
Our simulation results revealed that antirolling characteristics, such as roll reduction rate and rotor mass, improved effectively through gyro parameter optimization. In addition, the convergence speed of the BFOA was faster than that of the PSOA, and the antirolling characteristics obtained by the BFOA were better than those obtained by the PSOA, and this was due to the excellent global search ability of the BFOA.  
Conceptualization, Y.Z. and S.S.; methodology, Y.Z.; software, Y.Z.; validation, Y.Z., S.S. and Y.Q.; formal analysis, Y.C.; investigation, W.T.; data curation, Y.Z. and S.S.; writing--original draft preparation, Y.Z. and Y.Q.; writing--review and editing, Y.C. and S.S.; visualization, Y.Z.; supervision, S.S.; project administration, S.S.; funding acquisition, S.S. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Postgraduate Research and Practice Innovation Program of Jiangsu Province (Grant No. KYCX19_1662) and the Industry-University Joint Research Project of Jiangsu Province (Grant No. BY2019038).  
The authors would like to thank the editors and the reviewers for their professional suggestions.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Before Optimization** & **After Optimization** & **Variation** & **Changing Rate** \\ \hline Inner diameter \(D_{1}\) (m) & 1.070 & 1.200 & +0.130 & +12.1\% \\ Outer diameter \(D_{2}\) (m) & 1.240 & 1.263 & +0.023 & +1.9\% \\ Shaft diameter \(D_{3}\) (m) & 0.180 & 0.150 & \(-0.030\) & \(-16.7\%\) \\ Rotor thickness \(H_{1}\) (m) & 0.700 & 0.500 & \(-0.200\) & \(-28.6\%\) \\ Ribbed plate thickness \(H_{2}\) (m) & 0.050 & 0.058 & +0.008 & +16.0\% \\ Gyro’s damping coefficient \(C\) (N/m) & 25,300 & 27,419 & +2119 & +8.4\% \\ Motor speed \(\omega\) (\(r_{\text{min}}\)) & 1255 & 1300 & +5 & +3.6\% \\ Mass Ms (kg) & 2520 & 1560 & \(-960\) & \(-38.1\%\) \\ Roll reduction rate TT (\%) & 73.7 & 75.1 & +1.4 & +1.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Gyro parameters before and after optimization for the minimum rotor mass.  
Figure 15: Comparison of the results between the two algorithms.
# Extended Spatial-Index LED Modulation for Optical MIMO-OFDM Wireless Communication
## 1 Introduction
Here, a novel optical MIMO-OFDM communication modulation scheme termed as extended spatial-index light-emitting diode (ESI-LED) modulation is proposed. The key idea of the proposed ESI-LED is to maximize the LED's indices spatial position (spatial domain) utilization, whereas the optical system SE of the ESI is improved by varying both the power weight allocation and the positions of the complex OFDM time domain components several times over the same active LED indices combination. According to the aforesaid above concepts, the major contributions of this paper are as follows:  
* An efficient ESI-LED modulation scheme is proposed. In contrast to a GLIM-OFDM scheme, there is no barrier to utilize any number of LEDs in ESI-LED as long as \(n_{t}\geq 3\). Moreover, the attainable data rates of the ESI are semi-nonlinearly proportional to the number of LEDs deployed. The key idea of ESI-LED is to extend the resultant LED indices SD by maximizing the resulting LED combinations, whereas the same active LED indices are utilized many times with different power allocation values for OFDM real and imaginary components. Thus, the LED indices could be distinguished based on the real and imaginary part power allocation. Moreover, the number of active LEDs used for data transmission varies from the state in which only two transmit LEDs are activated to the state in which multiple/all transmit LEDs are activated. This will improve the utilization of the resultant spatial domain, thus the overall system spectral efficiency will be improved. * A low complexity maximum a posteriori probability (MAP) estimator is proposed on the ESI receiver side. The MAP estimator in [31] is reformulated to be compatible with ESI-LED, where multiple LEDs or all LED indices are active simultaneously with different OFDM real part power allocations to convey OFDM time domain components. * A general computational complexity (CC) form for both the proposed ESI and the GLIM are derived and tested in the simulation result section. Unlike the GLIM complexity analysis given in [31], the given CC analysis is derived to support any \(n_{t}\times n_{r}\) MIMO-LED configurations with/without spatial modulation. The rest of this paper is structured as follows: In Section 2, the system model for the proposed ESI-LED scheme is presented as well as the indoor optical wireless channel model. The simulation results with different scenarios with the receiver CC are introduced in Section 3. Finally, the paper is concluded in Section 4.
