esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of On-Chip Generated Weak Microwave Radiation Using Superconducting Normal-Metal SET
###### Abstract  
The present work addresses quantum interaction phenomena of microwave radiation with a single-electron tunneling system. For this study, an integrated circuit is implemented, combining on the same chip a Josephson junction (Al/AlO\({}_{x}\)/Al) oscillator and a single-electron transistor (SET) with the superconducting island (Al) and normal-conducting leads (AuPd). The transistor is demonstrated to operate as a very sensitive photon detector, sensing down to a few tens of photons per second in the microwave frequency range around \(f\sim 100\) GHz. On the other hand, the Josephson oscillator, realized as a two-junction SQUID and coupled to the detector via a coplanar transmission line (Al), is shown to provide a tunable source of microwave radiation: controllable variations in power or in frequency were accompanied by significant changes in the detector output, when applying magnetic flux or adjusting the voltage across the SQUID, respectively. It was also shown that the effect of substrate-mediated phonons, generated by our microwave source, on the detector output was negligibly small.  
single-photon detector; microwave photon; single-electron transistor; photon-assisted tunneling +
Footnote †: journal: Applied Physics Letters
## 1 Introduction
Experimentally obtaining a functional relationship between the stress state and the failure strain greatly simplifies the process of calibrating the unknown parameters [9; 17; 18; 19; 20; 21]. In uncoupled damage models, the stress state is the critical factor affecting material fracture. In the damage models proposed by researchers, the stress state considers hydrostatic pressure and stress invariants, which are usually expressed as the stress triaxiality and the Lode angle. Considering the effect of the stress state on fracture behavior, [PERSON] and [PERSON] [9] conducted several sets of experiments on aluminum alloy 2024, including shear, tensile and compression, and simulated the experiments numerically through ABAQUS software. It was found that the fracture strain and the stress triaxiality are strongly correlated. For medium- and high-strength steels, [PERSON] [22] investigated the relationship between failure strain, stress triaxiality, and Lode parameters. It was found that the ductility of steel could not be fully characterized by triaxiality alone. [PERSON] et al. [8] conducted sets of experiments on aluminum alloy 2024 and established a phenomenological fracture criterion by considering stress invariants. According to the experimental results, there is a non-monotonic functional relationship between fracture strength and the stress triaxiality. Considering the hydrostatic pressure, stress invariants and Lode angle parameters, [PERSON] and [PERSON] [18; 23] proposed the MMC failure model based on the Mohr-Coulomb equation, and applied the MMC model to successfully simulate the failure modes of aluminum and TRIP steel. [PERSON] [1] conducted uniaxial, notched, plane strain and plane shear experiments for 6061 aluminum alloy and calibrated the MMC model based on the experiment results. The calibrated MMC failure model can more accurately simulate the failure behavior of aluminum alloy in the U-bending experiment. From previous studies [1; 6; 18; 19; 23; 24], the failure criterion for predicting the fracture behavior of metals can be characterized by the stress triaxiality and Lode angle parameter. In order to accurately simulate and predict the failure behavior of die-cast materials and ensure the reliability of die-cast structural parts in whole-vehicle applications, this paper conducted an investigation on the fracture behavior of die-cast aluminum alloy A356. By combining finite element simulation and experimental methods, the MMC failure model of A356 was established and the model parameters were calibrated. The purpose of this paper is to investigate the performance of the MMC failure model in predicting the fracture performance of A356 and to verify the feasibility of its application in structural safety simulation.
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 3 Methodology
### Machine Learning Technique Overview
#### 3.1.6 Step 6: ML Implementation  
This study's machine learning models were implemented using a robust and efficient computational environment. The process began with preparing the data set through preprocessing steps, including handling missing values, encoding categorical variables, and normalizing numerical features to ensure compatibility with the algorithms. A diverse set of machine learning techniques, including Linear Regression, Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), gradient boost machine (GBM), bounded decision trees, Support Vector Machines (SVM), and XGBoost, were selected to capture different data patterns and complexities.  
