esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Verification of Chatter Suppression Effect with TMDs
### Effect of Various Circumferential Arrangements of TMDs on Chatter Suppression
Therefore, the period and direction of movement of the nodes coincided with the period and direction of rotation of the workpiece. In addition, the number of nodes during a single rotation cycle in the vibration waveform of each measurement point is six, which is consistent with the number of nodal diameters in the (3,1) mode of chatter vibration. The results of the workpiece vibration waveforms (Figure 11) suggest that the chatter vibration mode rotated in synchronisation with the workpiece rotation. We presumed that the chatter could not be completely suppressed since the mounting positions of the TMDs or the masses coincided with the positions of the nodes of the (3,1) mode of chatter vibration. Next, we investigated the chatter suppression effect of the masses or TMDs arranged at an unequal pitch in the circumferential direction to avoid coincidence with the pitch of the nodes of the chatter vibration mode. The masses or TMDs were mounted at the workpiece tip (\(l_{\text{d}}=35\) mm) with an unequal circumferential installation pitch, which did not coincide with the pitch of the vibration nodes of the (3,1) mode. The mounting position of the masses or TMDs was set to \(\theta_{1}=110^{\circ}\) and \(\theta_{2}=220^{\circ}\), which was slightly displaced from the position of the node to avoid an excessive imbalance. The workpiece vibration waveforms measured by the eddy current sensor of number 2 measuring point with and without the masses or TMDs are presented in Figure 12a,c wherein the workpiece vibration amplitude was considerably reduced upon using both the masses and the TMDs. The frequency analysis results of the time waveforms for the 0.05 s period in the initial 25 s after cutting (vertical dotted line in Figure 12a,c are presented in Figure 12b,d). As observed from Figure 12, the chatter could be suppressed if the masses or TMDs were arranged such that their installation pitch did not coincide with the pitch of the nodes of the generated chatter vibration mode. Figure 11: Workpiece vibration waveforms during chatter generation with attached TMDs (mounting arrangements: \(\theta_{1}=\theta_{2}=120^{\circ}\), \(l_{\text{d}}=35\) mm).
## 3 Results
### Implementation of Proposed Wireless DYU Air Box  
First of all, the BH1750, BME680, SCD30, and PMS7003T sensors as well as the TTGO ESP32 Wi-Fi device were aggregated in the well-designed PCB board. The implemented hardware of the proposed wireless, interdisciplinary, and heterogeneous multi-sensor monitoring module is shown in Figure 5. Figure 5a illustrates that the BME680 AOI/temperature/humidity sensor, PMS7003T PM\({}_{1,0}/\text{PM}_{2.5}\) sensor, SCD30 CO\({}_{2}\) sensor, and BH1750 light sensor were integrated into an ESP32 Wi-Fi module with four standard 4-pin socket terminals of 2.54 mm. Such a design means the devices can be easily removed for testing and maintenance. Figure 5b presents that the proposed multi-sensor monitoring module can be arranged on a decorated platform such as the DYU Air Box. The acquireddata of the AQI value and level, PM\({}_{1.0}\) and PM\({}_{2.5}\) concentrations, CO\({}_{2}\) concentration, brightness, ambient temperature, and humidity could be directly read by the TTGO ESP32 Wi-Fi module through the same I\({}^{2}\)C interface. In addition, the data acquisition of PM\({}_{1.0/2.5}\) from the PMS7003T dust sensor was read by the same Wi-Fi module through the UART interface. The proposed wireless DYU Air Box has been evaluated in the H708 teaching classroom at Da-Yeh University over two months starting on 1 October 2023. The accuracy of the AQI value and level, the concentrations of PM\({}_{1.0}\), PM\({}_{2.5}\), and CO\({}_{2}\), as well as brightness, temperature, and humidity was validated using commercially available instruments: a TES-1337B light intensity meter, a TES-1364 temperature and humidity meter, a TES-1370H CO\({}_{2}\) meter, a TES-5110 particle counter, and a TES-5322A IAQ meter.
