esa-sceva/satcom-chunk-collection huggingface dataset:
# The Virial Effect--Applications for SF\({}_{6}\) and CH\({}_{4}\) Thermal Plasmas
## 2 Equations and Methods
### The Equation for the Calculation of the LTE Plasma Composition
#### 2.2.5 Conservation Equations  
For closing the system, several equations of conservation are used.  
_Conservation of nuclei:_  
\[\varepsilon_{j}\sum_{i=1}^{N}n_{i}.C_{i,k}=\varepsilon_{k}\sum_{i=1}^{N}n_{i}.C_{ i,j}\text{where }j,k\left\{\begin{array}{l}\epsilon\left\{1,M-1\right\}\\ \
eq\text{charge}\end{array}\right. \tag{11}\]  
with the atomic proportion \(\varepsilon\).  
_Electric neutrality:_  
\[\sum_{i=1}^{N}n_{i}.Z_{i}=0 \tag{12}\]  
where \(Z_{i}\) is the electric charge of the species \(i\) (which can be directly expressed from \(C_{i,\text{charge}}\)).  
_- Pressure conservation:_  
The plasma pressure \(P\) is determined by the Dalton law (the perfect gas law) with Debye-Huckel, and with virial correction for real fluids:  
\[P=\sum_{i=1}^{N}n_{i}.k_{b}.T+\Delta P_{Debye}+\Delta P_{virial} \tag{13}\]  
where  
\(\Delta P_{Debye}\) is the Debye-Huckel pressure correction:  
\[\Delta P_{Debye}=-\frac{k_{b}.T}{24.\pi.\lambda_{D}^{3}}; \tag{14}\]  
\(\Delta P_{virial}\) is the virial pressure correction. For its calculation, several coefficients describing the difference in behavior between real fluids and perfect gases are needed. Taking into account the second- and third-order corrections, the virial pressure correction is given in Equation (15):  
\[\Delta P_{virial}=k_{B}.T.B(T).\left(\sum_{i=1}^{N}n_{i}\right)^{2}+k_{B}.T.C (T).\left(\sum_{i=1}^{N}n_{i}\right)^{3} \tag{15}\]  
where \(B(T)\) and \(C(T)\) are the second and third virial coefficients, respectively, and they depend on the temperature. The methods to obtain them are described in Section 3 of this paper (cf. Section 3 << _virial coefficient_ >>).  
More generally, all conservation equations can be written in the form given in Equation (16):  
\[\left\{\sum_{i=1}^{N}A_{l,i}.n_{i}=A_{l}^{{}^{\circ}}\right\}_{l=1}^{M} \tag{16}\]  
where \(A_{l,i}\) and \(A_{l}^{{}^{\circ}}\) are the coefficients of the conservation equation \(l\) for species \(i\). A summary of all of these equations is given in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Conservation Equation** & \(A_{l,i}\) & \(A_{l}^{\alpha}\) & **Number of Equations** \\ \hline Atomic nuclei & \(\varepsilon_{j}C_{i,k}-\varepsilon_{k}C_{i,j}\) & \(0\) & \(M\)-\(2\) \\ Electric neutrality & \(C_{i,charge}\) & \(0\) & \(1\) \\ Pressure & \(1\) & \(\frac{(P-\Delta P)}{kT}\) & \(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Conservation coefficients.
# Algorithm for Generating S-Boxes with Prescribed Differential Properties
## 3 New Algorithm for Generating S-Boxes with Prescribed Differential Properties  
The goal of an S-box generation algorithm is to produce an S-box, which is a vectorial Boolean function  
\[S:\mathbb{Z}_{2}^{n}\rightarrow\mathbb{Z}_{2}^{m}\]  
that fulfills specific criteria. For a given \(m,n\) there are \(2^{m2^{n}}\) possible functions \(S\). In general, for each potential \(S\), we define some evaluation function \(eval(S)\) that returns a score with respect to selected criteria. If the score is below some limit, we reject the Boolean function (a weak S-box); otherwise, we produce \(S\) as a result of the S-box generation. We aim to obtain an S-box with as high a score as possible, but are limited by computational resources, as even for small \(m,n\) it might be impossible to examine the whole search space.  
Our new algorithm is based on a randomized search with early rejection. Our search criteria are reduced to a single dimension; we evaluate the Difference Distribution Table (DDT) of the S-box and reject all S-boxes with DDTs containing values above some pre-selected threshold \(\delta\). Threshold \(\delta\) should be selected according to the security requirements. Our algorithm either produces a \(\delta\)-uniform S-box or proves that no such S-box exists. Note that if we set the threshold \(\delta\) too low, the algorithm might take too long to find a suitable S-box or exhaust the search space. The complexity analysis provided in Section 4 can help to select a suitable threshold that balances security requirements and the running time of the algorithm.  
