esa-sceva/satcom-chunk-collection huggingface dataset:
# Siberian Pine and Larch Response to Warming-Drying Climate in the Southern Boundary of Their Range
## 3 Results
### The Satellite Data Analysis
#### 3.4.2 The MODIS-Based Analysis of Land Cover Changes in the Tannu-Ola Ridge  
Within the Tannu-Ola Ridge, the majority of changes occurred on the northern megaslope. During 2001-2022, the area of Siberian pine stands decreased by \(-12\%\) (-142 sq. km) (Figure 12a; Table 4). Siberian pine stands mostly transformed into mixed forests, i.e., the area of forests with dominance of Siberian pine decreased (Table 5). On the contrary, the area of larch forests increased by \(+147\%\) (-806 sq. km; Figure 12b; Table 4). That increase was caused mostly by the partial transformation of \"mixed forests\" into larch-dominant ones (Table 5). Siberian pine loss and gain occurred mostly at low and high elevations, respectively, whereas the larch area increase was observed at both high and low elevations. Meanwhile, the area of mixed forests also increased (\(+15\%\)) due to income from the \"shrubs and grasses\" class, while \"shrubs and grasses\" itself decreased by \(-13\%\) (Tables 4 and 5). Notable is an increase in the birch stands' area (\(+101\%\)). The total area of closed stands increased by \(+21\%\) (\(+1486\) sq. km), which was caused mostly by the transformation of the \"shrubs and grasses\" class.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Class Name** & **Area in 2001–2003 (sq. km)** & **Area in 2020–2022 (sq. km)** & \(\Delta\) **(sq. km)** & \(\Delta\) **(\% of 2001–2003)** \\ \hline
1 & Siberian pine stands & 1235.8 & 1093.7 & \(-142.1\) & \(-12\) \\ \hline
2 & Larch stands & 547.3 & 1352.9 & \(+805.6\) & \(+147\) \\ \hline
3 & Birch stands & 21.7 & 43.5 & \(+21.9\) & \(+101\) \\ \hline
4 & Mixed stands & 5211.7 & 6012.2 & \(+800.5\) & \(+15\) \\ \hline
5 & Shrubs and grasses & 11,135.4 & 9646.6 & \(-1488.8\) & \(-13\) \\ \hline
6 & Background & 3.4 & 6.4 & \(+3.0\) & \(+88\) \\ \hline \hline \end{tabular} Note: “Shrubs and grasses” class cannot be separated based on the MCD12 QI IGBP maps.  
\end{table}
Table 4: Changes in areas of land cover classes within the Tannu-Ola Ridge determined based on the MCD12 QI IGBP maps.  
Figure 12: **(a)** During 2001–2022, the area of Siberian pine stands decreased by \(-12\%\) (\(-142.1\) km\({}^{2}\)), whereas (**b**) larch stands gained \(+147\%\) (\(+805.6\) km\({}^{2}\)). Burned areas were removed.
