esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Description of the Case Study and Climatic Conditions  
The model chosen for reference in this case study considers a typical vast office structure built in 'Diva for Rhino' with a depth of 8.0 m and a width of 4.6 m. As depicted in Figure 4, the office was aligned with the southern facade using a lateral typology. In the context of the reference model, the window-to-wall ratio (WWR) is 0.3. At the same time, the opening is double glazed using low-E material, and has a visible light transmittance (VLT) value of 0.79. Table 3 lists the radiance parameters used for daylighting simulation.  
This study used data pertaining to the Ha'il area as representative of the hot desert-like climate of Saudi Arabia, which is summarized in Table 4. This area is located around the center of the Arabian Peninsula, and its coordinates are 27\({}^{\circ}\)31\({}^{\prime}\) N, 41\({}^{\circ}\)41\({}^{\prime}\) E. The average temperature is 31.1 \({}^{\circ}\)C and the coldest month is January, which has a mean temperature of 10.6 \({}^{\circ}\)C. The area receives intense solar radiation--the average monthly incident solar energy experiences significant seasonal variation over the course of the year. The monthly maximum global horizontal radiation values range from 243 kWh/m\({}^{2}\) in summer and 118 kWh/m\({}^{2}\) in winter. At the same time, the global diffuse radiance is approximately 29%. There is shallow cloud cover in this area. The area experiences clear skies on more than 70% of the days in an average year, while 29% of the days are overcast or mostly overcast; hence, this area has clear sky conditions according to the CIE standard [33]. Furthermore, the daylight range is between 10.4-13.6 h, which indicates plenty of daylight through the year.  
Figure 4: Exterior and interior 3D reference model and furniture arrangement.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Radiance Parameter** & **Ambient Bounces** & **Ambient Divisions** & **Ambient Sampling** & **Ambient Accuracy** & **Ambient Resolution** \\ \hline
**Value** & 7 & 1500 & 100 & 0.1 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Radiance parameters used in the daylighting simulation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **January** & **February** & **March** & **April** & **May** & **June** & **July** & **August** & **September** & **October** & **November** & **December** \\ \hline Average air temperature (\({}^{\circ}\)C) & 10.2 & 13.5 & 17.8 & 23.3 & 28.1 & 31.9 & 33.2 & 33.8 & 30.6 & 25.1 & 17 & 12.2 \\ Global horizontal irradiance (\(\mathrm{\delta Wh}/\mathrm{m}^{2}\)) & 125 & 132 & 183 & 204 & 228 & 243 & 238 & 225 & 195 & 168 & 125 & 118 \\ Global diffuse irradiance (\(\mathrm{\delta Wh}/\mathrm{m}^{2}\)) & 38 & 40 & 53 & 62 & 64 & 56 & 61 & 60 & 51 & 45 & 40 & 29 \\ Sun hours & 10.6 & 11.2 & 12 & 12.8 & 13.5 & 13.8 & 13.7 & 13.1 & 12.3 & 11.5 & 10.8 & 10.4 \\ Cloud cover (\%) & 26 & 22 & 23 & 24 & 17 & 3 & 10 & 9 & 6 & 17 & 26 & 29 \\ \hline \end{tabular}
\end{table}
Table 4: Monthly climatic conditions of Ha’il city.
# Grassland Wildfires in the Southern Great Plains: Monitoring Ecological Impacts and Recovery
## Materials and Methods  
### Study Area  
The geographic focus of the assessment was wildfire-impacted areas of the Southern Great Plains in Kansas, Oklahoma and Texas. In particular, locations affected by the Anderson Creek (2016), Starbuck (2017) and Perryton (2017) wildfires were analyzed. These fires were selected based on the magnitude of the fires, their regional locations, and their multi-state footprints. The Anderson Creek started on 23 March 2016 in Oklahoma and burned 180,240 ha in Oklahoma and Kansas. The Starbuck fire started on 6 March 2017 in the Oklahoma panhandle and burned 246,570 ha in Oklahoma and Kansas. The Perryton fire started on 6 March 2017 in the Texas Panhandle and burned 145,358 ha in Texas and Oklahoma.
