esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Experimental Results
#### 3.3.3 Experiment 3 Using the Jiuzhaigou Dataset  
The third image pair shows significant differences in temporal and hue. In the experiment, the proposed method also provided the largest number of tie points (\(N_{cp}\) = 118), whereas the Patch-SIFT and SIFT methods yielded 94 and 46 tie points, respectively. The proposed method has a significant advantage in the running time over the other two methods. The running times of the proposed method were 2.05 and 1.93 s for the cubic and quadratic polynomial models, respectively. The former is only 7.36% of the SIFT method (27.85 s) and 32.23% of the Patch-SIFT method (6.36 s) using the same model. The proposed method provided \(RMSE_{M}\) values of 1.37 and 1.39 pixels, respectively, which are higher than those of the Patch-SIFT (\(RMSE_{M}\) = 1.27 pixels and \(RMSE_{M}\) = 1.32 pixels, respectively) and SIFT (\(RMSE_{M}\) = 0.72 pixels and \(RMSE_{M}\) = 0.76 pixels, respectively) methods. Although the SIFT method yielded the lowest model error, the validation error (\(RMSE_{T}\) = 2.48 and \(RMSE_{T}\) = 2.51 pixels for the cubic and quadratic polynomial models, respectively) was extremely high. The main reason is that the number of matched tie points was small, and the spatial distribution of tie points was uneven. The spatial distribution of matched tie points is shown in Figure 15. It can be seen that the spatial distribution of the matched tie points (dots in red) extracted using the proposed method (as shown in Figure 15a) was the most even. In contrast, the matched tie points obtained by the SIFT are mainly located in the image patches with significant features (such as roads and houses). However, in the image patches with complex terrain, the number of matched tie points extracted by the SIFT method was very limited compared with the two methods using the image partitioning strategy.  
Figure 13: Matched tie points of the GF-1 image. (**a**) The proposed method, (**b**) Patch-SIFT, and (**c**) SIFT.  
The registered images of the Jiuzhaigou dataset using the cubic polynomial model are shown in Figure 16. As shown in Figure 16b, the registered image of the proposed method still had slight displacements with the original image. In contrast, the other two methods had noticeable displacements for rivers and roads (Figure 16c,d). This is also consistent with the \(RMSE_{T}\) values provided by the three methods. Consequently, compared with the other methods, the proposed method can achieve a larger number of tie points, which are more evenly distributed. This is helpful for improving the accuracy of image registration.  
Figure 14: Registration results of the Yaan dataset. (**a**) The original image; (**b**) registered image of the proposed method; (**c**) registered image of Patch-SIFT; and (**d**) registered image of SIFT.  
Figure 15: Matched tie points of the GF-2 image. (**a**) The proposed method; (**b**) Patch-SIFT; and (**c**) SIFT.
## 5 Conclusions  
The aim of the study was to investigate the effectiveness of the frequency-band-limited attenuation of vertical interchannel crosstalk (VIC) (i.e., direct sound presented in the height loudspeaker layer) on vertical phantom image localisation in the context of quadraphonic sound reproduction with height channels. The main findings of the study are as follows.  
* It is possible to successfully prevent the localisation interference effect of VIC by attenuating only some specific octave bands of the VIC, each by the amount specified Table 1; for example, (i) all ofthe bands centred at 1 kHz and above, (ii) only the band centred at 4 kHz only, and (iii) only the band centred at 8 kHz.
* The influence of VIC on vertical localisation has a sound source dependency due to frequency-related psychoacoustic phenomena, such as the pitch-height effect and the phantom image elevation effect. The results suggest that, in vertical quadraphonic reproduction, sound sources with a narrow frequency spectrum (e.g., oboe) would not have a significant VIC influence on perceived vertical image position, since the image would be localised at a vertical position inherent from the frequency content, regardless of the presentation method.
* In addition, the results support a previous finding that neither the precedence effect nor localisation dominance operates vertically. That is, in the vertical domain, time delay between two loudspeakers alone would not cause the auditory image to be localised at the position of the earlier loudspeaker, and a certain level reduction would be necessary for this.  
