esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Cutting Parameters and MQL on Surface Finish and Work Hardening of Inconel 617
## 4 Results and Discussion
### Study B: Influence of Cutting Parameters  
The tests performed using the cutting parameters in Table 7 were cooled using flood coolant and machined to a total cutting length of 2000 m. This was the distance identified in the first tool coating tests and the initial break in region of the tool with the AITiN coating in Figure 2. The average surface roughness was measured at the final pass for each cutting parameter and final tool flank wear was recorded. Cutting tests that reached tool failure before the 2000 m initial break-in region do not have a recorded final flank wear and their tool failure mechanism is recorded.  
Tests T1, T4, and T7 exhibited the lowest average surface roughness and final flank wear. These tests also correspond with the lowest feed rate tested in the array, 0.075 mm/rev. This aligns with the main effect plot for the mean of means for average roughness Ra (Figure 3); this shows that the feed rate has the highest impact on the surface roughness, especially as the feed rate increases. A large depth of cut has an increased mean influence on the average roughness, whereas cutting speed displays an inverse effect, with low cutting speeds generating increased roughness. The impact of these cutting parameters on the surface roughness in turning Inconel 617 exhibited similar results to tests performed on Inconel 718, where the feed rate has the largest impact on surface roughness. Additionally, decreasing the feed rate and depth of cut while increasing cutting speed can improve the surface finish [47]. To further assess methods of improving surface quality, tests T1, T4, and T7 are used in the coolant deposition tests.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multirow{2}{*}{\begin{tabular}{c} **Depth of Cut** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Feed (mm/rev)** \\ **(mm/min)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Cutting Speed** \\ **Ra (um)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Final Rank** \\ **Wear (um)** \\ \end{tabular} } \\ \hline
1 & 0.1 & 0.075 & 50 & 0.890 \(\pm\) 0.015 & 112.1 \\
2 & 0.1 & 0.125 & 75 & 1.105 \(\pm\) 0.018 & 112.3 \\
3 & 0.1 & 0.25 & 100 & 1.917 \(\pm\) 0.024 & 131.9 \\
4 & 0.3 & 0.075 & 100 & 0.941 \(\pm\) 0.014 & 108.4 \\
5 & 0.3 & 0.125 & 50 & 1.056 \(\pm\) 0.022 & 130.7 \\
6 & 0.3 & 0.25 & 75 & 1.906 \(\pm\) 0.037 & Chipping \\
7 & 0.5 & 0.075 & 75 & 0.955 \(\pm\) 0.018 & 105.9 \\
8 & 0.5 & 0.125 & 100 & 1.039 \(\pm\) 0.037 & Nose Fracture \\
9 & 0.5 & 0.25 & 50 & 2.803 \(\pm\) 0.053 & Nose Fracture \\ \hline \hline \end{tabular}
\end{table}
Table 7: Taguchi L9 orthogonal array for cutting tests.  
Figure 3: Main effect plot for depth of cut, feed rate, and cutting speed on mean of means for average roughness.
# Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory
## 4 Experimental Results Analysis
### Comparison of Recognition Performance of Different Models
#### 4.3.2 Comparison of Robustness  
During the training process, the initialization of the networks has a severe impact on the results, and it is difficult to ensure that the network achieves optimal recognition by fixing the initial network parameters and weights.  
In the CPN model, a fixed distance threshold was used to detect the unknown samples, but the distribution of the sample features changed significantly during each training process. Therefore, the test results after each training fluctuated. In this paper, we incorporated EVT to improve the classification rules. Firstly, prototypical networks and joint loss were used to make the feature space of each class more independent and separate. Meanwhile, the distance was also more suitable for measuring the attribution. Finally, the classification results were weighted and revised by the probability from the Weibull model. Thus, our algorithm effectively increased the robustness and alleviated the influence of the model using the initial parameters, SNR, and instability of the network.  
