esa-sceva/satcom-chunk-collection huggingface dataset:
# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
## 1 Introduction
The existence of emergency vehicles and the context of the competing traffic flows are also considered in the obtained recommendations of the protocol. Moreover, digital maps are considered in each vehicle to determine the design of the coming road intersection. The proposed protocol aims mainly to assist drivers in passing through road intersections controlled by stop signs safely and efficiently. Autonomous vehicles can use the proposed protocol to direct their robotic control system to drive safely and efficiently around stop sign road intersections. An extensive set of experiments on traffic distributions and contexts have been executed to test the protocol. These experiments show that the proposed protocol has successfully enhanced the safety conditions around road intersections controlled by stop signs. This is by reducing the percentage of accident occurrences. Moreover, the traffic efficiency of these road intersections has been enhanced by reducing the average waiting delay time of the vehicles. It has also increased the throughput of the road intersection and decreased the fuel consumption and gas emissions of traveling vehicles there. The rest of this paper is organized as follows: Section 2 investigates previous related studies in traffic management and driving assistant protocols. Section 3 presents the considered design typologies of road intersections that can be controlled by stop signs and the number of competing traffic flows at each topology. Section 4 introduces the proposed driving assistant protocol that helps drivers to pass through road intersections controlled by stop signs safely and efficiently. The performance of this protocol is evaluated in Section 5. Finally, Section 6 concludes the paper.
###### Abstract  
Traditional ensemble methods rely on majority voting, which may fail to recognize correct answers held by a minority in scenarios requiring specialized knowledge. Therefore, this paper proposes two novel ensemble methods for supervised classification, named Confidence Truth Serum (CTS) and Confidence Truth Serum with Single Regression (CTS-SR). The former is based on the principles of Bayesian Truth Serum (BTS) and introduces classification confidence to calculate the prior and posterior probabilities of events, enabling the recovery of correct judgments provided by a confident minority beyond majority voting. CTS-SR further simplifies the algorithm by constructing a single regression model to reduce computational overhead, making it suitable for large-scale applications. Experiments are conducted on multiple binary classification datasets to evaluate CTS and CTS-SR. Experimental results demonstrate that, compared with existing ensemble methods, both of the proposed methods significantly outperform baseline algorithms in terms of accuracy and F1 scores. Specifically, there is an average improvement of 2-6% in accuracy and an average increase of 2-4% in F1 score. Notably, on the [PERSON] and Hilly datasets, our method achieves a 5% improvement compared to the traditional majority voting approach. Particularly on the Hilly dataset, which generally exhibits the poorest classification performance and poses the greatest prediction challenges, our method demonstrates the best discriminative performance. validating the importance of confidence as a feature in ensemble learning.  
ensemble learning; supervised classification; Bayesian Truth Serum; confidence
## 3 Heat Transport Pipeline Damage Probability Analysis
### Sensor Wire Condition  
A damage sensor wire is attached to the exterior of a heat transport pipeline, allowing operators to detect damage. When internal hot water leaks due to steel pipe damage or moisture from surrounding soil infiltrates because of insulation damage, moisture penetrates the sensor wire, causing a drop in its insulation level [13; 14; 15]. The sensor wire condition data are categorized into two states: normal and disconnection. In the normal state, insulation levels range from stages 1 to 13. Disconnection indicates that the sensor wire is malfunctioning.  
There were 240 damage cases with normal sensor wire condition and complete attribute data, which were used to analyze damage probability. Figure 5 presents the number of damage cases and damage probability according to insulation level. Damage probability generally decreased as insulation level increased, with a sharp decline observed from level 5 onwards. Pipelines with disconnected sensor wires totaled 1728.02 km in length, with a damage probability of 0.066.  
Figure 4: Number of damage cases and damage probability according to the pipe diameter.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multirow{2}{*}{**Pipe Length (km)**} & \multicolumn{2}{c}{**Number of Damage Cases (\#)**} & \multicolumn{1}{c}{**D Damage**} \\  & & **Damage Cases (\#)** & **Probability** \\  & & **(\#/km/Year)** \\ \hline Small diameter & 2228.41 & 1099 & 0.034 \\ Medium diameter & 968.40 & 311 & 0.022 \\ Large diameter & 1666.15 & 805 & 0.033 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of damage cases and damage probability according to the pipe diameter category.
