esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Inference Algorithm  
\(D=\{D_{1},\ \ldots,\ D_{n}\}\) represents a set of end-to-end measurement data. In which, \(D_{i}=(r_{i}f_{i}),\ r_{i}\) is the number of packets arrived of path \(p_{i}\) during measurement, \(f_{i}\) is the number of loss packets. Arrival rate of packets of path \(p_{i}\) is \(\varphi_{i}=r_{i}/(r_{i}+f_{i})\). Threshold value of packets arrival rates of path \(p_{i}\) is set as \(t_{p}\), which can be used to distinguish good or bad path: if \(\varphi_{i}\geq t_{p}\), path \(p_{i}\) is good, otherwise it is bad. The path set \(P\) is divided into good path set \(p_{G}\) and bad path set \(p_{B}\) according to \(t_{p}\).  
\(t_{p}\) is set artificially, two kinds of judgment errors are introduced inevitably. False positive error means that good paths are judged on bad paths, and false negative error means that a path is considered to be good but in fact it is bad. When \(t_{p}=t_{l}\) is chosen, if a bad link \(l_{k}\ (\ \forall\ l_{k}\in\ M_{i},\ \phi_{l_{i}}\ <t_{l})\) exits _in_ path \(p_{i}\), transfer arrival rate of the path is lower than \(t_{p}\ (\varphi_{i}=\underset{l\in M}{\prod}\phi_{l}\leq t_{l}-t_{l})\), at this time there is not exit false positive error. When \(t_{p}=\ t_{l}\) is chosen (\(m=|M_{i}|\) is the number of links contained in \(P_{i}\)), if the transfer arrival rate of the path is lower than \(\ t_{l}\), then there is at least a link whose delivery success rate is less than link \(t_{l}\), so there is no false negative error. When specific distribution of link packets loss rates is not known, the optimal choice of \(t_{p}\) cannot be acquired by analysis. This paper chooses \(t_{p}\) as \((t_{l}+\ t_{l}\cdot)/2\).
###### Abstract  
In Korea, for heavy metal-contaminated soil, there are insufficient basic data as well as field application cases for soil washing. This study was conducted to suggest the significant factors and optimal operation conditions for the maximum heavy metal removal efficiency. Five types of washing solution [acid solution (HCI), citric acid, oxalic acid, EDTA, and CaCl\({}_{2}\)], washing time (10-360 min), and solid-solution ratio (1:2-1:100) were selected as significant operational factors. Non-observed effect concentration (NOEC) and central composite rotatable design (CCRD) were applied to derive the optimal conditions. The soil pH was slightly alkali, and it was highly contaminated by Cd (17.34 mg kg\({}^{-1}\)), Cu (307.76 mg kg\({}^{-1}\)), Pb (714.78 mg kg\({}^{-1}\)), and Zn (1755.47 mg kg\({}^{-1}\)), complexly. For all heavy metals, CaCl\({}_{2}\), a neutral salt, was difficult to achieve the remediation goal. On the other hand, Cd, Cu, and Pb were removed successfully from the soil by acid solution, citric acid, oxalic acid, and EDTA. For Zn, only acid solution and oxalic acid could meet the remediation goal (300 mg kg\({}^{-1}\)). As the washing time increased, the heavy metal removal efficiency showed a tendency to increase overall. Especially, oxalic acid and acid solution were able to reach the highest efficiency point within 30 min, whereas citric acid and EDTA took 2 to 4 h at the longest. In the case of 1 M citric acid, through the CCRD experiment, optimal operation conditions (solid-solution ratio and washing time) could be deduced for each heavy metal. Furthermore, this series of processes can be utilized as a useful tool to find the optimal operating conditions and, at the same time, achieve the remediation goal.  
