esa-sceva/satcom-chunk-collection huggingface dataset:
# Drilling Condition Identification Method for Imbalanced Datasets
## 5 Experiment and Analysis of Drilling Condition Identification  
### Model Evaluation Indicators  
In the study of classification problems, the confusion matrix serves as an effective tool for representing prediction results, providing a clear presentation of the various situations that may arise during the model's prediction process [33]. For binary classification, the confusion matrix compares the model's predicted values with the actual labels of the data to assess the model's performance.  
Specifically, True Positive (_TP_) refers to the number of positive samples that the model correctly classifies as positive. True Negative (_TN_) represents the number of negative samples that the model correctly classifies as negative. False Positive (_FP_) refers to the number of negative samples that the model incorrectly predicts as positive. False Negative (_FN_) indicates the number of positive samples that the model incorrectly predicts as negative. These metrics are crucial for evaluating the performance of classification models, including accuracy, precision, recall, and _F1_ score. Accuracy represents the proportion of correctly predicted samples among the total number of samples. Precision measures the proportion of actual positive samples among those predicted as positive, reflecting the model's prediction accuracy. Recall refers to the proportion of positive samples that the model correctly identifies, indicating the model's ability to detect positive cases. The _F1_ score is the harmonic mean of precision and recall, combining the model's prediction accuracy and recall ability, providing a more valuable reference for performance evaluation and improvement. The formulas for calculating these evaluation metrics are as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{18}\]  
Figure 7: BiLSTM-GRU model architecture.  
\[recision=\frac{TP}{TP+FP} \tag{19}\] \[Recall=\frac{TP}{TP+FN}\] (20) \[F1=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{21}\]
## 4 Cost of Energy Results and Discussion
### System Levelized Cost of Energy  
The levelized and system levelized cost of electricity and hydrogen for Hamburg and Murcia in the Near Future and the Mid Century scenario are listed in Table 4. The values represent the average of the five simulated years. The levelized cost of energy (LCoE) and SLCoE parameters are calculated using the total costs (TC\({}_{i}\), Appendix E) of the various components and the corresponding energy flows (Figures 4 and 5). Detailed calculation methods can be found in Appendix C and [36]. * _The system levelized cost of energy of electricity (SLCoE) is 239 and 176 &MMWh in the Near Future scenario for, respectively, Hamburg and Murcia, and 104 and 71 &MMWh in the Mid Century scenario._ The SLCoE\({}_{e}\) is calculated by summing the costs of solar and FCEV2G electricity for buildings and dividing it by the total building electricity consumption. The total costs of solar electricity for buildings are calculated by multiplying the solar electricity consumption of buildings (Figures 4 and 5) by the levelized cost of energy of solar electricity (LCoE\({}_{e,S}\)). The total FCEV2G electricity costs are calculated by multiplying the FCEV2G electricity for buildings by the system levelized cost of energy of FCEV2G electricity (SLCoE\({}_{e,V2G}\)). * _All SLCoE\({}_{e}\) reduce by approximately 60% in the Mid Century scenario compared to the Near Future scenario._ Also, in Murcia, the SLCoE\({}_{e}\) is about 30% lower compared to Hamburg. In Murcia, a larger part of the building load can be directly covered by cheap and abundant solar electricity (even for hydrogen production) in both scenarios. As a result, less hydrogen production, storage, dispensing, and FCEV2G electricity are required. * _The levelized cost of energy of hydrogen from surplus solar electricity (LCoE\({}_{H,S}\) in &kg H\({}_{2}\)) in this system is always higher than the levelized cost of energy of hydrogen from wind electricity (LCoE\({}_{H,W}\) in &kg H\({}_{2}\))._ The levelized cost of energy of hydrogen (LCoE\({}_{H,W,S}\)) before transportation and storage is based on hydrogen from both wind and solar. Even in Murcia, in the Mid Century scenario, the cost of solar electricity (LCoE\({}_{e,S}\)) is lower than the cost of wind electricity LCoE\({}_{e,W}\). The reason for this is that a significantly higher capacity factor is achieved when the electrolyzer is connected to the wind turbine than to the solar electricity system, which only uses surplus solar electricity peaks. * _The system levelized cost of energy of hydrogen (SLCoE\({}_{H}\)) is 70-80% higher than the combined levelized cost of energy of hydrogen from solar and wind (LCoE\({}_{H,W,S}\))._ The SLCoE\({}_{H}\) includes the costs of hydrogen transportation by tube trailers, seasonal and fueling station storage, and dispensing on top of the solar and wind electricity costs, and the electrolyzers and low-pressure compressors, which is only the case for the LCoE\({}_{H,W,S}\).
