esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
We demonstrated the security of the BB84 QKD protocol under the collective-rotation noise channel by studying scenarios where the eavesdropper, [PERSON], conducts an intercept-resend attack on the transmitted photons sent via a quantum communication channel shared by [PERSON] and [PERSON]. Notably, we distinguished the impact of collective-rotation noise and that of the eavesdropper. From the analysis, we observed that, for small rotation angles, one can extract a secure secret key under a collective-rotation noise channel when there is no eavesdropping. For instance, in a noise-free environment (\(\theta=0\)), [PERSON] will induce a 25% QBER, corresponding to the well-known error bound for the BB84 protocol under the simple intercept-resend attack. However, when both eavesdropping and noise exist in the channel, the QBER obtained would always be greater than the 25% error threshold for the intercept-resend attack. Hence, no secret key can be distilled by the legitimate parties. In addition, the BB84 protocol experiences the worst QBER \(Q_{1}\) for every rotation angle \(\theta=n\pi+\frac{\pi}{4}\). Notably, this corresponds to noise that will flip the prepared quantum states into a state on the opposite basis (i.e., a phase flip error). Thus, \(Q_{1}\) reaches its lowest level at every \(\theta=n\frac{\pi}{2}\). However, when \(\theta=\frac{\pi}{4}\), the case that results in phase flip errors, the QBER is at 50%, meaning that half of the sifted key bits will be erroneous. Most significantly, we observed that noise induced by a rotation of 0.35 radians of the prepared quantum state will cause a QBER of 11%. This phenomenon corresponds to the lower bound on the tolerable error rate for the BB84 QKD protocol against general attacks. Finally, a rotational angle of 0.53 radians produces a 25% QBER, which corresponds to the bound on the error rate brought about by the intercept-resend attack.  
Conceptualization, M.M. and C.S.; methodology, M.M and C.S.; software, C.S.; validation, M.M. and C.S.; writing--original draft preparation, M.M.; writing--review and editing, C.S., M.M., and M.S.; visualization, C.S.; supervision, M.M.; project administration, M.M.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 3: Variation of the mutual information between [PERSON] and [PERSON] _I(A:E)_ as a function of the collective-rotation noise \(\varepsilon\).
# Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa
###### Abstract  
This study proposes a new approach to gait recognition using LoRa signals, taking into account the challenging conditions found in underground coal mines, such as low illumination, high temperature and humidity, high dust concentrations, and limited space. The aim is to address the limitations of existing gait recognition research, which relies on sensors or other wireless signals that are sensitive to environmental factors, costly to deploy, invasive, and require close sensing distances. The proposed method analyzes the received signal waveform and utilizes the amplitude data for gait recognition. To ensure data reliability, outlier removal and signal smoothing are performed using Hampel and S-G filters, respectively. Additionally, high-frequency noise is eliminated through the application of Butterworth filters. To enhance the discriminative power of gait features, the pre-processed data are reconstructed using an autoencoder, which effectively extracts the underlying gait behavior. The trained autoencoder generates encoder features that serve as the input matrix. The Softmax method is then employed to associate these features with individual identities, enabling LoRa-based single-target gait recognition. Experimental results demonstrate significant performance improvements. In indoor environments, the recognition accuracy for groups of 2 to 8 individuals ranges from 99.7% to 96.6%. Notably, in an underground coal mine where the target is located 20 m away from the transceiver, the recognition accuracy for eight individuals reaches 93.3%.  
