esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Excitation Beam Divergence on the Goos-Hanchen Shift Enhanced by Bloch Surface Waves
## 1 Introduction  
As is well known, a bounded beam of light undergoes a tiny lateral displacement, which is known as the Goos-Hanchen (GH) shift, between the incident light and the reflected light when the total reflection happens at the interface between two media. The GH shift has been studied both theoretically and experimentally ever since being discovered [1; 2]. It reveals that this phenomenon is due to the phase change during the reflection and universally exists at the order of the wavelength. Various structures containing different kinds of media have been studied and demonstrated in order to enhance the GH shift, such as metal [3; 4], photonic crystals [5; 6; 7], surface wave devices [8; 9], negative refractive index (left-hand) materials [10], absorbing media [11], and graphene [12; 13]. Among them, the GH shift could be boosted to tens of microns and even sub-millimeters of magnitude [5; 14], which makes the study of the GH phenomenon not only of scientific interest, but also feasible for potential practical applications like sensing, switching [15], and so on.  
In previous work [9; 16], the optical field distribution of the reflected beam for the case of a typical surface electromagnetic waves, configuration in both near and far field has been studied. The results show that observational distance affected the phase distribution of reflected light. In other words, the curve of the GH shift became fast distortion in the near-field region, while in the far field this distortion disappeared.  
In this paper, we demonstrate the influence of the excitation beam divergence on GH shift from both simulation and experiment. In Section 2, GH shifts are simulated for different beam waist radiuses. We find that as the waist radius is increased, the GH shift is close to the theoretical value.  
In Section 3, GH shift enhanced by the Bloch surface wave is experimentally studied, and the simulation result introduced in Section 2 is verified by experiments. Simulations and experiments show that the excitation beam waist radius is one of the important factors affecting the GH shifts enhanced.
## 1 Introduction
### Climatology and Microgrid Architectures at the Three Locations  
As shown in Figure 5 the UTSA SkyImager has been deployed at 3 geographically diverse locations: Golden, Colorado on the rooftop of the ESIF building at NREL, in San Antonio, Texas at the CPS Energy microgrid facility at Joint Base San Antonio (JBSA) and the Engineering Building at UTSA, as well as in the Canary Islands, Spain at Tenerife and Caleta de Sebo. Each location presented unique challenges in terms of local climate, physical and cyber access, and microgrid design, equipment, operation, and customer needs.  
The UTSA SkyImager was first conceived as a technology for providing accurate intra-hour irradiance forecasts as inputs to a microgrid management system that would then provide the utility with command and control of the microgrid in either connected or islanded mode. The Department of Energy INTEGRATE project [43] lasted for 18 months beginning on 6 March 2015 and partnered NREL, Ommetric-Siemens, CPS Energy, Duke Energy, and UTSA. The project goal was to increase the capacity of the electric grid to incorporate renewables by upgrading and optimizing architectures for control and communication in microgrids. There were three major components: (1) OpenFMB, a reference architecture that allows real time interaction among distributed intelligent nodes, (2) optimization with the Spectrum Power Microgrid Management System based on the Siemens SP7 Platform, and (3) PV and Load Forecasting using UTSA's applications for both intra-hour and day-ahead irradiance and building load forecasts. The OpenFMB framework leverages existing standards such as IEC's Common Information Model (CIM) semantic data model and the Internet of Things (IoT) publish/subscribe protocols (DDS, MQTT, and AMQP) to allow flexible integration of renewable energy and storage into the existing electric grid. The OpenFMB standard was ratified by the North American Energy Standards Board (NAESB) in March of 2016 and allows communication between diverse grid devices-meters, relays, inverters, capacitor bank controllers, etc. It allows federated message exchanges with readings such as kW, kVAR, V, I, frequency, phase, and State of Charge (SOC) published every 2 seconds as well as data-driven events, alarms, and control in near-real-time.
## 2 Concept of Unit Cell
### Relative Permittivity of Designed Cylindrical Unit Cell  
The successive change in the radius of cylinder determines the corresponding value of relative permittivity whose value extends from a minimum of 0.6687 to maximum of 2.4395. Figure 2 depicts the value of obtained relative permittivity, which highlights the fact that a decrease in radius of the cylinder corresponds to a decreased relative permittivity whereas an increase in its radius shows increased permittivity. The dimension of the cylindrical unit cell varies as 0.5, 1, 1.5, 2, 2.5, 3 and 3.5 mm in radius while height is kept constant at 11 mm (0.44\(\lambda\)). The choice of a particular cylindrical unit cell depends on the required permittivity values of seven different circular rings, which is based on the Luneburg lens principle. The relative permittivity (\(\epsilon\)) can be calculated as [27],  
\[\epsilon(r/D)=2-(r/D)^{2} \tag{1}\]  
where, \(\epsilon\) is the permittivity, r is the relative radial distance from the center of the cylindrical plat, D is the diameter of the parallel plates.
