esa-sceva/satcom-chunk-collection huggingface dataset:
# Safety Evaluation Method and Management Strategy for Aviation Flight Plans
## 3 Method
### Different Conflict Scenarios
#### 3.1.1 Conflicts over Special Airspace  
Special airspace refers to areas where the entry of a civil aircraft is restricted or prohibited as a result of political or military activities. Special airspace includes no-navigation, dangerous, and restricted airspaces.  
No-navigation airspace refers to the land or territorial sea zones of a country where aircrafts are forbidden to fly (Figure 1); it is usually represented by the letter P on maps. Without special approval in accordance with relevant state regulations, it is forbidden to fly civil aircrafts in no-navigation airspaces, except in the case of a special emergency. No-navigation airspace can be divided into permanent and temporary no-navigation airspace; the former cannot be entered at any time, while the latter appears only during special periods.  
Restricted airspace refers to border areas where the flight time or flight altitude of aircraft in the land or territorial sea of the country is restricted under certain conditions (Figure 2). Such airspace is often marked with the letter R on aeronautical charts. Aircrafts that are not authorized by air traffic control for a given period are not allowed to fly in restricted airspaces. Outside the specified time limit, qualified aircrafts may fly over.  
Dangerous airspace refers to airspace in which navigational hazards exist for a certain period (Figure 3), and they are usually marked with the letter D on aeronautical charts. Aircrafts are not allowed to fly in dangerous airspace during the specified time period. Eligible aircrafts may fly into it outside of the stated hours.  
The opening times of special airspace communication are dynamic, and the overflight and entry of aircrafts are restricted by certain conditions, such as time and altitude. The main information of special airspaces includes temporal and spatial information. In this paper, the former is considered, and the special airspace containing temporal information is defined as the spatiotemporal special airspace (STSA). The entry of an aircraft into special airspaces beyond the specified time can be regarded as an aircraft conflict.  
Figure 1: Example of no-navigation airspace (no-navigation airspace of P-40 R-4009 around Camp David).
# All-Optical Non-Inverted Parity Generator and Checker Based on Semiconductor Optical Amplifiers
###### Abstract  
An all-optical non-inverted parity generator and checker based on semiconductor optical amplifiers (SOAs) are proposed with four-wave mixing (FWM) and cross-gain modulation (XGM) non-linear effects. A 2-bit parity generator and checker using by exclusive NOR (XNOR) and exclusive OR (XOR) gates are implemented by first SOA and second SOA with 10 Gb/s return-to-zero (RZ) code, respectively. The parity and check bits are provided by adjusting the center wavelength of the tunable optical bandpass filter (TOBPF). A saturable absorber (SA) is used to reduce the negative effect of small signal clock (Clk) probe light to improve extinction ratio (ER) and optical signal-to-noise ratio (OSNR). For Pe and Ce (even parity bit and even check bit) without Clk probe light, ER and OSNR still maintain good performance because of the amplified effect of SOA. For Po (odd parity bit), ER and OSNR are improved to 1 dB difference for the original value. For Co (odd check bit), ER is deteriorated by 4 dB without SA, while OSNR is deteriorated by 12 dB. ER and OSNR are improved by about 2 dB for the original value with the SA. This design has the advantages of simple structure and great integration capability and low cost.  
all-optical parity generator; all-optical parity checker; semiconductor optical amplifier; all-optical signal processing; all-optical XOR logic; all-optical XNOR logic +
Footnote †: journal: Applied Sciences
## 7 Limitations  
This proposed approach improves instrument recognition by leveraging scaled multi-spectrogram features and hierarchical attention mechanisms; however, it has several limitations. Firstly, the method relies on manually selected spectrogram scaling configurations, which may not be optimal for all instruments. This reliance on fixed scaling parameters can lead to inconsistent performance across various instrument types, as certain scales might emphasize features relevant to some instruments while underrepresenting others. Additionally, the model's performance heavily depends on the quality and diversity of the training dataset. Limited training samples for less common instruments could result in reduced accuracy for these classes, as the model may not generalize as effectively to instruments with fewer examples.  