LoRa; gait recognition; identification of underground personnel in coal mines; received signal amplitude; autoencoder +
Footnote †: journal: Applied Sciences
# Novel Pointing and Stabilizing Manipulator for Optical Space Payloads
## 2 Characteristics and Algorithms for Large-Scale Pointing Adjustment
### Mechanism Singularity
It is also drawn that matrix \(\mathbf{J}_{\theta f}\) frees from \(\delta\overset{\rightarrow}{\boldsymbol{d}}_{c}\) since matrices \(\mathbf{M}_{pf}\), \(\mathbf{M}_{pc}\), \(\mathbf{N}_{f}\), and \(\mathbf{N}_{c}\) are all free from \(\delta\overset{\rightarrow}{\boldsymbol{d}}_{c}\). Similarly, in the case that matrix \(\mathbf{N}_{f}\) is nonsingular, matrix \(\mathbf{J}_{\theta c}\) would be free from \(\delta\overset{\rightarrow}{\boldsymbol{d}}_{f}\) and nonsingular if, and only if, \(\mathbf{J}_{lp}\) is nonsingular. In conclusion, on the condition that coarse actuator directions are mutually linearly independent of each other and fine actuator directions are also mutually linearly independent of each other, the division of labor of the pointing and stabilizing manipulator would be feasible as long as the corresponding 6-UPS configuration of the manipulator is nonsingular. Besides, the singularity relationship between matrices \(\mathbf{J}_{\theta f}\), \(\mathbf{J}_{\theta c}\) and matrix \(\mathbf{J}_{lp}\) implies that the proposed manipulator would have a large angular workspace if the corresponding 6-UPS configuration could remain non-singular across a wide range of roll-pitch-yaw (RPY) angles.
# Infrared and Visible Image Fusion via Feature-Oriented Dual-Module Complementary
## 3 The Proposed Method
### Infrared Brightness Supplement Module
#### 3.2.2 Exposedness Intensity Operator  
Exposedness features are often extracted in the MEIF task because it can localize the exposed regions well. Since infrared images are similar to exposure images on the premise of considering the brightness information, this work introduces exposedness to IVIF. However, we also notice that there are two constants (0.5 and 0.2) in Equation (6), which means that the equation does not consider the differences in the source images. This design is actually similar to the commonly used \"weighted average\" fusion rule, which means that all pixel values are promoted to be equal to 0.5, regardless of the difference in image distribution, while ignoring the regions where the pixel values in the source images are 0 or 1, resulting in loss of structural details and infrared brightness in the fused image. To address this defect, we improved the exposedness intensity \((A_{n}(x,y))\) by replacing the constants in Equation (6) with the mean and standard deviation to obtain a new equation operator as follows:  
\[A_{n}(x,y)=\exp\left(-\frac{(I_{n}(x,y)-(1-\mu_{I_{n}}))^{2}}{2{\sigma_{I_{n}}} ^{2}}\right), \tag{34}\]  
where \(\mu_{I_{n}}\) and \(\sigma_{I_{n}}\) are the means and standard deviations of the pixel intensities in \(I_{n}\), respectively. In this case, each exposedness is determined by each input, which can be seen as an adaptive operator.  
Next, the supplementary map \((S_{n}(x,y))\) is obtained through the two feature maps:  
\[S_{n}(x,y)={G_{n}}^{\omega_{1}}(x,y).*{A_{n}}^{\omega_{2}}(x,y). \tag{35}\]  
where \(\omega_{1}\) and \(\omega_{2}\) are weights that determine the ratio of the two feature maps injected into the fused image.  
Then, a Gaussian filter is used to smooth \(S_{n}(x,y)\) to reduce the noise artifacts as follows:  
\[R_{n}(x,y)=\text{Gaussian}(S_{n}(x,y),\sigma_{r}), \tag{36}\]  
where \(\sigma_{r}\) denotes the standard deviation of the Gaussian kernel.  
Finally, the fused image is obtained as  
\[F=P(x,y)+\sum_{i=1}^{n}I_{i}(x,y)\cdot R_{i}(x,y). \tag{37}\]  
Compared to the resulting image in Figure 2c,d, the fused image in Figure 2e achieves better visual results and maintains similar color of the infrared target as that in the infrared input image.
# Applicability Study on Modified Argillaceous Slate as Subgrade Filling for High-Speed Railway
## 3 Experimental Evaluation of Cement/Lime-Modified Soil
### Results
#### 3.1.4 Compaction Characteristics  
The variation in MDD and OMC at different quicklime/cement contents is illustrated in Figure 3. The samples were sieved through a 20 mm sieve and the tests were evaluated by Z2 compaction [33].  
