esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Complementary Model Description
### Setting the EMI Constraints
#### 3.3.3 Filtering Capacitor Losses Modelling  
Filtering capacitors are usually high-quality components, with fairly low equivalent series resistance (ESR). Nonetheless, large ripple currents may flow in these components making it necessary to estimate their losses accurately. For this purpose, manufacturers provide ESR of their capacitors. From their characteristics, it is possible to derive a representative ESR model. In our experiments, we are using ceramic capacitors from the manufacturer Murata. Murata is providing accurate ESR curve for all its components from a web application [28]. In Figure 14 below are plotted a set of ceramic capacitors ESR versus frequency.  
Since the various ceramic capacitors have a comparable ESR, the model considered here, will be the same for all capacitor values.  
The chosen interpolation curve (in red) follows the mathematical variation of Equation (17) below. This curve has been manually fitted with \(R_{c0}=0.0012\); \(k_{c1}=112\); \(k_{c2}=160E3\). With \(k_{c1}\) and \(k_{c2}\) fixed to match the elbow.  
\[ESR(f)=R_{c0}+\frac{\sqrt{f}}{k_{c1}}+\frac{k_{c2}}{f} \tag{17}\]  
In order to derive losses in \(C_{x}\) capacitors, the RMS current in each component must be derived. If no \(L_{filter}\) is inserted, the current flowing through the capacitors \(C_{x1}\) and \(C_{x2}\) is the one flowing inside inductors \(L\). Therefore \(I_{L}=I_{C1}\). If \(L_{filter}\) are inserted, the currents flowing inside \(C_{x1}\), \(C_{x2}\) and \(L_{filter}\) must be derived. \(I_{Lfilter}\) is derived thanks to Equations (18)  
Figure 14: Ceramic capacitor equivalent series resistance with respect to frequency. **(Left)**, manufacturer data, **(right)**, equivalent model representation for the series resistance with respect to frequency.  
and (19). \(C_{x1}\) capacitor current is the difference between \(I_{L}\) and \(I_{Lfilter}\). As well, \(C_{x2}\) current is expected to be the same as \(I_{Lfilter}\). These Equations are supposing an open output load.  
\[\frac{I_{c1}}{I_{L}}=\frac{\frac{C_{1}}{C_{2}}-\omega^{2}LC_{1}}{1+\frac{C_{1}} {C_{2}}-\omega^{2}LC_{1}} \tag{18}\]  
\[\frac{I_{c2}}{I_{L}}=\frac{1}{1+\frac{C_{x1}}{C_{x2}}-\omega^{2}LC_{x1}} \tag{19}\]
# SATFuzz: A Stateful Network Protocol Fuzzing Framework from a Novel Perspective
## 6 Conclusions  
In this paper, we proposed a stateful network protocol fuzzing framework called SATFuzz. The key idea is to improve the vulnerability discovery efficiency from three perspectives: reducing the extra time overhead, exploring the state space in depth, and avoiding invalid interactions. Specifically, we prioritized states according to their characteristics, screened out corresponding optimal test sequences for the states to be tested (i.e., high-priority states), and directly filtered invalid test cases that were destined to be unable to successfully participate in the fuzzing process.  
To systematically evaluate SATFuzz, we selected stateful network protocols with hidden security risks as the research object and conducted comparative experiments with the most representative method. The experimental results show that the vulnerability discovery efficiency of the proposed approach increased by at least 1.48 times (at most by 3.06 times). The experimental results verified that the vulnerability discovery efficiency of SATFuzz was far superior to that of its competitors of the same type. We proved that SATFuzz can effectively compensate for some shortcomings of the existing methods and achieved significantly improved vulnerability discovery efficiency.  
Conceptualization, Z.P. and L.Z.; methodology, Z.P. and Z.H.; software, L.Z. and Z.H.; validation, Z.P., L.Z. and Z.H.; investigation, Y.L. and Y.C.; resources, Y.L. and Y.C.; writing--original draft preparation, Z.P.; writing--review and editing, Z.P., L.Z., Z.H., Y.L. and Y.C.; supervision, Z.P.; project administration, Z.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program \"Cyberspace Security\" grant number 2017 YFB0802900.  
Not applicable.  
**Data Availability Statement:** We built a QRNN template used for processing long time-series data. We uploaded it to a GitHub project to support related research on test case filtering in stateful network protocol fuzzing; the template can be downloaded from [[https://github.com/Whoolocked/QRNN-For-LTSData](https://github.com/Whoolocked/QRNN-For-LTSData)]([https://github.com/Whoolocked/QRNN-For-LTSData](https://github.com/Whoolocked/QRNN-For-LTSData)) (accessed on 20 July 2022).  