The main advantage of our algorithm in comparison with a simple search is that we can evaluate the DDT based on a piece of partial information about the function \(S\). This allows us to reduce the search tree size by cutting whole unproductive branches, essentially performing an early rejection sampling in the search space. Thus, it can produce good S-boxes with less work than a simple exhaustive or random search.
# A Review of Advanced CMOS RF Power Amplifier Architecture Trends for Low Power 5G Wireless Networks
###### Abstract  
The structure of the modern wireless network evolves rapidly and maturing 4G networks pave the way to next generation 5G communication. A tendency of shifting from traditional high-power tower-mounted base stations towards heterogeneous elements can be spotted, which is mainly caused by the increase of annual wireless users and devices connected to the network. The radio frequency (RF) power amplifier (PA) performance directly affects the efficiency of any transmitter, therefore, the emerging 5G cellular network requires new PA architectures with improved efficiency without sacrificing linearity. A review of the most promising reported RF PA architectures is presented in this article, emphasizing advantages, disadvantages and concluding with a quantitative comparison. The main scope of reviewed papers are PAs implemented in scalable complementary metal-oxide-semiconductor (CMOS) and SiGe BiCMOS processes with output powers suitable for portable wireless devices under 32 dBm (1.5 W) in the low- and high- 5G network frequency ranges.  
Keywords:power amplifier; architecture; radio frequency; wireless; network; 5G; trends +
Footnote †: journal: Electronics Electronics
## 2 The BEAM Toolbox and the Land Surface Temperature Processor
### The LST Processor as a BEAM Plug-In
#### 2.2.1 Theoretical Background of the LST Algorithm
The surface reflectances retrieved from the MERIS and AATSR sensors are necessary for a correct estimation of the LSE using the NDVI-THM method introduced before [18]. The water vapour content is also a required input (see Equation (1)). The effects of aerosols and absorbing gases are modelled as well, scattering reflectance determination being a key issue for a good atmospheric correction. The great variability of the aerosol scattering induces a high uncertainty in the derivation of surface reflectance. The magnitude of the scattering increases with view angle, that is, the off-nadir view of AATSR and SLSTR--and in high latitudes the nadir view--constitutes a very important factor, especially for S3 due to their wider swath width. The method employed in SEN4 LST is based in a modification of the synergy approach ([24; 25]). The output is the aerosol optical depth at reference wavelength, an estimate of the aerosol model and the Angstrom coefficient, and the atmospherically corrected reflectance. Many of the ideas and approaches performed in the MERIS/AATSR Synergy project are applied here ([26; 27]). For instance, the cloud screening strategy is taken from the SYNERGY and Globalbedo cloud masks, which generates a cloud probability from an ensemble of multilayer perceptron artificial neural networks using inputs from both MERIS and AATSR. Other approaches were also tested in the SEN4 LST project [7], like the Bayesian approach by [28].
# A Novel Tempogram Generating Algorithm Based on Matching Pursuit
## 4 Discussion
### Sparsity  
The sparsity of tempogram is defined as the number of the none-zero coefficients. The smaller the number is, the sparser the tempogram is. Meanwhile, the sparser tempogram indicates that the energy is better-concentrated, which could help us to distinguish the dominant tempo components. In other words, the sparser tempogram would lead to a more accurate estimation of tempo.  
For one novelty curve, the tempo dictionary that was designed with rich atoms makes it easy to find a similar one to match with. Better similarity makes the energy more concentrated on the atom so that the tempogram could be sparser. Otherwise, the energy would scatter over many less similar atoms, which would produce many more none-zero coefficients.  
From Figure 4, we can observe from the image that the sparsity of the proposed algorithm is stronger than those of the method of ACF and FT under the same condition. If we estimate the number of the none-zero coefficients as the degree of sparsity, we can get the same results. Firstly, we normalized the image data, and then defined the none-zero coefficient as the coefficient whose value is smaller than 0.1. Table 2 shows the number of the none-zero coefficient of those tempograms in Figure 4. The total number of coefficient of each tempogram is 83,937. It shows that the tempogram of MP is the sparsest one.