# Directional Support Vector Machines
## 9 Experiments
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Dataset** & **vMNB** & **dLR** & **dRBF-SVM** & **cos-SVM** & **t-SVM** & **T-SVM** & **a-SVM** & **A-SVM** \\ \hline
**Colposcopy** & \(69.56\pm 11.1\) & \(80.56\pm 9.0\) & \(83.52\pm 8.9\) & \(81.67\pm 7.7\) & \(83.00\pm 9.6\) & \(83.48\pm 8.5\) & \(\mathbf{85.41}\pm 8.5\) & \(82.67\pm 9.6\) \\
**Behavier** [14] & \(46.22\pm 11.1\) & \(82.09\pm 6.0\) & \(80.45\pm 42.4\) & \(80.85\pm 3.5\) & \(82.99\pm 5.5\) & \(81.85\pm 4.0\) & \(81.97\pm 5.3\) & \(80.96\pm 3.8\) \\
**Arrhythmia** [13] & \(67.59\pm 47.9\) & \(\mathbf{79.69}\pm 6.2\) & \(78.51\pm 47.7\) & \(89.52\pm 5.2\) & \(82.81\pm 6.0\) & \(76.49\pm 6.1\) & \(78.82\pm 5.7\) & \(76.41\pm 5.9\) \\
**eBay** [16] & \(83.95\pm 5.9\) & \(85.47\pm 49.\) & \(\mathbf{86.00}\pm 5.1\) & \(83.94\pm 4.6\) & \(83.36\pm 6.2\) & \(82.02\pm 5.7\) & \(83.55\pm 6.4\) & \(81.21\pm 5.3\) \\
**Megasopes** [18] & \(76.77\pm 3.4\) & \(\mathbf{76.94}\pm 42.4\) & \(76.15\pm 4.4\) & \(75.59\pm 47.6\) & \(76.18\pm 47.2\) & \(76.28\pm 3.7\) & \(75.69\pm 3.7\) \\
**Characterts** [15] & \(71.50\pm 3.7\) & \(96.20\pm 1.6\) & \(97.83\pm 1.5\) & \(96.05\pm 1.5\) & \(97.09\pm 1.6\) & \(89.30\pm 1.4\) & \(96.50\pm 2.1\) & \(98.90\pm 1.3\) \\
**OnlineNews** [19] & \(55.33\pm 3.3\) & \(\mathbf{56.89}\pm 5.3\) & \(55.06\pm 5.1\) & \(49.50\pm 5.3\) & \(54.60\pm 5.1\) & \(50.40\pm 5.1\) & \(55.19\pm 4.5\) & \(50.06\pm 4.9\) \\
**Continents** & \(94.82\pm 1.6\) & \(\mathbf{56.46}\pm 1.3\) & \(95.68\pm 1.3\) & \(96.03\pm 1.3\) & \(94.81\pm 8.6\) & \(9.53\pm 1.6\) & \(97.18\pm 1.0\) \\
**Wall** [20] & \(51.26\pm 2.1\) & \(68.36\pm 1.8\) & \(80.58\pm 1.6\) & \(64.85\pm 2.5\) & \(64.14\pm 1.8\) & \(82.23\pm 1.5\) & \(66.14\pm 1.2\) & \(82.97\pm 1.6\) \\
**Temperature1** & \(65.24\pm 1.3\) & \(\mathbf{69.40}\pm 1.3\) & \(65.41\pm 5.2\) & \(65.53\pm 4.0\) & \(67.23\pm 2.4\) & \(69.33\pm 1.2\) & \(65.73\pm 1.2\) & \(68.05\pm 2.1\) \\
**Temperature2** & \(67.08\pm 2.8\) & \(71.62\pm 1.3\) & \(73.69\pm 2.1\) & \(70.27\pm 7.7\) & \(67.95\pm 3.0\) & \(76.55\pm 1.0\) & \(76.51\pm 1.2\) & \(\mathbf{79.83}\pm 1.3\) \\
**MAGIC** [17] & \(73.00\pm 0.6\) & \(80.78\pm 0.7\) & \(\mathbf{81.75}\pm 1.3\) & \(79.66\pm 0.9\) & \(78.77\pm 0.7\) & \(80.27\pm 0.8\) & \(78.74\pm 0.7\) & \(80.26\pm 0.8\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average accuracy and standard deviation per model on 30 runs for 12 datasets, which are presented in increasing order of their respective cardinality. The evaluated models are: Naive Bayes with von Mises distribution (vMNB), directional Logistic Regression (dLR), Multilayer Perceptron (MLP), SVM with directional RBF kernel (dRBF-SVM), SVM with cosine kernel (cos-SVM), primal SVM with triangle wave (t-SVM), SVM with Triangle wave kernel (t-SVM), primal SVM with asymmetric triangle wave (a-SVM), and SVM with Asymmetric triangle wave kernel (A-SVM). Best results are given in bold.
# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 4 Results and Discussion
### Geographical Distribution of Tea Plantations  
From Figure 18a, it can be observed that over 57.17% of tea plantations are located at altitudes below 100 m. Additionally, there are considerable tea plantation areas at altitudes of 100-200 m and 200-300 m, accounting for 33.76% and 6.72% of the total Anji white tea planting area, respectively. This indicates a wide distribution of Anji white tea cultivation across various altitudes. Tea plantations below 300 m make up 97.65% of the total Anji tea planting area, showing that the vast majority of Anji tea is cultivated in low-altitude regions, with only a few high-altitude tea varieties planted in higher elevations.  
From Figure 18b, it is evident that the proportion of tea plantations facing north (56%) is significantly higher than those facing south (44%). This is primarily because Anji white tea prefers shade, and north-facing slopes can create relatively cool microclimates, which are beneficial for accumulating amino acids and other beneficial components during the growth process, resulting in a fresher taste. Excessive sunlight can cause tea leaves to become coarse, reduce internal quality, and negatively affect the taste and quality of the tea.  
Figure 18c clearly shows that only about one-third of the Anji white tea plantations have a slope of 5\({}^{\circ}\) or less, while the remaining two-thirds have slopes greater than 5\({}^{\circ}\). Combined with the altitude statistics of the Anji white tea planting areas, it can be seen that Anji white tea is mainly grown in low-altitude hilly areas. The unique geographical distribution and planting characteristics of Anji tea plantations make Anji white tea a representative of high-quality tea in China.
# Prediction of RF-EMF Exposure by Outdoor Drive Test Measurements
## 3 Spatial Reconstruction on EMF Exposure
### ANN Model  
We consider a feedforward ANN model, which consists of one input layer, several hidden layers, and one output layer. Each layer is composed of several artificial neurons, inside which an activation function is applied to the dot product between the output of the previous layer and weights \(W_{i}\) (including bias). In the training process of the ANN model, the weights \(W_{i}\) are optimized in order to minimize the loss function. However, one must notice that an ANN model cannot be overtrained to avoid overfitting.  
The ground truth is the total Eq900, which takes into account all frequency bands from all operators. Accordingly, the ANN model's inputs include the relative distances from each measurement point to the \(N\) nearest base stations. Additional inputs concern publicly accessible information, as mentioned in the previous section.  
As mentioned in Section 2, whether taking into account the network operator or not when choosing the \(N\) closest BSs motivates us to propose two different architectures for the ANN model. The first one is a fully connected ANN model, and the second one is a hybrid model with \(M\) locally connected blocks followed by fully connected layers.  
The two ANN models are illustrated in Figure 5 and described as follows:  
**(a) Fully connected ANN model.** We consider the \(N\) nearest distance to a mixture of all BSs without distinguishing operators, as the ground truth is used to reconstruct the total Eq900.  
A fully connected feedforward neural network is used in this model (as seen on the left in Figure 5). Inputs \(\{x_{1},x_{2},\ldots,x_{N}\}\) represent distances to \(N\) nearest neighboring BSs, while \(\{x_{P,1},\ldots,x_{P,Q}\}\) represent hour of the measurement and normalized UTM coordinates.  
