esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Switching Characteristics of Flip-Chip GaN Half-Bridge Modules Integrated with Drivers
## 4 Conclusions  
We designed and fabricated a half-bridge module whose gate driver dies were closely integrated with the power devices. Both the HEMTs and driver dies were flip-chip bonded onto the AlN substrate without bonding wires. The parasitic inductance of the power loop for the module was measured to be 3.4 nH. The switching performance of the module was evaluated using the double-pulse test. The module showed an ultrasmall overshoot and oscillation in the \(V_{gs}\) waveform as well as a high switching speed in the \(V_{ds}\). The  
Figure 4: Gate-source voltage waveforms of the modules prepared by different processes under \(V_{dc}=40\) V: (a) turn-on period; (b) turn-off period.  
Figure 5: Drain-source voltage waveforms of the integrated module under \(V_{dc}=100\) V: (a) turn-on period; (b) turn-off period.  
\(V_{ds}\) rise and fall times of the module were measured to be 5.8 and 12.9 ns, respectively. Compared with a module packaged by the wire bonding process, the flip-chip module had reduced trace length and achieved lower parasitic inductance, showing excellent switching characteristics. This work is encouraging for the realization of an integration module with low parasitic inductance and excellent switching performance.  
Methodology, L.W., D.-F.L. and Y.Z.; investigation, L.W. and Z.-G.Y.; validation, L.W. and Z.C.; data curation, L.W. and L.-F.J.; writing--original draft preparation, L.W.; writing--review and editing, Y.Z.; project administration, Z.L. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China under Grant No. 2017 YFB0402903, the Science Challenge Project under Grant TZ018003, and the National Natural Science Foundation of China under Grant 61874114, 61674143 and 61974137.  
The authors would like to express their appreciation for Xiamen Sanan Integrated Circuit Co., Ltd. and Qin-Song Qian of Southeast University for providing the HEMTs and driver dies, respectively.  
The authors declare no conflict of interest.
## 2 IEEE 802.15.4-2015 Overview
### IEEE 802.15.4-2015 OQPSK-DSSS  
OQPSK-DSSS was introduced in the original IEEE 802.15.4 standard in 2003 and it uses a 16-ary quasi-orthogonal modulation where each data symbol represents 4 bits of information. Therefore, there are 16 possible symbols and each of them is represented by a PRNG (Pseudo-Random Noise Generator) chip sequence that is almost orthogonal.  
When operating in the 780 MHz, 915 MHz, 2380 MHz and 2450 MHz bands, the PRN sequence is 32 chips long for 4 bits of data, hence providing a data rate of 250 kbps. In contrast, when operating in the 868 MHz band, the PRN sequence is 16 chips long for 4 bits of data, thus offering a data rate of 100 kbps.  
As depicted in Figure 1, OQPSK-DSSS frames are composed of an SHR (Synchronization Header), PHR (PHY Header) and PSDU (PHY Service Data Unit). The SHR contains a preamble and an SFD (Start of Frame Delimiter); the PHR contains the frame length (7 bits) and a reserved bit. Hence, the maximum length of the PSDU is 127 bytes.
## 6 Future Research Directions
### Development of a Spatially Explicit Ecohydrological Modeling Framework
#### 6.1.1 Reviewing of the Existing EASS Model [112]  
EASS is based on a single layer vegetation canopy overlying a seven-layer soil, and includes physically-based treatment of energy and moisture fluxes from the vegetation canopy and through it. It also incorporates explicit thermal separation of the vegetation from the underlying ground [134]. Moreover, EASS includes a scheme with stratification of sunlit and shaded leaves to avoid shortcomings of the \"big leaf\" assumption [197, 238]. It has been referred as a \"two-leaf\" canopy model [81, 239]. The structure of EASS is shown in Figure 4.  
With spatially explicit input data on vegetation, meteorology and soil, EASS can be run pixel by pixel over a defined domain, such as Canada's landmass, or any of its parts, or the globe. EASS has flexible spatial and temporal resolutions, as long as the input data of each pixel are defined.  