## 3 The Proposed Model
### Process Involved in SA-GSO-Based Multihop Routing
The maximal fitness function can be evaluated using the following formulas:  
\[B=\frac{1}{4}[b+(1-h)+(1-\beta)+\mu] \tag{25}\]  
\[b=\frac{1}{a}\sum_{K-1}^{a}b_{K} \tag{26}\]  
\[h=\frac{a}{m} \tag{27}\]\[\beta=\frac{1}{a^{2}\times\eta}\sum_{K=1}^{a}\frac{\sum_{T=1}^{a}\beta(K,\ T)}{T=K+1} \tag{28}\]  
\[\mu=\frac{1}{3a^{2}\times\eta}\sum_{K=1}^{a}[DT+RT+HT] \tag{29}\]  
whereas \(B\) represents fitness function, \(b\) indicates energy, \(h\) denotes delay, \(\beta\) represents the distance, \(\mu\) shows trust, and \(\eta\) indicates the normalization factor. ```
Initialization: \(m\) dimension Initialization: \(n\) glowworms Let \(s\) be step size Let \(x_{i}(t)\) indicates the location of glow-worm \(t\) at time instant \(t\) Deploy agents in an arbitrary way \(\textit{depolv}-\textit{agents}\_randomly\); for \(i=1\) to \(n\)do \(\ell_{i}(0)=\ell_{0}\) \(r_{d}^{i}(0)=r_{0}\)  Consider highest number of iterations \(=\) max_iter; assume \(t=1\); while (\(t\leq\) max_iter) do: { for every glowworm \(i\)do \(\ell_{i}(t)=(1-\rho)l_{i}(t-1)+\gamma\) Fitness \((x_{i}(t))\); for every glow-worm \(i\)do \(N_{i}(t)=\left\{j:d_{ij}(t)<r_{d}^{i}(t);\ell_{i}(t)<\ell_{j}(t)\right\}\); for every glow-worm \(j\in N_{i}(t)\)do: \(p_{ij}(t)=\frac{\ell_{i}(t)-\ell_{i}(t)}{\sum_{p\in N_{i}(t)\ell_{j}(t)-\ell_{ i}(t)}}\); \(j=\textit{select}\_\textit{glowworm}\left(\overrightarrow{p}\right)\); \(x_{i}(t+1)=x_{i}(t)+step\left(\frac{x_{i}(t)-x_{i}(t)}{|x_{i}(t)-x_{i}(t)|}\right)\) \(r_{d}^{i}(t+1)=\min\left\{r_{sy},\ \text{maximum}\left\{0,\ r_{d}^{i}(t)+\beta(n_{t}-|N_{i}(t)|) \right\}\right\}\); \(t\gets t+1\); }
```  
**Algorithm 1**Pseudocode of GSO Algorithm.
## 2 Materials and Methods
### Methods
#### 2.3.1 Calculation of Indicators
* Land surface temperature (LST) and modified wetness (WET)  
Temperature and humidity are critical factors affecting vegetation growth and driving ecological environment changes. The DN value of LST_DAY_1 KM band in the MOD11 A2 data product was converted to the commonly used unit of degrees Celsius, and the formula is expressed as:  
\[\text{LST}=0.02\times\text{DN}_{\text{LST\_DAY}}-273.15 \tag{4}\]  
where \(\text{DN}_{\text{LST\_DAY}}\) denotes the DN value of the daytime land surface temperature waveband images. Some indicators of humidity or dryness, such as the TVDI, are not suitable for measuring humidity due to the particularity of MBSs in arid regions. After comparison, it was found that the moisture component of tasseled cap transformation could reflect the comprehensive moisture of soil and vegetation in the arid area of NW China well, so it was used as the wetness indicator in this paper. The wetness indicator of the third component in the tasseled cap transformation reflects the moisture of soil, vegetation, and water. The tasseled cap transformation mainly exists in Landsat and IKONOS images, but some scholars had found that tasseled cap transformation also exists in MODIS images in recent years [50]. Based on the improved MODIS tasseled cap transformation formula and the surface reflectance product of MOD09A1 [51], the wetness indicator of Fuyun County was calculated by the following formula:  
\[WET=0.1147\text{b}_{1}+0.2489\text{b}_{2}+0.2408\text{b}_{3}+0.3132\text{b}_{4 }-0.3122\text{b}_{5}-0.6416\text{b}_{6}-0.5087\text{b}\text{\  
could reflect the comprehensive information of the indicators, so it could be used to create the MODEI based on MODIS data.  
