esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and Framework
### System Architecture
#### Communication among Clusters  
Communication is not possible in the following conditions:  
1. if u \(\epsilon\) NN1 in L_CLS, v \(\epsilon\) NN1 in U_CLS and d \(\left(\text{i},\text{j}\right)\leq\) r
2. if u \(\epsilon\) NN1 in L_CLS, v \(\epsilon\) RN1 in U_CLS and d \(\left(\text{i},\text{j}\right)\leq\) r * if u \(\epsilon\) RN1 in L_CLS, v \(\epsilon\) RN1 in U_CLS and d(u,v) \(\leq\) R
* if u \(\epsilon\) CH1 in L_CLS, v \(\epsilon\) CH1 in U_CLS and d(u,v) \(\leq\) r  
Communication is possible in the following conditions only:  
* if u \(\epsilon\) CC1 in L_CLS, v \(\epsilon\) BS in U_CLS and d(u,v) \(\leq\) r, and \(\text{CLS\_id}\left(\text{L\_CLS}\right)=\text{CLS\_id}+1\left(\text{U\_CLS}\right)\)  
With these symbols and notations, we have made the following assumptions of the system model.  
* The sensor nodes are stationary and symmetric. Nodes can communicate using the same transmission power level that means links are symmetrical and each node is allocated a unique ID.The importance and potential of all the nodes are equal to extend the lifetime of the network.
* Routing techniques are required to balance the energy consumption among the nodes because the network supplies information to stationary observers positioned at the border of the area, which entails that energy consumption is not uniform for all nodes.
* The number of transmission power levels are fixed for each node.
* Nodes are equipped with GPS-capable antennae. The location of the nodes is tracked in the initial phase after that GPS will be turned off because the nodes are not mobile.
* It is not possible to recharge the batteries of the nodes as they are left unattended after deployment. Hence there is requirement of energy efficient routing protocols.
* Data is transmitted to the BS in multi-hop manner and BS layer are not constrained in energy while the other layers have the constraint of limited energy.
* The network of IoT is fully connected as BS is reachable to each and every node.  
In Section 4, an energy efficient IoT is modeled based on the assumptions discussed here.
# A Compact Low-Power LoRa IoT Sensor Node with Extended Dynamic Range for Channel Measurements
###### Abstract  
As sub-GHz wireless Internet of Things (IoT) sensor networks set the stage for long-range, low-data-rate communication, wireless technologies such as LoRa and SigFox receive a lot of attention. They aim to offer a reliable means of communication for an extensive amount of monitoring and management applications. Recently, several studies have been conducted on their performance, but none of these feature a high dynamic range in terms of channel measurement. In this contribution an autonomous, low-power, LoRa-compatible wireless sensor node is presented. The main uses for this node are situated in LoRa channel characterization and link performance analysis. By applying stepped attenuators controlled by a dynamic attenuation adjustment algorithm, this node provides a dynamic range that is significantly larger than what is provided by commercially available LoRa modules. The node was calibrated in order to obtain accurate measurements of the received signal power in dBm. In this paper, both the hardware design as well as some verification measurements are discussed, unveiling various LoRa-related research applications and opportunities.  
wireless sensor networks; Internet of Things; LoRa; radiowave propagation Article
## 4 Discussion
### Limitations of the Research and Future Perspectives  
While this study provides valuable insights, it is limited by its focus on a single tournament. Future research should analyse multiple tournaments across different surfaces to determine if tactical patterns remain consistent. Moreover, expanding the sample to include both male and female players would offer a more comprehensive perspective on tactical development in junior tennis. Besides this, future studies should explore how psychological and physical attributes influence tactical decision-making. Additionally, the integration of tracking technologies, such as Hawk-Eye, could provide deeper insights into shot placement and movement patterns, further refining training methodologies for young players.
# Improved MUSIC Method against Range Dimension Deceptive Jamming Based on FDA-MIMO
The rest of the article is structured in such a way that the signal models of FDA-MIMO are analyzed in Section 2. Section 3 analyzes the existing range dimension deceptive jamming models and methods. In Section 4, an improved MUSIC method against range dimension deceptive jamming is proposed. Section 5 provides a simulation analysis and validation of the method, and Section 6 concludes the text.
# PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems
## 2 Literature Review
### Prediction Problems in Semiconductor Scheduling
The identification of kernel functions and other pertinent parameters is the most significant issue for SVMs during model construction [34]. A grid search is a popular technique for choosing parameters, but it has certain drawbacks, including the need for a large amount of training data and a dramatic rise in time costs as searching grids become denser. The optimization mechanism related to the hyperparameter setting of kernel optimization in SVM training may be realized using the Particle Swarm Optimization (PSO) method [35; 36; 37]. As with other swarm intelligence (SI) algorithms like Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC), PSO is one of the most popular SI-based bios-inspired algorithms that makes use of the social sharing of information model [38; 39; 40]. The PSO algorithm, which belongs to a common class of intelligent algorithms and is typically used to calculate the parameters of an SVM model for simplicity and speed, was introduced by [PERSON] et al. [41]. [PERSON] et al. [42] suggested a novel IPSO-SVM model for online defect detection issues in eddy current testing, which might increase recognition efficiency and accuracy. Based on the above-mentioned, a novel hybrid algorithm coalescing a PSO and SVM will be used in this research to build the performance prediction model and the dispatching-parameters optimization model.
## 2 Sensors
### Camera
#### 2.3.4 Why Are Cameras Almost Always Required?  
Cameras, as lightweight and cost-effective sensors, play a vital role in MMSs due to their versatility and the wide range of data that can be extracted from images. For instance, image classification methods are significantly easier to implement for object recognition compared to processing LiDAR point clouds. Additionally, cameras complement LiDAR data by adding rich visual information, such as color and texture, to the point clouds. This integration enhances the overall quality and usability of the collected data, making cameras an essential component of most MMSs. Their ability to provide both visual and spatial context ensures that MMSs can deliver comprehensive and detailed representations of the environment.
# Secure Data of Industrial Internet of Things in a Cement Factory Based on a Blockchain Technology
###### Abstract  
The Industrial Internet of Things (IIoT) has become a pivotal field of development that can increase the efficiency of real-time collection, recording, analysis, and control of the entire activities of various machines, and can actively enhance quality and reduce costs. The traditional IIoT depends on centralized architectures that are vulnerable to several kinds of cyber-attacks, such as bottlenecks and single points of failure. Blockchain technology has emerged to change these architectures to a decentralized form. In modern industrial settings, blockchain technology is utilized for its ability to provide high levels of security, low computational complexity, P2P communication, transparent logs, and decentralization. The present work proposes the use of a private blockchain mechanism for an industrial application in a cement factory, which offers low power consumption, scalability, and a lightweight security scheme; and which can play an efficient role in controlling access to valuable data generated by sensors and actuators. A low-power ARM Cortex-M processor is utilized due to its efficiency in terms of processing cryptographic algorithms, and this plays an important part in improving the computational execution of the proposed architecture. In addition, instead of proof of work (POW), our blockchain network uses proof of authentication (PoAh) as a consensus mechanism to ensure secure authentication, scalability, speed, and energy efficiency. Our experimental results show that the proposed framework achieves high levels of security, scalability and ideal performance for smart industrial environments. Moreover, we successfully realized the integration of blockchain technology with the industrial internet of things devices, which provides the blockchain technology features and efficient resistance to common cyber-security attacks.  
blockchain technology; industrial IoTs; IoT applications in Industry 4.0; decentralization applications; machine to machine communication; distributed databases +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
In this work, we have proposed a new modification of the mathematical model of the stress-strain evolution during the extraction of two adjacent panels of an inclined coal seam. Judging by the results of the numerical modeling obtained in this study, the proposed model adequately describes the regularities of plastic deformation, damage, and roof-caving behavior of the study site compared to the field data.  
We have also proposed a numerical approach for the assessment of the protective pillar stability of an inclined seam based on the analysis of the stress-strain evolution in one of the south Kuzbass mines. The approach is based on an extension of the classical formula for the evaluation of the factor of safety to the case of a generalized stress state. The results suggest that modern methods of geomechanics may be used as an additional tool in pillars' design, ensuring the geodynamic safety of mining operations.  
