esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion
### RS Data Used in Wetland Studies
#### 3.4.2 Single-Source Versus Multi-Source Data  
Although individual RS data have proven successful in monitoring wetland areas, data fusion techniques employing multi-source satellite data sets can improve the determination of wetland hydrological, vegetation, and topographic characteristics, which are important indicators of wetland characteristics and offer the potential for improved wetland classification accuracy [21; 75; 76; 77]. For example, optical images can reflect the rich spectral information of objects, and SAR is capable of providing valuable geophysical parameters. The combination of polarimetric and optical data to classify wetlands fully exploits the spectral and backscattering characteristics of the habitat. In addition, it reduces radar speckle noise and enhances the separation between wetland objects, making it a helpful monitoring strategy. Many experts and geoscientists have recently utilized multi-source RS imagery to monitor wetlands with varying results [11]. Many works have attempted to employ multi-source imagery to monitor wetlands considering data fusion and feature fusion techniques to use the complementary merit of different types of RS data. For instance, in a newly published paper, [PERSON] et al. [21] established a two-stream DL framework based on a graph convolutional network and convolutional neural network for wetland classification using Sentinel-1 and Sentinel-2 images. Based on their ablation analysis, the accuracy of classification reached its highest when two kinds of data sets were combined. By combining LiDAR and SAR data, [PERSON] et al. [78] observed improved wetland extent mapping and classification accuracy of wetland types compared to individual data source analysis. The significant advancements and improvements in spaceborne RS in parallel with the rise of the trend toward open-source data sharing have led to opportunities for combining multiple data types in monitoring and fully understanding the unique characteristics of wetland environments.  
Using multi-source satellite images helps in understanding, modeling, and projecting land change effectively by providing timely and spatially distributed information. In particular, moderate-resolution multispectral sensors (e.g., Landsat, SPOT, and ASTER) have been successfully used to study flooding and affected land cover, detect wetlands in heterogeneous landscapes, and monitor invasive plants. More recent studies reported high accuracies in obtaining wetland characteristics when fusing the datasets from Sentinel-1 and Sentinel-2 sensors [21; 50; 73; 77]. This meta-analysis reveals that about 55% of studies benefited from multi-source or multi-sensor RS data combination, while the remaining utilized only a single-source or single sensor imagery (see Figure 12).
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 5 Analysis and Discussion
### Statistical Robustness  
To assess the consistency and reproducibility of the proposed method, we performed multiple independent runs of the embedding and extraction process using different pseudo-random watermark sequences. Each test was repeated 10 times on the CIF dataset, using the same embedding parameters and video sequences. The average and standard deviation (\(\sigma\)) were computed for key performance metrics, including BER, PSNR, SSIM, and VMAF.  
Table 10 shows the results obtained for three representative CIF videos. The low standard deviation values across the runs confirm the robustness and stability of the method, indicating that performance is not significantly influenced by the choice of watermark sequence. These findings strengthen the statistical validity of the proposed scheme.
# The Response of Vegetation Dynamics to Climate in Xinjiang from 1991 to 2018
## 3 Results
### The Response of Vegetation to Climate Change  
#### 3.3.1 The Changing Trend of Climate Factors  
The unique topography led to great regional differences in the distribution of temperature and precipitation in the study region. The long-term annual average temperature in the region ranged from \(-18.2~{}^{\circ}\)C to \(17.9~{}^{\circ}\)C, with an average of \(11.6~{}^{\circ}\)C. The temperature distribution showed a pattern of higher temperatures in basins and lower temperatures in mountainous areas (Figure 7). The areas with high temperature were mainly located in the Tarim Basin, Hami Basin, and other areas, with the highest temperatures found in the Turpan Basin in eastern Xinjiang. Low-temperature areas were primarily distributed in the Altai, Tianshan, and Kunlun Mountain ranges. Areas in Xinjiang with increasing temperatures were mainly located in lower-elevation basins and valleys, with significant warming observed in the Junggar Basin, Hami Basin, and the northern Tianshan region (\(p<0.05\)). Regions with decreasing temperatures were mainly distributed in higher-altitude mountainous areas and the central part of the Tarim Basin. Although the central Tarim Basin showed a cooling trend, its annual average temperature remained relatively high.  