Keywords:central composite rotatable design; low molecular weight organic acid; non observed effect concentration; soil remediation goal +
Footnote †: journal: Applied Sci. 2021, 11, 6398. [[https://doi.org/10.3390/app11146398](https://doi.org/10.3390/app11146398)]([https://doi.org/10.3390/app11146398](https://doi.org/10.3390/app11146398))
# Vegetation Loss Measurements for Single Alley Trees in Millimeter-Wave Bands
## 2 Additional Attenuation of Vegetation
### Tree Alley Perspective
#### 2.2.2 Possible Ground Reflection  
In addition, in some cases, ground reflection may play an important role, especially when small antenna height, long distance, and wide beamwidth of the antennas are observed. The reflection coefficient \(\Gamma\) then depends on the incident/reflection angle \(\beta\) and the relative permittivity of the medium \(\eta\):  
\[\Gamma=\frac{\cos\beta-\sqrt{\eta-\sin^{2}\beta}}{\cos\beta+\sqrt{\eta-\sin^{2} \beta}}. \tag{21}\]  
The ground reflection loss for the reflected path with distance \(d_{\text{ref}}\) of the reflected path is then equal to  
\[L_{\text{reflection}}=20\log_{10}\left(\frac{d_{\text{ref}}}{\Gamma}\right). \tag{22}\]
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 5 Security Analysis
### Exhaustive Key Search  
A good encryption scheme must overcome the brute-force attack. This implies that the total number of available keys is huge, and then, the number of attempts is great enough. In our proposal, the key is composed of two parts: the first one related to the scrambling process, the second one related to the hiding process. For the first part of the key, its length is equal to the number of samples of the secret message (\(L\)), and it corresponds to the numbers \([1\)_to_\(L]\) in a disordered way. Then, the total number of available keys is \(L!\). For example, if the secret message has 8 K samples, an attacker needs to test \(L!\) attempts to try to reveal the secret content. For the second part of the key, the total number of keys in the hiding process is very small (there are six available choices). Therefore, the major part of the security in our system relies on the key space of the scrambling process.  
For example, if the attacker knows the scheme and accesses stego signals, he can extract the wrong message from the stego signals, if the key is not right (Figure 8). The reader can compare those results with the obtained in Figure 7. In both figures, the recovered messages are from the same stego signals, with \(BH\) equal to 4, but for different keys (correct/incorrect). After several tests, we found that the similarity between the original message and the wrong message obtained by the attacker with incorrect keys is lower than \(1\times 10^{-3}\). Figure 9 shows the results for the messages obtained from ten different host signals.  
Figure 8: Secret messages recovered without access to the key/password, varying the host signal and with BH equal to 4.
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project  
5G 5 th Generation of Cellular Networks  
BER  Bit Error Rate  
CDL  Clustered Delay Line  
DLSCH  Downlink Shared Channel  
DM-RS  Demodulation Reference Signals  
DSSS  Direct-Sequence Spread-Spectrum  
EVM  Error Vector Magnitude  
FFT  Fast Fourier Transform  
IQ  In-phase and Quadrature  
JNSR  Jamming plus Noise-to-Signal Ratio  
LOS  Line of Sight  
LTE  Long-Term Evolution  
MIMO  Multiple Input, Multiple Output  
MMSE  Minimum Mean Squared Error  
NLOS  Non-Line of Sight  
OFDM  Orthogonal Frequency Division Multiplexing  
PDSCH  Physical Downlink Shared Channel  
PSK  Phase Shift Keying  
QAM  Quadrature Amplitude Modulation  
QPSK  Quadrature Phase Shift Keying  
RB  Resource Block  
RF  Radio Frequency  
RSS  Received Signal Strength  
Rx  Receive  
SCS  Subcarrier Spacing  
SIMO  Single Input, Multiple Output  
SJR  Signal-to-Jamming Ratio  
Tx  Transmit  
UE  User Equipment
Figure 17: EVM vs. RB, BER and Throughput Measurements for Chirp Jammer, (**a**) EVM vs. RB for SJR = 0 dB, (**b**) EVM vs. RB for SJR = 20 dB, (**c**) Peak-EVM vs. SJR, and (**d**) Throughput and BER vs. SJR.  
Figure A6: Tone Jammer Case, SJR = \(-\)10 dB, SCS = 15 kHz. (**a**) the Rx signal spectrum and (**b**) EVM vs. RB.
