esa-sceva/satcom-chunk-collection huggingface dataset:
# A 4.1 W/mm\({}^{2}\) Hybrid Inductive/Capacitive Converter for 2-140 mA-DVS Load under Inductor +
## 4 Measurement Results
The design presented here is the only hybrid converter design having both capacitive and inductive converters connected in parallel. As can be seen from the table, [3; 31] have comparable output power performance. This work achieves a 70\(\times\) output load range which is comparable to the design in [31] but this design is fully integrated whereas [31] uses off-chip passives. The design in [3] performs better in terms of the maximum output range but this design has a much higher power density. Figure 23: Leakage measurements in capacitive converter. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Technology & IBM 32 nm SOI \\ Converter type & Hybrid (Capacitive + Inductive) \\ Area with decays & 0.4 mm\({}^{2}\) \\ Peak efficiency(Inductive) & 74.5\% (@300 MHz and room temp) \\ Max O/P power & 140 mW \\ Min O/P power & 2 mW \\ Input voltage & 1.2 v \\ Max output voltage & 0.9 V \\ Min output voltage & 0.4 V \\ O/P ripple @ 750 mV & 28 mV \\ O/P current range & 70\(\times\) \\ Filter capacitor & 2 nF \\ Inductor & 2 nH \\ \hline \end{tabular}
\end{table} 
Table 1: Design summary. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & [28] & [29] & [30] & [21] & [3] & [22] & [33] & [8] & [94] & **This Work** \\ \hline Technology(nm) & 1500 & 500 & 250 & 180 & 130 bulk & 32 bulk & 600 & 32 SOI & 130 & 32 SOI \\ Ind(L)/Cap(SC) & L & L & L & L & L & SC & SC & SC & & Hybrid \\ Power Eff \(\times\) & 40-60 & 75-837 & 70–92 & 35-64 & 77.9 & 60 & 87 & 79.6 & 82 & 74 \\ In(Out Voltage (V) & 5/2.5 & 3.3/2.5 & 5.5-2.8/1.8 & 2.8/1.5-2.0 & 1.2/0.3-0.88 & 1/2.1 & 1.8/3-2/3.3 & 2.088 & 1.2/2-2.1 & 1.2/0.4-0.9 \\ Perv Den(W/mm)\({}^{2}\) & - & - & - & - & 0.21 & 1.123 & 2.185 & 0.86 & 0.67 & 0.38-41 \\ Ripple(W)\({}^{2}\) & - & - & - & 40 & - & 20 & - & 8.9 & 30 \\ Passive Size & 0.1 \(\mu\)H/30 nF & Offchip & 10 \(\mu\)H/47 \(\mu\)F & 22 \(\mu\)H/6 nF & 2 \(\mu\)H/5 nF & 2 pF & 1 pF & - & 1 pF & 2 nH/2 pF \\ Perv Rang(W) & 50–200 & 10–450 & 0.15–600 & 95–400 & 0.6–266 & 1–16 & 66–530 & - & 2–130 \\ Area (mm\({}^{2}\)) & - & - & - & 5.12 & 1.59 & 0.000714 & 5.4752 & 0.378 & 2.25 & 0.431 \\ \hline \end{tabular}
\end{table} 
Table 2: Summary comparison with other related work.
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 4 Discussion
### Information System Software Failure and Its Causes
#### 4.2.2 Configuration Failure and Its Causes  
In the application process of information system communication technology, the probability of configuration problems is relatively high, and the performance of this configuration problem is also multifaceted, which has a close relationship with many system links and needs to be paid more attention. The problems in this configuration are mainly closely related to the relevant management personnel, because there are obvious mistakes in the operation of the management personnel, which will affect the operation effect of the relevant software systems, such as the current common unreasonable VLAN (Virtual Local Area Network) division and improper setting of the end port, which may bring a greater threat.