During the study period, the long-term average annual precipitation ranged from \(108.3\) mm to \(183.4\) mm, with an average of \(147.0\) mm. The spatial distribution of precipitation  
Figure 6: Result of land use transfer matrix. Cr is crop land, Gr is grassland, Fo is forest, Sh is shrub, Ofto is other forest, De is desert, and Wa is water.  
Figure 7: Annual characteristics of spatial distribution (**a**) and trend (**b**) of air temperature from 1991 to 2018.  
showed a pattern of more precipitation in the north and less in the south (Figure 8). In northern Xinjiang, the Altai and Tianshan Mountains were the main areas with high precipitation values (>450 mm). In southern Xinjiang, areas with higher precipitation values (>15 mm) were mainly distributed in the southern Tianshan and eastern Kunlun Mountains. Precipitation in the Altai Mountains, Turpan Basin, eastern Tarim Basin, and the eastern Kunlun Mountains showed a decreasing trend, while other areas experienced increasing precipitation. Regions with the most significant decrease in annual precipitation were concentrated in the Altai Mountains, where the decline was statistically significant (\(p<0.05\)). Annual precipitation in the western and southern Tianshan Mountains increased by more than 1.2 mm/year.  
Overall, from 1991 to 2018, both temperature and precipitation in Xinjiang showed an upward trend, with _slopes_ of 0.02 and 0.21, respectively. These findings confirmed that, in recent decades, Xinjiang had been gradually shifting towards a warmer and wetter climate, which was expected to have a significant impact on variations in vegetation in study area.
###### Abstract  
In response to the demand for high-precision positioning within confined or indoor environments, the application of acoustic ranging methods has been widely adopted by numerous engineers. Currently, time-of-flight (TOF)-based acoustic ranging positioning systems face challenges such as the susceptibility of sound velocity to environmental factors and the loss of acoustic signals at both short and long distances, which leads to a reduction in positioning accuracy. This paper addresses these issues by proposing a high-precision confidence interval weighting method for acoustic ranging and further introduces a method for base station deployment and self-optimization positioning within fixed indoor base station scenarios. The method is based on trilateration positioning, establishing criteria for the division of central and boundary areas. It categorizes mobile terminal nodes based on their coordinates from the previous moment, selects distance information from nearby base stations in different modes, and employs weights for decision-making and computation, ultimately yielding two-dimensional positioning coordinates. Experiments demonstrate that the proposed method can effectively enhance the positioning accuracy of acoustic positioning systems compared to traditional four-base station weighted average positioning algorithms.  
acoustic positioning; time of flight (TOF); base station selection; cross-area; high-precision positioning +
Footnote †: journal: Applied sciences  
0000-0001-5000-0001-5000][PERSON]  
1 School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; [EMAIL_ADDRESS] (Z.Z.); [EMAIL_ADDRESS] (J.C.); [EMAIL_ADDRESS] (B.G.); 1
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 4 Active Device Technology S-Parameter Extraction
### I-V Characteristics of pHEMT Devices  
DC and RF test simulations are carried out under a variety of bias situations to ascertain the operating point of the transistor. This is done to gain an understanding of the non-linear features of the pseudomorphic high electron mobility transistor (pHEMT) active device via \(S\)-parameter extraction. Figure 3 displays the I-V characteristics of Indium Gallium Arsenide (InGaAs) pHEMTs at various biasing points for a \(2\times 50\) um InGaAs pHEMT while Figure 4 displays the I-V characteristics of Indium Gallium Arsenide (InGaAs) pHEMTs at various biasing points for a \(4\times 50\) um InGaAs pHEMTThe drain current (\(I_{ds}\)) is plotted versus the drain-source voltage (\(V_{ds}\)), where the operating point is chosen at \(V_{ds}=2\) V, \(V_{gs}=-0.3\) V, and \(I_{ds}=0.021\) A. The power dissipation for a three-stage low-noise amplifier (LNA) design is determined by:  
\[P_{diss}=V_{ds}I_{ds} \tag{28}\]  
where \(P_{diss}<0.14\) W.
