esa-sceva/satcom-chunk-collection huggingface dataset:
# A Wireless Monitoring System for Cracks on the Surface of Reactor Containment Buildings
## 3 Hardware Selection and Assembly
### Sensor Selection  
The most commonly used sensors for structural monitoring are accelerometers, with which the structural vibration response can be captured. In addition, two or three axes, high or low accuracy accelerometers are selected based on specific monitoring requirements. Our monitoring targets are width changes of the cracks on the surface of RCBs, then crackmeters are chosen, and they are based on linear variable differential transformers (LVDTs). LVDTs are displacement transducers and are widely used in industrial applications. They are made up of a central core, a primary coil and a secondary coil with two equal parts that are connected with opposite polarities. The primary coil is supplied with an alternating current, and the two parts of the secondary coil induce different voltages because of different mutual inductances when the core moves between the coils. The difference between the two voltages is the output voltage of the sensor. LVDTs have the advantage of high immunity to electrical noise, and as there is no mechanical contact between the core and coils, LVDTs could increase the expected life. The LVDT (Soway, Shenzhen, China) we choose can be seen in Figure 1a. It has a measurement range from \(-5\) mm to \(+5\) mm with a resolution of \(1\)\(\mu\)m. The output of the sensor is digital with RS485 interface and the work voltage is 12 Volt direct-current. It also has a built-in temperature compensation mechanism. The precision of the sensor is tested through comparison with the linear comparator, and the result shows that it meets the monitoring requirement. Besides crackmeters, we also use PT100 platinum resistance temperature detectors (Fenghe technology, Wuhan, China) to capture the temperature of the environment for data analysis purposes. The resistance of PT100 remains \(100\)\(\Omega\) at \(0\)\({}^{\circ}\)C and changes with temperature. In addition, the temperature information  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Node** & **Microprocessor** & **Radio Chip** & **Data Storage (Bytes)** & **Batteries** \\ \hline Mica2 & ATmega128L & CC1000 & 512 K & \(2\times\) AA \\ MicaZ & ATmega128L & CC2420 & 512 K & \(2\times\) AA \\ Imote2 & XScalePXA271 & CC2420 & 32 M & \(3\times\) AAA \\ Tmote-sky & MSP430 Fi1611 & CC2420 & 1 M & \(2\times\) AA \\ S-note & MSP430 Fi1611 & CC2420 & 1 M & \(2\times\) AA lithium \\ WiSSM & ATmega128 & Maxstream 9 XCite & 128 K & \(5\times\) AA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typical nodes used in structural monitoring.  
can be obtained through signal converter according to the continuous function relationship between the resistance and temperature. The PT100 platinum resistance temperature detector is shown in Figure 1b.
## 3 Shield Pitch Angle and Yawing Angle Calculation Method
### Calculation Example  
Based on the above derivation results, the following set of typical parameters for calculation was selected. Suppose a shield tunnel is excavated in a uniform sand layer. The soil weight \(\gamma\) is 19 kN/m\({}^{3}\), the cohesion \(c\) is 0 kPa, and the internal friction angle \(\varphi\) is 30\({}^{\circ}\). The shield diameter \(D\) is 12 m, the length \(L\) is 10.8 m, the shield mass \(G_{shield}\) is 3000 t, and the thickness of covering soil is 24 m. The displacement of the dome is 5 mm, and the eccentricity of gravity \(l_{s}\) is 3.5 m. \(M_{A}^{\beta}\ =\ 20\) MN\(\cdot\)m and \(M_{A}^{\alpha}\ =\ 75\) MN\(\cdot\)m. The ground reaction curve coefficient is shown in Table 1[11].  
Through the Matlab programming calculation, the shield attitude parameters could be calculated. As shown in Figure 12a, the weight of the shield machine was 3000 t, and \(\mathbf{\Delta s_{v}}\) was obtained (\(\mathbf{\Delta s_{v}}\) = 8.1 mm). In the second stage, the sum of \(M_{G}\) and \(M_{A}^{\beta}\) is 125 MN\(\cdot\)m, then the pitch angle \(\beta\) could be obtained (Figure 12b) (\(\beta\) = 1.16\({}^{\circ}\)). In the third stage, it was found that \(M_{A}^{\alpha}\) = 75 MN\(\cdot\)m, and then the yawing angle \(\alpha\) could be obtained (Figure 12c) (\(\alpha\) = 0.7\({}^{\circ}\)).  
