esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Assessment of Model Performance  
#### 2.4.1 Confusion Matrix  
A confusion matrix is a table that shows how well a classification model performs on a set of test data for which the true values are known. In this research, the following definitions have been set:  
* True Positive (_TP_): Infected seedling correctly identified as infected.
* False Positive (_FP_): Healthy seedling incorrectly identified as infected.
* True Negative (_TN_): Healthy seedling correctly identified as healthy.
* False Negative (_FN_): Infected seedling incorrectly identified as healthy.  
The performance of each classification model in this research was described by analyzing its value of accuracy, sensitivity, and specificity extracted from the confusion matrix. Accuracy measures how correct a model identifies and excludes a given condition. The accuracy is the proportion of correct predictions (both _TP_ and _TN_) among the total number of cases examined and calculated as in Equation (4)  
\[Accuracy\left(ACC\right)=\frac{TP+TN}{Total\text{ \emph{Number of Population}}} \tag{4}\]  
Sensitivity (also called the true positive rate (_TPR_)) evaluates how good the test is at detecting infected seedlings. It is the probability that an actual positive will test positive and calculated as in Equation (5)  
\[TPR=\frac{TP}{TP+FN} \tag{5}\]  
Specificity (also called the true negative rate (_TNR_)) estimates how likely healthy seedlings can be correctly ruled out. It is calculated as the number of correct negative predictions divided by the total number of negatives as in Equation (6)  
\[TNR=\frac{TN}{TN+FP} \tag{6}\]
###### Abstract  
This paper presents an alternative approach for high spatial resolution vegetation productivity mapping at a regional scale, using a combination of Normalised Difference Vegetation Index (NDVI) imagery and widely distributed ground-based Above-ground Net Primary Production (ANPP) estimates. Our method searches through all available single-date NDVI imagery to identify the images which give the best NDVI-ANPP relationship. The derived relationships are then used to predict ANPP values outside of field survey plots. This approach enables the use of the high spatial resolution (30 m) Landsat 8 sensor, despite its low revisit frequency that is further reduced by cloud cover. This is one of few studies to investigate the NDVI-ANPP relationship across a wide range of temperate habitats and strong relationships were observed (R\({}^{2}\) = 0.706), which increased when only grasslands were considered (R\({}^{2}\) = 0.833). The strongest NDVI-ANPP relationships occurred during the spring \"green-up\" period. A reserved subset of 20% of ground-based ANPP estimates was used for validation and results showed that our method was able to estimate ANPP with a RMSE of 15-21%. This work is important because we demonstrate a general methodological framework for mapping of ANPP from local to regional scales, with the potential to be applied to any temperate ecosystems with a pronounced green up period. Our approach allows spatial extrapolation outside of field survey plots to produce a continuous surface product, useful for capturing spatial patterns and representing small-scale heterogeneity, and well-suited for modelling applications. The data requirements for implementing this approach are also discussed.  
net primary production; Landsat; Normalised Difference Vegetation Index (NDVI); vegetation mapping; habitat condition monitoring; remote sensing 20179 801; doi:10.3390/rs9080801
# Modeling Intruder Reconnaissance Behavior through State Diagrams to Support Defensive Deception
## 5 Implementation  
In this section, an implementation based upon the analysis scheme presented in Section 4 is presented. We consider the scheme that defines how the processes of host discovery, port scanning, intruder behavior, network topology, and firewall setup are modeled. A software system that detects a computer network intruder, as well as the prediction of its next move, has been implemented. The implementation consists of the following components: (a) attack graph extractor, (b) path extractor, and (c) metric calculator. The components of the system are shown in Figure 7.  
The attack graph extractor component receives the network topology, the firewall setup, and intruder's decisions as inputs and creates an attack graph as output. In order to create an attack graph, state diagrams from all layers are combined into a large diagram. For example, if an intruder chooses to perform host discovery using an ICMP Echo Request, then the Echo Request state diagram will be integrated into the main diagram. Similarly, in the case of port scanning, if the intruder performs a TCP Port Scan, then the TCP Port Scan state diagram will be added to the main diagram. All of the above are repeated for every single port of all computers in the network topology. An example of an attack graph is shown in Figure 8.  
