esa-sceva/satcom-chunk-collection huggingface dataset:
# Semi-Supervised Learning for Intrusion Detection in Large Computer Networks
## 4 Experimental Results and Discussion
### Results and Analysis
#### 4.7.3 Classification Report  
A classification report is a detailed performance evaluation of a classification algorithm. It provides key metrics for each class in a classification problem, helping to understand the performance of the model in more depth. The main metrics included in a classification report are precision, recall, F1-score, and support [37].  
* **Accuracy**: The proportion of correctly classified instances (both true positives and true negatives) among the total instances: \[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}.\]
* **Precision**: The proportion of true positive instances among those instances predicted as positive: \[\text{Precision}=\frac{TP}{TP+FP}.\]
* **Recall (Sensitivity or True Positive Rate)**: The proportion of true positive instances among the actual positive instances: \[\text{Recall}=\frac{TP}{TP+FN}.\]
* **Specificity (True Negative Rate)**: The proportion of true negative instances among the actual negative instances: \[\text{Specificity}=\frac{TN}{TN+FP}.\]
* **F1-Score**: The harmonic mean of precision and recall, which provides a balance between the two metrics: \[\text{F1-Score}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{ Precision}+\text{Recall}}.\]  
In addition to per-class metrics, the classification report also provides the macro average and weighted average:  
* **Macro Average**: Unweighted mean of the metrics for each class.
* **Weighted Average**: Mean of the metrics weighted by the support of each class.  
The confusion matrix provides a comprehensive view of the performance of a classification model, allowing for the calculation of key metrics such as accuracy, precision, recall, specificity, and F1-score. These metrics help in understanding not only how many predictions were correct but also the types of errors made by the classifier. These insight are crucial for model evaluation and subsequent improvement.  
In addition to providing a comprehensive overview of the performance of a classification model by detailing precision, recall, F1-score, and support for each class the classification report includes the macro and weighted averages, offering a balanced view of the model's performance across all classes. This detailed analysis helps in understanding the strengths and weaknesses of the model and can guide further improvements.
## 3 Results and Discussion
### Effect of Cold Plasma Treatment on Physical Change of Seed's Surface
#### 3.1.3 Effect of Cold Plasma Treatment on the Wettability of Seeds  
Figure 4 shows that plasma treatment caused a transition from partial to almost complete wetting between untreated and plasma-treated mung bean seeds. The water contact angle was determined in sessile drop mode with distilled water with a drop volume of 5 \(\upmu\)L. Image) software was used to measure static contact angles and the values of the three measurements were averaged. The water contact angle reduced from 73\({}^{\circ}\) with untreated seeds, to 63\({}^{\circ}\) with 1-min plasma treatment, to 47\({}^{\circ}\) with 2-min plasma treatment, and to almost 0\({}^{\circ}\) with seeds that were treated for longer than 4 min. This observation also agreed with other reports, in which plasma treatment led to a dramatic decrease in the apparent contact angle of different seeds [21; 22]. More spreading and wetting and \(\upmu\)m-sized cracks lead to better water absorption on the surface of treated seeds. Consequently, this effect could enhance the germination rate and promote the growth of the sprout stem. Moreover, the increase in hydrophilicity of the treated seeds might save a significant amount of water necessary for irrigation. A previous study performed on pea seeds demonstrated an acceleration in water uptake following treatment with cold plasmas [23].  
Figure 3: SEM images of the morphology of the seeds’ surface without plasma treatment (0 min) and with 1, 2, 4, 6, 8, 10, and 15 min of plasma treatment.  
While the physical changes on the seed's surface were dose-dependent, and the change in the wettability of seeds was saturated after 4 min of plasma treatment, longer plasma treatment induced a higher germination rate and better development of the radices, and the enhancement effect only saturated after 10 min of plasma treatment. Therefore, not only physical changes induced by plasma treatment, but also the \"chemical\" changes in the endogenous hormone of the seeds, can even have a higher responsibility for this enhancement effect. The influence of cold plasma treatment on the genetics of seeds and other biological factors--such as the endogenous hormones that result in enhanced seed growth--remains unclear. Here, we varied the ratio of the plasma-generated reactive species while maintaining the maximum physical change condition (10 min plasma treatment time) to understand how they regulate GA\({}_{3}\) and affect plant growth. We extracted, separated, and quantified GA\({}_{3}\) from untreated and plasma-treated germinated seeds to study the effect of cold plasma treatment on this natural plant growth hormone.
