esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
A comparison of Equation (28) with Equation (29) yields  
\[S_{CC}(t;\omega)=\sigma_{\xi}^{2}\frac{\lambda\tau_{C}^{2}}{\pi(1-\gamma_{2}) \mu_{1}\mu_{2}\mu_{3}}\{\phi_{1}+2[\phi_{2}+\phi_{3}\cos(\omega t)+\phi_{4} \rho\sin(\omega t)]\} \tag{23}\]1  
or  
\[S_{CC}(t;\omega)=\sigma_{\xi}^{2}\frac{\lambda\tau_{C}^{2}}{\pi(1-\gamma_{2}) \mu_{1}\mu_{2}\mu_{3}}\{\phi_{1}+2[\phi_{2}+\phi_{3}\cos(\omega t)+\phi_{4} \rho\sin(\omega t)]\} \tag{23}\]2  
At large times, Equation (31) reduces to  
\[S_{CC}(t;\omega)=\sigma_{\xi}^{2}\frac{\lambda\tau_{C}^{2}}{\pi(1-\gamma_{2}) \mu_{1}\mu_{2}\mu_{3}}\{\phi_{1}+2[\phi_{2}+\phi_{3}\cos(\omega t)+\phi_{4} \rho\sin(\omega t)]\} \tag{23}\]3  
where  
\[S_{CC}(t;\omega)=\sigma_{\xi}^{2}\frac{\lambda\tau_{C}^{2}}{\pi(1-\gamma_{2}) \mu_{1}\mu_{2}\mu_{3}}\{\phi_{1}+2[\phi_{2}+\phi_{3}\cos(\omega t)+\phi_{4} \rho\sin(\omega t)]\} \tag{23}\]4  
Given finite observations on the input (inflow concentration) and output (outflow concentration) processes, Equation (32) enables us to estimate the form of the transfer function by replacing the terms in the numerator and denominator by their respective sample estimates. Most of the stochastic literature on the analysis of output time series from a linear time-invariant catchment system treats the input to the system as a stationary process, while this work is concerned with a temporal nonstationary input process, which leads to a nonstationary output process from the catchment system. In addition, the analyses presented in the above-mentioned references are carried out using the field observations (or experimental data) as an input time series to the system. In this work, the input nonstationary process is generated from solving a lumped solute transport model (the Langevin equation). That is, the present analysis is carried out using a combination of the convolution integral and Langevin models. To our knowledge, the closed-form expression for the variance of outflow concentration from the catchment system, such as Equation (24) or Equation (27), has not been presented in the literature so far using a nonstationary process as an input to Equation (1). The expression provides a useful way to evaluate the influence of parameters appearing in the models on the variability in outflow concentration. It is also interesting to note that the closed-form expression for the variance of nonstationary process with respect to a lumped solute transport model given by Equation (19) has never been presented in the literature before. The proposed approach has the advantage of operating at the catchment scale and therefore, if an appropriate input-output transfer function can be specified at that scale, predicting the response of the catchment to any inflow concentration does not require specification of the small-scale spatial heterogeneity of precipitation and soil properties.
# Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments
## 5 Multi-Operator Game for SF Assignment
### Best Response  
In game theory, a rational solution is one where all of the competing players adhere to a Nash equilibrium (NE) [14]. An NE is a profile of strategies in which no player will take advantage of the others by unilaterally deviating its strategy. Thus, the primary challenge in game theory is to propose algorithms that are capable of reaching such an equilibrium. The simplest of these algorithms are the repeated best response dynamics. Following these dynamics, each player selects the best, and locally optimal, response to other players' strategies, until the algorithm converges.  