## 4 Experiments and Results
### Evaluation Metrics  
To evaluate the module of aspect detection, the standard precision (\(P\)), recall (\(R\)) and F1 measures were used (\(S\) is the set of aspects calculated by SentiRank, and \(G\) is the set of correct aspects of each sentence).  
\[P=\frac{|S\cap G|}{|S|} \tag{8}\]  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Word-** & **Char-** & **POS** & **Clusters** & **WA-** \\  & **Ngram** & **Ngram** & & **Clusters** & **Lexicon** \\ \hline Food & \(\checkmark\), N = 3 & & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline Price & \(\checkmark\), N = 4 & & \(\checkmark\) & \(\checkmark\) & \\ \hline Service & \(\checkmark\), N = 1 & \(\checkmark\), N = 6 & & \(\checkmark\) & \\ \hline Ambience & \(\checkmark\), N = 3 & & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline Anecdotes & \(\checkmark\), N = 3 & & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The features used to detect each aspect category.  
\[R =\frac{|S\cap G|}{|G|} \tag{9}\] \[F1 =\frac{2\cdot P\cdot R}{P+R} \tag{10}\]  
The correctness of the polarity of the opinion with respect to an aspect is measured by the accuracy (correct polarity labels associated with an aspect by the system divided by the number of correct labels).
# GNSS Jamming Observed on Sounding Rocket Flights from Northern Scandinavia +
## 2 Jamming Observed on Sounding Rocket Flights
### MAIUS-2 Mission
The profile of the power spectrum is not as flat as at 1562.098 MHz, which suggests that the jamming signal is either not only pure bandlimited  
Figure 2: C/N\({}_{0}\) ratios for the GPS signals tracked by the DLR Phoenix-HD receiver on MAIUS\(-\)2.  
white noise or that there is another narrow band jammer superposed. Interestingly, the power spectra of the jamming signal between 1598 MHz and 1605 MHz at 30 km and at 233 km do not differ as much as for the two other jamming signals. The jamming primarily affects the GPS L1 C/A, GPS L1C, Galileo E1-B/C and BeiDou B1C signals centred at 1575.42 MHz, the BeiDou B1I signal centred at 1562.098 MHz and the GLONASS FDMA L1 C/A and CDMA L1 OC signals between 1598.0625 MHz and 1605.375 MHz. The GPS M-Code and Galileo E1-A signals are not affected by the jamming. Apart from the jamming signal between 1598.0625 MHz and 1605.375 MHz, the power spectrum recorded on the aircraft fits very well to the spectra observed on MAIUS-2, even though the aircraft was much closer to the jammer. Figure 4 combines all periodograms over the entire flight time in a spectrogram. The frequency is limited to the range between 1550 MHz and 1610 MHz to better recognize the jamming signals. The spectrogram illustrates that the jamming starts quite abruptly at T + 33.6 s at an altitude of 22.5 km and stops quite abruptly at T + 465.7 s. The jamming signal power is strongest when it appears and slightly decreases with increasing altitude. However, the jamming is still noticeable at the apogee at 233 km.
## 3 Materials and Methods
### Dataset
#### 3.3.2 Data Curation  
In order to use the data from the raw dataset, we need first to implement a data curation process. First, we have to remove all events from zeroed users as they avoid building users' paths, which are not valid for our analysis.  
The next step is building the user path. We consider the user path from the first impression up to the last event (click or impression) associated with the user. However, if two consecutive impressions are separated more than seven days apart, we consider the last impression to be part of a new path.  