## 4 Numerical Simulation of Roof Cutting Parameters
### Numerical Simulation of Cutting Height  
3.1 Vertical Stress Distribution Law of Surrounding Rock of Roadway with Different Roof Cutting Heights  
The stress distribution cloud diagram of the roof at different roof cutting heights is shown in Figure 8. When the roof cutting height is 8 m, the peak abutment pressure of the working face reaches 3.8 MPa and the peak lateral stress of the solid coal pillar reaches 3.3 MPa. The stress around the roadway is in a low-stress state under the action of roof cutting and pressure relief, and there is no stress concentration in the leading working face of the gob-side entry. As the roof cutting height increases to 9 m, the pressure relief effect improves. The peak value of the advanced abutment pressure of the working face is 3.3 MPa. The overburden load carried by the short-arm beam structure of the roof cutting is transmitted to the fixed-constraint end of the solid coal, and the peak value of the side  
\begin{table}
\begin{tabular}{c c c c} \hline
**Serial Number** & **Scheme** & **Variable** & **Invariant** \\ \hline \multirow{3}{*}{I} & Change the roof cutting & 8 m & Roof cutting angle 15\({}^{\circ}\) \\  & height & 9 m & Diameter of borehole 48 mm \\  & 10 m & Hole spacing 200 mm \\  & \(10^{\circ}\) & Roof cutting height 8 m \\ II & Change the roof cutting & \(15^{\circ}\) & Diameter of borehole 48 mm \\  & angle & \(20^{\circ}\) & Hole spacing 200 mm \\  & & 150 mm & Roof cutting height 8 m \\ III & Change the hole spacing & 200 mm & Diameter of borehole 48 mm \\  & & 300 mm & Roof cutting angle 15\({}^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 4: Numerical simulation scheme.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Lithologic Character** & **Cohesion/MPa** & \begin{tabular}{c} **Angle of Internal** \\ **Friction/(\({}^{\circ}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Joint Normal** \\ **Stiffness/(Gpa\(\cdot\)m\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **Joint Shear** \\ **Stiffness/(Gpa\(\cdot\)m\({}^{-1}\))** \\ \end{tabular} \\ \hline Silstone & 2.7 & 15 & 1.05 & 1.35 \\ Fine sandstone & 1.8 & 12 & 0.73 & 0.48 \\ Mudstone & 1.1 & 20 & 0.65 & 0.25 \\
2\# Coal seam & 0.6 & 10 & 0.33 & 0.12 \\ Mudstone & 1.1 & 20 & 0.65 & 0.25 \\ Sandy mudstone & 1.3 & 16 & 0.75 & 0.72 \\ \hline \end{tabular}
\end{table}
Table 3: Mechanical parameters of joints.  
stress of the solid coal pillar increases to 3.8 MPa. When the roof cutting height increases to 10 m, it has exceeded the roof control range of the constant-resistance anchor cable. The stable structure formed by the short-arm beam of the roof cutting and the support body has difficulty bearing the load of the overburden rock. The peak value of the advanced abutment pressure of the working face reaches 3.5 MPa and the peak value of the side stress of the coal pillar reaches 4.4 MPa. There is obvious stress concentration in the advance support section of the roadway.
## 6 Results
### Monte Carlo Simulation  
The results of the Monte Carlo simulation are presented below. The rural network presented in Figure 5 reveals high levels of VU, exceeding 2% limit. The only strategy that is able to keep VU below the limit, is strategy 4. Another great benefit of strategy 4 is the low variance of the results. The VU levels are less likely to deviate from an average value. Strategies 2 and 3 result in quite similar VU levels with relatively low variance. Figure 6 shows results of suburban (a) and urban (b) networks. VU levels never exceed VU limit over the course of this simulation. The higher impact of single-phase PVs can be noticed in suburban networks, since the difference of variance of strategies 1 and 4 is high. Compared to the urban network, the variance does not change within strategies. This indicates a dominant role of background VU source on the total VU.
## Materials and Methods
### BESS GPP Product  
The input data of the BESS GPP algorithm use three ancillary datasets, four reanalysis datasets, seven MODIS atmosphere (Collection 6) and land (Collection 5) products, and four additional satellite datasets [26]. A global continuous GPP product at high spatiotemporal (8-day, 1 km) resolutions is generated by a process-based satellite-driven method, instead of those widely-used machine-learning and semi-empirical models. The core algorithm comprises an atmospheric radiative transfer model, a forest light environmental simulator, and a two-leaf canopy radiative transfer model [27]. Meanwhile, a two-leaf longwave radiative transfer model jointly with a carbon-water-coupled module distinguishing C\({}_{3}\) and C\({}_{4}\) vegetation by employing [PERSON]'s photosynthesis model [9; 48], the quadratic Penman-Monteith [49; 50] and energy balance equations, and a stomatal conductance equation are applied to calculate GPP and ET for sunlit and shaded canopy through an iterative procedure. BESS GPP had a detailed description in the paper of [PERSON] and [PERSON] [26]. The work extracted BESS products from 2001 to 2015 in comparison with MODIS GPP over different climate zones in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Product** & **Description** & **Spatial** & **Temporal** & **Release Time** \\ \hline MODIS & MODIS GPP product derived & & & \\  & from satellite observations & 500 m & day & 10/2015 \\  & (MOD17A2H.006) & & & \\ BESS & BESS GPP product derived from a & 1 km & 8-day & 9/2016 \\  & process-based model & & & \\  & VPM GPP product derived from & & & \\  & MODIS observations and NCEP & 0.05\({}^{\circ}\) & 8-day & 10/2017 \\  & Reanalysis II climate data & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets of the satellite-based gross primary production (GPP) used in this study.  
