esa-sceva/satcom-chunk-collection huggingface dataset:
# An IoT-Based Non-Invasive Glucose Level Monitoring System Using Raspberry Pi
## 2 Proposed Internet of Things (IoT) Glucose Monitoring System
### IoT Cloud Computing
#### 2.5.2 System Implementation  
The whole system was implemented using a Flask server, which allows performing multiple functions or microservices, through a variety of API calls, and it is structured. This server is running on Windows operating system with 8 GB RAM, Core i7 processor, SSD 240 GB, Python 3.5, Flask 1.02 and MariaDB 10.1.35. Its structure is designed so that it contains the GET and POST interaction methods.  
Figure 3: Zones on the Clarke error grid analysis.  
Figure 2: Cross-validation division of data for system training and results validation.  
The API structure for the present monitoring system is shown in Table 1, and the main functions implemented on this system, are explained in detail below.  
In the _user glucose profile generation_ function (/signUp), the glucose profile of a user is stored for registered users. With the intention of registering new users in the database, a form with username, e-mail and password must be filled.  
On the other hand, the _glucose estimation_ function (/glucose/estimation) receives a parameter along with a JSON object with information such as the user ID, and the mean histogram obtained from the 12 fingertip images as computed by the RPi board. This also incorporates an artificial neural network model, trained previously as specified in the model selection section, giving as output the estimated glucose concentration value. Results obtained in this function, i.e., the estimated glucose value, the user ID, so as the current time and hour, are then saved on the database.  
Finally, the _share data to end device_ function (/glucose/query) is a function consisting in returning the current estimated glucose value. However, it also allows the retrieval of the glucose historical record of a user by an end device.  
Please note that /glucose/estimation and /glucose/query are general purpose functions, so the integration of measurements from other sensors, e.g., ECG (/ecg/estimation and /ecg/query) or SpO2 (/spo2/estimation and /spo2/query) is straightforward, resulting in a flexible and scalable system.
# Condition Monitoring of Industrial Equipment Based on Multi-Variables State Estimate Technique
###### Abstract  
Unexpected failures commonly occur in industrial equipment, and condition monitoring could significantly improve the efficiency of maintenance and failure of early alarm. A condition monitoring method using multi-variables state estimate technique (MSET) is proposed, and an improved multi-variables memory matrix construction method is employed, furthermore, an analysis of comprehensive similarity index that considering variable weights is accomplished, and incipient failure alarm thresholds are determined, which lead to effective early detection of failure. The method proposed in this paper is validated using actual data for blower fan in a thermal power plant, and the simulation and comparison results are discussed. The verification results reveal that the proposed method is effective for failure monitoring modeling and achieve a superior accuracy, and incipient failure could be accurately detected.  
condition monitoring; vibration; multi-variables state estimate technique Article
# ICRP: Internet-Friendly Cryptographic Relay-Detection Protocol
###### Abstract  
Traffic hijacking over the Internet has become a frequent incident over the last two decades. From minor inconveniences for casual users to massive leaks of governmental data, these events cover an unprecedently large spectrum. Many hijackings are presumed to be caused by unfortunate routing mistakes, but a well-organized attacker could set up a long-term stealthy relay, accessing critical traffic metadata, despite suitable encryption schemes. While many studies focus on the mitigation of known attacks, we choose to design a complete detection method regardless of the attacker's strategy. We propose a two-party cryptographic protocol for detecting traffic hijacking over the Internet. Our proposal relies on a distance-bounding mechanism that performs interactive authentication with a \"Challenge-Response\" exchange, and measures the round-trip time of packets to decide whether an attack is ongoing. Our construction is supported by worldwide experiments on communication time between multiple nodes, allowing us to both demonstrate its applicability and evaluate its performance. Over the course of this paper, we demonstrate our protocol to be _efficient_--itrequires only two cryptographic operations per execution inducing negligible workload for users and very few loss of throughput, _scalable_--no software updates are required for intermediate network nodes, _routing protocol independent_--this means that any future update of the route selection process will not induce changes on our scheme, and _network friendly_--the added volume of transiting data is only about 1.5%.  