## 4 Materials and Methods
### Selection of Reference Genes and Genes Involved in Limonin Biosynthesis  
The reference genes that existed in both previous reference gene screening studies and _E. rutuecarpa_ var. _officialis_ were selected. These genes included _\(\beta\)-TUB2_, _\(\beta\)-TUB6_, _\(\alpha\)-TUB_, _Act1_, _Act100_, _GAPDH_, _CHS1_, _His3_, _UBQ10_, and _UBQ_. Additionally, leveraging the \"Full-length transcriptome + expression profile + metabolome\" co-expression analysis, 18 genes associated with the established limonin biosynthesis pathway in _E. rutuecarpa_ var. _officialis_ were identified. These genes encompassed _DXS_, _DXR_, _ISPD_, _ISPE_, _ISPF_, _ISPG_, _ISPH_, _ACAT_, _HMGCS_, _HMGCR_, _MVK_, _MVK_, _MVK_, _IDI_, _GPS/FPS_, _SQS_, _SQE_, and _CYP450_. Primers were meticulously designed for these genes (see Table 3). All primers were designed using primer 5.0 software.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gene** & **Full Name and the E.C. Number** & **Primer Sequence (5\({}^{\prime}\)\(\rightarrow\) 3\({}^{\prime}\))** \\ \hline _DXS_ & 1-deoxy-D-xylulose-5-phosphate synthase (EC: 2.2.1.7) & F:ACTGCACCAGAACA \\  & & R:GCTACGAGGAGGAGAAGA \\ \hline _DXR_ & 1-deoxy-D-xylulose-5-phosphate reductoisomerase (EC: 1.1.1.267) & F:TCCATCCGTGACTTTG \\  & & R:GCTGCGTAATCTCGTGCC \\ \hline _ISPD_ & 2-C-methyl-D-erythnitol4-phosphate cytidylyltransferase (EC: 2.7.7.60) & F:AGGATTICCATAACCAGA \\  & & R:CAATCGGTTGACCAGA \\ \hline \hline \end{tabular}
\end{table}
Table 3: qRT-PCR primer sequences related to limonin biosynthesis pathway.
# Underwater Accompanying Robot Based on SSDLite Gesture Recognition
## 4 Algorithm and System Experiment
### System Experiment  
To test the overall performance of the system, we conducted outfield experiments on the UAR in a swimming pool. The experimental scene is shown in Figure 13.  
The whole UAR is shown in Figure 14. The ROV used was the Trench Wanderer 110 ROV, which is equipped with four thrusters to give it three degrees of freedom (surge, heavy, and yaw). In terms of hardware equipment selection, the underwater camera we used was GoPro7, because it has a wider viewing angle and an enhanced anti-shake function. It can be connected with the processing unit through USB interface to provide a better picture for the UAR. As the core processing unit, we chose NVIDIA Jetson Xavier NX to carry and run the recognition network. With the powerful computing power of 21 TPOS and memory bandwidth of more than 51 GB/s, combined with low power consumption (10 W) and a comprehensive expansion interface, Jetson Xavier NX is suitable for the perception and detection of complex underwater environments.  
We chose the STM32 microcontroller (STM32F407 ARM) to cooperate with the NX3 flight control to complete the control of the underwater robot: on the one hand, the attitude change of the robot was monitored through an on-board gyroscope and accelerometer to avoid excessive inclination or rollover. On the other hand, the control information sent by Jetson Xavier NX was received through UART, and 8 PWM channels were used to drive the motor of the robot to complete the set underwater work. The overall control flow of the  
Figure 14: Overall UAR structure. It consists of the ROV, Jetson Xavier NX (core processing unit), a GoPro7 (underwater camera), an STM32 control board, and other components.  
Figure 13: The outfield experimental scene.  
system is shown in Figure 15. With the exception of the floodlight, all other devices were installed in a waterproof shell made of transparent polycarbonate (PC). Finally, the built-in battery pack supplied power to all electronics inside through various up/down modules.  
During the experiment, the diver entered the water with the UAR, initialized the UAR and determined the main tracking target through the preset gestures. Then, the UAR invoked its thrusters and continuously tracked the target diver, always keeping the target diver in the center of the frame. At the same time, the UAR performed gesture recognition on the cropped video steam (crop by the position of the target). Due to the elimination of complex environmental background, the recognition area was narrowed, and the missed and false recognition was greatly reduced. Moreover, it also had a good recognition effect on fuzzy moving images, as shown in Figure 16.  
In terms of recognition speed, the target tracking time was 33.0 ms, and the model inference time was 31.9 ms. By setting the gesture recognition frequency to once a second, our algorithm was able to achieve 29.4 fps on Jetson Xavier NX, satisfying the real-time requirements.
# Overview of Voice Conversion Methods Based on Deep Learning
## 2 Voice Conversion Process
### Model Inputs  
Most VC studies [7; 8; 14; 16] used logarithmic mel spectrograms as the soundtrack input. The same was true for [11] as an input to the speaker encoder. However, for the pre-trained Wav2 vec, the authors gave a wave as the input. In [7], the authors gave the F0 fundamental frequency to extract information such as tonality. In [13], the MFCCs (mel frequency cepstral coefficients) were used instead of the mel spectrogram.
# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
###### Abstract  
The study of human torso medical image segmentation is significant for computer-aided diagnosis of human examination, disease tracking, and disease prevention and treatment. In this paper, two application tasks are designed for torso medical images: the abdominal multi-organ segmentation task and the spine segmentation task. For this reason, this paper proposes a net-work model CI-UNet improve the accuracy of edge segmentation. CI-UNet is a U-shaped network structure consisting of encoding and decoding networks. Firstly, it replaces UNet's double convolutional backbone network with a VGG16 network loaded with Transfer Learning. It feeds image information from two adjacent layers in the VGG16 network into the decoding grid via information aggregation blocks. Secondly, Polarized Self-Attention is added at the decoding network and the hopping connection, which allows the network to focus on the compelling features of the image. Finally, the image information is decoded by convolution and Up-sampling several times to obtain the segmentation results. CI-UNet was tested in the abdominal multi-organ segmentation task using the Chaos (Combined CT-MR Healthy Abdominal Organ Segmentation) open challenge dataset and compared with UNet, Attention UNet, PSPNet, DeepLabv3+ prediction networks, and dedicated network for MRI images. The experimental results showed that the average intersegmental union (mIoU) and average pixel accuracy (mPA) of organ segmentation were 82.33% and 90.10%, respectively, higher than the above comparison network. Meanwhile, we used CI-UNet for the spine dataset of the Guizhou branch of Beijing Jishuitan Hospital. The average intersegmental union (mIoU) and average pixel accuracy (mPA) of organ segmentation were 87.97% and 93.48%, respectively, which were approved by the physicians for both tasks.  
computer-aided diagnosis; medical image segmentation; CI-UNet; information aggregation block; Polarized Self-Attention; loss function +
Footnote †: journal: Medical Image and Image Processing
# Research on Routing Algorithm of Construction Robot Cluster Enhanced Ad Hoc Network
## 2 Problem Statement and System Model  
### Network Model  
Represent the communication network by an undirected graph \(G=(V,E)\), where \(V=(v_{1},\ldots,v_{n})\) denotes the set consisting of all nodes in the network and \(E\) denotes the set consisting of all link edges in the network, and \(V(G)\) and \(E(G)\) represent the set of vertices and the set of edges of \(G\), respectively. Furthermore, make the following assumptions:  
1. \(N\) multi-class construction robot nodes equipped with airborne ad hoc network equipment are randomly located on each floor of a building, and each node has a unique ID;
2. All construction robot nodes move randomly within the site according to their tasks and can only be active on the initial floor;
3. Nodes contain the same amount of energy in the initial stage, can obtain the remaining energy at any time, and can interact with ID and location information between other nodes;
4. The nodes can dynamically adjust the transmitting power according to the communication demand.
###### Abstract  
In many complex manufacturing environments, the running equipment must be monitored by Wireless Sensor Networks (WSNs), which not only requires WSNs to have long service lifetimes, but also to achieve rapid and high-quality transmission of equipment monitoring data to monitoring centers. Traditional routing algorithms in WSNs, such as Basic Ant-Based Routing (BABR) only require the single shortest path, and the BABR algorithm converges slowly, easily falling into a local optimum and leading to premature stagnation of the algorithm. A new WSN routing algorithm, named the Quantum Ant Colony Multi-Objective Routing (QACMOR) can be used for monitoring in such manufacturing environments by introducing quantum computation and a multi-objective fitness function into the routing research algorithm. Concretely, quantum bits are used to represent the node pheromone, and quantum gates are rotated to update the pheromone of the search path. The factors of energy consumption, transmission delay, and network load-balancing degree of the nodes in the search path act as fitness functions to determine the optimal path. Here, a simulation analysis and actual manufacturing environment verify the QACMOR's improvement in performance.  
wireless sensor network (WSN); energy; ant colony optimization (ACO); routing algorithm; quantum-inspired evolutionary algorithms +
Footnote †: journal: Sensors
## 2 Multimodal Learning Techniques for Fault Diagnosis
### Transfer Learning  
Transfer learning is a powerful technique that enables a model trained on one task to be applied to a different, yet related, task. This approach is especially beneficial in fault diagnosis, particularly when labeled data are scarce or costly [18]. Rather than starting from scratch, transfer learning harnesses the knowledge acquired from one task (the source domain) and applies it to another task (the target domain).  