The following conclusions can be drawn on the basis of the results obtained:  
1. The inclination of the seam causes a strong stress-strain asymmetry;
2. The formation of a larger damage zone dome is manifested in the vicinity of roadway III;
3. Roadway III is probably more prone to the violation of its functional properties than roadway II;
4. In order to maintain the stable state of the protective pillars and to increase the coal recovery ratio and the economic efficiency of the mine, a pillar's W/H ratio should not fall below \(\approx\)11.  
In our future work, we plan to simulate the artificial methods for abutment pressure management and compare their relative effectiveness regarding, for example, discharging boreholes and directed hydraulic fracturing.  
M.E.: Conceptualization, Methodology, Writing; A.P.: Visualization, Software, Formal analysis; G.E.: Conceptualization, Formal analysis; L.P.: Field data, Formal analysis, V.F.: Field data, Formal analysis. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Dependence of \(F_{s}\) on \(W/H\) ratio averaged in the protective pillar volume.  
**Funding:** This work was supported by the Russian Science Foundation, Project No. 21-71-10079.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors express gratitude to the anonymous reviewers whose critical comments allowed for the improvement of the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
\(\rho\) density  
\(P\) pressure  
\(S_{ij}\) components of deviatoric stress tensor  
\(K_{i}\mu\) bulk and shear modulus  
\(\epsilon_{ij}^{p}\) components of inelastic strain tensor  
\(\lambda\) multiplier used in theory of plasticity  
\(g(\sigma_{ij})\) plastic potential equation  
\(f(\sigma_{ij},D)\) yield surface equation  
\(\alpha\) internal friction factor  
\(\Lambda\) dilatancy factor  
\(J_{2}\) the second invariant of deviatoric stress tensor  
\(Y_{0},Y\) reference and current values of cohesion  
\(t^{*}\) time parameter of damage accumulation measure  
\(D\) measure of damage
# Improving Lossless Image Compression with Contextual Memory
## 3 PAQ8 PX Algorithm for Lossless Image Compression in Detail
### Image Compression
#### 3.4.5 Grayscale 8 bpp  
In the analyzed version of PAQ8 PX, a number of 62 stationary maps are used for grayscale images. Five of them are used in conjunction with OLS modeling, in order to model quadrants of the causal pixel neighborhood of different lengths. The others accept as keys various clipped and clamped predictions. An indirect context map is used which accepts 27 entries as keys computed as hashed predictions. This means that the estimated number of probabilities which are the output of the image model for grayscale images is 62 * 2 + 27 * 5 = 259.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 4 From Basic Connectivity to Full Autonomy (Levels 0 to 5)
### Level 2: Partial Vehicle Automation  
Level 2 driving automation signifies a cutting-edge driver assistance system capable of handling steering, acceleration, and braking functions. However, it requires the driver to maintain continuous vigilance and actively oversee the technology's operation [63]. In such systems, the driver remains responsible for steering, accelerating, and braking while holding the steering wheel, even though technology aids in these tasks. While using BlueCruise, drivers briefly take their hands off the wheel, but they must stay alert and ready to resume control at any moment [64]. Even with the introduction of over-the-air software updates, such as Autosteer for city streets, Level 2 technology still requires human supervision.
## 3 The MGSOBFO Algorithm
### Fast Non-Dominated Sorting Approach and Crowding Distance  
Before introducing the multi-objection firefly bacteria foraging algorithm, we first introduce the following two basic concepts: fast non-dominant sorting and crowding distance [22].  