\[\text{MODEL}=\text{f}(\text{GPP},\text{FVC},\text{LAI},\text{WET},\text{LAT}) \tag{7}\]  
The PC1 was selected as the initial MODEI. In order to facilitate the measurement and comparison of different years, the initial MODEL was standardized through Equation (8):  
\[\text{MODEL}=\frac{\text{PC1}-\text{PC1}_{\text{min}}}{\text{PC1}_{\text{max} }-\text{PC1}_{\text{min}}} \tag{8}\]
## 3 Proposed Algorithmic Approach
### CC2531 USB Hardware  
Texas Instruments offers the CC2531 USB dongle (see Figure 1) and corresponding documentation to support a PC interface in IEEE 802.15.4/ ZigBee applications. The dongle can be connected directly to your PC and used as an IEEE 802.15.4 packet sniffer, among other purposes. With the CC2531 USB firmware library available online, developing your software to operate this device is possible. An external programmer is required to program the debugger. Both the software and the hardware allow for the verification of the information sent from the nodes to the route-generating application and vice versa. In addition, verifying the new routes that algorithms generate through the Packet Sniffer application is possible. This is achieved by observing the similarity between the source and destination addresses of the 802.15.4 frames presented in both applications.
# Translational Echocardiography: The Dog as a Clinical Research Model of Cardiac Dysfunction
## 3 Cardiac Anatomical Similarities between the Human and the Dog
### Similarities in Cardiac Valves  
The dog heart has four cardiac valves with structures and locations similar to humans. As illustrated in Figure 3, its transthoracic echocardiographic appearance and other cardiac structures are similar to that of humans. The heart has two atrioventricular valves and two semilunar valves situated between the ventricles and the outflow tracts of the pulmonary artery and aorta on either side. These valves play a critical role in regulating the flow of blood through the heart. The fibrous leaflets of the atrioventricular valves are connected to the papillary muscles of each ventricle by the chordae tendineae. This connection is essential in preventing atrial valve prolapse during ventricular contraction. On the other hand, the closure of the pulmonary, semilunar, and aortic valves does not involve chordae tendineae but instead relies on pressure gradients to facilitate their proper functioning [11; 19].  
In both dogs and humans, the tricuspid valve separates the right atrium from the right ventricle and is almost symmetric in structure. The right ventricle has three papillary muscles associated with it. In some cases, the anterosuperior and inferior leaflets of the tricuspid valve may appear to have only two leaflets in certain canine patients due to the fusion of their commissures [11]. Variations in the number of papillary muscles have been observed not only between different mammalian species but also among individualswithin the same species. These variations are important to consider when evaluating cardiac function and diagnosing any abnormalities or pathologies. The left atrium and left ventricle are separated by the bicuspid valve, which typically has two leaflets-anterior or septal and posterior or parietal. However, it has been reported in humans that the mitral valve may have multiple leaflets, with some individuals exhibiting significant variations in the number and morphologic variability of the mitral valve leaflets [23]. This anatomical variation is relatively rare, but it is important to be aware of it when evaluating mitral valve function or performing interventions on the valve. In both humans and dogs, there is a fibrous connection between the mitral and aortic valves that extends from the central fibrous body to the left fibrous trigone. This fibrous continuum is important for maintaining the structural integrity of the heart and for the proper functioning of the valves [7]. There are variations in the annulus fibrosus that support the mitral valve and its leaflets among different species. In humans, there is an annular segment at the base of the mural cusp. However, this segment can be difficult to identify in some breeds of dogs [11]. The semilunar pulmonary valve has a similar appearance to the aortic valve but is thinner. Unlike the aortic valve, there are no coronary ostia located behind the cusps of the pulmonary valve [7].
## 3 Results and Discussion
### Preliminary Analysis of Experimental Signals  
Figure 8 illustrates the testing signals of the pressure bar 01, the elementary theoretical wave velocity \(C_{0}\) in this bar can be obtained by dividing twice the length of the bar by the period of signal reflection, as the elastic stress wave reflects back and forth between the front and rear end and the length of the bar is known (1500 mm).  
Table 3 summarizes the experimental results of eight pressure bars, where the average wave velocity is 5.2586 km/s and the standard deviation is 2.2 m/s. Table 3 also gives the free surface velocity-pressure conversion coefficient (\(k=\rho_{0}C_{0}/2,\rho_{0}=7800\) kg/m\({}^{3}\)), calculated from the elastic wave velocity according to the elementary theory.  
Figure 6: The diagram of testing system.  
