esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Gene Expression Analysis  
In order to confirm that 3D nanofibrous scaffolds are suitable for cell proliferation and infiltration, and to understand the cellular behaviors in seeded scaffolds, the expression  
Figure 5: NIH 3T3 cells proliferation tests on different 3D nanofibrous scaffolds and membrane at culturing day 1, 3 and 7 by MTT assay. * \(p\) < 0.05, compared with pure chitosan/RHC nanofibrous membrane. Bars represent mean \(\pm\) SD, number of repeats \(n\) = 3.  
Figure 6: AO/EB double fluorescence staining of NIH 3T3 cultured on 3D nanofibrous scaffolds. (**A–C**) Images of the transversal section after 1, 3, and 7 days with 10\(\times\) objective lens. (**D–F**) Images with 20\(\times\) objective lens corresponding to (**A–C**). (**G–I**) Vertical sections corresponding to (**A–C**). The red dotted lines represent the surface of the scaffolds.  
levels of the associated genes were determined by RT-PCR. The gene expression levels were found increased along with the culturing time (Figure 7). The expression of collagen I remained much higher in the 3D nanofibrous scaffold groups compared to the electrospun membranes. In contrast, the expression of collagen type III was much higher in the electrospun membranes groups. Since collagen I is the predominant collagen and maintains cellular integrity by providing mechanical strength, while collagen III is abundant in tissues having elastic properties [41], the current results confirmed that 3D scaffolds with porous structures could provide a mechanical stable environment, which accelerated the ECM deposition from the seeded fibroblasts [42]. Meanwhile, the expression of collagen I was found to be relatively higher than that of collagen III in both 3D scaffolds and membranes. A possible explanation is that these samples are rich in RHC; thus, there is no need to produce much of their own collagen III during the culture. The expression of MMP-13 was detected in our study, as it acts as a kind of collagenase in cellular activates. Following an injury, MMP facilitates the removal of degraded ECM components in a wound and reorganizes the provisional matrix for efficient cell migration; then, it accelerates the wound healing [43]. Similar to collagen I, MMP-13 showed higher expression levels in the 3D nanofibrous scaffolds, revealing a more effective migration of NIH 3T3 [44]. The expression of vimentin, a guarantee of fibroblast proliferation and coordinated in collagen accumulation, increased with culture time, and this value was also higher for the 3D nanofibrous scaffold groups throughout the experiments. All these results proved that the 3D nanofibrous scaffolds are capable of supporting cell migration and proliferation.
# Operation Optimization of the Sea Container Fleet Based on the Double-Level Planning Model
## 2 The Establishment of the Model
### The Establishment of Comprehensive Optimization Model
#### Variable Relationships
When comparing ships of the same type, the weekly fuel consumption of the auxiliary engine remains constant, whereas the power of the main engine is roughly proportional to the cube of its speed. \[P_{M}=0.7355D^{2/3}s^{3}/C \tag{4}\]  
where \(P_{M}\)(kw) is the actual engine power, \(D\)(t) is the ship's displacement, and \(C\) is a naval constant. The daily total oil consumption of a single ship's main engine is directly proportional to the power of the main engine as follows:  
\[F_{f}=24\times 10^{-3}P_{M}g=17.625\times 10^{-3}gD^{2/3}s^{3}/C \tag{5}\]  
where \(g\)(kg/(kw\(\cdot\)h)) refers to the heavy fuel oil consumption rate of the main engine. As the fuel consumption of the main engine is proportional to the cube of speed, let \(k_{v}\) be the proportionality coefficient for \(V\)-type ships, then \(F_{f,v}=K_{v}s_{v}^{3}\). In this way, the weekly heavy fuel oil consumption of ships can be expressed as \((F_{1,v}+7K_{v}s_{v}^{3})\). 4. The composition of the cost of the fleet The cost of the liner company's fleet mainly considers the variable cost, which primarily consists of fuel and operating expenses. The fuel cost encompasses auxiliary diesel oil and main engine heavy oil expenses. In practical operations, if the average weekly auxiliary fuel consumption of various ship types is \(F_{1,\
u}\), the total fuel cost per ship type can be expressed as \((P_{1}F_{1,\
u}+7P/F_{\
u})\). The operating expenses of the ships cover crew salaries, daily ship maintenance, and upkeep, management fees, etc. Depending on the number of ships deployed on a route, there can be variations in operating costs. Assuming that the daily operating cost of each type of \(V\) ship is \(C_{V}\), the total weekly operating cost of ships on route r is as follows:  
\[c_{r}=\sum\limits_{v=1}^{V}\Big{(}P_{1}F_{1,\
u}+7K_{v}s_{v}^{3}\Big{)}x_{vr} n_{vr}+\sum\limits_{v=1}^{V}x_{vr}t_{r}C_{v}/7 \tag{6}\]  
5. The composition of the carbon emissions cost  
The ships emit carbon emissions during the process of fuel consumption. In view of the coefficient of conversion from ship fuel to carbon dioxide, this paper transforms the cost of carbon emissions into the cost of carbon tax, as follows:  
\[t_{s}=56\times 3.17\times(F_{1,\
u}+7K_{v}s_{v}^{3}) \tag{7}\]  
where 56 is the national tax per ton of carbon dioxide emissions, and 3.17 is the IPCC coefficient, which means that 1 t of marine fuel produces 3.17 t of carbon dioxide.