(1) Fast Non-dominated Sorting Approach  
First, we calculated two values for each solution.1 domination count \(N_{p}\), the number of solutions which dominate the solution q. 2\(S_{q}\), a set of solutions that the solution q dominates. The pseudo code of the MaBFOA operator is listed in Algorithm 1:  
```
for each \(p\in P\) \(S_{p}=0\), \(n_{p}=0\) for each \(q\in P\) if \(p\prec q\) // if p dominated q then \(S_{p}=S_{p}\cup\{q\}\) else if \(q\prec p\) then \(n_{p}=n_{p}+1\) end if if \(n_{p}=0\) then \(p_{rank}=1\) // p belong to the first front \(F_{1}=F_{1}\cup\{p\}\) end i = 1 //Initialize the front counter  While \(F_{1}\
eq 0\), \(Q=0\)//Q represents the next front for store  For each \(q\in S_{p}\) \(n_{q}=n_{q}-1\) if \(n_{q}=0\)//q belong to the next front then \(q_{rank}=i+1\), \(Q=Q\cup\{q\}\) i=i+1 \(F_{i}=Q\) end
```  
**Algorithm 1**Fast non-dominated sort approach  
(2) Crowding-distance calculation approach  
The crowding distance sorting procedure is shown in Figure 1a. The crowding-distance computation requires sorting the population according to each objective function value in ascending order of magnitude. All populations' members are assigned a distance metric; we can compare two solutions for their extent of proximity with other solutions. The boundary solutions are assigned an infinite distance value. In Figure 1b, the crowding-distance of the I-th solution in its front is the side length of the cuboids. The crowded-comparison operator guides the selection process towards a uniformly spread-out Pareto-optimal front. The crowding distance of each individual be computed by Equation (12).  
\[d_{i}=\sum_{k=1}^{m}|f_{k}(i-1)-f_{k}(i+1)| \tag{12}\]In most situations, the last level is accepted partially. In such a case, these solutions with better crowding distances are picked up.
# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
## 2 Literature Review
### Clustering-Based Data Aggregation Schemes
[25] have introduced an energy improvement scheme for homogenous WSNs. To select an appropriate CH from nodes, the rank is calculated. The higher rank increases the likelihood of a node being selected as CH. The CMs select clusters by using a fuzzy system. When clustering is finalized, CMs transmit data to CH, which CH aggregates and forwards to BS. The proposed scheme gains better energy utilization and reduces the chances of hotspot issues. Some other parameters should be considered for the intelligent selection of CH to overcome latency [25]. To improve energy utilization and lessen the issue of congestion, [PERSON] et al. have presented a cluster-based strategy that includes two crucial stages. The first stage involves establishing sensors in clusters. For transmitting data from sensors to CH, and CH to BS, the shortest path is selected by using the Capuchin Searching algorithm that helps in overcoming energy consumption. The scheme is simulated over MATLAB and it has a better network lifetime, lower delay, and higher rate of packet delivery. The scheme uses a Capuchin searching algorithm that is not very efficient in the case of large networks [26]. To enhance the network lifetime of sensors in the medical field, multi-hop routing is checked. The scheme has a better lifetime, but not enough parameters are considered for hop selection and the security aspect for maintenance is neglected [27; 28]. To securely transmit healthcare data, active sensors are selected by the Archimedes algorithm and the shortest path is selected by an attribute-centered binary scheme [29]. Similarly, clustering and data aggregation is performed for underwater WSNs [30; 31]. The first layer contains medical sensors to capture the patient's condition and data is transferred to a fog server. The fog server prioritizes the data based on the health state and intensity of diseases. This task is performed by fog clusters. If the computing cost is greater than available resources then by offloading mechanism, data is transferred to the cloud. In the end, reports are created for medical staff to take better steps in treatment [32].