# New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System
## 3 Bridge Experiment
### Quality Measures
#### 3.2.1 QDOP and Redundancy  
The first considered quality measures were QDOP and redundancy, as shown in Figure 9. The number of changes made to the tracking satellites affected QDOP and redundancy. In the section with a reduced number of satellites, QDOP values were increased and redundancy was decreased. The relationship between the change in the number of tracking satellites and redundancy likewise showed a similar trend; it was determined that QDOP had the opposite trend.
## 3 Results and Discussion
### Algal Growth in Different Diluted ADPEs Pretreated by Fungus  
To further reduce the dilution ratio of ADPE through fungal pretreatment, NCU-39 was cultured in each of 3\(\times\), 5\(\times\), 10\(\times\) diluted ADPEs pretreated by three-day NU-27 decolorization. Due to the addition of extra glucose for fungal decolorization as described above, initial COD concentrations in the diluted ADPEs (Table 2) were quite higher than those of the corresponding diluted one without glucose addition (Table 1). After fungal decolorization, the concentrations of NH\({}_{4}\)+-N, TN, and COD decreased by 10-29%, 7-19%, and 18-32% in all diluted ADPEs, respectively (Table 2). NH\({}_{4}\)+-N is one of the nitrogen sources for some fungal growths [27], including _Lichtheinia_ spp. [23]. Moreover, fungi can also degrade nitrogenous compounds and other organic matters during wastewater remediation [27]. The above reasons decreased concentrations of NH\({}_{4}\)+-N, TN, and COD during fungal decolorization. In contrast, the TP concentration was improved by 43-45% after decolorization, maybe due to declining pH to 3.0 (Table 2). With declining pH in ADPE, phosphorus can be released from phosphate precipitates, such as struvite (MgNH\({}_{4}\)PO\({}_{4}\) 6H\({}_{2}\)O) [31].  
After fungal decolorization, algal growth was not completely inhibited in all diluted ADPE (Figure 5). Specifically, algal growth in 5\(\times\) diluted ADPE was faster than that in 3\(\times\) and 10\(\times\) diluted ones in the first two days, which might be attributed to the inhibition effect of high NH\({}_{4}\)+ concentration (586 mg L\({}^{-1}\), Table 2) in 3\(\times\) diluted ADPE, and limitation effect of low NH\({}_{4}\)+ concentration (153 mg L\({}^{-1}\)) in 10\(\times\) diluted ADPE, suggesting a high NH\({}_{4}\)+ tolerance of NCU-39 in 5\(\times\) diluted ADPE at initial NH\({}_{4}\)+-N concentration of 380 mg L\({}^{-1}\). When NCU-39 was isolated from an NH\({}_{4}\)+-rich waterbody, it might already adapt to high NH\({}_{4}\)+ concentration at 380 mg L\({}^{-1}\). [PERSON] et al. [32] showed that _Chlorella_ spp. isolated from high NH\({}_{4}\)+ condition can be cultured in ADE from an abattoir at NH\({}_{4}\)+-N concentration of 220 mg L\({}^{-1}\). [PERSON] et al. [13] reported that _Chlorella_ spp. isolated from high NH\({}_{4}\)+ conditions can even grow in ADPE with an NH\({}_{4}\)+-N concentration of 800-1600 mg L\({}^{-1}\). Using isolated algal species/strains capable of survival under high NH\({}_{4}\)+ conditions is highly feasible because of their high tolerance to NH\({}_{4}\)+, high algal yield and nutrient removal in NH\({}_{4}\)+-rich wastewater [13; 32; 33]. Although 5\(\times\) low-diluted ADPE pretreated by NU-27 was more suitable for algal growth, it had quite lower algal yields than Tap medium (Figure 5). Therefore, subsequent process optimization of microalgae cultivation is needed for maximum algal yield in the pretreated ADPE.
# Key Distribution Scheme for Optical Fiber Channel Based on SNR Feature Measurement
## 5 Conclusions  
In this paper, a key generation scheme based on the SNR characteristic of the optical fiber channel's physical layer is proposed. In the scheme, the rate of SNR change is obtained using loopback measurements, and the changed SNR is compared with the threshold system to judge whether the system is attacked and the legitimate equipment can be correctly authenticated. If [PERSON] launches an active attack on the system, it impacts the security and reliability of the generated key. Therefore, we can use physical layer  
Figure 8: (**a**) The measured value curve of [PERSON] and Bob Eve SNR, and (**b**) the change curve of the error rate of Eve key.  
Figure 7: Influence of SNR measurement interval on Alice–Bob consistency.  
authentication for intrusion detection. The simulation results show that the KGR can reach 25 kbps, and the KCR can reach 98%. The correlation coefficient of BER measurement samples of [PERSON] and Eve is relatively low. The correlation coefficient \(cc=0.02\) and the KER of Eve is only 50%, indicating that this system has high security. Due to the physical characteristics of using the SNR as a key distribution scheme, normal communication requirements can be guaranteed to set the SNR values. The scheme can be used together with other security methods in the higher network layer, to enhance communication security and withstand active intrusion attacks. It has excellent application value and is suitable for popularization.  