In our game \(\mathcal{G}\), for every network \(i\), \(U_{i}\) is strictly concave with respect to \(\mathbf{p}^{i}\) and continuous in \(\mathbf{p}^{-i}\) (the strategy of all other players). Hence, a unique NE exists, and it can be reached via the repeated best response dynamics. As such, we implement a best response algorithm, where, in each iteration \(t_{b}\), network \(i\) seeks to find its optimal SF distribution as a response to \(\mathbf{p}^{-i}(t_{b}-1)\) by solving the following problem:  
\[\text{Maximize }U_{i}(\mathbf{p}^{i},\mathbf{p}^{-i})\] (6a) Subject to \[\sum_{s=1}^{S}p_{s}^{i}\leq 1, \tag{6b}\] \[\sum_{k=1}^{s}p_{k}^{i}\leq\sum_{k=1}^{s}\frac{N_{k}^{i}}{N_{co} ^{i}},\ \ \forall s=1,...,S. \tag{6c}\]  
The optimum \(p_{s}^{i*}\) must satisfy the Karush-Kuhn-Tucker (KKT) conditions. There exists a unique Lagrange multiplier \(\alpha\), such that:  
\[p_{s}^{i*}=\frac{1}{\alpha+2\lambda^{i}N_{co}^{i}T_{s}}, \tag{7}\]  
\[\alpha(1-\sum_{s=1}^{S}p_{s}^{i})=0. \tag{8}\]  
This produces two cases:  
* if \(\alpha\)=0, given that \(\sum_{s=1}^{S}p_{s}^{i*}\leq 1\), then \(p_{s}^{i*}\)=\(\frac{1}{2\lambda^{i}N_{c}^{i}T_{s}}\).
* if \(\alpha\)\(\
eq\)0, and, given that \(\sum_{s=1}^{S}p_{s}^{i*}\leq 1\), then \(\sum_{s=1}^{S}\frac{1}{\alpha+2\lambda^{i}N_{c}^{i}T_{s}}\)=1. The best response algorithm we implemented is sketched in Algorithm 1.
## 2 The Proposed Schemes: DAE-EDMR and T-DAE-EDMR
### Method I: DAE-EDMR
#### 2.2.1 A Denoising Process
Finally, we can define \(\mathcal{H}^{\text{NN}}\) consisting of the optimal encoding and decoding functions, denoted by \(f_{\theta*}\) and \(g_{\theta^{\prime}*}\), respectively, as follows:  
\[\mathcal{H}^{\text{NN}}=\{f_{\theta*},g_{\theta^{\prime}*}\}=\underset{f_{ \theta},g_{\theta^{\prime}}}{\text{argmin}}\,\frac{1}{M}\sum_{i=1}^{M}||\mathbf{ s}^{(i)}-g_{\theta^{\prime}}(f_{\theta}(\mathbf{\hat{s}}^{(i)}))||_{2}^{2}, \tag{9}\]  
where \(M\) is the number of training datasets. Let \(\Theta\) be all of the model parameters, i.e., \(\Theta:=\theta\cup\theta^{\prime}=\{\mathbf{W},\mathbf{W}^{\prime},\mathbf{b},\mathbf{b}^{\prime}\}\); then, it simultaneously updates every parameter in \(\Theta\) at each iteration toward the direction of the steepest descent as follows:  
\[\Theta^{(n+1)}=\Theta^{(n)}-\eta\
abla_{\Theta}\Big{(}\frac{1}{M}\sum_{i=1}^{M }||\mathbf{s}^{(i)}-g_{\theta^{\prime}}(f_{\theta}(\mathbf{\hat{s}}^{(i)}))||_{ 2}^{2}\Big{)}, \tag{10}\]  
where \(\
abla_{\Theta}\) is the gradient operator with respect to \(\Theta\) and \(\eta\) is the learning rate related to the step size. Through this procedure, we can optimize the fully connected NN model \(\mathcal{H}^{\text{NN}}\) for denoising the eigenvalues. Next, in the test phase, let \(\mathbf{\hat{s}}^{(j)}\) denote the \(j\)-th denoised eigenvalues with the optimized \(\mathcal{H}^{\text{NN}}\), and this can be obtained as follows:  
\[\mathbf{\hat{s}}^{(j)}=\mathcal{H}^{\text{NN}}(\mathbf{\hat{s}}^{(j)})=g_{ \theta^{\prime}*}(f_{\theta*}(\mathbf{\hat{s}}^{(j)})). \tag{11}\]Finally, the denoised EDM \(\hat{\mathbf{D}}^{(j)}\) can be reconstructed as follows:  
\[\hat{\mathbf{D}}^{(j)}=\tilde{\mathbf{V}}^{(j)}\text{diag}(\mathbf{s}^{(j)}) \big{[}\tilde{\mathbf{V}}^{(j)}\big{]}^{T}, \tag{12}\]  
where \(\tilde{\mathbf{V}}^{(j)}\in\mathbb{R}^{N\times(k+2)}\) is the matrix consisting of \(k+2\) eigenvectors of \(\tilde{\mathbf{D}}^{(j)}\).