The band-limited VIC attenuation methods might produce a greater magnitude of perceptual effects, such as increases in vertical image spread and fullness when compared to the blanket method (i.e., attenuation as a whole). Their practical application areas include vertical sound image rendering in 3D sound mixing and post-processing of 3D sound recording.  
Future works will investigate (i) what the most salient effects of vertical interchannel crosstalk are, (ii) how these vary when the different VLT methods are applied, and (iii) which method is most preferred subjectively.  
[PERSON] conducted the experiment, analysed the data and wrote majority of the paper. [PERSON] supervised the project, contributed to the data analysis and co-wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Engineering and Physical Sciences Research Council (EPSRC), UK, Grant Ref. EP/L019906/1.  
The authors would like to thank all subjects who participated in the listening test of this study.  
The authors declare no conflict of interest.
## 2 Principles of GNSS Remote Sensing Technique
### Gnss-\(R\)  
As the wind blows, the ocean surface becomes rough due to the wind-caused waves. The GNSS satellite signals illuminate the rough ocean surface and diffuse reflection occurs. Actually, we can collect the peak value and shape of the reflection waveform to represent the roughness of the ocean surface. The higher the wind speed, the rougher the ocean surface, leading to more diffuse reflection. At last, it results in the peak value of the waveform reduction and waveform edges being flat [18].  
The delay-Doppler map (DDM) shown in Figure 4 is a fundamental physical parameter based on a GNSS-R measurement, and it describes the characteristics of the time and signals. It is generated by cross-correlating the received signal with a replication of the transmitted signal, covering a range of delays and Doppler frequencies. DDM is then calibrated into a bistatic radar cross-section (BRCS) using geometry and the power parameters of the transmitter and receiver  
\[\sigma=\frac{P_{g}(4\pi)^{3}(R_{r})^{2}(R_{t})^{2}L_{a1}L_{a2}}{P_{t}\lambda^{ 2}G_{t}G_{r}} \tag{5}\]  
where \(\sigma\) is the radar cross-section, \(P_{t}\) is the GNSS transmit power, \(P_{g}\) is the noncoherently processed scattered signal power, \(\lambda\) is the GNSS signal carrier wavelength, \(G_{t}\) is the antenna gain of GNSS, \(G_{r}\) is the antenna gain of the receiver, \(R_{r}\) and \(R_{t}\) are the distances to and from the Earth surface, respectively, and \(L_{a1}\) and \(L_{a2}\) represent the atmospheric losses along the signal's propagation to and from the surface, respectively.  
Figure 4: (**a**) Is DDM power, and (**b**) is DDM BRCS. In (**b**), the white rectangle highlights the region of the \(\sigma\) BRCS that can be used for estimating the wind speed. The white X marks the estimated location of the specular bin in the DDM.  
To retrieve the ocean surface wind speed from the DDM BRCS, a number of algorithms have been developed [19; 20; 21]. Typically, there are two key observables computed using a 3 \(\times\) 5 delay-Doppler window in DDM, shown in Figure 4b, the normalized bistatic radar cross-section (NBRCS) and leading-edge slope (LES). The center of the window is usually the bin nearest the predicted specular point delay, to catch the peak value of reflection, and the spatial resolution can achieve 25 km [20]. Then, utilizing empirical geophysical model functions (GMFs) that link the surface speed with NBRCs and LES [22; 23; 24], we can derive the wind speeds. Figure 5 shows the retrieval results of ocean surface wind speeds from FY-3E for a typhoon in 2023.
# Decision Support Application for Energy Consumption Forecasting
## 3 Proposed Model
### Forecasting Strategies  
Every forecasting method to estimate a value requires two sets of data, the train data, and the test data. The way that these two data sets have been selected and structured is one the most important facts to have a reliable forecast. All the decisions that the forecasting methods make are based on these input data (Table 1). This way, this application includes four different strategies to collect the train and test data for an hour-ahead energy forecasting. These strategies have been chosen based on the most effective variables on the energy consumption and the behavior of the consumption in the target building. However, the application has the possibility of receiving any other structured data using an excel file.  