The path extractor component takes the attack graph from the previous step, the network topology, and the intruder's decisions as inputs and outputs all probable attack paths. Each intruder decision will create a new path. For instance, if an intruder opts for ICMP Echo Request for host discovery and TCP SYN Scan or TCP FIN Scan for port scanning, then two probable paths are created. The number of extracted paths is equal to  
Figure 6: Node Occurrence Probability.  
Figure 7: Components.  
the number of different combinations between all open options for the intruder with regard to host discovery and port scanning.  
In our example, the number of paths is two (one host discovery method * two port scanning methods); the first path corresponds to the case in which a TCP SYN Scan is used for each port, while the second corresponds to the case in which a TCP FIN Scan is used. The two distinct attack paths extracted for the example analysis in Figure 8 are shown in Figures 9 and 10.  
The metric calculator component calculates metrics from the generated attack paths. Given that the path extractor component has produced the probable attack paths for a specific network topology and a specific level of knowledge of the intruder's behavior, the metric calculator component can efficiently calculate the metrics defined in Section 4.6 for any node. The metric calculator is of high significance, as it enables instantly assessing the risk with respect to a specific asset in the network when an intruder is detected. The results obtained from the metric calculator component can be used to enable fast and efficient decisions with regard to applying deception measures. On top of this, the same results can also be used to better understand the increase
## 4 Methodology
### Long Short-Term Memory (LSTM)  
The neural unit of an LSTM (Long Short-Term Memory) consists of a cell state and three gates: the input gate, the forget gate, and the output gate. The cell state is responsible for updating the information state. The input gate controls the input of new information, the forget gate filters previous information, and the output gate determines the output of the information. The specific formulas are as follows:  
\[f_{t}=\sigma(W_{f,s}+W_{f,x}X_{t}+b_{f}) \tag{9}\]  
\[i_{t}=\sigma(W_{i,s}O_{t-1}+W_{i,x}X_{t}+b_{i}) \tag{10}\]  
\[C_{t}^{\prime}=\tanh(W_{u,s}O_{t-1}+W_{u,x}X_{t}+b_{u}) \tag{11}\]\[C_{t}=f_{t}C_{t-1}+i_{t}C_{t}^{\prime} \tag{12}\]  
\[h_{t}=\sigma(W_{h,s}O_{t-1}+W_{h,x}X_{t}+b_{h}) \tag{13}\]  
\[O_{t}=h_{t}\text{tanh}(C_{t}) \tag{14}\]  
\[\text{tanh}(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{15}\]  
\[\sigma(x)=\frac{1}{1+e^{-x}} \tag{16}\]  
where \(f_{t}\) represents the weight for retaining past information in the forget gate; \(i_{t}\) denotes the weight for processing input information in the input gate; \(C_{t}\) is the state of the memory module that captures all current information; \(C_{t}^{\prime}\) represents the updated information state at the \(t\)-th layer; \(h_{t}\) indicates the weight for processing output information in the output gate; \(O_{t}\) is the final output information of the recurrent memory module; \(W_{f,s}\), \(W_{f,x}\), \(W_{i,s}\), \(W_{i,x}\), \(W_{u,s}\), \(W_{u,x}\), \(W_{h,s}\) and \(W_{h,x}\) represent the weights of interactions across layers; \(b_{f}\), \(b_{i}\), \(b_{u}\) and \(b_{h}\) are the bias terms for the respective interaction layers.
# Field Trials of SC-FDMA, FBMC and LP-FBMC in Indoor Sub-3.5 GHz Bands
## 4 Experiment Results
### BER Comparison According to Per-User Bandwidth  
We can change the bandwidth (equivalently data rate) per user by two different ways. One is to change the subcarrier spacing while keeping the number of users and the other way is to change the number of subcarriers while keeping the subcarrier spacing. Figure 14 shows the BER results with the same system parameters as those for Figure 13d, except that the per-user bandwidth is enlarged twice as that of Figure 13d by doubling the number of sub-carriers \(N\). It is shown that the BER of LP-FBMC decreases due to increased frequency diversity by increased DFT-spreading size (equal to \(N\)). This also holds for SC-FDMA, but it is limited to the case with perfect inter-user synchronization. Whereas, owing to lack of DFT-spreading, FBMC is relatively much worse than SC-FDMA and LP-FBMC.  
