esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 5 Key Cooperative and Cognitive Technologies
### NOMA
#### 5.1.2 NOMA-Based HSTN Cognitive Networks  
A NOMA scheme to serve multiple secondary users simultaneously and improve the efficient use of limited resource in the secondary network was considered for a cognitive HSTN in [166] by analyzing the ergodic capacity to understand the performance limitations of the considered system.  
Formulating a sum rate maximization problem, ref. [167] proposed NOMA-based beamforming in a cognitive HSTN operating at the Ka band, where a uniform planar array (UPA) was deployed at the base station (BS) to compensate for the path loss of high-frequency signals.  
In [168], a NOMA-based power allocation scheme considering instantaneous channel conditions was proposed to improve the spectral efficiency of an overlay-cognitive HSTN. However, only a single PU with no direct satellite (DS) communication was considered.  
Different from [168], the authors of [169] considered an overlay scheme that integrated the secondary terrestrial relay cooperation to with the primary DS communication in order to achieve a diversity gain for the primary network for a NOMA-assisted multi-user overlay-cognitive HSTN.  
The performance of a cooperative NOMA-assisted underlay-cognitive HSTN wherein a secondary terrestrial network shares the spectrum with the primary satellite network to communicate with intended receivers was investigated in [170].  
To mitigate the effects of inter-carrier interference (ICI) and intra-group interference (IGI) caused by bandwidth compression and power-domain multiplexing in a NOMA-based HSTN, an iterative and successive interference cancellation (ISIC) approach was proposed in [171] with symmetrical coding (SC) to avoid error propagation due to ISIC.  
A user-pairing scheme to group multiple users into clusters and a joint optimization design of beamforming and power allocation in a downlink NOMA-based integrated satellite-terrestrial network operating at mmWave band was proposed in [172].
## 2 Materials and Methods
### Study Aims and Biometry  
The primary study aim is to determine the percentage of participants for which 5-ALA-induced PpIX fluorescence status allows the surgeon to visually obtain correct information as to the presence or absence of tumor in tissue where there is uncertainty regarding that tissue's tumor status based on white light visualization alone.  
We assume that if a minimum of 30% of study participants achieve the primary efficacy endpoint, this proportion is clinically meaningful. This means that at least 30% of the study participants will have at least one indeterminate or unexpected fluorescent end-of-surgery tissue area where 5-ALA-induced PpIX fluorescence status is consistent with histological assessment of meningioma. With 100 participants included in the per-protocol population, the lower bound of a 95% confidence interval of the success rate will be 40.4% using a Wilson (score) confidence interval. This lower bound is considered clinically meaningful in this population.  
Key secondary endpoints are:  
* The biopsy-level positive predictive value (PPV) of 5-ALA for the real-time visualization of tissue locations on the tumor margin in newly diagnosed or recurrent meningioma during resection surgery.
* To determine the biopsy-level negative predictive value (NPV) of 5-ALA for the real-time visualization of tissue locations on the tumor margin in newly diagnosed or recurrent meningioma during resection surgery.
* To determine the participant-level PPV of 5-ALA for the real-time visualization of bulk tumor in newly diagnosed or recurrent meningioma during resection surgery.
* To determine the biopsy-level diagnostic accuracy of meningioma identification with (i) 5-ALA-induced PpIX fluorescence status under BL vs. (ii) visualization under WL in indeterminate tissue and unexpected fluorescent EOS tissue locations, as assessed by the operating surgeon.  
In this study, safety is assessed in a safety analysis population of all patients meeting the eligibility criteria for the study and who receive 5-ALA. Efficacy is assessed in a per-protocol population defined as having participants dosed with 5-ALA, who undergo surgery, have a histologically confirmed meningioma (all grades), and have at least one tissue biopsy included in the biopsy efficacy analysis population. For sensitivity purposes, all assessments at a biopsy level will also be performed in an all-biopsy population which includes any biopsies collected during surgery. The biopsy efficacy analysis population will only contain biopsies not eliminated due to possible bias.  
Patients are followed for a total of 6 weeks to assess pharmacological safety of 5-ALA.