Another limitation is the increased computational complexity introduced by the layered attention mechanisms. While the hierarchical attention layers enhance the model's ability to focus on relevant frequency and time features, they also make the approach computationally intensive. This complexity could restrict the model's usability in real-time applications or deployment on devices with limited processing power, such as mobile or embedded systems. Furthermore, the proposed method has not been extensively tested across different audio environments, which may impact its robustness in noisy or acoustic varied settings. Future work could address these challenges by investigating adaptive scaling techniques, optimizing model efficiency, and testing the approach under diverse real-world conditions to enhance its applicability and reliability.
## 3 Implementation
### Proposed Method
#### 3.2.4 Step-by-Step Image Decryption Based on Quad Key System (Receiver's Side)  
The image decryption process is illustrated in Figure 7b. This is the reverse process of the encryption algorithm, and image reconstruction or detailed decryption steps are as follows.  
**Step 1:** The encrypted image \(A4\) is input, and then row and column permutations using \(x\) and \(y\) sequences are used to produce a shuffled image. Then, using the XOR operation with the shuffled image and the \(z\) sequence, one may obtain the compressed decrypted image \(B4\). (Pseudo-random sequences \(x\), \(y\), and \(z\) are obtained by a 3D Gauss map, as explained above).  
**Step 2:** Using Equation (2) (logistic map), the chaotic sequence \(y_{i}\) is created and \(y_{i}\) is used to generate the diffusion sequence by (14).  
Perform a bitwise XOR operation on the \(B4\) image, The inverse diffusion operation can be expressed as follows:  
\[B3=B4\oplus Y_{i} \tag{16}\]  
**Step 3:** To obtain the unscrambled image (\(B2\)), apply the inverse Arnold transformation. (See Algorithm 1 in Section 2.2.3).  
**Step 4:** Using Equation (3) (sine map), the chaotic sequence \(x_{i}\) is created and \(x_{i}\) is used to generate the diffusion sequence by (12).  
Perform the bitwise XOR operation on image \(B2\). The inverse diffusion operation can be expressed as follows:  
\[B1=B2\oplus X_{i} \tag{17}\]  
**Step 5:** Finally, we combine all of the subcomponents of SVD to obtain the decrypted image \(B\), as described by (18).  
\[B_{M\times N}=U_{M\times M}\cdot(B1_{M\times N})^{-1}\cdot(V_{N\times N})^{T} \tag{18}\]
# Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility
## 3 Methodology and Proposed Approach
### Communication Layer  
This layer enables the data transmission and interaction between the control and management layer and the physical layer. It can use the 5G, WiFi, or LoRaWan technologies to enable communication and data transfer between the edge device in the physical layer and the Data Access and Integration module in the Control and Management layer. Our previous work in [30] discussed the details and the evaluation of the LoRaWan technology in a shared mobility environment.  
Figure 1: Shared mobility reference model.
## 1 Introduction
[18] demonstrated the capacity to derive multiple biophysical variables from multispectral satellite imagery, including the leaf area index (LAI) and the fraction of absorbed photo-synthetically active radiation (FAPAR). These variables exhibit a strong correlation with crop yields at specific growth periods, presenting opportunities for their use in the training and validation of models assessing cropland suitability [19]. The significance of the LAI and FAPAR is underscored in determining the impacts of climate change on vegetation, aligning with United Nations guidelines, thus highlighting their potential in suitability analyses [18]. The initiation of the Sentinel-2 and Sentinel-3 satellite missions has further augmented these capabilities, allowing the modeling of biophysical vegetation variables at both high and medium spatial resolutions. Moreover, employing remote sensing data circumvents the prohibitively costly and time-intensive process of terrestrial data collection, particularly in expansive areas with underdeveloped transport infrastructure, which is characteristic of many agricultural regions [20]. To provide an alternative to conventional GIS-based multicriteria analysis and overcome its limitations, the primary goal of this study was to propose and validate a method for predicting cropland suitability using soybean (_Glycine max_ L.), ultimately improving agricultural land management strategies. This endeavor aims to enhance the efficient utilization of natural resources while minimizing reliance on fertilizers and pesticides. There were two specific objectives:  
* To develop and ensure quality control of ground-truth datasets for training and testing machine learning models, thereby guaranteeing the accuracy of predictions. * To implement cropland suitability predictions in alignment with FAO standards, categorizing results into five classes through a fully objective methodology using machine learning while ensuring global applicability and accuracy evaluation. The successful fulfilment of these objectives includes a zoned cropland suitability map classified into five FAO suitability categories and quantitative indicators for assessing the prediction accuracy of machine learning models.