**Acknowledgments:** We are very thankful to [PERSON] and [PERSON] for their help with the preparation of the experiment and reviewing the paper. We thank the anonymous reviewers for their detailed comments, which helped to improve the quality of the paper.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.
## 2 The Concept and Design: The Function Design of the CBTC System
### Transport units tracking  
At the 2013 13 th International Conference on Control, Automation and Systems (ICCAS 2013), [PERSON] et al. [48] claimed that in non-CBTC systems, train position information is inaccurate and discontinuous; therefore, it is very necessary to establish the train tracking equation. For underground mine transportation, there are also such problems.  
Based on the disturbance vibration and other factors, [PERSON] et al. [49] established the train compensation tracking equation in 2018. In order to solve this problem, the concept paper draws on the method of [PERSON]. [PERSON] et al. [50] of tracking equations: The transport units location is tracked based on the ground beacon data, airborne information, Correlation Interval Algorithm, and the digraph. Specially, transport units tracking equations are established using the transport unit measurement error, track wear, and information transmission delay, as the following Equations (1) and (2) [50] show.  
\[T_{hesway\ of\ mine(m)}=\frac{S_{b}+L_{u}+L_{u-a}}{V_{max}} \tag{1}\]  
\[S_{b}=Y_{\beta}-m \tag{2}\]  
Equations (1) and (2) [50] Tracking equations.  
In Equations (1) and (2), \(m\) represents the line mileage of the transport unit, \(S_{b}\) represents the blocked area, \(Y_{\beta}\) represents the mileage from \(m\) to the axle counting area end, \(L_{u}\) represents the length of the transport unit, \(L_{u-a}\) represents the transport unit position's uncertainty amount, and \(V_{max}\) represents the transport unit's maximum allowable operating speed.  
In the application of tracking equations based on the digraph, the followings are essential: (1) The digraph is stored in the linked list. (2) The ATS system receives the transport units' main information including the units' running direction, the offset and the corresponding points in the directed graph. (3) The directed graph edges and offsets at the beginning and end of the unit section are determined based on the relevant data in the packet, and then, the coordinates of the start and end of the track are found in the directed  
Figure 9: The route adjustment.  
graph. (4) The specific position of the unit is determined according to the running direction of the unit and the edge direction of the directed graph.
# Electron Beam Characterization of REBCO-Coated Conductors at Cryogenic Conditions
## 2 Material and Methods
### Experimental Techniques
#### 2.3.2 SEY Measurement  
The SEY, denoted as \(\delta\), is defined as the ratio between the primary electrons impinging the sample and the secondary electrons leaving the sample, irrespective of their physical origins. The collector current \(I_{C}\) and the sample drain current \(I_{S}\) were directly measured in our experiment. The beam current was recollected as a sum of the sample drain current and the collector current \(I_{B}=I_{S}+I_{C}\). Then, the SEY was directly calculated from the observables.  
\[\delta=\frac{I_{Secondary}}{I_{Primary}}=\frac{I_{C}}{I_{B}}=\frac{I_{C}}{I_{C}+I _{S}} \tag{1}\]  
The SEY and ESD energy dependencies were measured point-wise, starting from 0 eV. The SEY data points were spaced 0.25 eV apart at low energy and up to 50 eV apart around 1.4 keV. The beam current of \(\sim\)0.5 nA preserved a signal-to-noise ratio (SNR) of \(\sim\)50 on the measured currents, while avoiding a significant conditioning effect during the measurement.
