esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT-Portrait: Automatically Identifying IoT Devices via Transformer with Incremental Learning
## 4 System Design
### Multi-Classifier Training
#### 4.3.3 Training Data Generation  
After the data preprocessing phase and data sampling phase, we obtained many samples, each sample contains a fixed number of consecutive packets, and each packet has a fixed length. Assume the number of consecutive packets is _packet_num_, and the length of each packet is _packet_len_. We generate the training data by converting the sample into a _packet_num_\(\times\) (_packet_len_\(+1\)) matrix, where 1 contains information on the packet number within 100 ms, representing the packet rate, and other contains the data of the packet. The types of features involved are as follows:  
* Some obviously important features obtained from the data packet. As analyzed in Section 3, there are distinguishable patterns between the traffic information of IoT devices and non-IoT devices, IoT devices and IoT devices (the IP and MAC addresses of the device's communication endpoints, communication ports, and the use of protocols). In addition, the data packet length is also an important feature that data packets of different IoT devices may have different lengths.
* Other features obtained from the data packet. The data packet information (the version of IP, etc.) designed and used by each device may be different, and it is not easy for us to find the rules of this information. Therefore, we convert the information in the data packet within _packet_len_ length into numbers and store them in the matrix.
* Calculated statistical characteristics of multiple packets. As analyzed in Section 3, IoT devices have regular communication patterns, so the traffic rate characteristic is also important. We count the number of packets within 100 ms to represent the traffic rate of the device.  
For the sample labeling, we use information such as product type and device category, collected through the device information collection module, as labels. Specifically, we extract the IP address from the data packet, and actively collect information based on this IP address. Afterward, the packet undergoes data preprocessing and data sampling, and the information obtained before is used as the label of this sample. If the collected information is sufficient, the samples will be automatically labeled. Otherwise, they need to be manually labeled. After labeling, the samples will be used as the training input of the transformer model.
###### Abstract  
Leaf-device interaction can effectively be modeled with a finite element model when proper finite element model parameters are applied. In order to investigate the contact mechanism of picking up sugarcane leaf during the operation of a sugarcane leaf cutting and returning machine, a geometric solid model of sugarcane leaf picking was established. A finite element numerical model to analyze the large deformation problem of flexible bodies was developed in LS-DYNA to simulate the picking process of the returning machine. A dynamic simulation of the sugarcane leaf-picking process was carried out to obtain the change of stress field and the motion posture of the sugarcane leaf and the elastic teeth. The picking process of the picking mechanism, the change in posture of the sugarcane leaves, the change in stress on the sugarcane leaf, the change in the bending angle of the sugarcane leaf and the change in stress on the elastic teeth were analyzed in detail. The results showed that the picking process can be divided into four stages: picking, lifting, pushing and retrieving. The posture changes of sugarcane leaf are \"C\", logarithmic curve, wavy shape and \"V\", in turn. During the picking process, the sugarcane blade showed some breakage, the sugarcane vein remained intact, and the elastic teeth did not fail. During the whole picking cycle, the maximum Von Mises stress of the blade, vein and elastic teeth were 22.8 MPa, 17.5 MPa and 900 MPa, respectively. An evaluation criterion of bending angle was creatively put forward to measure the bending deformation of leaves. The trend in the sugarcane leaf bending angle shows that it is largely variable, gradually decreasing, fluctuating and increasing with interval fluctuations. The working process of the picking mechanism was observed through a quick camera experiment. Comparing the experiment with the simulation, the changing trend of the simulation data and experimental data was generally similar. The experimental and simulation values of the average sugarcane leaf bending angle were 27\({}^{\circ}\) and 19\({}^{\circ}\), respectively. The relative error of the average bending angle was 29.6%. It was concluded that the developed finite element model is substantial and could be applied to optimize and improve the picking mechanism. In addition, some references were provided for the contact mechanism between the picking mechanism and the sugarcane leaf.  