hijacking; distance-bounding; protocol 2022
## 5 Numerical Analysis  
### Governing Equations and Numerical Solutions  
This study was performed under the assumption that the fluid flow type inside the MCR represents a three-dimensional steady state, incompressible, and turbulent flow. The physical properties of the working fluid were kept at a constant level for temperature and pressure for numerical calculation. Due to the disturbance of the fluid by the cooling fan, the flow in the machine room will behave in a complex manner. Therefore, the standard \(k-\varepsilon\) turbulence model that represents excellent estimation performance in an internal flow analysis that has been widely adopted was used to perform flow analysis in the refrigerator machine room [9; 10; 24]. The governing equations were composed of the continuous equation, the momentum equation, the turbulent kinetic energy equation, and the turbulent kinetic energy dissipation rate equation, and are as shown in the following Equations (5)-(10) [25]:  
\[\frac{\partial u_{i}}{\partial x_{i}}=0, \tag{5}\]  
\[\frac{\partial u_{i}}{\partial t}+u_{j}\frac{\partial u_{i}}{\partial x_{j}}=- \frac{1}{\rho}\frac{\partial p}{\partial x_{j}}+\
u\frac{\partial^{2}u_{i}}{ \partial x^{2}_{j}}, \tag{6}\]  
\[v_{t}=\frac{C_{\mu}k^{2}}{\varepsilon}, \tag{7}\]  
where \(v_{t}\) is the turbulent viscosity, \(C_{\mu}\) is the constant. \(k\) and \(\varepsilon\) can be obtained from the following equations for turbulent kinetic energy and turbulent dissipation rate, respectively  
\[\frac{\partial u_{j}k}{\partial x_{j}}=\frac{\partial}{\partial x_{j}}\left( \left(\
u+\frac{v_{t}}{\sigma_{k}}\right)\!\!\frac{\partial k}{\partial x_{j} }\right)+P_{k}-\varepsilon, \tag{8}\]  
\[\frac{\partial u_{j}k}{\partial x_{j}}=\frac{\partial}{\partial x_{j}}\left( \left(\
u+\frac{v_{t}}{\sigma_{\varepsilon}}\right)\!\!\frac{\partial \varepsilon}{\partial x_{j}}\right)+\frac{\varepsilon}{k\rho}(C_{\varepsilon 1 }P_{k}-C_{\varepsilon 2}\varepsilon), \tag{9}\]  
Here, \(C_{\varepsilon 1}\), \(C_{\varepsilon 2}\), \(\sigma_{\varepsilon}\), and \(\sigma_{k}\) are model constants. \(P_{k}\) is the turbulence generation term according to viscosity and buoyancy and is defined as follows:  
\[P_{k}=\
u_{i}\frac{\partial u_{i}}{\partial x_{j}}\!\!\left(\frac{\partial u_ {i}}{\partial x_{j}}+\frac{\partial u_{j}}{\partial x_{i}}\right) \tag{10}\]  
Figure 4: Compressor On/Off cycle for defrost state.  
where the coefficients were defined as follows.  
\[C_{\mu}=0.09,o_{k}=1.0,C_{\epsilon 1}=1.44,C_{\epsilon 2}=1.92, \tag{11}\]
###### Abstract  
Electrical stimulation is a promising therapeutic approach for the regeneration of large bone defects. Innovative electrically stimulating implants for critical size defects in the lower jaw are under development and need to be optimized in silico and tested in vivo prior to application. In this context, numerical modelling and simulation are useful tools in the design process. In this study, a numerical model of an electrically stimulated minipig mandible was established to find optimal stimulation parameters that allow for a maximum area of beneficially stimulated tissue. Finite-element simulations were performed to determine the stimulation impact of the proposed implant design and to optimize the electric field distribution resulting from sinusoidal low-frequency (\(f=20\,\mathrm{Hz}\)) electric stimulation. Optimal stimulation parameters of the electrode length \(h_{\mathrm{el}}=25\,\mathrm{mm}\) and the stimulation potential \(\varphi_{\mathrm{stim}}=0.5\,\mathrm{V}\) were determined. These parameter sets shall be applied in future in vivo validation studies. Furthermore, our results suggest that changing tissue properties during the course of the healing process might make a feedback-controlled stimulation system necessary.  
Keywords:finite-element simulation; electric stimulation; bone regeneration; computational modelling; electrically active implants; bioelectromagnetism; critical size defect; maxillofacial; minipig
## 3 SLD-GEDIR Protocol
### SLD-GEDIR Algorithm  
In this section, a geographic routing algorithm for VANETs is presented. It is aimed at reducing the forwarding overheads and improving the NHV selection criteria. The algorithm considers segment vehicles, one-hop link quality, and degree of connectivity of NHVs, direction of vehicle, speed, and sector angle for making a precise NHV selection decision. The SLD-GEDIR algorithm is presented below:  
Figure 6: NHV selection based on degree.  