## 2 Materials and Methods  
### General Information  
X-ray powder diffraction (XRD) patterns were measured using a D8 advanced diffractometer (Bruker AXS, Karlsruhe, Germany) with Cuk\(\alpha\) radiation. A PerkinElmer (FTIR 65) spectrometer was utilized to record the infrared spectra. Scanning electron microscopy (SEM) was carried out using a Sirion SEM microscope (FEI Company, Hillsboro, OR, USA), a Shottky-type emission source, and a secondary electron (SE) detector, operated at a voltage of 5 kV. Transmission electron microscopy (TEM), scanning transmission electron microscopy (STEM), and electron diffraction spectroscopy (EDS) analyses were accomplished through (S)TEM Tecnai F20 G2 (FEI Company, Hillsboro, OR, USA) operated at 200 kV. Size distribution and zeta potential were determined by a Nano Series instrument, model Nano-Zetasizer ZEN3600 (Malvern Instruments, Worcestershire, United Kingdom). Thermogravimetric analysis (TGA) was done on a Mettler Toledo TG 50 analyzer (Greifensee, Switzerland). Measurements were conducted at a temperature range from 25 to 900 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min under an inert atmosphere (N\({}_{2}\)). The specific surface areas were calculated by means of the Brunauer-Emmett-Teller (BET) equation by utilizing a high-speed gas sorption analyzer, and a Quantachrome Nova 1200e instrument (Quantachrome Instruments, Boynton Beach, USA). The reaction mixtures were analyzed by gas chromatography (GC) (Agilent Technologies, 7890A, Santa Clara, USA) with a universal capillary column (HP-5, 30 m) to determine the yields and the conversions of the reactions. \({}^{1}\)H NMR and \({}^{13}\)C NMR spectra were recorded using a Bruker DRX-400 and DRX-500 instrument (Rheinstetten, Germany). Inductively coupled plasma mass spectrometry measurements (ICP) were done on a 7500 cx instrument (Agilent company, Tokyo, Japan) using an external standard calibration to determine the ruthenium loadings.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results
### Thermal Analysis
#### 3.7.1 Solar Radiation  
The solar radiation is the main heating source for an S/C in orbit. Its intensity varies regarding the distance between the S/C and the Sun. For a satellite orbiting the Earth, the solar constant has an average value of 1367 W m\({}^{-2}\), with a maximum value of 1414 W m\({}^{-2}\) and a minimum value of 1322 W m\({}^{-2}\), that varies while the Earth orbits the Sun. The above values are recommended by the World Radiation Center in Davos in Switzerland. Another variation of the solar constant is created due to the 11-year solar cycle, but it is in the magnitude of 0.1%, and it is considered negligible [19].  
The heat gained (W) due to the direct solar flux is as follows:  
\[Q_{solar}=aS_{solar}A_{sun}\]  
where  
\(a:\) the absorptivity coefficient of the spacecraft's surface;  
\(S_{solar}:\) the solar radiation flux (W m\({}^{-2}\));  
\(A_{sun}\): the area of the spacecraft vertical to the solar rays (m\({}^{2}\)).  
For this analysis, it is considered that the CubeSat maintains nadir pointing attitude, with one of its 8U sides facing to nadir as illustrated in Figure 12.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Phasing Duration** & \(\Delta\)**V Required (m/s)** & \(\Delta\)**V with 50\% Margin** \\ \hline
1 day (15 orbits) & 175.2 & 262.8 \\
3 days (45 orbits) & 57.8 & 86.7 \\
5 days (75 orbits) & 35.0 & 52.5 \\
6 days (90 orbits) & 29.2 & 43.8 \\
7 days (105 orbits) & 25.2 & 37.8 \\
10 days (150 orbits) & 18.0 & 27.0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: \(\Delta\)V with respect to phasing duration.  
While the CubeSat orbits a celestial body, it rotates in a way that both its 2U sides and its one 8U side (since the other is constantly facing to nadir) are exposed to direct sunlight. If a surface A is inclined at an angle \(\gamma\) to the solar rays, the projected area of the surface vertical to the rays (\(A_{p}\)) is given by the following:  
\[A_{p}=Acos\gamma\]  
While in orbit, if the given surface performs a complete rotation, the angle \(\gamma\) is constantly changing. Since a surface rotates in and out of the Sunview, the average surface area over a full revolution (i.e., 0 to 2\(\pi\) radians) needs to be determined.  
\[A_{p,average}=\frac{1}{2\pi}\int\limits_{0}^{2\pi}A_{p}d\gamma=\frac{1}{2\pi }\int\limits_{0}^{2\pi}Acos\gamma d\gamma\]  
By considering the zero radians position being with the surface normal facing away from the solar vector, the integral then goes to zero when the surface normal rotates between \(\pi/2\) and \(3\pi/2\) (i.e., the active surface is out of Sunview). Thus, the integral limits can be reduced, and the \(A_{p,average}\) relation rewritten as [21].  
