esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results  
### Metrics Evaluation of the Trained Models  
In object recognition methods, the detection result and the performance of the classifier are the two primary indices used to calculate the performance of the models. IoU, precision, F1 score, recall and mAP@IoU = 0.5, mAP@IoU = 0.5:0.95 are commonly used to evaluate the bounding box positioning results. The IoU is a basic metric used to compare object recognition systems [49]. A confusion matrix is a general analysis tool for describing a classifier's performance. For model recognition, IOU calculates the distance between the expected and actual bounding boxes to assess the scenario (Figure 7). IoU is a ratio that is calculated by intersecting and joining the actual and anticipated boxes. The classified object detection findings are regarded as true positives (TP) if the IOU value is greater than 0.5. If the IoU value is less than 0.5, the result is a false positive (FP).  
In object detection, a false negative (FN) denotes that the models should have predicted a positive result but detected it incorrectly. Using the output from IoU, the indicators precision, recall, F1 score, and mAP compute object recognition models performance. For object recognition models, precision, recall, and mAP is popular technical indicators to evaluate overall performance. Precision, recall, and mAP are defined in Equations (1) to (4) respectively. With the obtained values TP, TN, FP, and FN. For mAP@IoU = 0.5, the threshold was set 0.5, and for mAP: IOU = 0.5:0.95, the threshold has taken 10 different values between 0.5 and 0.95 in steps of 0.05.  
Our goal is to develop an algorithm suitable for real-time applications, such as insect pest infestation detection, reducing labor costs, avoiding economic losses, and retaining beneficial insects.  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Number of Layers** & **Training Parameters** \\ \hline YOLO-Lite & 362 & 5,467,452 \\ YOLOv5n & 213 & 1,790,284 \\ YOLOv5s & 270 & 7,072,156 \\ YOLOv5m & 290 & 20,941,836 \\ YOLOv5l & 468 & 46,256,764 \\ YOLOv5x & 567 & 83,365,852 \\ YOLOv3 & 269 & 62,664,988 \\ YOLOR & 665 & 36,957,216 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Information of model variation used in training.  
Figure 7: IOU calculation.  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{2}\]  
\[\text{mAP}=\frac{1}{N}\sum\limits_{\text{i=1}}^{\text{N}}\text{APiN} \tag{3}\]  
number of queries, \(\text{AP}:\) average precision  
\[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{4}\]
## 2 Materials and Methods  
### Materials and Reagents  
Male landrace pigs (8-month-old, \(\approx\)48 kg) were supplied by The Original Pig Farm, South China Agricultural University, China. Sprague-Dawley neonatal rats and adult male rats were supplied by the Laboratory Animal Center of Sun Yat-sen University, China. Collagen I (#354249) was purchased from Corning Incorporated, Corning, NY, USA. DNA extraction kit, DNA detection kit, PageRank pre-staining protein marker, colorimetric peptide assay kit, silver plating staining kit, liquid chromatography column, and live/dead cell staining solution were purchased from Thermo Fisher Scientific, Inc., Waltham, MA, USA. A commercially available kit containing a 1,9-dimethylmethylene blue (DMMB) dye-binding assay was purchased from Genned Scientists Inc., Arlington, MA, USA. The hydroxyproline test kit was purchased from Nanjing Jiancheng Bioengineering Institute, Nanjing, China. Cell Counting Kit-8 was purchased from Dojindo, Kumamoto, Japan. Trypsin was purchased from Promega, Fitchburg, WI, USA. Pepsin, collagenase, DMEM/F12 basic medium, Complete medium/DMEM/F12, fetal bovine serum, horse serum, and penicillin, were purchased from GIBCO, Grand Island, NY, USA. Anti-cTn1 (#SAB4500539), -Cx43 (#SAB4301326) antibodies, and DAPI were purchased from Abcam, Cambridge, UK. Human umbilical vein endothelial cells (HUVECs) were purchased from ScienCell, Carlsbad, CA, USA. Triton X-100, sodium deoxycholate, tetramethylenediamine (TEMED), trimethylol aminomethane (Tris), urea (UA), iodoacetamide (IAA), dithiothreitol (DTT), formic acid (FA), triethylammonium bicarbonate (TEAB), trifluoroacetic acid, 5-bromodeoxyuridine (BrdU), and paraformaldehyde, were purchased from Sigma Aldrich, St. Louis, MO, USA. Dichloromethane and other reagents were purchased from Guangzhou Chemical Reagent Factory, Guangzhou, China.