The increase in OMC for quicklime-modified WAS is recorded from 12.4% to 15%, while for cement-treated soil it is noted to have increased up to 14%. The MDD decreased from 1.844 g/cm\({}^{3}\) to 1.804 g/cm\({}^{3}\) for lime-modified WAS, while in the case of cement-modified WAS, it decreased to 1.835 g/cm\({}^{3}\). With the increase in the mixing ratio of quicklime and cement, the compaction curve tends to be flatter and the limit of moisture content in the error range increases gradually. An increase in the specific surface area of the mixture and the cation exchange reaction may attribute to the increases of the OMC. Additionally, the low density of cement/quicklime and the chemical reactions may lead to the decrease in MDD of the cement/lime-modified WAS. Similar observations have been reported by other researchers [46], who had utilized lime, cement or their admixture to enhance the geotechnical properties of various type of soils [47; 48; 49; 50].  
Figure 3: Compaction curves of modified soil. (**a**) Quicklime-modified soil; (**b**) Cement-improved soil.
# A Novel UWB Pulse Expander Using an Integrated Microstrip Splitter, Delay Lines, and a Combiner
## 2 Design and Modeling  
### Initial Considerations  
This paper examines the approach of expanding a pulse in the time domain by initially splitting it into three equal pulses. These pulses are then time-shifted against each other and fed to a 3-way combiner. The output of the combiner produces a composite pulse, the duration of which is several times longer than the original input signal, preserving the steep rising edge of the pulse at the same time. The device diagram shown in Figure 1 is used as the pulse expander in this research. The multi-tap pulse expander is intended to be designed using a pulse splitter, several different delay lines, and a pulse combiner. The expected result is a time-shifted input pulse with a smooth waveform and enough steep slope for the first transition. The main requirements for the design and implementation of this pulse expander are its simplicity and low cost. Reasonable power losses in the pulse expander are acceptable. Considering the design aspects mentioned above, the most suitable option is to use the microstrip technology to implement the design, which also determines the construction of the design used in simulations, optimization, and fabrication stages. The behaviors of the separate parts of the pulse expander--pulse splitter, delay lines, and combiner--as well as the combinations of their connections, were designed and simulated with Ansys 2022 R1 HFSS software separately and in the following combinations: (a) pulse splitter-delay lines, (b) delay lines-pulse combiner, and (c) all parts together. This approach helps to select the appropriate shapes and dimensions of the pulse splitter, delay lines, and pulse combiner, which would lead to the desired result--a compact UWB pulse expander. The designed pulse expander, calculated and simulated in HFSS, is shown in Figure 2.  
Figure 1: Diagram of multi-tap pulse expander realization. Figure 2: Model of the designed pulse expander. The proposed model is examined for use with unipolar Gaussian pulses of a specific duration. This pulse shape was chosen because it has a low time-bandwidth product and can be easily generated. Equations (4)-(7) are used for the graphical representation of input pulse waveforms and spectral density and to validate the results of numerical calculations (time domain waveforms, frequency spectra, energy) and performed simulations. If the pulse duration is given at a different level of pulse waveform amplitude, then Formulas (2) and (3) can be used for graphical representations and validation of results.
## 3 Results
### Seismic Refraction  
Through the current study, a seismic refraction survey was principally applied to delineate subsurface layering, and the bedrock in particular, to evaluate the extension of Helwan city for engineering projects and other inter-related development projects. A sum of twenty profiles of seismic refraction were conducted in the investigated site, with a maximum length of profiles reaching 75 m, the used inter-geophone spacing ranging from 1.5 to 2 m, and the shot-to-first-geophone-spacing was 1.5 m, as presented in Figure 7.  
After picking the first arrivals for the conducted records, the evaluation of the collected seismic refraction travel time data was achieved by applying a refraction analysis program \"Seis REFA\", OYO GEOSPACE [38]. The analysis routine begun with irregularity, reciprocity, and parallelism checks. Velocity-depth models were then calculated using the Ray tracing technique. The calculated parameters (i.e., depth and velocity) are represented in two forms: (1) two-dimensional (2-D) depth-velocity models, which display both vertical and lateral variations in each detected stratum along the profiles; and (2) velocity and depth or thickness contour maps to illustrate the horizontal variation in these parameters through the investigated site.  