The first strategy uses the value of the electricity consumption to train the methods. The objective is to forecast the value of the electricity consumption of the next hour. Therefore, the test data will include this value from the past 24 h before the hour which is meant to be forecasted. The train data in this strategy includes the value of the energy consumption from past 10 days before the date of the target value. This way, the method will be trained 10 times by the data from past 10 days.  
The second strategy trains the methods by a combination of the electricity consumption and the environmental temperature values of the related place in the past 10 days. The environmental temperature has been chosen to be used in this strategy due to having a high influence on the value of the energy consumption. The test data in this strategy includes the value of these two variables from the last 24 hour before the target hour.  
The third strategy works in a similar way as the first strategy and only uses the value of the electricity consumption to train the method. The difference between them is the dates of the train data. This strategy does not use the data from the last 10 days, but it includes a process that chooses special dates to train the methods, these dates are chosen base on two properties:  
* The difference between the average environmental temperature during these days and the target day should be a maximum of 2 \({}^{\circ}\)C.
* These days should have the same type of working day (week or weekend) as the target day.  
After the process of date selection, the method will be trained 10 times by the values of the energy consumption of these chosen dates. Also, the test data as same as the first strategy includes the energy consumption of the past 24 h before the target hour.  
Finally, the fourth strategy is a combination of the second and the third strategies. Where it uses the same process of choosing special dates to train the methods as the third strategy and as same as the second strategy uses the value of the environmental temperature plus the value of the energy consumption as the input of the method. Also, the test data in this strategy is the same as the second one.
## 3 Perturbation Analysis and Modulation Equations
Similarly, the Cartesian form of modulation equations are also obtained\[q_{w2} =\frac{\Lambda_{w1}}{\omega_{w}^{2}-(\omega_{v}+\omega_{w})^{2}}A_ {v}A_{w}\mathrm{e}^{\mathrm{i}(\omega_{v}+\omega_{w})t}+\frac{\Lambda_{w1}}{ \omega_{w}^{2}-(\omega_{v}-\omega_{w})^{2}}A_{v}\bar{A}_{w}\mathrm{e}^{ \mathrm{i}(\omega_{v}-\omega_{w})t}+c.c..\]0 \[q_{w2} =\frac{\Lambda_{w1}}{\omega_{w}^{2}-(\omega_{v}+\omega_{w})^{2}}A_ {v}A_{w}\mathrm{e}^{\mathrm{i}(\omega_{v}+\omega_{w})t}+\frac{\Lambda_{w1}}{ \omega_{w}^{2}-(\omega_{v}-\omega_{w})^{2}}A_{v}\bar{A}_{w}\mathrm{e}^{ \mathrm{i}(\omega_{v}-\omega_{w})t}+c.c..\]1 \[q_{w2} =\frac{\Lambda_{w1}}{\omega_{w}^{2}-(\omega_{v}+\omega_{w})^{2}}A_ {v}A_{w}\mathrm{e}^{\mathrm{i}(\omega_{v}+\omega_{w})t}+\frac{\Lambda_{w1}}{ \omega_{w}^{2}-(\omega_{v}-\omega_{w})^{2}}A_{v}\bar{A}_{w}\mathrm{e}^{ \mathrm{i}(\omega_{v}-\omega_{w})t}+c.c..