esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Many researchers are devoting attention to the so-called \"Internet of Things\" (IoT), and wireless sensor networks (WSNs) are regarded as a critical technology for realizing the communication infrastructure of the future, including the IoT. Against this background, virtualization is a crucial technique for the integration of multiple WSNs. Designing virtualized WSNs for actual environments will require further detailed studies. Within the IoT environment, physical networks can undergo dynamic change, and so, many problems exist that could prevent applications from running without interruption when using the existing approaches. In this paper, we show an overall architecture that is suitable for constructing and running virtual wireless sensor network (VWSN) services within a VWSN topology. Our approach provides users with a reliable VWSN network by assigning redundant resources according to each user's demand and providing a recovery method to incorporate environmental changes. We tested this approach by simulation experiment, with the results showing that the VWSN network is reliable in many cases, although physical deployment of sensor nodes and the modular structure of the VWSN will be quite important to the stability of services within the VWSN topology.  
virtual topology; human-brain network; routing; hierarchical modular structure; small-world properties +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Evaluation Metrics
#### 2.5.1 Ranking Metrics
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **GRU** & **LSTM** & **Transformer** \\ \hline Train Loss & 0.01433 & 0.01610 & 0.01423 \\ Train MRR & 0.9918 & 0.9905 & 0.99115 \\ Train Avg. Pos. Sim. & 0.68324 & 0.69887 & 0.75792 \\ Train Avg. Neg. Sim. & 0.02040 & 0.03455 & 0.01873 \\ \hline Validation Loss & 0.03157 & 0.03679 & 0.03253 \\ Validation MRR & 0.98314 & 0.97741 & 0.97876 \\ Valid Avg. Pos. Sim. & 0.61753 & 0.65069 & 0.69815 \\ Valid Avg. Neg. Sim. & 0.00836 & 0.03975 & 0.02107 \\ \hline Test Loss & 0.03586 & 0.03703 & 0.03140 \\ Test MRR & 0.97800 & 0.97750 & 0.97900 \\ Test Avg. Pos. Sim. & 0.60579 & 0.63980 & 0.69249 \\ Test Avg. Neg. Sim. & 0.00540 & 0.04778 & 0.02460 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance metrics (loss, MRR, average similarities) for GRU, LSTM, and Transformer models across train, validation, and test sets.
###### Abstract  
For reliability and thermal management of power devices, the most frequently used technique is to employ heatsinks. In this work, a new configuration of offset strip fin heatsink based on using the concept of curvy fins and U-turn is proposed with the aim of improving the heat transfer performance. With this aim, a three-dimensional model of heatsink with Silicon Insulated-Gate Bipolar Transistors (IGBTs) and diodes, solder, Direct Bonded Copper (DBC) substrate, baseplate and thermal grease is developed. [PERSON]'s extrapolation is used for increasing the accuracy of the numerical simulations and to validate the simulations. To study the effectiveness of the new offset design, results are compared with conventional offset strip fin heatsink. Results show that in aspects of design of heatsinks (including heat transfer coefficient, maximum chip temperature and thermal resistance), the new introduced model has advantages compared to the conventional offset strip fin design. These enhancements are caused by the combination of the longer coolant passage in the heatsink associated with generation of disturbance and recirculation areas along the curvy fins, creation of centrifugal forces in the U-turn, and periodic breaking up boundary layers. Also, it is shown that due to narrower passage and back-and-forth route, the new introduced design can handle the hot spots better than conventional design.  