**Algorithm:** SLD-GEDIR  
**Notations**  
_CFV_: Current Forwarding Vehicle  
_SSV_: Set of Segment Vehicle (potential Next Forwarder Vehicle)  
_ODV_: Original Destination Vehicle  
_OSV_: Original Source Vehicle  
_NHV_: Next hop Vehicle  
SONV: Set of One hop Neighbor Vehicles  
_V\({}^{t}\)_: Individual \(i^{th}\) Vehicle  
_d\({}_{i}\)_: Degree of connectivity of \(i^{th}\) segment vehicle  
_\(\alpha\)_: Sector angle used for segment area calculation  
_Q\({}_{i}^{link}\)_: Quality of one-hop link of \(i^{th}\)segment vehicle  
**Input**: \(R,\lambda,L,M,P_{t},P_{r},G_{t},G_{r},H_{t},H_{r},l,d_{link}\)  
**Process**  
**1. initialization**: \(CFV=OSV\)  
_SSV_ = \(\phi\)  
_NHV_ = \(\phi\)  
_SONV_ = \(\phi\)  
_\(\alpha\)_ = \(50^{0}\)  
**2. \(SONV=\{\)neighbors of \(\mathrm{CFV}\}\)**  
**3. if (\(ODV\in SONV\)) then**:  
Send the packet to \(ODV\) using available direct link  
**exit**  
**4. else**:  
**while** (\(SSV==\phi\))  
a. **Calculate** segment area \(\mathrm{S_{area}}\) using equation (2)  
b. \(\mathrm{SSV}=\{\)Vehicles in \(\mathrm{S_{area}}\}\)  
c. **if** (\(SSV==\phi\) and \(\alpha<180^{0}\))  
\(\alpha=\alpha+10^{0}\)  
d. **Else**:  
Wait for random amount of time  
\(\alpha=50^{0}\)  
**end while**:
**end if**:
**5. for** each vehicle \(V_{i}\in SSV\)  
Calculate packet error rate \(PER_{link}^{N}(i)\) using equation (19)  
\(Q_{i}^{link}=(1-PER_{link}^{N}(i))\)  
**end for**:
**6. for** each vehicle \(V_{i}\in SSV\)  
Calculate degree of connectivity \(d_{i}\)  
**end for**:  
**7. \(Q_{j}^{link}+d_{j}=Max\{\)\(Q_{1}^{link}+d_{1},Q_{2}^{link}+d_{2}+Q_{3}^{link}+d_{3},\)...\(Q_{n}^{link}+d_{n}\}\)  
**8. \(HV=V_{j}\)**  
**9. transmit** the packet to \(NHV\) and \(NHV=CFV\)  
**10. exit**:  
**Output:**\(NHV\) (\(V_{j}\))
# Hysteresis Compensation and Trajectory Tracking Control Model for Pneumatic Artificial Muscles
## 4 Results and Discussion
### Validation Results of PAM Displacement Tracking Control
A complex harmonic signal contains multiple frequency components, exhibiting more complex fluctuation characteristics and a larger dynamic range, which poses higher demands on the adaptability and real-time performance of the controller. The comparison of the two control schemes under complex harmonic signals is shown in Figure 12. Figure 12a sequentially displays the changes in feedforward control compensation, the compensation amounts of the two feedback control schemes, and the total compensation under the complex harmonic input signals, where the compensation of Control Method 2 is smoother and responds faster. Figure 12b shows the real-time fitting results and error variations of the two control schemes. Control Method 2 exhibits significantly smaller errors during rapid signal changes, with the final error converging within 0.2 mm, while the error convergence speed of Control Method 1 is slower, stabilizing at around 0.4 mm. Overall, the comparison shows that Control Method 2 reduces the error by approximately 30%, and its response speed is about 20% faster than Control Method 1. The use of iterative learning in Control Method 2 allows it to achieve stability more quickly and smoothly than Control Method 1 during rapid signal \begin{table}
\begin{tabular}{c c c} \hline \hline
**Control Plan** & **1** & **2** \\ \hline MAE (mm) & 0.189 & 0.0185 \\ RMSE (mm) & 0.221 & 0.0267 \\ \hline \hline (3) & Complex Triangular Wave Signal \\ \end{tabular}
\end{table} 
Table 6: Comparison of error values between two control schemes under complex harmonic signals. Figure 13: Comparison of two control schemes under complex triangular wave signals. of magnitude for the mean absolute error and the root mean square error can reach 0.01 mm, with a maximum error of 0.2 mm. Compared to open-loop control with only feedforward control [22], the control accuracy is greatly enhanced. In contrast, the maximum tracking error of the existing PI inverse model feedforward control combined with the adaptive projection algorithm in closed-loop control is 3.62 mm [23], indicating that the control method proposed in this paper achieves higher precision.
