esa-sceva/satcom-chunk-collection huggingface dataset:
# Uncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation
## 2 Materials and Methods
### Dynamic Saliency U-Net
#### 2.2.1 Contextual Dynamic Encoder (CDE)
The dynamic features are then fed into a shared multi-layer perceptron (MLP) with one hidden layer to generate channel-wise attention scores  
\[V_{\text{avg}}=MLP\big{(}D_{\text{avg}}\big{)},V_{\text{max}}=MLP(D_{\text{ max}}) \tag{12}\]  
The outputs of the MLP for both average- and max-pooled features are combined through element-wise summation and passed through a sigmoid activation function to produce the channel attention map \(M_{c}\)  
\[M_{c}=\sigma\big{(}V_{\text{avg}}+V_{\text{max}}\big{)} \tag{13}\]  
where \(\sigma\) denotes the sigmoid function and \(M_{c}\in\mathbb{R}^{1\times 1\times C_{i}}\) is the channel attention feature map. Spatial Attention Mechanism  
A dynamic convolution is applied to the input feature map F to obtain spatial features, which are then fed into the Spatial Pyramid Pooling (SPP) layer to capture multi-scale spatial information:  
\[F_{s}=\text{DynamicConv}(F),P_{spp}=\text{SPP}(F_{s}) \tag{14}\]  
where \(F_{s}\) is the feature map after dynamic convolution, and \(P_{spp}\) is the result of the SPP layer. The multi-scale spatial features \(P_{spp}\) are then processed through another dynamic convolution and a sigmoid function to obtain the spatial attention map \(M_{s}\):  
\[M_{s}=\sigma\big{(}\text{ DynamicConv}\big{(}P_{spp}\big{)}\big{)}, \tag{15}\]  
where \(M_{s}\in\mathbb{R}^{H\times W\times 1}\) is the spatial attention map. The final attention map is derived by an element-wise multiplication of the channel attention map with the input feature map, followed by an element-wise multiplication with the spatial attention map:  
\[F^{\prime}=(F\odot M_{c})\odot M_{s} \tag{16}\]  
where \(F^{\prime}\) is the recalibrated feature map that incorporates both channel and spatial attention.
## 4 Discussion
The authors found that the growth rate of biomass cultivated in the form of a suspension was 30.5 mg/Ld with PAR 20 \(\upmu\)mol/m\({}^{2}\)s light and biomass growth rate of 42.1 mg/Ld, with a lighting of 60 \(\upmu\)mol/m\({}^{2}\)s. The author of the present work used monoculture and industrial wastewater with high ammonium nitrogen concentrations, which led to the differences in the rate of biomass growth due to different physico-chemical composition of wastewater. The experiments were carried out during the day at 29 \({}^{\circ}\)C, which was beneficial for the growth of microalgae. According to the literature, the range of optimal temperatures for the growth of _S. amatus_ microalgae is 20-40 \({}^{\circ}\)C, whereas for _C. vulgaris_ it is 25-30 \({}^{\circ}\)C, with the best biomass growth at 30 \({}^{\circ}\)C [27]. Higher temperatures accelerate the diffusion of substrates into microbial cells [29]. [PERSON] [29] showed that higher temperatures increase the phosphorus content in the microalgae biomass. Moreover, with the temperature rising the rate of nitrification increases [16]. The present research proved that it is possible to use industrial wastewater for cultivation of microalgae _C. vulgaris_ and _S. armatus_, in batch and repeated fed-batch culture technique, suspended in the solution or immobilized on alginate biopolymers. The present investigations fulfill the principles of the sustainable circular bioeconomy by helping to reduce resource consumption and contaminants emissions to the environment. The microalgae could be used for wastewater remediation, biomass production, and water quality control [30]. They could utilize wastewater as an inorganic nutrient source or production of biofuel [31]. Mixed bacteria-microalgae cultures are found in high-rate algal ponds (HRAPs) to treat wastewaters (e.g., municipal, industrial, agricultural) [32]. The microalgae-bacteria consortium could be used for industrial wastewater treatment. The removal of organic matter and nutrients could be achieved as well as the utilization of algal biomass for producing several products [33]. Microalgae-bacteria consortium can utilise both organic nitrogen (such as urea) and inorganic nitrogen (in the form of ammonium/ammonia) as well as nitrite and nitrates. The microalgae-bacteria consortium into wastewater treatment helps the generation of O\({}_{2}\) through photosynthesis, necessary for heterotrophic bacteria to biodgrade carbonaceous materials. During the microalgae-bacteria consortium treatment processes a greenhouse gas emission rate is lowering, because the ma jority of N is assimilated by the microalgae-bacteria consortium instead of being converted to oxides of nitrogen [34]. The algae could be used for water and wastewater treatment, as feedstock for producing biofuels, food, cosmetics, pharmaceuticals, fertilizers and also could be used as feed for animals [35].