Keywords: sugarcane leaf; returning machine; picking mechanism; finite element; dynamic simulation +
Footnote †: journal: Applied Sciences
## 3 Design of Approximate QAM Demodulation Circuits
### Arithmetic Approximation Techniques
#### 3.5.1 Floating-Point Approximations  
Both the mantissa adder and exponent subtractor are replaced with an approximate carry look-ahead adder [19] that decreases the circuit complexity of the floating-point (FLP) addition. By splitting the calculations in two segments and omitting the least-significant one, an approximation is inserted in the generation of the carry output of the \(i\)-th stage. As a result, the calculation is performed approximately in a window of size \(W\).  
\[c_{(i+1)}=\sum_{j=i-W+1}^{i}G_{j}\bigg{(}\prod_{m=j+1}^{i-1}Pm\bigg{)} \tag{5}\]  
where \(P_{i}=A_{i}\oplus B_{i}\) and \(G_{i}=A_{i}\cdot B_{i}\) are the propagate and generate signals of the \(i\)-th stage, respectively.  
Figure 5a presents the high-level architecture of the approximate FLP adder, where the inexact mantissa addition and exponent subtraction are highlighted. The rest of the components remain the same as in the accurate FLP addition, except that we do not apply any rounding technique in the mantissa sum. In the right-hand side (Figure 5a), we present the basic logic block of the approximate carry look-ahead adder, which approximately calculates the carries according to Equation (5) and stores them in DFFs. The total latency of the approximate FLP adder is 5 CCs.  
Figure 4: \(M\)-QAM demodulation architecture of approximate maximum likelihood (AML).
# Research on Equivalent Static Load of High-Rise/Towering Structures Based on Wind-Induced Responses
## 5 Conclusions  
This paper presents an assessment method for the equivalent static wind load based on random vibration theory and quasi-steady aerodynamic theory, and investigated a method for analyzing the background response, the resonant response, and the coupling components between these two responses, to wind vibration in the time domain and the frequency domain. The main conclusions are as follows:  
(1) The displacement and bending moment responses obtained by the time domain and frequency domain methods are in good agreement, and the deviation in the base shear force response of the structure is slightly increased. However, the deviation in the total pulsation response is within 1.5%, and the percentage of \(\sigma_{\text{BR}}\) in the total pulsation response of each response is within 2%, so the influence of neglecting \(\sigma\)BR on the total pulsation response is small;  
Figure 3: _GLF_ values obtained by the method in this paper and their difference rates.  
(2) The _MGLF_ and _QGLF_ obtained by the new assessment method in this paper increase significantly with height, while the traditional GLF methods underestimate the maximum wind effects. The _DGLF_ is constant as the height increases, and the _MGLF_ at the top of the structure is about 22% larger than the _DGLF_. At this time, if only the _DGLF_ or the base location _MGLF_ values are considered, the wind effect of the structure will be seriously underestimated. The _DGLF_, _MGLF_ and _QGLF_ values increase as the \(\beta\) increases, and the _MGLF_ and _QGLF_ values decrease as the \(\lambda\) increases;  
(3) The differences between the _GLF_ obtained in this paper and the traditional gust load factor method are compared through the case of a high-rise building, and the validity of this paper's method is verified. The GLF assessment method of this paper considers the higher-order vibration pattern of the structure and the whole wind-induced response, including displacement, bending moments and shear forces. It has a wide range of applications.  
[PERSON] and [PERSON] conceived the idea and designed the framework; [PERSON] set up the numerical model; [PERSON] analyzed the simulation data; and [PERSON] and [PERSON] writing--review and editing the paper. All authors have read and agreed to the published version of the manuscript.  
Shenzhen Basic Research Program (JCYJ20190806145216643) and National Natural Science Foundation of China (51778200).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
This research has received support from the Shenzhen Basic Research Program (JCYJ20190806145216643) and National Natural Science Foundation of China (51778200), all of which are gratefully acknowledged.  