Figure 7: A photograph of the testing system.  
Figure 9 summarizes the waveforms within the first peak intervals of the eight bars obtained from the testing experiments without containing the subsequent reflected signals. The curves' baselines are shifted, and the peaks are normalized and then aligned to time zero. It can be seen that the first peaks are followed by high-frequency oscillations, amongst which the large amplitude appears at about 0.16 ms.  
Figure 8: Typical velocity curve of free surface of pressure bar obtained by photonic doppler velocimetry (PDV).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Bar** & **Peak Time (ms)** & **Reflection Period (us)** & **Wave Velocity \(C_{0}\) (km/s)** &
\begin{tabular}{c} **Conversion Coefficient** \\ **(MPa/ms\({}^{-1}\))** \\ \end{tabular} \\ \hline
01 & 3.666 & 570.42 & 5.2595 & 20.51 \\
02 & 3.394 & 571.08 & 5.2537 & 20.49 \\
03 & 3.897 & 570.67 & 5.2577 & 20.51 \\
04 & 3.612 & 570.46 & 5.2589 & 20.51 \\
05 & 3.903 & 570.49 & 5.2591 & 20.51 \\
06 & 3.902 & 570.29 & 5.2610 & 20.52 \\
07 & 3.637 & 570.29 & 5.2610 & 20.52 \\
08 & 4.036 & 570.65 & 5.2578 & 20.51 \\ Average & - & 570.54 & 5.2586 & 20.51 \\ standard deviation & - & 0.24 & 0.0022 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The preliminary results of the pressure bar obtained by the testing experiment.  
Figure 9: The testing signal of eight pressure bar sensors.
# Can IMU Provide an Accurate Vertical Jump Height Estimate?
## 2 Materials and Methods
### Statistical Analysis  
For the purposes of this research, all raw data were subjected to descriptive statistical analysis in order to provide basic statistical indicators (Mean, Standard Error Mean--SEM, Standard Deviation--SD, Coefficient of Variation--CV, Minimum--Min and Maximum--Max). The assumption of normality of the distribution was tested by application of the Shapiro-Wilk test. Differences in the scores in relation to the measurement equipment were determined using a Paired sample \(t\)-test. Cohen's d was provided for effect size, with a value of 0.2, 0.5 and 0.8 considered small, medium, and large effect, respectively [33]. Intraclass Correlation Coefficient (ICC) was used to provide a measure of inter and intra-instrument reliability. Based on the 95% confidence interval of the ICC estimate, values less than 0.5, between 0.5 and 0.75, between 0.75 and 0.9, and greater than 0.90 are indicative of poor, moderate, good, and excellent reliability, respectively [34]. In addition, the mean coefficient of variation (McV) was provided [35]. Bland-Altman plots were used to evaluate the discrepancies of the results between the two measurement devices [36]. The level of statistical significance was defined based on the criterion \(p\leq 0.05\)[37]. Statistical analyses were conducted using IBM SPSS 23 and Python3 Pandas and SciPy libraries [38; 39].  
Figure 3: Squat jump—starting (**left**), take-off/landing position (**middle**), and IMU placement (**right**).
# Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection
## 3 Proposed System Model
### Data Preprocessing  
The primary objective of preprocessing is to guarantee that the data are transparent, standardized, and analytically ready. We implement data cleansing, standardization/normalization, feature scaling, and categorical variable encoding as preprocessing techniques for this investigation. The IoT-23 and APA-DDoS datasets are processed similarly. Each process and the relevant methodologies and computations are described below [29]:  
**Data cleaning:** Identifying and fixing (or eliminating) errors and inconsistencies in the dataset to improve its quality is known as data cleaning. The most common issues are outliers, duplicate data, and missing values.  
**Handling missing values:** Many techniques can be used to handle missing data, such as removing rows with missing values, applying mean/median/mode, or putting more sophisticated interpolation techniques into use [29].  
\[y_{\text{new}}=\begin{cases}y&\text{if }y\
eq\text{NaN}\\ \
u&\text{if }y=\text{NaN}\end{cases} \tag{1}\]  
\(y_{\text{new}}\) denotes the updated value, \(y\) denotes the initial value, and \(\
u\) denotes the average of the non-missing values in the feature.  