## 3 Data and Methodology
### Data Acquisition and Processing  
#### 3.2.1 Single-Station Measurements  
An ambient noise measurements campaign was conducted in the city of Ain Te-mouchent in 2024. Ambient noise was recorded at 62 sites (Figure 2), at night and under calm weather conditions, in accordance with the recommendations of the Site Effects Assessment using Ambient Excitations (SESAME) project [57]. The standard acquisition time was 16 min. However, in the industrial zone located northeast of the study area, the recording time was extended to 20 min due to the influence of human activity. The additional recording time ensured that the obtained spectral ratios were representative of the actual characteristics of the site and were not significantly affected by short-term disturbances. Besides, previous experimental tests showed that the HVSR curves were stabilized after 15-20 min at these sites, minimizing possible errors. The equipment used for the recordings was a SARA short-period triaxial velocity station, with a natural frequency of 1 Hz and a sampling frequency of 100 Hz.  
The HVSR processing was carried out using GEOPSY software (release 2.10.1) [58]. First, the recording was divided into windows of a predefined duration. In this study, a duration of 30 s for each window was used. Then, an anti-triggering algorithm was applied, which ensured that only stationary windows were selected for the required processing. The spectrum of each component was calculated for each window, and the results were smoothed using the Konno-Ohmachi [59] technique to obtain the spectral amplitudes of each component (NS, EW, Z) for each window using a smoothing coefficient equal to 40. Subsequently, the horizontal spectrum was calculated as the quadratic mean of the NS and EW spectra. Thereafter, the H/V spectral ratio was determined for each window. Finally, a geometric mean of the H/V spectral ratio was calculated in the frequency band between 0.5 and 15 Hz. As a result, the HVSR curve, with amplitude as a function of frequency, was obtained.
## 1 Introduction  
In high-speed serial port design, multi-level modulation technology has been applied, such as a four-level pulse amplitude modulation (PAM-4). In the transmission protocol of peripheral component interconnect express (PCIe), PAM-4 has been adopted since PCIe 5.0. In the optoelectronic interfaces, PAM-4 is the current mainstream electrical interface and has evolved into the main growth trend of replacing non-return to zero (NRZ) with PAM-4. In the current situation of limited transmission bandwidth, to improve the transmission rate and bandwidth utilization, the development of high-order modulation technology has become inevitable.  
In the process of high-speed signal transmission, compressing data bandwidth to improve transmission efficiency is an effective way to improve signal transmission quality. Duo binary (DB) modulation has the function of compressing signal bandwidth at the same transmission rate [1]. In order to reduce the transmission bandwidth, the DB PAM-4 combined with the DB modulation technology can compress the bandwidth. At the same working rate, the bandwidth required by DB PAM-4 is greatly reduced, but the 7-level characteristic of DB PAM-4 makes the signal eye height compressed to half [2; 3; 4], increasing the difficulty of the subsequent judgment.  
The high-speed signal channel is seriously lost, and clock and data recovery (CDR) needs to extract the clock information contained in the data, generate a synchronous clock to resample the data, and complete the data retiming and synchronous clock recovery. The phase information extraction of conventional CDR relies on the Bang-Bang (BB) phase-detection technique [5; 6; 7], which requires two samples 1 unit interval (UI) to obtain variable edge information and data information in turn, and requires high sampler bandwidth. Using Mueller-Muller (MM) phase-detection technique [8; 9; 10; 11], with only sample 1 UI, reducing the sampler bandwidth requirement and the amount of data processing. The original structure of the sampler plus CDR for Data Retiming under pam-4 modulation [11; 12; 13] is not applicable to the current modulation mode. The analog to digital converter (ADC) + digital signal processing (DSP) architecture is a modular architecture, in which the output clock of the phase interpolator (PI) controls the high-speed ADC for data sampling, and the DSP controls the clock phase of the PI [14; 15; 16], which is applicable to DB pam-4. ADC integrate to the system through intellectual property (IP) core for data sampling and quantization. DSP performs data processing and loop control. For DB PAM-4, a generalized low-complexity phase-detection method based on multiple-level MM phase-detection technique is proposed, and the CDR based on this phase-detection method is built in ADC + DSP architecture.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 8 Underwater SLAM Systems Evaluation and Comparison
### Comparison of Underwater SLAM Methods
#### 8.3.1 Traditional Methods  
Traditional SLAM methods demonstrate varying performance on the Aqualoc dataset. Multi-sensor fusion approaches, such as U-vip-SLAM and Visual-inertial-pressure odometry, achieve lower localization errors (0.103 m and 0.0873 m, respectively) compared to vision-only methods like _Improved Underwater VSLAM_ (0.19 m). This highlights the importance of integrating multiple sensor modalities, for example visual, inertial, and pressure data, to improve localization accuracy in underwater environments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