In 5G and beyond, various subcarrier spacings (i.e., \(\Delta f(=\frac{1}{\tau})=15\) kHz, 30 kHz, and 60 kHz) are used to support high-quality service [1]. Figure 15 shows the case when the per-user bandwidth is enlarged twice by doubling the subcarrier spacing. For doubling the subcarrier spacing, the symbol duration decreases to one half of that presented in Figure 13d and, thus, the CP length (=\(T/4\)) also decreases to one half. The reduced CP results in larger inter-symbol interference (ISI) to SC-FDMA. This explains a remarkable result in Figure 15 that SC-FDMA more severely deteriorates with increasing timing offset as compared to the case in Figure 14. Summing up, the BER performance gain of LP-FBMC over SC-FDMA is more increased for lower carrier frequency, larger subcarrier spacing, and larger timing/frequency offset among the users.  
Figure 14: BER of the SC-FDMA, FBMC, and LP-FBMC according to timing/frequency offset, \(N=128\), \(\Delta f=15\) kHz, \(F=3.5\) GHz.
## 4 Results
### Comparison of LOS and GSI Trends  
#### 4.2.1 Converging/Diverging Trends in LOS and GSI  
A direct comparison of trends in LOS and GSI of GIMMS3g (1982-2013) (Figure 3) is made for the pixels of significant trends in Figure 2C,D. Pixels with converging trends in LOS and GSI (consistent significant positive/negative trends in LOS and GSI) (24.53%, (a + d) Table 1) cover considerably larger areas than pixels with diverging trends (opposite significant trends in LOS and GSI) (5.78%, (b + c) Table 1), indicating that the NH is dominated by converging trends in LOS and GSI. However, areas of pixels of NA with converging trends (18.70%) are considerably smaller than those of EA (27.22%), whereas areas of pixels of NA with diverging trends (11.08%) are considerably larger than those of EA (3.33%) (Table 1). This suggests that the vegetation metrics in NA are characterized by more diverging trends than EA. More specifically, areas of pixels of the NH with consistent significant positive trends in LOS and GSI (converging trends type 1) (19.46%, (d) Table 1) are considerably larger, when compared to pixels with consistent significant negative trends (converging trends type 2) (5.07%, (a) Table 1), indicating that the NH is dominated by converging trends type 1. However, areas of pixels of NA with converging trends type 1 (12.23%) are considerably smaller than those of EA (22.80%), whereas areas of pixels in NA with converging trends type 2 are somewhat larger than those of EA (Table 1), suggesting that the dominance of converging trends type 1 is more pervasive in EA. In contrast, only 0.41% pixels of the NH are characterized by significant positive trends in LOS/significant negative trends in GSI (diverging trends type 1) ((c) Table 1), whereas the opposite pattern (diverging trends type 2) ((b) Table 1) covers considerably larger areas (5.37%), suggesting that diverging trends in the NH are dominated by diverging trends type 2. At the continental scale, areas of pixels of NA with diverging trends type 1 are somewhat larger than those of EA (Table 1), while pixels of NA with diverging trends type 2 (10.28%) are considerably larger than those of EA (3.11%), thus indicating that the dominance of diverging trends type 2 is more pervasive in NA.  
Spatially, large areas with converging trends type 1 are mainly found in central and eastern NA, northern and eastern Europe, north and south Russia, north Mongolia, and north and northeast China ((+1, +1) in Figure 3). On the contrary, areas with converging trends type 2 are primarily observed in northeast Canada, central America, east and central Russia and northeast Tibetan Plateau ((\(-1\), \(-1\)) in Figure 3). In contrast, areas with diverging trends in LOS and GSI can be seen in high latitudes of NA and EA, central NA, northern Europe, Kazakhstan, and eastern Russia ((\(-1\), +1) and (\(+1\), \(-1\)) in Figure 3), suggesting that diverging trends are mainly in high latitudes and arid/semi-arid areas.