curvy fins; heatsink; numerical simulation; offset strip fin; thermal management; U-turn +
Footnote †: journal: Applied Sciences
# Problem-Driven Scenario Generation for Stochastic Programming Problems: A Survey
## 4 Problem-Driven Scenario Generation
### Methodologies  
The present section entails an investigation into several examples of problem-driven scenario generation methodologies. As mentioned by [PERSON] [60], property-matching scenario generation methodologies investigated in [47] can be considered one of the first problem-driven scenario generation methodologies. The aim is to construct sets of scenarios with prescribed statistical properties, such as moments of the marginal distributions, correlations of the margins, percentiles of the marginal distributions etc. [61]. It is a generalization of the moment-matching algorithms [14; 47] and focuses on capturing the key statistical features instead of guaranteeing an exact representation of the distribution. Consequently, it focuses only on problems with specific properties, usually determined by empirical analysis. Subsequent advancements have been centered on establishing methodologies that can be mathematically verified and their application to real-world problems. Two examples of the problem-driven scenario generation methodologies have been mentioned in [53]: Importance sampling [62] and Forward Selection in Recourse Clusters [48; 63]. Importance sampling is a general technique that effectively samples from a distribution and has been used for distribution-based scenario generation. It involves drawing samples from a proxy distribution and modifying the weights assigned to each sample to approximate the required expectation. The construction of the proxy distribution depends on the underlying loss function of the stochastic program, the scenario generation approach is therefore considered as problem-driven when the problem-specific loss function is defined, e.g., a solution-based loss function instead of a distribution-based loss function. Nevertheless, compared to other variance reduction techniques, importance sampling is demonstrated to be computationally expensive. The Forward Selection in Recourse Clusters technique aims to prevent scenario redundancy by initially clustering scenarios based on their behavior in relation to the problem. Following this, a standard reduction technique known as forward selection is employed to select a single scenario from each cluster in the reduced set. An application of this approach to a stochastic unit commitment problem can be found in [48], along with a discussion of the practical challenges involved. Alternative modifications to the method are described in [48; 63]. The research by [53] also put forward two specific approaches for handling stochastic programs that incorporate tail risk measures: aggregation sampling and aggregation reduction. The tail risk measure is a statistical or quantitative measure employed to assess the potential losses or risks associated with extreme events or outliers in a probability distribution. As demonstrated in Figure 5, the red node represents an event characterized
## 1 Introduction  
Orthopedic maxillary expansion can be achieved using different fixed appliances that use deciduous or permanent teeth or miniscrews for support to transmit a lateral force to the two palatal processes of the maxillary bone, producing the splitting of the midpalatal suture and a certain amount of widening of the maxillary sutures [1]. The force is generated by a transversal screw that delivers a variable amount of force depending on several factors such as the activation protocol, its relative position in the palatal vault and its type of support. Palatal expansion allows decreasing nasal resistances and enlarging the upper airways, therefore improving breathing [2, 3].  
It also allows correcting crossbite, thus preventing the worsening of craniofacial asymmetries during growth [4; 5].  
Rapid maxillary expansion is a common activation protocol of the median screw that allows correcting maxillary hypoplasia and posterior crossbite in 1/3 weeks by activating the median screw of the appliance twice a day (total activation = 0.4-0.5 mm) [6]. The other commonly used expansion protocol is slow maxillary expansion, which requires less frequent activation of the median screw for a longer period of time. Despite the different kinds of protocols, both of the maxillary expanders have similar orthopedic effects in growing subjects [7].  
A Ni-Ti leaf spring palatal expander (leaf expander) has a small size body and is similar to a conventional Hyrax expander [8]. The maxillary expansion is promoted by calibrated and continuous forces. In particular, the constant lateral force of 450 g delivered by the Ni-Ti leaf spring allows for a 6 or 9 mm maximum expansion. The clinician reactivates the device once a month with 10 quarter turns (1 mm of activation) until the achievement of the needed expansion. The advantage of the presented appliance leads to no required compliance from patients' parents; moreover, it is less painful compared to conventional expanders and provides a similar amount of expansion [9; 10; 11; 12; 13]. Moreover, leaf expander treatment appeared effective in increasing upper airway and maxillary sinus volume in patients with maxillary hypoplasia, demonstrating no difference from a conventional Hyrax RME [9].  
The aim of this study is to evaluate and compare, through bidimensional cephalometry performed on lateral radiographs, sagittal and vertical skeletal and dental changes obtained from an RME and a leaf expander anchored to deciduous teeth and compare them with an untreated control group.