In short, EASS has the following characteristics: (i) satellite data are used to describe the spatial and temporal information on vegetation [143]; (ii) energy and water exchanges and carbon assimilation in the soil-vegetation-atmosphere system are fully coupled and are simulated simultaneously. The Ball-Woodrow-Berry stomatal conductance formulation [24] was employed (see Section 4.4); (iii) the energy and C assimilation fluxes are calculated with stratification of sunlit and shaded leaves to avoid shortcomings of the \"big-leaf\" assumption; and (iv) the lateral movement of water is ignored.
## 2 Materials and Methods
### Accuracy Assessment  
The accuracy of cropping intensity quantification was assessed at the pixel scale using a stratified random sampling dataset. Sample points were randomly collected from each cropping intensity region estimated for the year 2019. The actual cropping intensity was independently determined through visual interpretation of EVI time series, very high-resolution imagery, and true color Sentinel-2 images. This process utilized tools such as Collect Earth v1.20.6 [37] and the Copernicus Browser [38]. Notably, during the interpretation, the points whose buffer zones contained noisy features such as ridges and trees were deliberately excluded. As a result, a total of 578 individual sample points were retained for the accuracy assessment (Figure 7). To enhance the independence of the validation, only sample points for the years from 2020 to 2023 were used for assessment, with each year containing more than 500 sample points. Subsequently, the confusion matrix and various metrics were calculated, such as overall accuracy and the Kappa coefficient. The overall accuracy serves as a fundamental metric for assessing the performance in terms of correct classifications across the entire dataset. It is determined as follows:  
\[p_{o}=\frac{t}{n} \tag{2}\]  
where \(p_{o}\) represents the overall accuracy, \(t\) is the number of correctly classified samples, and \(n\) is the total number of samples. The Kappa coefficient is a statistic that measures the performance considering class imbalances and the possibility of agreement occurring by chance. It is calculated as follows:  
\[k=\frac{p_{e}-p_{e}}{1-p_{e}} \tag{3}\]  
where \(p_{e}\) is the expected proportion of samples correctly classified by chance. The Kappa coefficient (\(k\)) ranges from \(-1\) and \(1\), with a value of \(1\) indicating perfect agreement between the truth and predictions.
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## 3 Mobile Platforms
### Underwater Platforms  
Unmanned underwater vehicles (UUVs) can be divided into Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs).  
The exploration of inactive mines (in Europe, it is estimated that there are 30,000 inactive mining stations) may constitute a severe risk to humans. These environments are characterized by a network of tunnels (possibly flooded), in some cases with an unknown topography, and by the presence of metallic and industrial materials (e.g., cobalt, gallium, indium and rare earth elements). Thus, UUVs, particularly AUVs, are of great help to explore these sites allowing to extract topographic, geological, and mineralogical information [57]. Afterwards, this information can be used to determine if a mine can be drained and re-opened minimizing costs or if represents any risk of collapsing [58].  
In the framework of the UNEXMIN European project, an underwater robotic system was developed for the autonomous exploration and 3D mapping of flooded and deep mines [59].  
ROVs are also used in the inspection of nuclear reactor pressure vessels and other water-filled infrastructures (Figure 3), such as piping systems [60; 61; 62]. For that, robots normally carry water-resistance and radiation tolerance cameras and non-destructive evaluation sensors [61]. These inspections normally occur when the reactor is shut down for refueling and is becoming increasingly important with the aging of the worldwide NPPs [60]. In order to access confined spaces with complex structures, the robots should be: compact, highly maneuverable, untethered, body shape completely smooth (with minimal appendages), and radiation tolerant [61; 62].  
Specific underwater platforms are commercially available for the visual inspection and non-destructive examination of an NPP [63]. Water-resistant and radiation tolerant cameras are also available in Reference [63; 64].