# Compliant Human-Robot Collaboration with Accurate Path-Tracking Ability for a Robot Manipulator
## 4 Conclusions  
We successfully achieved compliant path tracking motion using an industrial 6-DOF robot manipulator by employing the proposed dual-loop control structure including an inner motion control loop and an outer admittance control loop. The safety of the application is guaranteed by adhering to the constraints presented in our previous work [23], which include singularity avoidance, joint limits, and workspace limits. Additionally, the modified SMC designed and implemented as the inner motion controller showed a satisfactory tracking performance. A linear motion test successfully demonstrated the system ability to modify the robot compliance with respect to the external force direction applied to the end effector. The results from this linear test show the capability of our proposed method to customize the compliance behavior of the robot for a path tracking task. Furthermore, a 3D helix path was used to test the path-tracking performance and display the generality to many applications such as rehabilitation, assisted drawing, assisted hand-motor skills development, and so on. For this test, three cases were compared depending on the level of the interaction; FM, FAM, and CVF motion. The error difference between cases is less than 0.1 mm, which is suitable for path tracking applications where the user can dynamically interact with the robot. Our future research directions include testing the framework in more specific applications such as rehabilitation or assisted hand-motor skills development as well as in different types of robot manipulators, given that our framework implementation can be easily ported to other manipulators with the addition of an F/T sensor. Moreover, a user study will be carried out to further explore the perceived performance in each specific application.  
Conceptualization, D.R.-U. and T.H.; Data curation, D.R.-U.; Formal analysis, D.R.-U.; Funding acquisition, T.H.; Investigation, D.R.-U.; Methodology, D.R.-U. and T.H.; Project administration, T.H.; Resources, T.H.; Software, D.R.-U.; Supervision, T.H.; Validation, D.R.-U. and T.H.; Visualization, D.R.-U.; Writing-original draft, D.R.-U. and T.H.; Writing-review & editing, D.R.-U. and T.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan, grant number MOST 109-2221-E-009-092-.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Optimal Harmonic Mitigation in Distribution Systems with Inverter Based Distributed Generation
## 6 Simulation Results and Discussion  
### Test Distribution Systems  
The proposed method is applied to the modified IEEE 69-bus distribution system [9]. The total active and reactive loads of the original system are 3.802 MW and 2.694 MVAR, respectively. Six nonlinear loads are added and located at buses (12, 26, 27, 28, 43, 49, 53, and 63) in the system shown in Figure 7. Four DG units are also considered to be installed at buses (28, 44, 47, and 53) each one has a capacity equals to (\(190.1+j\,134.7\)) kVA.
# A Novel mHealth Approach for the Monitoring and Assisted Therapeutics of Obstructive Sleep Apnea
## 3 Materials and Methods  
### ESP32 Microcontroller  
The ESP32 is a powerful SoC (system on chip) microcontroller, released in 2016 by the Chinese company, _Espressif Systems_ (Shanghai, China). The ESP32 offers the ability to use Bluetooth and BLE (Bluetooth Low Energy), and it integrates an 802.11 b/g/n HT40 Wi-Fi transmitter, allowing it to connect to and interact with a Wi-Fi network. It is also possible for it to configure its own network to allow other devices to connect to it.  
It also has a single or dual-core 32-bit Tensilica Xtensa LX6 microprocessor, with adjustable clock frequencies from 80 to 240 MHz, and a total of 48 pins for different uses.  
It is a low cost, low power system, as it has a ULP (ultra-low-power) coprocessor, which allows analog-to-digital conversions, computation and threshold levels to be performed while in \"deep sleep\" mode. This is ideal for battery-powered applications.  
It is designed to achieve the best RF and power performance, as well as for robustness, versatility, and reliability in a wide variety of applications, with different power profiles. This chip also has 15 channels of 12-bit ADCs; 2 channels of 8-bit DACs (digital-to-analog converters) to produce true analog voltages; 10 capacitive touch sensors; 3 SPI (serial peripheral interface) interfaces, one I2C (inter-integrated circuit) interface to connect all types of sensors, and 2 I2S (Inter-IC Sound) interfaces for digital audio; and 25 PWM outputs.  
Since the ESP32 works with a voltage range of 2.2 V to 3.6 V, this board has a voltage regulator to keep it stable, at 3.3 V, which is the ideal voltage for this microcontroller to reliably supply up to 600 mA.  
The ESP32 microcontroller can be programmed in different development environments and the code can be written in C/C++ or MicroPyton. The _Espressif Company_ has provided its own development framework for this chip so that it is possible to make use of all its features. The ESP-IDF (Espressif IoT Development Framework), however, is the easiest and most common way to use the Arduino IDE, which, although is not the best environment to work with the ESP32, has the advantage of being a familiar platform, with a flatter learning curve.