## 3 Numerical Simulation
### Building a Computational Model  
Assuming a constant atmospheric temperature of 22 \({}^{\circ}\)C and an initial soil temperature of 22 \({}^{\circ}\)C and taking into account the influence range of tunnel freezing and the speed of calculation, the specific simulation steps of this simulation are as follows:  
(1) Soil generation: The minimum radius of soil particles in the simulation is 0.02 m and the maximum radius is 0.04 m; to study the impact of the frozen tube on the surrounding soil, the center of the tunnel is set as the origin of coordinates to establish a two-dimensional initial soil model of 20 m \(\times\) 30 m size. The total number of particles is approximately 170,000. The model is given the mechanical parameters shown in Table 1, and the particles are given gravity to compact them to their natural state.  
(2) Generating freeze tubes: We import 43 frozen pipes with a radius of 0.054 m into the soil, use the \"ball delete\" command to delete the soil particles at the location of the frozen pipes, and then create a frozen pipe wall with a radius of 0.054 m at that location. Finally, the internal forces of the soil are balanced to complete the model generation.  
(3) Set initial conditions: We set the wall as a fixed unit with no displacement and use the \"configure thermal\" command to enable the program thermal calculation module. The particles were then given the unfrozen soil thermodynamic fine view parameters as in Table 2, and the boundary wall was set to a fixed 22 \({}^{\circ}\)C. According to the actual brine cooling curve of the project (Figure 2) in the freezing tube temperature of the brine temperature set, the beginning temperature of the ground is set to 22 \({}^{\circ}\)C.  
(4) Thermal coupling calculation: The thermal coupling calculation module is enabled by the command \"set thermal on mechanical on\". The inclusion of a judgment statement enables the particles to be given a coefficient of frost heave (thaw settlement) if the temperature of the soil particles meets the conditions for frost heave (thaw settlement) [32; 33; 34; 35]. The specific calculation process is shown in Figure 5 below.  
\begin{table}
\begin{tabular}{c c c c c c c}
**Effective Modulus** & **Normal-to-Shear** & **Bond Effective Modulus** & **Bond Normal-to-Shear** & **Cohesion (kPa)** & **Tensile Strength** & **Density (kg:m\({}^{*}\))** \\ \hline silty clay & 6 & 3 & 6 & 3 & 15 & 50 & 1801 \\ \hline \end{tabular}
\end{table}
Table 1: Soil physical parameters (drained).  
\begin{table}
\begin{tabular}{c c c c c c}
**Specific Heat** & **Thermal Conductivity** & **Latent Heat** & **Frost Heave** & **Thaw** \\
**(J/(kg\({}^{\circ}\)C\({}^{-1}\)))** & **Conductivity (W/(m-K))** & **(kJ/kg)** & **Coefficient (\%)** & **Settlement Coefficient (\%)** \\ \hline silty clay & 1710 & 1.05 & 92 & 4.5 & 6.8 \\ \hline \end{tabular}
\end{table}
Table 2: Soil thermophysical parameters.
## 1 Introduction
We prove that even if an attacker has unlimited computing power, he does not have enough information to compute the exact value of the other party's data. 3. The proposed scheme is computationally efficient. It does not require encryption and does not incur additional computation due to distribution of the datasets. 4. The proposed scheme is also communication efficient, as it only transmits aggregated data to other parties. The size of the aggregated data correlates with the number of features. In practical applications, whether data are encrypted or not, the number of data typically far surpasses the number of features. 5. The proposed linear regression learning scheme supports incremental updates, allowing for the seamless integration of new datasets into the system. 6. The proposed scheme also enables the removal of old datasets from the system, facilitating the refinement of the linear regression model without the need to recompute the model parameters from the beginning. Organization of the paper. The remainder of this paper is organized as follows. In Section 2, we provide an overview of prior work in the field of federated learning; we focus on linear regression. We also presents a comparison of our scheme with other related schemes. In Section 3, we review linear regression, presenting it in matrix form and emphasizing the mathematical model that is the foundation for our approach. Our main results are described in Sections 4-6. In Section 4, we introduce our approach to privacy-preserving federated learning for linear regression. Section 5 is dedicated to the security analysis of our proposed scheme. Section 6 outlines techniques for implementing our scheme involving the continuous collection of data. In Section 7, we conclude our research work with key findings and contributions to the field of federated learning for linear regression. Finally, we describe our experience in implementing our proposed scheme in Appendix A. The results of these implementations are all consistent with our analysis.