# Low-Temperature and Low-Pressure Silicon Nitride Deposition by ECR-PECVD for Optical Waveguides
## 3 Characterization and Results
### Optical Loss
The inherent uncertainty in this method is reflected in the data and increased for Film 3, which shows propagation losses on the order of 0.1 dB/cm beyond 1570 nm in Figure 3a, which is the measurable limit for the system. This loss trend is promising for applications in the telecommunications L-band (1570-1620 nm) and potentially longer wavelengths where new telecommunication and sensing windows are demanding low-loss waveguides. Figure 3b shows that the thicker film has significantly higher losses from 1510 to 1580 nm, with still relatively low losses at 638, 847, 980 and 1310 nm. Increasing the film thickness from 369 to 650 nm increases the modal interaction and overlap with the planar waveguide layer from \(\sim\)70.0 to \(\sim\)89.0 % at 1510 nm, while decreasing the interaction with the air from \(\sim\)8.9 to \(\sim\)3.5 %. Doing so resulted in a film which demonstrates visible-NIR losses as low as 0.3 dB/cm at 980 nm, with increased C-band losses as high as \(\sim\)8.0 dB/cm at 1550 nm shown in Figure 3b and the inset, respectively. The increase in loss for Film 6 is expected to be attributed to the increased modal interaction with the hydrogen in the SiO\({}_{\text{N}}\)N\({}_{\text{y}}\):H\({}_{\text{z}}\) films due to increased thickness. It may also be the case that increased pre-deposition pressures lead to higher hydrogen incorporation in comparison to Film 3. Annealing the samples at 1000 \({}^{\circ}\)C for 1 h caused the majority of the films to be immeasurable due to stress-induced lifting of the film off the SiO\({}_{\text{2}}\), or in one case of Film 5, total failure showing visible cracks throughout the whole deposited layer. This was expected, as the films are relatively thick for Si\({}_{\text{k}}\)N\({}_{\text{y}}\) depositions and therefore are expected to have high tensile stress. However, this was not the case for Film 2, which demonstrated a loss decrease of 0.8 \(\pm\) 0.1 dB/cm from 1520 to 1550 nm after annealing and not lifting off. This might be attributed to the higher Si content in the film and decreased tensile stress. These data are shown as Film 2A in Figure 2a, with a dotted line to guide the eye, in comparison to the losses measured prior to annealing. The remaining \(\sim\)1.6 dB/cm of loss in this range suggests that other loss mechanisms rather than hydrogen content still need to be overcome for the S-L bands in our ECR-PEVCD SiO\({}_{\text{x}}\)N\({}_{\text{y}}\):H\({}_{\text{z}}\) films.
## 6 Pitfall Trap Deployment
### Hardware Setup  
Here, we describe the specifics of the hardware and an overview of the configurations used in the pitfall trap deployment. We deployed our system in an array of pitfall traps at James Reserve.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Operation** & _Mica2_ & _MicaZ_ \\ \hline Idle & 9.6 mW & 9.6 mW \\ MCU Active & 24.0 mW & 24.0 mW \\ MCU + Radio TX (0 dBm) & 76.2 mW & 63.0 mW \\ MCU + Radio RX & 45.3 mW & 69.9 mW \\ \hline
**Operation** & \multicolumn{2}{c}{_Cyclops_} \\ \hline Capture Image & 42 mW \\ Extended Memory Access & W = 53, R = 50 mW \\ MCU Active & 28 mW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Power consumption of Mica2/MicaZ [17] and Cyclops [9].  
Figure 4: Pitfall trap monitoring deployment topology at James Reserve.  
Figure 4 depicts the network topology of the deployment. One array of pitfall traps consists of seven traps in a star configuration. We placed seven traps, each equipped with a camera sensor node, one Stargate master near the array and a Linux laptop acting as a server at an indoor location about 100 yards away from the array (Figure 5). The laptop communicates with the Stargate via 802.11, and they together constitute the upper tier of the network. Stargate is connected to a MicaZ mode, which is used to communicate with nearby sensor nodes, which comprise the low power, lower tier of the network. Some of the nodes were behind the trees with foliage, which blocked the line of sight to the base station.  
Each pitfall trap contains a pair of D cell alkaline batteries, an antenna and the embedded hardware necessary for imaging and communication. Figure 5(right) shows the outside of a pitfall trap. Plastic traps are all custom-made and contain a sensor node composed of a 2.4-GHz MicaZ mode for communication and a Cyclops camera for imaging. The sensor node was enclosed in a box with a clear plastic bottom through which the camera takes images, while protecting the hardware from dirt and moisture. For most nodes, a pair of D cell batteries was enough to keep them running for more than three days, and all nodes had 9 dBi omnidirectional antennas.  
