esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Cutting Test
### Analysis of the Effect of Cutting Parameters on Surface Roughness
#### 3.2.5 Effect of Tool Nose Radius on Surface Roughness  
The bar graph of the variation of surface roughness of the workpiece machined with the tool nose radius for the parameters of Table 4 is given in Figure 15. The parameters of cutting speed of 2 m/min, feed of 16 m/rev, depth of cut of 2.5 m, and ultrasonic amplitude of \(A_{y}\) = 4.5 m and \(A_{z}\) = 1.2 m were selected. From the figure, it can be seen that the surface roughness decreases significantly with the increase of the tool nose radius. When the tool nose radius is \(r_{e}\) = 0.8 mm, the machined surface morphology of the workpiece becomes flat, and the surface roughness is only 63 nm. In contrast, when the tool nose radius is \(r_{e}\) = 0.2 mm, the surface of the workpiece is defective, with a surface roughness of up to 115 nm.  
During ultrasonic elliptical vibratory machining, the size of the tool nose radius is a critical parameter that directly affects the residual height in the feed direction, which in turn affects the surface roughness of the workpiece. When other cutting parameters are fixed, and the feed is specific, the tool feed residual height is inversely proportional to the tool nose radius. Figure 16 clearly shows this relationship: The larger the tool nose radius, the smaller the feed residual height, the smoother and flatter the machined surface, and the lower the surface roughness of the workpiece. On the contrary, a smaller tool nose radius will result in a more considerable cutting feed height, making the machined surface uneven and prone to surface defects, thus increasing surface roughness and reducing surface quality. However, care should be taken in choosing the tool nose radius, as the tool nose radius of the cutter is too large, leading to an increase in the contact area between the blade and the workpiece, which not only increases the cutting force but also may cause an increase in the cutting temperature, which may ultimately lead to the phenomenon of chattering.  
Therefore, in ultrasonic elliptical vibration machining, the tool nose radius should be reasonably selected according to the specific machining requirements and conditions to achieve the best machining effect and surface quality.
## 1 Introduction
However, the manoeuvre of tilting the nanosatellite, with the telescope, with the use of reaction wheels, enables to prolong the time of observation of a specific area without the need to consume fuel. In this case, the satellite uses the energy generated by solar panels, which significantly lowers operating costs and improves the flexibility of the mission. 4. The research results may be used to improve the planning of nanosatellite missions, in particular, by identifying the manoeuvres that are the most cost-effective in terms of fuel and energy, which will allow to maximise their operational efficiency. 5. Combining orbital manoeuvres (such as changing the altitude or inclination) with the satellite tilting manoeuvre ensures the best balance between fuel and energy efficiency and operational precision. However, in order to effectively combine these two types of manoeuvres, it is necessary to ensure the appropriate amounts of fuel and energy. Therefore, when planning a satellite mission, one should take into consideration both the fuel resources that are necessary to conduct impulse orbital manoeuvres and the appropriate availability of electric power for propulsion systems, such as reaction wheels. For future nanosatellite missions, in particular, those devoted to long-term observations, it is recommended to increase the focus on the accurate planning of fuel and energy consumption. This will allow the use of tilting manoeuvres efficiently together with the change in the inclination of the orbit. The novelty of this study lies in integrating the evaluation of various types of manoeuvres, encompassing both orbital manoeuvres and platform tilting operations, in termsof their overall energy efficiency and fuel consumption. Unlike previous works, which focused on the analysis of individual manoeuvres, this study offers a comparative system-level analysis supported by numerical simulations, which can aid in the decision-making process during the early stages of mission planning. The article consists of the following parts: Section 1 contains a review of the literature on orbital manoeuvres and the optimisation methods that have been used so far, focusing on the current state of knowledge and research gaps. Section 2 covers the current state of knowledge concerning the directions in which engines are set during orbital manoeuvres, the manoeuvres that may be performed by a nanosatellite (which will be discussed in detail in further sections) as well as the methodology of the conducted numerical experiments. Section 3 presents the results of the conducted analyses, while Section 4 provides their detailed interpretation. In Section 5, the main conclusions from the research are summarised, and the potential directions of further experiments on the possibilities to optimise orbital manoeuvres for nanosatellites are indicated.