###### Abstract  
This investigation focuses on the dynamical effects caused by varying the central-cut width within the Box Assembly with Removable Component (BARC) system. The central-cut widths included in this study are a 0.5\({}^{\prime\prime}\) cut, a 0.25\({}^{\prime\prime}\) cut, a thin 0.1\({}^{\prime\prime}\) cut, and a structure that did not have a cut at all. Finite element analysis was conducted to determine the mode shapes and natural frequencies of each of the BARC structures. Structural dynamics experiments were run to examine the effects of the central-cut width on the dynamical responses and nonlinear characteristics of the BARC system. Free vibration testing with an impact hammer was carried out to excite the system and extract the dominant frequencies and directions of the significant responses. A pseudorandom vibration test that allows for the qualitative determination of any nonlinear behavior within the system was performed. This type of behavior can include nonlinear softening, nonlinear hardening, and the most common, nonlinear damping due to the presence of several bolted-joint connections and the possible activation of geometric and inertia nonlinearities. To quantitatively investigate the impacts of the central-cut width on the dynamics of the system, swept sinusoidal testing was conducted. It is determined that almost all systems with central cuts demonstrate the presence of nonlinear softening, but at times, nonlinear hardening trends are seen, particularly in the 0.1\({}^{\prime\prime}\) cut and no-cut systems when testing harmonically. Each of the central-cut systems displays nonlinear damping, with the amount of damping generally increasing as the central cut decreases in size. The effect of the central cut of the BARC system on the mode-switching ability of the system is negligible; however, mode switching takes place when comparing the central-cut configurations to the no-cut one. These results show the significance of accurately measuring the central-cut width and how geometric uncertainty may change the dynamical responses and nonlinear properties of the system.  
BARC; central-cut width; computational modeling; experimental structural dynamics; nonlinear characterization 2025
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 5 Conclusions  
This work reports on single-band and multi-band object-based time-constraint DTW workflow in an OBIA environment for crop mapping using Sentinel-2 time series data. Our findings represent a helpful step towards generating ready-to-use spatial data products that describe agricultural fields. For testing purposes, we used two complex agricultural landscapes from California and Texas, achieving satisfactory classification accuracy when time delays of 30 and 45 days were used for DTW. This outperformed the classifications using the Euclidean distance or DTW without time constraints. These kinds of approaches are increasingly necessary as we move into imagery-dense workflows with satellite constellations providing increasing spatial and temporal resolutions. This work demonstrated its efficiency for crop mapping. Nevertheless, other applications such as deforestation monitoring, disaster mapping or mapping of tree species can successfully benefit from using this approach.  
The rule set developed in eCognition, sample data and a user guide are available at www.csillik.com/research and as supplementary material. Tables S1-S4 details the error matrices for best single-band and multi-band DTW classification for the two test areas, TA1 and TA2.  
Conceptualization, O.C. and M.B.; Methodology, O.C. and M.B.; Software, O.C.; Supervision, G.P.A. and M.K.; Writing--original draft, O.C. and M.B.; Writing--review and editing, G.P.A. and M.K.  
[PERSON] started the work reported in this article at the Department of Geoinformatics - Z_GIS, University of Salzburg, Austria, being funded by the Austrian Science Fund (FWF) through the Doctoral College GIScience (DK W1237 - N23).  
We thank the two anonymous reviewers for their constructive comments and the eCognition team for their support.  
The authors declare no conflict of interest.
# Response to Variations in River Flowrate by a Spaceborne GNSS-R River Width Estimator
###### Abstract  
In recent years, the use of Global Navigation Satellite System-Reflectometry (GNSS-R) for remote sensing of the Earth's surface has gained momentum as a means to exploit existing spaceborne microwave navigation systems for science-related applications. Here, we explore the potential for using measurements made by a spaceborne GNSS-R bistatic radar system (CYGNSS) during river overpasses to estimate its width, and to use that width as a proxy for river flowrate. We present a case study utilizing CYGNSS data collected in the spring of 2019 during multiple overpasses of the Pascagoula River in southern Mississippi over a range of flowrates. Our results demonstrate that a measure of river width derived from CYGNSS is highly correlated with the observed flowrates. We show that an approximately monotonic relationship exists between river flowrate and a measure of river width which we define as the associated GNSS-R width (AGW). These results suggest the potential for GNSS-R systems to be utilized as a means to estimate river flowrates and widths from space.  
b 27 September 2019; Accepted: 18 October 2019; Published: 22 October 2019
The objective functions of the linear joint and standard kernel framework are solved using, the augmented Lagrange multiplier (ALM) and alternating direction method of multipliers (ADMM) techniques to obtain efficient values for the variables. The overall structure of our proposed model is depicted in Figure 1. This research work makes the following key contributions:  
* Introduction of a novel approach that combines feature selection and a multi-label SVM classifier within a multi-label classification framework. This is achieved through the utilization of manifold regularized matrix factorization in our proposed method to identify a robust low-rank subspace within the input space. This results in a more resilient SVM classifier, particularly in scenarios with high-noise samples. * Extending the proposed framework via a kernel learning approach to capture the nonlinear relationship between inputs and outputs. By taking the derivative of the kernel function, we establish a linear relationship between the kernel derivative and the kernel function itself. This leads to an improved solution for the nonlinear problem. * To effectively address our non-convex learning framework, we employ the augmented Lagrangian multiplier (ALM) and alternating direction method of multipliers (ADMM) techniques. These optimization methods ensure efficient convergence and provide a solution to the optimization problem.