# New Insights in the Ion Beam Sputtering Deposition of ZnO-Fluoropolymer Nanocomposites
## 3 Results and Discussion
### Water Contact Angle (WCA) Measurements  
Water contact angle values, acquired in dynamic mode, are reported in Table 3 as a function of \(\phi\). The advancing water contact angle is similar for all surfaces, and it is sensitive to the presence of the hydrophobic fluorinated moieties. Major differences can be observed in the receding water contact angle values, which decrease from 73 to 28 degrees. This trend can be explained considering the hydrophilicity of ZnO [50,51]. It is well-known, in fact, that a receding contact angle probes the density of high-surface energy moieties [52]. Therefore, samples with higher ZnO content show lower receding contact angles. Correspondingly, a very high hysteresis (i.e., difference between advancing and receding angles) was found for samples with a ZnO loading equal to 0.10 and 0.15. Based on the AFM results, since surface roughness is of a few nm maximum, its contribution to contact angle hysteresis can be neglected and chemical heterogeneity can be regarded as the main factor affecting dynamic sample wettability.
# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 2 Theoretical and Technical Foundations
### Key Technology  
#### 2.2.1 MAC Address Matching  
The MAC address of the device network card is assigned by the IEEE and is unique worldwide. MAC address matching compares the MAC address traversal in the Wi-Fi data sets of two different acquisition points and finds the MAC address that appears in both Wi-Fi data sets simultaneously. This is the MAC address matching of Wi-Fi data [37]. After MAC address matching, the spatial-temporal correlation information in the Wi-Fi data can be analyzed based on the corresponding detection timestamp and the actual location of the collection point.
# Data Naming Mechanism of LEO Satellite Mega-Constellations for the Internet of Things
## 2 LEO Mega Constellation for the IoT
### Interstellar Link of LEO Mega Constellation
#### 2.2.1 Keplerian Elements  
A satellite orbit can be described by seven orbital elements: the semimajor axis, eccentricity, right ascension of the ascending node, inclination, argument of the perigee, mean anomaly and true anomaly, as shown in Figure 3[46]. The relationships between these parameters are shown in Figure 4[47].  
Figure 3: Keplerian elements.  
Figure 2: Iridium constellation and its coverage of the Earth.  
The orbital period \(P\) is proportional to the semimajor axis \(\alpha\):  
\[P\ =\ 2\pi\sqrt{\frac{a^{3}}{G(M_{e}+m)}} \tag{1}\]  
where \(G\) is the gravitational constant, \(M_{e}\) is the mass of the earth, and \(m\) is the mass of the satellite.  
The eccentric anomaly angle is denoted as \(E\) and the average angular velocity is denoted as \(n\). At time \(t\), the geometric relationship among \(M\), \(v\) and \(E\) is shown in Figure 5, and there are:  
\[M\ =\ nt \tag{2}\]  
\[E-e\sin E\ =\ M\ =\ nt \tag{3}\]  
\[v\ =\ 2\tan^{-1}\sqrt{\frac{1+e}{1-e}\tan^{2}\frac{E}{2}} \tag{4}\]  
\[r\ =\ \alpha(1-e\cos E) \tag{5}\]  
where \(r\) is the modulus of the satellite's vector radius.  
For the circular orbits commonly used in LEO satellite constellations, \(e\ =\ 0\), \(M\ =\ E\ =\ v\). Therefore, only \(v\) is used to describe the Keplerian elements of the constellation. The Keplerian elements of the Iridium constellation are shown in Table 3.  
Figure 4: The geometric relationship between \(M\) and \(v\).  
Figure 5: Geometric relationship among \(M\), \(v\) and \(E\).