For instance, consider a model trained to detect faults in one type of machine. With transfer learning, this model can adapt to diagnose faults in a different type of machine with minimal additional training. Mathematically, this can be expressed as follows:  
\[\hat{y}_{target}=f(X_{target};\theta_{source}) \tag{3}\]  
In this equation, \(X_{target}\) represents the data from the target domain, while \(\theta_{source}\) signifies the parameters learned from the pre-trained model on the source domain.  
A widely used method in transfer learning is fine-tuning, which involves adapting a pre-trained model by retraining only the final layers of the network on new data while leaving the earlier layers unchanged. This greatly reduces the time and computational resources required to train the model [19,20].  
The advantages of employing transfer learning in fault diagnosis are evident: it reduces the necessity for extensively labeled datasets and speeds up the implementation of fault detection systems [18]. Nevertheless, the success of transfer learning significantly hinges on the degree of similarity between the source and target domains. Transfer learning may not produce optimal results if the two domains are too dissimilar. Numerous successful applications of transfer learning in fault diagnosis have been documented. For instance, in wind turbine systems, models trained on one type of turbine have been successfully utilized to diagnose faults in other turbine types with comparable structural characteristics [21,22].
## 2 Antenna Structure
### Two-Way Hybrid WG-to-SIW E-Plane PD  
Figure 5a presents the geometry of the two-way hybrid WG-to-SIW _E_-plane PD. Four matching posts are located symmetrically at two outputs to improve the impedance matching. An aperture with a size of \(w_{a}\times l_{a}\), which is the same as that of the input waveguide (WR-90), is echted on the bottom metal layer of the SIW. A rectangular coupling patch is loaded inside the coupled aperture to improve the coupling between the input waveguide and SIW. The detailed design parameters of the proposed two-way hybrid WG-to-SIW _E_-plane PD are described in Figure 5b, and their values are shown in the caption of Figure 5. Figure 5c illustrates the simulated S-parameters. A two-way hybrid WG-to-SIW _E_-plane PD without a coupling patch is also simulated, and its simulated \(|S_{11}|\) is also included in Figure 5c for comparison. As observed, the coupling patch not only improves impedance matching but also introduces an additional resonance, resulting in a much wider impedance bandwidth [21]. For the proposed two-way hybrid WG-to-SIW _E_-plane PD, a \(-15\) dB reflection bandwidth ranges from 9.2 to over 11 GHz. The insertion loss of less than 0.22 dB is achieved. The phase difference between two output ports, namely, Port 2 and Port 3, is approximately \(180^{\circ}\), with a phase error of less than \(0.5^{\circ}\) across the entire frequency band of interest.  
Figure 4: Simulated S–parameters of the six-way unequal PD: (**a**) amplitudes and (**b**) phases.  
Figure 5: (**a**) Perspective view, (**b**) top view and (**c**) simulated S–parameters of the two-way hybrid WG-to-SIW _E_-plane PD. Dimensions are \(a_{0}=14.54\), \(r_{0}=0.5\), \(d_{0}=1\), \(n=28.74\), \(m=13.75\), \(l_{a}=22.86\), \(w_{a}=10.16\), \(l_{p}=16\), \(w_{p}=6.75\), \(s_{p}=10.8\), and \(d_{p}=1.5\) (Units: mm).
# Validation of a Wearable IMU System for Gait Analysis: Protocol and Application to a New System
## 2 Materials and Methods  
### Validation Protocol Design Guidelines  
To define how a wearable IMU system for gait analysis can be evaluated with respect to GA systems, we define some guidelines to design a robust validation protocol. We think that this approach can help to define a validation protocol that can be followed in future studies. These aspects have to be considered: (a) _coherent measurement_; (b) _statistical significance_; (c) _robust dataset consistency_; (d) _assurance of objectivity_; and (e) _statistical methods_.  
_Coherent measurement_ means using the gold standard technique as a reference for the measurements conducted with the system under validation, and the two protocols of measurements have to be structured so that the same variables are the outputs to allow for a real data comparison, point-to-point (for continuous matching) or for single values (for overall matching).  
_Statistical significance_ means that the dataset allows for a reliable statistical analysis. It requires one specific setup to be defined or to cover all the different predictable experimental conditions for the measurements: the first option is preferable for a complex situation where several variables or conditions could be defined in order to have a segmented approach and a step-by-step systematic validation.  