Figure 11: Shield attitude calculation flow chart.
###### Abstract  
The new generation of wireless communication technologies is already in development. Sixth Generation (6G) mobile communications are designed to push the limits for more bandwidth, more connected devices with minimal power requirements, and better signal quality. Previous technologies used in Fifth Generation (5G) are inadequate to handle the new requirements alone. One of the proposed solutions is the use of Reconfigurable Intelligent Surfaces (RISs). These surfaces, when combined with Artificial Intelligence (AI), may be a very powerful means of achieving this. In this paper, we review studies that focus on the use of RISs controlled by AI in determining the concept of Smart Radio Environment (SRE) for use in 6G wireless networks. We examine applications that span from Supervised to Federated Learning (FL) as enabled by the rise in Edge Computing. As the new generation of mobile devices is expected to have enhanced capabilities to perform computing and AI locally, thus reducing the need to transfer the data to a central hub, more opportunities are created for the extensive use of FL. In this context, we focus on research in FL as used in RIS-aided SRE.  
5G mobile communications; 6G mobile communications; artificial intelligence; edge computing; federated learning; reconfigurable intelligent surface; smart radio environment +
Footnote †: journal: Applied sciences
## 2 Dose Individualisation  
The optimum cytotoxic drug dosage should provide patients with the maximal anti-cancer effect, alongside acceptable levels of toxicity. For most cancer types, there is a plateau in the dose -response curve for cytotoxic chemotherapy, meaning that any increase in dosage above a certain level will lead to greater levels of toxicity but no additional effect of treatment on the tumour [2; 17]. For this reason, the avoidance of excessive toxicity caused by overdosing is regarded as the most important side-effect to control within cytotoxic chemotherapy, with high-dose chemotherapy dose-regimens reserved for acute leukaemia and aggressive lymphomas. However, inadvertent underdosing has been shown to be more common, affecting as many as 30% of patients receiving a standard chemotherapy regimen [3]. This could potentially lead to a reduced effectiveness of the treatment and response or survival rates. It has been suggested that an underlying issue of clinical trials used to determine the dose of cytotoxic drugs is that the primary consideration is the prevention of toxicity rather than identifying the dose that produces the greatest anti-cancer effect [3].  
An alternative proposed method of optimising chemotherapy treatment in individual patients is to adjust successive doses throughout a course of treatment based on their levels of myelosuppression, known as 'toxicity-adjusting dosing' [2,18]. Though this method seeks to account for known interpatient variations in cytotoxic drug clearance, it is still more likely that doses will be reduced, dose intervals increased, or courses of treatment skipped entirely than it is to increase the dose intensity in cases where treatment is well tolerated and there is no significant toxicity. Relative dose intensity (RDI) is defined as the ratio of delivered dose intensity (per unit body surface area per unit time (mg/m\({}^{2}\) per week)) to the planned dose intensity for a chemotherapy regimen [19], with an RDI value of less than 85% considered a clinically significant reduction in dosage compared to planned therapy [20,21]. Though it has been shown that patients who receive higher dose intensities typically achieve better disease-free, progression-free, and overall survival than patients that receive lower dose intensities than planned [22], significant numbers of patients are frequently given less than 85% of the suggested dose [23]. For this reason, the desire among oncology practitioners would be to have a method that enables them to prescribe the correct individualised dosage at the start of treatment, rather than finding the correct dose by the third or fourth cycle through trial and error, resulting in a suboptimal therapeutic effect.
# Approximate Image-Space Multidirectional Occlusion Shading Model for Direct Volume Rendering
## 3 Image Space Multidirectional Occlusion Shading Model
### Occlusion Computation  
The occluded path between a pixel, \(P\), and a light sample point, \(L_{sp}\), implies that the material between them obstructs the light transmittance either fully or partially. Full occlusion occurs when the material completely blocks the light transmittance on the paths between the pixel and the light sample points, which means that the material is fully opaque. On the other hand, partial occlusion appears when the material is transparent. The amount of the transmitted light intensity to the pixel diminishes as the light sample travels through the transparent material along the path. Generally, the full occlusion computation is faster than the partial occlusion computation.