## 2 System Model
Then the signal before quantization received at \(i\)-th RF chain of the BS from user \(k\) can be expressed as  
\[y_{k,i}=h_{k,i}s_{k}+f_{\text{RF},k,i}^{H}\mathbf{z}, \tag{5}\]  
where \(s_{k}\) is the finite-alphabet signal with covariance \(\mathbb{E}\left[s_{k}s_{k}^{H}\right]=E_{\text{s}}=1,k\in\left\{1,\ldots,N\right\}\), \(E_{\text{s}}\) is the average symbol energy transmitted from user \(k\), \(\mathbf{h}_{k}\in\mathbb{C}^{M\times 1}\), \(k\in\left\{1,\ldots,N\right\}\), is the channel vector between the BS and user \(k\), and \(\mathbf{z}\in\mathbb{C}^{M\times 1}\sim\mathcal{CN}(\mathbf{0},\sigma^{2} \mathbf{I})\) is the additive noise assuming to be complex white Gaussian with zero-mean variance \(\sigma^{2}\). Figure 1: The considered hybrid mmWave system with 1-bit A/D equipped at transceivers for strongest AoA components estimation. The received signal after 1-bit quantization can be represented as:  
\[ \begin{split}|\widehat{y}_{k,i}|&=\mathcal{Q}_{\text{BS} }[|y_{k,i}|]=\mathcal{Q}_{\text{BS}}\Big{[}|h_{k,i}\mathcal{Q}_{\text{users}}(s_{k})+f^{H}_{ \text{RF},k;i}\mathbf{z}|\Big{]}\\ &=\text{sign}[|y_{k,i}|-\tau]=\begin{cases}1&\quad(|y_{k,i}|-\tau )\geqslant 0,\\ 0&\quad(|y_{k,i}|-\tau)<0,\end{cases}\end{split}  \tag{6}\]  
where \(\mathcal{Q}_{\text{BS}}(\cdot)\) and \(\mathcal{Q}_{\text{users}}(\cdot)\) are 1-bit quantization operations at the BS and users, respectively. Parameter \(\tau\) is the quantization threshold. Conditioned on any underlying beamforming \(f_{\text{RF},k;i}\) and the quantization threshold, \(\widehat{y}_{k,i}\) follows a Bernoulli distribution, for which the probability mass function (PMF) over possible outcomes \(r\) can be expressed as  
\[f_{\text{Pmf}}(i,r,p)=\left\{ \begin{array}{ll}p_{i}&r=1,\\ q_{i}=1-p_{i}&r=0,\end{array} \right. \tag{7}\]  
where \(p_{i}\in[0,1]\) denotes the yield value that need to be estimated.
# Perspective on Double Pulsed Gas Metal Arc Welding
## 4 Evolution Process and Future Trends of DP-GMAW
### Future Trend of DP-GMAW
#### 4.2.2 Variable Polarity Double Pulsed GMAW (VPDP-GMAW)  
When the base current reduces to a value that is less than zero (electrode negative), DP-GMAW evolves into variable polarity double pulsed GMAW (VPDP-GMAW). Figure 14 provides a schematic waveform of the welding current of VPDP-GMAW [61]. The pulse current during the electrode positive phase is used to generate a high electromagnetic force for projected spray transfer. During the electrode negative phase, the welding arc climbs to the solid part of the welding wire, and the arc root completely covers the droplet surface [62]. Arc energy concentrates primarily on the welding wire and droplet. The highest rate of electrode extension burn off and droplet growth occurs during the electrode negative phase, whereas the least heat is transferred to the weld pool [63].  
VP-GMAW has the following advantages compared with conventional GMAW: (1) High melting rate [64]. (2) Low heat input characteristic [65] and associated advantages, such as shallow penetration for welding of sheets, average low temperature [66] and decreased distortion [67]. (3) Weld pool stir property and associated advantages, such as reduced porosity, grain refinement [68] and improved mechanical properties [69].  
Joining of Fe-Al dissimilar material by VPDP-GMAW has been reported [70; 71]. [PERSON] et al. [38] revealed that VPDP-GMAW featured reduced porosity incidence due to the stirring action of weld pool. [PERSON] et al. [72] observed that VPDP-GMAW could effectively reduce the heat input. For the same welding current and voltage, VPDP-GMAW exhibited thinner intermetallic compounds than did DP-GMAW. Other benefits of VPDP-GMAW from VP-GMAW and DP-GMAW have not been reported.
# Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks
## 3 Methods
### Parameter Tuning  
A qualitative sensitivity analysis is presented to aid users in selecting the optimal set of parameters when employing METEOR with their own datasets. The Markov lag \(m\) is the most significant parameter when modeling a DBN structure. Increasing \(m\) causes the complexity of the network to increase, causing each window captured to include information of \(m+1\) time slices, which decreases the number of windows available. Longer MTS analysts are thus advised to model a high order DBN correctly. Users should avoid a high value of \(m\), being common values \(m=1\) or \(m=2\). It is presumed that attributes are better explained by their immediate previous values, for most scenarios, than from long memory.  