# Adaptive BBU Migration Based on Deep Q-Learning for Cloud Radio Access Network
## 5 Simulation Results
### A Comparative Analysis with Some Time Series Methods
Nodes with forecasted values above the upper threshold are considered overutilized and are not selected. **Selection:** Preference is given to underutilized nodes (lowest forecasted load), and if none are available, then acceptable nodes are considered. If no node meets the criteria, a fallback strategy (such as FCFS) is used. This ARIMA-based approach introduces a dynamic, time series forecasting element to the node selection process, allowing decisions to be made not just on the current snapshot but on an informed prediction of future load, which can lead to more proactive resource management in BBU cloud environments. The proposed DQN with these time series methods is also simulated for about two hours for the scenario listed above. Figures 11-13 show the energy consumption, number of migrations, and migration time. As observed from the evaluated results, DQN performs better than SES and ARIMA in terms of energy consumption, number of migrations, and migration time while providing more computational and complex matters. Table 5 gives a comparative analysis between the proposed DQN vs. ARIMA and SES. Figure 11: Energy consumption for the comparative study with time series methods. Figure 12: Number of migrations for the comparative study with time series methods.
## 2 Materials and Methods
### Handling Missing Traffic Flow Data
In addition, there is a cell state used to store and pass information, and each component is composed of one or more fully connected layers for weighting, activation, and gating operations on the input. The forget gate controls which information from the previous time step's cell state \(C_{t-1}\) needs to be retained or forgotten to avoid irrelevant information interfering with the current state. The updated formula for the forget gate is as follows:  
\[f_{t}=\sigma\Big{(}W_{f}[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{2}\]  
The input gate is responsible for controlling the update of the cell state \(C_{t}\) by the current input \(x_{t}\), determining which information needs to be added to the cell state \(C_{t}\). The sigmoid layer determines which values need to be updated, and the tanh layer creates a new candidate value vector and generates candidate memory. The state update rule for the input gate is:  
\[\left\{ \begin{array}{l}i_{t}=\sigma(W_{t}[h_{t-1},x_{t}]+b_{i})\\ \stackrel{{\sim}}{{C}}_{t}=\tanh(W_{c}[h_{t-1},x_{t}]+b_{c})\\ \end{array} \right. \tag{3}\]  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\times\stackrel{{\sim}}{{C}}_{t} \tag{4}\]  
Figure 1: Framework tunnel graph and node A’s computational diagram. Figure 2: Framework of LSTM. The output gate is responsible for calculating the output \(h_{t}\) at the current time step based on the input \(x_{t}\) and memory cell state \(C_{t}\), which can be passed to the next LSTM unit or used as the final output. The state update rule of the output gate is:  
\[\left\{ \begin{array}{l}o_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o})\\ h_{t}=o_{t}\times\tanh(C_{t})\end{array} \right. \tag{5}\]  
The above formula shows the LSTM cell state update process, where \(\sigma\) denotes the sigmoid function; \(\overset{\sim}{C}_{t}\) represents the candidate cell information; \(W_{f}\), \(W_{i}\), \(W_{c}\), and \(W_{o}\) represent the weight coefficient matrices in the LSTM cell state update process; and \(b_{f}\), \(b_{i}\), \(b_{c}\), and \(b_{o}\) represent the bias matrices in the state update process.
# Low-Complexity Synchronization Scheme with Low-Resolution ADCs
## 3 Analysis of Synchronization Algorithm
### Low-Resolution Quantization Scheme  
In view of the good reliability and excellent BER performance of Gray code [28], we consider the QPSK modulated signal with Gray code through an additive white Gaussian noise (AWGN) channel. For the convenience of analysis, a \(K\)-level uniform quantizer \(Q\) is used to process the received signal. The low-resolution quantization model is shown in Figure 3.  
The quantized output of the AWGN channel can be expressed as  
\[Y=Q(R)=Q\{\Re(R)\}+j\cdot Q\{\Im(R)\}, \tag{10}\]  
Figure 3: Low-resolution quantization mode.  