The models were implemented using Apache Spark 3.4.2 for distributed computing, leveraging its scalability and speed to handle complex datasets effectively. The Java Development Kit (JDK) 21 provided a stable framework for developing and executing custom algorithms. Ubuntu Linux 22.04 is an operating environment known for its reliability and open-source ecosystem.  
Each algorithm was hyperparameterized to optimize its performance, ensuring the best results for the data set. Evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) were used to compare  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Category** & **Attribute** & **Percentage (\%)** \\ \hline
**Total Employees** & & 100\% \\ \hline
**Academic Skills** (K1) & Seminar 1 & 47.02\% \\  & Seminar 2 & 55.49\% \\  & Seminar 3 & 51.41\% \\  & Bachelor Degree & 53.29\% \\  & Second Bachelor Degree & 57.05\% \\  & Master Degree & 35.74\% \\  & NSPA Degree & 21.94\% \\  & PhD & 19.12\% \\ \hline
**Public Sector** (K2) & Head of Small Department & 23.51\% \\  & Head of Department & 12.38\% \\  & General Manager & 3.13\% \\ \hline
**Private Sector** (K3) & Head of Small Department & 26.02\% \\  & Head of Department & 8.31\% \\  & General Manager & 5.02\% \\ \hline
**Age Groups** (K4) & 20–29 years & 5.64\% \\  & 30–39 years & 16.46\% \\  & 40–49 years & 30.41\% \\  & 50+ years & 47.49\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset Summary Table.  
model accuracy. Additionally, computational metrics such as CPU time and memory usage were recorded to assess each model's efficiency.  
This systematic and well-orchestrated implementation process ensured a thorough exploration of the selected machine learning techniques, providing valuable insights into their suitability for modeling employee performance and workload management in the public sector. The results of this implementation not only highlight the strengths and weaknesses of each approach but contribute to the development of a more effective and efficient evaluation framework.
## 5 Simulation Results and Discussion
### Case 3--Existence of SOC Difference among Energy Storage Systems (ESSs)  
Unlike Case 2, the SOC may not always be identical among ESSs. In Figure 8, simulation results are shown to illustrate the performance of the proposed controller while the SOCs differ among grid-forming units. Here, the SOC of DG3 is 1% larger than that of DG1. At 0.8 s, the SOC of DG1 drops below \(SOC_{min}\) (20%) and the output frequency drops due to the change in \(P_{n\!f\!f}\). However, the SOC of DG3 is still above \(SOC_{min}\) immediately after 0.8 s, and DG3 does not change its \(P_{n\!f\!f}\). Therefore, the two grid-forming units instantaneously output different frequencies. Since DG1 outputs a lower frequency than DG3 at 0.8-0.86 s, the output powers of DG1 and DG3 are decreased and increased, respectively. Consequently, the SOC of DG3 drops faster, and finally drops below 20%. At 0.99 s, \(\Delta f_{int}\) of the grid-feeding units is turned on and the frequency and SOC are restored.  
In Figure 9, simulation results of a larger SOC difference between DG1 and DG 3 are shown. Here, the SOC of DG3 is 5% larger than that of DG1 in order to prove the robustness of the proposed control method in for the worse situation. As shown in Figure 9, DG1 and DG3 deviated their output power  
Figure 8: SOC restoration results for SOC difference of 1% between DG1 and DG3.  
from the initial value in an opposite direction for a longer period than that of 1% SOC difference case in order to set their difference smaller. At 1.17 s, both SOCs are increased and thereby prove they worked as intended.  
To show a more extreme case, even larger SOC difference among DG1 and DG3 is tested. In Figure 10, simulation results of 10% SOC difference among DG1 and DG 3 are shown. As shown in Figure 10, DG1 and DG3 deviated their output power from the initial value in an opposite direction for a longer period than that of the 5% SOC difference case in order to set their difference smaller. The cases of SOC difference of 1%, 5%, and 10% take 0.22 s, 0.37 s, and 0.56 s, respectively, to start to recover their SOCs within the allowable range. Interestingly enough, for all cases including Case 2 (zero SOC difference), the maximum SOC drop (the minimum SOC) is all the same in every case. It is slightly above 18%. From these simulation results, it can be concluded that the system can be stably operated even for a larger SOC difference among DGs.  