# Rank-Based Ant System with Originality Reinforcement and Pheromone Smoothing
## 4 Experimental Results
### Comparison with ACO Algorithms  
The comparative Tables 5 and 6, and the evolutionary curves in Figures 6 and 7 show the performance of several ACO algorithms: Ant System (AS) [9], Rank-based Ant System (AS\({}_{Rank}\)), [10], Max-Min Ant System (MMAS) [11], Ant Colony System (ACS) [12] and the proposed extension of the Rank-based Ant System (AS\({}_{ORank}^{ps}\)). On the one hand, Tables 5 and 6 allow the comparison of the final solutions obtained by the ACO algorithms, showing that AS\({}_{ORank}^{ps}\) outperforms all analysed ACO algorithms, including MMAS and ACS which are the most widely used and often best performing ACO algorithms [14; 15; 16]. More concretely, AS\({}_{ORank}^{ps}\) obtained better results than AS and AS\({}_{Rank}\) in all problem instances, better results than ACS in all instances with the exception of _brazil58_, and better results than MMAS in all SOP instances, and all TSP instances with the exception of _ftv64_ and _ftv70_, where both algorithms obtained similar results. Regarding the computational times, ACS requires slightly longer computational times than the rest of the algorithms. On the other hand, the evolutionary curves of Figures 6 and 7, which represent the evolution of the average best-found tour lengths along the number of tours constructed, enable the comparison of the performance of the ACO algorithms along the runs of the algorithms, and not just the comparison of the fitness of final solutions. Note that these graphs allow to compare the performance of the algorithms at different iterations, but due to the scale, the comparison of the fitness of the final solutions is better observed in the comparative Tables 5 and 6. The evolutionary curves of Figures 6 and 7 show that during the initial iterations, all ACO algorithms outperform MMAS, which presents a slower convergence to high-quality solutions. For example, at iteration 100 of the TSP instance _brazil58_, the mean percentage deviations from the optimal solution of AS\({}_{Rank}\) and AS\({}_{ORank}^{ps}\) are, respectively, 1.26% and 1.6%, while MMAS clearly presents a higher error equal to 26.2%. The reason for the observed behaviour is that, while MMAS relies on a high initial exploration of the search space in order to finally converge to high-quality solutions, AS\({}_{ORank}^{ps}\) maintains the rapid convergence of AS\({}_{Rank}\) and relies on search diversification of the originality reinforcement and pheromone smoothing mechanism to improve the quality of the solutions. The behaviour of AS\({}_{ORank}^{ps}\) can be especially beneficial in those optimization problems where the available computational time is limited or not known beforehand, as its faster convergence would return a significantly better solution if the number of iterations is restricted.
## 5 Lidar Scan Pattern Optimization
### Lidar System Configurations
Because pulsed lidars typically require longer sampling times than CW lidars, only the CW configuration is analyzed for the circular scan scenario due to the high sampling rate required to complete the scan in a reasonable amount of time relative to the lidar preview time. All of the commercial lidars that these scan patterns are inspired by can complete a full scan in approximately 1 s. Because of the relatively short scan times compared to typical feedforward control time scales, the calculations of measurement quality are simplified in this investigation by assuming each point in the scan pattern is measured simultaneously. Each of the scan patterns shown in Figure 3 is optimized by varying the preview distance \(d\) and scan radius \(r\) of the scan (or, equivalently, the measurement distance \(F\) and cone angle \(\theta\)). For the pulsed lidar configurations, the 10 range gates measured are evenly spaced between 50 m and the maximum range given by the measurement distance parameter \(F=\sqrt{d^{2}+r^{2}}\). Figure 3: Lidar scan pattern scenarios investigated for scan pattern optimization.
## 4 Proposed Trust-Based Optimized Reporting Scheme
### Trust Propagation
#### 4.3.3 Root Node Overhead Reduction  
Here, we highlight how the proposed methodology optimizes cost by minimizing the burden on the root node. As outlined in Algorithm 3, the forgetting curve mechanism, coupled with a predefined threshold, filters out calculated trust values after a certain number of feedback values are received. This strategic design choice ensures that the root node is spared from processing excessive feedback, leading to more efficient resource utilization. The reduced load on the root node is particularly valuable for large-scale IoT networks, where minimizing overhead is crucial for scalability. Thus, the proposed methodology not only enhances trust management but also strategically reduces the overall costs associated with root node processing and memory consumption.
# Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems
## 3 Methodology
### Long Short-Term Memory Model  
LSTM, which is designed to overcome the short-term memory limitations of traditional recurrent neural networks (RNNs), features a cell state that retains information for capturing both short- and long-term dependencies. As shown in Figure 2, it employs three key gate mechanisms, i.e., the input gate, the forget gate, and the output gate, all of which are essential for controlling cell state modifications and output production.  
These gate mechanisms enable the learning and determination of whether information should be updated, retained, or outputted. This capability is crucial for LSTM to effectively capture and remember long-term dependencies within sequence data, thereby enhancing its ability to understand contextual information.  
The forget gate determines the retention of information from the previous moment's cell state \(\rm C_{t-1}\) to the current moment's cell state \(\rm C_{t}\)  
\[\rm f_{t}=\sigma(w_{f}\cdot[h_{t-1},x_{t}]+b_{f}) \tag{8}\]  
In the equation, \(\sigma\) represents the sigmoid function, \(\rm w_{f}\) denotes the weight of the forget gate, \(\rm h_{t-1}\) is the hidden state of the cell at time \(t-1\), \(\rm x_{t}\) is the input data at time \(tt\), and \(\rm b_{f}\) is the bias of the forget gate.  
The input gate determines what information is fed into the cell module, which consists of a sigmoid layer and a tanh layer  
\[\rm i_{t} =\sigma(w_{i}\times[h_{t-1},x_{t}]+b_{i}) \tag{9}\] \[\rm a_{t} =\tanh(w_{c}\times[h_{t-1},x_{t}]+b_{c})\]  
In the formula, \(\rm w_{i}\) represents the weight of the input gate, \(\rm b_{i}\) signifies the bias of the input gate, \(\tanh\) denotes the hyperbolic tangent function, \(\rm w_{c}\) refers to the candidate state information that is about to be updated into the cell state, and \(\rm b_{c}\) is the bias of the candidate cell state.  
Figure 2: LSTM model structure.  
The update process of the LSTM cell module is:  
\[c_{t}=f_{t}\times c_{t-1}+i_{t}\times a_{t} \tag{10}\]  
The output gate determines the output value based on the state of the cell module, controlling the influence of long-term memory on the current output and selectively emitting information  
\[\begin{array}{ll}o_{t}&=\sigma(w_{0}\times[h_{t-1},x_{t}])+b_{0}\\ h_{t}&=o_{t}\times\tanh(c_{t})\end{array} \tag{11}\]  
In the formula, \(o_{t}\) represents the output of the output gate, \(w_{o}\) denotes the weight of the output gate, \(b_{0}\) signifies the bias of the output gate, and \(h_{t}\) is the hidden state at time \(t\).
# Soil Moisture Retrieval by Integrating SAR and Optical Data over Winter Wheat Fields
## 4 Results and Discussion  
### SM Inversion  
The soil roughness was assumed to be a constant value from 16 April to 10 May. The multi-temporal Sentinel data on 16 April, 28 April, and 10 May were utilized to retrieve the SM and roughness Z, simultaneously, so the four unknowns were solved from six equations based on the Newton iteration method. In the process of solving the equation, the SM was limited to a range from 0.05 to 0.45, and the roughness of \(Z\) was limited to a range from 0.05 to 0.9. The spatial distribution of SM is shown in Figure 4.  
The spatial distribution of SM on 16 April was very similar to that on 28 April with a slightly lower SM in the northern part of the study area. The spatial distribution of SM on 28 April was drier than the one on 16 April, while the SM distribution in the entire area on 10 May was generally higher. According to the local meteorological data, there was basically no rainfall from 16 April to 28 April, while a widespread rainfall in the study area occurred on 9 May, with rainfall exceeding 22 mm, so the soil moisture in the total study area remained high on 10 May.  