## 3 Methods
### Problem Formulation  
In order to make the recommendation formalization, we establish sets \(\mathrm{U}=\{u_{1},u_{2},u_{3},\ldots\ldots\,u_{M}\}\) and \(\mathrm{V}=\{v_{1},v_{2},v_{3},\ldots\ldots\,v_{N}\}\) to represent the users and items, respectively, where M is the users' number and N is the items' number, respectively. The items could refer to movies, books, and music. The interaction between users and items is captured in the matrix \(\mathrm{Y}\in\mathrm{R}^{M\times N}\), where Y indicates the user's implicit feedback on the item. If \(\mathrm{y}_{uv}=1\), this denotes the interaction of the user and the item. If \(\mathrm{y}_{uv}=0\), this denotes that the user did not interact with the item. To assist in the recommendation process, the KGE is a triple \(\mathrm{G}=\{(\mathrm{h},\mathrm{r},\mathrm{t})|\mathrm{h},\mathrm{t}\in \varepsilon,\mathrm{r}\in\mathrm{R}\}\), where \(\mathrm{h}\) and \(\mathrm{t}\) are entities and \(\mathrm{r}\) represents the relationship between them and where \(\varepsilon\) represents the set of all entities and R represents the set of all relations.  
In order to calculate the user-item interaction matrix Y, we need to input data about the users and items into our model. Our ultimate goal is to analyze the preferences of users to items that they have not yet interacted with, but may potentially like. To achieve this, we use a prediction function \(\hat{\mathrm{y}}_{uv}=\mathrm{F}\left(\mathrm{u},\mathrm{v}|\Theta,\mathrm{Y}, \mathrm{G}\right),\) where \(\hat{\mathrm{y}}_{uv}\) represents the probability of user \(\mathrm{u}\) interacting with item \(\mathrm{v}\). The prediction function takes into account various hyperparameters denoted by \(\Theta\).
# Remote Sensing Image Target Detection Method Based on Refined Feature Extraction
## 3 Experimental Results and Analysis
### Comparison with Other Algorithms
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Params** & **FLOPs(G)** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & **AP\({}_{S}^{val}\)** & **AP\({}_{M}^{val}\)** & **AP\({}_{L}^{val}\)** & **Year** \\ \hline YOLOv3 & 61.6 & 154.8 & 87.0\% & 61.4\% & 24.0\% & 53.5\% & 74.0\% & 2018 \\ YOLOv3-Spp & 62.6 & 156.6 & 88.0\% & 62.8\% & 25.7\% & 53.6\% & 75.3\% & 2018 \\ YOLOv4-Csp & 52.5 & 119.1 & 88.3\% & 63.1\% & 25.9\% & 55.1\% & 76.2\% & 2020 \\ YOLOR-Csp & 46.2 & 107.9 & 89.5\% & 66.1\% & 27.1\% & 55.9\% & 77.8\% & 2021 \\ YOLOv5l(7.0) & 46.4 & 108.7 & 88.9\% & 65.8\% & 26.2\% & 55.6\% & 77.4\% & 2021 \\ YOLOv5-Bifpn(7.0) & 7.0 & 15.7 & 85.0\% & 57.0\% & 21.4\% & 49.6\% & 67.9\% & 2021 \\ YOLOv5s- & 52.5 & 119.1 & 88.5\% & 63.8\% & 25.4\% & 54.9\% & 77.0\% & 2021 \\ Transformer(7.0) & 59.6 & 150.5 & 87.4\% & 65.8\% & 25.4\% & 54.8\% & 79.3\% & 2022 \\ YOLOv7 & 36.5 & 103.4 & 88.7\% & 65.0\% & 25.6\% & 56.1\% & 78.2\% & 2022 \\ DCN-YOLO & 38.1 & 98.4 & 90.1\% & 66.7\% & 29.0\% & 57.4\% & 79.6\% & ours \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results of the DIOR dataset. After conducting an in-depth analysis of the results, we found that the DCN-YOLO algorithm demonstrates the ability to accurately capture and detect dense small objects, exhibiting higher localization precision. This improved performance allows it to better recognize and distinguish objects in complex scenes. It is worth noting that, consistently with the comparative analysis results in other research works, our proposed method exhibits improved detection accuracy and [EMAIL_ADDRESS] values on various datasets, further validating the robustness and generalization capability of our approach. Figure 6: Illustrates the detection results of various algorithms for different scenarios: (**a**) pool detection, (**b**) airplane detection, (**c**) car detection, and (**d**) oil tank detection.