Figure 9: SOC restoration results for SOC difference of 5% between DG1 and DG3.  
SOC difference larger than 10% can be regarded as a problem of SOC estimation and/or the tertiary control level and hence should be solved in terms of a different category. Otherwise, utilizing only a single ESS as the grid-forming unit could be one of the solutions for the SOC difference problem.  
By conducting various simulations, the proposed controller is proved to be effective in many cases. However, it is worth noting that this paper is focused on the basic concept of the controller and hence the stability of the system should be further analyzed.
## 3 Results and Discussion
### Volumetric Flow Rate and Volumetric Efficiency  
Figure 6 shows the effect of the rotating speed, discharge pressure, and cooling water flow rate on the volumetric flow rate. It can be seen that the volumetric flow rate showed a certain degree of increase as the cooling water flow rate increased. In fact, the increase in the volumetric flow rate was about 0.5%. The eight-shaped inner wall of the working chamber of the compressor will undergo irregular deformation at high temperatures, and its left and right sides will expand outward while the upper and lower sharp points will expand inward. The increase in the cooling water flow rate will effectively slow down the deformation of the inner wall of the working chamber and reduce the average gap between it and screw rotors, thereby reducing air leakage backflow and increasing the discharge flow rate. In addition, an increase in the flow rate of cooling water can also result in the sharp points of the inner wall being far away from the screw rotors, which will effectively improve the extreme working conditions of the dry screw compressor. Moreover, it can also be seen that the volumetric flow rate showed an almost linear increase with the increase in the rotating speed and a decrease with the increase in the discharge pressure. This is because the higher rotating speed results in more air being discharged per unit time and the higher discharge pressure increases exhaust leakage.  
Figure 7 shows the variation in volumetric efficiency with the rotating speed, discharge pressure, and cooling water flow rate. The volumetric efficiency showed the same but more pronounced pattern, as shown in Figure 6, with the change in the cooling water flow rate, indicating that the increase in the cooling water flow rate effectively improves volumetric efficiency. And when the discharge pressure was high, its improvement effect was more significant. The reason is also due to the influence of the variation in the average gap between the screw rotor and the working chamber wall on the leakage rate.  
Figure 6: Effect of the rotating speed, discharge pressure, and cooling water flow rate on the volumetric flow rate.  
Figure 7: Effect of the rotating speed, discharge pressure, and cooling water flow rate on the volumetric efficiency.  
In addition, volumetric efficiency showed an increase with increasing rotating speed and a decrease with increasing discharge pressure. The leakage rates are nearly constant at different rotating speeds, so the shorter leakage time caused by increased rotating speed will reduce the net leakage mass. And the increase in the discharge pressure will increase gas leakage.
## 4 Discussion
This study showed that significantly different VOCs were detected in the three selected cucumber cultivars. This is mainly because the genotype can affect the VOC content of a fruit, so that different cultivars have different tastes [4]. However, research into the molecular mechanisms of cucumber VOC biosynthesis lags behind that of nonvolatile compounds [43]. Existing studies have identified representative candidate genes that are likely involved in the production of terpenoids, benzenoids, and C6/C9 aldehydes/alcoholsin cucumber. Moreover, _TPS11_/_TPS14_, _TPS01_, and _TPS15_ may be involved in the synthesis of terpenoids in the roots, flowers, and fruits of cucumber, respectively. Studies have also found that _SSUII_ may regulate the synthesis of the volatile monoterpenoid precursor geranyl diphosphate in roots and flowers [7; 27; 44], whereas the molecular basis of other important VOCs remains to be explored. In addition to genetic mechanisms, the VOCs synthesized by fruit are also closely related to the external growth conditions [45]. Temperature, light, and moisture all affect the contents of VOCs in the fruit [46; 47]. The contents of C6/C9 aldehydes/alcohols in fruits can be increased by adjusting the temperature, light, and relative humidity [48]. Hence, attention also should be paid to these management details in actual production when aiming to improve the taste and quality of cucumbers.