# A Review on SIW and Its Applications to Microwave Components
###### Abstract  
Substrate-integrated waveguide (SIW) is a modern day (21 st century) transmission line that has recently been developed. This technology has introduced new possibilities to the design of efficient circuits and components operating in the radio frequency (RF) and microwave frequency spectrum. Microstrip components are very good for low frequency applications but are ineffective at extreme frequencies, and involve rigorous fabrication accessions in the implementation of RF, microwave, and millimeter-wave components. This is due to wavelengths being short at higher frequencies. Waveguide devices, on the other hand, are ideal for higher frequency systems, but are very costly, hard to fabricate, and challenging to integrate with planar components in the neighborhood. SIW connects the gap that existed between conventional air-filled rectangular waveguide and planar transmission line technologies including the microstrip. This study explores the current advancements and new opportunities in SIW implementation of RF and microwave devices including filters, multiplexers (diplexers and triplexers), power dividers/combiners, antennas, and sensors for modern communication systems.  
radio frequency; filter; diplexer; triplexer; power divider/combiner; antennas; sensors; substrate-integrated waveguide +
Footnote †: journal: Electronics Letters  
1
Footnote 1: [[https://doi.org/10.3390/electronics11071160](https://doi.org/10.3390/electronics11071160)]([https://doi.org/10.3390/electronics11071160](https://doi.org/10.3390/electronics11071160))
# Privacy-Preserving Federated Learning Using Homomorphic Encryption
## 6 Performance Analysis
### Computational Overhead
#### 6.1.2 Computational Overhead on the Server's Side  
The computational complexity of the averaging local model parameter algorithm performed in the conventional FL algorithm can be expressed as \(\mathcal{O}(N_{w}\cdot N_{c})\). On the other hand, in the proposed PPFL algorithm, additional encryption, partial decryption, and homomorphic addition operations are required to perform the proposed secure averaging local model vector algorithm on the server's side. The encryption and partial decryption operations have a dominant impact on the computational complexity because the exponentiation operation in the encryption and partial decryption requires much more computation than the other operations. Moreover, as the number of clients and model parameters increases, the number of encryption and partial decryption operations to be performed also grows. Thus, the computational complexity of the secure averaging local model vector algorithm on the server's side can be represented as \(\mathcal{O}(N_{r}\cdot N_{w}\cdot N_{c})\).
###### Abstract  
Airborne molecular contamination (AMC) represents a wide range of gaseous contaminants in the cleanroom air environment. It is difficult to monitor AMC in the cleanroom air using conventional methods in real-time due to its ultra-low concentrations, such as part-per-billion or -trillion (ppb or ppt). In this study, AMC in the real university cleanroom air environment was investigated to figure out hot spots through portable soft X-ray radiolysis detector, which converts gaseous AMC into nanoparticles (gas-to-particle conversion) under the soft X-ray irradiation. A soft X-ray was connected to a clean stainless steel chamber to convert the introduced AMC into nanoparticles, and the size distributions of nanoparticles were measured through a scanning mobility particle size, which consists of a differential mobility analyzer and a condensation particle counter. By converting the size distribution information into total particle volume concentrations, equivalent AMC concentrations can be calculated using an appropriate calibration curve between AMC and the total particle volume concentration. The volume concentration of nanoparticles were converted into an equivalent sulfur dioxide (SO\({}_{2}\), a major acidic AMC in the cleanroom air) concentration by a calibration curve between SO\({}_{2}\) and the particle volume concentrations. AMC levels at different locations in the cleanroom at the University of Minnesota were measured by the soft X-ray-assisted AMC detector, and revealed that several tenth-order of pptV (lower than 15 pptV) in terms of the equivalent SO\({}_{2}\) concentration existed in the cleanroom air environment.  
