esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Frameworks and Methodologies
### Research Methodologies  
#### 2.2.1 Feature Selection: RF-RFECV  
1. Random forest (RF)  
The random forest (RF) model, introduced by [PERSON] in 2001, is a supervised learning algorithm based on bagging [26]. Its distinct feature is the introduction of random feature extraction to construct independent training subsets, thereby increasing randomness in the generation of decision trees. It builds multiple decision trees on various resampled data samples and integrates them, with each tree casting a vote to produce the final prediction.  
2. Recursive feature elimination and cross-validation (RFECV)  
Recursive feature elimination (RFE) [27] is frequently used for feature selection. RFE involves the iterative assessment of the importance of features in a base model, where the least important feature is removed in each iteration until a specified number of features is reached. The sequence of elimination determines the final order of feature importance. Recursive feature elimination with cross-validation (RFECV) extends RFE by incorporating resampling and cross-validation processes. It calculates validation errors for all of the feature subsets and selects the subset with the lowest error rate as the optimal feature subset [28]. The RFECV method includes the following four steps: (a) train a base model using the original feature dataset; (b) calculate the importance scores of each feature; (c) form a new feature subset by removing a feature variable through cross-validation; and (d) repeat steps (a) to (c) until a suitable number of feature variables are determined.  
3. RF-RFECV  
As mentioned, the random forest algorithm can quickly select features for efficient dimensionality reduction. However, the random forest algorithm only calculates the importance of each feature and cannot determine the optimal number of features; thus, it requires manual setting. This approach somewhat reduces the feasibility of the results, and due to the high randomness of the random forest algorithm, the calculated feature importance also has a degree of uncertainty. Relying solely on a single random forest algorithm to select the optimal feature subset is typically unreliable. Therefore, incorporating the RFECV method into the random forest algorithm when establishing an efficient and accurate prediction model can improve the accuracy of the selection of the optimal feature subset. This method allows for effective feature selection by choosing the best feature subset based on their importance. The feature selection process designed using the RF-RFECV method is illustrated in Figure 1b (2).
# Crawling Magnetic Robot to Perform a Biopsy in Tubular Environments by Controlling a Magnetic Field
## 5 Experiment and Verification
### Experiment in Y-Shaped, Water-Filled Tubular Environment  
To evaluate the crawling motion and biopsy performance of the CMR, we used a pseudo-tissue made of agar filled at one end of a Y-shaped watery glass tube with a diameter of 14 mm, as shown in Figure 10a. In step 1, the CMR crawled to a target point, propelled by the external precessional magnetic field (\(B\) = 8 mT, \(f\) = 15 Hz, \(\delta\) = 30\({}^{\circ}\)), and the cover was closed during navigation as shown in Figure 10b. In the bifurcated branch, we controlled the joystick to change the unit vector of the rotating axis (**N**), which can be accomplished by controlling the applied currents of the developed MNS. In step 2, the cover was opened by a uniform magnetic field of 12 mT. In step 3, the uncovered biopsy needle punctured the tissue by the crawling motion generated by the external precessional magnetic field (\(B\) = 14 mT, \(f\) = 25 Hz, \(\delta\) = 30\({}^{\circ}\)). In step 4, the turning motions of the crawling bodies and flexible legs along the y-axis as well as the covering motion of the biopsy needle were performed by rotating the uniform magnetic field of 12 mT by 180\({}^{\circ}\) along the y-axis. Finally, in step 5, the CMR crawled back to the original point, propelled by the external precessional magnetic field (\(B\) = 12 mT, \(f\) = 15 Hz, \(\delta\) = 30\({}^{\circ}\)). Figure 10c shows the uncovered  
Figure 9: (**a**) Experimental setup to measure the puncturing force of CMR. (**b**) Puncturing force of CMR according to frequency and external magnetic field.  
needle before the biopsy needle was inserted into the pseudo-tissue. Figure 10d shows a sampled tissue of 1 mm thickness extracted through this experiment. We confirmed from this experiment that the proposed CMR could crawl through a tubular structure and extract sample tissue successfully.