The results showed that the SM value in the surrounding areas of the town was significantly higher than the value of the winter wheat areas. The field investigation revealed that there were a large number of scattered poplar and fruit trees in the surrounding area of the town. For the woodland areas, tree trunks and branches have a strong microwave scattering ability, and this is not involved in the algorithm. The algorithm attributes the strong scattering of branches to a high soil moisture content, so the soil moisture in the forested areas will be overestimated.
## 2 Materials and Methods
### Static GNSS Measurements  
Static GNSS measurements were taken approximately every 20 km at the respective overnight camps in 2002, 2006, 2010, 2012, and 2015, with each expedition member reaching the camp established in 2002 to ensure comparability. Upon arrival, the antenna was set up, aligned, and connected to the receiver (see Figure 3). The system is powered by solar cells. The measurement time is between 8 and 12 h, and the equipment is stored at a sufficient distance to avoid interference with the signal. The earlier measurements were made using ground stations on both coasts (Kangerlussuaq and Kulusuk and Tasilialo, Kangerlussuaq and Ilulissat, respectively), and later, precision was achieved using Precise Point Positioning (PPP) by correcting the orbits afterward. WGS84 was used as a reference frame.
## 2 Theoretical Background
### Deep Residual Convolutional Neural Networks  
Because the bearing degradation process has strong nonlinear characteristics, the shallow, deep learning model is difficult to find the optimal results. Increasing the width and depth of traditional CNN can improve network performance, but with the increase in network layers, some local features are lost, and there are problems such as gradients disappearance and explosion [21]. The residual network structure [22] effectively solves these problems and significantly improves model performance. The residual network consists of a series of residual blocks, as shown in Figure 1, which can be expressed as  
\[y=F(x,\{W_{i}\})+x \tag{4}\]  
where \(x\) and \(y\) are the input and output vectors of the corresponding network layer, respectively, the function \(F(x,\{W_{i}\})\) represents the residual mapping to be learned. The shortcut connections introduce neither extra parameter nor computation complexity, and the performance degradation of traditional neural networks with increasing depth is solved.
## 3 RFID Security and Threats
### RFID Challenges
* **Space division multi-access:** Its main concern is to split the channel into distinct areas to enhance the channel's connection capability. Unfortunately, SDMA is extremely costly and requires some complex designs for the antennas. * **Time division multi-access:** This approach is widely used and covers many anti-collision algorithms. TDMA divides the transmission channel between tags to ensure the reader's identification ability at separate times to overcome interference. This method is not costly and reduces the number of tag interrogations after each successful response (broadcast message response).
# Experimental Study on Damage Constitutive Model of Rock under Thermo-Confining Pressure Coupling
## 4 Damage Constitutive Model of Rock
### Rock Damage Theory Based on the Drucker-Prager Criterion
#### 4.1.2 Rock Damage under Temperature  
Based on the theory of macroscopic damage mechanics, the thermal damage of rock can be characterized using macroscopic mechanical parameters. Therefore, the elastic modulus can be used to define the thermal damage variable, which can be expressed as:  
\[D_{T}=1-\frac{E_{T}}{E_{0}} \tag{15}\]  
where \(E_{T}\) is the elastic modulus of the rock under the temperature \(T\); \(E_{0}\) is the elastic modulus of the rock at room temperature (20 \({}^{\circ}\)C).
## 3 Results
### Observed Correlation of Rainfall, GNSS-IWV, and CAPE at the GNSS Station Locations
#### 3.1.2 Correlating Seasonal Pattern of GNSS-IWV and CAPE with Rainfall  
Next, we show the seasonal pattern of both variables with respect to rainfall. Our results show that the daily values of GNSS-IWV and CAPE increased during the austral summer months and coincided with an increase in rainfall at both station locations (TUCU and CATA) (Figure 4). In contrast to the GNSS-IWV, which represents higher absolute values during austral summer at the TUCU station location (Figure 4A,C), CAPE showed larger absolute values for the CATA station (Figure 4B,D) during austral summer months.  