# Sensor-Based Hand Gesture Detection and Recognition by Key Intervals
## 3 Proposed Algorithm
### Inference Operations
7:Require: SKI_Detected \(\leftarrow\) false. 8:\(\forall i\) is the first window of \(S\). 9:repeat
10: Find \(X_{t}\) and \(Y_{t}\) from \(S_{t}\) by the trained neural network. 11: Compute \(j^{*}\leftarrow\operatorname*{argmax}_{1\leq j\leq K+1}X_{tj}\). 12:if\((j^{*}\
eq K+1)\)and\((X_{tj^{*}}>T)\)then\(\triangleright\) PKI detection
13: PKI_Detected \(\leftarrow\) true. 14: PKI_Detected_Time \(\gets t\). 15: PKI_Class \(\leftarrow\)\(j^{*}\). 16:endif
17: Compute \(j^{*}\leftarrow\operatorname*{argmax}_{1\leq j\leq K+1}Y_{tj}\). 18:if\((j^{*}\
eq K+1)\)and\((Y_{tj^{*}}>T)\)then\(\triangleright\) SKI detection
19: SKI_Detected \(\leftarrow\) true. 20: SKI_Detected_Time \(\gets t\). 21: SKI_Class \(\leftarrow\)\(j^{*}\). 22:endif
23:if(PKI_Detected \(=\) true) and (SKI_Detected \(==\) true) then\(\triangleright\) Matching of key intervals
24:if(PKI_Class \(==\) SKI_Class) then\(\triangleright\) Matching of key intervals
25:\(\delta\leftarrow\) SKI_Detected_Time \(-\) PKI_Detected_Time. 26:if\((t_{\min}<\delta<t_{\max})\)then\(\triangleright\) Postprocessing
27: Gesture_Class \(\leftarrow\) SKI_Class. 28:\(\mathcal{C}\leftarrow\mathcal{C}\cup\operatorname*{Gesture_Class}\). 29:\(\mathcal{T}\leftarrow\mathcal{T}\cup(\) SKI_Detected_Time, PKI_Detected_Time). 30: PKI_Detected \(\leftarrow\) false. 31: SKI_Detected \(\leftarrow\) false. 32:endif
33:endif
34:\(t\gets t+\Delta\). 35: Form new window \(S_{t}\) based on new \(t\). 36:until Final window of \(S\) is processed. 37:return\(\mathcal{C}\), \(\mathcal{T}\)
```  
**Algorithm 1** Proposed Gesture Inference Procedure
# DMCNet-Pro: A Model-Driven Multi-Pilot Convolution Neural Network for MIMO-OFDM Receivers
## 5 Experimental Design and Results  
### Experimental Design  
The experiments in this section were conducted on a laboratory server platform. The server used had two NVIDIA GeForce RTX 2080 Ti GPUs, 16 GB of RAM, and 64 GB of GPU memory. The neural network was implemented using the Keras deep learning framework. The channel dataset used during the training process was collected from a wireless channel model following the Wireless World Research Forum's New Radio [11] initiative (WINNER II). The experimental process included two scenarios: 8 pilots and 32 pilots. In the eight-pilot scenario, there were 8 known complex training sequences for each of the two signals, totaling 16 known complex values. Additionally, 32 random bits (0 s and 1 s) were generated using random numbers. Similarly, for the 32-pilot scenario, 128 random bits were generated. The generated random bits were written to files named \"Pilot8\" and \"Pilot32\", respectively, and the pilot sequences remained unchanged throughout the subsequent experiments.  
The channel model was the same as described in Section 2. Two antennas at the transmitter generated random data of 512 bits each, which were then transmitted through an Additive White Gaussian Noise (AWGN) simulated channel. The output of the AWGN channel serves as the input to the network. During the training process, the batch size was set to 128. Batches of 128 samples were generated using a Python generator for training, and the number of steps per epoch was set to 1000, meaning that 1000 batches of samples were input per epoch for training. The total number of epochs was set to 200. The Adam optimizer was used to optimize the model parameters. Since the final output is a binary sequence, binary cross-entropy was used to compute the model loss. It can be mathematically represented by the following equation:  
\[\text{Loss}=-\frac{1}{N}\sum_{i=1}^{N}y_{i}\cdot\log(p(y_{i}))+(1-y_{i})\cdot \log(1-p(y_{i})). \tag{7}\]  
In this equation, \(y\) represents the binary label (0 or 1) and \(P(y)\) denotes the probability of the output label. The learning rate follows a step-wise decay approach, where it is reduced by 0.75 times every 30 epochs.  
For the model-driven receiver network DMCNet-pro, model training is carried out in a phased way, which is divided into four stages: training the denoising module (100 epochs); fixing the denoising module parameters and training the channel estimation module (100 epochs); training the parameters of the fixed-signal denoising module and the cardio estimation module to the signal detection module (100 epochs); and end-to-end training with a small learning rate (0.0001), fine-tuning the model, and training (200 epochs). The other settings for the experiment are the same as for DMCNet.
# Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics
## 3 Methodology for the Elevation Angle Characterization
### Error Analysis  
The error between the empirical CDF curves of the elevation angle and the ones obtained with the parameters in Tables 6 and 7, was quantified by means of the absolute error, \(\epsilon\), and its CDF. Figure 11a-f in Appendix A show the mean absolute error, \(\bar{\epsilon}\), between the empirical CDF's for the simulated points of Figure 9c, and for the orbit altitudes \(h_{k}\in h_{s}\).  