Figure 2: Superframe structure.  
where \(R\) is the received signal, \(Y\) is the quantized output, \(Q(\cdot)\) denotes a quantization function, and \(\Re(\cdot)\) and \(\Im(\cdot)\) represent the real part and imaginary part, respectively. Assuming the quantization resolution is \(b\) bits, we characterize the ADC by a set of \(2^{b}+1\) quantization thresholds \(\tau_{b}=\{\tau_{0},\tau_{1},\cdots\tau_{2^{b}}\}\), such that \(-\infty=\tau_{0}<\tau_{1}<\tau_{2}\cdots<\tau_{2^{b}}=\infty\), and a set of \(2^{b}\) quantization labels \(L_{b}=\{\ell_{0},\ell_{1},\cdots\ell_{2^{b}-1}\}\) where \(\ell_{i}\in(\tau_{i},\tau_{i+1}]\). The function \(Q(\cdot)\) of \(b\) bits can be expressed by mapping relation \(Q_{b}(\cdot):\mathbf{C}^{N}\rightarrow\Omega_{b}^{N}\), which maps the received signal \(r_{n,t}\) to the quantized output \(y_{n,t}\) in the following way: if \(\Re\{R_{n,t}\}\in(\tau_{k},\tau_{k+1}]\) and \(\Im\{R_{n,t}\}\in(\tau_{l},\tau_{l+1}]\), then \(y_{n,t}=\ell_{k}+j\ell_{l}\).  
It can be seen from Equation (10) that the quantization distortion is caused by the fact that the low-resolution quantization changes the amplitude of the real and imaginary parts of the signal. Particularly in the case of one-bit, the quantization distortion can severely deteriorate the information of the signal. To minimize the impact of quantization, we define the quantization distortion as  
\[e_{n,t}=Q_{b}(R_{n,t})-\xi\cdot R_{n,t}. \tag{11}\]  
In Equation (11), the proportional coefficient \(\xi\) is used to minimize the quantization distortion variance \(E=\mathrm{E}\Big{[}|e_{n,t}|^{2}\Big{]}\). From the Wiener minimization solution [29], \(\xi\) can be expressed as  
\[\xi=\frac{\mathrm{E}\big{[}R_{n,t}^{*}Q_{b}(R_{n,t})\big{]}}{\mathrm{E}\Big{[} \big{|}R_{n,t}\big{|}^{2}\Big{]}}. \tag{12}\]
## 4 Conclusions  
In this study, the characterization of an advanced ceramic material, such as a C/C composite in terms of erosion by AtOx high-energy impact, was presented, focusing on both the effect of the material's surface/incident flux mutual orientation (attitude) and the influence of synergic UV irradiation on the erosion mechanism. LEO space system deterioration and failure due to AtOx-induced processes are today not yet fully known, due to the many mechanical and chemical parameters involved in such complex dynamics. The present research represents a novel attempt to clarify the critical roles of spacecraft attitude and synergic AtOx/UV irradiation, with the aim of providing a technical baseline to explain the significant gap noticed so far between in-flight experiments and ground-test simulations. The measurements here reported demonstrate evidence that, during a whole LEO mission, favorable attitude positions (in relation to the mutual orientation between the sensitive surface and the ram direction--i.e., AtOx flux) may successfully inhibit the detrimental AtOx oxidative effects on C/C structures, while, on the other hand, a critical boosting of the AtOx erosion may arise as a consequence of even weak exposure to highly energetic UV rays. Semi-empirical modeling was thus introduced in order to give a physical interpretation of the experimental results obtained within the adopted LEO simulation setup; both functional and quantitative considerations attest to the reliability of the theoretical approach proposed, thus establishing the coupling between the materials' constitutive space properties (measured by ground testing) and the specific mission parameters as an effective strategy for predicting the actual behavior of aerospace structures in the LEO environment. Tackling this task is expected to be extremely useful for improving the design of spacecraft's protective systems, with particular regard to C/C-based advanced TPS for re-entry vehicles and, more generally, for applications in large space structures operating during long-term missions within LEO regions, such as the ISS.  
Conceptualization, A.D. and R.P.; Methodology, R.P.; Validation, M.A.; Investigation, A.D. and R.P.; Writing--original draft, A.D. and R.P.; Supervision, F.S., F.P. and M.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data are contained within the article. The authors declare no conflicts of interest.