## 2 Research Motivation
### Problem Identification  
To approach the constraint mitigation and identify areas to research, the ISFs were mapped and compared to identify areas not formally covered by those references. Figure 1 illustrates the initial gaps (depicted as red shapes) in the formal delineation of activities found in the primary BCM references for each BCM common component (located in the outer ring). Figure 1 presents the organised activities, objectives, or practices within the ISFs and highlights relevant components of BC within the circle's centre.  
The intention here is to emphasise that despite the complexity and relevance of the references we have reviewed, there appears to be an opportunity to address the gaps identified, particularly concerning the definition of BC metrics. While the standards' guidance concentrates on the methods, timing, and parties responsible for measurement, they also underscore that organisations need to determine what exactly should be measured.  
Score metrics can improve the planner's involvement and the plan content [23]. They can also improve communication of the plan's content and activities' achievements and tendencies between the BC teams and the top management. They also can provide the ability to perform a quick assessment of the quality of the plan.  
The definition of the BC teams is not formally addressed in most of the studied ISFs. ISO 22301:2019 states that the organisation needs one more teams responsible for responding to disruptions, with roles and responsibilities. Thus, the BCP must provide guidance and information to assist teams in responding to a disruption [9]. The other standards mention the BC teams as given facts, for example, by considering that the recovery phase must detail a suggested course of action for recovery teams to restore system operations [24].  
The existence of BC teams, although relevant for the success of the BCM [25] and, for example, for the BCP exercises [26], is not necessary at all times [27]. Considering this information, this area of research was deferred. The other gaps are partial, considering that some ISFs address them and others do not, and therefore, not eligible to be considered an undervalued research path.  
Acknowledging the ISF guidelines, the framework to be developed should also include the best practices and considerations stated in the literature for defining metrics that reveal a particular BC performance or program implementation. The SLR focuses on identifying frameworks, models, and other contributions that address the key areas of interest identified in this paper.  
Figure 1: BCMS components, relevant activities, and gap analysis. Source: [22].  
Taking into account the guidelines provided by the ISF, any BC framework should encompass the best practices and insights outlined in the literature for defining metrics that effectively showcase specific aspects of BC performance or program implementation. The research primarily concentrates on pinpointing frameworks, models, and other contributions that tackle the key areas of interest outlined in this paper.
# Negotiation-Free Encryption for Securing Vehicular Unicasting Communication
## 4 Negotiation-Free Encryption Method
### Algorithm Implementation
#### 4.2.3 Decryption Authentication  
\(V_{r}\) receives the ciphertext and the timestamp. Now it is time to decrypt the ciphertext. However, the current time is later than the timestamp. Therefore, the decryption key is its own history of the state. For the state value of the history, only the vehicle itself can submit a request to the cloud, but others cannot. The decryption authentication steps are shown in Figure 4.  
Figure 3: Data transmission.  
1. \(V_{r}\) sends the request for its status value to the \(VC\) and the time of the request state is the same time of timestamp time (\(TS\)). \[Ciphertext_{(V_{r}-to-VC)}=Enc_{VC_{pk}}(ID_{r}+ID_{r}+TS)\] (7)
2. The \(VC\) first decrypts the \(Ciphertext_{(V_{r}-to-VC)}\) with its own Secret Key. \[Message_{rec}=Dec_{VC_{cl}}(Ciphertext_{(V_{r}-to-VC)})\] (8) Then it finds \(V_{r}\)'s historical states \((Attr_{r_{1}},Attr_{r_{2}},Attr_{r_{3}},...)\) and hashes them. The hash value has a _key_ that is used to decrypt the received ciphertext. \[key=Hash(Attr_{r_{1}},Attr_{r_{2}},Attr_{r_{3}},...)\] (9)
3. The cloud uses \(V_{r}\)'s public key to encrypt the \(key\) and the timestamp (\(TS\)) and then sends it back to \(V_{r}\). \[Ciphertext_{(VC-to-V_{r})}=Enc_{V_{pk}}(ID_{r}+key+TS)\] (10)
4. \(V_{r}\) uses its private key to decrypt the ciphertext to get the real \(key\). \[key+TS=Dec_{V_{sk}}(Ciphertext_{(VC-to-V_{r})})\] (11)
5. \(V_{r}\) uses the key to decrypt the ciphertext (\(M\)) and receives the message. \[Data=Dec_{key}(Message)\] (12)
## 3 Experimental Setup
### Photon Counting with PCM
#### 3.3.1 Output Pulse Width and Dead Time  
The output pulse width and dead time of the PCM are two important parameters which directly influence the bandwidth of the photon-counting receiver. For a PCM, if a single-photon detection event occurs, the detection system will become blind to the incoming photon. The time after an initial detection during which no new input photons can be detected is named dead time. A typical dead time for a PCM ranges from microseconds to nanoseconds. Short dead times are significant for the high-rate photon-counting receiver. As shown in Figure 9a, we sampled the analog output of PCM using high-speed oscilloscopes under high input power. In addition, they are influenced by afterpulses caused by the elastic scattering of electrons on the first dynode of PMT and the ionization of residual gas molecules in the PMT tube, which leads to the output pulse spreading. Moreover, due to the effect of the detector itself and pulse-counting electronics, the dead time can randomly change. Therefore, we calculated the mean value of the output pulse width and dead times. They were 11.7 ns and 18.5 ns, respectively.