\[A_{p,average}=\frac{A}{2\pi}\int\limits_{3\pi/2}^{\pi/2}\cos\gamma d\gamma= \frac{A}{\pi}\]  
The total surface of the CubeSat projected vertically to the solar rays is as follows [21]:  
\[A_{p,total}=\frac{1}{\pi}(A_{8U}+A_{2U}+A_{2U})=\frac{1}{\pi}(A_{8U}+2\cdot A _{2U})\]
# Skin Cancer Classification Using Fine-Tuned Transfer Learning of DENSENET-121
###### Abstract  
Skin cancer diagnosis greatly benefits from advanced machine learning techniques, particularly fine-tuned deep learning models. In our research, we explored the impact of traditional machine learning and fine-tuned deep learning approaches on prediction accuracy. Our findings reveal significant improvements in predictability and accuracy with fine-tuning, particularly evident in deep learning models. The CNN, SVM, and Random Forest Classifier achieved high accuracy. However, fine-tuned deep learning models such as EfficientNetB0, ResNet34, VGG16, Inception _v3, and DenseNet121 demonstrated superior performance. To ensure comparability, we fine-tuned these models by incorporating additional layers, including one flatten layer and three densely interconnected layers. These layers play a crucial role in enhancing model efficiency and performance. The flatten layer preprocesses multidimensional feature maps, facilitating efficient information flow, while subsequent dense layers refine feature representations, capturing intricate patterns and relationships within the data. Leveraging LeakyReLU activation functions in the dense layers mitigates the vanishing gradient problem and promotes stable training. Finally, the output dense layer with a sigmoid activation function simplifies decision making for healthcare professionals by providing binary classification output. Our study underscores the significance of incorporating additional layers in fine-tuned neural network models for skin cancer classification, offering improved accuracy and reliability in diagnosis.  
deep learning; transfer learning; skin lesions; VGG16; Resnet; Densenet; Inceptionnet; CNN +
Footnote †: journal: Applied sciences
## 2 Problem Formulation
### Reconstruction Error Model
The Jacobian determinant, then, denotes a scalar multiplier when the Jacobian matrix is square, and it  
Figure 3: The reconstruction uncertainty of two camera sensors, where C stands for camera. converts the differential volume in the pose space to the corresponding differential volume in the measurement space, which is described as  
\[\text{vol}(\delta z)=|\text{det}(H)|\cdot\text{vol}(\delta x) \tag{8}\] \[\text{vol}(\delta x)=|\text{det}\big{(}H^{-1}\big{)}|\cdot\text{vol} (\delta z)=\tfrac{1}{|\text{det}(H)|}\cdot\text{vol}(\delta z)\]  
where \(\text{vol}(\delta x)=|\delta x_{1}\delta x_{2}\delta x_{3}|\), \(\text{vol}(\delta z)=|\delta z_{1}\delta z_{2}\delta z_{3}|\) represent the absolute value of the product of the components in pose error vector \(\delta x\) and observation error vector \(\delta z\), respectively, and are a measure of the error range. \(|\text{det}(H)|\) is the absolute value of the Jacobi determinant; then,  
\[\text{GDOP}=|\text{det}\big{(}H^{-1}\big{)}|. \tag{9}\]  
It is noted that the relationship between determinants and the volume of parallel polyhedra is used here, and the following theorem is available in the literature [30], p. 232. Assuming that \(A\in\mathbb{R}^{n\times n}\), matrix transformation \(T\): \(\mathbb{R}^{n}\to\mathbb{R}^{n}\) meets \(T(x)=Ax\). If \(S\) denotes any region in \(\mathbb{R}^{n}\), then we have  
\[\text{vol}(T(S))=|\text{det}(A)|\cdot\text{vol}(S) \tag{10}\]  
where \(T(S)\) denotes the mapping of region \(S\) after matrix transformation \(T\), and \(\text{vol}(S)\) denotes the region volume. In a more general case, the measurements are implicit functions as  
\[f(x,z)=0. \tag{11}\]  
Similarly, when the system is fully determined, we have the definition as  
\[ \begin{array}{c}|\text{det}(H)|=|\text{det}(f_{x})|/|\text{det}(f_{z})|\\ \text{GDOP}=|\text{det}\big{(}H^{-1}\big{)}|=\tfrac{1}{|\text{det}(H)|}\end{array}  \tag{12}\]  
where \(f_{x}=\partial f/\partial x\) and \(f_{z}=\partial f/\partial z\) are Jacobian matrices, which are both square.
# Remote Sensing Road Extraction by Road Segmentation Network
## 2 Materials and Methods  
### Materials  
Two large public datasets were used for experiment: the DeepGlobe dataset and the Massachusetts dataset. A brief introduction is as follows.  