\]2 \[q_{w2} =\frac{\Lambda_{w1}}{\omega_{w}^{2}-(\omega_{v}+\omega_{w})^{2}}A_ {v}A_{w}\mathrm{e}^{\mathrm{i}(\omega_{v}+\omega_{w})t}+\frac{\Lambda_{w1}}{ \omega_{w}^{2}-(\omega_{v}-\omega_{w})^{2}}A_{v}\bar{A}_{w}\mathrm{e}^{ \mathrm{i}(\omega_{v}-\omega_{w})t}+c.c..\]3 \[q_{w2} =\frac{\Lambda_{w1}}{\omega_{w}^{2}-(\omega_{v}+\omega_{w})^{2}}A_ {v}A_{w}\mathrm{e}^{\mathrm{i}(\omega_{v}+\omega_{w})t}+\frac{\Lambda_{w1}}{ \omega_{w}^{2}-(\omega_{v}-\omega_{w})^{2}}A_{v}\bar{A}_{w}\mathrm{e}^{ \mathrm{i}(\omega_{v}-\omega_{w})t}+c.c..\]4 \[q_{w2} =\frac{\Lambda_{w1}}{\omega_{w}^{2}-(\omega_{v}+\omega_{w})^{2}}A_ {v}A_{w}\mathrm{e}^{\mathrm{i}(\omega_{v}+\omega_{w})t}+\frac{\Lambda_{w1}}{ \omega_{w}^{2}-(\omega_{v}-\omega_{w})^{2}}A_{v}\bar{A}_{w}\mathrm{e}^{ \mathrm{i}(\omega_{v}-\omega_{w})t}+c.c..\]5 \[q_{w2} =\frac{\Lambda_{w1}}{\omega_{w}^{2}-(\omega_{v}+\omega_{w})^{2}}A_ {v}A_{w}\mathrm{e}^{\mathrm{i}(\omega_{v}+\omega_{w})t}+\frac{\Lambda_{w1}}{ \omega_{w}^{2}-(\omega_{v}-\omega_{w})^{2}}A_{v}\bar{A}_{w}\mathrm{e}^{ \mathrm{i}(\omega_{v}-\omega_{w})t}+c.c..\]6 \[q_{w2} =\frac{\Lambda_{w1}}{\omega_{w}^{2}-(\omega_{v}+\omega_{w})^{2}}A_ {v}A_{w}\mathrm{e}^{\mathrm{i}(\omega_{v}+\omega_{w})t}+\frac{\Lambda_{w1}}{ \omega_{w}^{2}-(\omega_{v}-\omega_{w})^{2}}A_{v}\bar{A}_{w}\mathrm{e}^{ \mathrm{i}(\omega_{v}-\omega_{w})t}+c.c..\]7  
where \(a_{i}=\sqrt{p_{i}^{2}+q_{i}^{2}}\ \ (i=v,w)\). \(\Omega\approx\omega_{v}\) (the in-plane excitation), \(v_{v}=\sigma_{1}\), \(v_{w}=\sigma_{1}-\sigma_{2}\) and \(\Omega\approx\omega_{w}\) (the out-of-plane excitation), \(v_{v}=\sigma_{1}+\sigma_{2}\), \(v_{w}=\sigma_{1}\). The fixed points, also known as equilibrium or constant solutions, can be obtained by setting \(a_{v}=a_{w}=0\) and \(\dot{p}_{v}=\dot{p}_{w}=0\) in Equation (15). Using a pseudo-arclength path-following algorithm, we trace the rest response branches and determine the corresponding stability of the fixed points based on the eigenvalues of the Jacobian matrix in Equation (16). Utilizing XPPAUTO, the four coupled solution branches that involve non-zero solutions and determines their stability properties are exhibited [47].
## 3 Propagation Model
### Temporal Response of the Channel
Although the normal \(\left(Gaussian\right)\) density of data forms the basis for Equation (18), actually it is not as strong as the constant normal assumption, i.e., use of Equation (18) on non-normally distributed data, will not result in a histogram that looks like normal. The bin size \(t_{bw}\) also determines the maximum frequency of the channel due to the reciprocal frequency-time relationship. Therefore, we can say the maximum frequency that can be represented in estimating the channel time dispersion is derived as  
\[f_{max}=\frac{c}{t_{bw}n_{w}}. \tag{19}\]  
where \(c\) is the speed of light in vacuum, \(t_{bw}\) is bin size, and \(n_{w}\) is the refractive index of water.