## 2 Materials and Methods
### Perturbation and Observe Algorithm  
This algorithm is based on an intentional and periodical perturbation on the control command with a following observation and evaluation of the system output [43]. Applied to the PV system, the perturbation is generated through a change in the voltage \(V_{in}(k)\) and current \(I_{in}(k)\), such that the power of the PV is measured. This implies that the slope \(\Delta P/\Delta V\) can be calculated, which helps with knowing whether the MPP is achieved, as Figure 5 shows.  
Based on the previous description and on the detailed logic of Figure 6, the knowledge of \(P_{in}(k)\) and \(V_{in}(k)\) and its delay in \(k-1\) allows for the calculation of the mentioned slope. Therefore, if the latter mentioned value is positive, the duty cycle \(d\) (which modifies the voltage by Equation (6)) will increase such that the algorithm output \(u=d+\Delta d\) and aims to reach the MPP; on the contrary, when the position is at the right side of the MPP, the control signal decreases through \(u=d+\Delta d\).  
Figure 4: Implementation architecture of FLC controller.  
Figure 5: Power–Voltage curve with the P&O mechanism to reach the MPP.
## 3 Results
### Soil Desalinization  
Mean salt storage to the depth of 30 cm for different macropore depth treatments was calculated in Table 3, in order to compare the effect of AMSF method on soil desalinization. The mean total salt appeared to be a similar tendency of decrease for both initial salinization conditions with the increasing of macropore depth, i.e., the order of means was 15 cm \(<\) 10 cm \(<\) 5 cm \(<\) 0 cm macropore depth treatment.  
Figure 4: Mean water storage in top 30 cm soil layer 25 days after irrigation under different macropore depth treatments for artificial macropores with sandy fillings (AMSF) method. Control means soils without macropore. Mean values within same initial soil condition followed by different letters are significantly different (\(p\) = 0.05).  
Figure 3: Mean water storage at top 30 cm under different salinization conditions without artificial macropores. Label “ns” means values within the same sampling time are not significantly different, and label “**” means values within the same sampling time are significantly different (\(p\) = 0.05).  
Total desalinization in top 30 cm soil profile was determined by the difference in total salt before irrigation and after 25 days of irrigation. More specifically, in a 1200 m\({}^{3}\) ha\({}^{-1}\) irrigation event, under mild salinization condition, 10 cm and 15 cm macropore depth treatments reduced total salt by 41.7 and 88.3 g m\({}^{-2}\), and accordingly increased total desalinization by 83.5% and 176.6% significantly (\(p\) = 0.05), respectively, as compared to control group without macropore. Meanwhile, the total desalinization under severe salinization condition with \(\geq\)10 cm macropore depth treatments were 52.1% to 94.7% (\(p\) = 0.05) higher than control group.
## 4 Discussion  
The authors discussed the results of their research with the involvement of leading experts in the field of observation control theory, monitoring systems, and computer security.  
At present, the mathematical apparatus of random processes is used in the description and solution of a large number of theoretical and practical problems in various fields of science and technology. An important place in this series is occupied by tasks described by dynamic models, which are used both in the humanities and in the natural and technical sciences. In technical sciences, such tasks include those related to estimating the motion parameters of moving objects by location and navigation systems, monitoring information systems, describing communication networks, controlling traffic flows, and many other applied tasks. Despite the fact that the mathematical apparatus for solving such problems has been researched quite extensively, the potential for its further development remains very high. New technical problems appear, in which classical approaches can be successfully applied. These tasks include optimization of IDS computing resources.  
The scientific novelty of this research lies in the generalization of existing methods for optimizing observations and filtering for a class of practically important problems of optimal resource management of an intrusion detection system, in which these approaches have not been previously employed.  
The practical implementation of the research results will help to significantly optimize the distribution of computing resources of the IDS information system, which uses the Kalman filter to filter data. Today, such systems are gradually gaining popularity [24,25]. The approach given in this article for the organization of observations in IDS using the Kalman filter is also of scientific interest.  