## 6 Alternative Methods of Articular Cartilage Diagnosis
### Numerical Methods and Artificial Intelligence
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Modality** & **Diagnostic Accuracy** & **Invasiveness** & **Operator Dependence** & **Clinical Readiness** & **Ability to Assess Biomechanics** & **Other Notes** \\ \hline Ultrasound & Low-moderate (surface only) & Non-invasive & High & Widely available & No & Inexpensive; real-time imaging; limited depth penetration \\ \hline MRE & Emerging; promising for stiffness maps & Non-invasive & Moderate & Limited clinical use & Yes (shear stiffness mapping) & Requires advanced MRI hardware and postprocessing \\ \hline T2/T1\(\rho\)/dGEMRIC & High (biochemical properties) & Non-invasive & Low & Research settings & Indirect (composition-based) & Enables early detection of degeneration; requires contrast (dGEMRIC) \\ \hline Ultrasound & Moderate (surface  stiffness) & Non-invasive & Moderate & Limited clinical use & Yes (local stiffness) & Operator-dependent; resolution limitations \\ \hline Vibroarthography & Moderate (early & & & & & Simple to use; promising for dynamic detection) & Simple to use; promising for dynamic functional assessment \\ (VAG) & degeneration & Non-invasive & Moderate & Experimental & Yes (mechanical friction detection) & Simple to use; promising for dynamic functional assessment \\ \hline OCT & High (microscopic structure) & Minimally invasive (intra-articular) & High & Experimental & No & Intimately to accessible joints (e.g., during arthroscopy) \\ \hline Raman & High (biochemical & Minimally invasive & Moderate & Experimental & Indirect (molecular composition) & Promising for collagen/proteoglycan content analysis \\ \hline Modal Analysis & Moderate (mechanical dynamics) & Non-invasive & High & Experimental & Yes (dynamic stiffness) & Requires controlled loading; research-stage tool \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._artificial intelligence algorithms has the positive effect of enhancing both the diagnostic and prognostic potential of these techniques. However, the focus should be on the further development of alternative diagnostic methods of AC in order to implement them in daily clinical practice. These measures will positively improve the efficiency of early diagnosis of degenerative changes, improving patients' quality of life. Further research on the standardisation and validation of these solutions is therefore crucial so that they can be implemented in the care of patients affected by OA.
# Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
## 1 Introduction
For example, in a cement plant in an industrial park, it takes several hours to arrange the response time from receiving instructions to starting the cement mill, while for a thermal storage boiler, it takes about 30 min from the start of the response to the end. Ignoring the control-response characteristics of different time scales will result in significant deviations between the control results of each controlled object and the control target, making it impossible to achieve precise control. Therefore, this paper presents a unified modeling of the control characteristics of multi-type loads in industrial parks, quantifying the control-delay characteristics, start-stop characteristics, and control response speed during their control response process. Based on this, the paper proposes a model for the aggregated control of multi-type loads in industrial parks and their control strategies, providing a predictive control rate for individual load that considers delay and segmented response characteristics to achieve precise control of multi-type loads in industrial parks. The main innovations of this paper are listed as follows:  
1. The paper abstracts and unifies the multidimensional control-response characteristics of multi-type of controllable loads in industrial parks, including control-delay characteristics, start-stop characteristics, and control-response-speed characteristics, and establishes a phased response model for controllable loads. This lays the foundation for achieving precise system-level control in industrial parks. 2. Establish a day-ahead and intra-day aggregate optimization dispatch model for an industrial park that includes controllable loads with multidimensional control response characteristics. The optimization dispatch objective is to minimize the overall operation cost of the industrial park. A two-stage solving method is implemented to transform the mixed-integer nonlinear programming problem into a mixed-integer linear programming problem, achieving efficient solutions for both day-ahead optimization dispatch and intra-day rolling optimization dispatch strategies. 3. Based on the multidimensional control response characteristics and phased response model of controllable loads in the industrial park, a real-time forecast and control model for a single controllable load unit is established. The minimum variance control rate of the controllable load is calculated with the goal of minimizing the error while tracking the rolling optimization dispatch strategy. Precise control of controllable loads is realized through rolling forecasting and closed-loop optimization control.