The maximum observed mean error for the individual characterizations shown in Appendix A was about 5% (\(\bar{\epsilon}_{max}\approx 0.05\)) for the worst case. However, the greatest error values were observed to occur at lower elevation angles (below 20\({}^{\circ}\) were the link is often unreliable since line-of-sight is harder to find) that are often not considered by satellite communication systems due to greater attenuation.  
The absolute errors \(\epsilon\) were also analyzed through their individual CDF's, showing that most of the errors where below 5%. Figure 15a-d show the CDF's of the absolute error, \(\epsilon\), corresponding to Figure 10a-d. In addition, \(\epsilon\) was analyzed for all the simulated satellite orbits and according to their elevation angle range.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Satellite 1** & **Satellite 2** \\ \hline Dry mass & 5 kg & 200 kg \\ Drag area & 1 m\({}^{2}\) & 10 m\({}^{2}\) \\ Solar radiation pressure area & 1 m\({}^{2}\) & 10 m\({}^{2}\) \\ \hline \end{tabular}
\end{table}
Table 10: Satellites with different mass and drag area characteristics for additional performed simulations.  
Figure 14: CDF \(F_{\Theta}(\theta)\) for different satellite mass and drag area characteristics.  
The PDF, CDF, and the complementary CDF (CCDF) showing the resultant absolute errors, \(\epsilon\), for all the simulations are shown in Figure 16, where it can be observed that around 95% of the values of \(\epsilon\) are below 0.05. Figure 17 shows the same absolute errors as in Figure 16b, but now expanded and classified by elevation angle range; in this figure, it can be observed that most of the absolute errors are below 5% for all the elevation angles, and that errors' CDF distributions are approximately the same for all the elevation angle ranges. Figure 18 expands the 90 th-percentile of the CDF's in Figure 17, showing that in fact, most of the errors are below 2%; the 90 th-percentile mean value is shown as a red cross for each boxplot.  
Figure 16: Distribution of the absolute errors behavior of all the performed simulations: (**a**) PDF, (**b**) CDF, and (**c**) CCDF.
## 2 Bluetooth Low Energy Protocol Stack
### GAP and Application Profiles  
At the highest level of the core BLE stack, the GAP specifies device roles, modes and procedures for the discovery of devices and services, the management of connection establishment and security.  
The BLE GAP defines four roles with specific requirements on the underlying controller: Broadcaster, Observer, Peripheral and Central. A device in the Broadcaster role only broadcasts data (via the advertising channels) and does not support connections with other devices. The Observer role is complementary for the Broadcaster, _i.e._, it has the purpose of receiving the data transmitted by the Broadcaster. The Central role is designed for a device that is in charge of initiating and managing  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \cline{3-6} \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{**Pairing**} & **Encryption** & **Data Integrity** & **Layer** \\ \hline \multirow{3}{*}{**LE Security Mode 1**} & **Level 1** & No & No & No & \\  & **Level 2** & Unauthenticated & Yes & Yes & Link Layer \\  & **Level 3** & Authenticated & Yes & Yes & \\ \hline \multirow{3}{*}{**LE Security Mode 2**} & **Level 1** & Unauthenticated & No & Yes & \\  & **Level 2** & Authenticated & No & Yes & ATT layer \\ \hline \end{tabular}
\end{table}
Table 1: Security services and features for the security modes and levels defined in BLE.  
multiple connections, whereas the Peripheral role is designed for simple devices which use a single connection with a device in the Central role. In consequence, the Central and Peripheral roles require that the device's controller support the master and slave roles, respectively. A device may support various roles, but only one role can be adopted at a given time.  
Finally, since certain types of applications may benefit from reusing common functionality, additional profiles can be built on top of the GAP. Bluetooth follows a profile hierarchy, whereby a new profile including all the requirements of an existing profile can be defined. A highest-level profile that specifies how applications can interoperate is called an application profile. Application profiles, which are also specified by the Bluetooth SIG, favor interoperability between devices from different manufacturers.
# Transparent CoAP Services to IoT Endpoints through ICN Operator Networks +
## 4 Design and Implementation
### Communication Scenario  
The CoAP Handler considers the following communication scenarios as CoAP services to CoAP endpoints:  
* Single request and immediate response: The CoAP server is ready to send the response immediately.
* Single request and delayed response: This scenario happens when the server is sleeping for a while and the resource is not yet available.
* CoAP observe extension: The CoAP client is interested in the update notification of a particular resource.