As shown in Figure 12, the test results of the three models were compared after multiple experiments at \(-4\) dB and \(0\) dB using the ZigBee dataset. The comparison showed that the test results of EVT-Shuffle-SE were more robust, while the CPN and OpenMax models fluctuated more. The fluctuation increased as the SNR decreased. Therefore, the recognition performance of our model was more stable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **EVT\_PN\_Shuffle** & **OpenMax** & **CPN** & **Center\_Loss** \\ \hline Recognition & 90.3\% & 85.8\% & 78\% & 81.5\% \\ Accuracy & 90.3\% & 85.8\% & 78\% & 81.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of recognition accuracy of different models.  
Figure 11: Confusion matrix under mixed SNRs of \(-4\)–\(10\) dB.
# Future Renewable Energy Communities Based Flexible Power Systems
## 7 Flexibility and Ancillary Services  
The high injection of intermittent renewable power into the power system adversely affects the efficient operation of power systems. Its primary impact is on the system frequency. Renewable generation fluctuates constantly because they are highly affected by environmental condition. For example, wind power is constantly varying according to the wind speed variation. In addition, cloud patterns affect PV generation. Hence, the large share of renewables worsens the mismatch between system generation and demand. In this regard, system operators need more flexible energy resources to control the system frequency. Different levels of frequency deviations require different flexibility services [38]. Small-scale flexible energy resources such as prosumers need to be aggregated by an aggregator or in a form of energy communities to provide frequency control services. Besides, they have to pass some qualification tests before their participation. Figure 11 reviews the potential flexibility services that can be provided in a smart city environment. In addition to frequency, the high injection of renewables and distributed generations (DG) affects the voltage control at different levels of the power system and may cause congestion within feeders.  
As a result, the system needs more flexible energy resources that also provide non-frequency flexibility services such as voltage control and congestion management services [39]. The system operators need to unlock flexibility potential at different nodes and buses to be able to control voltage and avoid congestion.
## 3 Sensor Data Security Estimator
### Proposed Security Metrics  
As stated earlier, SDSE defines a security metric for each considered security mechanism. The three security mechanisms considered by SDSE are: the cryptography algorithm, key management scheme and intrusion detection system. These mechanisms were chosen, because together, they represent the two types of security mechanisms needed to ensure the security of networked systems, which are: prevention mechanisms (represented by the cryptography and the KMS) and detection mechanisms (represented by the IDS). Prevention mechanisms act as a first line of defense, preventing or reducing the potential of attacks. These mechanisms are critical to ensure the main security requirements, such as authentication, confidentiality and integrity. On the other hand, intrusion detection mechanisms act as a second line of defense, identifying and possibly eliminating intruders who evaded prevention mechanisms.  
These mechanisms considered by SDSE are the basis for the operation of secondary security mechanisms, such as secure aggregation and secure routing, for example. Therefore, by evaluating the security of the primary mechanisms (cryptography, KMS and IDS), SDSE also addresses the security provided by any other secondary mechanism that include them.  
To estimate the security level provided by each of the considered mechanisms, one must initially identify their key vulnerabilities and then define metrics to quantify security, taking into account the impact caused by the exploitation of those vulnerabilities. These details are presented in the following subsections, according to each considered mechanism and the respective security metric defined by SDSE to evaluate it, as listed below:  
* The key strength probability metric, for the cryptography algorithm;
* The resilience probability metric, for the key management scheme;
* The legitimacy probability metric, for the intrusion detection system.  
Figure 1: Operation flow of the sensor data security estimator (SDSE).
## 4 Results
### Numerical Results
#### 4.1.2 Group Peak Signal-to-Noise Ratio  
For the Group PSNR with depth 1 (see Figure 8b), the results were similar to the PSNR, with an error rate of 59 and an average execution time of 0.0977 s. Using the depth of 2 produced 167 errors and had an average execution time of 0.2899 s. It is also worth noting that in Period 3 this method exhibited a distinct pattern, where a significant number of High-Quality measurements were clustered within the range of Low-Quality measurements (see Figure 8c).