After building the paths for all the users, we have to retrieve which impressions have received a click. Note that if the same ad impression receives multiple clicks from a user, we consider it as a clicked impression as it had received a single click. Once we have labeled all the impressions as clicked or not clicked, we will include this information in the user path.  
At this moment, we build the states the user travels for each impression and the subsequent ones in the path. We can aggregate all the information to retrieve how many users in our dataset have stayed in each of the possible states.  
We implement a final step to simplify the complexity of the analysis. We calculate the number of users in the previous state and those who have received an impression for each state. With that information, we compute the following ratios: the probability of transition from the previous node to this one, probability of clicking in after receiving an impression from the previous state, and probability of receiving an impression from the previous state.
## 3 Methodology
### Object Segmentation and Integration with Pixel-Based Results  
Based on the homogeneity of the spectral value, the image can be segmented into many small-scale objects consisting of several pixels to avoid the noise of pixel-based classification. In this paper, multiresolution segmentation in eCognition software was employed in the cloud-free composite segmentation. The segmentation scale, shape, and compactness were the three major parameters that first determined the size of the object, while the last two set the threshold of relative homogeneity, including the color and shape [65].  
Determining the optimal scale is a critical step in performing multiresolution segmentation [66]. We adopted the estimation of scale parameter (ESP) tool to select the appropriate scale of segmentation. Introduced by [PERSON], the ESP tool is commonly used to fit the scale parameter [67; 68]. The ESP tool iteratively calculated the local variance (LV) of object heterogeneity, and the local maximum in the rate of change (ROC) curve for LV indicated the potential optimal scale for the scene. As shown in Figure 6, the ROC curve suggested that 52, 64, 80, 88, and 96 were the optimal scales for segmentation. Considering that the cropland area per capita was only 0.126 ha in 2012 according to the National Bureau of Statistics [69], the smallest potential optimal scale of 52 was selected as the optimal scale to segment the fragmented and heterogeneous cropland. At this scale, the complex landscape can be depicted well (Figure 7c). Hence, we conducted multiresolution segmentation with a scale of 52 and selected 0.5 and 0.3 for compactness and shape, respectively.  
To remove the'salt-and-pepper effect', the pixel-based result was integrated with object output from multiresolution segmentation to produce a fusion map. If all the pixels classified as corn within an object accounts for less than 50% of the area of that object, then the object will be dropped from the fusion map. Otherwise, the object will be labeled as corn. This approach excludes some very small objects and confusing scattered pixels misclassified as corn in areas of no corn (Figure 7b,d).  
Figure 6: Curve of the local variable and rate of change graph exported from the estimation of scale parameter; the local maximum is the potential optimal scale for the scene, indicating that 52, 64, 80, 88, and 96 are potential scales for segmentation.  
Figure 7: Part of the output images of object segmentation and integration with pixel-based results: (**a**) false-color combination with near infrared (NIR), red, and green in the R, G, and B channels, respectively; (**b**) pixel-based result output from a random forest (RF) classifier; (**c**) object boundary generated with multiresolution segmentation; and (**d**) integration with pixel-based results and object-oriented segmentation.
# A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages
###### Abstract  
Despite the rapid evolution of Internet protocol-based messaging services, SMS still remains an indisputable communication service in our lives until today. For example, several businesses consider that text messages are more effective than e-mails. This is because 82% of SMSs are read within 5 min., but consumers only open one in four e-mails they receive. The importance of SMS for mobile phone users has attracted the attention of spammers. In fact, the volume of SMS spam has increased considerably in recent years with the emergence of new security threats, such as SMiShing. In this paper, we propose a hybrid deep learning model for detecting SMS spam messages. This detection model is based on the combination of two deep learning methods CNN and LSTM. It is intended to deal with mixed text messages that are written in Arabic or English. For the comparative evaluation, we also tested other well-known machine learning algorithms. The experimental results that we present in this paper show that our CNN-LSTM model outperforms the other algorithms. It achieved a very good accuracy of 98.37%.  
Keywords:SMS spam detection; deep learning; CNN; LSTM; SMS Classification +
Footnote †: journal: