esa-sceva/satcom-chunk-collection huggingface dataset:
# A 11 mW 2.4 GHz 0.18 \(\upmu\)m CMOS Transceivers for Wireless Sensor Networks
## 3 Circuit Implementation
### Frequency Synthesizer
The voltage swings of the input and output of the divide-by-two unit are about 600 mV (peak-to-peak). After a MCML-to-digital buffer, which is a differential to single-ended unit, the output becomes a rail-to-rail signal. Since the output frequency is now about 1.2 GHz, it is possible to use more power efficient logic, namely the TSPC logic in the following divide-by-4/5 prescaler, which is shown in Figure 9c. The D flip-flop used in this design is the conventional nine-transistor TSPC topology [13]. Finally, the output of the divide-by-4/5 prescaler is less than 400 MHz so that the following frequency division can be performed by the divide-by-14/15/16 counter implemented using standard static CMOS logic, which is stable and easy to implement. The total division ratios are determined by four bits of modulus control signals, which are connected to a sigma-delta modulator. In this system, the modulator is of three stages (in Mash 1-1-1) each of which is a 10-bit configuration. The total division ratio is set to be (2 \(\times\) (52 + M)), where M is a programmable fraction number. Using this fractional-N topology, it is able to achieve division ratios which is a fractional, instead of an integer, number. It is now possible to use a higher reference clock for the PLL system, which results in fast settling time and maximized functionality. Figure 8: Simulated F-V curve and phase noise of the VCO; (**a**) F-V curve; and (**b**) phase noise at 2.45 GHz. The other building blocks in the PLL, as shown in Figure 10, are quite conventional. The PFD is a dead zone-free digital detector and the charge-pump (CP) is a conventional one, as shown in Figure 10. The charge current is about 100 \(\upmu\)A. A third-order RC-based loop-filter is used for better suppression of the reference spurs. In this work, key consideration is that the capacitor should be small enough to have a fully-integration solution. The parameters of the components in the loop filter are determined by the closed-loop transfer function of the PLL [15]. The whole PLL system is now a fourth-order system [13]. Since the gain of the VCO frequency tuning, operating frequency, division ratio, and charge current have been determined, the PLL has the freedom in optimization of loop filters. As the key target is the silicon area which is occupied by the loop capacitor. In this design, the maximum capacitance after optimization is 60 pF, which occupies 240 \(\upmu\)m \(\times\) 200 \(\upmu\)m. The loop bandwidth is 500 kHz to suppress the Delta Sigma Modulator (DSM) quantization noise. Figure 10: _Cont._  
Figure 9: (a) The frequency divider; (b) CML divide-by-two circuit; and (c) divide-by-4/5 circuit. Simulation of the PLL is carried out using Cadence Spectre RF (San Jose, CA, USA) at the transistor-level in a 0.18 \(\upmu\)m CMOS technology (post-layout). Simulation suggests that the proposed PLL is able to work at 2.4 GHz with less than 20 \(\upmu\)s settling time, as shown in Figure 11.
# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
###### Abstract  
We propose Depth-to-Space Net (DTS-Net), an effective technique for semantic segmentation using the efficient sub-pixel convolutional neural network. This technique is inspired by depth-to-space (DTS) image reconstruction, which was originally used for image and video super-resolution tasks, combined with a mask enhancement filtration technique based on multi-label classification, namely, Nearest Label Filtration. In the proposed technique, we employ depth-wise separable convolution-based architectures. We propose both a deep network, that is, DTS-Net, and a lightweight network, DTS-Net-Lite, for real-time semantic segmentation; these networks employ Xception and MobileNetV2 architectures as the feature extractors, respectively. In addition, we explore the joint semantic segmentation and depth estimation task and demonstrate that the proposed technique can efficiently perform both tasks simultaneously, outperforming state-of-art (SOTA) methods. We train and evaluate the performance of the proposed method on the PASCAL VOC2012, NYUV2, and CITYSCAPES benchmarks. Hence, we obtain high mean intersection over union (mIOU) and mean pixel accuracy (Pix.acc.) values using simple and lightweight convolutional neural network architectures of the developed networks. Notably, the proposed method outperforms SOTA methods that depend on encoder-decoder architectures, although our implementation and computations are far simpler.  