# Chaotic Pulse-Shaping Filter Based on Root-Raised-Cosine Division
## 2 Backgrounds  
In wireless communication systems, to maintain the minimum ISI, the overall responses of the transmit filter, channel response and receive filter have to satisfy Nyquist ISI criterion. The RC pulse has become the de facto standard, which has the impulse response  
\[h_{rc}(t)=\begin{cases}\dfrac{\pi}{4}\dfrac{\sin\left(\dfrac{\pi}{2\beta} \right)}{\frac{\pi}{2\beta}},&t=\pm\dfrac{T_{s}}{2\beta},\\ \dfrac{\sin\left(\dfrac{\pi t}{T_{s}}\right)}{\frac{\pi t}{T_{s}}}\dfrac{\cos \left(\dfrac{\pi nt}{T_{s}}\right)}{1-\left(\frac{2\beta t}{T_{s}}\right)^{2}},&\text{otherwise },\end{cases} \tag{1}\]where \(\beta\) (\(0<\beta\leq 1\)) is the roll-off factor, and \(T_{s}\) is the reciprocal of symbol rate. Its frequency-domain description is a piecewise-defined function, given by:  
\[H_{rc}(f)=\begin{cases}1,&|f|\leq\frac{1-\beta}{2T_{s}},\\ \frac{1}{2}\bigg{[}1+\cos\bigg{(}\frac{\pi T_{s}}{\beta}\bigg{[}|f|- \frac{1-\beta}{2T_{s}}\bigg{]}\bigg{)}\bigg{]},&\frac{1-\beta}{2T_{s}}<|f| \leq\frac{1+\beta}{2T_{s}},\\ 0,&\text{otherwise}\;.\end{cases} \tag{2}\]  
The total effective filter in the transmission system is the convolution of the transmit filter and receive filter and the convolution in time domain is equal to the multiplication in the frequency domain. Taking the square root of the RC filter in the frequency domain, we obtain the so-called RRC filter, which has the frequency response \(H_{\textit{rrc}}(f)\) as follows:  
\[H_{\textit{rrc}}(f)=H_{\textit{rrc}}(f)H_{\textit{rrc}}(f), \tag{3}\]  
or:  
\[H_{\textit{rrc}}(f)=\sqrt{|H_{\textit{rrc}}(f)|}. \tag{4}\]  
The impulse response of RRC is written as:  
\[h_{\textit{rrc}}(t)=\begin{cases}\frac{1}{T_{s}}\bigg{(}1+\beta( \frac{4}{\pi}-1)\bigg{)},&t=0,\\ \frac{\beta}{T_{s}\sqrt{2}}\bigg{[}(1+\frac{2}{\pi})\sin(\frac{ \pi}{4\beta})+(1-\frac{2}{\pi})\cos(\frac{\pi}{4\beta})\bigg{]},&t=\pm\frac{T _{s}}{4\beta},\\ \frac{1}{1-\left(\frac{4\beta t}{T_{s}}\right)^{2}}\bigg{[}\cos \frac{(1+\beta)\pi t}{T_{s}}+\frac{T_{s}}{4\beta t}\sin\frac{(1-\beta)\pi t}{ T_{s}}\bigg{]},&\text{otherwise}.\end{cases} \tag{5}\]  
Unlike RC, the impulse response of RRC is not zero at intervals of \(\pm T_{s}\). However, the combination of two RRC filters forms a RC pulse, which is zero at intervals of \(\pm T_{s}\). Furthermore, since the filter is real-valued and symmetric, RRC is its own matched filter.