## 3 Security and Privacy Challenges
### Intrusion Detection System  
An intrusion detection system (IDS) is a software or hardware-based system, able to detect malicious activity in an IoT network [178]. Also, IDS can track down any violations in the established network protocols or anomalies. Upon threat detection, IDS has two possible responses [179]:  
* _Issue alerts:_ This class of responses comes from passive IDS systems that issue security alerts via email or text messages. Also, a notification is issued to the security information and event management (SIEM) system, which helps security teams detect user behavior anomalies and apply AI for threat detection and incident response.
* _Countermeasure:_ In this class, Active IDS not only sends alerts but also takes countermeasures like changes in access control lists on firewalls to block the suspicious traffic, kill communication-related processes, and redirect traffic to a legitimate part of the network while assessing the threat.  
A typical IDS system has three significant units that monitor the network traffic, detect any suspicious activity, and trigger an alert. An IDS can be active, also known as an Intrusion Prevention System (IPS), or passive. An IPS monitors the activities at the system or network level and issues real-time countermeasures in case of threat detection. On the other hand, a passive IDS detects suspicious activity and just alerts the administrators without taking any corrective actions [180]. Traditional IDSs were originally designed for conventional networks but struggled to adapt to the diverse and complex IoT ecosystems. These legacy IDSs proved insufficient in addressing security threats posed by advanced and constantly evolving attacks, such as zero-day exploits. The vast amount of data generated within IoT environments, coupled with highly variable traffic patterns, makes it challenging for IDSs to accurately distinguish between legitimate and malicious activities, increasing the likelihood of errors [181].  
In contrast, machine learning (ML)-based IDSs provide more adaptable, scalable, and intelligent solutions to tackle the dynamic nature of IoT security threats. Linear Support Vector Machines (LSVMs), a type of ML algorithm, are commonly used for classification tasks, including intrusion detection, due to their effectiveness in identifying patterns and anomalies [182]. A classification of IDS based on four main characteristics, i.e., detection method, source of collected data, type of architecture, and response type, is shown in Figure 13. Host-based IDS (HIDS) sits on the host computer and detects malicious behaviors for a single host only. HIDS attempts to detect the presence of unwanted applications in a computing system by analyzing the local data, application registers, log access, and system calls. On the contrary, network-based IDS (NIDS) focuses on detecting malicious patterns in network traffic [183; 184]. IDSs normally use one or both of the two primary threat detection methods: signature-based or anomaly-based detection [185].
## 2 Materials and Methods
### Endpoint Localization Method
#### 2.4.2 Wavelet Transform (WT)  
After the step transformation, the waveforms of vibration signal \(w\) appear as intensive step jumps at endpoints, while they appear as smooth straight lines in the middle of motions. The positions of the step jumps can be easily detected by the wavelet transform.  
Wavelet transform is often used for time-frequency analysis of unstable signals and is suitable for the detection of mutation points. Since the collected signal is discrete, discrete wavelet transform (DWT) is utilized here. As shown in Equations (27) and (28), the signal can be decomposed into a set of linear combinations of wavelet series with different scales and displacements by DWT:  
\[f(t)=\sum_{k}\sum_{j}a_{j,k}\psi_{j,k}(t) \tag{27}\]  
\[\psi_{j,k}(t)=2^{-\frac{j}{2}}\psi\left(2^{-j}t-k\right) \tag{28}\]  
where \(j\) represents the scale, and \(k\) represents the displacement. In the discrete wavelet trans-form, the scale \(j\) is a discrete value representing the frequency resolution, and \(k\) is the time shift. \(a_{j,k}\) is the wavelet coefficient representing the energy of the signal at the corresponding time-frequency point.  
On this basis, the Mallat algorithm uses a set of orthogonal wavelet bases to decompose the signal, as shown in Equations (29) and (30).  
\[f(t)=\sum_{k}c_{j_{0},k}\phi_{j_{0},k}(t)+\sum_{k}\sum_{j}d_{j,k}\psi_{j,k}(t) \tag{29}\]  
\[\left\{\begin{array}{cc}c_{j_{0},k}=&\left\langle f(t),\phi_{j_{0},k}(t) \right\rangle\\ d_{j,k}=&\left\langle f(t),\psi_{j,k}(t)\right\rangle\end{array}\right. \tag{30}\]  
The first term is the approximation of the signal where \(c_{j_{0},k}\) is the approximation coefficient. The second term contains the detailed information of the signal and \(d_{j,k}\) is the detail coefficient of the signal. The coefficient equals the sum of the inner products between the signal and the wavelet bases.  