# FF-MR: A DoH-Encrypted DNS Covert Channel Detection Method Based on Feature Fusion
## 5 Experimental Evaluation
### Validation of Effectiveness
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Metrics** & **1D-CNN** & **2D-CNN** & **1D-Resnet** & **2D-Resnet** & **MHA-Resnet** \\ \hline \multirow{4}{*}{Macro-averaging} & Macro\_P & 0.9061 & 0.9098 & 0.9838 & 0.9514 & **0.9972** \\  & Macro\_R & 0.9089 & 0.9157 & 0.9835 & 0.9519 & **0.9973** \\  & Macro\_F1 & 0.9075 & 0.9126 & 0.9836 & 0.9516 & **0.9978** \\ \hline \multirow{4}{*}{iodine} & Precision & 0.847 & 0.8557 & 0.9696 & 0.9262 & **0.994** \\  & Recall & 0.8456 & 0.8513 & 0.9673 & 0.9188 & **0.9935** \\  & F1-Score & 0.8463 & 0.8535 & 0.9684 & 0.9225 & **0.9951** \\ \hline \multirow{4}{*}{dnscat2} & Precision & 0.7007 & 0.7081 & 0.954 & 0.8424 & **0.9939** \\  & Recall & 0.7238 & 0.7524 & 0.9558 & 0.8563 & **0.9942** \\  & F1-Score & 0.712 & 0.7296 & 0.9549 & 0.8493 & **0.9954** \\ \hline \multirow{4}{*}{dns2 tcp} & Precision & 0.9851 & 0.9874 & 0.9959 & 0.9899 & **0.9993** \\  & Recall & 0.9825 & 0.9825 & 0.9959 & 0.9892 & **0.9995** \\ \cline{1-1}  & F1-Score & 0.9838 & 0.985 & 0.9959 & 0.9896 & **0.9995** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: MHA-Resnet vs. Baseline Models. Figure 12: MHA-Resnet vs. Baseline Models on Macro-averaging Metrics. Comparing 1D-Resnet and MHA-Resnet reveals that both contain one-dimensional Residual Neural Networks with the same structure; however, the difference is that statistical features are added to MHA-Resnet, and important features are highlighted using the Multi-Head Attention mechanism that not only enrich the training information but also enhance the representation ability of the model. This is the reason why the MHA-Resnet performs better in classification. The above comparative analysis verifies that statistical features are an important factor in improving the classification performance of a model when applied to the CIRA-CIC-DoHBrw-2020 dataset. It also verifies that the Multi-Head Attention mechanism improves the detection performance of the model by fusing both features.
## 3 Results and Discussion
### Water Content  
The value of water content in foodstuffs has an important role that can affect the texture and determine the shelf life against damage by microbes. According to Figure 4, the increase in concentration and extraction time affects the reduction in gelatin water content. The immersion treatment with different extraction times and solvents showed different water content values. The treatment at an extraction time of 4 h produced a water content relatively higher than 6 h. The extraction time affects the water content of gelatin. The water content of the extracted gelatin for 6 h tends to be smaller. This is because at a longer extraction time, there is a longer contact and heating of the ossein, and it breaks down the guanidine and arginine groups which reduce the hygroscopic properties of gelatin [19]. The use of acid for soaking is considered to provide good performance as well.
## 2 Materials and Methods  
### Experimental Bench  
This work was developed with the vibration signal obtained from the experimental bench represented in Figure 1. It has a 1.5 kW, 1200 rpm, three-phase 220 V motor, and a 1.5 kW frequency inverter. The motor is coupled to a single-stage spur gearbox; the gears have Z1 = 32 and Z2 = 48 teeth. The load is simulated by an 8.83 kW electromagnetic brake on the output shaft. The vibration signal was obtained through six accelerometers (A1-A6) in (m/s\({}^{2}\)). Accelerometers A1, A4, A5, and A6 were installed in a vertical position defined by the \(z\)-axis. A1 and A4 were mounted on the gearbox's input shaft, whereas A5 and A6 were installed on the output shaft to capture vibration signals associated with both transmission stages. Accelerometer A2 was mounted inclined 45\({}^{\circ}\) concerning the \(x\), and \(z\) axes, while A3 was mounted inclined 45\({}^{\circ}\) concerning the \(x\), \(y\), and \(z\) axes. The vibration signal is fed to a computer, which collects the data using LabVIEW 2024 Q1 and Matlab R2024a software.  
Four failure types were simulated on the gear Z1, breaking (Figure 2a), cracking (Figure 2b), pitting (Figure 2c), and scuffing (Figure 2d). Each failure mode was evaluated under baseline operating conditions (P1) and across nine progressive severity levels (P2-P10) to characterise the system's response under varying failure intensities. Motor speed was adjusted to F1 = 8 Hz, F2 = 14 Hz, and F3 = 20 Hz using a frequency inverter, while load conditions were varied to L1 = 0 V, 10 V, and L3 = 20 V via an electromagnetic braking system. Considering ten severity levels, three motor speeds, three load conditions, and ten experimental repetitions, a database comprising 900 observations per accelerometer was generated.  
Figure 1: Testbench layout.  