The results of studies by other scientific groups confirm the effectiveness of using this approach to solving measurement control problems in systems for estimating motion parameters [26, 27, 28]. However, the use of the considered mathematical apparatus in optimizing IDS resources also made it possible to obtain high results, which confirmed the effectiveness of this approach.  
At the same time, information security experts recommended a more in-depth study of the possibilities of using this approach. Real intrusion detection systems can monitor a large number of parameters: traffic intensity, RTT delays, CPU-cores load, CPU temperature, disk access intensity, number of running processes, RAM consumption, power consumption, etc. Many of them can be approximated by Gaussian processes and can be included in the state vector of the controlled object, and accordingly considered when planning AIDS resources.  
Figure 5: Optimal observation plan.
###### Abstract  
Industrial controls, and in particular, Programmable Logic controllers (PLC) currently form an important technological basis for the automation of industrial processes. Even in the age of industry 4.0 and industrial internet, it can be assumed that these controllers will continue to be required to a considerable extent for the production of tomorrow. However, the controllers must fulfill a range of additional requirements, resulting from the new production conditions. Thereby, the introduction of the service paradigm plays an important role. This paper presents the concept of smart industrial control services (SICS) as a new type of a PLC. As a distributed service-oriented control system in an IP network, a SICS controller can replace the traditional PLC for applications with uncritical timing in terms of Industry 4.0. The SICS are programmed as usual in industry, according to the standard IEC 61131-3, and run in a SICS runtime on a server or in a cloud. The term Smart Service is introduced and the uses of SICS as a smart service, including a clearing system for the creation of new business models based on control as a service, are described. As a result, two different SICS prototype implementations are described and two application examples from manufacturing automation, as well as the evaluation of the real-time features and the engineering of a SICS controller, are discussed in the paper.  
control service; smart service; control as a service; cloud-based control system; automation system +
Footnote †: journal: Applied Systems
## 4 Dynamic Characteristics Analysis of New TBM Integrated Cutter System
### Vibration Response Solution of Cutter System  
#### 4.1.1 Numerical Solution of Differential Equations  
There are two main methods for solving dynamic differential equations: the analytical method and numerical method. The analytical method gives the exact solution of the equation. However, the existence of the initial value makes the solution process harder and more complicated when solving the cutter system's dynamic response. The numerical method obtains the approximate solution of the equation. It is easier to program and solve, even though it is less accurate than the analytical method. Currently, the numerical methods for solving dynamic differential equations mainly include Euler, Newmark, Runge-kutta, Gill, etc. [19; 20]. The Newmark method is a direct integration method mainly used to solve the structural dynamic response. It has the characteristics of a fast solving speed and a high accuracy [21]. This section uses the Newmark method to solve the cutter system's dynamic equation and obtain each component's time-domain dynamic response.  
When \(\alpha\) and \(\beta\), which are the control parameters in the Newmark numerical method, are equal to 0.5 and 0.25, the solution is unconditionally stable and is not affected by the time step \(\Delta t\). Combined with the dynamic differential equation of the cutter system, the solution process based on the Newmark method is shown in Figure 8.  
Figure 8: Dynamic response solving process based on Newmark method.
## 3 Model Predictive Control-Based Longitudinal and Lateral Tracking Control
### Model Predictive Control  
The errors can be regulated from (11) as:  
\[\chi_{k+1}=\mathcal{C}_{\chi}^{k+1}\left(x_{\text{ref}}^{k+1}-x_{k+1}\right)= \mathcal{C}_{\chi}^{k+1}x_{\text{ref}}^{k+1}-\mathcal{C}_{\chi}^{k+1}x_{k+1} \tag{12}\]  
where \(\mathcal{C}_{\chi}^{k+1}=M_{\theta_{\text{ref}}^{k+1}}\left[\begin{array}{ cccc}sin\;\theta_{\text{ref}}^{k+1}&0&cos\;\theta_{\text{ref}}^{k+1}&0\\ 0&1&0&0\\ 0&0&0&1\end{array}\right],x_{k+1}^{k+1}=\left[\begin{array}{c}X_{k+1}^{k+1} \\ v_{k+1}^{k+1}\\ \gamma_{k+1}^{k+1}\\ \theta_{\text{ref}}^{k+1}\end{array}\right],x_{k+1}=\left[\begin{array}{c}X _{k+1}^{k+1}\\ v_{k+1}\\ \gamma_{k+1}\\ \theta_{k+1}\end{array}\right]\) represent the coordinate transform matrix, the reference and predicted states at the prediction step \(k+1\), respectively.  
