esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 4 Common Preparation Methods
### Liquid Phase Method
#### 4.4.4 Hydrothermal Method  
In the hydrothermal method, an inorganic or organic compound is mixed with water at a temperature of 100-350 \({}^{\circ}\)C and a high pressure, and then an improved inorganic substance is obtained through several steps. The resulting inorganic substance is filtered, washed, and dried to obtain ultrafine particles of high purity [7; 215; 216; 217; 218; 219; 220].  
The hydrothermal preparation of the film is carried out in the liquid phase, and no post treatment is required. This feature of the hydrothermal method avoids defects such as curl, cracks, particle coarsening, and film-to-gas reactions that can occur during heat treatment of the film. The hydrothermal method uses an inorganic substance as the precursor and water as the reaction medium, which avoids a series of disadvantages resulting from the use of organometallic materials. The hydrothermal methodhas many advantages, including mild conditions, a stable system, little environmental pollution, an easy process, and a low cost. In addition, films prepared by the hydrothermal method have good uniformity, firmly adhere to the substrate, and are not limited by the shape and size of the substrate [221]. The hydrothermal method can also achieve uniform doping and preparation of the nanocomposite by adjusting the proportion of the product or by adding a surfactant.
## 3 Influence of Face Advancing Rate on Roof Caving Interval
### Simulation of Main Roof Initial Weighting Intervals Under Different Advance Rates  
Field measurements revealed that during the initial mining phase, the initial caving interval of the main roof measured 54 m. Building upon this, simulations were conducted to investigate its variation under different advance rates.  
Figure 7 illustrates the initial weighting behavior of the main roof under varying advance rates, with key parameters summarized in Table 3. The initial weighting interval of the main roof increases progressively with higher advance rates: 52 m at 4 m/d (minimum)  
Figure 6: Immediate roof caving parameters under different advance rates.  
and 60 m at 10 m/d (maximum). Caving positions under all rates occur 1-4 m ahead of the coal wall, with maximal offsets observed at 4 m/d and 10 m/d.  
Based on the simulation results of overlying strata collapse positions under different mining advance rates, it is demonstrated that the caving intervals of both the immediate roof and main roof exhibit an increasing trend with accelerated advance rates. Among the four simulated schemes, the minimum advance rate of 4 m/d corresponds to an immediate roof caving interval of 12 m and a main roof caving interval of 52 m, while the maximum advance rate of 10 m/d yields respective values of 20 m and 60 m. The maximum discrepancy in both cases measures 8 m. The simulation analysis aligns fundamentally with field-measured data, particularly in terms of roof collapse positioning relative to the coal wall, which displays negligible spatial variation. This parametric study conclusively indicates that the mining advance rate exerts limited influence on both immediate roof caving and main roof failure mechanisms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Advance Rate/(m-d-1)** & **Initial Weighting** & **Position Relative to Coal** \\  & **Interval/(m)** & **Wall/(m)** \\ \hline
4 & 52 & 4 \\
6 & 54 & 2 \\
8 & 56 & 1 \\
10 & 60 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main roof caving parameters under different mining advance rates.  
Figure 7: Main roof caving patterns under different advance rates: (a) 4 m/d; (b) 6 m/d; (c) 8 m/d; (d) 10 m/d.
# Support Vector Machine-Based EMG Signal Classification Techniques: A Review
## 2 EMG Signals
### Feature extraction from an EMG signal
#### 2.2.1 Time Domain (TD)  
MESs have a very particular structure during muscle contraction, which varies according to the movement performed by the extremity. For this reason, MES classification can be used to actuate a prosthesis. By processing signals in the time domain, there is an increase in the available time for analysis since there is no need for the time-consuming task of transforming the signal to a different domain [27].  
Since the signals are usually sampled in the time domain, it is more common to extract features in that domain, since they do not need to be converted and can be processed directly. These time-domain signals are studied in depth and used by researchers from the medical and engineering fields.  
Time-domain features are more natural and simpler to extract since they are calculated from the sampled MES time series (the EMG signal) without any intermediate transformation [23; 27]. Notwithstanding, time-domain EMG signals also present some disadvantages, which come from the non-stationary properties of the MES, with time-varying statistical properties. Nonetheless, features in this domain are highly used, because of their performance during classification presents a very reduced amount of noise and their processing time is lower compared with those features found in the frequency domain and timescale.