The authors declare no conflict of interest.
# A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar
## 2 Detection Principle and Data Characteristics of GPR Radar  
As shown in Figure 1a, the GPR detection trolley is used to detect the buried object in a sand bunker. The trolley moves at a constant speed along a straight line to collect 512 channels of A-Scan data; each has 1024 samples. Generally, the convolution of GPR wavelet and reflection sequence constitutes the echo of GPR detection system. The radar first receives the strong reflection signal of the upper surface. When the transmitted pulse encounters the target in the underground, there is a reflected signal of the target, which is relatively weak. Figure 1b is the time stacking diagram of the first 512 sampling points. It can be seen that the strong wavelet submerges the signal of target. Moreover, the wavelets at the adjacent monitoring points on the same survey line in the same detection area are very similar.  
Three correlation coefficients of [PERSON]'s linear correlation coefficient [14], [PERSON]'s tau coefficient [15], and [PERSON]'s rho [16] are used to evaluate the relevance of adjacent A-Scan echoes. The correlation coefficient greater than zero indicates that the two groups of data are positively correlated. On the contrary, a correlation coefficient less than zero indicates that they are negatively correlated. Moreover, the greater absolute value indicates the stronger correlation. Three correlation coefficients of the A-Scan echoes in Figure 1 are shown in Figure 2. The mean values are 1, 0.9998, and 0.9894, respectively, which proves the strong correlation between adjacent echoes. Several isolated valley points on the curves correspond to the buried target, especially on the line of [PERSON]'s tau. This strong correlation in time series could be well explored by neural networks with memory.
## 1 Introduction
found that the SAT model continues to be an effective tool for facilitating shared understanding and proper calibration of trust in human-agent teams [24]. And in recent studies, conveying the automation's decision-making rationale via the display resulted in an increase in the participants' level of trust, which became more calibrated over time [28; 29]. While understanding the effect of uncertainty at various SAT levels is considered important in ADS, [PERSON] et al. noted that all three levels may not be absolutely necessary to achieve transparency, and in time-sensitive situations, drivers may only need to know the agent's actions (level 1) and the projections (level 3) to make a sufficiently informed decision [24]. This is due to the fact that human attention resources are limited, and under time pressure, it may be more feasible to provide the correct levels of information than to provide all of the data, especially in regards to visual or auditory information [30]. Recently, researchers have begun to emphasize the effect of transparency in time-critical decision and have found that integrating AI transparency into time-critical decision support is complex and requires consideration of the impact on human-machine teaming [31]. However, according to our review, studies involving ADS transparency have not emphasized time or  
Figure 1: The SAT Model. scenario urgency, but have focused primarily on the effects of the sequential stacking of transparency levels (i.e., comparing SAT level 1, SAT level 1 + 2, and SAT level 1 + 2 + 3) on human trust and reliance regarding ADS [20; 28; 32]. One of the studies most relevant to our current research is the work of [PERSON] et al. [28], in which the role of the SAT model in different driving scenarios was considered. Another relevant work was the study of [PERSON] et al., which discussed the implications of transparent interfaces to improve human-autonomy teaming outcomes in time-critical environments [32]. However, these two studies did not focus on the particular role of SAT level 3 information in trust calibration. As of yet, there is still a limited understanding on how SAT affects trust and human-automation team performance, especially what transparency information supports trust calibration in different driving scenarios.