**Figure 5.** An array of pitfall traps consisting of seven traps in a star configuration (**left**) and the outside of a pitfall trap. Notice the barrier walls on the sides to lead the lizards into the trap and the legs to hold the trap lid above ground. The Cyclops node is under the lid pointing down into the trap (**right**).
###### Abstract  
In recent deregulated power systems, demand response (DR) has become one of the most cost-effective and efficient solutions for smoothing the load profile when the system is under stress. By participating in DR programs, customers are able to change their energy consumption habits in response to energy price changes and get incentives in return. In this paper, we study the effect of various time-based rate (TBR) programs on the stochastic day-ahead energy and reserve scheduling in residential islanded microgrids (MGs). An effective approach is presented to schedule both energy and reserve in presence of renewable energy resources (RESs) and electric vehicles (EVs). An economic model of responsive load is also proposed on the basis of elasticity factor to model the behavior of customers participating in various DR programs. A two-stage stochastic programming model is developed accordingly to minimize the expected cost of MG under different TBR programs. To verify the effectiveness and applicability of the proposed approach, a number of simulations are performed under different scenarios using real data; and the impact of TBR-DR actions on energy and reserve scheduling are studied and compared subsequently.  
demand response (DR); scheduling; time-based rate (TBR) programs; renewable energy resources (RESs); electric vehicles (EVs) +
Footnote †: journal: Applied Sciences
## 3 Methodology
### Object-Level Distance  
The image-level and region-level distances have been considered by many state-of-the-art outfit-completion models, though the embedding method and distance definition may vary from ours. However, the key point for outfit completion is to learn the compatibility of items in the outfit, which has not been dealt with by other completion models for scene-based images. To address that, we use one fast and accurate object-detection model YOLOv3 [26] to extract clothing items from the masked scene. YOLOv3 is first pre-trained on a large, annotated clothes dataset, where the most commonly used ones are ModaNet [27] and DeepFashion2 [28] (Table 1). The categories of ModaNet are more diverse, while the data size of DeepFashion2 is larger and the partitions are more detailed.  
During object detection, to ensure that the query item is fully covered by the bounding box, we first enlarge the bounding box of each scene by 1% and then feed the masked scenes to YOLOv3. Figures 3 and 4 show the detection results pre-trained on ModaNet and DeepFashion2 on Street2 Shop dataset, where the model pre-trained on ModaNet has more accurate detection results. Here are several explanations for the low accuracy of YOLOv3 pre-trained on DeepFashion2. First, DeepFashion2 only contains clothes (Table 1), hence the item categories it can detect are limited. Secondly, the image quality of DeepFashion2 is lower than ModaNet, which leads to a lower detection accuracy of YOLOv3. Thirdly, the item partitions of DeepFashion2 are too detailed, which increases the difficulty of object detection. Lastly, the category partitions of our targets Street2 Shop and STL-Fashion are more similar to ModaNet, hence the model pre-trained on ModaNet reports a higher performance.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Dataset** & **\#Images** & **Categories** \\ \hline \hline \multirow{3}{*}{ModaNet} & \multirow{3}{*}{55 K} & bag, belt, headwear, sunglasses, scarf\&tie, top, pants, shorts, skirt, outer, dress, boots, footwear \\ \cline{3-3}  
The input of YOLOv3 is a masked scene, and the output of YOLOv3 contains all bounding boxes of the detected items. Each bounding box is a 4-dimensional array. We can easily obtain the image of each item according to the predicted bounding box. Let \(\{\alpha_{1},\alpha_{2},\dots\alpha_{M}\}\) denote the \(M\) objects detected in the masked scene. We use ResNet-50 to extract the image features \(\sigma_{\alpha_{m}}\) for each object, and apply the embedding function \(g(\Theta,\cdot)\) to obtain the visual vector \(v_{\alpha_{m}}\). \(g(\Theta,\cdot)\) is the one used in image-level distance computation, and the embedding process can be described as follows:  
\[v_{\alpha_{m}}=g(\Theta,\sigma_{\alpha_{m}})=E\sigma_{\alpha_{m}} \tag{10}\]  
Then we calculate the \(l_{2}\) distance between each object and item \(i\), and sum them up to obtain the object-level distance \(L_{O}(s,i)\):  
\[L_{O}(s,i)=\sum_{m=1}^{M}\left\|v_{\alpha_{m}}-v_{i}\right\|_{2} \tag{11}\]
## 2 Evolution of Maritime Communication Networks  
### Legacy Wireless Maritime Networks  
While this paper emphasizes the role of 6G technologies in shaping next-generation MCNs, it is important to recognize that various heterogeneous wireless systems, including commercial-off-the-shelf (COTS) technologies, have long been used to enable communication at sea. These systems include Wi-Fi and mesh-based S2S links, maritime LTE deployments, VHF Data Exchange System (VDES), and hybrid satellite-terrestrial architectures. Among others, the existing literature has explored link selection algorithms that dynamically switch between Wi-Fi, LTE, and satellite links depending on cost, link quality, and context (e.g., coastal vs. open sea) [24]. Other studies for legacy maritime networkshave analyzed routing protocols for MANETs and floating content networks, which account for mobility patterns and signal degradation over water [25].