## 3 Experiments and Results
### The Performance of Collecting Vibration Energy  
H-TENG can convert vibration energy into electrical energy, so the functioning process of the sensor is also the device's power generation process. If multiple H-TENGs are combined, other downhole drilling instruments can be powered. The sensor's power generation performance was tested, and the results are shown in Figure 5. As illustrated in Figure 5a,b, the output voltages of a single H-TENG or four H-TENGs are proportional to the frequency and external load. When the frequency of vibration is 11 Hz and the series resistance value is \(10^{10}\)\(\Omega\), a single H-TENG can provide a maximum output voltage of 12 V. When four H-TENGs are connected in parallel, the output voltage of multiple H-TENGs is up to 13 V. The output current of H-TENG is proportional to frequency and inversely proportional to resistance, as shown in Figure 5c,d. When the frequency of vibration is 11 Hz and the series resistance value is \(10^{10}\)\(\Omega\), the maximum output  
shown in Figure 5f, for multiple H-TENGs, the maximum output power is 1.57 uW at a frequency of \(11\) Hz and a resistor of \(10^{7}\)\(\Omega\).  
A rectifier bridge is used to convert the output AC signal to a DC signal and charge the 4.7 uF capacitor in order to display the output power in real-time. As depicted in Figure 6a, when the charging time is 50 s, a single H-TENG can charge the voltage to 0.8 V, while multiple H-TENGs can charge the voltage to 2 V, demonstrating the linear charging  
Figure 4: Result of the H-TENG sensing performance. (**a**) Voltage output at various frequencies; (**b**) voltage output at various amplitudes; (**c**) measurement error of vibration frequency; (**d**) measurement error of vibration amplitude; (**e**) voltage output at various temperatures and relative humidity; (**f**) voltage output at various cycles.  
characteristics. The real-time output power of the sensor, which can light twelve LEDs, is shown in Figure 6b.  
Figure 5: The sensor’s power generation performance. (**a**) The output voltage of a single H-TENG under various external loads and frequencies with a 15 mm amplitude; (**b**) the output voltage of four H-TENGs under various external loads and frequencies with a 15 mm amplitude; (**c**) the output current of a single H-TENG under various external loads and frequencies with a 15 mm amplitude; (**d**) the output current of four H-TENGs under various external loads and frequencies with a 15 mm amplitude; (**e**) the output power of a single H-TENG under various external loads and frequencies with a 15 mm amplitude; (**f**) the output power of four H-TENGs under various external loads and frequencies with a 15 mm amplitude.
## 3 Result
### Evaluation Metrics  
Detect attacks should be evaluated in real time to assess their effectiveness and efficiency. The primary studies we reviewed used various strategies to evaluate the efficiency of their proposed approach. Figure 5 shows the percentages of specific evaluation metrics used in the primary studies. Numerical measures and graphical measures are two types of measurement. Numerical measures consist of precision, accuracy, F-measure, and others, whereas graphical measures consist of ROC curves, etc.  
Accuracy was frequently used in the primary studies. Accuracy can be described as the number of IoT attacks that are correctly detected divided by the number of IoT attacks. The second most commonly used performance measure for the identification of IoT attacks is recall. This measurement relates to the quantity of IoT attack classes correctly predicted among the actual IoT attack classes. Precision is the third most commonly used evaluation metric, and it measures the correctness of the model. F-measure is the fourth most commonly used evaluation metric, and it shows the trade-off between the performances of a classifier. The detection rate is the fifth most commonly used evaluation metric, and it indicates the efficiency of a classifier with respect to its ability to detect malicious behaviors.