## 3 System Model Based on Time Switching and NOMA
### Stage 1  
In this stage, the source transmits signal \(x_{s}\) with power \(P_{s}\) to the \(IoT_{R}\) for half of the block time \(T\) i.e., \(T/2\) period of time. Here, \(IoT_{R}\) node works as TS-based relay. The \(IoT_{R}\) node divide the time block in the ratio \(\alpha T\):\(\frac{(1-\alpha)T}{2}\):\(\frac{(1-\alpha)T}{2}\). Here \(\alpha T\) is for energy harvesting by \(IoT_{R}\) and \(\frac{(1-\alpha)T}{2}\) is for information processing by \(IoT_{R}\) respectively, \(0\leq\alpha\leq 1\). The information signal received at \(IoT_{R}\) during this stage is given as:  
\[\hat{g}_{IoT_{R}}=\sqrt{P_{s}}h_{IoT_{R}}x_{s}+n_{IoT_{R}}, \tag{1}\]  
where \(n_{IoT_{R}}\sim CN(0,\sigma_{IoT_{R}}^{2})\) is the additive white Gaussian noise at \(IoT_{R}\) with mean zero and variance \(\sigma_{IoT_{R}}^{2}\). \(h_{IoT_{R}}\sim CN(0,\lambda_{h})\) is the channel coefficient between source node and \(IoT_{R}\) node with zero mean and variance \(\lambda_{h}\).  
The energy harvested at \(IoT_{R}\) in \(\alpha T\) duration of time is given as:  
\[\hat{E}_{h_{IoT_{R}}}=\eta P_{s}|h_{IoT_{R}}|^{2}\alpha T, \tag{2}\]  
where \(0\leq\eta\leq 1\) is the energy conversion efficiency. Here, we assume that the pre-processing power for the energy harvesting is negligible in contrast to the transmission power \(P_{s}\) which is in line with the previous works [31; 32; 33].  
The transmit power of \(IoT_{R}\) i.e., \(\hat{P}_{IoT_{R}}\) in \(\frac{(1-\alpha)T}{2}\) block of time can be given as:  
\[\hat{P}_{IoT_{R}}=\frac{\hat{E}_{h_{IoT_{R}}}}{(1-\alpha)T/2}=\frac{2\eta P_{s} |h_{IoT_{R}}|^{2}\alpha}{(1-\alpha)}, \tag{3}\]
# Routing Topologies of Wireless Sensor Networks for Health Monitoring of a Cultural Heritage Site
## 2 WSN
### The Hardware Platform  
Two WSNs composed of Mica2 motes purchased from MEMSIC (Andover, MA, USA) [28] using different routing topologies, mesh or tree, were employed. The Mica2 motes come in three models according to their RF frequency band: the MPR400 (915 MHz), MPR410 (433 MHz), and MPR420 (315 MHz). The motes use the Chipcon CC1000, FSK modulated radio and are controlled by an Atmega128L micro-controller with a frequency-tunable radio with extended range. The MPR400 model, emitting maximum power, was used in this experiment. Software programs for motes were written in NesC (an object-oriented extension of C) and run using the TinyOS operating system [29].  