# Augmented Reality and the Digital Twin: State-of-the-Art and Perspectives for Cybersecurity
## 3 Literature Review and State-of-the-Art
### Qualitative Results
#### 3.3.1 Why?--Motivation for Combining AR with the Digital Twin  
The overarching, central motivation why AR is applied for DT concepts within the identified literature is to allow easy access to the underlying data and improve understanding through visual representation [6]. AR is utilized to support the connection between the DT's physical and digital part enabling a new way of monitoring and analyzing of the physical part [31]. Although respective simulations, analyses, etc., are possible via standard (primarily web-based) user interfaces, AR offers a more intuitive and efficient interaction with the DT data [32]. Literature sees AR as a unique way to contextualize the digital information of a DT with the actual real-world twin enabling a new way to improve situational awareness. The improved human-machine interaction, achieved by using AR and this perceptual combination of digital and physical twin, makes AR so beneficial in various use cases. They range from advanced analysis or to train and educate staff members [33]. Overall, motivations for combining AR with the DT can be summarized under monitoring, training, and malfunction detection [34].
# Assessing Abrasion Resistance in Concrete Pavements: A Review
## 4 Factors Affecting Abrasion Resistance
### Texturing  
Concrete pavements with surface finishing typically have a smooth surface. The lack of texture on the surface causes insufficient friction. By using an appropriate texturing technique, sufficient macrotexture and, subsequently, skid resistance are produced. The type of pavement macrotexture must be chosen after considering numerous parameters, including weather, pavement surface noise, aggregate type and gradation, traffic characteristics, and the necessary skid resistance [66].  
The skid resistance is affected by the abrasion imposed on the concrete, which depends on the traffic volume and road surface characteristics [67]. The link between abrasion resistance, mean texture depth (MTD), and skid resistance for various macrotextures in concrete pavements was studied by [PERSON] et al. Their study showed that texturing concrete pavement increases its resistance to skids, typically decreasing the resistance to surface abrasion. Among the investigated texturing techniques, brushing produces the highest amounts of abrasion, regardless of whether it is performed parallel or perpendicular to the direction of traffic. On the other hand, burlap dragging increases abrasion resistance by 2.5 percent, which makes it a technique that might be appropriate if adjustments are made to increase surface uniformity [68].
# Research on the Coverage of Ultraviolet Communication Network in the Arbitrary Polygon Area
## 3 k-Connectivity
### The 2-Connectivity Analysis for Two Distribution Regions  
The 2-connected UV network has good robustness, which can meet the use of an actual UV network. In our previous research, the UV network connectivity in the circular area under different working modes has been studied [10]. We compared the model built in an arbitrary polygonal area with the model built in the circular area, and the numerical comparison results are shown in Table 2.  
From Table 2, we find that the model of UV nodes evenly distributed in a circular area requires more stringent communication parameters than the model in a regular quadrilateral area. It requires higher \(\rho\), \(P_{t}\) and lower \(R_{b}\) to reach the 2-connected network. The \"boundary effect\" of the circular area is stronger, which leads to more terminals distributed on the boundary of the region, thereby reducing the connectivity of the model [17].
###### Abstract  
The sensory quality of black tea is intrinsically linked to cultivar genetics, yet comprehensive characterization of flavor compounds in emerging northern Guangdong black tea (NGBT) remains limited. This study employed high-performance liquid chromatography-ultraviolet (HPLC-UV) and headspace solid-phase microextraction coupled with GC-MS (HS-SPME-GC-MS) to analyze non-volatile and volatile compounds in five NGBT cultivars--Jinshahong (JSH), Danxia No.1 (DX), Danxia No.2 (DXE), Yingde Black Tea (QTZ), and Yinghong No.9 (YH)--alongside sensory evaluation. Orthogonal partial least squares-discriminant analysis (OPLS-DA) identified key non-volatile discriminants (VIP > 1) ranked by contribution: total catechins > simple catechins > CG > EGCG > ester catechins > EGC. HS-SPME-GC-MS detected 97 volatiles, with eight aroma-active compounds exhibiting OAV > 1 and VIP > 1: Geraniol > Methyl salicylate > Linalool > \(\beta\)-Myrcene > Benzyl alcohol > (Z)-Linalool Oxide > Phenethyl alcohol > (Z)-Jasmone. These compounds drive cultivar-specific aromas in NGBTs. Findings establish a theoretical framework for evaluating cultivar-driven flavor quality and provide novel insights for targeted breeding and processing optimization of NGBTs.  
black tea; HPLC-UV; GC-MS; northern Guangdong black tea +
Footnote †: journal: _foods_