# A Network Architecture and Routing Protocol for the MEDIcal WARNING System
## 6 Implementation and Performance Evaluation
### Evaluated Scenario  
The considered scenario, as shown in Figure 4, consists of three hospital rooms and a corridor. Each room hosts two patients, each one with their own monitor. The patients (each one with a monitor and a Raspberry Pi) represent the end nodes (N1-N6), i.e., the senders, while the MEDIWARN Virtual Biosensor, which is located at the end of the corridor in a dedicated room, is the sink (S), i.e., the receiver. Next to the latter room, there is the monitoring room that hosts the monitoring station. Five relay nodes (R1-R5) are placed in the corridor. The monitors acquire the vital signals of the patients using multiple sensors, as shown in Figure 3, and send them to MEDIWARN Virtual Biosensor through a Raspberry Pi.  
The location of the relay nodes is fixed, as shown in Figure 4, to ensure that multiple paths always exist. This way, the messages can be forwarded to the destination even in the case of relay node failures, for fault-tolerance purposes. The relay nodes forward the received messages through the best two paths according to the MP-RPM routing algorithm.  
The aim of this assessment is to evaluate the timing and reliability of the communication system when the messages are transmitted through two different paths. No retransmission mechanisms are implemented at the application layer.  
Each end node generates one data flow, whose transmission period is set to 1 s. Each message is 60 bytes long. The sampling and sending times of the end nodes are not synchronized with each other. Each relay node implements a priority transmission queue that contains the outbound messages. The priority of messages can be assigned according to a configurable policy. In the considered scenario, to keep it simple, we adopted First-In First-Out (FIFO). The experimental assessment duration was set to 1800 s, i.e., 30 min.  
Figure 3: The components of an end node of the MEDIWARN system.
## 3 Methods
### Synthetic Data Generation Techniques
#### 3.4.1 Jacobian-Based  
The Jacobian-based technique is a gradient-based strategy that creates synthetic samples for model extraction by utilizing the target model's Jacobian matrix [22]. The sensitivity of the model's outputs to its inputs is captured by the Jacobian matrix, which offers useful information for creating instructive examples. A target model \(f\) and an input sample \(x\) are used to calculate the Jacobian matrix \(J(x)\).  
\[J(x)=\frac{\partial f(x)}{\partial x}\]  
The partial derivatives of the model's outputs in relation to each input feature are contained in the Jacobian matrix \(J(x)\), which essentially captures the local behavior of the algorithm around the input value \(x\).  
We can apply a perturbation onto the data \(x\) in the way of the Jacobian matrix to produce synthetic samples. In particular, a synthetic sample \(x^{\prime}\) can be acquired as follows:  
\[x^{\prime}=x+\epsilon\cdot\text{sign}(J(x))\]  
where the step size that regulates the perturbation's magnitude is \(\epsilon\), and \(\text{sign}(\cdot)\) is the sign function applied element-wise to the Jacobian matrix.  
The intuition behind this approach is that perturbing the input sample in the direction of the Jacobian matrix is likely to produce synthetic samples that exhibit high sensitivity to the target model's outputs. These synthetic samples can then be used to train a replica model, potentially improving its performance and accuracy in approximating the target model. Various strategies can be employed to optimize the synthetic sample generation process, such as exploring different step sizes, combining multiple Jacobian-based samples, or incorporating additional constraints or regularization techniques. It is crucial to remember that the Jacobian-based approach relies on being able to reach the gradients of the target model, which could not be possible in some circumstances. In such cases, alternative gradient estimation techniques or other synthetic data generation approaches may be necessary.
## 3 Memory Management in MIROS
### Dynamic Memory Allocators in the Current WSN OSes  
Segregated free list (SFL) allocator has been used in Contiki, SOS and uCOS. It divides the memory heap into segregated partitions. Each partition holds a set of fixed-size blocks, and a free list is used for the block allocation. Upon allocation, a block is deleted from the header of the matched free list. Upon releasing, the released block is added to the header of the matched free list. In Figure 6a, the SOS SFL allocation mechanism is depicted.  