Keywords:convolutional neural networks; semantic segmentation; real-time computer vision +
Footnote †: journal:
## 3 Materials and Methods
### Ground Truth Labels for Model Training  
To train our ML models, our satellite images needed to be linked to plant-level generation data. We used satellite image timestamps to match each image to the nearest record of plant-level generation data, as described in A.4. Our training plants are located in the US, Europe, and Australia. For regression models, each satellite image was labeled with the capacity factor at that timestamp: the generation of the plant divided by its capacity at the given timestamp (see Figure 1). For classification, we labeled plants with a greater than 5% capacity factor as \"on\" and everything else as \"off\". We used this nonzero threshold because there are a handful of plants reporting very low levels of generation that can functionally be considered \"off\".
## 6 Open Issues and Future Trends
### Robustness of Dima Optimization  
The robustness of DIMA optimization should be considered with the presence of uncertainty, physical variance and external disturbances. The worst case scenario, for example possible failures of some components, must be considered. The authors in [70] explored to provide Worst Case Execution Time (WCET) analysis for the application scheduling in IMA. Further, the authors in [71] presented tight probabilistic WCET estimates using Probabilistic Timing Analysis, which enables system utilization to be optimized. However, such a solution for DIMA is still not available.  
Moreover, sensitivity analysis for the ILP formulation is absent in the current research of DIMA optimization, which should also be the future goal for the software and hardware mapping. If solutions are quite sensitive to the variable perturbations, we are more interested in finding the robust solutions. In this case, we can apply robust optimization procedures to solve the DIMA multi-objective optimization problems [72].
## 1 Introduction  
Magnetic hyperthermia is a powerful technique envisaged to heat up materials or tissues. Used as probes for cancer therapy, iron oxide nanoparticles are largely studied coupled to hyperthermia to activate cell death [1] or molecular release. Iron oxide nanoparticles are non-toxic [2], and eliminated by the cells in vivo [3]. However, the heating properties of iron oxide nanoparticles are often deteriorated by their aggregation [1, 4]. This involves a drastic decrease of the specific loss power (SLP, or SAR in some publication, for specific absorption rate), which corresponds to the heat energy loss by the particles toward their environment [5]. Recovering high SLP values during application is really important because this normalized value is the one that is used to compare the efficiency of iron oxide nanoparticles directly depending on their synthesis procedure. Optimized nanoparticles excited with the right frequencies demonstrate large SLP values (see, for instance, the work of [PERSON] et al. [6]). When specific synthesis pathways are used, large SLP values can be obtained [7]. Thus, aggregation is the main drawback that hamper the recent improvements in nanoparticle synthesis pathways. As a consequence, this drastically limits their applicability or necessitates a larger nanoparticle amount to produce the expected macroscopic thermal effect. Against this macroscopic effect, some groups already showed that magnetic hyperthermia could be useful in close proximity of the nanoparticles [8, 9, 10, 11]. This is true for well-dispersed stable nanoparticles, but could also be applied to nanostructuredmaterials containing iron oxide material as local heating sources. To avoid detrimental interparticle dipolar interactions, it is necessary to tune the mean distance between magnetic nanoparticles inside nanostructured materials. However, this point is most always forgotten, or indirectly established by regarding the heating effect (either structure change or release ability) on the nanostructured systems. In this article, we investigated the role of dipolar interaction of maghemite nanoparticles embedded into aggregates between non-magnetic and magnetic nanoparticles with similar sizes. We thus wondered how to tune the interparticle dipolar interactions between magnetic nanoparticles into aggregates, to reach highly efficient nanostructured materials with embedded magnetic nanoparticles. To tune the mean distance between Fe\({}_{2}\)O\({}_{3}\) nanoparticles, we investigated their heating efficiency when aggregated with non-magnetic cerium oxide nanoparticles. They were selected because they exhibit similar surface chemistry reactivity and present the same dependence of the surface charges versus pH values. To ensure a homogeneous distribution into the aggregates, we selected an aggregation pathway that introduced a non-directed link between nanoparticles through electrostatic interactions with an oppositely charged polyelectrolyte.
## 3 Overview of IoT Technology
Zigbee: Although Zigbee has a maximum baud rate of 250 kbps, 115,200 bps was utilized for transmitting and receiving since this was the fastest speed that the microcontroller's UART could be configured to work. There is a start and stop bit for each byte sent. One of ZigBee's most important characteristics is covering huge regions with routers [52]. IPV6: IPV6, the most recent version of the IP protocol, visually shows the data information and transfers data without loss. The 0-bit compressed representation is utilized for resulting in high overlap data, providing a new approach for network viewing. IPV6 (6 LoWPAN) is a network architecture that combines low-power personal wireless area networks. One of the infrastructure layer protocols supports header compression to reduce transmission overhead [53]. In smart buildings, inter-device communication is organized using a hierarchical protocol paradigm, with each tier fulfilling a distinct function. At the physical layer, protocols such as WiFi, ZigBee, Bluetooth, LoRa, and NFC provide the wireless or wired connectivity of devices, including sensors, lighting, and security systems. The data connection layer guarantees reliable transmission via the management of error detection and repair. The network layer employs protocols such as IPv4, IPv6, and 6 LoWPAN to address and transport data among devices, facilitating communication across local or worldwide networks. IPSec is used to encrypt data for enhanced security. The transport layer ensures reliable data delivery between devices; TCP is used for secure communication, but UDP permits expedited transfer where dependability is not paramount, as in video streaming for security cameras. The application layer encompasses high-level protocols such as HTTP, MQTT, and CoAP, facilitating interaction and data sharing across many devices and systems inside the smart building. These protocols facilitate the seamless functioning of smart building management systems such as lighting, HVAC, and security, allowing for efficient and secure communication within the building's ecosystem.
## 1 Introduction  
High-order harmonic generation (HHG) in extreme ultraviolet (XUV) range, which occurs when an intense laser pulse interacts with laser-induced plasma (LIP) formed by another laser pulse on the surfaces of bulk targets, is a rich field for a variety of material studies [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. The high-order nonlinear optical properties of various inorganic nanoparticles (Ag, Au, Pd, Pt, Ru, BaTiO\({}_{3}\), and SrTiO\({}_{3}\)) appearing in LIP were reviewed in [16].  
The heating pulses (HP) are usually generated by splitting the amplified laser pulse beam (with pulse duration of a few hundred ps) before temporal compression in a chirped-pulse amplification laser system. The time delay between the picosecond HP and the femtosecond driving pulses (DP) for HHG is commonly created by the path length difference between the two optical paths, so the delays are usually restricted by \(\sim\)100 ns during most of the previous HHG experiments. Correspondingly, the distance between the focus spot and target surface due to above limitations usually does not exceed 200 \(\upmu\)m, thus involving particles with minimum velocities distribution at around 10\({}^{3}\) m/s. The presence of clusters consisting of hundreds to thousands of atoms in LIP requires the ability to control delay between HP and DP in a much more comprehensive range, which is difficult while using the optically driven delay between two pulses from the same laser.  
One way to overcome this problem is applying a second source, in particular, nanosecond pulses from the Nd: YAG laser. An analysis of the application of longer (nanosecond)pulses for target ablation during HHG in LIP compared to picosecond pulses, has previously been reported in [17]. Nanosecond laser ablation has been widely used to synthesize various metal and oxide nanoparticles (NPs). However, numerous factors such as the duration of the laser pulse, fluence, and characteristics of irradiated targets affect the properties of synthesized NPs. The pulse duration is one of the most important parameters when considering the in-situ synthesis of NPs in LIP.  