## 3 Experiments and Discussion
### Parameter Analysis
#### 3.3.4 Effectiveness of Residual Connections  
The residual connections can prevent network degradation, and their effectiveness is evident in Table 6.
## 3 Application Examples
### Reservoir Permeability Evaluation  
The evaluation of reservoir permeability involves comparing Stoneley wave data with the reservoir permeability index for verification purposes. Taking Wells A and B as case studies, Equation (2) is used to calculate the Stoneley wave index, which reflects the reservoir permeability. Equation (7) is used to calculate the Stoneley wave reflection coefficient. The Stoneley wave index and the Stoneley wave reflection coefficient are then compared with the reservoir permeability index, and a strong correlation is observed, indicating that the seepage index accurately reflects the reservoir permeability (Figure 7).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Well Name**} & **Difference in** & **Fracture** & **Fracture** & **Shear Wave** & \multirow{2}{*}{**Effectiveness**} & **Productivity** \\  & **Deep and Shallow** & **Density** & **Length** & **Amplitude** & **Index** & **Coefficient** \\  & **Resistivity** & **1/m** & **m/m2** & **Ratio** & & \\ \hline B well & 2.87 & 0.78 & 1.83 & 0.47 & \textless{}0.5 & 219.3 \\ D well & 2.49 & 0.49 & 1.64 & 0.65 & 0.5-0.7 & 58.2 \\ E well & 2.17 & 0.34 & 1.18 & 0.82 & \textgreater{}0.7 & 2.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effectiveness Evaluation of Multiple Inter-well Buried Hill Reservoirs.  
Figure 7: Processing Results of seepage Characteristics in Buried Hill Reservoirs of Wells A and B.
# An Overview on Atomization and Its Drug Delivery and Biomedical Applications
## 5 Mathematical Formulations
### Disruption of Liquid Jet  
The theoretical study on liquid jet was first done by [PERSON] by considering streamline flow of liquid jet from a round aperture and suggested that when the accelerated expanding disruption reaches an optimum wavelength of \(\lambda_{\mathrm{opt}}\) = 4.51d, the small-scale disruptions will cause breakup of the jet into drops [12], which is represented below in Equation (6).  
\[4.51\mathrm{d}\times\left(\frac{\pi}{4}\right)\mathrm{d}^{2}=\left(\frac{\pi }{6}\right)\mathrm{D}^{3} \tag{6}\]  
where, d is early jet diameter and D is diameter of the drop.  
From Equation (6) the diameter of the drop is obtained as  
\[\mathrm{D}=1.89\mathrm{d} \tag{7}\]  
The limitation in [PERSON]'s study was that he considered the variables surface tension of the liquid and forces acting at rest, neglecting the viscosity of the feed liquid and the aerodynamic forces. Later, [PERSON] included the effect of surrounding air into [PERSON]'s study and found that the optimum wavelength reduces when the aerodynamic forces comes into play and found it to be \(\lambda_{\mathrm{opt}}\) = 2.8d resulting in a smaller diameter of the drop, D = 1.6d [37]. Thus when the aerodynamic forces come into play, it results in the breakup of jet to smaller drops. [PERSON] also studied the effect of viscosity of the liquid in jet disruption and found that increase in liquid viscosity will escalate the optimum wavelength required for breaking up the liquid jet which is given by  
\[\lambda_{\mathrm{opt}}=4.44\mathrm{d}(1+3\mathrm{Oh})^{0.5} \tag{8}\]where,  
\[\text{Oh}=\sqrt{\text{We}}/\text{Re}=\mu_{\text{L}}/(\rho_{\text{L}}\sigma\text{d })^{0.5} \tag{9}\]  
\(\mu_{\text{L}}\)--viscosity of the liquid  
\(\rho_{\text{L}}\)--density of liquid  
\(\sigma\)--surface tension  
Oh--Ohnesorge number  
We--Weber Number = \(\rho\text{U}^{2}\text{d}/\sigma\), U is the jet velocity  
Re--Reynolds Number = \(\rho\text{U}\text{d}/\mu\)