**(b) Hybrid connected structure.** As seen in Figure 4, E-field strengths in different bands from each operator are highly correlated. It is of interest to utilize this information to help predict the total Eq900. Therefore, we sorted distance to neighboring BSs from each operator and adopted the same hybrid structure as proposed in [21] (as shown on the right in Figure 5). The hybrid design of locally connected (LC) blocks and fully connected (FC) blocks can reduce unnecessary interactions and enhance the interactions of correlated inputs. Input \(x_{m,n},m\in\{1,\ldots,M\},n\in\{1,\ldots,N\}\) represents n-th nearest distance to neighboring BS from m-th operator. Then \(\{x_{P,1},\ldots,x_{P,Q}\}\) contain the same information as in the previous model. Here, the ground truth is the same as model (a), i.e., Eq900 computed from drive test measurements.  
Furthermore, to explore the impact of \(N\) on the prediction of the measured E-field strength, we consider different values for \(N\) in the results.  
Figure 5: ANN structure for model (a) and model (b).
# LED Current Balance Using a Variable Voltage Regulator with Low Dropout \(v_{DS}\) Control
## 2 Aspect of Linear Current Regulator for LEDs  
### Linear Current Regulator  
Figure 2a shows the commonly-used LCR, which is a current-series feedback configuration composed of a MOSFET, an operational amplifier (OPA), and a sense resistor \(R_{s}\). A string LED is widely loaded on the MOSFET in application. We define the symbols herein such that \(v_{DS}=V_{DS}+v_{ds}\), where \(v_{DS}\) is for the total signal, \(V_{DS}\) is the dc component, and \(v_{ds}\) is the small signal. The small-signal model of the LCD is depicted in Figure 2b, in which the output current (drain current of MOSFET) \(i_{DS}\) is given by [17]. If \(A_{v}>>1\) and \(g_{m}A_{v}R_{s}>>1\), this yields:  
\[i_{DS}\cong\frac{V_{+}}{R_{s}}, \tag{1}\]  
Presuming that the MOSFET operates in the saturation region, \(i_{DS}\) in (1) is almost a constant current, varying with the input voltage \(V_{+}\). This drain current can also be directly found from the characteristic curve of the MOSFET, guided by a certain \(v_{GS}\).  
Figure 1: Commonly-used LED drives: (**a**) Voltage control or individual current control by constant voltage source supply; (**b**) Shunt current control with current source supply; (**c**) Digital PWM duty control using shunt current balance.
# COTS Battery Charge Equalizer for Small Satellite Applications
## 4 Experimental Setup
### Equipment  
To conduct the experiments, a controlled temperature of 25 \({}^{\circ}\)C in a dry environment was maintained. Moreover, in the tests of both circuits, specialized equipment was employed to ensure the precise control of the testing conditions and high-resolution data acquisition. The voltage of each cell was measured with an Agilent 34410A multimeter (Santa Clara, CA, USA). In addition, in order to automate data acquisition, a Python program was developed in its version 3.13.
## 3 Feature Extraction of Acoustic Signal
### Double-Domain Feature Extraction Method
#### 3.1.1 Analysis of Sectional Energy Ratio  
This paper studied the energy ratio of an acoustic signal produced by different attenuation speeds. Since the acoustic signal is a slow-changing short-time stable signal, the energy ratio at different time periods should be different. In order to improve the total energy ratio which could not be better used for comparison, a segmented energy ratio was adopted [21]. First, the acoustic signal of the column was divided into three parts and the energy of each part was calculated. Then, the energy ratio could be obtained by comparing the total energy of the acoustic signal with the total energy of the column. The expression is as follows:  
\[k_{i}=\frac{E_{i}}{E} \tag{1}\]  
where \(E\) is the total energy of the input signal. The three sections of energy are represented by \(E_{1}\), \(E_{2}\) and \(E_{3}\), respectively. The energy proportion of each segment is \(k_{1}\), \(k_{2}\) and \(k_{3}\).