Conceptualization, X.W. and B.W.; methodology, X.W.; software, B.W., K.Z. and H.S.; validation, X.W., B.W. and K.Z.; formal analysis, H.S.; investigation, H.S.; resources, R.L.; data curation, R.L.; writing--original draft preparation, X.W. and F.Z.; writing--review and editing, F.Z.; visualization, F.Z.; supervision, J.Z.; project administration, X.W.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the NSFC (Grant No.:61831003), University Natural Science Research Project of Anhui Province (Grant No.: KJ2019A0616), and the Key Projects in Natural Science of West Anhui University (Grant No.: WXZR201719). The authors are thankful for the funding support from the Anhui Provincial Quality Engineering Project (Grant No.: 2020 jyxm2152).  
Not applicable.  
This study does not report any data.  
We acknowledge the support given by [PERSON] and [PERSON] during the project.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# An Automatic Fault Diagnosis Method for the Reciprocating Compressor Based on HMT and ANN
###### Abstract  
The health management of the reciprocating compressor is crucial for its long term steady operation and safety. Online condition monitoring technology for the reciprocating compressor is almost mature, whereas the fault diagnosis technologies are still insufficient to meet the need. Therefore, in this paper, a novel fault detection method for the reciprocating compressor based on digital image processing and artificial neural network (ANN) was proposed. This method is implemented to the sectionalized pressure-volume (\(p\)-\(V\)) curves, which are obtained by dividing a working cycle in the cylinder into four thermal processes, including expansion, suction, compression and discharge. Hit-or-miss transform is adopted to extract the comprehensive gradients of expansion and compression curves, and vertical projection transform is applied to extract the vertical projection features. Finally, all of the features are fed to an ANN to do classification. To validate the proposed method, a seeded fault testing was conducted to collect real running data. The results showed that the new approach shows a good performance, with a high classification accuracy of 97.9%.  
reciprocating compressor; fault diagnosis; hit-or-miss transform; vertical projection transform Vol. 2022, 12, 5182. [[https://doi.org/10.3390/app12105182](https://doi.org/10.3390/app12105182)]([https://doi.org/10.3390/app12105182](https://doi.org/10.3390/app12105182))
# Entity Extraction of Key Elements in 110 Police Reports Based on Large Language Models
## 2 Related Work
### Development of Large Language Models for Information Extraction  
The technology for extracting key elements from police reports has evolved from basic rule-driven methods to complex natural language processing techniques. The rise of generative large language models such as GPT-3 [25] and ChatGPT [26] has introduced innovative possibilities for this field. Large language models have demonstrated excellent performance across various downstream tasks [17], showcasing superior practicality and flexibility over traditional methods in handling structured information extraction tasks [27].  
Currently, extensive research has applied large language model technology to information extraction tasks, including the universal information extraction (UIE) model by [PERSON] et al. [13], the instruction-based InstructUIE framework by [PERSON] et al. [14; 15], and the unified semantic matching information extraction framework proposed by [PERSON] et al. [16]. These works provide new perspectives and methods for police information extraction. Moreover, studies such as ChatIE [17] and the work by [PERSON] et al. [18] have utilized LLM for zero-shot training and clinical text mining, further proving the effectiveness of large language models in specific domains. However, using internet-connected large language models raises privacy concerns. Since most large language models are accessible only via their APIs, they are unsuitable for domains with confidential data. This issue can be addressed by leveraging open-source models to build domain-specific large language models locally.  
Domain-specific large language models are trained or fine-tuned on data from specific fields (e.g., biomedical, legal, or financial), aiming to absorb the expertise, terminology, and style of the target domain to enhance performance in various downstream tasks within that field. These models can be developed through training from scratch or fine-tuning pre-trained large language models, where fine-tuning is a form of transfer learning that provides a good starting point for specific domain applications [19]. For example, BioMedLM, a GPT model trained on PubMed data [20], has achieved advanced results in medical question-answering tasks, while Bloomberg GPT, trained on financial data [21], has excelled in multiple finance-specific tasks.  
For the public security domain, constructing domain-specific large language models can address the challenges of police information extraction. However, simply using open-source large language models is insufficient; fine-tuning and other methods tailored to police information are necessary to enhance the model's effectiveness.
# Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data
## 2 Related Works  
The field of road event classification has seen significant advancements, particularly with the increasing use of machine learning models to handle complex, high-dimensional sensor data. Thus, deep learning-based methods, such as convolutional neural networks (CNNs), have shown promise in learning intrinsic patterns and classifying road events [13, 14, 15]. Although such methods demonstrate the potential of deep learning to manage complex, high-dimensional data, they often require extensive labeled datasets and significant computational resources.  