# Hierarchical Visual Place Recognition Based on Semantic-Aggregation
## 2 Hierarchical Visual Place Recognition Based on Semantic-Aggregation
### Coarse Matching
Then, we compute feature residuals of each semantic class and aggregate all feature residuals to get a hybrid image descriptor \(H_{c}\). Subsequently, we keep the main semantic classes through semantic filtering to construct the final hybrid image descriptor \(H\). Finally, a query is matched with the reference images by cosine distance, getting the top \(n\)_Candidates_. where \(\dot{H_{1}}+\dot{H_{2}}+\ldots+\dot{H_{L}}\) refer to L2-normalized \(H_{1},H_{2},\ldots,H_{L}\) respectively. Meanwhile, in order to improve the ability to distinguish distance, \(H\) is normalized again as follows:  
\[H=\frac{(H-m)}{\sigma} \tag{5}\]  
where \(m\) and \(\sigma\) are the mean and standard deviation of descriptor calculated on the dataset, respectively. After that, the query is matched with the reference images by cosine distance \(d_{jk}\):  
\[d_{jk}=1-\frac{H_{j}\cdot H_{k}}{\left\|H_{j}\right\|_{2}\left\|H_{k}\right\|_ {2}},\forall j\in[1,N] \tag{6}\]  
where \(d_{jk}\) is the cosine distance between the query \(k\) and reference image \(j\) in the reference dataset, and \(N\) is the number of images in reference datasets. The top \(n\) reference images with the lowest distance to the query are kept as _Candidates_ and passed to the fine matching for the final match.
# A Multi-Loop Vehicle-Counting Method under Gray Mode and RGB Mode
## 3 Methods
### Experimental Dataset  
In this paper, the performance of the proposed method was experimented on six traffic videos. Video I and Video II are two videos recorded on a highway, which are called M-30 and M-30-HD and could be obtained from the GRAM-RTM dataset [29]. These two videos were shot from the rear of the vehicle on a sunny and cloudy day. Video III is also a video recorded on a highway, which is called Highway and could be obtained from the Change Detection Benchmark [35]. Unlike Video I and Video II, it was shot from the front. The weather of Video III is sunny.  
Video IV, Video V, and Video VI are three videos [36] recorded by us in Nanjing, Jiangsu, China, which are called NJ-1, NJ-2, and NJ-3. Similar to Video III, Video IV was also shot from the front, but the location is on an urban road and the weather is cloudy. Video V and Video VI were shot from the side on two expressways on a sunny and cloudy day, respectively.  
The basic information in each video and road is shown in Table 7, including the name of video, recording site, number of frames, and the average pixel number for a vehicle. Among them, the meaning of \"**Average Pixel No. for a Vehicle**\" needs some explanation. As we all know, the imaging principle of the camera is pinhole imaging. Therefore, through perspective, the same vehicle in the image appears to be small at a distance but large at close distances. Based on this, for the statistics of the pixel number for a vehicle in each video, we aim for the average pixel number of vehicles that are at close distance. At the same time, it is worth mentioning that the multi-loop is also set at close distance. From this point of view, the information of how many pixels for a vehicle is useful to apply the proposed method in different videos. The characteristics in each video and road is shown in Table 8, including road category, number of lanes, weather, and shooting angle.  
Figure 16: Example of the experimental results in Video II.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \(\mathbf{TP}\) & \(\mathbf{FN}\) & \(\mathbf{FP}\) & \(\mathbf{True}\) & \(\mathbf{Counted}\) & \(\mathbf{Recall}\) & \(\mathbf{Precision}\) & \(\mathbf{F-Measure}\) & \(\mathbf{Accuracy}\) \\ \hline \(Lane^{1}\) & 44 & 1 & 1 & 45 & 45 & 97.78\% & 97.78\% & 97.78\% & 100\% \\ \(Lane^{2}\) & 123 & 0 & 2 & 123 & 125 & 100\% & 98.40\% & 99.19\% & 98.37\% \\ \(Lane^{3}\) & 114 & 1 & 2 & 115 & 116 & 99.13\% & 98.28\% & 98.70\% & 99.13\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Experimental results of Video V.  