## 3 Results and Discussion
### Theoretical Assay
#### 3.7.2 Global Chemical Reactivity  
The interaction between HOMO\({}_{\text{inhibitor}}\) and LUMO\({}_{\text{surface}}\) has indirectly stabilized with energy gap \"\(\Delta G\)\". The stability index for AO and HERA adhering to Fe surfaces was determined by \(\Delta G\). AO and HERA exhibited lower \(\Delta G\) values in gas phase than liquid phase, respectively. These values showed promising reactivity liquid phase (Table 4). Furthermore, \(\Delta G\) related linearly with the soft-nucleophile and hard-electrophile. Thus, the molecule which has a low \(\Delta G\) displayed promising softness \"\(S\)\" properties, which leads to being a good inhibitor in acidic media [59; 60; 61]. The data obtained from Table 7 demonstrated greater softness values in liquid phase than gas phase.  
Furthermore, we examined the amount of electron transfer from the inhibitor (donor) to the Fe (acceptor) by calculating the \"\(\mu-\) & \(\omega-\)\" [59; 62]. The inhibition action increases with increasing values for \"\(\mu-\) & \(\omega-\)\", which refers to high ability for donating electron by the inhibitor, respectively, and vice versa for \"\(\mu+\) & \(\omega+\)\". The liquid phase exhibited higher capacity for sharing electrons than the gas phase. Besides, the highest electrons amount transferred (\(\Delta\)Nmax) for liquid phase (0.055 to 0.059) is more than for the gas phase (\(-\)0.28 to \(-\)0.46), which supported the claimed experimental findings regarding high efficiency for AO and HERA in liquid phase.  
The process of the electron contribution as \"Fe \(\rightarrow\) inhibitor\" is recognized as energy back-donation, \"\(\Delta\)E\({}_{\mathbf{B}\mathbf{D}}\)\", which determined the \"Inhibitor-Fe\({}_{\text{surface}}\)\" interaction. The higher \(\Delta\)E\({}_{\mathbf{B}\mathbf{D}}\) in the liquid phase than the gas phase, displayed promising penetration ability with higher inhibition efficiency for liquid phase. The negative values of \"\(\Delta\)E\({}_{\mathbf{B}\mathbf{D}}\), \(\mu+\), \(\omega+\)\" proposed that the \"Fe \(\rightarrow\) inhibitor\" electron follows in liquid phase are more favorable energy than gas phase (Table 7). These findings were consistent with the experimental data.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline  & **HOMO** & **LUMO** & **AG** & **DM** & \(\eta\) & **S** & **x** & **I** & **A** & **EP** & \(\omega\)**i** & \(\mu+\) & \(\mu-\) & \(\omega+\) & \(\Delta\)**EBD** & \(\Delta\)**Nmax** \\ \hline \multicolumn{11}{c}{**(Gas phase)**} \\ \hline
**Mytistic acid** & \multirow{2}{*}{\(-\)1.90} & \multirow{2}{*}{\(-\)3.54} & \multirow{2}{*}{1.63} & \multirow{2}{*}{36.37} & \multirow{2}{*}{1.63} & \multirow{2}{*}{438.65} &
# Drone and Controller Detection and Localization: Trends and Challenges
## 4 Drone Controller Detection and Localization
The computed geolocation parameters for the direction-finding methods include angle of arrival (AOA) [40], time of arrival (TOA) [64], direction of arrival (DOA) [18; 47], frequency difference of arrival (FDOA) [51], time difference of arrival (TDOA) [11; 35], and received signal strength (RSS) [65]. The proposed system in [12] utilizes a low-cost passive RF-based UAV detection and localization method. The system computes AOA for the RF-based signal to determine whether the transmitted signals' peaks correspond to the UAV or its controller. Then, it uses the triangulation technique to estimate the location of RF signal peak sources. The free-space path loss model and triangulation combination is reported in [50] to detect and localize a stationary drone controller. The proposed system contains two direction-finding systems for direction identification and localization for the drone and its controller. Each direction-finding system has an omnidirectional antenna for detecting drone signal occurrence and a mechanically agile directional antenna for directions identification and localization of RF signal peaks for UAV and/or its controller signals. The whole system is depicted in Figure 8.  