###### Abstract  
In the age of social networks, the number of tweets sent by users has led to a sharp rise in public opinion. Public opinions are closely related to user stances. User stance detection has become an important task in the field of public opinion. However, previous studies have not distinguished between user viewpoints and stances. These studies usually detected stance from the perspective of the tweet level but rarely the user level. Therefore, in this paper, we defined user stance, which is the user viewpoint (support, oppose, and neutral) toward the entire target event process. On this basis, we put forward a user stance detection method based on external commonsense knowledge (such as SenticNet) and environment information (such as a user's historical tweets, topic information, and neighbor tweets) and denote this method as ECKEI. First, in order to better integrate external commonsense knowledge into the neural network, we improved BiLSTM and called it CK-BiLSTM for complementary commonsense information to the memory cell. Secondly, we used LDA to extract the topic of user tweets and designed a topic-driven module to capture the users' neighbors' information. Finally, we used the attention mechanism to integrate information from users' historical tweets and neighbors' tweets obtained through topic information; then, we used the softmax layer to classify user stances into the support, neutral and oppose classes. In this paper, we conducted experiments and assessments on datasets containing information on Brexit and the elections to verify the practicability and effectiveness of our proposed method. Extensive experimental results on the Brexit and elections datasets show that our approach outperforms six baseline methods (SVM-ngram, NB, MTRE (RNN), Pkudblab (CNN), TAAT, and Aff-feature). We use the average micro-F1 and average accuracy to measure performance on the detection of a user's stance. The ECKEI model makes improvements of 4.30-16.89% and 1.22-16.58% on the Brexit and election datasets, respectively, in terms of average micro-F1. Meanwhile, this model makes improvements of 4.24-17.46% and 0.48-14.64% on the Brexit and election datasets, respectively, in terms of average accuracy. Our model makes improvements of 5.34-17.30% and 2.65-19.73%, respectively, on the Brexit and election datasets in terms of average recall.  
social network; Bi-LSTM; stance detection; external commonsense knowledge; neighborhood information +
Footnote †: journal: Applied Sciences
## 2 Methods
### Data Collections and Data Processing Procedures
#### 2.4.2 _HyPlant_ Data
Atmospheric corrections were applied using atmospheric functions supplied from the MODerate resolution TRANsmission model (MODTRAN, Spectral Sciences, Burlington, MA, USA) ([PERSON] et al., 2016; [PERSON] et al., 2016). Initial atmospheric parameters were estimated from sun-scan measurements, relying on reflectance measurements made of the tarps in each flight line to constrain the atmospheric correction. No correction for a digital elevation model was applied since the study area was very flat and thus no major topographic contribution of the 3-D surface was expected.
# Contrastive Enhanced Learning for Multi-Label Text Classification
## 4 Experiments
### Ablation Study  
We conducted ablation testing to analyze the impact of key components in the MPCM. The full MPCM is denoted as A and is compared with the following variants: the MPCM without contrastive label representation learning (denoted as A/e), the MPCM without positive label representation alignment (denoted as A/v), and the MPCM without contrastive global representation learning (denoted as A/c). The results for P@1, P@3, and P@5 on the AAPD dataset are presented in Figure 3, leading to several interesting observations.  
Firstly, the most pronounced performance degradation is observed in A/e, while A/c and A/v exhibit similar levels of decline. This difference arises because both positive label representation alignment and contrastive global representation learning refine the label space, thereby improving performance. In contrast, contrastive label representation learning directly constructs the label space, making it the most critical factor for classification performance. Additionally, positive label representation alignment, though a straightforward optimization method, significantly enhances overall performance despite not involving complex computations. This finding also implicitly validates the effectiveness and robustness of this optimization approach.  
Secondly, as shown in Figure 4, removing contrastive label representation learning led to a significant decrease in the model's training speed, with A/e requiring around 50 epochs to achieve the same performance that other variants reached in just 5 epochs. Since the majority of the model's computational load is concentrated in the BERT model, the time per epoch remains largely consistent across different variants. Thus, we believe that the increased number of epochs required to achieve comparable performance after removing the contrastive label representation learning module suggests its role in accelerating the construction of the label space. Despite this slowdown, the model without the contrastive label representation learning module can still achieve a good level of performance after an extended training period. This suggests that even in the absence of this module, the model can construct a similar label space through positive label representation alignment and contrastive global representation learning, although this process is evidently less efficient.  
Figure 3: Results of the ablation test on the AAPD dataset: (**a**–**c**) represent the best performance of P@1, P@3, and P@5 for the ablation test. “A” denotes the complete MPCM, A/e denotes the MPCM with contrastive label representation learning removed, A/v denotes the MPCM with positive label representation alignment removed, and A/c denotes the MPCM with contrastive global representation learning removed.