## 4 Electric Propulsion System Characterization
### Aerodynamic Forces and Moments  
The aerodynamic forces and moments (thrust force and drag torque), according to Equations (15) and (17), directly depend on the rotor angular velocity. Based on the obtained curves (Figures 16-18), it was possible to estimate the factors of aerodynamic forces in the EPU working area for the purposes of conducting computer simulations of the multirotor UAV behavior. The factors can be further investigated through the thrust coefficients and motor power coefficients according to Equations (16) and (18). Figures 16-18 show thrust forces and drag torques as a function of rotor angular velocity for three series of measurements (setups). It can be seen that the thrust and torque factors of the EPU depend on the geometry of the propeller, mostly the diameter. Motors with a higher \(K_{v}\) and/or batteries with a larger number of cells increase the angular velocities of the rotor, so as expected, higher thrust forces and consequently drag torques are achieved.  
Figure 16: Aerodynamic effects with respect to rotor angular velocity, 1806 BLDC series: (**a**) thrust force; (**b**) drag torque.  
Figure 15: Static maps—MN 4014 BLDC motor: (**a**) electric current; (**b**) electric power.
## 3 R-SIN Architecture Model Based on DaaC  
The DaaC architecture modeling method can realize the overall design of the SIN architecture and overcome the lack of versatility and portability of existing methods. The idea of reconfigurable modeling has been studied in the fields of computer software, robot, and compression systems [30; 31; 32]. Based on the DaaC architecture model idea, in order to realize the recombination of the task-oriented SIN architecture, we study the reconfigurable SIN (R-SIN) architecture model based on DaaC.  
Taking military application as an example, future war patterns will gradually evolve into a local war under the condition of informationization. If the whole SIN is involved in the information support and situational awareness of a combat task, it will inevitably increase the time of OODA (observation, confirmation, decision, and action). Correspondingly, it will also add some non-essential costs, as well as generate a huge waste of resources. On the contrary, if the scope of a particular operation is determined, the physical equipment of the SIN that meets the requirements of the mission will be rapidly reorganized to provide the required information support services. When the combat activity is over, it can be deconstructed to release these resources and bring them back to a natural state of operation. Based on this method, it can not only shorten the delay of OODA, improve the efficiency of combat, and ensure the effective information support, it can also reduce the cost of use and improve the efficiency of the SIN.  
Of course, the SIN defined usually is a whole, and the reorganization SIN (R-SIN) is a subnet of it. An R-SIN is a state in which structural reorganization and functional reproduction are achieved within a local area to achieve specific functions and provide specific services. Thus, based on the DaaC architecture model, taking the space entities as the minimum physical resources and the space data as the center, we propose a task-oriented architecture model of the SIN in order to provide some new ideas and methods in order to meet the top-level design of SIN architecture to realize multi-tasks.  
In this section, firstly, we define some concepts as the basis for the model presentation. Then, we build the reconfigurable principles. Finally, we analyze the realization mechanism and methods of the reconfigurable model of task-oriented architecture for the SIN based on DaaC. The details are as follows.
# A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model
## 3 The Calculation of Measurement Vector for Heading Estimation  
In this section, we discuss the calculation of measurement vector in KF for heading estimation. In general, two methods are used to calculate the heading. One is based on the outputs of the magnetometer, and the other is obtained from raw gyroscope data
## 4 Regression Analysis  
The visual analysis results show an approximately linear correspondence between the respective response values compressive strength (\(R_{p}\)), water absorption rate (\(W\)), softening coefficient (\(K\)) and bulk density (\(\rho\)) of NCRPB and the four factors. Using the regression method for fitting, the following linear regression equation can be derived:  
\[y=a_{0}+a_{1}x_{1}+a_{2}x_{2}+a_{3}x_{3}+a_{4}x_{4}+e \tag{5}\]  
where \(y\) is the calculated value of each performance, \(a_{i}\) (\(i\) = 0, 1, 2, 3, 4) is the regression coefficient; \(x_{1}\) is the W/C; \(x_{2}\) is the R/C; \(x_{3}\) is the \(w_{l}\); \(x_{4}\) is the \(w_{l}\); \(e\) is the experimental error.  