Figure 3: The power-law behaviour using a maximum likelihood approach of log-binned data for the independent variable CAPE (**A**) for TUCU and (**B**) for CATA following methods described in [50]. We identify a power-law like behaviour for CAPE values above 2500 J/kg (for TUCU) and 2900 J/kg (for CATA). The \(p\)-value greater than 0.1 (TUCU = 0.9, CATA = 0.4) confirms that a power law is a plausible hypothesis for the data.  
We then show the cyclic behaviour of rainfall and CAPE as well as rainfall and GNSS-IWV using the wavelet coherency analysis. As can be seen in Figure 5, the significant coherence area between CAPE and rainfall as well as GNSS-IWV and rainfall time series is observed from the cycle scale of 8 months to 16 months and more significantly around 12 months from 2008 to 2013. The arrows (phase), which are turning to the right at the period band 8-16, show the in-phase coherence and argue that the CAPE and GNSS-IWV contribute to the rainfall.  
Figure 4: The daily mean GNSS-integrated water vapour (orange line) vs. daily mean rainfall from TRMM data (blue line) (**A**) for TUCU and (**C**) for CATA stations and daily mean CAPE (red line) from ERA-interim vs. daily mean rainfall from TRMM data (blue line) (**B**) for TUCU and (**D**) for CATA stations for (2010–2013). Both datasets represent a high seasonal agreement with rainfall in both stations.
# Classification of Tropical Cyclone Tracks in the Northwest Pacific Based on the SD-K-Means Model
## 2 Data and Methods
### Clustering Method
#### 2.3.2 Soft-DTW Algorithm  
Dynamic Time Warping (DTW) is a common method for measuring the similarity of time series. Compared with direct measurement methods such as Euclidean distance, the DTW algorithm can handle nonlinear time changes in time series signals, that is, it can handle time offsets and length differences, and its effect is relatively good among a number of time series similarity calculation methods (for example, Longest Common Subsequence (LCSS) and Edit Distance for Real Sequences (EDR)).  
The DTW algorithm matches time series by stretching and compressing the nonlinear time axis to measure the similarity between the time series. Its essence is to find the optimal alignment path between two time series through dynamic programming to minimize the distance between them. If two time series \(X=(x_{1},x_{2},\ldots,x_{n-1},x_{n})\) and \(Y=(y_{1},y_{2},\ldots,y_{m-1},y_{m})\) are given, the calculation equation of DTW can be expressed as follows:  
\[D(i,j)=d\big{(}x_{i},y_{j}\big{)}+min\{DTW(i-1,j),DTW(i,j-1),DTW(i-1,j-1)\} \tag{11}\]  
where \(D(i,j)\) represents the DTW distance between subsequences \(X[1:i]\) and \(Y[1:j]\), \(d\big{(}x_{i},y_{j}\big{)}\) represents the distance between \(x_{i}\) and \(y_{j}\), and the DTW distance between sequences \(X\) and \(Y\) is \(D(n,m)\).  
At the same time, to reduce the size of the search space and find the optimal alignment path faster, it needs to meet the following five restrictions: first, monotonicity, that is, the path is unidirectionally corresponding and the features are not repeated in the path; second, continuity, that is, the path cannot jump, ensuring that no feature is skipped; third, boundary conditions, that is, the path starts from the upper left and ends at the lower right, ensuring that both time series are considered as a whole; fourth, regular window, that is, ensuring that the path with the shortest distance is selected when corresponding features; fifth, slope restriction, that is, preventing two time series with too long lengths from being matched.  
However, the DTW algorithm is discrete and non-differentiable, and its robustness is poor. To solve the above problems, a Soft-DTW (Soft Dynamic Time Warping) algorithm [20] that uses a smoother DTW distance to measure the similarity between two time series has emerged. The calculation equation of the Soft-DTW algorithm can be expressed as follows:  
\[DTW(X,Y)=min^{\gamma}\{D(n,m)\}=-\gamma log\left(\sum_{\pi}e^{-\frac{D\pi}{ \gamma}}\right) \tag{12}\]  
where \(\pi\) represents all possible paths, \(\gamma\) represents the smoothing parameter (\(\gamma>0\)). When \(\gamma\) tends to 0, Soft-DTW degenerates into standard DTW; when \(\gamma\) increases, the path becomes smoother.