# A Study on Dropout Prediction for University Students Using Machine Learning
## 3 Proposed Method  
### Data Description  
To perform supervised learning for dropout prediction, we used data stored in the academic information system of Sahmyook University, a medium-sized four-year university located in Seoul. In the system, the student data were stored separately in several tables to prevent duplication by applying database normalization. Table 2 shows the tables and attributes that can be used for dropout prediction, where underlined attributes indicate those that make up a primary key. All of the tables except StudentInfo contain student records by semester and use a primary key in the form of a composite key consisting of three attributes, including SID, year, and semester. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Ref\#** & **Source Data** & \begin{tabular}{l} **Dropout** \\ **Rate** \\ \end{tabular} & \
\begin{tabular}{l} **Imbalance** \\ **Processing** \\ \end{tabular} & **Algorithms** & **Measure** &
\begin{tabular}{l} **Best Score** \\ **(Algorithm)** \\ \end{tabular} \\ \hline
[19] & 2018 academic records & 4.5\% & SMOTE, ADASYN &
\begin{tabular}{l} Balanced Bagging, \\ DNN, DT, \\ CatBoost + XGBoost \\ \end{tabular} & F1-score & 0.976 (DT) \\
[8] & 2015–2021 academic records & 5\% & \
\begin{tabular}{l} SMOTE + Tomek \\ SMOTE + ENN \\ \end{tabular} & CatBoost + XGBoost & F1-score & 0.808 \\  & from 67,060 students & 2017-2021 survey & 6.4\% & - &
\begin{tabular}{l} DT, NB, RF \\ Ridge Regression \\ \end{tabular} & Precision & 0.739 (Ridge Regression) \\
[29] & 2011–2019 academic records & 37.5\% & ROS & DNN, RF, XGBoost & F1-score & 0.81 (RF) \\
[30] & Academic records & 55.2\% & SMOTE+Tomek & GB, RF, SVM & F1-score & 0.804 (SVM) \\  & from 1418 students & 2017–2018 academic records & 72.8\% & - & LR, DNN & Accuracy & 0.768 (DNN) \\
[32] & 2016–2017 academic records & - & - & DNN, LR, NB, & F1-score & 0.96 (NB) \\  & from 366 students & 2013–2022 academic records & 4.5\% & SMOTE & DNN, DT, LR, RF, SVM & F1-score & 0.817 (RF) \\  & from 20,050 students & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summarization of the existing studies: target data, dropout rate, algorithms, performance measure, and the best prediction score. (DNN: Deep Neural Network, DT: Decision Tree, GB: Gradient Boosting, K-NN: K-Nearest Neighbor, LR: Linear Regression, NB: Naive Bayes, RF: Random Forest, SVM: Support Vector Machine. )In a typical case, each student has 8 records in the tables because he or she attends 8 semesters until graduation. Table 3 shows an example of the records in the Grade table for dropout and non-dropout students. The SID values were partially masked to
## 2 Materials and Methods
### Antibacterial Activity  
Two reference strains belonging to the strain collection of the Department of Microbiology, Immunology and Laboratory Medicine, Pomeranian Medical University, in Szczecin (Poland), were used in the current study: _Staphylococcus aureus_ ATCC 29213 (methicillin-susceptible _S. aureus_ (MSSA)) and _Escherichia coli_ ATCC 8739. All microorganisms were cultured on Columbia agar with 5% sheep blood (bioMerieux, Warsaw, Poland) and incubated for 18 h at 37 \({}^{\circ}\)C.  
The minimum inhibitory concentrations (MICs) of the EOs, thymol (positive control; Sigma-Aldrich, Darmstadt, Germany), and gentamicin sulfate (positive control; solution for injections; Krka, Novo Mesto, Slovenia) against _S. aureus_ and _E. coli_ were evaluated via the serial microdilution method in Mueller-Hinton broth (MHB) (Sigma-Aldrich, Darmstadt, Germany). The study was performed according to the Clinical and Laboratory Standards Institute recommendations [18; 19; 20]. In brief, 50 uL of a suitable concentration of EOs/thymol/gentamicin sulfate was added to a microwell plate. Concentrations of EOs from 250 to 0.12 uL/mL were made using Tween 80 (1.0%, _v/v_) (Sigma-Aldrich, Darmstadt,Germany). Concentrations of thymol from 625 to 1.22 \(\upmu\)g/mL were made using dimethyl sulfoxide (DMSO) (2.0%, \(v/v\)) (Loba Chemie, Mumbai, India). Moreover, concentrations of gentamicin sulfate from 625 to 0.31 \(\upmu\)g/mL were prepared. In the next step, 50 \(\upmu\)L of microbial suspension at \(10^{6}\) CFU/mL was added to the microplate. After incubation (18 h at 37 \({}^{\circ}\)C), the MICs for the EOs/thymol/gentamicin sulfate were estimated via a resazurin (Sigma-Aldrich, Darmstadt, Germany) assay [21; 22]. The bacteria suspension with 1.0% (\(v/v\)) Tween 80 or 2.0% (\(v/v\)) DMSO was regarded as a negative control.  