The study [12] discussed that distinguishing between RF signals from the UAV and its controller from other RF signals in the surrounding area poses a challenge in drone controller localization. The reported direction-finding station consists of two modules: drone signal analysis to classify drone and remote controller (RC) signals and the direction-finding module. Each direction-finding station extracts acute parameters from detected RF signals and uses a mechanical steering antenna for identification and localization. The precision of the direction-finding function is dependent and affected by the antenna's directivity and gain, drone velocity, scanning velocity, and beam width of the used directional antenna. The reported detection system in [18] employs frequency hopping spread spectrum (FHSS) to detect and locate UAVs and RCs. The cyclostationarity analysis algorithm is used to identify the FHSS-type drone RC signals and differentiate them from other background signals operating in the same frequency band. After the successful classification of the drone RC signals, STFT and additional re-sampling processing are applied to enhance the detection accuracy of the reconstructed RC signal. Finally, the direction-finding phase is achieved by implementing the subspace algorithms to identify the AOA of the FHSS  
Figure 8: Diagram of direction finding mechanism in the proposed system [12]. drone RC signal. The proposed system [49] utilizes a set of a uniform linear array of quasi-Yagi antennas in the experimental setup to enhance the precision of the direction-finding function.
# Data Exfiltration through Electromagnetic Covert Channel of Wired Industrial Control Systems
## 5 Covert Channel Design and Implementation
### Structure and Encoding of Data Frame  
When designing the covert channel, simply modulating digital 1 and 0 values in the data using the two previously identified network traffic patterns is not reliable. A communication channel of this nature has two important requirements in order to be useful: (1) minimising errors and (2) time synchronisation between the sender and receiver. For the former requirement, a data frame structure was designed incorporating error correcting codes. The frame contains an eight bit preamble, which is an alternating 1 and 0 sequence to detect the start of the frame. The payload is 42 bits long, and an eight bit cyclic redundancy check (CRC) is generated on the payload and appended to the end of the frame. Overall, the data frame is 64 bits long. Both the payload and CRC are encoded with Hamming codes. Hamming codes are a family of binary linear error correcting block codes. They are considered to be a _perfect code_ that has a high throughput compared to other error correction codes. Hamming codes can correct one bit errors. Hamming(7,4) code is adopted in this research for error correction. It encodes a four bit word into a seven bit code word by adding three parity bits.  
Due to the specific Hamming code in use, seven bits in the payload of the data frame are used to represent four bits of data. Therefore, an ASCII character, i.e., with a size of eight bits, occupies fourteen bits in the payload of a data frame. Since the payload carried by a data frame has a capacity of 42-bits, a single data frame can be used to deliver three ASCII characters at a time. Meanwhile, for the modulation of data to the physical medium, the Manchester encoding scheme is used. With Manchester encoding, binary 1 is represented by a rising edge, while binary 0 is represented by a falling edge. These transitions take place at clock pulses. In this manner, the clock signal and the data stream are encoded together. As a result, any requirement for clock synchronization is eliminated. Since the amplitude of the signals varies over time, simple encoding schemes, such as On-Off Keying (OOK), cannot be used here [24].
# Experimental Study of Influence of Plant Roots on Dynamic Characteristics of Clay
## 3 Experimental Results Analysis
### Influence of Plant Root Systems on Soil Shear Modulus
#### 3.1.2 Different Root Mass Density
This phenomenon primarily results from the fact that as the confining pressure increases, the arrangement of soil particles, roots, and the interstitial space becomes denser. This decrease in porosity heightens the cohesion and frictional forces between soil particles and roots. Consequently, the impact of the roots diminishes, reducing their effect on the dynamic shear modulus of the specimen.