Figure 17: Example of the experimental results in Video III.  
Figure 18: Example of the experimental results in Video IV.  
The total number of vehicles that should be counted are 988, and there are only six missing errors and fourteen noise errors. As a whole, the total _Recall_, _Precision_, _F-measure_, and _Accuracy_ of thte proposed method achieved 99.39%, 98.59%, 98.99%, and 99.19%, which reached more than 98.5%.
# Dual-IoTID: A Session-Based Dual IoT Device Identification Model
## 3 Proposed Method
### System Overview and Methodology  
The system involves extracting features from flows and sessions, as well as frequent patterns in payloads and utilizes a two-layer machine learning model for effective IoT device identification. As depicted in Figure 1, our system workflow consists of the following four steps:  
1. Traffic and session splitting: dividing the original traffic into multiple individual sessions.
2. Session Communication Feature Extraction: extracting session communication features of different IoT devices.  
3. Frequent Item Extraction and Initial Classification: extracting frequent patterns from different IoT device sessions and conducting initial classification based on the extraction results.
4. Second Classification Prediction: performing a second round of IoT device classification based on session communication features and initial classification results. The detailed instructions for each step are as follows.
## 3 Results and Discussion  
Considering that multiple environmental factors (atmosphere and temperature) were investigated simultaneously, the outcomes of every analysis conducted were analysed from two points of view:  
* A comparison of the results of ArS and samples and the samples built in a sealed environment (SE-CB and SE-HB) to understand the influence of the process atmosphere;
* A comparison between the results of SE-CB and SE-HB samples to examine the effect of baseplate heating.
# Seismic Response Analysis of Reinforced Concrete Frame Structures Considering Slope Effects
## 2 Establishment of Soil-Structure Interaction Model
### Consideration of Pile-Soil Interaction  
Piles connect the soil and superstructure. The connection between the soil and the pile was assumed to be rigid to simplify the calculation used in previous studies so that the relative slip of the pile and soil could be ignored. Motivated to avoid the error caused by this connection mode, the study used the pile-soil spring to connect the soil and the pile for simulation. In the finite element model, the pile and soil body were connected by zero-length nonlinear soil springs, namely \(p\)-\(y\) spring (lateral resistance) [15]. (Figure 2 is the schematic diagram of the soil spring model.)  
For clay, the study used Formulas (1)-(3) to express the \(p\)-\(y\) spring [16].  
\[p_{ult}=c_{u}BN_{p} \tag{1}\]  
\[N_{p}=\left(3+\frac{\gamma^{\prime}x}{c_{u}}+\frac{J_{x}}{B}\right)\leq 9 \tag{2}\]  
\[y_{50}=2.5B\varepsilon_{50} \tag{3}\]  
where \(P_{ult}\) is the ultimate resistance of the soil; \(B\) is the pile diameter; \(Np\) is the lateral bearing capacity coefficient; \(c_{u}\) is the undrained shear strength of the soil; \(\gamma^{\prime}\) is the effective weight of the soil, \(x\) is the depth of the soil; \(\varepsilon_{50}\) is the strain corresponding to the ultimate stress of 50% stress in the stress-strain curve of the soil, and the value is taken as 0.005; \(y_{50}\) is the pile deformation when the soil resistance reaches half the ultimate bearing capacity; and \(J\) is the dimensionless coefficient, designated 0.5.
# Functional Safety Networks and Protocols in the Industrial Internet of Things Era
## 4 The Fail Safe over EtherCAT Protocol Implemented on WiFi  
In this section, we present an example of implementation of the FSoE protocol on general purpose commercial devices, using WiFi as wireless network. In particular, we provide some details about the testbed and the obtainable performance. A more comprehensive report about this activity is given in [19]. Notably, the proposed implementation does not make use of any industrial application layer, and relies only on the protocol stack of the adopted devices. As a consequence, the implementation cannot take benefit of the techniques typically adopted by the application layer protocols to increase reliability. Thus, from this point of view, the achieved performance can be considered, roughly, as a lower bound for reliability. Conversely, the measurement of the polling time does not include the time necessary to execute the application layer protocol stack, possibly leading to slightly more optimistic results.  