Also, for each node, the maximum number of connections from previous time slices, parameter \(p\), is useful in datasets where there is a high temporal dependency between attributes, i.e., when a certain attribute is better explained by a set of values from previous time slices. These connections form the inter-slice dependencies of the transition network. Conducted experiments in Section 4 demonstrate that \(p=1\) is sufficient in most cases; this typically leads for each attribute \(X_{i}[t]\) to have a connection to its previous value \(X_{i}[t-1]\) which is easily understandable. Inter-slice connections are normally between the same variable at different time slices. Please note that by using tDBNs, besides inter-slice connections, there are also intra-slice ones. Therefore, a large \(p\) value can easily cause overfitting since each network node begins to be allowed to connect with multiple nodes. Users are advised to experiment with \(p=1\) or \(p=2\), yielding favorable results for the majority of the datasets tested.  
The value \(s\) conveys the stationarity of the system and indicates if transitions should be modeled according to their temporal position on the series. In other words, if it is important that certain patterns occur on specific time slices. It is worth noting that the complexity is much larger in non-stationary models, and the learning phase could take a long time resulting in a large DBN encompassing the whole series time domain. Additionally, the trained model is more probable of overfitting to certain patterns at specific transitions. On the contrary, in a stationary DBN, every window captured is scored according to the general network modeling the whole MTS. Stationarity should always be active unless the analyst knows for sure that in its specific dataset, observations can be considered anomalous for occurring in specific time frames and not for their observed values.
# Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
## 3 Optimizing the volume of the residual set
### Dynamic Execution Phase
#### 4.2.1 Execution Model
Compared with the two-phase execution model, the two-level queue execution model dynamically adjusts execution order based on dependencies and thread status with the following objectives:  
* **Reduced lock contention:** Hot transactions experience fewer conflicts through fine-grained scheduling to reduce the probability of aborts. * **Higher resource utilization:** Cores remain active by executing transactions in the cold queue when hot transactions wait for a lock.
## 2 Design of the Six-Element MIMO Antenna Structure
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Term** & **m** & **1** & **P** & **g\({}_{\text{P}}\)**  
The bands derived from this calculation are shown in Table 3. We can identify that all the antennas are offering the minimum three operating bands, and the conditions of \(\mathrm{S_{\overline{9}}}<-10\) dB are satisfied. In Design 1 of the antenna, we observed the three operating bands with bandwidth values of 7.88 GHz, 5.17 GHz, and 5.46 GHz. This antenna covers an overall area of 74% of the 1-25 GHz band in the frequency spectrum for radiation conversion. The reflection coefficient's minimum value is observed at \(-34.51\) dB. The maximum peak gain observed is 12.64 GHz with a return loss of \(-16.11\) dB and a band of 5.46 GHz. We can observe similar types of antenna operating bandwidth, peak gain, and bandwidth for all the antennas in Table 3. According to our measurements, the bandwidth of Design 5 is the highest in the frequency range of 14.32 to 25 GHz, which is 10.68 GHz. The minimum bandwidth of 1.38 is observed in Design 2 for 14.95 to 16.33 GHz of the frequency band. The maximum gain of 28.23 dBi is observed in the third band of antenna operation in Design 5 of the antenna. It is observed that 49% of the band is covered for radiation in Design 2 of the antenna structure. In designs 3, 4, and 5, we have observed that 70%, 46%, and 77% of the band are covered for radiation over 1 to 25 GHz of the spectrum. \begin{table}
\begin{tabular}{c c c c} \hline
**Sr. No** & **Design** & **Images of Fabricated Antenna** & **Description** \\ \hline
1 & & & Tow element split ring patch antenna with rectangular patch \\ \hline
2 & & & Three element split ring patch antenna \\ \hline
3 & & & Circular patch shaped six element antennas with continuous ground \\ \hline
5 & & & Circular patch shaped six element antennas with rectangular patch \\ \hline \end{tabular}
\end{table} 
Table 2: Different split rings and circular patches shaped the proposed antenna with its fabricated images and its description of the design. Figure 1: Schematic of the six-element, split-ring resonator-based MIMO antenna with different antenna designs. **(a)** Three-dimensional view of the proposed MIMO antenna with the notation of the input ports. The top and bottom layers of the antenna of copper are formed on the dual-layer FR4 substrate material. Schematic view of **(b)**Tow element split ring patch antenna with the rectangular patch (Design 1), **(c)** Three element split ring patch antenna (Design 2), **(d)** Circular patch shaped six element antennas with the continuous ground (Design 3), **(e)** Circular patch shaped six element antennas with a rectangular patch (Design 4), and **(f)** split ring resonator shaped six element antennas with rectangular patch.