esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup  
### UV Driving Laser  
Figure 2 shows a schematic illustration of the experimental setup for VUV TRPES using 205-nm pump and 90-nm probe pulses. The fundamental output from a home-built Ti:sapphire amplifier (810 nm, 12 mJ/pulse, 35 fs at 1 kHz) was split by an uncoated fused silica plate, with the minor (a few percent) Fresnel reflection sent for fourth-harmonic generation (FHG, 205 nm) using nonlinear crystals. The main portion (>90%) was sent to a single-pass third-harmonic generation (THG, 270 nm) stage that consisted of a \(\beta\)-BaB\({}_{2}\)O\({}_{4}\) (BBO) crystal for second-harmonic generation, an \(\alpha\)-BBO delay-compensation plate, a quartz retardation plate for polarization control, and a \(\beta\)-BBO crystal for type-I THG [5]. These optical components have a fairly large aperture size (\(\diameter 12\) mm), and a collimated fundamental beam of a large diameter (>5 mm full width at half maximum) was used to avoid two-photon absorption in the THG crystal. The highest pulse energy obtained at 270 nm was 1.5 mJ, corresponding to 13% energy conversion efficiency. However, intense DUV pulses induce nonlinear processes while propagating through air, and the shot-to-shot intensity fluctuation of the 90-nm pulse after the 9 th harmonic generation was found to be far worse than that of the 270-nm driving pulse. Therefore, we operated the THG stage with a reduced conversion efficiency. The temporal duration of the 270-nm pulse was measured using a multiphoton ionization process of nitric oxide [18] to be 40-50 fs, which is consistent with the transform-limited (TL) pulse width (\(\sim\)40 fs) calculated from the THG spectrum (Figure 2 inset).  
Figure 1: Schematic diagram of time-resolved photoelectron spectroscopy (TRPES). Conical intersections and products with eBE higher than 6 eV have been outside of the observation window when using deep-UV (DUV) probe pulses.
## 2 Theoretical Framework
### Decision Variables  
\(ET_{i,m}=\) Total water for crop \(i\) in month \(m\) (mm)  
\(a_{izz}=\) Total area of the crop \(i\) planted in zone \(z\) (m\({}^{2}\))  
\(w_{z,m}=\) Total water of zone \(z\) in month m (m\({}^{3}\))  
\(TW_{m}=\) Total water in month m (m\({}^{3}\))  
The problem maximizes the net economic benefit and crop area under the available water in the irrigation zones (\(z\)). Each crop (\(i\)) can be described as a gross margin (yields and prices).  
\[MaxZ=\sum_{z=1}^{M}\sum_{i=1}^{N}a_{i,z}\cdot Y_{i}\cdot Cy_{i}\cdot(P_{i}-C_{i}) \tag{1}\]  
\[ET_{i,m}=Kc_{i}\cdot ET_{0m}\cdot M_{i,m}\cdot D_{m}\] for all  
\[m=\{1,2,\ldots,12\}\]  
, \[i=\{1,2,\ldots,N\}\]  
, \[w_{z,m}=\sum_{i=1}^{N}ET_{i,m}\cdot a_{i,z}\] for all  
\[z=\{1,2,\ldots,M\}\]  
, \[m=\{1,2,\ldots,12\}\]  
, \[TW_{m}=\sum_{z=1}^{M}w_{z,m}\] for all  
\[m=\{1,2,\ldots,12\}\]  
, \[\sum_{i=1}^{N}a_{i,z}\leq A_{z}\] for all  
\[z=\{1,2,\ldots,M\}\]  
, \[TW_{m}\leq FW_{m}\] for all  
\[m=\{1,2,\ldots,12\}\]  
, \[\sum_{z=1}^{M}a_{i,z}\leq\sum_{z=1}^{M}A_{z}\cdot(R_{i}/100)\] for all  
\[i=\{1,2,\ldots,N\}\]  
, \[\sum_{z=1}^{M}a_{i,z}\geq\sum_{z=1}^{M}A_{z}\cdot(RL_{i}/100)\] for all  
\[i=\{1,2,\ldots,N\}\]  
. (8)  
Objective (1) proposes a function for maximizing the net return for crop planning. Constraint (2) calculates the total water for each crop in each month. The crop coefficients \(\mathrm{Kc}\) are generated using Penman-Monteith method [33; 34]. Constraints (3) and (4), the amount of water distribution for each zone in each month is evaluated. Constraint (5) ensures that the total cropping area must not exceed the total irrigation area. Constraint (6) ensures that the total volume of water applied each year must not exceed the corresponding available supply. Constraints (7) and (8) are used to limit the crop area.