In this work, we apply the two-laser scheme to investigate HHG in carbon-based nanostructured plasmas. The nanosecond laser is used for LIP formation and the femtosecond laser is used for harmonics generation in plasma. The synchronization of the two laser sources may resolve, to some extent, the puzzle related to the enhancement of harmonics in the multi-atomic species produced during ablation of bulk materials or targets that initially contain NPs. The analysis of plasma components can be achieved by controlling the HP-DP delay in a wide range spanning between 0 and a few tens of microseconds, which should be sufficient for studying the fast and slow components of LIPs. Such an approach has already been used in reported studies to analyze the harmonics' delay dependencies from different ZnO-contained targets, resonance-enhanced harmonics in mixed LIPs, and HHG during propagation of femtosecond pulses through different ablated species [18; 19; 20].
## 3 The Problem of Selecting the Next Network
### Network Selection Process according to the Proactive Cross-Layer Protocol
#### 4.1.5 Model of k-Nearest Neighbors  
The k-nearest neighbors (KNN) model is one of the simplest and most straightforward supervised learning models in machine learning. The key idea of this algorithm is to decide a predicted value based on the labeled data points of the training set that are near the query data point.  
KNN starts by loading the training data points in memory. Then, the classification task is completed by finding the nearest k data points. Finally, a vote of the k closest points to the query point will determine the class of the query data point. One critical decision that needs to be made is the selection of the distance function. Several distance functions have been proposed to compute the distance between two data points; however, the most common methods are cosine similarity and Euclidean distance.  
The Euclidean distance can be calculated by subtracting the training data point from the point to be classified, as in Equation (12).  
\[E(x,y)=\sqrt{\sum_{i=0}^{n}(x_{i}-y_{i})^{2}} \tag{12}\]  
In this calculation, we determine the data point class based on the best posterior probability value. Although KNN contains a limited number of hyper-parameters (i.e., the k-value and distance function), which makes it a simple model, the K-value can dramatically affect the model performance.  
Five classification models were compared to each other to get the best fit for the data set on the network. They are the support-vector machines (SVM), naive bayes (NB), logistic regression (LR), decision tree (DT), and k-nearest neighbors (KNN).
## 5 Case Study
### Results Discussion  
#### 5.2.1 Scenarios Analysis  
The Copula function should be adopted to fit the MCPs in the provincial and external markets. In this paper, it is assumed that the prediction error is the Gaussian distribution with the mean value of 0. Therefore, the probability density distribution was symmetric for the MCPs in the provincial and external markets. Three symmetrically distributed Copula functions in Table 1, including the bivariate Gaussian-Copula function, bivariate t-Copula function and bivariate Frank-Copula function were candidate functions, but the final closed function should be subject to the fitting performance.  
There were 1000 groups of samples generated discretely. Based on the marginal distribution functions of MCPs in the provincial and external markets, the parameter estimation result of different Copula functions is shown in Table 4. The fitting performance of different Copula functions is tested by Equation (7), and the result is shown in Table 5. It can be seen that the values of the Gaussian-Copula function were the smallest, which indicates the better fitting performance of the Gaussian-Copula function in each month compared with the t- and Frank-Copula functions. Therefore, the Gaussian-Copula function was used to describe the correlation between the MCPs in  
It is well known that the more discrete scenarios are, the more precise the description of the original distribution will be. However, the typicality of each scenario will be poor, and the workload for computation will be more. Upon the test, there are 1000 sets of scenarios generated by monthly discretization. After scenario reduction, the minimum number of scenarios from January to December is 47, and the maximum number is 64. A diagram of silhouette values is shown in Figure 5. It was found that the silhouette values were positive for each original scenario after clustering, and more than 95% silhouette values were greater than 0.2 for the original scenarios. This indicated that the classification number determined by the inconsistent values was appropriate, and the scenario set after clustering could be used to describe the original scenario set.