## 2 Background: Electric Vehicle Charging Systems and Standards
### EV Charging Architecture  
Figure 1 depicts the architecture of an EV charging system and relevant standards. In EV charging, EV and EVSE are the main actors. The EV makes a physical connection to the EVSE using a charging cable ([PERSON] _et al._, 2018). They communicate through two standards: IEC 61851 for low-level communication and ISO/IEC 15118 for high-level communication. In particular, the EV Communication Controller (EVCC) in EV and Supply Equipment Communication Controller (SECC) perform high-level communication to control the overall charging procedure.  
Figure 1: Architecture and standards for the Electric Vehicle (EV) charging.  
On the other side of EVSE, the secondary actors play supporting roles, such as billing and power control. In this work, we are interested in a secondary actor, called the operator, which is involved in charging by dealing with power-related values. We assume the IEC 61850 standard for the exchange of power-related values between EVSE and the operator. However, the messages generated in ISO/IEC 15118 and that of IEC 61850 are incompatible. To bridge ISO/IEC 15118 and IEC 61850 communication, a technical report IEC 61850-90-8 was proposed [8]. In the following, we discuss relevant standards in more detail.
## 4 IFC Data Model for Supporting Safety Risk Identification in Deep Excavation
### Information Requirements for Safety Risk Identification in Deep Excavation
This research classifies the risk factors into two types: (a) static influential factors, which refer to construction information that can be obtained directly from design documentation before the construction operation stage (i.e., construction design, hydrogeological, and circumjacent environment factors); and (b) dynamic influential factors, which refer to construction information that can be obtained by monitoring concrete construction activities during the construction operation stage (i.e., human behavior, mechanical equipment, and working environment factors). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Risk Category** & **Description** & **Examples** \\ \hline
1. Technical risk & \
\begin{tabular}{c} Risks caused by project characteristics, such as \\ adopted construction technologies or methods. \\ \end{tabular} &
\begin{tabular}{c} Sand boiling at the bottom of the pit; \\ Ground heave of the pit bottom. \\ \end{tabular} \\ \hline
2. Geological risk & \
\begin{tabular}{c} Risks caused by harmful geological or underground \\ water conditions, such as caves, water capsules, and \\ poisonous gases. \\ \end{tabular} &
\begin{tabular}{c} Leakage of poisonous gas; \\ Water and mud gushing due to water capsules. \\ \end{tabular} \\ \hline
3. Environmental risk & \
\begin{tabular}{c} Risks related to environmental destruction within \\ the scope of construction influence, such as damage \\ to surrounding buildings, structures, and \\ underground pipelines. \\ \end{tabular} &
\begin{tabular}{c} Cracking, slopping, or collapsing of surrounding \\ buildings (or structures); \\ Leakage or fracture of underground pipelines. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Objective risk categories related to construction safety management of deep excavation Automatic risk identification is realized by defining construction parameters as various discontinuous value ranges, which correspond to different certainty factors during the risk reasoning stage. Some construction parameters are defined as 'Yes' or 'No'.