To locate the position of steps, a three-layer discrete wavelet transform based on db wavelet is performed on the processed signal. As shown in Figure 3, the approximate coefficients and detail coefficients are obtained in the decomposition of each layer, and then, the approximate coefficients are further decomposed in the next layer. The step signal is reflected in the high-frequency components after wavelet decomposition, i.e., the detail coefficient. D1 is selected for the later localization analysis, and its frequency band is \([\frac{1}{2},1]\) of the signal frequency.
# Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System
## 1 Introduction
The approach seeks to be autonomous and independent of Earth-based infrastructure to meet the requirements of users on the lunar surface and orbits, especially those that have a critical dependence on a lunar navigation satellite system. The lunar-based scenario is introduced in Section 2, exploring factors such as orbital and satellite design. Methodologies of each considered sensor are then described in Section 3, employing and evaluating performances with least-square formulations. Orbital propagation and filter models are then proposed and evaluated in Section 4. The architecture is assessed to ascertain whether it might meet performance targets required by JAXA and other space agencies, which target a surface position performance below 40 m [6]. This article continues work first presented at the 34 th ISTS in Kurume, Japan on 9 June 2023 [20]. Significant modifications, improvements, and extensions have been made. These include the use of ISLs, filter-based methodologies, and performance limitations of fundamental celestial sensor techniques.
## 6 Simulation Results

Table 4: Details of component settings used to obtain a specified \(f_{o}\) in Figure 11.  
Figure 6: Ideal (dashed line) and simulated (solid line) frequency responses for TAM: (**a**) LP, BP and HP; (**b**) BS; (**c**) AP. Figure 8: Transient analysis responses of the proposed filter in VM. Figure 7: Ideal (dashed line) and simulated (solid line) frequency responses for CM and TIM: (**a**) LP, BP, HP in CM; (**b**) LP and BP in TIM. Figure 11: Simulated BP gain responses in VM for variation in \(f_{0}\) with \(Q=9.5\). Figure 10: Simulated BP gain responses in VM for variation in \(Q\) with \(f_{0}=1.52\) MHz. Figure 9: THD variations of the LP, BP, and HP output waveforms in VM. To study the effect of temperature variations, the proposed filter was analyzed under various ambient temperatures. Figure 12 demonstrates the simulated frequency responses of the AP filter in VM for different temperatures (0 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, 75 \({}^{\circ}\)C, and 100 \({}^{\circ}\)C). At the natural angular frequency \(f_{o}\) = 1.52 MHz, the simulation results show that the gain and phase responses lie within the range of -1.3 dBV to -2.7 dBV, and -184\({}^{\circ}\) to -228\({}^{\circ}\), respectively. This variation does not have a strong effect on the gain and phase responses of the circuit. The noise behavior of the proposed filter versus the frequency has also been evaluated, as shown in Figure 13. The output voltage noises of the BP filter at the frequency of 1.52 MHz were found to be 20.50 nV/Hz\({}^{1/2}\) for VM and TIM operations, while the output current noises for CM and TAM were 8.45 pA/Hz\({}^{1/2}\). The Monte Carlo statistical analysis of the proposed filter is next performed to perceive the effects of the passive component deviations on the filter performance. Statistical analysis with 5% random deviation in both resistor and capacitor values was performed simultaneously for 200 samples. The results for LP, BP, and HP responses in VM operation  
Figure 12: Simulated AP frequency responses for various temperatures (0 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, 75 \({}^{\circ}\)C and 100 \({}^{\circ}\)C). Figure 11.0 Output voltage and current noises for the BP filters in VM, TIM and CM, TAM. are achieved as in Figure 11.1 Additionally, the corresponding histogram demonstrating the \(f_{o}\) variations in BP output is shown in Figure 11.2 According to the statistical results, the mean, median, and standard deviation were, respectively 1.50134 MHz, 1.49936 MHz, and 53.2151 kHz, which implies that the proposed filter exhibits a reasonable sensitivity figure to the passive component tolerances. This further validates the robustness of the design.
# Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification
## 4 Experimental Results
### Performance Evaluation of BW QR Codes  
To evaluate the fine-tuned pretrained models, standard performance metrics are used, including precision and accuracy, recall (or sensitivity), and specificity for each optimizer. These metrics are implemented to determine the best parts in terms of prediction accuracy, overall stability, and training duration, as shown in Table 5. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Augmentation Type** & **Purpose** & **Value** \\ \hline RandXReflection & Reflection in the left-right direction randomly appears, specified as a logical scalar. The image is reflected horizontally with a 50\% probability & 1 (true) \\ \hline RandXTranslation & Moving the image in the X or Y direction (or both) from the center coordinate. This forces the convolutional neural network to look everywhere within the image dimension. & \([-30,30]\) \\ \hline RandXScale & The image is scaled outward or inward. While scaling outward, the final image size is larger than the original. Most image frameworks cut out a section from the new image, with a size equal to the original image. Scaling inward reduces the image size, forcing the model to make assumptions about what lies beyond the boundary. & \([0.9,1.1]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Image Augmentation Parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & SGDM & 97.7 & 0.98 & 0.98 & 0.99 & 143 \\ \cline{2-6} AlexNet & Adam & 9.09 & 0.01 & 0.09 & 0.91 & \(-\) \\ \cline{2-6}  & RMSprop & 9.09 & 0.01 & 0.09 & 0.91 & \(-\) \\ \hline \multirow{3}{*}{GoogleNet} & SGDM & 98.3 & 0.98 & 0.98 & 0.99 & 65 \\ \cline{2-6}  & Adam & 89.8 & 0.94 & 0.90 & 0.99 & 68 \\ \cline{2-6}  & RMSprop & 84.2 & 0.86 & 0.84 & 0.98 & 66 \\ \hline \multirow{3}{*}{DenseNet201} & SGDM & 99.7 & 0.99 & 0.99 & 1.00 & 839 \\ \cline{2-6}  & Adam & 97.8 & 0.98 & 0.98 & 0.99 & 1090 \\ \cline{2-6}  & RMSprop & 97.8 & 0.98 & 0.98 & 0.99 & 984 \\ \hline \multirow{3}{*}{MobileNetv2} & SGDM & 99.7 & 0.99 & 0.99 & 0.99 & 117 \\ \cline{2-6}  & Adam & 99.3 & 0.99 & 0.99 & 0.99 & 151 \\ \cline{1-1} \cline{2-6}  & RMSprop & 99.7 & 0.99 & 0.99 & 1.00 & 136 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance Metrics for BW QR Code. For identifying the BW QR code printed source, ResNet50 SGDM took first place, with 100% accuracy, based on perfect scores for all three performance metrics. In second place, ResNet18 had a training time almost 50% faster than ResNet50 SGDM, with minimal loss in prediction accuracy, precision, and recall. MobileNetv2 took third place at 99.7%, with a training time of almost 117 min, or about 9 min longer than ResNet50. The results of our customized model are also included for comparison. The results for identifying each printer model's intrinsic signatures are shown at the bottom of Table 6, with the accuracy for each printer being measured to determine which printer model is the easiest and hardest to distinguish for each CNN model.
## 4 Proposed Scheme
### Step 2: Allocating Charging Tasks to the Vehicle and Drones  
This step involves creating an allocation for all the requesting nodes, with the exception of those within the inner circle. A crucial aspect of this stage is determining whether the nodes will be charged by the vehicle or by drones that are carried by the vehicle.  
**Theorem 1**.: _For any scheduling consisting of \(m\) (\(m<1\)/\(S_{d}\) 1) drones carried by the vehicle, at most \(m\) remote nodes can be charged by \(m\) drones, and the initial tour of the vehicle is formed with the NJNP [35] principle. If each of \(m\) drones is assigned with a single charging task, then the total movement distance of the vehicle achieves an optimal minimum._  
Proof of Theorem 1.: Given a set of charging requests \(S\), the subset of nodes that are designated for charging by either the vehicle or drones carried by the vehicle is represented by the set \(S/S_{in\_d}\). \(\square\)  
By way of contradiction, if \(m\) remote nodes are allocated to \(m\) drones carried by the vehicle, the resulting total movement distance \(l\) of the vehicle is not the optimal minimum. This implies that there is another total movement distance \(l^{\prime}\) less than \(l\) when \(m\)-\(l\) (where \(i=1\), \(2\), \(\ldots\), \(m-1\)) remote nodes are allocated to the drones.  