## 5 Results Analysis
Table 9 compares the different methods analyzed, including RFE, the GA approach, the variance penalty version of the GA, and DE applied to the five classifiers \(k\)NN, DTC, RFC, ETC, and XGBC. The results show that with RFE, DTC achieved the best validation accuracy (74.09%), separating itself from the other classifiers. However, in the GA and GA with variance penalty methods, RFC and XGBC showed superior performance, especially with penalties of 0.3 and 0.5. In particular, RFC achieved a remarkable validation performance (71.64%) with 16 features selected using the GA approach, while XGBC reached its maximum performance (70.35%) with 9 features and a penalty of 0.5. However, \(k\)NN showed inferior performance in most cases, although a higher penalty (0.7) improved its accuracy by selecting only four features. These results suggest that GA and GA with variance penalty methods are effective in improving generalization, especially in ensemble classifiers such as RFC and XGBC, while DTC remains the best classifier with the 13 features selected in the original study. With a similar running time, DE have not improved any of the results from RFE or GA. Although accuracy was used as the primary metric for comparison, the F1-score was also calculated to provide another assessment of model performance, given that this dataset has moderate class imbalance, which is common in medical datasets. This additional \begin{table}
\begin{tabular}{l r r r r r r r} \hline \hline
**Classifier** & **noF** & **Penalty** & **Min.** & **Avg. Test Accuracy \(\pm\) Std** & **Max.** & **Validation** \\ \hline \multirow{3}{*}{\(k\)NN} & 10 & 0.3 & 0.6397 & 0.6598 \(\pm\) 0.0091 & 0.6868 & 0.6702 \\  & 9 & 0.5 & 0.6393 & 0.6616 \(\pm\) 0.0099 & 0.6868 & 0.6714 \\  & 4 & 0.7 & 0.6564 & 0.6776 \(\pm\) 0.0089 & 0.6985 & 0.7070 \\ \hline \multirow{3}{*}{DTC} & 25 & 0.3 & 0.7283 & 0.7593 \(\pm\) 0.0119 & 0.7930 & 0.6871 \\  & 18 & 0.5 & 0.7401 & 0.7662 \(\pm\) 0.0091 & 0.7930 & 0.6830 \\  & 15 & 0.7 & 0.7404 & 0.7661 \(\pm\) 0.0108 & 0.7996 & 0.7228 \\ \hline \multirow{3}{*}{RFC} & 19 & 0.3 & 0.7099 & 0.7330 \(\pm\) 0.0094 & 0.7577 & 0.7245 \\  & 9 & 0.5 & 0.7169 & 0.7329 \(\pm\) 0.0071 & 0.7522 & 0.7170 \\  & 7 & 0.7 & 0.6824 & 0.7030 \(\pm\) 0.0103 & 0.7279 & 0.6661 \\ \hline \multirow{3}{*}{ETC} & 37 & 0.3 & 0.6688 & 0.6758 \(\pm\) 0.0047 & 0.6941 & 0.6456 \\  & 13 & 0.5 & 0.6765 & 0.6963 \(\pm\) 0.0086 & 0.7221 & 0.6906 \\  & 11 & 0.7 & 0.6746 & 0.6927 \(\pm\) 0.0080 & 0.7099 & 0.6743 \\ \hline \multirow{3}{*}{XGBC} & 34 & 0.3 & 0.7048 & 0.7181 \(\pm\) 0.0086 & 0.7518 & 0.6930 \\  & 13 & 0.5 & 0.6864 & 0.7047 \(\pm\) 0.0085 & 0.7335 & 0.6906 \\ \cline{1-1}  & 9 & 0.7 & 0.6981 & 0.7162 \(\pm\) 0.0092 & 0.7529 & 0.6848 \\ \hline \hline \end{tabular} Values in green indicate validation accuracy over the majority rate.  
\end{table} 
Table 8: Results using features selected by the genetic algorithm with a fitness penalization based on the variance in CV.
## 1 Introduction
As considered in this study, most existing studies on flow prediction using machine learning have focused on river discharge using average flow conditions against low and high flows. Furthermore, streamflow prediction within the Niger Basin is of utmost importance based on the representation of the river as it flows through nine West African countries, playing a pivotal role in shaping regional hydropolitics, influencing the agricultural landscape, and impacting the hydroeconomics of West African member states. The significance of studying the Niger flow becomes imperative for the continuing development and stability of the region. Studies have identified the River Niger as highly dynamic and chaotic, emphasizing the complexity and variability inherent in its hydrological patterns [10; 11]. Hence, highly efficient methods such as deep learning algorithms can help with short-term river flow predictions. This study aims to predict the high and low river flow across eight gauging stations within the Niger Basin using four deep-learning approaches. Identifying the most efficient algorithms will go a long way in managing water resources across the sub-region and support efficient regional decision-making.
# Design of an Underwater Acoustic Waveform and Integrated System for Communication and Detection
## 2 Waveform Design
### Signal Model
At the receiver, a matched filter can be used to remove the LFM and recover the data signal. Compared to LFM, the proposed waveform does not require additional bandwidth and has similar spectral characteristics, so the rapid out-of-band roll-off is retained.