## 1 Introduction
Additional data such as soil maps, geological maps, and climate and meteorological data, can all provide information to further explain observed deformation phenomena [18]. Therefore, remote infrastructure observation with PSI has the potential to prompt proactive asset maintenance, increase network resilience and reduce the need for expensive in situ monitoring. One example of a PSI investigation is [PERSON] et al. 's study [18], in which a large time series of 60 ERS and ENVISAT images in descending orbit were collected between March 1997 and December 2005, with ground movement being quantified over a 95 \(\times\) 55 km scene over London, UK. The study found that PSI targets in large parts of the Thames estuary subsided between 0.9 and 1.5 mm year\({}^{-1}\) with the fastest soil subsidence rate being, on average 2.1 mm year\({}^{-1}\). By combining PSI outputs with geological data in a GIS, the authors noted that regional patterns of uplift and subsidence are controlled by both deep geological features, such as the relative mass of underlying geology, and shallow geological features such as fault lines. [PERSON] et al. 's study [20] noted that the shrink swell behavior of the London clays can give rise to 50 mm year\({}^{-1}\) of vertical movement (cumulative shrink and swell) over wide areas. This rate of movement was attributed largely to dry summers and wet winters. Both studies [18; 20] noted the importance of external factors such as extreme meteorological conditions, local topography, urban fabric, vegetation, and anthropogenically-induced subsidence such as tunneling and water abstraction. National soil maps, such as the one used in this study [21], often estimate ground movements based on the laboratory assessment of clay soils' volumetric change potential. Such categorical maps are widely used by utilities, with classes such as \"high\" and \"low\" ground movement potential [22]. Quantification of the vertical movement of soils is difficult due to numerous external factors (vegetation, agriculture, erosion etc.). Therefore, applying PSI over engineered surfaces that maintain high coherence can act as one possible method to quantify soil movement. Previous studies have investigated the effects of engineering design on the rates of observed surface movement [3; 16]. The importance of foundation depth is consistently noted as a predominant control over surface deformation of structures. This paper seeks to measure directly how road and rail networks respond to the movement of different soils. By investigating the use of PSI over 6 different UK urban areas (Bristol, Bath, Bournemouth, Kings Lynn, Peterborough and Grantham), a wide range of environmental conditions, soil types and asset responses can be included in the analysis. This work will improve the current knowledge of how different types of infrastructure respond to soil movement--particularly in the UK--and will help determine the suitability of the PSI technique to monitor critical infrastructure networks nationally.
# Thermal-Structural Coupling Analysis of Subsea Connector Sealing Contact
Temperature has an important impact on the thermal deformation of the sealing structure in the temperature field, and an appropriate high temperature can improve the sealing ability. In 2020, [PERSON] et al. [14] considered the elastic deformation and thermal stress deformation of the micro-convex body based on fractal theory and analyzed the normal contact stiffness of the double friction interface of dry gas seals. These authors obtained the influence of the fractal dimension and characteristic dimension on the thermal and elastic normal contact stiffness, that is, the former had a positive ratio, and the latter was inversely proportional. [PERSON] et al. [15] established a fluid dynamics and heat transfer model of porous media through the thermal stress module of the ANSYS Workbench, corrected the friction heating model, and calculated the thermal deformation of fingers according to the pressure and temperature results. As can be seen from the above literature review, previous researchers mainly studied the mechanical properties of the seal structure under thermal-structural coupling and the structural contact stress under external load or thermal stress. However, the contact state and mechanical characteristics of the sealing structure under the action of thermal stress and structural stress were not considered sufficiently. Therefore, this paper analyzes the heat transfer process when the subsea connector is subjected to the action of internal high-temperature oil and external low-temperature seawater and simplifies the heat transfer by equivalent thermal conductivity and the composite heat transfer coefficient. The structural stress of the subsea connector under a mechanical load and thermal stress produced by temperature loads is coupled to analyze the contact characteristics and overall stress change of the subsea connector lenticular sealing structure in the thermal-structural coupling field. Because the contact stress of the sealing contact surface cannot be analyzed by a sticking strain gauge, the correctness of this method was verified by comparing the finite element numerical simulation with the theoretical analysis.