## 5 Conclusions  
This study is pivotal in advancing the application of machine learning to optimize oil recovery forecasts, aiming to significantly enhance the decision-making processes in oil production. By utilizing the black oil model, this research employed advanced machine learning algorithms to predict the oil recovery factor, with the RF and GB models achieving an R\({}^{2}\) score of 0.985. The PR and XGBoost models also performed well, each with R\({}^{2}\) scores of 0.98, while the LR, NN, and DT models showed slightly lower R\({}^{2}\) scores of 0.97, 0.97, and 0.975, respectively. Quantitative assessments were made on the key parameters that influence oil recovery, providing deeper insights into the production processes and optimization strategies. One of the notable computational advancements was the implementation of parallel computing techniques. In particular, the DT algorithm achieved a maximum speedup of 10.54 times when running on eight processes; other models, except LR, also showed speedups in the region of 8 times.  
This research primarily utilized synthetic data, which facilitated controlled conditions but might limit the generalizability of the findings to real-world scenarios. Recognizing this limitation, this study highlights the necessity for further validation using real-world datasets to ensure the practical applicability and robustness of the developed models. Future research will apply these machine learning and parallel computing methods to larger datasets with real data encompassing multiple parameters, aiming to provide forecasts under actual operational conditions. This research not only demonstrates the efficacy of machine learning in enhancing oil recovery forecasts but also underscores the potential of integrating advanced computational techniques with traditional petroleum engineering practices.  
Conceptualization, B.M. and T.I.; methodology, B.D. and M.M.; software, A.M.; validation, A.M. and N.K.; formal analysis, B.M.; investigation, T.I.; resources, B.D.; data curation, N.K.; writing--original draft preparation, N.K.; writing--review and editing, T.I. and M.M.; visualization, A.M.; supervision, B.M.; project administration, B.M.; and funding acquisition, T.I. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan, grant numbers BR18574136 and AP14871644.  
The data presented in this paper are available upon request from the corresponding author.  
The authors declare no conflicts of interest.
## 4 Discussion and Results of Experiment
### Data Acquisition  
For fault diagnosis testing, the VS1 vacuum circuit breaker (Changzhou Senyuan, Changzhou, China) was utilized as the test prototype in this study. The VS1 circuit breaker is a spring-operated mechanism with a rated voltage of 12 kV and a rated current of 1250 A. The vibration signal is collected by a piezoelectric acceleration vibration sensor (YD111T), and the collected charge is converted into a voltage value by a charge amplifier (TS5863). The vibration signal is then transmitted to the upper computer through the acquisition card via the vibration trigger circuit. The experimental equipment is shown in Figure 5.  
During operation a HVCB may fail, especially in the process of opening and closing. This failure may be caused by electrical or mechanical issues, for example, \"refuse to open\", \"refuse to close\", \"false open\", or \"false close\". When such a fault occurs, the circuit breaker has lost the normal function of opening and closing, and these fault phenomena can be easily observed manually. A HVCB can occasionally open and  
Figure 5: Experimental equipment setup.  
and closing distances of the contacts and the travelling distance after the action cannot reach the normal state. In this study, a total of four working conditions were designed: state 1: normal working condition, state 2: closed spring fatigue failure, state 3: open spring fatigue failure, and state 4: iron core jamming. Shortening the spring tension length by three effective coils (reducing the maximum tension \(\sim\)8%) can simulate the spring fatigue state. Simulating iron core jamming can be accomplished by adding weights to the electromagnet and to the bolts at the lower end of the movable iron core.  
During the test, vibration signals from the operating mechanism of the laboratory were collected under normal conditions and with three mechanical defects. The status signal was derived from the shape of the oscillation signals during opening and closing. Each status sample consisted of 1600 time points, with 40 data samples per defect type. The vibration signals are visually depicted in Figure 6. For dataset partitioning, 60% of the samples were allocated to the training set, 10% to the verification set, and the remaining 30% to the test set. This division ensures a balanced representation of samples across the three sets for training, validation, and evaluation purposes.  
Figure 6: Vibration signals under (**a**) normal working condition, (**b**) closing spring fatigue failure, (**c**) opening spring fatigue failure, and (**d**) iron core jamming.