# A Novel Computer-Aided-Diagnosis System for Breast Ultrasound Images Based on BI-RADS Categories  
[PERSON]  
1 Department of Computer Science and Information Engineering, National Chiayi University, Chiayi 60004, Taiwan; [EMAIL_ADDRESS]  
2 Department of Information Management, National Sun Yat-sen University, Kaohsiung 80424, Taiwan; [EMAIL_ADDRESS]  
[PERSON]  
3  
[PERSON]  
1 Department of Computer Science and Information Engineering, National Chiayi University, Chiayi 60004, Taiwan; [EMAIL_ADDRESS]  
2  
[PERSON]  
3  
[PERSON]  
2 Correspondence: [EMAIL_ADDRESS]; Tel. : +886-5-27177323323  
screening. This screening tool is suitable for women at any age, especially young women under the age of 35. Medical ultrasound transmits high-frequency sound waves via a transducer over the breast. Once the waves hits a tissue or structure, it bounces back such that the transducer receives changes of waves to create a black-and-white image of breast tissues and structures called a sonogram. Various tissues can be visualized via a sonogram under different volumes, pitches, and frequencies. This information can help a physician confirm the diagnosis that a breast lesion is identified as benign or malignant. The BUS has a panoramic view of different tissues of the breast which is composed of skin, subcutaneous, fat, muscle, glands, chest wall, and other tissues. Above all, the BUS is real-time that can obtain the result quickly. Therefore, it has become one of the major pieces of equipment for breast lesion detection--they include breast cancers, breast cysts, benign tumors, fibrocyystic breast, etc. Traditionally, the severity of the patient's lesion was assessed as benign or malignant by clinicians' subjective and qualitative judgment. This cannot be effectively used to provide diagnosis or treatment for patients. In addition, it may cause some unnecessary surgery or pathological biopsy for a patient having suspicious breast carcinoma. To address this problem, the American College of Radiology (ACR) proposed a gold standard in 1993; namely, the Breast Imaging Reporting and Data System (BI-RADS). The initial edition of BI-RADS was created in 1993 and the newest edition of BI-RADS is the 5 th edition [1]. BI-RADS provides a uniform standard in breast carcinoma severity and sorts the results into various categories numbered 0 through 6 according to different degrees of severity, as described in Table 1. It also provides mass descriptors examined with ultrasound, including shape, orientation, margin, echo pattern, and posterior features, as listed in Table 2. Clinically, radiologists analyzed and identified the characteristics of tumor composition intuitively and clinicians assessed the severity of breast tumors as benign or malignant according to this standard. Furthermore, this can reduce the differences between interpretations of breast cancer for physicians so that patients can obtain appropriate treatment.
# Virtual Power Plant Optimization in Smart Grids: A Narrative Review
## 3 VPP Concept and Technology
### Optimization and Coordination  
The optimal virtual aggregation of energy assets is a complex constraint satisfaction problem addressed using different heuristics [62] considering local and energy grid sustainability objectives [35]. Relaxation of time constraints is possible in the case of VPP operation on the day-ahead energy markets allowing the implementation of cloud-based optimization solutions [36]. Various heuristics are defined for minimizing the VPP operational cost and maximizing the energy profit of energy assets [37; 38; 3]. Stochastic programming models are defined for VPP management and portfolio optimization under various objectives [39; 40]. Moreover, multi-criteria optimization heuristics are investigated for VPP portfolio optimization to provide simultaneously cross-sector combined services for increased flexibility provisioning and to provide a transparent, verifiable, and trustworthy management framework [41; 42].  
Nevertheless, the decentralization of VPP coordination is only partially addressed in the literature even though it is a promising solution for better and timely consideration of local energy constraints [41; 43]. Local density, power flows, administrative, and economic or social factors are criteria considered for VPP optimization processes [44]. There is a strong need for decentralized decision support systems on energy assets coordination in community-level VPP energy assets considering data on local sustainability goals, assets size, communication efficiency or latency, local typology, and remuneration schemes [45]. Others are using games theory to achieve a certain degree of decentralization while reducing the uncertainty impact on VPP optimization [46]. The optimal virtual aggregation of energy assets aims to achieve a state of equilibrium among coordinated energy assets while optimizing the service to the DSO [47].  