# Advances in Fermentation Technology: A Focus on Health and Safety
## 3 The Effectiveness of Innovative Methods in Fermented Food Safety Assurance
### Precision Fermentation for Targeted Compound Production  
Precision fermentation involves using genetically engineered microorganisms to produce specific compounds and has raised essential safety considerations. While this technology has the potential to create high-quality fermented products with tailored health benefits, it also necessitates rigorous safety assessments to evaluate the potential risks associated with genetically modified organisms (GMOs) [138]. Recent studies have highlighted the importance of thorough testing to ensure these microorganisms do not introduce harmful traits, such as antibiotic resistance, into the food supply [139]. Regulatory frameworks are evolving to address these concerns, ensuring precision fermentation can safely be integrated into food production systems.  
Unlike conventional fermentation, which relies on mixed microbial communities with broad metabolic outputs, precision fermentation offers a high degree of control over the types of compounds produced, potentially enhancing both the consistency and safety of fermented products. [PERSON] et al. [140] emphasised the need for rigorous safety assessments of precision fermentation products, particularly concerning potential allergenicity and metabolic by-products. For example, precision fermentation can be used to produce rare bioactive peptides that enhance immune response or improve gut health, thereby contributing directly to food safety. However, using genetically GMOs raises regulatory and consumer acceptance challenges. Traditional fermentation has a well-established safety profile and consumer trust, while precision fermentation, being a novel technology, requires clear regulatory oversight and transparent communication to gain broader acceptance.
## 2 Materials and Methods  
### Study Flowchart  
The flowchart in Figure 2 presents the processing steps used in this study. First, the remote sensing data were organized through Google Earth Engine (GEE) platform and processed to extract the built-up area, annual average light intensity, and area of lit from Landsat 8 Operational Land Imager (OLI), Suomi NPP Visible Infrared Imaging Radiometer Suite (VIIRS) Day and Night Band (DNB), respectively. Second, the sum of light intensity (SL) using zonal statistic tools was calculated for each district through ArcGIS online. All datasets were then prepared for model building after cleansing and normalizing. The electricity consumption estimation models included MR, DT, and SVR were conducted in RapidMiner(r) software. The feature selection and the optimization of model parameters were also controlled.  
Figure 2: Flowchart of the study.
# Recent Advances in Photodetectors Based on Two-Dimensional Material/Si Heterojunctions
## 2 Figures of Merit for Photodetectors
### Linear Dynamic Range  
In practical applications, it is crucial for a photodetector to have a linear response when converting optical signals into electrical signals in order to avoid distortion. However, at high light power levels, the photocurrent tends to saturate. To evaluate the linear response capability, the concept of linear dynamic range (LDR) is introduced. LDR represents the power range within which a photodetector maintains a linear response to incident light. It is defined as follows [68]:  
\[r_{LDP}=10\;\text{lg}\frac{P_{max}}{P_{min}} \tag{12}\]  
Here, \(P_{\text{min}}\) and \(P_{\text{max}}\) correspond to the minimum and maximum light power levels, respectively, at which the photodetector exhibits linear response. The unit of LDR is decibel (dB). It is worth noting that most commercial photodiodes demonstrate linear response until they reach saturation. In these cases, \(P_{\text{min}}\) represents the NEP without normalization to bandwidth. However, it is important to note that many photoconductive detectors reportedin the literature exhibit sublinear behavior, making LDR inappropriate for characterizing them.  
It is important to emphasize that the commonly misused definition of 20 lg(\(P_{\text{max}}/P_{\text{min}}\)) in the literature is incorrect. In engineering, the use of 10 lg is appropriate for the power range with dB units, while 20 lg is used for the amplitude range with dB units. Moreover, it is not appropriate to use photocurrent to define LDR as 20 lg(\(I_{\text{pmax}}/I_{\text{pmin}}\)), where \(I_{\text{pmax}}\) and \(I_{\text{pmin}}\) are the maximum and minimum photocurrents that the device responds linearly to incident light. This is because the focus should be on the linear response to incident light power and not on the photocurrent itself, making such a definition meaningless.