**Removing duplicates:** each row in the dataset is unique by removing duplicates.  
**Encoding categorical variables:** categorical variables are converted to numerical representations so that machine-learning algorithms can handle them.  
**One-hot encoding:** This approach allows us to convert our category data into discrete binary variables. On a categorical variable with \(m\) different categories [30],  
\[\text{One-hot}(y)=\begin{cases}1&\text{if }y\text{ corresponds to the category}\\ 0&\text{otherwise}\end{cases} \tag{2}\]  
**Label encoding:** every category will have its label/number.  
\[y_{\text{encoded}}=\text{LabelEncoder}(y) \tag{3}\]  
**Feature scaling:** by guaranteeing that the features are standardized to an exact size, feature scaling improves the effectiveness of machine-learning algorithms [30].  
**Min-max scaling:** it functions similarly to normalization; however, it operates on the entire dataset.  
\[y_{\text{scaled}}=\frac{y-y_{\text{min}}}{y_{\text{max}}-y_{\text{min}}} \tag{4}\]  
**Z-score scaling:** similar to standardization, but guarantees that the data are distributed normally [30].  
\[y_{\text{z-score}}=\frac{y-\
u}{\sigma} \tag{5}\]  
The preparation techniques transform the IoT-23 and APA-DDoS datasets into clean, normalized, and appropriately scaled forms, which are then ready for rapid analysis and model training. This ensures the datasets are prepared for developing robust machine-learning models specifically designed to detect DDoS in IoT environments.
# IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial
## 2 Several Promising Techniques: IRS, LIS, and Radio Stripes
### Categorizing Recent Studies on Intelligent Reflecting Surfaces
#### 2.1.1 Capacity and Data Rate Evaluations of IRS-Aided Communications  
An IRS is a promising approach for increasing wireless network communication capacity while remaining economical and energy efficient by suitably modifying signal propagation through the tuning of many passive reflecting components [21]. The purpose of the publication [5] is to define the essential capacity restriction of IRS-assisted point-to-point MIMO communication networks with multi-antenna transmitters and receivers in general, by finding the optimal IRS reflection to maximize capacity [22].
# New Detection Paradigms to Improve Wireless Sensor Network Performance under Jamming Attacks
## 4 Analysis of Results
### Performance in a Different Network Structure  
To give stringency to the proposed scheme, simulations should be extensive in terms of analysis of the performance in different network structures. Because of the scheme that has the best results is Scenario 3, we take into account 4 protocols with different characteristics in WSNs: AODV, DSR, MPH, and ZTR. Please note that AODV and DSR are reactive protocols, MPH is a hybrid protocol between proactive and reactive, and ZTR (Zigbee Tree Routing) [37] is a proactive hierarchical protocol. ZTR is a simple protocol which establishes parent-child links and the nodes always carry information to their parent. It has a tree topology and is easy to implement. ZigBee requires that there is at least one full-function device with a more robust nature to act as a network coordinator, but the final nodes can have reduced function to reduce costs. The parent node is the one which has given the child access to the network, so parent-child links are created, but each child can only have one parent. Some of the advantages of ZTR are that in the algorithm implemented in the network layer, there is a balance between cost per unit, battery expense, complexity of implementation to achieve a proper cost-performance relation to the application [38].  
Regarding the structure of nodes, in the non-uniform random distribution, the coordinator node is in the center of the topology, and the remaining nodes are distributed along the area. We have 100 nodes in evaluation (with the collector node). Sixty percent of the nodes, including the jammer node, are located near the central part of the topology, and the rest of the nodes, 40%, are distributed in the remaining area. The area is 50 m \(\times\) 50 m.  