airborne molecular contamination (AMC); soft X-ray radiolysis; nanoparticle; portable gas detector; cleanroom air quality +
Footnote †: journal: Applied Physics Letters  
10 December 2019; Accepted: 27 January 2020; Published: 3 February 2020
# Automated Hybrid Model for Detecting Perineural Invasion in the Histology of Colorectal Cancer
## 2 Materials and Methods
### Segmentation Network Development  
A general scheme of the proposed model is displayed in Figure 1. A hybrid model was proposed to detect PNI using histology, comprising a semantic segmentation network and a rule-based PNI classifier.  
A multiclass semantic segmentation network was trained to detect tumors and nerves. As an alternative approach, experiments were conducted using binary segmentation models for tumors and nerves. For the segmentation frameworks, we used the U-Net [26], Deeplabv3+ [27], and SegFormer [28] networks. U-Net, Deeplabv3+, and SegFormer were built on a pre-trained backbone. For an ablation study, SegFormer was trained from scratch, comparing the performance of a transformer-based segmentation network with transfer learning. To train the U-Net, three backbone models, namely Inception-Resnet-v2, EfficientNet-B0, and SE-ResNeXt-101, were used. For the training of the Deeplabv3+, two models--MobileNet and Xception--were used as backbones. A pre-trained model using the ImageNet database was used [29]. For the training of SegFormer, MiT-B0 (Mix Transformer Encoder) was used. An adaptive moment estimation optimizer was used, with an initial learning rate of 10 \(\times\) 10\({}^{-3}\). The batch size for training was set to 32 and the maximum number of epochs was set to 200. In the multi-class segmentation network, a multi-loss function, calculated as a weighted sum of the dice loss and categorical focal loss (\(L_{\text{Multi}}=L_{\text{Dice}}+L_{\text{Focal}}\)) was used. To train the binary segmentation network, a combination of dice loss and binary cross-entropy loss (\(L_{\text{Binary}}=L_{\text{Dice}}+L_{\text{CE}}\)) was used.
# Non-Parametric Model-Based Estimation of the Effective Reproduction Number for SARS-CoV-2 +
## 2 Materials and Methods
### Estimation of Time-Dependent Parameters in Non-Linear ODE Models via the Augmented Kalman Smoother
#### 2.2.4 From the Time-Continuous ODE System to Its Time-Discrete State-Space Formulation  
Although we apply the presented Kalman Smoother method exclusively to epidemic models within this paper, it is in general applicable to any partially observed non-linear ODE systems, as is shortly outlined in the following. Let us consider the following time-continuous dynamic ODE model:  
\[\dot{x}=f(x,p)\,. \tag{18}\]  
In the time-discrete formalism, the time derivative of the state variables \(x\) is represented by a function \(f\) that depends on both the state vector itself and the parameter vector \(p\). Additionally, the initial conditions of the ODE system can also be regarded as parameters within \(p\).  
For the time-discrete formulation, the right-hand side of the ODE, denoted as \(f(x,p)\), is interpreted as an update equation for each discrete time step, transitioning from \(x_{k-1}\) to \(x_{k}\). Consequently, Equation (18) can be expressed as  
\[\begin{pmatrix}x_{k}\\ p_{k}\end{pmatrix}=\begin{pmatrix}x_{k-1}\\ p_{k-1}\end{pmatrix}+\begin{pmatrix}f(x_{k-1},p_{k-1})\\ 0\end{pmatrix}+\epsilon_{k-1}\,. \tag{19}\]  
The parameters \(p_{k}\) are considered as supplementary states in the state space and are not updated according to the ODE model structure, as indicated by the zero term in the equation. Instead, their time dependence solely arises from the additive noise term \(\epsilon_{k-1}\), which is simultaneously estimated by the Kalman Smoother at each step. This additional degree of freedom grants the Kalman-Smoother-based approach the ability to adapt these parameters based on the available data. Consequently, the approach achieves a data-driven estimation of time-dependent parameters in an ODE system without any constraints on the parameters' trajectory.