# Enhanced BLIP-2 Optimization Using LoRA for Generating Dashcam Captions
###### Abstract  
Autonomous driving technology has advanced significantly. However, it is challenging to accurately generate captions for driving environment scenes, which involve dynamic elements such as vehicles, traffic signals, road conditions, weather, and the time of day. Capturing these elements accurately is important for improving situational awareness in autonomous systems. Driving environment scene captioning is an important part of generating driving scenarios and enhancing the interpretability of autonomous systems. However, traditional vision-language models struggle with domain adaptation since autonomous driving datasets with detailed captions of dashcam-recorded scenes are limited and they cannot effectively capture diverse driving environment factors. In this paper, we propose an enhanced method based on the bootstrapping language-image pre-training with frozen vision encoders and large language Model (BLIP-2) to optimize the domain adaptation by improving scene captioning in autonomous driving environments. It comprises two steps: (1) transforming structured dataset labels into descriptive captions in natural language using a large language model (LLM), and (2) optimizing Q-Former in a BLIP-2 module with low-rank adaptation (LoRA) to achieve efficient domain adaptation. The structured dataset labels are originally stored in JSON format, where driving environment scene factors are encoded as key-value pairs that represent attributes such as the object type, position, and state. Using the Large-Scale Diverse Driving Video Database (BDD-100K), our method significantly improves performance, achieving BLEU-4, CIDEr, and SPICE scores that were each approximately 1.5 times those for the baseline BLIP-2, respectively. Higher scores show the effectiveness of LoRA-based optimization and, hence, its suitability for autonomous driving applications. Our method effectively enhances accuracy, contextual relevance, and interpretability, contributing to improved scene understanding in autonomous driving systems.  
Keywords:autonomous driving; adaptation optimization; domain adaptation; vision caption; vision language model (VLM)
## 2 Determination of Vertical Vibration Load from a Train  
### Expressions of Vertical Vibration Acceleration Caused by a Train  
The load from a train can be simulated by applying interaction forces between the wheel and rail of a train to the rail structure. The main methods for determining train loads include the field measurement method, equivalent load method, numerical simulation method [16], and empirical simulation method [17]. In this study, the equivalent load method was used to determine the vertical vibration load from a train. Based on the time domain signal of the measured vertical vibration acceleration of the rails and the simplified model of the vertical vibration of a train (see Figure 1), this method was used to calculate the vertical interaction force between the wheel and rail of a train. The specific calculation procedure is as follows.  
The rail vibration excited by a train can be regarded as a stationary random Gaussian process whose mean is zero [18]. The vertical vibration acceleration \(a(t)\) can be expressed as Equations (2)-(4) [19].  
\[a(t)=\sum_{n=0}^{\frac{p}{2}-1}(C_{n}\text{cos }nwt+D_{n}\text{sin }nwt) \tag{2}\]  
\[C_{n}=\frac{2}{P}\sum_{\theta=0}^{P-1}a(t_{\theta})\text{cos}\frac{2\pi n \theta}{P}\ \bigg{(}n=0,1,2\cdots,\frac{P}{2}-1\bigg{)} \tag{3}\]  
\[D_{n}=\frac{2}{P}\sum_{\theta=0}^{N-1}a(t_{\theta})\text{sin}\frac{2\pi n \theta}{P}\ \bigg{(}n=0,1,2\cdots,\frac{P}{2}-1\bigg{)} \tag{4}\]  
where \(P\) is the amount of interaction points between wheels and rails, \(w\) is the fundamental frequency (\(w=\frac{2\pi}{P\Delta t}\), \(\Delta t\) is the sampling interval), and \(t_{\theta}\) is the time corresponding to the \(\theta\)th interaction point between the wheels and rails.