Several authors use edge computing principles for VPP optimal operation while addressing the challenges related to localized decision-making [48]. The authors focus on households' or citizens' engagement and aggregation when dealing with limited local renewable energy budgets showing privacy features. The fog computing principles can be applied for VPP optimization heuristics [49] for better using computational resources and improving availability. They bring important features for sustainable energy transition such as reducing operational costs, better integration of renewable energy, local balancing of generation, demands and storage, etc. [50]. Finally, energy as a service platform can be used for microgrid and VPP operation to provide closer to real-time ancillary services [51]. The service-oriented optimization models consider potential congestion, power flows and structures, and privacy of citizens for reliable delivery of VPP services [52].
# Fast Spectral Approximation of Structured Graphs with Applications to Graph Filtering
## 2 Preliminaries
### Notation  
We use calligraphic letters to denote sets with the exception of \(\mathcal{G}\) and \(\mathcal{H}\) that are reserved for representing graphs. We use \(|\cdot|\) to denote the cardinality of a given set. \(\deg(v)\) denotes the degree of vertex \(v\). Upper (lower) case boldface letters such as \(A\) (\(a\)) are used to denote matrices (vectors). \((\cdot)^{T}\) represents transposition. \(\|\cdot\|\) represents a _generic_ norm. The use of a particular norm will be clarified in the subscript. Throughout the paper, asymptotic notation is used. Therefore, when we use \(\mathcal{O}(\cdot)\) and \(o(\cdot)\) we always refer to big-O and little-o notation, respectively. They must be read as \"is bounded above by\" and \"is dominated by\", respectively. The notation \(\tilde{O}(\cdot)\) is used to hide a factor of at most \((\log\log n)^{4}\). In addition, \(\Omega(\cdot)\) denote big-Omega and it must be read as \"is bounded below by\". All these symbols describe the asymptotic behavior and hence represent the behavior in the asymptotic regime.
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 4 Results
### Response of the Initial Sensor Array to the Detected Gases  
Figure 2 shows the typical response curves of the initial sensor array to CO-CH4 gas mixtures and each curve represents a different sensor's transition with respect to detection time. It can be seen that although their initial resistances were different in the reference gas (ambient air), all of the sensors maintained stable baselines. When the CO-CH4 gas was introduced into the sensor array chamber, the resistances of all the sensors decreased sharply, and then reached a stable response state. After the target gas was pumped away, the sensor resistances gradually increased and returned to their baseline values. It can also be observed that the responses of different sensors to the CO-CH4 gas were significantly different. These results indicate that the as-designed E-nose had a good response to the target gas. However, although these sensors differed significantly in response to the target gas, it is still difficult to identify gases only from the response of a single sensor due to the inherent cross-sensitivity of the semiconductor sensors. Therefore, we could not tell which sensor has the possible optimal response with respect to the measured resistances before the E-nose optimization.
## 4 Modeling of AV-Mixed Traffic Information Services
### Derivation of Services  
The AV-mixed Traffic Information types presented in Section 3.3 are primitive information that future traffic control centers need to provide. They can be used as the output message of each operation included in the primitive traffic information service (Table 3). Furthermore, services that provide reproduced information by processing and analyzing traffic information can be proposed. In this study, descriptive, diagnostic, predictive, and prescriptive information services are added by referring to the analytic ascendancy model, which listed the data analysis types in order of difficulty and value. The descriptive information service (Table 4) provided descriptive statistics of the AV-mixed Traffic Information that could be aggregated, whereas the diagnostic information service (Table 5) explained the causes of major incidents (e.g., traffic incidents). Moreover, the predictive information service (Table 6) provided information that could be predicted using each AV-mixed Traffic Information type. Finally, the prescriptive information service (Table 7) provided the analysis information for optimal traffic conditions using the simulation of each AV-mixed Traffic Information. Figure 4 shows the process of deriving the AV-mixed Traffic Information services and the information that each service needs to provide. This process was derived through brainstorming by a group of 10 experts comprising the providers and users of traffic information center systems and police officers. The traffic information that did not require statistical diagnosis prediction was excluded, and the analysis information for traffic indices required from a comprehensive road operation perspective was added.