## 2 Setup and Modeling  
### Setup  
Figure 1a shows the block diagram of a directly modulated RSOA followed by a serially connected BFL.  
An electrical data signal induces through the RSOA impedance a peak current deviation, \(\pm I_{m}\), superimposed on the RSOA fixed current, \(I_{dc}\), which is physically indispensable in order for the RSOA to function and chosen to lie on the linear part of the optical power-driving current (P-I) curve [25]. In this manner, the aggregate current is varied between an upper and a lower extreme from the dc bias, i.e., \(I_{dc}+I_{m}\) and \(I_{dc}-I_{m}\), respectively (Figure 1b). This variation alters analogously the RSOA gain, which is perceived by a lightwave beam of constant power in time, i.e., Continuous Wave (CW), after traveling twice inside the RSOA cavity. Accordingly, the RSOA driving the current modulation is mapped on the CW signal, which carries the original information but converted into optical form. Normally, an exact copy of the injected electrical signal should be transferred at the RSOA output. Nevertheless, because the RSOA modulation bandwidth is typically smaller than the equivalent bandwidth of the electrical excitation, the profile of the encoded signal after the RSOA is impaired (Figure 1c). A characteristic example of this is when the RSOA is modulated by non-return-to-zero (NRZ) data, which is the simplest data format. In this case, increasing the repetition rate, \(B_{rep}\), quickly scales the equivalent bandwidth that equals half this quantity beyond the modulation bandwidth, \(B_{mod}\), of the RSOA, which exhibits a low-pass filter response (Figure 1d) [26]. Thus, the key to allowing the RSOA to be modulated beyond its limited modulation bandwidth is to compensate for the RSOA response by a filter characteristic whose slope is in the opposite direction to that of the RSOA, i.e., high pass. This can be achieved by using a BFL right after the RSOA, whose comb-like response against the wavelength satisfies this requirement; i.e., the wavelength response maximum, \(\lambda_{max}\), is offset to the left-hand side of the encoded signal's position, \(\lambda_{enc}\), so that the transmission is decreased with increasing wavelength (Figure 1e). Equivalently, the BFL eliminates the lower-frequency components in the RSOA spectrum broadened due to SPM while leaving the higher-frequency ones, which are converted by the slope of the BFL spectral response into amplitude changes that counteract those at the RSOA output. As a result, the lack of uniformity between pulses of the same binary content and the poor distinction between pulses of different binary content is reduced. In this manner, the profile of the encoded signal becomes a closer replica of the digital information carried by the applied RSOA current (Figure 1f).
## 2 Theoretical Background  
### Hydraulic Fracturing  
The breakdown pressure and morphology of the induced fractures are key parameters in hydraulic fracturing and are dependent on both the fracturing fluid type and the applied stress condition (2016, 2017). Under triaxial conditions, two main stresses act on the rock sample: axial stress and confining stress, and the fractures created in hydraulic fracturing always propagate perpendicular to the minimum stress direction or in the plane of maximum principle stress. Therefore, under triaxial conditions, hydraulic fracturing can be done in two different ways: (1) when the axial stress is less than the confining stress, the fracture will open across the wellbore (horizontal plane) (Figure 1a); and (2) when the axial stress is greater than the confining stress, fractures will open along the wellbore (vertical plane) (Figure 1b). In this paper, only the fractures along the wellbore (vertical plane) are considered, and in this situation, the breakdown pressure can be estimated using Equation (1) (Hubert-Willis) criterion).  
\[P_{b}=3\sigma_{h}-\sigma_{H}+\sigma_{T} \tag{1}\]  
where, \(P_{b}\) is the breakdown pressure, \(\sigma_{h}\) is the minimum horizontal stress, \(\sigma_{H}\) is the maximum horizontal stress and \(\sigma_{T}\) is the tensile strength of the rock.  
Here, consideration of tensile strength is important, as failure can occur under tension or shear in the rock mass, rather than under compression. However, under triaxial conditions, the minimum horizontal stress and the maximum horizontal stress are equal to the confining stress (\(\sigma_{c}\)). Therefore, Equation (1) can be rearranged to a more simplified form (Equation (2)):  
\[P_{b}=2\sigma_{c}+\sigma_{T} \tag{2}\]  
According to Equation (2), only the tensile strength and confining stress influence the breakdown pressure of the rock mass under triaxial conditions. However, in real situations in the field, there may be other factors, such as degree of saturation, saturation medium (pore fluid), and the chemical and mineral composition of the rock mass, which may also have a considerable influence on the breakdown pressure relation.