### Divergences and Decomposable Divergences  
A statistical divergence \(D(p:q)\) shall satisfy the following two basic axioms:D1 (Non-negativity).\(D(p:q)\geq 0\) for all densities \(p\) and \(q\), D2 (Identity of indiscernibles).\(D(p:q)=0\) if and only if \(p=q\)\(\mu\)-almost everywhere.  
These axioms are a subset of the metric axioms, since we do not consider the symmetry axiom nor the triangle inequality axiom of metric distances. See [31; 32] for some common examples of probability metrics (e.g., total variation distance or Wasserstein metrics).  
A divergence \(D(p:q)\) is said decomposable [6] when it can be written as a definite integral of a scalar divergence \(d(\cdot,\cdot)\):  
\[D(p:q)=\int d(p(x):q(x))\mathrm{d}\mu(x), \tag{13}\]  
or \(D(p:q)=\int d(p:q)\mathrm{d}\mu\) for short, where \(d(a,b)\) is a scalar divergence between \(a>0\) and \(b>0\) (hence one-dimensional parameter divergence).  
The \(\alpha\)-divergences are decomposable divergences since we have  
\[I_{\alpha}^{+}(p:q)=\int i_{\alpha}(p(x):q(x))\mathrm{d}\mu \tag{14}\]  
with the following scalar \(\alpha\)-divergence:  
\[i_{\alpha}(a:b):=\left\{\begin{array}{ll}\frac{1}{\alpha(1-\alpha)}\big{(} \alpha a+(1-\alpha)b-a^{\alpha}b^{1-\alpha}\big{)},&\alpha\in\mathbb{R} \backslash\{0,1\}\\ i_{1}(a:b)=a\log\frac{a}{b}+b-a&\alpha=1\\ i_{0}(a:b)=i_{1}(b:a),&\alpha=0\end{array}\right. \tag{15}\]
# Hardware-Based Architecture for DNN Wireless Communication Models
## 6 Experimental Results and Discussions
### Software Results  
In our simulation, a PYTHON-based MIMO OFDM simulator is used to generate the received signals. The MIMO OFDM simulator consists of a bitstream generator, a baseband modulator, a MIMO OFDM transmitter, a MIMO OFDM channel, and AWGN. After the above process, the received MIMO OFDM signals are pre-processed and fed to the proposed architectures. In our work, the ZF and MLD are applied to type I and II models to clearly show their difference in efficiency when combined with the DNN model. In addition, we consider two cases where the clipping noise level of 5 dB and 7 dB shows an effect  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Model Type I** & **Model Type II** & **Model Type III** & **Model Type I** & **Model Type II** & **Model Type III** \\ \hline Tx antennas \(\times\) Rx antennas & \(2\times 2\) & \(2\times 2\) & \(2\times 2\) & \(4\times 4\) & \(4\times 4\) & \(4\times 4\) \\ \hline Number of subcarriers & 64 & 64 & 64 & 64 & 64 & 64 \\ \hline Number of epochs & 5000 & 5000 & 5000 & 5000 & 5000 & 5000 \\ \hline Batch size & 300 & 300 & 1000 & 300 & 300 & 300 \\ \hline Total batches every epoch & 5 & 5 & 50 & 5 & 5 & 20 \\ \hline Number of test cases & 200,000 & 200,000 & 200,000 & 200,000 & 200,000 & 200,000 \\ \hline Neural network size & 32; 256; 128; & 256; 512; 512; & 512; 512; 256; & 32; 256; 128; & 512; 1024; 1024; 512; 256; \\  & 64; 16 & 512; 256 & 128; 16 & 64; 16 & 1024; 512 & 128; \\ \hline Initial LR & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 \\ \hline LR decreasing step & 500 & 500 & 1000 & 500 & 500 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the proposed deep learning architecture.  
compared to the ideal signal. The following subsections show the difference in BER values between our proposed models and the conventional models.