Assuming there is a shortest tour base station \(=s_{o(0)}\)\(\rightarrow\)\(s_{o(1)}\)\(\rightarrow\)\(s_{o(2)}\)\(\rightarrow\)\(\ldots\)\(\rightarrow\)\(s_{o(m)}\)\(\rightarrow\)\(s_{i}\)\(\rightarrow\)\(s_{o(m+1)}\)\(\rightarrow\)\(\ldots\)\(\rightarrow\)\(s_{o(n)}\) for the vehicle, as illustrated by the solid arrow in Figure 2, there must be an unassigned drone \(d\) carried by the vehicle. It is evident that the length of the tour \(l^{\prime}\) is:  
\[l^{\prime}=\sum_{j=0}^{m}dist(s_{o(j)},s_{o(j+1)})+dist(s_{o(m)},s_{i})+dist(s _{i},s_{o(m+1)})+\sum_{j=0}^{o(n)}dist(s_{o(j)},s_{o(j+1)}) \tag{11}\]  
Suppose \(dist(s_{i},s_{o(m+1)})\leq D_{dmax}\). In this case, we can assign node \(s_{i}\) to drone \(d\). Thereafter,  
\[l=\sum_{j=0}^{m}dist(s_{o(j)},s_{o(j+1)})+dist(s_{o(m)},s_{o(m+1)})+\sum_{j=0} ^{o(n)}dist(s_{o(j)},s_{o(j+1)}) \tag{12}\]Nonetheless, the triangle inequality theorem states that \(l^{\prime}>l\), which contradicts the hypothesis. Consequently, \(l\) is undoubtedly the optimal route for the vehicle.  
According to Theorem 1, it is apparent that releasing all drones carried by the vehicle (i.e., assigning \(m\) nodes to \(m\) drones) results in the vehicle traveling the shortest possible distance. Consequently, the first guiding principle for allocating nodes with charging requests is to make full use of drones and distribute the vehicle's charging task among them.
## 3 Implementation
### Proposed V2X Protocol  
The main objective of the proposed protocol is to control communication among nodes of a V2X network in which the vehicle's ECU real-time generated error codes are transmitted. Also, that protocol is oriented towards connection, which implies the \"availability\" verification of the remote equipment to share the data (DTC codes). Figure 5 shows a diagram of the communication flow proposed.  
Figure 4: Real time communication protocol implementation schematic diagram using embedded system.  
Figure 3: Communications system design.  
Messages are sent by the sender (Slave), a time is set for the receiver to receive the messages, and the correct transmission is verified by sending an ACK = 0 value by the Master. In case one of the communication steps has errors or there's incorrect information coming from the slave node, the master node will send an ACK (Acknowledge) message with value 1, indicating to the slave node to re-send the message containing the corresponding DTC code. As previously mentioned, the proposed protocol is based on the implementation of DTMF (Dual Multifrequency Tones), which are generated by any cell phone.  
Figure 6 presents the proposed datagram, which starts with a bit called \"Initiation\" accompanied by the tone *, and ends with one called \"End\" and the tone #. Right after \"Initiation\", the data corresponding to the DTC code will be encrypted via XOR ciphering and transmitted. That ciphering is a telecommunications technique in which the XOR binary operator is applied over each one of the characters using a specific encryption key. Since DTMF tones are being used, the encryption key and all possible transmittable characters are located in a table relating each tone with a corresponding binary number (see Table 7).  
Figure 5: Implemented code communication flow schematics.  
Figure 6: Proposed protocol datagram.  
Table 8 corresponds to the same tones to transmit but is associated with a binary number randomly ordered. To realize the encryption, the DTC code is located element by element in Table 7, and the XOR operator is applied to it with a binary number located in the corresponding position to the number of ones (1 digit) in the complete DTC code (encryption key). Finally, tone codes are transmitted in the location: \"Binary code of the original tone XOR Key\", followed by PAR (parity), and by the CTRL bit related to the tone located in the corresponding encryption key position of Table 8.