# Weighted \(z\)-Distance-Based Clustering and Its Application to Time-Series Data
## 4 Experimental Results
### Time-Series Datasets
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **K-Means** & **TSKmeans** & **DSKmeans** & **FCM** \\ \hline \multirow{4}{*}{CBF} & Fscore & 0.6359/5 & 0.7028/2 & 0.6890/3 & 0.6316/6 \\  & RI & 0.7071/4 & 0.7331/2 & 0.7028/5 & 0.6973/6 \\  & NMI & 0.3616/5 & 0.4716/2 & 0.4701/3 & 0.3364/7 \\  & Time(s) & **0.030** & 1.900 & 1.600 & 0.140 \\ \hline \multirow{4}{*}{Coffee} & Fscore & 0.7578/5 & 0.7425/6 & 0.8441/2 & **0.8912/1** \\  & RI & 0.6667/3 & 0.6572/4 & 0.7662/2 & **0.8052/1** \\  & NMI & 0.3230/4 & 0.3051/6 & 0.5067/2 & **0.6001/1** \\  & Time(s) & 0.040 & 0.100 & 0.010 & 0.010 \\ \hline \multirow{4}{*}{ECG5} & Fscore & 0.5157/7 & 0.7091/2 & 0.5943/3 & 0.5147/8 \\  & RI & 0.4999/7 & 0.5780/2 & 0.5033/3 & 0.4999/7 \\  & NMI & 0.0007/7 & 0.1462/2 & 0.0327/3 & 0.0006/8 \\  & Time(s) & **0.010** & 1.500 & 1.050 & 0.030 \\ \hline \multirow{4}{*}{Face4} & Fscore & 0.6468/5 & 0.7394/2 & 0.6631/4 & 0.5765/8 \\  & RI & 0.7443/5 & 0.7977/2 & 0.7448/4 & 0.6881/7 \\  & NMI & 0.4585/4 & 0.6140/2 & 0.4493/5 & 0.3777/8 \\  & Time(s) & 0.009 & 0.780 & 0.160 & 0.070 \\ \hline \multirow{4}{*}{Light7} & Fscore & 0.5779/3 & 0.5677/4 & 0.5652/5 & 0.3664/8 \\  & RI & **0.8181/1** & 0.7937/4 & 0.8142/2 & 0.6317/8 \\  & NMI & 0.4990/2 & 0.4796/5 & 0.4890/4 & 0.2588/8 \\  & Time(s) & 0.020 & 1.500 & 0.500 & 0.020 \\ \hline \multirow{4}{*}{Oil} & Fscore & 0.8212/3 & 0.8175/4 & 0.8148/5 & 0.8226/2 \\  & RI & 0.8558/3 & 0.8524/5 & 0.8480/6 & 0.8757/2 \\  & NMI & 0.6906/2 & 0.6603/5 & 0.6688/4 & 0.6809/3 \\  & Time(s) & 0.009 & 1.100 & 0.050 & 0.060 \\ \hline \multirow{4}{*}{OSU\_leaf} & Fscore & 0.4154/3 & 0.4068/5 & 0.4070/4 & 0.3411/8 \\  & RI & **0.7456/1** & 0.7447/2 & 0.7391/3 & 0.5895/8 \\  & NMI & 0.2233/3 & 0.2091/5 & 0.2159/4 & 0.1030/8 \\  & Time(s) & 0.090 & 5.300 & 2.300 & 0.350 \\ \hline \multirow{4}{*}{Sony\_Surf} & Fscore & 0.8022/4 & 0.7883/5 & 0.7445/6 & **0.8610/1** \\  & RI & 0.6947/3 & 0.6863/4 & 0.6415/6 & **0.7710/1** \\  & NMI & 0.3828/3 & 0.3674/4 & 0.2727/6 & **0.4907/1** \\  & Time(s) & **0.009** & 0.420 & 0.400 & **0.009** \\ \hline \multirow{4}{*}{Synthetic} & Fscore & 0.7256/3 & **0.7543/1** & 0.7284/2 & 0.6393/6 \\  & RI & 0.8763/2 & **0.8908/1** & 0.8722/3 & 0.8386/6 \\  & NMI & 0.7859/3 & **0.8143/1** & 0.7756/4 & 0.6946/6 \\  & Time(s) & **0.010** & 0.560 & 1.200 & 0.040 \\ \hline \multirow{4}{*}{Trace} & Fscore & 0.5491/8 & 0.5820/5 & 0.6143/2 & 0.5643/6 \\  & RI & 0.7498/7 & 0.7401/8 & 0.7493/7 & 0.7521/2 \\ \cline{1-1}  & NMI & 0.5160/6 & 0.5142/8 & 0.5698/2 & 0.5204/5 \\ \cline{1-1}  & Time(s) & 0.008 & 0.600 & 0.500 & 0.040 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparisons of Fscore, RI, and NMI on 10 time series datasets—Part I.