The calculated velocity-depth models for the conducted seismic refraction profiles can be described by two main seismic layers: (1) thin surface layer with low seismic velocity in the range of 400-1100 m/s, which corresponds to highly weathered limestone, with an average thickness of 3 m; and (2) a bottom stratum with higher seismic velocity of in the range of 2100-3600 m/s, which corresponds to the bedrock that is composed of hard limestone that extends to the maximum penetrable depth, as shown in Figure 8.  
The areal distribution of seismic velocity throughout the study area was formalized by constructing two contour maps, which reflect that the seismic velocity of the first layer ranges between 900-1000 m/s for most of the investigated site while the southwestern part is characterized by low seismic velocity (400-350 m/s), as shown in Figure 9a. The  
Figure 8: Travel time curves and calculated velocity–depth model of seismic refraction profiles that describe in its upper part both observed and calculated travel time curves and on its lower part the calculated depth–velocity model. The good agreement between the observed and calculated travel times indicates the integrity of the calculated depth–velocity models of the profiles (**a**) SR#1 and (**b**) SR#15.  
Figure 7: Samples of collected seismic refraction records of the profile SR#11: (**a**) forward shooting and (**b**) reverse shooting.  
seismic velocity of the second strata is also characterized by low seismic velocity only at the southwestern parts of the investigated site, as indicated in Figure 9b.
# Smoothness of Graph Energy in Chemical Graphs
## 3 Computational Details and Results
### Linear Regression Analysis of Graph Energy and Correlations  
First, we compare the values of the calculated graph energies as a linear function of the number of vertices in a molecular graph, denoted by \(n\). Each of the four subsets \(M_{1},\ldots,M_{4}\) is considered separately. In the subset \(M_{4}\), we have structural isomers with the same number of vertices, so in this case the average of the graph energies is considered in the regression analysis. Linear regressions for predicting the graph energy for alkanes (1), cycloalkanes (2), methyl-cycloalkanes (3) and the average value of graph energy for methylalkanes (4) are:  
\[E(M_{1}) =1.2782\,n-0.8149,\quad R^{2}=0.9996 \tag{1}\] \[E(M_{2}) =1.2806\,n-0.1406,\quad R^{2}=0.9976\] (2) \[E(M_{3}) =1.2827\,n+0.8792,\quad R^{2}=0.9991\] (3) \[E(M_{4}) =1.2825\,n+0.0682,\quad R^{2}=0.9993 \tag{4}\]  
As we can observe, the correlations are very good, since the coefficients of determination are all higher than \(0.99\).  
Second, we are interested in the connection between the graph energies of acyclic and cyclic molecules and so we compare the values of graph energies between molecular graphs in the sets \(M_{1}\) and \(M_{2}\). More specifically, we perform the correlation analysis between alkanes and cycloalkanes with the same number of vertices. The results are gathered in Figure 2. We proceed similarly with the molecules in the subsets \(M_{3}\) and \(M_{4}\), i.e., with methyl-alkanes and methyl-cycloalkanes. As in the linear regressions for the prediction of graph energies, because of the structural isomers of methyl-alkanes with the same number of vertices, the average value of graph energies of isomers is included in the calculation. The results can be seen in Figure 3.  
Figure 1: Representatives of each of four sets \(M_{1},\ldots,M_{4}\).  
Figure 2: Correlation between graph energy of alkanes and cycloalkanes.  
Again, the correlations between graph energies of acyclic and cyclic molecules are very good, as the coefficients of determination are both above 0.99.