# Data Hiding Method for Color AMBTC Compressed Images Using Color Difference
## 3 Preliminaries
### Optimal Pixel Adjustment Process (OPAP)  
LSB substitution method is a DH by directly replacing the LSBs of the cover image according to the secret bits. [PERSON] et al. [18] introduced DH, which allows for optimal LSB substitution, where the worst mean square error (WMSE) was found to be \(1/2\) that obtained with the simple LSB substitution method. We assume that a pixel in a grayscale 8-bit is \(x_{i}\in\{0,1,\ldots,255\}\) and \(\mathcal{S}\) means \(n\)-bit secret bits as \(\mathcal{S}=\{s_{k}|0\leq k<n,s_{k}\in\{0,1\}\}\). The relationship between the \(\mathcal{S}\) and embedded bits \(\mathcal{S}^{\prime}\) is defined as \(s^{\prime}_{k}=\sum_{j=0}^{\delta-1}s_{k}\times\delta+j\times 2^{\delta-1-j}\).  
For embedding the \(\delta\)-bit, the pixel \(x_{i}\) is changed to be \(x^{\prime}_{i}=x_{i}-(x_{i}\bmod 2^{\delta})+s^{\prime}_{k}\). The hidden \(\delta\) LSBs of \(x_{i}\) are extracted by using \(s_{k}=x^{\prime}_{i}\bmod 2^{\delta}\). Let \(x_{i}\) and \(x^{\prime}_{i}\) be the pixels of the cover image and stego image, respectively. The pixel \(x^{\prime\prime}_{i}\) denotes the optimized pixel derived from \(x^{\prime}_{i}\) by using OPAP method. Let \(\Delta_{i}=x^{\prime}_{i}-x_{i}\) be the embedding error between \(x_{i}\) and \(x^{\prime}_{i}\). The pixel \(x^{\prime}\) optimizes as \(x^{\prime\prime}\) as the following rules:  
1. \(\quad\)\(\left(2^{\delta-1}<\Delta<2^{\delta}\right)\): if \(x^{\prime}_{i}\geq 2^{\delta}\), then \(x^{\prime\prime}_{i}=x^{\prime}_{i}-2^{\delta}\); else \(x^{\prime\prime}_{i}=x^{\prime}_{i}\);
2. \(\quad\)\(\left(-2^{\delta-1}\leq\Delta\leq 2^{\delta-1}\right)\): \(x^{\prime\prime}_{i}=x^{\prime}_{i}\);
3. \(\quad\)\(\left(-2^{\delta}<\Delta_{i}<-2^{\delta-1}\right)\): if \(x^{\prime}_{i}<256-2^{\delta}\), then \(x^{\prime\prime}_{i}=x^{\prime}_{i}+2^{\delta}\); else \(x^{\prime\prime}_{i}=x^{\prime}_{i}\).
# A Miniaturized and Fast System for Thin Film Thickness Measurement
## 3 Film Thickness Calculation Algorithm
\[P_{LS}(z)=\frac{1}{2}\Bigg{\{}\frac{\left[\sum_{i=1}^{N}I_{i}\cos(2\pi z(\sigma _{i}-\delta))\right]^{2}}{\sum_{i=1}^{N}\cos^{2}(2\pi z(\sigma_{i}-\delta))}+ \frac{\left[\sum_{i=1}^{N}I_{i}\sin(2\pi z(\sigma_{i}-\delta))\right]^{2}}{ \sum_{i=1}^{N}\sin^{2}(2\pi z(\sigma_{i}-\delta))}\Bigg{\}} \tag{2}\]  
where \(\delta\) is specified for each \(z\) to ensure time-shift invariance,  
\[\delta=\frac{1}{4\pi z}\tan^{-1}\Bigg{(}\frac{\sum_{i=1}^{N}\sin(4\pi z\sigma _{i})}{\sum_{i=1}^{N}\cos(4\pi z\sigma_{i})}\Bigg{)} \tag{3}\]  
Under normal circumstances, the full width at half maximum of the single-layer film interference spectrum after LSP transformation is small (30-50 nm) as shown in Figure 4. Therefore, the peak value \(z_{peak}\) can be directly extracted by the maximum value method. The built-in false alarm probability of LSP evaluates the significance of the peak value to further ensure the correct extraction. If \(n_{1}\) is the refractive index of the film, the film thickness \(d\) is  
\[d=z_{peak}/2n_{1} \tag{4}\]  
In the PPS algorithm, EMD as a self-adaptive high-pass filter realizes the non-destructive extraction of spectral interference terms. While expanding the measurement range of the film, it effectively reduces the influence of the non-uniform distribution of the light source spectrum. The essence of LSP is to fit the data with a single sine function \(y=acos\left(\omega t\right)+bsin\left(\omega t\right)\) with the same frequency [20], which implicitly requiring the mean value of the measurement data to be zero. The IMF components after EMD filtering approximately meet this requirement, thus the combination of EMD and LSP can obtain a significant deterministic solution. Besides, LSP uses full range spectrum data and thus can better correct the gross errors introduced by local spectral noise. At the same time, using the built-in statistical tool of LSP to evaluate the reliability of the solution can greatly eliminate the false frequency interference caused by environmental noise and improve the stability of the system.