Figure 4: Lateral error description in Frenet coordinate system.  
Therefore, the errors of the predictive horizon can be regulated as:  
\[\left\{\begin{array}{l}\mathbf{\chi}=\hat{\mathbf{C}}_{\chi}\big{(}\hat{\mathbf{A}}_{d}x_{k}+ \hat{\mathbf{B}}_{d}\mathbf{U}\big{)}-\hat{\mathbf{C}}_{\chi}Y_{\text{ref}}\\ \hat{\mathbf{C}}_{\chi}=\left[\mathbf{C}_{\chi}^{k+1},\mathbf{C}_{\chi}^{k+2},\cdots,\mathbf{C}_ {\chi}^{k+N_{p}}\right]^{T}\\ \mathbf{\chi}=\left[\chi_{k+1},\chi_{k+2},\cdots,\chi_{k+N_{p}}\right]^{T}\end{array}\right. \tag{13}\]  
where \(\hat{\mathbf{A}}_{d}\) and \(\hat{\mathbf{B}}_{d}\) are the augmented matrices of the prediction formulation; \(\mathbf{U}\) and \(\mathbf{Y}_{\text{ref}}\) are the vectors of control and reference variables during the prediction horizon.  
Based on the optimal control theory, a quadratic cost function is designed for the MPC tracking control. The optimisation problem can be described as follows:  
\[\begin{array}{l}\underset{\Delta U_{t}}{\text{min}}\sum\limits_{k=1}^{N_{p}} ||y_{t+k,t}-y_{t+k,t}^{des}||_{\mathbf{Q}}^{2}+||\Delta u_{t+k-1,t}||_{\mathbf{R}}^{2 }\\ \mathbf{s.t.}\big{(}k=t,\centerdot+N_{p}-1\big{)}\\ x_{t}^{k+1}=\mathbf{A}_{d}x_{t}^{k}+\mathbf{B}_{d}u_{t}^{k}\\ y_{k,t}=\mathbf{C}_{d}x_{t}^{k}\\ y_{min}\leq y_{k,t}\leq y_{max}\\ u_{min}\leq u_{k,t}\leq u_{max}\\ \Delta u_{k,t}\in[\Delta u_{min},\Delta u_{max}]\\ u_{k,t}=u_{k-1,t}+\Delta u_{k,t}\end{array} \tag{14}\]  
where \(\mathbf{Q}\) and \(\mathbf{R}\) are the weights of the cost terms, \(k\) represents the prediction of \(k^{th}\) step ahead of \(t\); \(\mathbf{A_{d}}\) and \(\mathbf{B_{d}}\) are the discrete matrices of the state-equation based on [PERSON] method; \(\mathbf{C_{d}}\) denotes the output matrix, \(y_{t+k,t}^{des}\) represents the reference path; \(y_{t+k,t}\) is the real-time position of the ego vehicle. The Optimal problem is solved by an open-source solver Qp OASES [21].
## 6 Clustering with Overlap
### Results with Overlap Detection
#### 6.1.2 LFR Benchmark Networks with Overlap  
LFR benchmark networks were generated with overlap as described in [61]. All graphs have 1024 nodes, and are scale-free with a degree sequence exponent of \(-2\) and a community size distribution exponent of \(-1\). The mixing factor is 0.1, and the cluster size is not fixed, but ranges from 50 to 200 nodes. Each graph has a fixed number of overlap nodes and a constant number of memberships for each overlap node. The number of overlap nodes were tested at 25, 50 and 100, and the number of memberships for each overlap node were tested at 2, 4 and 6.  
The results of the clustering and reassigning are shown in Table 10. On the easiest graphs, VAT scored an NMI of 0.98, and integrity scored 0.93. Even with the very most difficult clustering problem, 100 nodes overlapping six different clusters, VAT and integrity got NMI scores of 0.88 and 0.84, respectively. These scores imply that less than 2% of the nodes were incorrectly identified. Additionally, integrity was able to cluster each type of graph in a median of one step. VAT clustered in a median of three steps.