### Nomenclature  
\begin{tabular}{l l} g & Gravitational Acceleration (m/s\({}^{2}\)) \\ \(G_{b}\) & Direct Normal Irradiance (W/m\({}^{2}\)) \\ h & Specific Enthalpy (J/kg) \\ H & Height (m) \\ \(L_{loc}\) & Location Longitude (degrees) \\ \(L_{sl}\) & Local Standard Meridian (degrees) \\ m & Mass Flow Rate (kg/s) \\ N & Lifetime of a System (years) \\ \(N_{hd}\) & Total Number of Heliostats \\ \(N_{hd,el,i}\) & The heliostats' Number in Cell \(i\) \\ Q & Heat Transfer Rate (W) \\ \(r_{a}\) & Air to Fuel Ratio \\ r & Discount Factor (Assumed to be 3\%) \\ \(r_{ap}\) & Effective Size of Receiver Opening (m) \\ \(r_{cell,i}\) & Distance of the receiver and the Cell Center (m) \\ \(D_{int}\) & Inner Diameter of the Concentric Piping (m) \\ \(D_{outer}\) & Outer Diameter of the Concentric Piping (m) \\ s & Specific Entropy (J/(kg\(\cdot\)K)) \\ T & Temperature (K) \\ \(T_{a}\) & Air Temperature (K) \\ \(U_{wind}\) & Wind Velocity (m/s) \\ V & Velocity (m/s) \\ W & Work Rate (W) \\ \(x_{i}\) & Mass Fraction of Component \(i\) \\ \(y_{i}\) & Molar Fraction of Component \(i\) \\ z & Height (m) \\ \(z_{total}\) & Total Cost of a System (USS) \\ \(Z_{cont}\) & Additional Funding Required for Unexpected \\  & Technological and Regulatory issues (USS) \\ ZF & Annual Cash Flow (USS) \\
**Greek symbols** \\ \(\alpha\) & Absorption Factor of the Solar Receiver (degrees) \\ \(\alpha_{S}\) & Angle of Solar Altitude (degrees) \\ \(\beta\) & Parameter in equation (9) \\ \(\delta\) & Deflection Angle (degrees) \\ \(\varepsilon\) & Emissivity of a Surface \\ \(\eta\) & Efficiency \\ \(\theta_{z}\) & Angle of Solar Zenith (degrees) \\ \(\lambda_{s}\) & Angle between Vertical Direction and Reflected \\  & Irradiation (degrees) \\ \(\rho_{cell,i}\) & Density of Heliostats in Cell \(i\) \\ \(\sigma\) & Constant of Stefan-Boltzmann (W/(m\({}^{2}\cdot\)K\({}^{4}\))) \\ \(\varphi\) & Latitude Angle (degrees) \\ \(\varphi_{S}\) & Angle of Solar Azimuth (degrees) \\ \(\omega\) & Sunset Hour Angle (degrees) \\
**Subscripts** \\
0 & Environment Condition \\ am & Ambient \\ at & Atmospheric Attenuation \\ BC & Booster Compressor \\ CC & Combined Cycle; Combustion Chamber \\ \end{tabular}
## 3 Test Results and Discussion
### Out-of-Plane Behaviour  
In each test, a clear, heat-induced bowing of the wall inwards, in the direction of the furnace, was observed. The recordings of the horizontal deflection are shown in Figure 7. The monitoring of the horizontal deflection was prescribed by the testing code, but it was also important also because relatively large deformations were expected. The one-sided fire exposure of the concrete sandwich walls resulted in heat-induced bowing, which was expected to be larger than in the case of solid concrete walls of the same thickness due to the larger temperature gradient between the fire-exposed and non-fire-exposed sides.  
A phase of intensive deformation was observed on each test 7-35 min after the furnace ignition. Subsequently, the rate of horizontal deformation tended to decrease. In each test, the temperature between the insulation and the wythe not exposed to fire (see Figure 8, TC10) did not rise above the level of 100 \({}^{\circ}\)C; this was primarily due to the boiling water in the concrete and mineral wool. The observed deflection resulted from the distributions of the strains between the unheated load-bearing wythe and the facing wythe, which underwent large heat-induced expansion. The bowing of the walls was also caused by the bowing of particular wythes resulting from the temperature difference between their sides. The small (2-4 mm) initial deflection in the case of Test 1 was caused by short pre-heating of the furnace, caused by technical problems with the furnace controller.  
Figure 6: Deformation recordings in the vertical direction measured at the wall’s head.  
Figure 7: Horizontal deformations.  
The largest deflection was observed in the case of Specim 3 (56.89 mm). This can be explained by two effects. Firstly, the degree of composite action and the flexural stiffness in this case were deemed to be lower as the connectors with free lengths of 200 mm had lower shear stiffness than those in Specimens 1 and 2. Secondly, the thicker insulation changed the temperature gradient in the fire-exposed wythe, resulting in higher temperatures throughout its cross-section and a higher temperature difference between the two wythes, which in turn resulted in larger thermal expansion and bowing. The temperature gradient through the fire-exposed wythe, however, was not measured.  