## 2 Related Works  
### Time Series with the Missing Values Classification Problem  
A considerable amount of literature has been published on time series with missing values. Many of these works focus on the imputation of missing values [15; 25]. The classification problem can be solved after the imputation procedure using traditional classification methods such as kernel method [26], support vector machines [27] and random forest [12]. However, most of the traditional methods can not directly handle multivariate time series of varying length. [PERSON] et al. used multi-task Gaussian processes to directly transform the irregular sampled time series with missing values into a more uniform representation and then do the classification task [28]. Recently, [PERSON] et al. proposed a kernel method called time series cluster kernel (TCK) [29] to learn the similarities between multivariate time series. It can directly handle time series of varying lengths and with missing data without an imputation method. However, the maximum missing rate shown in this research is 50%, which is not enough to deal with massive missing values in clinical time series and TCK assumes that missing values in time series occur at random. Later, they proposed an improved kernel to exploit the informative missingness when the missing values occur non-randomly [30].  
Besides kernel methods, researchers also choose RNN to address the missing values problem in time series. [PERSON] et al. indicated that the information within variables is as important as the information across variables in the procedure of imputation [31]. They used multi-directional recurrent neural networks with interpolation blocks and imputation block to impute the missing values. [PERSON] et al. considered the missing mark very important. They concatenated the missing mark and the time series and input them into LSTM [13]. They used the forward or backward imputation methods to address missing values. If the variable in a time series is completely missing, it will be filled with expert experience values. Later, [PERSON] et al. proposed an improved method [18], which is added to the mean value, standard deviation, and the first and the last observation of each variable in a multivariate time series into the model. These added features characterize the difference of each variable, but they are calculated manually and remain the same during the training process. [PERSON] et al. changed the framework of GRU and treated the missing mark as another input and fed both missing mark and time series into a GRU model, called GRU-D [23]. [PERSON] et al. proposed channel-wise LSTM [24]. They input a single variable along with its missing mark into an independent bidirectional LSTM layer and then concatenated all the outputs and fed them into the same LSTM layer. Thus, the information related to univariable can be learned before the variables are mixed in LSTM. However, because each variable needs to be trained in an independent network, it causes intensive computations.
## 2 Survey for Outdoor Agricultural UAV Applications
### Mapping  
Mapping applications aim to follow agricultural or forest areas locally. This is achieved by obtaining 2D or 3D maps of agricultural land observed by UAVs. In this way, over time, changes in factors such as current production, efficiency and environmental conditions are observed. In this context, mapping can be performed by showing information such as the area of the agricultural land, soil diversity and crop status on images taken with the UAV. For example, understanding soil variability is one of the oldest challenges facing farmers and researchers. Soil analysis and periodic observations provide a variable fertilization system [4; 24]. Low-flying, high-resolution images and low-cost UAV mapping applications have been frequently applied recently. As an example of mapping agricultural products with a UAV, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [32] used both multispectral UAV and WorldView-3 images to map macadamia gardens and mapped different macadamia trees using a random forest approach.  
Agricultural mapping applications often aim to mark or indicate information about plants or soil in an image. In contrast, mapping for mobile robotics, which is discussed later, aims to determine the geometric boundaries of the environment. In mobile robotics, if the boundaries of the environment are known, the mobile vehicle acts with the knowledge of its limits in the environment and also localizes itself in this environment. To perform autonomous tasks in an indoor environment, the map of the environment must be known. This is why mapping in robotics is very different from the term mapping in agriculture.
# Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework
## 2 Related Work  
### ML  
ML is a branch of AI and refers to a data analysis method that automates the building of analytic models. ML uses a large number of data for training and is mainly used to classify and predict data through a trained model. This section describes ML procedures and classification according to the learning methods [2; 3; 4].  
The ML procedure involves developing a model using training data and performing a test using the developed model. For example, ML can be used to create a model that can identify a person based on their picture, and then the generated model can be used to test and identify a picture with people in it. Although interpretations of ML procedures can vary, they are generally classified into seven stages when data management and processes for learning, such as data collection and preparation, are included in the ML stages [3; 4; 5]:  
1. Because the quantity and quality of the collected data directly affect the predictive model performance in ML, the first step is to collect high-quality data to build the model.
2. The collected data are prepared using various preprocessing methods for model building. For example, preprocessing can be used to increase the size of the dataset or to denoise and/or segment images. Then, the dataset is split into two parts for training and testing.
3. Many ML algorithms and models have been developed to solve various problems. In this step, the most appropriate ML model for the problem to be solved is selected. For example, some models are suitable for detecting image patterns, whereas others are more suitable for processing image data.
4. This step is the heart of the ML process. A part of the dataset allocated for training is used to train the selected model. A model can be built based on trial and error. Thus, detailed experiments are required.
5. When training is completed, the maturity of the model is verified through a test. This stage can test the generated model by evaluating how it actually works.
6. After the evaluation, further improvement for learning can be considered, and the performance of the model can be improved by adjusting the parameters through hyper-parameter tuning.
7. Because the ultimate goal of ML is to use data to answer problems, the value of ML models is determined by predicting what the given data mean using the models built and improved in the previous steps.