The DeepGlobe dataset is a satellite dataset that uses images from Thailand, Indonesia and India. The land area of the dataset is about 2220 km\({}^{2}\) and contains a variety of image scenes (urban, rural, wilderness, tropical rainforest, seaside, etc.). The DeepGlobe dataset consists of 6226 images with the size of 1024 \(\times\) 1024. The spatial resolution of DeepGlobe is 50 cm/pixel, which is used for the road extraction challenge. In the experiment, 4358 images are used for training, and the remaining 1868 images are used for testing.  
The Massachusetts dataset is an aerial dataset that uses images from Massachusetts, US. The dataset has a resolution of 1.2 m/pixel, covers a land area of 2.25 km\({}^{2}\), and contains urban, suburban, and rural environments. The size of each image is 1500 \(\times\) 1500. The Massachusetts dataset consists of 1108 training images, and 49 testing images.  
The experiments were performed on PyTorch library with GPU. Within the limits of computational cost, the size of the input image was cropped to 512 \(\times\) 512 pixels in both the DeepGlobe dataset and the Massachusetts dataset. To alleviate the over-fitting problem, data augmentations were used, such as image flipping, image shifting and scaling, and color jittering.  
The backbone network of the proposed method was ResNet34, due to its powerful encoder capability. The Adam optimizer was used to train the proposed network. The training epoch was set to 50. The batch size was 24 for the Massachusetts Dataset and 16 for the DeepGlobe dataset. The learning rate was initially set to \(2\times 10^{-4}\) and reduced by a factor of 0.1. During the testing phase, each input image was predicted a total of eight times by flipping different angles, and the final result was the average output of these outputs.  
To verify the performance of the proposed method, four widely used metrics were employed: overall accuracy (\(OA\)), precision (\(P\)), recall (\(R\)), and \(F1\)-score. After the calculation of true positive (_TP_), true negative (_TN_), false positive (_FP_), and false negative (_NT_), the measures of \(OA\), \(P\), \(R\), and \(F1\)-score were calculated as follows:  
\[P=\frac{TP}{TP+FP},R=\frac{TP}{TP+FN},F1=2\times\frac{P\times R}{P+R}.\]  
\[OA=\frac{TP+TN}{TP+FP+TN+FN}\]  
The precision and recall were hoped to remain at a high level, indicating improved performance, although the two values are contradictory in some cases and have a negative correlation. \(F\) score is used to consider the precision and the recall simultaneously. It can be regarded as a weighted average of precision and recall, where its maximum value is 1 and minimum value is 0.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 4 Analysis of Performance Results
### Theoretical Mathematical Model Results  
To achieve the objectives of this study, this section applies the theoretical mathematical model to obtain the values of CR, L, and DR of protocols 802.11p, 802.11 bd, LTE-V2X, and NR-V2X that were analyzed.
## 2 Materials and Methods
### Methods
#### 2.3.3 Model Building
\[\omega=\omega_{\mathrm{max}}-\left(\omega_{\mathrm{max}}\ -\ \omega_{\mathrm{min}} \right)*t/t_{\mathrm{max}} \tag{6}\]  
\[\mathrm{V_{i}^{t+1}}=\omega V_{i}^{t}\ +\ c_{1}r_{1}\left(\mathrm{P_{i}^{t}-X_{i}^ {t}}\right)\ +\ c_{2}r_{2}\left(\mathrm{G_{i}^{t}-X_{i}^{t}}\right) \tag{7}\]  
\[X_{i}^{t+1}=X_{i}^{t}\ +\ \mathrm{V_{i}^{t+1}} \tag{8}\]  
where \(\omega\) is the inertia weight; \(\omega_{\mathrm{max}}\), \(\omega_{\mathrm{min}}\) represent the maximum and minimum inertia weight; \(t\) is the number of current iterations; \(t_{\mathrm{max}}\) is the total number of iterations; \(V_{i}\) is the velocity of the particle; \(r_{1}\) and \(r_{2}\) are random numbers distributed in the interval \([0,1]\); \(c_{1}\) and \(c_{2}\) are the acceleration factors, which are non-negative constants; \(P_{i}\) is the current local optimal position of the ith particle; \(G_{i}\) is the global optimal position of the particle swarm; and \(x_{i}^{t}\) represents the position of the ith particle in the tth iteration. In this study, a PSO-optimized BPNN was constructed using 30 particles, with a maximum of 100 iterations at a speed of 1, and using inertia weights in the range of [0.4, 0.9]. The optimization process is shown in Figure 3.0 Simultaneously, both a BPNN and an SVR model were constructed. Model accuracy validation was conducted, followed by a comparative analysis to assess the performance of the PSO-optimized BPNN and evaluate its capacity for PC concentration retrieval. Detailed information regarding the code and hyperparameter settings for constructing PSO-BPNN can be found in Figure 3.1  
Figure 3.2 Flowchart of BPNN optimized with PSO.