Next, the minimum bactericidal concentrations (MBCs) were evaluated by transferring 20 \(\upmu\)L from the microwells that displayed no signs of growth to a new 96-well microplate containing MHB [20; 21; 22]. The microplate was incubated for 18 h at 37 \({}^{\circ}\)C. The MBCs were examined in the wells in which no bacterial growth was noticed. To evaluate the effectiveness of the EOs/thymol/gentamicin sulfate, the MBC/MIC ratio was determined (MBC/MIC ratio \(\leq\) 4--bactericidal effectiveness; MBC/MIC ratio \(>\) 4--bacteriostatic effectiveness) [23].
To enhance the comparison of optical properties among different wave spectra, the often-overlooked truncation error of the Elfouhaily spectrum at low wind speeds is analyzed and refined. Subsequently, the mean square slope (MSS) of the simulated sea surface is computed to aid in selecting the most suitable spectrum for accurately modeling sea surface dynamics and laser channel transmissions. Afterward, the effects of different wind speeds and underwater depths on the beam power reaching the ideal and optimal receiving plane are investigated. In particular, this study introduces the time evolution property alongside spatial averaging statistics, enabling a comprehensive comparison of the laser transmission characteristics through both statistical and spatial sea surfaces. The remainder of this paper is structured as follows: In Section 2, the sea surface channel model is presented, including the classical statistical sea surface and the wave spectrum simulation sea surface, with a focus on refinement of the truncated characteristics of the Elfouhaily spectrum at low wind speeds. In Section 3, the uplink seawater-sea surface-air channel is described, incorporating simplifications for the optical properties of the water and air columns. In Section 4, numerical results, including power spatial distributions, and beam offsets, are analyzed. Finally, this paper concludes with a summary in Section 5.
# R-Curve Behavior of Polyhedral Oligomeric Silsesquioxane (POSS)-Epoxy Nanocomposites
## 2 Materials and Methods  
The polymer system adopted in this work consists of diglycidyl ether of bisphenol-F based resin (EPON 862(r), Hexion Specialty Chemicals, Columbus, OH, USA), that is cured with a low viscous aliphatic amine (EPIKURE 3274(r) Hexion Specialty Chemicals, Columbus, OH, USA). Both the resin and the curing agent were acquired from Miller-Stephenson Chemical Company, (Danbury, CT, USA). This epoxy resin system is a highly cross-linked network that results in excellent mechanical properties and high chemical resistance.  
The POSS selected in this work is glycidylI POSS (Hybrid Plastics, Hattiesburg, MS, USA), as shown in Figure 1, which is an epoxide-based filler. GlycidylI POSS can show compatibility with epoxy resin through hydrogen bonding, a similar chemical structure, and \(\pi\)-\(\pi\) interaction. In addition, glycidylI POSS can also form covalent bonds with the polymer matrix during the cross-linking process due to the presence of epoxide groups on the POSS molecule.  
Mixing of the EPON 862 epoxy resin and POSS was carried out in a magnetic stirrer at 70 \({}^{\circ}\)C and 200 rpm for 12 h on a magnetic stirrer (VWR Advanced Hot Plate Stirrers, Sugar Land, TX, USA). The mixture was then cooled to room temperature, and a stoichiometric amount of amine-based curing agent (EPIKURE 3274) was added by 40% wt. of epoxy. This was followed by mixing for 10 min. Subsequently, the mixture was placed in a vacuum chamber for 30 min for degassing. The degassed mixture was poured into a prepared mold for casting and cured at room temperature for 24 h. The cast plate was then released from the mold and post cured at 121 \({}^{\circ}\)C for 6 h. The same procedure was used for preparing neat resin samples for comparison. Materials were prepared at POSS loading levels of 0.5%, 1%, 3%, 5%, and 8%.  
Figure 2 exhibited how inclusion of POSS molecule in the epoxy network increases the free volume which increases the ductility parameter hence change in failure mode was observed.  
Figure 1: Molecular structure of glycidylI POSS.