###### Abstract  
A full proof of concept for low-cost millimeter wave (MMW) communication link is demonstrated in this study. The suggested MMW channel is based on a very inexpensive commercially available off-axis dish antenna usually used for TV satellites, two Arduino Uno micro controller boards, and glow discharge detectors (GDD). The GDD is a robust and inexpensive room-temperature plasma device which was found to be a sensitive MMW radiation detector. The Arduino micro controllers are used to encode a text message into serial bits and also decode it. Those serial bits were used to modulate the MMW radiation in On-Off keying. The detection of MMW radiation was performed using a simple and inexpensive GDD. The suggested MMW channel can be used as point to point backhaul wireless communication for the 5 th generation of cellular communication.  
Keywords:plasma detectors; Quasi-optical design; millimeter wave (MMW) communication +
Footnote †: journal: Electronics
# Rapid Tracking Satellite Servo Control for Three-Axis Satcom-on-the-Move Antenna
## 4 Experiment Analysis  
This section verifies the effectiveness of the proposed schemes through experimental methods. The experimental equipment utilizes a 60 cm caliber three-axis SOTM antenna. In this section, the ADRC based on the conventional fal function is referred to as conventional ADRC, while the ADRC based on the improved fal function is referred to as modified ADRC.
# Thermal and Thermomechanical Analysis of Amorphous Metals: A Compact Review
## 1 Introduction
### Amorphous Metals: Characterization Techniques  
Some of the crucial tools that have been employed to closely monitor the development of BMGs and amorphous metals every step of the way are state-of-the-art characterization techniques. These include thermal characterization techniques, such as differential scanning calorimetry and fast scanning calorimetry; mechanical analysis techniques, which involve the measurement of any kind of mechanical property (strain, hardness, deformation, etc.) with respect to the applied mechanical stress [37]; and structural analysis methods, such as electron and neutron diffraction and scattering. Additionally, spectroscopy methods, such as Fourier transform infrared spectroscopy and Raman spectroscopy, have been employed as helpful tools in resolving the complex structural challenges that metallic glasses present. Moreover, microscopy has always provided good insight into systems from a morphological viewpoint, and has thus been used consistently, both in the past and present day [38]. These techniques have revealed and enhanced the understanding of many mechanisms and kinetic processes involved in glass formation. Advanced structural analysis has been used to link varying compositions to their physical and chemical properties [39], and mechanical analysis is obviously important, as it speaks to the application potential of newly developed materials, but out of all of them, thermal analysis seems to play a pivotal role in the modern development of new materials. This is because in recent decades, thermal analysis has expanded into a whole new range of techniques with newfound precision and sensitivity, such as fast scanning calorimetry [40]. Thermomechanical analysis, as a family of techniques focused on the study of the interplay between thermal and mechanical properties of the material, has witnessed a great improvement with upgraded sensing and temperature modulation equipment. With the rapid development and broadening of research techniques, it is important to summarize and give a simplified overview of the technical improvements, which have helped overcome many challenges that this class of materials poses. It is also important to highlight the versatility to which modern science has had to resort in order to resolve some of these issues. For example, nowadays one might even consider resolving the structure of materials in situ with a temperature-dependent thermal analysis; thus, it has become important to discern between a plethora of methods and their impact on research in the field.  
In this work, we will review the most important thermal analysis methods, which have contributed and continue to add to the development of amorphous metals and bulk metallic glasses. Moreover, in situ structural analysis under temperature control will also be discussed.