By substituting the data into the regression model in Equation (5), the least squares estimate is obtained. The regression equation is given below.  
Compressive strength (\(R_{p}\)) calculation formula:  
\[\begin{array}{c}y=11.525+0.683x_{1}-0.820x_{2}+1.683x_{3}+3.381x_{4}\\ R^{2}=0.960,n=9,\text{F}=36.024\end{array} \tag{6}\]  
Water absorption rate (\(W\)) calculation formula:  
\[\begin{array}{c}y=0.052+0.008x_{1}+0.038x_{2}-0.103x_{3}-0.182x_{4}\\ R^{2}=0.912,n=9,\text{F}=16.043\end{array} \tag{7}\]  
Softening coefficient (\(K\)) calculation formula:  
\[\begin{array}{c}y=1.114-0.028x_{1}-0.057x_{2}-0.054x_{3}+0.055x_{4}\\ R^{2}=0.883,n=9,\text{F}=12.798\end{array} \tag{8}\]  
Bulk density (\(\rho\)) calculation formula:  
\[\begin{array}{c}y=1627+4.908x_{1}+32.040x_{2}+1.203x_{3}-1419.21x_{4}\\ R^{2}=0.990,n=9,\text{F}=151.790\end{array} \tag{9}\]  
Figure 10: Intuitive analysis of the bulk density of four factors.  
The orthogonal test was divided into nine groups with five variables, so the degree of freedom was 4, and the determination coefficients \(R^{2}\) were close to 1. This high \(R^{2}\) value suggests that the linear regression equation obtained is meaningful. In addition, the critical value of variance F\({}_{0.05}\)(4,5) of the regression equation is 6.256, and all variance values of the above formulas are larger than 6.256, so the obtained equation is significant.
## 3 Reduced Dynamic Orbit Determination of COSMIC-2
### Assessment of Orbit Accuracy
#### 3.3.3 Validation with Reference Orbits  
In this study, we carried out an orbit comparison between the resolved orbits with the RD method and the reference orbit released by Central Weather Bureau and evaluated the external coincidence accuracy of the RD orbit according to the RMS values of the differences between them. The RMS values of differences between the RD orbits and external reference orbits during DOY 138-142 are shown in Figure 9, and the corresponding mean RMS values for each day are shown in Table 7.  
It can be seen from Table 7 and Figure 9 that the mean RMS values of the orbit differences of the six COSMIC-2 satellites fluctuate between 7.35 cm and 8.79 cm during DOY 138-142 in the 3D direction, and the mean RMS value of Sat-3 in the 3D direction  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **R** & **T** & **N** & **3D** \\ \hline Sat–1 & 5.23 & 5.98 & 2.20 & 8.26 \\ Sat–2 & 4.97 & 5.55 & 1.78 & 7.67 \\ Sat–3 & 4.23 & 5.68 & 1.93 & 7.35 \\ Sat–4 & 5.44 & 6.59 & 2.04 & 8.79 \\ Sat–5 & 5.33 & 6.44 & 2.13 & 8.64 \\ Sat–6 & 5.61 & 6.36 & 2.29 & 8.79 \\ \hline \end{tabular}
\end{table}
Table 7: Mean RMS of difference between the solved orbits and the reference ones (cm).  
Figure 9: RMS of difference between the solved orbits and the reference ones.  
is the smallest and the ones of Sat-4 and Sat-6 are 8.79 cm, which is the largest among the six satellites. The orbit difference of the six COSMIC-2 satellites in the N direction in these five days is smaller than that in the T and R directions. The mean RMS of difference between the RD orbit and the reference orbit of DOY 138-142 for five days are below 5.61 cm, 6.59 cm and 2.29 cm in R, T and N directions, respectively. The mean RMS of 3D of the six COSMIC-2 satellites is better than 9 cm, which meets the orbit determination accuracy requirements of COSMIC-2.
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 4 Implemented Attacks and Relative Mitigations
### Impersonation Attack
#### 4.2.3 Use of Symmetric Cryptography  
Considering the scenario in which symmetric cryptography is used, each sensor shares with the server a secret key, which is supposedly unknown to anyone except by the two entities, the sensor, and the server. In order to mitigate the attack, therefore, sensor nodes send to the server their packets, which are composed of the message \(m\) (as a plaintext), a _ciphered field_ (the one we want to make confidential) and a \(tag\).  