Both WSNs were composed of seven motes and the base station. Motes were programmed to send a data package of 41 bytes every 2 min, which was enough to monitor the slow temperature changes in the buildings. As mentioned above, this work arose from a project to study the effects of heating in historical heritage sites using WSNs to monitor the temperature and humidity conditions inside and outside the walls during periods of weeks.
## 2 Background
### Regression Analysis  
Regression analysis is a tool to evaluate the statistical association between at least one 'explanatory variable' and an 'outcome variable'. A regression model linking the explanatory variables to the outcome variable is generally formulated on the basis of hypotheses that the analyst makes about the underlying relation. A typical regression model assumes the following general form:  
\[g(E(Y))=c+\beta_{1}Var_{1}+\beta_{2}Var_{2}+\cdots+\beta_{n}Var_{n}+\epsilon \tag{3}\]  
where \(g(\cdot)\) is the link function, \(Y\) is the outcome variable with mean \(E(Y)\), \(c\) is the intercept, and \(\beta_{1},\ldots,\beta_{n}\) are the regression coefficients of the \(n\) explanatory variables \(Var_{1},\ldots,Var_{n}\), and \(\epsilon\) is the error term. If the analyst believes that there are interactions between explanatory variables (i.e., they have a joint effect on the dependent variable), she can capture these interactions by considering the product of the explanatory variables, denoted by \(Var_{i}\cdot Var_{j}\), as an additional explanatory variable. Equation (3) is called a _regression equation_; the estimation of the coefficients is the key aspect, and which link function one adopts depends on the nature of the data (e.g., a _logit_ or a _probit_ function for a binary outcome).  
A model formulation (also called _parametrization_) is generally directly derived from the formulated hypotheses; however, in _exploratory_ settings (where those hypotheses do not yet exist [7]) the model definition can generally be automated by employing techniques based on the analysis of variance (ANOVA). Again, the choice of the test to compare models depends on the type of model considered; logit models, for example, may be compared using likelihood ratio tests. to select the _explanatory variables_ that have the highest _power_ in 'explaining' the data. Given two models of the same power, the model with the fewer explanatory variables is preferred (principle of'minimality'). Regardless of the adopted approach to parameterize a model, it is important to verify that the chosen explanatory variables are not highly correlated, to avoid model multicollinearity that may bias the coefficient estimation. This check can be performed by calculating a correlation matrix across the variables, or a variance inflation factor (VIF) for a given model.
## 4 Applications
### Comparison with FEM  
The aim of this application is to validate the present approach by means of a comparison with results obtained through a commercial FEM code. Two examples are conducted in this Subsection: a CNT-reinforced fully clamped square plate (Figure 4a) made of single-ply of constant thickness  
Figure 5: Distributions of the CNT-reinforcing phase along the thickness. For each layer, the parameters for the distribution itself are shown: (**a**) (5P); (**b**) (2P-W); (**c**) (2P-E).  
(\(h=0.1\) m); and the same square plate with 3 layers (CNT/matrix/CNT) of various thickness (\(0.02\) m/\(0.06\) m/\(0.02\) m). The agglomeration parameters and mass fraction value of the CNTs are given in Tables 2 and 3. All the phases were considered to be isotropic. The 3D FEM solution was obtained by the commercial software Strand7. Several kinematic models were considered with reference to the GDQ solution. In particular, in the first example, the following theories were used: FSDT\({}_{RS}^{x=5/6}\), ED2\({}^{x=5/6}\) and ED3; and in the second example, the following theories: FSDT\({}_{RS}^{x=1}\), FSDT\({}_{RS}^{x=5/6}\), ED2Z\({}^{x=1}\), ED2Z\({}^{x=5/6}\) and EDZ3. The notation \(RS\) means that reduced stiffness is used (hypothesis of plane stress). Analogously, \(\kappa\) stands for the shear correction factor. It should be noted that the zig-zag theories up to the second order of kinematic expansion are taken with and without the shear correction factor. Further details concerning this choice can be found in the following papers [101; 102; 103; 104; 105; 106; 107; 108; 109; 110]. The results of the comparison can be observed in Tables 2 and 3, showing good agreement between GDQ solutions and FEM ones. It should be recalled that, in the second circumstance, only zig-zag theories were used, since the structure has a soft-core. As highlighted in [109], sandwich structures with soft inner cores require the Murakami's function to be well analyzed.