China. In this study, the BESS GPP from 2001 to 2015 over the different climatic zones in China was freely extracted from [[http://environment.snu.ac.kr/bess_flux/](http://environment.snu.ac.kr/bess_flux/)]([http://environment.snu.ac.kr/bess_flux/](http://environment.snu.ac.kr/bess_flux/)).
## 4 Conclusions  
Simultaneous high-speed PIV, flame tomography, and high-speed spark imaging is applied in an optical port-injected gasoline engine at two different operating conditions: 1200 rpm--A/F18 and 1700 rpm--A/F18. A dichroic mirror splits the signal to 2 cameras, with one setup for PIV and flame imaging, while the other is dedicated to high-speed spark imaging. The deduced results of the flow field, burned region, and spark imaging is combined and analyzed to understand the interaction between in-cylinder flow, spark behavior, flame development, and combustion performance. The following observations are made.  
Higher flow velocity across the spark plug will result in a longer spark stretch distance. However, with a given ignition energy, the spark channel can only handle a certain flow speed limit before it breaks off and restrikes as the spark gap occurs.  
Figure 9: PIV-flame-development images after initial kernel development of three selected cycles (A, B, and C) at 1700 rpm condition.  
A longer spark stretch and higher flow surrounding the spark plug (both sides, with and against the flow) shorten the ignition delay and allow an earlier start of combustion and flame kernel formation.  
The flame will develop with the bulk in-cylinder flow and will follow the flow. It does not grow against the flow and will also enhance the existing flow in the same direction with its growth.  
The combustion duration does not depend much on the spark stretch but mostly on the in-cylinder flow in the region of the combustion chamber where it is developing in.  
Conceptualization, Y.I.; methodology, F.T. and M.K.L.; software, M.K.L.; validation, M.K.L.; formal analysis, F.T. and M.K.L.; investigation, F.T.; resources, Y.I.; data curation, F.T.; writing--original draft preparation, M.K.L; writing--review and editing, A.N.; visualization, M.K.L.; supervision, Y.L; project administration, A.N.  
This research received no external funding.  
The authors declare no conflict of interest.
# A Full-Duplex 60 GHz Transceiver with Digital Self-Interference Cancellation
###### Abstract  
This paper presents the design and measurement of an IEEE 802.11 ad standard compatible RF transceiver for 60 GHz wireless communication systems. In addition to the traditional half-duplex (HD) mode, this work supports full-duplex (FD) operations to deliver better channel utilization and faster response times for the system. The isolation between the transmitter and receiver from the architecture design to system integration for FD operations has been fully considered. A digital self-interference cancellation (DSIC) is implemented in MATLAB to verify the FD performance. The super-heterodyne architecture with an intermediate frequency (IF) of 12 GHz is designed to suppress the image frequencies without using extra filters. A flexible phase-locked loop (PLL) synthesizer provides a local oscillator (LO) frequency with a 2 kHz resolution. Other than the time division duplex (TDD) mode used in the conventional 60 GHz system, a wide-bandwidth baseband digital variable-gain amplifier (DVGA) with a 3 dB bandwidth of more than 4 GHz also supports frequency division duplex (FDD) operations. The transceiver chip is fabricated using the Tower Jazz 0.18 \(\upmu\)m SiGe BiMOS process. With an on-board antenna, the transceiver covers all four channels in the 802.11 ad standard, with MCS-12 (7.04 Gbps under 1.76 GSym/s and 16-QAM) under 15 m. In the proposed system design, the RF frontend-based self-interference (SI) suppression from the local transmitter to receiver LNA is around 54 dB. To achieve a practical FD application, the SI is further suppressed with the help of a digital SI compensation. The measured power consumption for the transmitter and receiver configurations are 194 mW and 231 mW, respectively, in HD mode and 398 mW for the FDD or FD operation mode.  