In contrast, traditional feature-based methods, such as statistical feature extraction and wavelet transforms, continue to be relevant for certain applications due to their interpretability and lower computational cost [16]. These techniques rely on manually constructed features to identify specific patterns in vibration data. However, their performance may degrade in environments with overlapping or unpredictable event characteristics, where the variability of sensor data challenges the effectiveness of handcrafted features.  
More recently, researchers have explored the use of ensemble learning methods, including Random Forest, to address some of these limitations. Random Forest offers a robust solution to classification tasks by aggregating predictions from multiple decision trees, making it less sensitive to overfitting and more effective in handling noisy and high-dimensional data. For example, authors of ref. [17] demonstrated that Random Forest could achieve high precision in classifying events from vibration and acceleration data, outperforming traditional methods and offering a more efficient solution than deep learning models for real-time applications.  
In a similar trend, [PERSON] et al. [6] proposed the use of a genetic algorithm to detect speed bumps, utilizing the characteristics of the accelerometer. Their approach demonstrated the potential of heuristic search techniques, such as genetic algorithms, to optimize feature selection for classification tasks. By combining genetic algorithms with sensor data, their method improved the accuracy and robustness of road event detection, especially in challenging environments with noisy and variable sensor data. This highlights the versatility of ensemble learning and optimization algorithms in enhancing the performance of event detection systems.  
Sensor fusion techniques have also been widely explored to enhance classification accuracy. For example, refs. [18, 19, 20, 21, 22] highlighted the benefits of combining vibration data with accelerometer and gyroscope input to improve robustness and reduce classification errors. By merging data from multiple sensors, these hybrid methods leverage the complementary strengths of each sensor modality, improving overall performance in noisy, dynamic environments. Our approach similarly utilizes sensor data from multiple axes of vibration and acceleration to improve event classification accuracy.
# The Challenges of Advanced CMOS Process from 2D to 3D
## 3 Process Integration of New Transistor Architecture
### Common Challenges: Less Lateral Space Left  
Continuous scaling suggests endless reduction of CPP. This results in a trade-off between \(Lg\), S/D spacer thickness, and S/D contact area in a lateral device. \(Lg\) is limited by short-channel effects. Minimum spacer thickness is defined by reliability requirements and/or by capacitance between gate and S/D electrodes. Narrow S/D contacts result in high access resistance.  
Gate-all-around (GAA) FETs offer the best potential solution to electrostatic control, and can be implemented in a lateral (with one or more lateral wires which are vertically stacked) [94; 95] or a vertical configuration [96; 97]. The lateral nanowire FETs are closer to FinFETs in terms of processing,circuit design, as well as footprint constraints. The vertical nanowire FETs are less constrained on _Lg_, spacer thickness, and S/D contact area, as they are oriented vertically and thus should possess even better scalability [96; 97]. However, the move towards a vertical architecture will require more disruptive technology and design changes to be considered and implemented.  
Down scaling has always acted against wiring performance. At and below 22 nm, interconnect resistance is set to rise dramatically. Besides the wire cross-section reduction, carrier scattering from the boundaries of individual copper crystal grains, and interfaces with barrier layers are rapidly pushing up interconnect resistivity and the resistance of individual wires. Double patterning also leads to routing challenges, and the access to standard cell pins is more difficult because of constraints on interconnect pitch. Interconnect resistance and capacitance (RC) starts to dominate the delay. The line/via resistance and capacitance need to reduce, while reliability (electromigration (EM), time dependent dielectric breakdown (TDDB), bias temperature instability (BTI) and hot carrier injection (HCI)) needs to be maintained. Before these, defect-free metal filling is a prerequisite. However, reducing line resistance needs enough space for actual wiring material, leaving less room for barrier and thus poor reliability like EM. Lower via resistance needs thinner barrier at via bottom causing EM blocking boundary insufficient [98].
## 4 Results and Discussion
### Classification Model: Degree of Corrosion vs. Attack Type  
In Part 1 of this work, calibration and fitting the classification model were carried out in accordance with the degree of corrosion and double layer capacity [33]. In line with that study, and according to both parameters, it was possible to identify not only the corrosion level of a rebar embedded in concrete, but also the attack type (carbonation or chlorides) that caused corrosion. This study was performed on samples under laboratory conditions [33]. In this second work phase, the model to be implemented in the INESSCOM system was generated and validated from a study about structural elements. The objective of these tasks is, by means of such calibrating, to determine the limits of the areas where the different attacks are defined. Figure 9 shows the graph of the input of the data from the model that was derived in the first study part. Here, the objective is for the results obtained in this second work to be introduced into the model, which will allow the aggressive agent that is present, which is also the corrosion precursor to corrosion intensity and double layer capacity, to be defined.