Upon the receipt of the message, the server, first of all, will verify the authenticity of the source verifying the tag. If the tag-verify operation is successful, then it proceeds with the deciphering operation. Suppose that, considering the scenario described above, there exists an attack attempt, the attacker sensor node forges a new packet with the message \(m\) (as a plaintext) and a \(tag\), generated using the key shared with the server. Also, in this case, the attacker will change, as we explained earlier, the source IP address of the packet impersonating a legacy sensor of the WSN.  
Then, upon the receipt of the packet, the server, considering the packet as generated by a legacy sensor, will go through the verification phase of the tag using the key shared with the legacy node, described by its source address. Of course, the verification will fail since the \(tag\) provided to the server is generated using the key of the malicious node and not using the key of the legacy node. Thus, the server will be able to immediately detect the attack attempt, and refuse to decipher the data.
## 4 Discussion
While other researchers used dust sensors, noise sensors, and cameras to have a precise prediction, it is also shown that dust, noise can be interrupted by other external and internal factors like dragging of chairs and so on. Data captured by the camera shows that they can expose privacy thus currently eliminated from the discussion. This research took advantage of a mixture of different parameters as multivariate data in the prediction models instead of univariate data for each parameter among temperature, humidity, light, humidity ratio, and occupancy to enhance the experimental accuracy of the models. In future research, the consideration of collecting data from additional similar enclosed environments using deployed IoT sensor kit at various places across cities of Rwanda will be taken care of. Further, the FIWARE, an open-source platform for smart environments will be explored. Moreover, the temporal dependence of data on the occupancy level should be examined. Besides, the impact of external factors (weather conditions, noise, and airflow) on the prediction accuracy should be considered. The extension of both univariant and multivariant data with Noise-canceling/avoiding techniques to precisely measure real-time prediction models with even better accuracy is also planned. Furthermore, we will explore how the deeper layers of LSTM could affect the performance of the model concerning the tested multivariate time series data set. Finally, further evaluations with other more deep-learning models will be performed to investigate the gates that have the most influential role. Conceptualization, E.H. and G.B. ; methodology, E.H.; software, E.H.; validation, E.H., G.B., and J.K.; formal analysis, E.H
## 6 Sensitivity Analysis on the Inter-Laminar Damage Behaviour  
In this section, the sensitivity analysis performed to assess the influence of the omega stringer joints on the damage behaviour of the composite components of the structure is summarized. An initial artificial 70 mm-long debonding was introduced between the interface of the composite skin and stringers. Then, the virtual crack closure technique (VCCT) [57] was adopted to numerically simulate the skin-stringer debonding growth in configurations with and without the omega stringer joints (see Figure 24). By comparing the results obtained on these configurations, in terms of skin-stringer debonding growth, for a tensile applied displacement equal to 2.5 mm (which is, actually, the displacement at collapse for the configuration without omega stringer joints) the effects of the omega stringer joints on debonding evolution were assessed.  
Figure 25 reports the tensile load as a function of the applied displacement for both the investigated configurations (with and without omega stringer joints).  
Figure 23: Plastic strains—load: 300 kN.  
According to Figure 25, the maximum tensile load reached by the configuration without omega joints is 48 kN, which is considerably lower if compared to the 160 kN tensile load reached by the configuration with the omega joints. Indeed, in the model without omega stringer joints, the propagation of the skin-stringer debonding critically reduces the structure-carrying load capability, leading to a premature collapse. This trend is confirmed by Figure 26, which introduces the skin-stringer debonding propagation, in the configuration without omega joints, as the applied displacement increases.  
Figure 26: Skin-stringer debonding propagation. (**a**) skin-stringer debonding growth onset; (**b**) intermediate skin-stringer debonding evolution; (**c**) maximum attained load; (**d**) ultimate skin-stringer debonding.  
Figure 25: Load vs. applied displacement.  
The skin-stringer debonding growth onset occurs at a 12.5 kN load (Figure 26a). The skin-stringer debonding then propagates at both skin-stringer interfaces, up to the maximum applied tensile load of 48 kN (see Figure 26c) at which structural collapse takes place (see Figure 26d).  