Regarding the retransmissions, MPH has 16% fewer retransmissions per node than AODV, 13% less than DSR, and 3% less than ZTR. Regarding the CSMA retries, MPH exhibits an average reduction of 13% against AODV, 11% against DSR, and 2% than ZTR. The good news is that these two metrics are broadly related to network redundancy, and MPH performs well, much like an algorithm as simple and fast as ZTR, which is good. MPH also has hierarchical characteristics and adds the multi-parent concept, which generates more redundancy and fewer retransmissions and CSMA retries. Concerning the overhead, MPH and ZTR protocols have a similar behavior, due to their hierarchical structure, while reactive protocols have more routes and more types of packets. It is logical that the ZTR protocol has the lowest energy expenditure of the four protocols, because it is the simplest. MPH only has 2% more energy consumption than ZTR. It is important to mention that Extended Mechanism continues presenting approximately 3% improvement in overall performance in the 4 studied protocols.
## 2 The Theoretical Background
### Gap Analysis  
The review of the existing literature on EC management and the PI paradigm reveals several key gaps that this research aims to address. While both topics have been studied independently, their integration remains limited, especially in practical, real-world contexts  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Authors** & **Problem** & **Case of Study** & **Purpose of the Model** & **Method/Tool** \\ \hline [PERSON] et al. [31] & Sustainable truck scheduling and container grouping in road-rail PI-hubs & Theoretical approach & Optimising scheduling and grouping of PI-containers & Multi-agent simulation/integer programming model/hybrid meta-heuristics \\ [PERSON] et al. [32] & Unsustainable logistics systems & Theoretical approach & Improve logistics efficiency & Framework \\ [PERSON] et al. [33] & 3D container loading problem in dynamic environments & Theoretical approach & Minimise the number of bins and optimise space utilisation in logistics using real-time data & Heuristic method \\ [PERSON] et al. [34] & Inefficient routing of PI-containers in the Physical Internet & Real case scenario & Optimise the PI-container flow by implementing a protocol similar to the digital BGP. & Simulation-based approach \\ \hline \hline \end{tabular}
\end{table}
Table 2: Approaches to PI management.  
involving multi-modal hubs that incorporate both maritime and road transportation. Most of the research has concentrated on conceptual or simulation-based techniques, with no actual validation to determine the true impact of the PI on logistics efficiency.  
On the other hand, it is observed that PI initiatives typically focus on improving operational efficiency, but few have measured the accompanying environmental benefits. Furthermore, while MASs have been demonstrated to be useful for dynamic management in simple contexts, their use in more complex multi-modal environments, such as at large-road hubs, is limited.  
Based on the gaps listed above, this research focuses on a multi-modal barge-road hub. The goal is to improve the environmental efficiency in transportation logistics through the use of a 2 Stage-Opt model to optimise the container motion. Furthermore, a multi-agent system simulation (MASS) is presented to test the validity of the 2 Stage-Opt optimisation model.
## 3 Results and Discussion
### FT-IR Analysis  
The changes in the functional groups of the microcapsules were analyzed by FT-IR spectroscopy. It was previously proven that FT-IR microspectroscopy in the ATR mode can successfully study the composition of the global cell wall due to the reduced depth of penetration of the photon beam [50]. The spectra of the resulting changes are shown in Figure 4. The absorption spectra of the YBMCs showed no significant changes resulting from the content of various oil blends. A characteristic absorption band was observed at wave numbers 3400 cm\({}^{-1}\) and 2900 cm\({}^{-1}\), responsible for the OH vibration bands of yeast and the asymmetric CH\({}_{2}\) stretching vibrations of lipids [19; 51]. Absorption maxima were recorded at the wave number 1600 cm\({}^{-1}\), which corresponds to amide I and amide II bands in proteins, and the 1050 cm\({}^{-1}\)\(\beta\)-1,3-glucans absorption band [52]. FT-IR analysis also showed a band related to the COO stretching vibration of triglycerides at 1742 cm\({}^{-1}\)[51].  
Figure 3: Particle size distribution of NY-based (**A**) and AY-based (**B**) YBMCs.