## 4 Results
### Path Analysis and Hypothesis Test  
Using standardized path coefficients to reflect the magnitude of the influence between the variables, Table 4 and Figure 2 present the structural equation path coefficients and hypothesis testing results. The relationships between the variables are all significant at the 99% confidence level (\(p<0.05\)); therefore, the hypotheses H1-H5 and H7-H13 are supported. However, the initial hypothesis, H6, is not supported, as it did not yield significant results in this study.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Hypothesis** & **Estimate** & **S.E.** & **Standard Error** & **C.R.** & \(p\) & **Outcome** \\ \hline H1: SQ\(\rightarrow\)PU & 0.087 & 0.089 & 0.035 & 2.55 & 0.011 & Supported \\ H2: IQ\(\rightarrow\)PU & 0.581 & 0.573 & 0.061 & 9.435 & *** & Supported \\ H3: SI-\(\rightarrow\)PU & 0.348 & 0.384 & 0.051 & 7.504 & *** & Supported \\ H4: PA\(\rightarrow\)IM & 0.603 & 0.592 & 0.046 & 12.793 & *** & Supported \\ H5: PC\(\rightarrow\)IM & 0.425 & 0.428 & 0.042 & 10.091 & *** & Supported \\ H6: IM\(\rightarrow\)PU & \(-\)0.031 & \(-\)0.033 & 0.042 & \(-\)0.784 & 0.433 & Not supported \\ H7: IM\(\rightarrow\)SA & 0.093 & 0.102 & 0.051 & 2.001 & 0.045 & Supported \\ H8: PC\(\rightarrow\)SA & 0.216 & 0.239 & 0.072 & 3.325 & *** & Supported \\ H9: PU\(\rightarrow\)SA & 0.483 & 0.494 & 0.109 & 4.539 & *** & Supported \\ H10: PA\(\rightarrow\)SA & 0.229 & 0.246 & 0.083 & 2.963 & 0.003 & Supported \\ H11: IM\(\rightarrow\)CI & 0.164 & 0.166 & 0.033 & 5.004 & *** & Supported \\ H12: PU\(\rightarrow\)CI & 0.824 & 0.779 & 0.05 & 15.435 & *** & Supported \\ H13: SA\(\rightarrow\)CI & 0.06 & 0.056 & 0.027 & 2.07 & 0.038 & Supported \\ \hline \hline \end{tabular} Note: In the table, \(p\) cannot be equal to 0.00, but rather \(p<0.01\). “***” indicates that the number is less than 0.001, and “\(\rightarrow\)” indicates that the variable has an effect on the variable pointed to by the arrow. “S.E.” represents Standard Errors.  
\end{table}
Table 3: Goodness-of-Fit indices.
# Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics
## 3 Proposed Method
### Proposed Fusion Strategy to Generate Recommendation  
Based on the improved similarity calculation method above, the final user comprehensive similarity calculation method can be obtained by weighted fusion, namely:  
\[Sim_{p}(u,v)=\xi Sim_{\text{User}}(u,v)+\mu Sim_{\text{Character}}(u,v) \tag{10}\]  
where \(\xi+\mu=1\), and \(\xi,\mu\in(0,1)\) represent the weights for the similarity obtained based on the TF-IDF method and the user characteristics. For different recommender systems, \(\xi\) and \(\mu\) should be optimized. In this paper, a searching algorithm is proposed to obtain the optimal values of \(\xi\) and \(\mu\), which is shown in Algorithm 1:  
```
1:\(\mu=0;\xi=1-\mu\); %Initialization parameters
2:\(for\)\(i\) in \(range(N)\) :
3:\(if\)\(\mu==1\) :
4:\(break\);
5:else:
6:\(Simp=\xi Sima+\mu Simb\); %Model fusion
7:\(MAE(\mu,\xi);RMSE(\mu,\xi)\);
8:\(f(\mu,\xi)=\min(MAE(\mu,\xi),RMSE(\mu,\xi))\);
9:\(\mu=\mu+0.5;\xi=1-\mu\); %Parameter update
10:end
11:end
12:\(if\)\(i>=N\)\(or\)\(\mu==1\);
13:\(\mu,\xi=f(\mu,\xi)\);
14:end
```  
**Algorithm 1** Optimal solution search algorithm  
After obtaining the user's comprehensive similarity \(Sim_{p}(u,v)\), \(K\) users which are most similar to the target user are selected as the nearest neighbors to form the similar neighbor set of the target user. Combined with the rating information of all neighbors and the similarity with the target user, the target user's rating on the \(i\)-th unknown item is predicted. In this study, the rating prediction in (3) is changed to:  
\[P_{u,i}=\bar{R}_{u}+\frac{\sum_{v\in S(u,K)\cap N(i)}Sim_{p}(u,v)\times(R_{v,i}- \bar{R}_{v})}{\sum_{v\in S(u,K)\cap N(i)}\mid Sim_{p}(u,v)\mid} \tag{11}\]  
The total work processing of the proposed collaborative recommendation algorithm is summarized as follows:  
* Step 1: Preprocess the rating data and construct the user-item rating matrix \(R(m\times n)\);
* Step 2: Use TF-IDF method and rating data to calculate the user similarity matrix \(Sim_{\text{User}}(u,v)\);
* Step 3: Use user characteristics information to calculate the user characteristics similarity matrix \(Sim_{\text{Character}}(u,v)\);
* Step 4: Fuse the similarity matrices from Step2 and Step3 to generate the final user comprehensive similarity matrix \(Sim_{p}(u,v)\);
* Step 5: After the comprehensive similarity matrix of a user is obtained, the nearest neighbor set of the target user is selected to make rating prediction and generate recommendations.