The experimental bench also has an encoder (E1), a laser encoder (LE1), two acoustic emission sensors (EA), and two microphones (M) for the gearbox and the power supply lines to the motor with three voltage meters (V) and three electric current clamp (CC).
# Classification of Parkinson's Disease Using Machine Learning with MoCA Response Dynamics
## 4 Discussion
Furthermore, this study underscores the limitations of MoCA in accurately diagnosing cognitive impairment across different age groups and in web-based settings. This is significant because it challenges the one-size-fits-all approach to cognitive assessment and calls for more personalized or adaptable testing methods. It is important to acknowledge the limitations of this study. These include the small size of the research groups, potentially limiting the generalizability of these findings. The demographic diversity of the described sample may not fully represent the broader population, particularly in terms of age, gender, ethnicity, and education. The use of a web-based platform for MoCA assessment has its constraints and may not accurately replicate the nuances of an in-person testing environment. The study's focus on a single tool, MoCA, may not encompass the multifaceted nature of cognitive impairments. The absence of longitudinal data prevents us from observing changes over time. External variables that could affect cognitive performance were not fully controlled. Finally, interpreting interactions in a web-based format poses unique challenges in understanding the complete cognitive assessment scenario. Therefore, research should aim to address these limitations by including larger, more diverse study groups, employing a combination of cognitive assessment tools, and potentially incorporating longitudinal designs to track cognitive changes over time. Further, more detailed research is crucial for progress in the prevention of neurodegenerative diseases.
# A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
###### Abstract  
Quick and accurate detection of inside packet drop attackers is of critical importance to reduce the damage they can have on the network. Trust mechanisms have been widely used in wireless sensor networks for this purpose. However, existing trust models are not effective because they cannot distinguish between packet drops caused by an attack and those caused by normal network failure. We observe that insider packet drop attacks will cause more consecutive packet drops than a network abnormality. Therefore, we propose the use of consecutive packet drops to speed up the detection of inside packet drop attackers. In this article, we describe a new trust model based on consecutive drops and develop a hybrid trust mechanism to seamlessly integrate the new trust model with existing trust models. We perform extensive OPNET (Optimized Network Engineering Tool) simulations using a geographic greedy routing protocol to validate the effectiveness of our new model. The simulation results show that our hybrid trust model outperforms existing trust models for all types of inside packet drop attacks, not only in terms of detection speed and accuracy as it is designed for, but also in terms of other important network performance metrics, such as packet delivery rate, routing reliability, and energy efficiency.  
insider packet drop attack; trust model; trust-based routing; wireless sensor networks 202323407. [[https://doi.org/10.3390/s23094407](https://doi.org/10.3390/s23094407)]([https://doi.org/10.3390/s23094407](https://doi.org/10.3390/s23094407))
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 5 Discussion
### ROIs  
It is possible to observe in Table 3 that Test 2 identified more pixels in class 12, while Test 1 detected more pixels between classes 13 and 16. Notably, classes 15 and 16 are the most important for identifying iron oxides.  
Test 2 identified more zones of interest than Test 1. However, upon analyzing the results, we can observe that this higher number of ROIs in Test 2 was primarily identified by pixels in classes 12 and 13. In contrast, Test 1 contains more pixels in the higher classes (14, 15, and 16). This suggests that PS influenced the fuzzification process, reducing the number of pixels representing iron oxides with lower values and increasing the number of pixels identified with higher values. This effect, as seen in Figure 5c,d, is the formation of well-defined and restricted areas of interest, allowing for a more objective and effective field analysis.  
For example, in ROI 3 (Figure 5a), there is a concentration of high-value pixels (classes 15 and 16) in the southwestern corner of the ROI. In ROI 4 (Figure 5d), the high-value pixels (classes 15 and 16) are concentrated in the center of the ROI. The clusters observed in Figure 5 allow the field validation team to directly target locations with a higher probability of iron oxide occurrences.  
Conversely, Test 2 identified ROIs 1 (Figure 6a), 2 (Figure 6b), 3 (Figure 6c), and 5 (Figure 6e), with clusters predominantly composed of class 12 and 13 pixels, which hinders a more direct approach and requires a broader area to be analyzed for field validation compared to Test 1. However, Test 2 yielded good results for ROI 4 (Figure 6d).  
Overall, although the fuzzification process with PS identified fewer ROIs, this is not necessarily a negative outcome, as the results indicate that PS influenced the detection of a higher number of pixels with elevated values, which are more accurate for iron oxide identification. This demonstrates that the high spatial resolution of PS enhances sensitivity in detecting areas of interest, possibly leading to fewer false positives.