# A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
## 5 Experimental Results
### Visual Evaluation  
Visual assessment is a fundamental procedure when analysing the results of satellite imagery. In this way, it is possible to evaluate areas where there is a greater difference in surface albedo with respect to the ABOVE reference images. This visual comparison is made using the QGIS(r) Geographic Information System.  
Figure 4 shows one of the dates selected for the study in the Illinois area, where the SURFAD BND station is located. This workspace is characterized by the presence of a large urban core: Champaign. In Figure 4, the conclusions in the quantitative assessment section are visually checked. On the one hand, the V03 (Figure 4f) method works better in urban areas (NDVI values below 0.20), while the V01 (Figure 4d) and V02 (Figure 4e) methods generally work better in the non-urban areas (NDVI values over 0.20). On the other hand, the combined method 3 (Figure 4i) gives better results than the combined methods 1 (Figure 4g) and 2 (Figure 4h). As shown in Figure 5, the combined methodology 3 (Comb3) gets 3 scores very similar to the V01 and V02 methodology but improves the results in urban areas like the V03 methodology.  
This improvement in the results of combined methodology 3 (Comb3) in urban areas is due to the use of the adequate existing method for the adequate pixels, such as the application of the V03 methodology only for NDVI values below 0.20. This difference in results can be seen in Figure 5, which shows in more detail the difference in surface albedo values in the center of the town of Champaign between the V02 methodology (similar to V01) and V03.  
This visual assessment allows us to confirm the conclusions of the quantitative assessment and to show again that the combined methodology 3 (Comb3) offers improvements over the original methods and the remaining combined methods.  
In addition, as shown in the ABOVE surface albedo image in Figure 4b, the difference in spatial resolution between the reference image and the output images is visually shown, creating a mixture of surface albedo since the resolution of the pixels is greater than the surface elements. The situation is similar with other types of satellite imagery, such as those of the Earth surface temperature [63], so the range of some errors is understandable, especially in urban areas due to the heterogeneity of the surfaces.  
Figure 4: 12 September 2016 Visual Assessment of Illinois Area: (**a**) Open Street Map of the area under study; (**b**) ABOVE surface albedo; (**c**) NDVI; difference between ABOVE surface albedo and (**d**) V01 (**e**) V02 (**f**) V03 (**g**) Comb1 (**h**) Comb2 (**i**) Comb3 methods. Figure (**a**) shows the North orientation, scale, and coordinates for all images.
## 1 Introduction
The authors considered the computational cost for future applications and used a random forest classifier to identify the movement of the container close to the mouth. Finally, the classifier achieved 97.4% precision, 97.1% recall and 97.2% \(F1\)-score on a data set containing 312 drinking actions and 216 other daily actions. Additionally, [PERSON] et al. [27] used a microphone worn near the throat to recognize drinking events. Afterward, the linear discriminative classifier was used to determine whether each segmented frame was a fluid intake behavior, and a recall of 72.09% was achieved. The above wearable-based methods still have the possibility of misjudgment in certain situations. In terms of wrist-worn inertial sensors, the activity design of the experiment usually contains only unitary drinking gestures and common daily activities [26]. They do not include events analogous to drinking, such as eating, combing hair, pushing glasses, etc. For wearable microphones, the signals of swallowing water and saliva are very similar in the frequency domain. Overall, in the task of fluid intake identification, single-modal signals may cause recognition errors in some analogous activities. In order to enable fluid intake monitoring, better recognition performance must be achieved in practical situations. The purpose of this study is to develop a drinking activity identification approach using multimodal signals for fluid intake monitoring. This method identifies drinking events through the signals of wrist-worn IMUs, smart containers with built-in sensors and in-ear microphones. In addition, the experimental design also involves non-drinking behaviors that can easily lead to misjudgment and various drinking postures to correspond more to the actual situation. Finally, the performance of single-modal and multimodal signals in the drinking activity recognition task was compared. The main contributions of this study are listed as follows:  
* Experimental settings: In order to make the identification approach closer to the real situation, this study introduces non-drinking behaviors that are easily confused with drinking activities, such as eating, pushing glasses, scratching necks, etc. * Approach of drinking activity identification: When the movement or acoustic signals of drinking and non-drinking activities are similar, the abundant information provided by multimodal signals can effectively enhance activity recognition performance. Hence, this study develops a multi-sensor fusion approach that considers the signal characteristics of drinking movements and swallowing sounds to improve the identification performance of drinking activities.