Sequential fit (SF) allocator is different from the segregated free list (SFL) allocator in that the memory heap is not divided into segregated partitions. Instead, all the allocations are performed inside the heap in a sequential way, and only one free list rather than several segregated free lists is used to manage the free memory. Upon allocation, the free list is searched to find a suitable free entry, and the searching algorithm can be the best fit, the first fit, the next fit or the good fit [22]. In Figure 6b, the mantisOS SF allocation mechanism is depicted.  
The SFL allocator shows its advantage in the short and deterministic allocation time. This feature is significant for the real-time systems. However, each SFL partition needs to be pre-reserved. If the pre-reserved size is too small, the memory overflow problem will occur. If it is too large, the memory utilization efficiency will be low and the memory insufficiency problem can occur. Due to these reasons, the SFL allocator is not flexible to adapt to the varied application contexts.  
The key advantages of the SF allocator are that no memory area needs to be pre-reserved and no internal memory fragmentation exists. However, the allocation time is not deterministic as an appropriate entry needs to be searched upon the allocation. Moreover, the external memory fragmentations exist, e.g., in Figure 6b, if a 30-byte object needs to be allocated, the allocation will fail although the total size of the free memory is larger than 30 bytes. Currently in the mantisOS, no mechanism is implemented to solve this fragmentation problem, and this will decrease the utilization efficiency of the memory resources.  
Figure 6: **(a)** SOS segregated free list (SFL) allocation. Three partitions (_A,B,C_) are pre-reserved. The block allocation is applied in each partition, and the block sizes in the different partitions are different. **(b)** mantisOS sequential fit (SF) allocation. Only one double-link free list is used to manage all the free memory.
# Study on the Influence of Dust Removal Fan Structure Parameters on Dust Removal Performance
## 4 Numerical Simulation Results and Analysis
### Influence of Driving Impeller Blade Airfoil on the Performance of Dust Removal Fan  
The driving impeller blades of the dust removal fan adopt the C-4 airfoil. To investigate the influence of the driving impeller blade airfoil on the performance of the dust removal fan, impeller models of the dust removal fan using the C-4 airfoil and equal thickness circular arc plate airfoil are established, respectively, without changing other parameters. The blade root cross-sections of the C-4 airfoil and equal thickness circular arc plate airfoil are shown in Figure 15.  
The two driving impeller blades were simulated by dust removal fan models with different airfoil profiles. The inlet speed was set to 16.405 m/s, the driving impeller speed  
Figure 14: Influence of the number of driving impeller blades on dust removal performance. (**a**) Number of driving impeller blades—total pressure efficiency; (**b**) number of driving impeller blades—static pressure efficiency; (**c**) number of driving impeller blades—dust removal efficiency.  
Figure 15: C-4 airfoil and equal thickness circular arc plate airfoil blade diagram. (**a**) C-4 airfoil; (**b**) arc plate airfoil of equal thickness.  
was set to 2900 r/min, and the dust particle size was set to 10 \(\upmu\)m during the discrete phase simulation. The numerical simulation results of the dust removal fan model with the C-4 airfoil and equal thickness circular arc plate airfoil are summarized in Table 7.  
According to the numerical simulation results, when the driving impeller blade adopts the C-4 airfoil, the total pressure efficiency and static pressure efficiency of the dust removal fan are higher but the dust removal efficiency is lower compared to the same thickness circular plate airfoil. This occurs because when the driving impeller blade of the dust removal fan adopts the C-4 airfoil, the streamlined structure of the C-4 airfoil effectively reduces the resistance of the driving impeller blade during the rotation process, thus reducing the shaft power of the dust removal fan and improving the total pressure efficiency and static pressure efficiency. Additionally, the C-4 airfoil also reduces the torque exerted by the driving impeller blade on the dust-bearing airflow. This reduction in torque leads to a decrease in the centrifugal force acting on the dust particles, resulting in lower dust removal efficiency. However, despite the reduction in dust removal efficiency, the C-4 airfoil can significantly improve the aerodynamic performance of the dust removal fan. Therefore, the C-4 airfoil is preferred over the equal thickness arc plate airfoil for the driving impeller blade of the dust removal fan.