## 4 Results
### Results of the Analyses of the Effects of Four Variables
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera Type, Rotation, Contrast Ratio, Device Used** & **Estimate** & **Standard Error** & \(z\)**Value** & **Pr (\textgreater{}|z|)** \\ \hline Intercept & 0.51385 & 0.02670 & 19.246 & \(<\)2 \(\times\) 10\({}^{-16}\) \\ Orthographic, NO\_R, NO\_C, Gear VR & \(-\)0.03202 & 0.07884 & \(-\)0.406 & 0.6847 \\ Orthographic, NO\_R, INC\_C, desktop display & 0.14107 & 0.06717 & 2.100 & 0.0357 \\ Orthographic, NO\_R, INC\_C, Gear VR & \(-\)0.22087 & 0.19458 & \(-\)1.135 & 0.2563 \\ Orthographic, INC\_R, NO\_C, desktop display & 0.07053 & 0.05559 & 1.269 & 0.2045 \\ Orthographic, INC\_R, NO\_C, Gear VR & 0.13040 & 0.08391 & 1.554 & 0.1202 \\ Orthographic, INC\_R, INC\_C, desktop display & 0.21036 & 0.03966 & 5.305 & \(1.13\times 10^{-7}\) \\ Orthographic, INC\_R, INC\_C, Gear VR & 0.35379 & 0.06417 & 5.513 & \(3.52\times 10^{-8}\) \\ Perspective, NO\_R, NO\_C, desktop display & 0.19790 & 0.03904 & 5.069 & \(4.00\times 10^{-7}\) \\ Perspective, NO\_R, INC\_C, Gear VR & 0.23262 & 0.07695 & 3.023 & 0.0025 \\ Perspective, NO\_R, INC\_C, desktop display & 0.31400 & 0.06772 & 4.637 & \(3.54\times 10^{-6}\) \\ Perspective, NO\_R, INC\_C, Gear VR & \(-\)0.04214 & 0.15152 & \(-\)0.278 & 0.7809 \\ Perspective, INC\_R, NO\_C, desktop display & 0.41798 & 0.05303 & 7.882 & \(3.22\times 10^{-15}\) \\ Perspective, INC\_R, NO\_C, Gear VR & 0.25528 & 0.06062 & 4.211 & \(2.54\times 10^{-5}\) \\ Perspective, INC\_R, INC\_C, desktop display & 0.23513 & 0.03959 & 5.940 & \(2.86\times 10^{-9}\) \\ Perspective, INC\_R, INC\_C, Gear VR & 0.43645 & 0.06032 & 7.236 & \(4.62\times 10^{-13}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 37: Logistic regression analysis results investigating the effects of the camera type, rotation, contrast ratio, and device used.
These studies suggest that (1) only specific monitoring of CC-specific Abs would elucidate the causal relationship between the levels of DSAs and NDSAs with graft failure or rejection, and (2) the reactivity of the sera to naturally occurring OC that are admixed with CC in SAB assay may result in the denial of an otherwise compatible organ, or may result in inappropriate pre- and post-transplantt therapies. Therefore, there is a need to monitor HLA Ab binding to CC, without the interference of OC, during the sensitization of end-stage organ disease patients. Another salient factor of Ab, which may impact the precise measurement of the MFI or density of serum HLA Abs, is the clonality of the detection Abs [51]. The most commonly used detection antibody in SAB immunoassay is _IgHPolyFab_[51], which is recommended by histopathologists for better resolution and localization of the surface antigens on cells and tissue sections, due to the signal amplification caused by the IgG heavychain binding polyclonal Fab fragments, binding to an antigen-bound primary Ab (Cusabio Catalog, Cusabio Product Center. Introductory Notes for Secondary Antibodies. Available at: [[http://www.cusabio.com/catalog-21-1.html](http://www.cusabio.com/catalog-21-1.html)]([http://www.cusabio.com/catalog-21-1.html](http://www.cusabio.com/catalog-21-1.html)), accessed on 12 October 2015.). For the detection of IgG subclasses, Fc-specific monoclonal IgG (_FcMonolgG_) is used as a detection Ab [52]. For the detection of anti-HLA IgG, _FcMonolgG_ has not been used to compare or validate the sensitivity and reliability of the different detection Abs for monitoring Abs in the Luminex SAB assay. We have documented that _FcMonolgG_ may provide a better quantitative assessment (MFI or titer) of anti-HLA IgG measurements, as it binds to a single Fc domain of the primary anti-HLA IgG, in a one-to-one ratio [46; 51]. The studies further revealed that when the serum Ab density was low, the binding of _IgHPolyFab_ was amplified. When the serum density was high, the binding of _IgHPolyFab_ diminished. No such variations were observed with _FcMonolgG_[48]. However, it remains to be elucidated whether the nature (mono- versus poly-clonality, and whole IgG versus Fab fragments) of the detection Abs impacts the assessment of the sensitization of patients before transplantation. These reports led to the following hypotheses: (1) the number and the MFI of anti-HLA CC Abs may differ due to the presence or absence of OC on the SAB, and (2) the sensitivity of the assay may differ depending on the clonality of the detection Abs, i.e., _IgHPolyFab_ and _FcMonolgG_. The present investigation is aimed at testing these hypotheses in a clinically defined sensitized ESRD patient cohort waiting for donor organs.