## 2 Materials and Methods  
Earlier studies suggest that achieving laser irradiance on the target in TOF mass spectrometry at the order of 1 GW/cm\({}^{2}\) is necessary to generate a plasma plume with minimal elemental fractionation, reflecting the surface's true composition [15]. To attain such high irradiance states from dense solids, lasers equipped with short-pulse passive Q-switching systems are required, offering advantages in coupling with the time-of-flight mass analyzer [16]. Given the stringent mass and power budget constraints in deep space exploration missions, the microchip laser adopts a design strategy utilizing semiconductor laser-pumped passive Q-switching. In contrast to conventional lasers, the microchip laser's resonant cavity is notably short, resulting in a brief photon lifetime and facilitating the generation of short pulses lasting on the order of nanoseconds or sub-nanoseconds. Passive Q-switching eliminates the need for electronic switching devices, reducing the system's size and complexity while enhancing power efficiency. The repetition frequency can surpass a kilohertz, and the single-pulse energy can reach tens of microjoules. In addition, such systems provide excellent pulse to pulse energy stability, which is of high importance for laser ablation techniques. This approach addresses the unique challenges posed by deep space exploration missions, ensuring efficient and precise performance of the LA-TOF-MS under the constraints of limited mass and power resources.  
The operating principle of the microchip laser, designed for application in deep space exploration TOF mass spectrometers, revolves around employing a short-cavity resonant structure with a saturable absorber to regulate laser generation. Initially, the saturable absorber exhibits a high absorption coefficient, which gradually decreases as the optical intensity rises. Upon reaching the saturation intensity, the saturable absorber swiftly transitions to a transparent state, facilitating the generation of a robust pulsed laser output [17]. Contrasted with continuous pumping, pulsed pumping notably mitigates thermal effects on the crystal, resulting in improved pulse output energy, beam quality, and pulse stability. This paper aims to offer an introduction to the microchip laser, encompassing both structural and electronic design perspectives.
## 4 Design of Dynamic Cache Allocation Mechanism
### Weight Definition
#### 4.1.3 Definition 3: Rate Weight  
The higher the transmission rate of a multicast group is, the more multicast packets arrive per unit time. Therefore, more cache space should be allocated for the multicast group to perform a retransmission response when the NACK is received. The MTN periodically collects statistics on the number of the received multicast data packets. Let \(S_{i,n}\) denote the number of multicast data packets of multicast group \(i\) received by MTN in the \(n\)-th statistical period. According to Equation (4), at the same MTN, the rate weight \(R_{i,n}\) of multicast group \(i\) in the \(n\)-th statistical period can be obtained by  
\[R_{i,n}=S_{i,n}/\sum_{j}^{M}S_{j,n} \tag{4}\]
## 3 Experiments
### Experiments on AIRS Dataset  
To further validate the effectiveness of the proposed approach, we also performed experiments on a large segmentation dataset, i.e., AIRS. The AIRS dataset consists of 951 aerial images captured over the area of Christchurch, the largest city on the South Island of New Zealand. The size of each image is \(10,000\times 10,000\) pixels with a spatial resolution of \(0.075\) m. In AIRS, the ground truth is only given for the training set. For the ease of comparison, we split the dataset as mentioned by [PERSON] et al. [64]. For the AIRS dataset, the training set contains \(309,377\) images and the validation set encompasses \(33,934\) images. For the ease of experimentation, the original images were randomly cropped to a size of \(526\times 526\) and then resized to \(256\times 256\). For data augmentation, we followed the same data augmentation as used in TernausNet [61], where random vertical and horizontal flips were used in the experiments. Table 2 presents our pruning results for TernausNet and U-Net on the larger AIRS dataset under different weight initialization strategies.
# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
## 5 Optimal Dispatching of Micro-Energy-Grid
### Results
Combining with Figure 8, it can be seen that both TOD and real-time tariffs can transfer load to the night trough. However, compared with real-time tariff, TOD tariff produces new load peaks at 6:00, thus the effect of real-time tariff to reduce peak-trough difference is better. The electricity purchasing and selling status, PV absorption status and integrated operation cost in five cases are shown in Table 15. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Load Type** & **Case 2** & **Case 3** & **Case 5** \\ \hline Water and fertilizer integration & 17:00–18:00 & 17:00–18:00 & 15:00–16:00 \\ Plasma nitrogen fixation and water treatment & 13:00–14:00 & 11:00–12:00 & 12:00-14:00 \\ Ventilator & 2:00–700, 23:00–24:00 & 7:00,17:00, 23:00–4:00 & 10:00–17:00 \\ Biogas pool heat pump & 6:00, 24:00 & 2:00, 23:00 & 20:00, 24:00 \\ Lighting in shed & 3:00–6:00, 24:00 & 1:00–4:00, 23:00 & 20:00–2:10:20, 24:00–2:00 \\ Physical insecticide & 3:00–4:00, 6:00–7:00, 24:00 & 1:00–4:00, 23:00 & 11:00–15:00 \\ Space electric field dehumidification & 13:00, 23:00–7:00 & 17:00, 23:00–7:00 & 8:00–17:00 \\ Acoustic wave facilitation & 6:00–70, 12:00–15:00 & 6:00–7:00, & 10:00–17:00 \\  & 17:00–18:00 & 9:00–11:00, 31:00–14:00, 18:00 & 10:00–17:00 \\ Nutrient solution circulation system & 12:00–14:00 & 12:00, 17:00–18:00 & 12:00–14:00 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: The input time schedule of time-shifting load. Figure 9: The load response of TOD tariff and real-time price. In Table 15, the following can be seen:  
1. In Cases 1-3, PV has been completely eliminated, because it allows the sale of surplus power from micro-energy-grid to distribution grid. Cases 4 and 5 can eliminate 92.97% and 95.88% of PV energy, respectively, without selling surplus power, but the phenomenon of light abandonment still can be curbed. 2. Compared with Case 1, Cases 2 and 3 reduced the synthesized operation cost by 43.4% and 56.5%, respectively, which indicates that a reasonably working DR scheme can reduce the synthesized operation cost of a greenhouse.
# Transformation of Trigonometric Functions into Hyperbolic Functions Based on Cable Statics
## Appendix B  
The solution is expressed in terms of \(p\):  
\[\ln\biggl{(}p+\sqrt{1+p^{2}}\biggr{)}=\frac{\mu x}{T_{0}}. \tag{11}\]  
The exponential function with base \(e\) is applied to both sides of the equation:  
\[p+\sqrt{1+p^{2}}=e^{\frac{\mu x}{T_{0}}}. \tag{12}\]  
By moving \(p\) to the other side of the equation through subtraction, the following expression is obtained:  
\[\sqrt{1+p^{2}}=e^{\frac{\mu x}{T_{0}}}-p.\]  
Raising both sides of the equation to the power of 2:  
\[1+p^{2}=\biggl{(}e^{\frac{\mu x}{T_{0}}}-p\biggr{)}^{2}.\]  
Expanding the squared binomial in the equation:  
\[1+p^{2}=e^{\frac{2\mu x}{T_{0}}}-2 pe^{\frac{\mu x}{T_{0}}}+p^{2}.\]  
Solving for \(p\) after eliminating the \(p^{2}\) terms:  
\[p=\frac{e^{\frac{2\mu x}{T_{0}}}-1}{2e^{\frac{\mu x}{T_{0}}}}. \tag{13}\]  
The numerator and denominator are multiplied by \(e^{-\frac{\mu x}{T_{0}}}\):  
\[p=\frac{\biggl{(}e^{\frac{2\mu x}{T_{0}}}-1\biggr{)}\times e^{-\frac{\mu x}{T_ {0}}}}{2e^{\frac{\mu x}{T_{0}}}\times e^{-\frac{\mu x}{T_{0}}}}.\]  
The Euler function in the denominator cancels:  
\[p=\frac{e^{\frac{\mu x}{T_{0}}}-e^{-\frac{\mu x}{T_{0}}}}{2}. \tag{14}\]  
The following expression represents the hyperbolic sine (sinh):  
\[p=\sinh\frac{\mu x}{T_{0}}. \tag{15}\]