60 GHz communication; 802.11 ad; digital self-interference cancellation (DSIC); frequency division duplex (FDD); full duplex (FD); low power consumption; self-interference (SI); transceiver SoC; SiGe BiCMOS +
Footnote †: journal: Electronics Electronics
# Drilling Condition Identification Method for Imbalanced Datasets
## 3 Sliding-Window-Based SMOTE Oversampling Method  
Classical oversampling methods include random oversampling, SMOTE, and its derivative algorithms (such as SMOTE-Tomek, Borderline-SMOTE, and ADASYN) [28]. These methods achieve class balance by either randomly duplicating minority-class samples or generating new samples through interpolation in the feature space [29]. However, different strategies have certain limitations:  
1. Random duplication of minority-class samples may cause the model to overfit to the characteristics of the minority-class samples. This issue is particularly problematic when the minority class contains noise, as it can further increase the number of anomalous samples;
2. Interpolation-based generation of minority-class samples, while leveraging the distribution information of the minority-class samples, may disrupt the temporal relationships in the original data. This can cause the model to learn a distribution that deviates from the true distribution.  
To overcome the aforementioned issues, this paper proposes a sliding-window-based SMOTE oversampling method, which integrates a local temporal preservation mechanism with the SMOTE approach, ensuring the retention of temporal relationships during the synthesis of minority-class samples. First, the drilling time-series data are segmented  
Figure 1: Weighted symmetric uncertainty feature selection flowchart.  
using a sliding window. Oversampling is then performed within each window, ensuring that the newly generated samples retain the local temporal structure. Meanwhile, the sliding window mechanism enables the gradual expansion of minority-class samples across different segments, overcoming the temporal discontinuities caused by traditional global oversampling. As a result, the generated drilling parameter sequences maintain the continuity of the physical process. By reducing noise interference and enhancing the alignment between the resampled data and the actual distribution, this method significantly improves the quality of data input to the classifier. Consequently, it effectively enhances model classification accuracy and is particularly well suited for applications with strong temporal dependencies, such as drilling state recognition.
## 1 Introduction  
It has been estimated that 1 million patients underwent total knee replacement (TKR) surgery in 2020, and this is projected to increase to 1.9 million in 2030 just in the US [1]. In Asia, the reported incidence of DVT after TKR is about 34-48.6% [2, 3]. Deep venous thrombosis (DVT) is a serious complication that usually occurs within 2 weeks after TKR [4, 5, 6, 7],although the incidence is rare [5; 8; 9]. Possible damage to the vessel wall during the surgery, venous stasis caused by bed rest, and hypercoagulability of the blood after surgery are the three main reasons for the formation of DVT. In most cases, the thrombi resolve spontaneously; however, some of them (approximately 1-2.3%) may develop into symptomatic and even fatal DVT [9]. Anticoagulants such as aspirin are traditionally used for patients after TKR to prevent DVT [8; 10], but carry a risk of GI bleeding [11; 12]. In addition to aspirin, physical therapies, including active ankle pumping exercise and intermittent pneumatic compression, have shown reduction in the risk and incidence of DVT [13; 14; 15; 16; 17; 18]. The mechanism of active ankle pumping exercise for DVT prevention is the extrusion of the veins in the lower leg with the contraction of the calf muscles [19]. The extrusion force from muscle contraction helps the venous blood to go against gravity and back to the heart. The mechanism of intermittent pneumatic compression for DVT prevention is similar to that of active ankle pumping exercises [20]. Ankle pumping exercises and pneumatic compression differ in the power source. Muscle contraction is the power source of ankle pumping exercises, whereas the external motor power is the power source of pneumatic compression. Both ankle pumping exercise and intermittent pneumatic compression can reduce the risk of DVT [13; 14; 16; 17].  
For DVT prevention with ankle pumping exercises after TKR, patients are asked to exercise automatically, particularly after returning home from the hospital. However, exercise execution and effects of active ankle pumping exercises on DVT prevention is uncertain. In addition, since ankle pumping exercises and intermittent pneumatic compression can both prevent DVT, which one is more effective method is still not reported. To remind the patients to exercise, a device with a motion sensor and vibration alarm, manufactured by ZMI Electronics (Kaohsiung, Taiwan), was developed to promote patient compliance with exercise regularly for their ankles after TKR. The alarm turns off if an adequate number of ankle movement with more than 40 degrees has been performed.  
This study compared 1. the effect of active ankle pumping exercise performed with and without an alarm reminder, and 2. the effect of intermittent pneumatic compression and active ankle pumping exercise without alarm reminder on the prevention of DVT in the hospital. It is hypothesized that reminders can improve the performance of active ankle pumping exercises to improve lower leg circulation.