Following this approach, the smallest deflection, as expected, was observed for Specim 2 (10.23 mm). The lower thickness of the fire-exposed wythe resulted in a lower temperature gradient and less thermal bowing than in Tests 1 and 3. The relatively low stiffness of the facing wythe, as well as its partial disconnection from the load-bearing wythe (due to the failure of the connectors in the inner area of the wall) and the concrete spalling observed in this test, resulted in a much smaller deflection of the wall in Test 2 until a time of 25 min. Subsequently, once the gradually heated load-bearing wythe started to thermally bow itself outward, the deflection gradually proceeded (compare Figure 9).
# Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine
## 3 Results and Discussions
### Performance Evaluation
Also, for babble noises, the proposed FE-SVM based VAD manages \(\sim\)90% accuracy rate, showing the proposed FuzzyEn feature is better in discriminating speech from background noises. The \(\mathrm{HR_{ns}}\) for all the noises is \(\sim\)95% and above, therefore this FuzzyEn feature is well-suited for various speech applications, namely, compression and speech coding.
## 3 Filter-Level Pruning Based on RL
### DCNN Pruning as a Markov Decision Process  
In filter pruning, the determination of which filters need to be pruned often depends on filter importance. The problem can be formulated as a Markov decision process (MDP) in the form of a tuple \((S,A,R,P,\gamma)\), where \(S\) is the set of all possible states, \(A\) is the set of available action in all states, \(R\) is the reward function, \(P\) is the transition probability, and \(\gamma\) is the discount factor.  
The MDP provides a mathematical framework for decision-making in the stochastic environment. At each time step, the RL agent takes an action \(a_{t}\in A\) according to the current state \(s_{t}\in S\), and receives a reward \(R(s_{t},a_{t})\). Consequently, the state is updated to \(s_{t+1}\in S\) with transition \(p(s_{t+1}|s_{t},a_{t})\). The target of the RL agent is to find an optimal policy \(\pi(a_{t}|s_{t})\) that maximizes the expected cumulative rewards. In this paper, the task of filter-level pruning of DCNN is formulated as an MDP, which is later solved by the DDPG algorithm. The details of the modeling of MDP and DDPG are as follows.
# Algebraic Point Projection for Immersed Boundary Analysis on Low Degree NURBS Curves and Surfaces
## 4 Methodology of Algebraic Point Projection
### Extension to NURBS Surfaces  
Analogous to the algebraic level sets in three-dimensions, the algebraic point projection can be naturally extended to three-dimensional Bezier and NURBS surfaces by replacing Bezout matrix \(\mathbf{M}^{B}\) with Dixon matrix \(\mathbf{M}^{D}\). Since the Dixon matrix also has the linearity property, as given in Equation (17), the basic procedure for point projection remains the same, as outlined in Section 4.1.  
Figure 12: Illustration of the second projection onto an adjacent Bézier curve segment if the first projection yields an out-of-span solution.
# A Network Analysis on Cloud Gaming: Stadia, GeForce Now and PSNow
###### Abstract
We show that Stadia and GeForce Now adopt the standard real-time protocol (RTP) for streaming [3], largely adopted by real-time communication applications [4], while PS Now uses an undocumented protocol or encapsulation mechanism. Stadia and GeForce Now transmit video up to 45 Mbit/s, while PS Now does not exceed 13 Mbit/s. For comparison, a Netflix 4K video consumes approximately 15 Mbit/s ([[https://help.netflix.com/en/node/87](https://help.netflix.com/en/node/87)]([https://help.netflix.com/en/node/87](https://help.netflix.com/en/node/87)), accessed on 1 October 2021), making cloud gaming potentially a new heavy hitter of the future Internet. With these characteristics, mobile networks can sustain high-definition gaming sessions only in the case of good-quality 4\(G\) connections, as we quantify using real-world speedtest measurements. Their client applications contact cloud servers located in the autonomous systems (ASes) of the respective corporations. They are no further away than 20 ms in terms of round trip time from our testing location in northern Italy, a figure low enough for playing a videogame with no noticeable lag. To the best of our knowledge, we are the first to perform a study on these cloud gaming services, namely Stadia, GeForce Now, and PS Now, showing their characteristics and peculiarities in terms of network usage. The remainder of the paper is organized as follows. Section 2 presents the related work. Section 3 describes our experimental setup, while Section 4 illustrates the findings that we obtain while analyzing the packet traces. Finally, Section 5 concludes the paper. To let other researchers replicate and extend our results, we release sample packet traces available at [5].