## 2 Methodology
### Interviews with Clinicians  
Interviews with clinicians were a vital part of our methodology, offering insights into the clinical requirements, diagnostic challenges, and practical needs of healthcare providers working with children with ADHD. These semi-structured interviews were designed to delve deeper into the experiences and expectations of medical staff, focusing on their interaction with current diagnostic tools and their perspectives on integrating innovative, privacy-preserving technologies like those proposed in AURORA MINDS.  
The interviews were conducted over a four-week period, involving three medical professionals, including one pediatrician specializing in neurodevelopmental disorders and two speech therapists with expertise in behavioral assessments. Each interview lasted between 60 and 90 min and followed a structured guide that covered the following themes:  
* **Current Diagnostic Tools:** Strengths and limitations of existing solutions.
* **Challenges in ADHD Management:** Practical issues in diagnosis, monitoring, and patient engagement.  
* **Privacy Concerns:** Specific worries about data handling and compliance with regulations like GDPR.
* **Expectations for New Technologies:** Features and functionalities desired in a next-generation application.  
All three participants highlighted significant limitations in current diagnostic tools. While existing systems like checklists and standardized questionnaires provide a baseline for diagnosis, they lack dynamic and contextual insights into a child's behavior. Additionally, medical staff pointed out that current tools are often poorly integrated into their workflows, creating inefficiencies.  
Privacy emerged as a major concern among clinicians. While recognizing the value of digital tools, medical staff expressed apprehension about data breaches and unauthorized access to sensitive patient information. The clinicians also emphasized the need for compliance with data protection regulations, particularly GDPR. They highlighted the importance of transparency in data handling and the ability of parents to exercise control over their children's information. Lastly, the concept of Federated Learning received positive feedback. Clinicians appreciated the idea of collaboratively improving diagnostic models without compromising the privacy of patient data.
## 2 The Vision of Future Manufacturing Industry Networks
### Enabling Techniques for M2M-Based Collaborative Automations
#### 2.2.2 Semantic Level Techniques  
Semantic-level techniques are responsible for the understanding of messages and interoperability between machines, and therefore allow data to be shared and tasks to be collaboratively fulfilled across machines. This should be based on the normalisation of data and modelling of machine profiles, tasks, events, data interactions, and so on. For data normalisation, Extensible Markup Language (XML) and Javascript Object Notation (JSON) are widely used tools for the presentation of arbitrary data structures, which are already mature for data transfer of connected machines. This provides common frameworks to allow data to be shared and reused across applications with the standards to facilitate the data formats and exchange protocols.  
For system modelling, ontology provides a formal naming and definition of types, properties, and interrelationships of the entities for a particular domain, which is widely used to categorise variables to establish the interrelationships for computations. The semantic data can be expressed through the definition of a common set of ontologies that describe the data items produced, exchanged, and consumed. For the IIoT, industrial scene, production policy, and machine configuration can be expressed with ontology concepts for computation and reasoning. An expressive ontology for IoT (IoT-O) has achieved device interoperability by extending one M2M standard [13]. An ontology-based smart production system for adaptive production management (ARUM) is discussed in [14]. Admittedly, ontology has been found a useful tool for specialised models or more general ones in IoT applications.
# A High-Sensitivity Cesium Atomic Magnetometer Based on A Cesium Spectral Lamp
## 4 Optimization Processes for Improving the Magnetometer's Sensitivity
### Optimization of Magnetic Resonance Linewidth and Signal Amplitude  
As shown in Figure 11, the relationships between the magnetic resonance linewidth, signal amplitude, and \(\beta\) value with respect to the cell heating temperature are presented with a constant feedback magnetic field of roughly 13 nT. Within the temperature scanning range, all three parameters exhibit optimal values. However, the optimal operating points are not the same. The magnetic resonance linewidth reaches its optimum value at approximately 48 \({}^{\circ}\)C, the signal amplitude achieves its optimum value at around 54 \({}^{\circ}\)C, while the \(\beta\) value has its optimal value at 52 \({}^{\circ}\)C. We thus conclude that the sensor achieves its optimal sensitivity at a temperature of 52 \({}^{\circ}\)C at the current feedback magnetic field, with a linewidth of 215 Hz and signal amplitude of 51.00 mV.  