# A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure
## 6 Open Issues and Research Challenges/ Future Challenges and Research Opportunities
### Charging Safety  
_What is the challenge?_  
EV charging safety seems to be a major concern to be mitigated to ensure safe and secure EV charging. However, there are certain factors affecting the safety of EV charging. These factors can be overheating, the battery short circuiting, a fault in charging equipment, environmental factors, etc., which can lead to accidents with EVs [137]. These factors can adversely affect the performance of EV charging, especially the economic performance of the charging process due to the replacement of a faulty battery or equipment involved in charging. Ev accidents represent the main challenge needing to be tackled and mitigated to ensure safety and protection.  
_Possible solution: Insulation and short circuit detection_  
Charging safety is the major issue that can be protected by adapting insulation for electric cables and the communication lines used in EV charging. Additionally, EVs accident can be controlled by minimizing the impact of environmental factors on the charging equipment. If the battery short circuits, this will majorly affect the charging safety of the EV. It needs to be detected beforehand to attenuate the number of EV accidents. The internal short circuit detection method is a promising solution that analyzes parameters such as the temperature, voltage, and current to detect the short circuit earlier.
## 2 Methods and Materials
### Data Collection  
The measurement campaign was carried out in four different cities in southern Nigeria. The cities include Agbor and Asaba in Delta State and Ontisha and Awka in Anambra State. A total of 16 eNodeB were monitored, four in each of the four cities categorized as locations 1, 2, 3, and 4. A TEMS software installed in a laptop with GPS and TEMS handset connected to it was housed in a test vehicle. The TEMS software was launched on the laptop after which the relevant Nigerian routes and cell reference details were uploaded. The TEMS was locked on a 4G LTE network at the 2600 MHz frequency band. Following this, a drive test was conducted on the specified routes in and around the coverage area of the base stations and the reference signal received power was continuously recorded with the TEMS tools. The GPS data were recorded simultaneously at all instants of the collected field data. The Reference Signal Received Power (RSRP) of the serving base stations was recorded on a log file by the TEMS software with detailed information, such as the coordinates of the mobile station location, transmitting frequency, cell identity, distance covered, altitude, and other system parameters. The log file containing the measured RSRP in all the studied locations was saved for further processing and analysis.
## 2 Materials and Methods
#### 3.1.4 Three-Dimensional Representation of Process Parameter Changes  
With constant input parameters (\(i\) and initial pH), the final values of the process responses (\(T\), pH value, and \(c\)) can be represented as a function of time (i.e., the process duration) in a three-dimensional (3D) coordinate system. Such a representation results in 3D surfaces with different shapes, contours, and colours (Figure 4).  
Any changes in the final values of the system responses at a constant current density (\(T\), pH, and \(c=f\) (\(t\), initial pH) can be more easily observed by following the colour change over a flat or curved surface in the 3D diagram. As the colour changes from orange to yellow and from green to blue, the final values of the output parameters (\(T\), pH, and \(c\)) decrease. A reverse colour transition indicates an increase in the output parameter's final value (the transition's limits are outlined on the grey base of the diagram).  
The surface describing the change in the final pH value as a function of its initial value and time is more complex but remains the same for all applied currents. The final solution pH increases with EC duration at lower initial pH values. As the initial pH of the solution increases, the observed surface curves bend, and its colour changes from green to yellow to orange (with a slight decrease in intensity towards the end of the process). In other words, as the initial pH of the solution increases, its final value increases to a maximum and then gradually decreases. The greater the current density, the less pronounced these changes are.  
The most important parameter is the concentration of the solution, as its decrease over time indicates the rate of TY dye removal. Under all experimental conditions, the concentration decreases with time and, in some cases, reaches very low values (close to zero). Colour changes accompany these changes on a flat rectangular area in the 3D diagram. As the colour changes from yellow to green and then to blue, the concentration of the TY dye decreases, and its removal efficiency increases. Accordingly, the lowest concentration (represented by the most intense blue colour) is reached at higher current densities and lower initial pH values. Thus, at the highest current density, depending on the initial pH, the dye concentration drops to zero after 20-25 min (which means that \(R\) is \(\approx\)99%). At the medium current density, this occurs after 35-40 min, while at the lowest current density, the concentration does not reach zero and is 0.01 mmol L\({}^{-1}\) (and \(R\approx\) 80%) after 50 min (at pH = 3).