# Interspectral Error Tracking and Compensation of DSDT in Satellite Internet of Things
## 4 Synchronization in Multiple RF Channels
Figure 8: Labview implementation of inter-spectrum error synchronization and adaptive compensation. Figure 7: DSDT system receiver structure. In practical applications, the resource consumption will increase with the increase in the number of sub-spectrum. The FPGA hardware model of this system is Xilinx Kintex 7-410T, and the maximum number of molecular spectra supported by this system is six. The table shows that the hardware USRP-X310 can meet the resource requirements of the synchronization scheme, and it can be predicted that the system will have better performance on the later version of USRP.
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 4 Integration of Quantum Money with Quantum Networks
### Benefits of Entanglement-Based Money Transfer  
One key advantage of using quantum teleportation for quantum money transfers is its decentralized nature. Transactions can occur directly between any two parties (such as individuals, banks, or institutions) that share an entangled state. Unlike traditional banking systems, which depend on centralized ledgers or intermediaries, quantum money transfers could theoretically operate on a decentralized quantum network, facilitating peer-to-peer transactions across potentially vast distances.  
Furthermore, since quantum teleportation relies on quantum principles rather than classical cryptography, it is inherently secure against attacks from quantum computers. Quantum computers pose a significant threat to current classical cryptographic systems, but a quantum-enhanced financial system would be resilient to such attacks. This is because its security is derived from quantum mechanics, specifically entanglement and the no-cloning theorem, rather than classical mathematical problems that quantum computers can solve efficiently.  
Another benefit of quantum teleportation is the potential for nearly instantaneous transfer of funds. Unlike traditional money transfers, which may take time to process through banks or financial networks, quantum teleportation transfers the quantum state representing money instantly upon receipt of the classical communication. This feature would significantly improve transaction efficiency, with no need to wait for a transfer to clear. As a result, users could experience real-time movement of funds, which could be transformative for applications in global finance.
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### Security
#### 2.6.2 Activation by Personalization (ABP)  
In ABP mode, end-devices store DevAddr, NwkSKey, and AppSKey to eliminate Join Request/Join Response stages. These devices can directly communicate with the Network server with given keys. Storing and securing critical keys is a vital task, any hardware attack can compromise these keys and unwanted network access may occur. Also, ABP strictly binds end-nodes to the specific Network Server where these types of devices cannot benefit roaming features available since LoRaWAN v1.1.
## 3 Control System Design
### Adaptive Supervisor and Switching  
When the plant parameter vector switches rapidly (or discontinuously) between two values, p1 and p2, the adaptive method must be able to detect the change and switch to the appropriate controller to avoid catastrophic failure. The use of multiple (fixed) models permits the rapid selection of a fixed controller from a set of controllers, which can also be considered adaptive [22].  
The following are some of the important questions that have to be addressed when multiple models are used for adaptive control:  
1. When is it appropriate to make the transfer from one model to another? We need to decide which model to use;2. When is a switching scheme stable? Will switching stop after a finite time? Will the switching scheme improve performance? The switching criterion (or switching rule), which provides the answer to question (1), plays an important role in system design. Various performance indices can be chosen based on the estimation error to determine which of the models best fits the plant at any given time and should thus be used to regulate it. These may assume the following forms for continuous-time systems and their discrete-time counterparts. The main method of switching depends on two main criteria, as shown in Figure 10. The first one is the set point because our multi-models depend on the level of the set point or zones, and the second one is the error, but this works just when the set point or model is in the common zones between two models, where the error identifies which controller is suitable. Three different zones are identified in the preview sections: Z1 (0-40%), Z2 (30-70%), and Z3 (60-100%). If we consider the desired position between 0 and 100 percent, the common zones are between Z1 and Z2 (30-40%) and between Z2 and Z3 (60-70%) [17].