# Depth-Optimized Quantum Circuit of Gauss-Jordan Elimination
## 3 Parallel Implementation for Quantum Circuit of Gauss-Jordan Elimination
### Obstacles to the Parallelization of Gauss-Jordan Elimination  
In this section, we analyze the obstacles to the parallelization of the Gauss-Jordan elimination quantum circuit. In the swap stage in Gauss-Jordan elimination, swap operations between rows are performed depending on the pivot (in quantum implementation, these conditional operations must be designed to be reversible). If the pivot is \(|0\rangle\) and the element in the target row is \(|1\rangle\), an actual swap operation between the rows (one containing the pivot and the other being the target row) is executed. In Gauss-Jordan elimination, the pivot is checked repeatedly many times. However, this implies a sequential flow that increases the circuit depth by iteratively checking pivots and elements. Figure 1 shows the case of sequential flow in the swap operation. For a comprehensive understanding, throughout Section 3, we represent a matrix \(H\) (size \(4\times 4\)) and a syndrome \(c\) (size 4) as follows, and our method is described based on the following arrangement:  
\[H|c=\begin{bmatrix}H_{0,0}&H_{1,0}&H_{2,0}&H_{3,0}&H_{4,0}(c_{0})\\ H_{0,1}&H_{1,1}&H_{2,1}&H_{3,1}&H_{4,1}(c_{1})\\ H_{0,2}&H_{1,2}&H_{2,2}&H_{3,2}&H_{4,2}(c_{2})\\ H_{0,3}&H_{1,3}&H_{2,3}&H_{3,3}&H_{4,3}(c_{3})\end{bmatrix} \tag{4}\]  
Depending on the pivot \(H_{0,0}\), swap gates between the rows are performed (the pivot \(H_{0,0}\) is copied to the ancilla qubit \(anc_{0}\)). However, subsequent swap gates for the remaining elements of the rows (i.e., CSWAP (\(anc_{0}\), \(H_{1,0}\), \(H_{1,1}\)), CSWAP (\(anc_{0}\), \(H_{2,0}\), \(H_{2,1}\)), and CSWAP (\(anc_{0}\), \(H_{3,0}\), \(H_{3,1}\))) must wait until the current pivot operation (CSWAP (\(anc_{0}\), \(H_{0,0}\), \(H_{0,1}\))) is completed. This sequential flow also occurs in the elimination stage (not only in the swap stage).
###### Abstract  
The classification of pulmonary nodules using computed tomography (CT) and positron emission tomography (PET)/CT is often a hard task for physicians. To this end, in our previous study, we developed an automated classification method using PET/CT images. In actual clinical practice, in addition to images, patient information (e.g., laboratory test results) is available and may be useful for automated classification. Here, we developed a hybrid scheme for automated classification of pulmonary nodules using these images and patient information. We collected 36 conventional CT images and PET/CT images of patients who underwent lung biopsy following bronchoscopy. Patient information was also collected. For classification, 25 shape and functional features were first extracted from the images. Benign and malignant nodules were identified using machine learning algorithms along with the images' features and 17 patient-information-related features. In the leave-one-out cross-validation of our hybrid scheme, 94.4% of malignant nodules were identified correctly, and 77.7% of benign nodules were diagnosed correctly. The hybrid scheme performed better than that of our previous method that used only image features. These results indicate that the proposed hybrid scheme may improve the accuracy of malignancy analysis.  
lung cancer; pulmonary nodule; PET/CT; patient information; classification +
Footnote †: journal: Medical  
reported that FDG-PET and FDG-PET/CT perform better on the classification of benign and malignant tumors than CT examinations alone [3; 4; 5].  
Notably, benign nodules associated with inflammatory disease often exhibit elevated FDG uptake as malignant nodules. Therefore, it is often difficult to distinguish benign from malignant nodules [3]. In these cases, bronchoscopic biopsy is performed to confirm the diagnosis. However, patients are at risk for pain and complications, such as bleeding and/or pneumothorax.  
Here, we focus on patient metadata for lung cancer diagnosis; these metadata can be collected non-invasively. Age, gender, and smoking status have all been shown to correlate with the risk of lung cancer; this information can be used to further improve the analysis accuracy. Further, minimally invasive laboratory tests can be performed for determining the patient's general condition. If such information can be analyzed along with PET and CT images, a comprehensive evaluation of the nodules' malignancy may become possible. Therefore, in this study, we focused on the automated analysis of the malignancy potential of the pulmonary nodules using PET/CT images combined with patient-related information.
# Influence of Drift on Robot Repeatability and Its Compensation
## 3 Data Acquisition
### Evaluation of the Drift Compensation  
The use of this technique allows to almost completely neutralize the effect of drift, as can be seen on the graph (Figure 8). Without drift compensation, the repeatability is in 210 \(\upmu\)m range (according to the trend of the TCP Z axis drift in the graph). When applying the calculated drift compensations based on the current robot temperature, the resulting repeatability is in 18 \(\upmu\)m range (the value is represented by the green line in Figure 8). For these specific conditions, an increase in repeatability of one order of magnitude was achieved.