_Robust dataset consistency_ means having a proper number of subjects recruited in clusters of homogenous populations, as required by the selection protocol rules.  
_Assurance of objectivity_ means ensuring that the protocol of data analysis is not dependent on how the operator interprets this procedure. Using a blind protocol data analysis, it must have access only to the necessary data and guarantee the non-accessibility to other information that may condition or direct the processing of data by the operator.  
_Statistical methods_ have to be properly identified in accordance to protocol and data typology (normal distribution, accuracy analysis, etc.).  
From all the above the guidelines, Figure 1 has been defined for designing an experimental protocol dedicated to the validation of a wearable system for gait analysis.  
Figure 1: The flow chart relative to protocol design guidelines.
# CFD Analyses: The Effect of Pressure Suction and Airflow Velocity on Coronavirus Dispersal
## 3 Results
### Velocity and Turbulence Results at Two Different Suction Levels (0.85 and 0.7 bar)  
This section demonstrates the effects of increasing the suction levels (i.e., from 0.85 bar to 0.7 bar) on the velocity and turbulence profiles. As shown in Figure 9, the velocity at the 1.7 m plane was approximately unaffected by the change in the suction level. On the other hand, the velocity was decreased in the areas a, b and c in the 1 m plane at the suction level of 0.7 bar, in comparison to the suction level of 0.85 bar. However, as shown in Figure 9, the difference between the two velocity profiles of the chosen two suction levels is minor.  
Remarkably, as shown in Figure 10, the turbulence kinetic energy profile for the 1.7 m plane increased (i.e., area a) at the suction level of 0.7 bar. On the other hand, the turbulence kinetic energy profile at the 1 m plane was not affected by varying the suction level from 0.85 bar to 0.7 bar.  
By correlating the velocity profiles to the turbulence profiles, it can be concluded that increasing the suction level failed to reduce the velocity at the 1.7 m plane. In fact, the turbulence kinetic energy was increased, thus demonstrating a negative effect on preventing the spread of COVID-19. On the other hand, increasing the suction level has  
Figure 8: Velocity streamlines from all inlets.  
shown a positive impact at the 1 m plane since the velocity has been reduced (i.e., areas a, b, and c as shown in Figure 9). This essentially highlights the dilemma of choosing a suction level while reducing the spread of COVID-19 for both planes at the same time. Therefore, one potential solution could be re-designing the ducts (i.e., the number of inlets and outlets and their vertical and horizontal positions).
## 2 Related Studies
In addition, the experiment results proved that this model had a good performance on GOES satellite data. However, there are few studies using a GRU-based model to predict typhoon tracks. The present study builds on the existing literature focusing on deep learning algorithms using meteorological data. Through this research, we propose a novel GRU-based neural network aiming at making a more accurate prediction of tropical cyclone tracks. The model is composed of two layers, an AE layer and a GRU layer. The AE layer can perform implicit feature extractions from a tropical cyclone track dataset with a number of meteorological factors in spatio-temporal dimensions, and the GRU layer can train the extracted spatio-temporal features and make a prediction.
## 4 Performance Evaluation in Edge Scenarios
### System Call Overhead
#### 4.5.1 Process Creation Latency  
Gramine and Occlum differ significantly in their process management models. Gramine only allows one process per enclave, while Occlum permits multiple processes within a single enclave. This leads to differences in their process creation methods: Occlum discards the fork system call and instead uses spawn to create new processes. Gramine, on the other hand, uses the traditional fork to create child processes, where the child and parent processes share the enclave properties defined by the manifest file. During testing, child processes are generated with varying memory sizes allocated via the malloc function. For parent processes, both native Linux and Gramine use fork system call, while Occlum uses spawn. The time required to create a new process with different memory sizes reflects the process creation latency.  
In Figure 8, the process creation latency in Occlum is significantly lower than in Gramine. When the memory size of the new process is small (less than 100 MB), the performance can match that of native Linux. In Gramine, after a fork call, a new enclave is created, and complex interactions occur between the parent and child processes, such as identity authentication and key exchange, resulting in higher latency. Occlum, however, allows multiple processes to exist within a single enclave, where these processes share resources and are transparently managed by the operating system, greatly reducing process creation time. However, when the memory size of the new process exceeds the capacity of the EPC, it is constrained by the secure memory limit, requiring some of the memory to be  
Figure 7: Impact of file size on latency.  
moved to regular memory. This triggers repeated encryption and decryption operations, leading to additional performance overhead and a sharp increase in latency.