# Feasibility, Design, and Deployment Requirements of TCR for Bistatic SAR radiometric Calibration
## 4 Error Factors of TCR RCS
### Thermal Deformation  
The thermal deformation caused by environment mainly changes the length of the TCR, which is similar to the machining error of length. The thermal deformation can be calculated according to the temperature range, and then the relationship between thermal deformation and the corresponding bistatic RCS error can be obtained from Figure 10. Aluminum is commonly used for the construction of plates; the linear thermal expansion coefficient of the aluminum is \(23\times 10^{-6}/^{\circ}\)C as the measurement temperature is 20 \({}^{\circ}\)C. For the TCR with a length of 0.4 m, the length will change \(-\)0.552-0.368 mm in the temperature range of \(-\)40 \({}^{\circ}\)C to 60 \({}^{\circ}\)C. According to Figure 10, it can be seen that the relative change on RCS is very small (less than 0.05 dB) for this temperature range.  
To sum up the errors in the above Sections 4.1-4.4, it can be concluded that RCS errors due to interplate orthogonality and machining errors of length were more severe for monostatic calibration under the current machining accuracy, and the thermal deformation is similar to the matching error of length. However, the effect of the pointing deviation on the RCS errors was more severe for bistatic calibration when the misalignment is the same. These results indicate that these error factors can introduce different RCS errors for monostatic and bistatic calibration, so we cannot replace the conclusions of monostatic calibration with those of bistatic calibration. Moreover, these results will have important reference values and guidance significance for guaranteeing the calibration accuracy for bistatic SAR calibration.
# In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
## 3 Methodology
### Sensors
#### 3.1.1 Inductive Proximity Sensor for Ferrous Materials  
Ties are typically connected to rails with metal fasteners like spikes and tie plates. One way to detect ties and identify their locations is to detect tie plates and possibly spikes using inductive proximity sensors for ferrous materials. Typically, an \"inductive proximity sensor\" is equipped with a Hall effect probe, which triggers a pulse when it detects a ferrous object in its proximity (Hall effect sensors, named for the physicist [PERSON], incorporate one or more Hall elements, generating a voltage proportional to an axial component in a magnetic field. They are commonly used in proximity sensing, positioning, speed detection, and current sensing applications in many industrial devices). For rail applications, the sensor can be positioned above the tie plate and spikes, and the generated pulses can be used to identify tie locations.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results
### Mission Analysis
#### 3.3.2 Contact Analysis  
The contacts between a single S/C and the service area have been simulated and resulted in an average contact duration of 10.7 min and to an average of 7.2 contacts per day. The average revisit time (ART) of each S/C is about 3 h.  
Additionally, the contacts between a single S/C and GCSs have been simulated. In the STK analysis performed, a minimum elevation of 20\({}^{\circ}\) was assumed and any contact with a duration less than a minute was considered insufficient for establishing a link and was discarded. This resulted that each S/C has an average contact duration of 267 s with both GCS and an average contact number per GCS of 2.6 per day.