## 3 Numerical Method  
### Governing Equations  
With the improvement of computer hardware, it is possible to use Direct Numerical Simulation (DNS) to study fluid motion characteristics [24]. However, DNS could not be widely used in industrial fluid numerical simulations up to now due to its high requirements on mesh. In this study, the three-dimensional incompressible Reynolds-Averaged Navier-Stokes (RANS) equation is used to solve the flow field. Since no heat transfer is involved, there is no need to solve the energy equation. In RANS, the solution variables represent time-averaged values. The mass and momentum conservation equations for the steady RANS system in Cartesian coordinates can be written as follows [1]:  
\[\frac{\partial\overline{u_{i}^{\prime}}}{\partial x_{i}}=0 \tag{1}\]  
\[\rho\overline{u_{j}}\frac{\partial}{\partial x_{j}}(\overline{u_{i}})=-\frac{ \partial\overline{p}}{\partial x_{i}}+\frac{\partial}{\partial x_{j}}\bigg{(} \mu\frac{\partial\overline{u_{i}^{\prime}}}{\partial x_{j}}-\rho\overline{u_ {i}^{\prime}u_{j}^{\prime}}\bigg{)}+S_{i} \tag{2}\]  
where \(\rho\) is the air density, \(u_{i}\) is the air velocity, \(p\) is the a pressure, \(\mu\) is the air viscosity, \(u_{i}^{\prime}\) is the fluctuating velocity component in the \(i\) direction, \(S_{i}\) is the source term. \(-\rho\overline{u_{i}^{\prime}u_{j}^{\prime}}\) is the Reynolds stress term which needs to be modeled via an appropriate turbulence model. The turbulence models commonly used in the Reynolds mean N-S equation include the \(k\)-\(\varepsilon\) model and the \(k\)-\(\omega\) model. [PERSON] et al. [23] have discussed the influence of the turbulence model on the flow field results of the Invelox wind turbine in their research, so the Realizable \(k\)-\(\varepsilon\) model is applied in this study.
# Experimental Analysis of Pore and Permeability Characteristics of Coal by Low-Field NMR
## 3 Discussion
### NMR Porosity
#### 3.2.1 Porosity of Different Types of Pores  
Precious research shows that the intensity of the NMR signal was proportional to the water content in the saturated coal samples [22; 23]. By NMR testing of the known porosity standard samples, the unit volume NMR signal of different porosity standard samples was obtained, and the linear relationship between the porosity and the NMR unit volume signal intensity was established as follows:  
\[\varphi_{\text{NMR}}=0.0079S_{\text{uv}}+0.1073 \tag{2}\]  
where \(\varphi_{\text{NMR}}\) is the NMR porosity, %; and \(S_{\text{uv}}\) is the NMR unit volume signal intensity.  
We used density balance and precise mass balance to determine the density and mass of each saturated coal sample and to calculate their volume. The total amount of NMR signals was divided by the coal sample volume to obtain the signal intensity of the unit volume. Then, substituting Equation (2), the total NMR porosity of the coal sample was obtained. According to the proportions of various peaks, the porosity of different types of pores was calculated, and the results are shown in Table 2.  
Figure 6: Migration of water in coal under centrifugal force.  
[PERSON] et al. [20] studied porosity by NMR, MIP, and helium adsorption and considered that NMR porosity is relatively accurate and reliable. Table 2 shows that the total porosity of the coal samples from the different mining areas differed greatly. The porosity of WY-2 was the lowest, only 2.75%, and the porosity of YJL-1 was the highest, up to 15.12%. The porosity of the coal samples from the same mining area was very close, and the porosity of the bedding coal samples was slightly higher than that of the vertical coal samples. The porosity proportion of the three types of pores of samples from different mining areas was obviously different. The porosity of CZ was mainly from nanopores and partly from fissures, and the micro-fissures porosity was zero. The porosity of WY mainly came from nanopores, and the contribution of micro-fissures and fissures was very small. The porosity of YJL was also mainly from nanopores, followed by micro-fissures, and the contribution to fissures was very small.