FSoE is a Master-Slave protocol, with a unique device referred to as FSoE master, and several FSoE slaves. During normal operation, the master cyclically polls the slaves. The data exchange takes place over FSoE connections that are virtual communication channels established between the FSoE master and each FSoE slave in the initialization phase.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Network** & **Polling Time** \\ \hline Wireless HART & \(\approx\)10 ms \\ WISA & \(\approx\)1 ms \\ WIA-FA & \(\approx\)100 μs \\ \hline \hline \end{tabular}
\end{table}
Table 2: Indicative Values of Device Polling Times for Industrial Wireless Networks.  
The FSoE PDU has two different formats depending on the amount of safe data bytes that has to be exchanged. The simplest format is shown in Figure 2 and it is used to transfer a single byte of safety data from master to slave and vice versa. The first byte contains the command that identifies the specific state of the FSoE connection. This allows to determine the meaning of the safety data. The command field is followed by the data field, by the CRC (2 bytes) and, then, by the Connection Id (2 bytes).  
In the following of this section, we provide an experimental assessment of FSoE. Besides the performance indicators discussed in the former section, we address an additional index, namely the Safety Function Response Time (SFRT) which has been introduced for a specific functional safety protocol but may be adopted by FSoE as well.
# Noise Reduction Using Synthetic Microjet Excitation in Supersonic Rectangular Jets
## 2 Methodology
### Problem Statement  
The simulations utilized a computational domain encompassing the nozzle and the surrounding fluid region. The geometry consisted of a cross-section with exit dimensions of 12.95 mm\(\times\) 25.91 mm (aspect ratio of 2), an exit Mach number of 1.5, a nozzle pressure ratio (NPR) of 3.67, and a nozzle temperature ratio (NTR) of 3, as reported in the experimental paper [2; 3]. The ratio of the throat-exit area was 1.18 in the plane parallel to the minor axis, while the nozzle walls in the major axis plane were straight from the inlet to the nozzle exit. The Reynolds number for the experiment was \(Re\approx 277,500\) for air. The jet flow was expected to be fully expanded when operating under these conditions.  
This rectangular jet nozzle has been extensively studied experimentally [2; 3], making it a suitable reference for comparison in this study. Also, a previous study utilizing the same computational setup performed a similar simulation [34].  
In the following discussion, the simulation results will be presented and discussed for a baseline case, as was carried out in our previous work [33]. Figure 1 shows the rectangular jet nozzle, showing its key characteristics and highlighting its dimensions. Furthermore, this computational investigation considers the nozzle's shorter lip length (\(D\)) to be the characteristic length for determining the Strouhal Number (\(St\)). This facilitates further comparison with geometries at different aspect ratios, with \(D\) kept constant. Note that if the nozzle's effective (hydraulic) diameter (\(D_{\text{eff}}=0.020665\)) is considered for the definition of \(St\), then the values of \(St\) should be multiplied by 1.5957.
## 1 Introduction
Its dynamic equations are based on the non-Keplerian motion. Two issues need to be solved in this model: (i) The system has a high dimension, which means that it will suffer from the curse of dimensionality [21] when solving the problem; (ii) two objectives, intercepting the evader and evading the defender, should be considered by the pursuer, and the corresponding weights should be determined according to the current state. For the first issue, as the zero effort miss (ZEM) can be used to simplify the linear system [4], the dimension of the system is reduced by introducing the relative state variables and the ZEM variables [22]. For the second issue, the pursuer's objective function is designed based on the fuzzy comprehensive evaluation, and the pursuer's control strategy which is suitable for the orbital pursuit-evasion-defense game is proposed. Based on the above model, the orbital pursuit-evasion-defense game is transformed into a TPBVP by applying the differential game theory. A hybrid method combining the multi-objective genetic algorithm and the multiple shooting method is presented to solve the TPBVP.