## 3 Results and Discussion
### Prediction Models
#### 3.4.1 Prediction Model for Chemical Analysis
NIRS studies related to TMRs are relatively limited compared to those conducted on forages and concentrates considered individually, with a notable lack of research focused on TMRs for dairy buffalo. The study by [PERSON] et al. [8], conducted on rations for cows producing milk for Parmigiano Reggiano cheese, could serve as a useful point of comparison, as the rations used in this experiment were significantly richer in fibre than those typically used for dairy cows. However, it is important to note that the rations for Parmigiano Reggiano do not include silage, while silage is commonly used in buffalo diets, including the rations considered in this study. [PERSON] et al. [8] achieved good results for CP, starch, aNDF, and ADF (R\({}^{2}\)c \(>\) 0.80), with lower values for ADL (R\({}^{2}\)c = 0.76) and ash (R\({}^{2}\)c = 0.57). The RPD values for these components were greater than 2.20, while they were lower for ADL and ash (<1.90). They reported RMSE values for CP, ADL, and ash lower than 1, while for the other parameters, the RMSE was found to be greater than 2, which is higher compared to our data. [PERSON] et al. [50] developed calibration models for 394 TMR samples for dairy cows, obtaining coefficients of determination (R\({}^{2}\)c) greater than 0.80, except for starch, which had an R\({}^{2}\)c of 0.75. The SEC ranged from 0.71 to 1.25, and the RPD values ranged from 2.31 to 5.76. The best calibration results were achieved for DM. These models were developed using the Foss NIRSystems 6500 instruments (1100-2498 nm). Excellent calibration results were also published by [PERSON] et al. [18] in a study involving 125 samples of TMRs for dairy cows. They achieved high coefficients of determination for calibration (R\({}^{2}\)c \(>\) 0.98) with very low errors (SEC \(<\) 0.67) and cross-validation RPD values greater than 3, indicating excellent model capability according to the discussed metrics.
## 3 Methodology
### Data Preparation
#### 3.2.4 Data Discretisation  
In data discretisation, numeric data are transformed by mapping values to interval or concept labels. This could be achieved using various techniques such as binning, correlation, cluster, and decision tree analysis. The binning method was utilised for data discretisation in this study. Additionally, the equal-frequency interval-based discretisation method was employed. In this method, the minimum and maximum values of all discretised attributes are determined. Then, these values are sorted in ascending order. The sorted values are further divided into k intervals, as each interval contains \(n/k\) data instances. There may occur continuous value, which can cause the occurrence to be assigned into different bins. The limitation of equal width interval discretisation is overcome by adopting the domain's approach according to the same distribution of data points. This method also tries to overcome the limitation of equal-width interval discretisation. In this research, all attributes of the dataset were discretised by this method.
# Routing Schemes in FANETs: A Survey
## 2 Background and Motivating the Need for FANETs
### What Are the Mobility Models in FANETs?  
Mobility models have been used to characterize the movement of UAVs, including the speed, direction, and acceleration of UAVs, in a fixed space. Such models have been used to develop real-timesimulation environment for FANETs. The rest of this section presents various kinds of mobility models used in the investigation of FANETs.
# Review of Photodetectors for Space Lidars
## 2 Space Lidar Detectors from UV to near Infrared
### Photoelectron Multiplication Tubes
A major advantage of PMTs is the combination of large detector active area and wide electrical bandwidth, which is important for space lidars, which need fast time response and a relatively wide field of view (FOV). PMTs also have afterpulsing, which is caused by the ionization of residual gas molecules inside the PMT after a relatively strong optical input. Positive ions can hit the photocathode and produce spurious output after the incident optical signal. However, the afterpulsing amplitude and rate of occurrences in modern PMTs are relatively low. They do not usually cause problems in the lidar measurement. PMTs are far less susceptible to space radiation damage compared to semiconductor devices. Major radiation effects on PMTs are scintillation and darkening of the glass window and housing [13]. Scintillation is a transient effect that produces spurious pulses  
Figure 1: Photographs of the PMT used in ICESat-2/ATLAS. They were made by Hamamatsu Corporation, Model R7600-300-M16, and up-screened and space-qualified at NASA. The PMT consists of an extended bi-alkali photocathode, a 10-stage dynode chain, and 4 \(\times\) 4 segmented anodes. There are 16 outputs, one for each anode. at the PMT output. Window darkening is a permanent effect that causes a reduction in the incident optical signal power. Another radiation effect is the transient response to high-energy particles that hit the photocathodes and dynodes. There is no permanent damage from these transient events except for spurious noise outputs as the spacecraft flies through high-radiation regions, such as the South Atlantic Anomaly (SAA) [5]. PMTs are known to have a limited lifetime. The photocathodes can be damaged gradually by the ion bombardment from the residual gases during the photoelectron multiplication process. PMT lifetime mainly depends on the amount of electron charge drawn from the photocathodes. The lifetime of PMTs with MCP was generally shorter than regular PMTs in the past but has improved significantly in recent years [14]. The lifetime of modern PMTs is usually adequate for a multi-year space lidar mission, as has been demonstrated by ATLAS on the ICESat-2 mission.