In contrast, the skin-stringer debonded area in the configuration with the omega joint (shown in Figure 27) does not increase, reaching a maxim applied tensile load of 160 kN for an applied displacement of 2.5 mm as confirmed by Figure 25.
# Text Indexing for Faster Gapped Pattern Matching
## 3 An Improved Solution
### Space Analysis  
First, considering only the Gapped Set Intersection Data Structure from Lemma 1 on non-leaf nodes, this requires space logarithmic factors from  
\[\sum_{i=0}^{\log N}2^{i}\left(\frac{N}{2^{i}}\right)^{2-\delta/3}=N^{2-\delta/3 }\sum_{i=0}^{\log N}\left(\frac{1}{2^{1-\delta/3}}\right)^{i}.\]  
Since \(0\leq\delta\leq 1\), we have \(1/2^{1-\delta/3}<1\), and the geometric series converges to a constant. Hence, ignoring leaf nodes, the space is \(\widetilde{\mathcal{O}}(N^{2-\delta/3})\).  
Next, we include the leaves. We first show that the number of leaves is \(\mathcal{O}(N/G)\).  
Lemma 4: _Every non-middle child leaf's associated subarray has size at least \(G\)._  
Proof: Suppose, for the sake of contradiction, there exists a leaf \(u\) that has a subarray size less than \(G\). Let \(v\) be the parent of \(u\) with range \(l\) to \(r\) and midpoint \(m\). If \(u\) is a left child, it has a subarray size  
\[m-l+1=\left\lfloor\frac{l+r}{2}\right\rfloor-l+1<G.\]  
The above implies  
\[\frac{l+r}{2}-1-l+1<G,\]  
which leads to \(r-l<2G\). However, this implies \(r-l+1\leq 2G\), so \(v\) had a subarray size of at most \(2G\). In such a case, our algorithm would not recursively create a left child for \(v\), a contradiction.  
Similarly, if \(u\) is a right child with subarray size less than \(G\), it has size  
\[r-(m+1)+1=r-\left(\left\lfloor\frac{l+r}{2}\right\rfloor+1\right)+1<G,\]  
meaning \(r-\lfloor(l+r)/2\rfloor<G\). This implies \(r-(l+r)/2<G\). Hence, \(r-l<2G\). Again, we conclude \(v\) has a subarray size small enough that our algorithm would not recursively create a right child for \(v\), a contradiction.  
Lemma 5: _The number of leaves in the tree structure created by Algorithm 1 is \(\mathcal{O}(N/G)\)._  
Proof: Since all leaves created as non-middle children have disjoint associated subarrays, their union represents a set of cardinality \(N\), and (by Lemma 4) each represents a disjoint subset of size at least \(G\). Therefore, there are at most \(\mathcal{O}(N/G)\) non-middle child leaves. Next, because the tree (still excluding middle children) is a binary tree, the total number of internal nodes is also \(\mathcal{O}(N/G)\). Furthermore, including the middle child leaves at most doubles the total number of nodes in the tree.  
Because each leaf contains a subarray of size \(\mathcal{O}(G)\), the space for the data structures for each leaf is \(\widetilde{\mathcal{O}}(G^{2-\delta/3})\). Combined with Lemma 5, the total space for leaves is \(\widetilde{\mathcal{O}}(G^{2-\delta/3}\cdot N/G)=\widetilde{\mathcal{O}}(G^{ 1-\delta/3}\cdot N)\), which, since \(G\leq N\), is also \(\widetilde{\mathcal{O}}(N^{2-\delta/3})\).
# Interference Suppression in EEG Dipole Source Localization through Reduced-Rank Beamforming
## 2 Materials and Methods
### EEG Data  
In order to prove the applicability of the previously proposed methods, next we present two types of EEG data used in our numerical examples: simulated and real EEG data. In the examples with simulated data, the aim is to evaluate the performance of (7) and compare it to those of (13) and (15) on the inverse problem of estimating the position of \(L=3\) brain sources located on the occipital lobe, specifically in the visual cortex. For the examples with real data, their purpose is to show the applicability of the proposed methods to resolve the topography of cortical activation produced by visual flicker stimulation.