Figure 12 shows the variations in magnetic resonance linewidth, signal amplitude, and \(\beta\) value of the atomic sensor under different feedback magnetic field amplitudes, while keeping the operating temperature constantly at 52 \({}^{\circ}\)C. It can be observed that both the magnetic resonance linewidth and signal amplitude increase with the increasing feedback magnetic field amplitude, as discussed in Section 4.1. In conclusion, the \(\beta\) value reaches its minimum when the feedback magnetic field amplitude is roughly 15.81 nT, and the sensor achieves its estimated optimal sensitivity of 100.3 fT/\(\sqrt{\text{Hz}}\).  
Figure 11: Resonance linewidth and signal amplitude dependences on temperature.  
Figure 10: System noise of the detected signal, with the photon-shot noise N\({}_{\text{sn}}\) from the power of the lamp as the dominating source.
## 3 Possibilities for the Efficient Design of FEA for the Calculation of the Load-Notch-Strain Curve
### Definition of the Material Law in the FE Software  
As described in Section 3.1, it is sufficient to determine the load-notch-strain curve for the hysteresis branch by simulation. For the material in the FE model, the material behaviour according to Equation (6) must be stored as nonlinear hardening. Since only single-sided increasing stresses are simulated in the FEA, it is irrelevant whether kinematic or isotropic hardening is used, since in both cases the same stress-strain path results up to the first load reversal point [69]. In commercial FE programs (such as the Ansys Workbench software used in the following), the material law is often not specified as a function, but as a path of linear sections. The number of supporting points of the material law \(\text{n}_{\text{M}}\) has an effect on the calculation time and accuracy of the load-notch-strain curve.  
The material law is specified in the FE programme up to a stress range of twice the tensile strength \(\Delta\sigma=2\cdot\text{R}_{\text{m}}\), as this normally covers all stress ranges that occur. At the end of the FEA, whether the limits of the material law have been exceeded must be checked and, if necessary, the material law must be adjusted. The stresses and corresponding strains are specified with constant spacing in the stress direction.  
Figure 5: Relationships between the load–notch-strain curves for the initial load and the hysteresis branch.  
The required calculation time and the accuracy with which the material law is represented in comparison to Equation (6) depend on the number of support points \(\mathrm{n_{M}}\) (index M for material behaviour). The more support points are used, the more accurately Equation (6) is approximated, but the computational effort is also expected to increase.  
\[\Delta\sigma_{\mathrm{k}}=\frac{2\cdot\mathrm{R_{m}}}{\mathrm{n_{M}}}\cdot \mathrm{k}\qquad\qquad\mathrm{with}\ \mathrm{k}=1,\ \ldots,\ \mathrm{n_{M}} \tag{11}\]  
\[\Delta\epsilon_{\mathrm{k}}=\frac{\Delta\sigma_{\mathrm{k}}}{\mathrm{E}}+2 \left(\frac{\Delta\sigma_{\mathrm{k}}}{2\cdot\mathrm{K}^{\prime}}\right)^{ \frac{1}{2^{\prime}}}\qquad\mathrm{with}\ \mathrm{k}=1,\ \ldots,\ \mathrm{n_{M}} \tag{12}\]
## 4 Case Study
### Case Study 3: Performance Comparison between DRNN-LADRC and BPNN-LADRC  
In this part, tracking control of BPNN-LADRC for the radar position servo system is simulated as compared with DRNN-LADRC, which is studied in Case Study 2. The parameters of the BPNN-LADRC are given as: \(w_{0}=85\); the number of the neurons in the input layer, hidden layer and output layer, are 4, 5 and 2, respectively. Additionally, the initial value of the weight vectors with respect to the hidden layer and output layer are also random vectors in a certain range. The remaining parameters and input signal are invariant, just as in Case Study 2.  
The tracking outputs of the DRNN-LADRC and BPNN-LADRC for the radar system are demonstrated in Figure 16, and the tracking errors are shown in Figure 17. It is clear from Figure 17 that the proposed DRNN-LADRC has better control performance compared with the BPNN-LADRC. It can be seen that the DRNN-LADRC has higher control accuracy and will be more effective in high-precision control. In addition, the tracking error of the BPNN-LADRC fluctuates from \(-0.043\) to \(0.026\) as shown in Figure 17, which is lower than the tracking error of the LADRC, which fluctuates from \(-0.048\) to \(0.044\) as shown in Figure 11. The adaptive adjustment curves of \(k_{p}(k)\) and \(k_{d}(k)\) in the BPNN-LADRC are shown in Figure 18.  
Figure 14: Sensitivity curve for the radar system with respect to its input.  
Figure 15: Curves of the internal nonlinear disturbances. (**a**) The friction nonlinearity, (**b**) The dead zone.