# A Digital Bang-Bang Clock and Data Recovery Circuit Combined with ADC-Based Wireline Receiver
## 3 The Proposed DBB-CDR Circuit with an ADC-Based Wireline Receiver
### The Structure of the DBB-CDR Circuit  
When ADC-based receivers are used in high-speed wireline transceivers, digital CDR circuits offer a more attractive solution compared with an analog implementation. Compared with analog CDR circuits, digital CDR circuits have many advantages [25]. MMPD circuits are the most common type of digital CDR circuit used in ADC-based wireline receivers, which include a Mueller-Muller phase detector, a decimation block, a frequency integrator, a phase integrator and a phase interpolator. When using an MMPD, the digital signal processing needs to send parallel sign-sign data and error-direction information to the MMPD.  
Figure 5: (**a**) A diagram of a bang-bang phase detector; (**b**) the waveform of a bang-bang phase detector.  
However, we adopted a partial oversampling system for the ADC-based receiver, as shown in Figure 6. We added a sub-ADC 1.5 to the partial oversampling system (between sub-ADC 1 and sub-ADC 2) to sample the edge information, so the DSP only needed to send the Data<1>, Data<1.5> and Data<2> results to the CDR circuit (as judged from sub-ADC 1, sub-ADC 1.5 and sub-ADC 2) for the CDR circuit to adopt a more stable DBB-CDR structure. This also meant that the circuit did not require a summing block to decimate the input signals from the DSP.  
The DBB-CDR circuit included a digital bang-bang phase detector, a frequency integrator, a phase integrator and a phase interpolator, as shown in Figure 7. The digital signal processing received the ADC data and equalized them adaptively, based on the sign-sign least mean square (SS-LMS) algorithm. Then, the sign-sign Data<1>, Data<1.5> and Data<2> data were sent to the DBBPD. The DBBPD generated up/down signals according to the Data<1>, Data<1.5> and Data<2> results. The proportional path through \(K_{P}\) combined with the integral path through \(K_{I}\) filtered the phase detector decisions. The phase interpolator used a multi-bit digital control bus to control the phases of the ADC sampling clocks.
# AI-Driven Waste Management in Innovating Space Exploration
## 4 Smart Waste Management Technologies for Space
### Smart Waste Management and Enhances Mission Sustainability  
Plasma arc and pyrolysis are promising advanced waste management technologies for space missions, significantly reducing waste volume and converting it into valuable by-products. Plasma arc technology uses electrically generated plasma at temperatures up to 5000 \({}^{\circ}\)C, decomposing organic waste into hydrogen and carbon monoxide gases and inorganic material into stable slag. While highly effective in sterilisation and waste reduction, plasma arcs' substantial energy requirements pose challenges in energy-constrained space environments, potentially necessitating larger solar arrays or nuclear power systems, thus increasing mission costs and complexity.  
Pyrolysis, operating at lower temperatures (400 \({}^{\circ}\)C to 700 \({}^{\circ}\)C) without oxygen, decomposes organic waste into bio-oil and syngas, products valuable for soil enrichment, refinement, and power generation. Although less energy-intensive than plasma arc, pyrolysis still demands consistent heating and additional processing systems, complicating its deployment in spacecraft. To mitigate energy constraints, integrating renewable energy sources or efficient nuclear systems, capturing waste heat for reuse, enhancing unit insulation, and designing modular systems can optimise overall energy efficiency.  
Automated waste management systems leveraging AI and robotics significantly enhance efficiency in microgravity by reducing manual handling. Automation minimises  
Figure 3: Energy consumption of plasma arc and pyrolysis technologies.  
human intervention, allowing crew members to focus on critical mission tasks, which is crucial for distant missions with limited resupply options. These systems convert organic waste into vital resources such as water and oxygen, reducing storage needs and resupply frequency. AI-driven classification and sorting utilise convolutional neural networks (CNNS) for visual identification and support vector machines (SVMS) with sensors for accurate sorting. Deep reinforcement learning (DRL) further optimises robotic handling strategies. Integrating these advanced technologies into waste management significantly reduces logistical costs and enhances mission sustainability and success (Figure 4) (Table 4).