* CoAP group communication: The client is interested in multiple resources hosted on multiple IoT devices.
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measure** & **Right Ankle \(p\) Value** & **Left Ankle \(p\) Value** & **Asymmetry \(p\) Value** \\ \hline \multicolumn{3}{c}{_Soccer_} \\ \multicolumn{3}{c}{Baseline} \\ \multicolumn{3}{c}{Pre-training} & 0.007 & 0.001 & 0.116 \\ Post-training & 0.021 & 0.611 & 1.000 \\ Pre-training & & & \\ Post-training & 1.000 & 0.054 & 0.184 \\ \multicolumn{3}{c}{_Basketball_} \\ \multicolumn{3}{c}{Baseline} \\ \multicolumn{3}{c}{Pre-training} & 0.001 & 0.001 & 0.007 \\ Post-training & 1.000 & 0.575 & 0.361 \\ Pre-training & & & \\ Post-training & 0.001 & 0.001 & 0.286 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Bonferroni correction values for the intra-subject (time) effects. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **Baseline** & **Pre-Training** & **Post-Training** & **Time F (Df): \(p\) (Eta2)** \\ \hline
**Soccer** & & & \\ Right ankle & 39.71 \(\pm\) 5.33 & 36.00 \(\pm\) 6.55 & 36.88 \(\pm\) 5.32 & F (2,32) = 7.558; \(p=0.002\) (0.321) \\ Left ankle & 38.82 \(\pm\) 4.87 & 34.58 \(\pm\) 5.86 & 37.49 \(\pm\) 5.10 & F (2,32) = 9.813; \(p=0.001\) (0.380) \\ Asymmetry & 6.44 \(\pm\) 3.44 & 10.40 \(\pm\) 6.90 & 6.25 \(\pm\) 5.68 & F (2,32) = 3.213; \(p=0.057\) (0.167) \\ \hline
**Basketball** & & & \\ Right ankle & 41.00 \(\pm\) 6.6 & 37.67 \(\pm\) 6.4 & 40.58 \(\pm\) 5.6 & F (2,36) = 17.687; \(p=0.001\) (0.496) \\ Left ankle & 39.56 \(\pm\) 6.7 & 34.9 \(\pm\) 5.3 & 38.70 \(\pm\) 5.7 & F (2,36) = 35.204; \(p=0.001\) (0.662) \\ Asymmetry & 4.56 \(\pm\) 3.9 & 8.7 \(\pm\) 5.1 & 5.95 \(\pm\) 4.5 & F (2,36) = 5.913; \(p=0.001\) (0.247) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: One-way ANOVA for the ankle ROM and asymmetry variables. Figure 1: Right ankle ROM values for each group in three measurement times. Figure 2: Left ankle ROM values for each group in three measurement times.
## 5 Conclusions  
In summary, our study indicates that both liming and plant residue incorporation can ameliorate acidic soils, with liming more effective than plant residue incorporation. In parallel with pH increase, plant residue incorporation also increased the TOC and DOC contents. However, soil nutrient levels and enzyme activities were increased by liming or plant residue incorporation, the effect of plant residue incorporation was more comprehensive than that of liming. PCR-DGGE analysis revealed that liming changed the microbial community structure more greatly than plant residue incorporation, and in contrast, plant residue incorporation altered the microbial community composition much more than liming. The alteration of the microbial community composition by liming and plant residue incorporation should be characterized in detail in the future. When plant species are considered, the growth responses of _S. guianensis_ (legume) and _P. natatum_ (graminoid) to liming and plant residue incorporation are different, with plant residue incorporation more promotive for _S. guianensis_ and liming more promotive for _P. natatum_. Our results provide a new sight in the amelioration of acidic soils, and help select appropriate practices in a combination of plants.  
Conceptualization, Q.Y. and H.Z.; methodology, X.L., Y.Z. and Z.F.; formal analysis, X.L. and Y.Z.; investigation, X.L., Y.Z. and Z.F.; data curation, X.L., H.Z. and Q.Y.; writing--original draft preparation, X.L. and Q.Y.; writing--review and editing, Q.Y. and H.Z.; supervision, Q.Y.; funding acquisition, Q.Y. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Guangdong Technological Innovation Strategy of Special Funds (key areas of research and development program, grant no.: 2018B020205003), and Guangdong Science and Technology Projects (2016A020210071).  
We thank the colleagues at Heshan Hilly Land Interdisciplinary Experimental Station, Chinese Academy of Science for collecting the tested soil.  
The authors declare no conflict of interest.