###### Abstract  
Both liming and plant residue incorporation are widely used practices for the amelioration of acidic soils--however, the difference in their effects is still not fully understood, especially regarding the microbial community. In this study, we took the acidic soils from a subtropical orchard as target soils, and implemented liming and plant residue incorporation with a leguminous and a gramineous cover crop as test plants. After six months of growth, soil pH, total organic carbon (TOC), dissolved organic carbon (DOC) and nutrient contents were determined, soil enzymes involving C, N, P cycling were assayed, and microbial communities were also analyzed using Polymerase Chain Reaction-Denaturing Gradient Gel Electrophoresis (PCR-DGGE). Results showed that liming was more effective in elevating soil pH, while plant residue incorporation exerted a more comprehensive influence--not only on soil pH, but also on soil enzyme activity and microbial community. PCR-DGGE analysis revealed that liming changed the microbial community structure more greatly than plant residue incorporation, while plant residue incorporation altered the microbial community composition much more than liming. The growth responses of test plants to liming and plant residue incorporation depended on plant species, indicating the necessity to select appropriate practice for a particular crop. A further, detailed investigation into the microbial community composition, and the respective functions using metagenomic approach, is also suggested.  
acidic soil improvement; liming; microbial community; plant residue incorporation; soil enzyme activity; subtropical orchard soil +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Farm Management System Integration
#### 2.4.2 The Challenging Task of FMS Interoperability  
The integration of data generated by the infrastructure developed during this project, which includes meteorological and agro-meteorological information to enhance crop management, poses significant challenges when attempting to interface with third-party systems such as Farm Management Systems (FMS). These challenges stem primarily from the proprietary nature of these systems, which often require custom development efforts by their publishers to enable compatibility with external data sources.  
However, these publishers are typically reluctant to invest in such developments unless there is substantial demand and pressure from their user base. This creates a bottleneck for  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Site** & **Window of Forecast** & **SCDAB** & **Tetto-Bernardo** \\ \hline \multirow{8}{*}{**Romania**} & 29 June–3 July 2024 & 10.5 & \multirow{8}{*}{43.7} \\  & 22–26 July 2024 & 3.2 & \\ \cline{1-1}  & 5–8 August 2024 & 5 & \\ \cline{1-1}  & 7–11 September 2024 & 12.1 & \\ \cline{1-1}  & 20–24 June 2024 & & 43.7 \\ \cline{1-1}  & **Total rainfall (evaluated period)** & **30.8** & **43.7** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Forecasting time windows tested per pilot site and total rainfall measured.  
the adoption of innovative data-driven solutions in agricultural management, as the value of integrating these new data streams is often not immediately recognized or prioritized by the FMS providers.  
To address this issue and ensure the usability of the project's outputs across diverse systems, we adopted an open API (Application Programming Interface) approach. By providing an open API, the results and insights generated through the project can be made accessible to any third-party system willing to integrate them, without requiring proprietary developments from the FMS providers themselves.  
This approach not only removes the dependency on specific publishers but also empowers end users and stakeholders to leverage the data in ways that suit their unique requirements.  
The dashboard itself is designed to meet the need for accessible, actionable data from the MAGDA infrastructure. It also uses MAGDA's API and demonstrates the interest of MAGDA to the end user, using visualizations for both raw in situ sensor data and model outputs.  
Dashboard components can also be embedded within third-party systems, such as graphs integrated into extranets, thus facilitating interoperability and wider adoption.
## 3 Proposed Approach
### Clustering Methods  
In this paper, we use five different clustering methods (both hard and soft clustering approaches) namely \(k\)-means [53], \(k\)-modes [54], fuzzy \(c\)-means [55; 56], agglomerative hierarchical clustering, and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) [57; 58] (note that this is a soft clustering approach). For the \(k\)-means and \(k\)-modes, default parameters are used. For the fuzzy \(c\)-means, the clustering criterion used to aggregate subsets is a generalized least-squares objective function. For agglomerative hierarchical clustering, a bottom-up approach is applied, which is acknowledged as the agglomerative method. Since the bottom-up procedure starts from anywhere in the central point of the hierarchy and the lower part of the hierarchy is developed by a less expensive method such as partitional clustering, it can reduce the computational cost [59].  
HDBSCAN is not just density-based spatial clustering of applications with noise (DBSCAN) but switches it into a hierarchical clustering algorithm and then obtains a flat clustering based in the solidity of clusters. HDBSCAN is robust to parameter choice and can discover clusters of differing densities (unlike DBSCAN) [58].