# Fungal Pigments and Their Roles Associated with Human Health
## 4 Interaction of Fungal Pigments with the Host Immune System
neoformans_ produces melanin to protect against the oxidative environment inside the phagolysosome [122]. For _A. fumigatus_, it is believed that melanins protect the conidia against reactive oxygen species, mask the recognition of various _A. fumigatus_ pathogen-associated molecular patterns (PAMPs) by the host, inhibit macrophage apoptosis and phagolysosome fusion, and attenuate the host immune response. The protective effect of melanin might also be due to its role in modifying the surface properties of conidia and vegetative cells by altering the charge and hydrophobicity, thereby reducing the effectiveness of the immunological functions of the host [123]. For example, in contrast to the wild type conidia, melanin-deficient mutant conidia can activate human DCs (dendritic cells) and subsequent cytokine production, leading to their elimination by the host immune system. The protective effects of melanin for fungal pathogens have also been demonstrated for autophagy. Autophagy plays an important role in host immunity to microbial pathogens. The autophagy system targets pathogens in phagosomes, promotes phagosome maturation and prevents pathogen escape into the cytosol. Autophagy protein LC3 is believed to be a key player during phagocytosis [124]. During _Aspergillus_ germination, exposure of PAMPs enables the activation of host LC3-associated phagocytosis (LAP), which promotes the killing and eradication of fungi. Shielding PAMPs from detection by pattern recognition receptors is a predominant strategy adopted by fungi for evasion from host immunity. Recent studies with _A. fumigatus_ have demonstrated that LAP activation also requires the removal of fungal cell wall melanin, based on the genetic, biochemical or biological (germination) data [125]. Fungal melanin is known to inhibit the NADPH oxidase-dependent activation of LAP via excluding the p22 phox subunit from the phagosome. Therefore, LAP blockade is a general trait of melanin pigments [126]. Physiologically, melanin-triggered LAP blockade is able to enhance fungal virulence.
# Freshness Monitoring of Packaged Vegetables
## 2 Classification  
In the last two decades smart packaging has become popular especially because it leads the consumer and businesses to make better decisions regarding food quality. It can be further classified into two main categories: intelligent packaging (IP) to monitor the freshness and safety, and active packaging (AP) to keep or improve the quality. IP is defined as one of the packaging systems that plays an important role by providing means of sensing/indicating and communicating the information about the freshness and the safety of the consumed vegetables and aims to prolong the shelf life. This emerging packaging technology, that serves as a barrier against environmental influences such as odors, dust, micro-organisms, facilitates decision making regarding the quality of the packaged food. IP contains smart devices that are capable of tracing and monitoring the freshness of the produce. It can also store and transfer the sensed information to retailers and stakeholders to improve the technology. Intelligent packaging (IP) can be classified into three main categories: sensors, indicators, and data carrier, as shown in Figure 2. Each category can be either placed inside or on the package to provide information about each package, or to monitor the environment surrounding the packages [1,23]. In contrast, active packaging (AP) is defined as the system that incorporates active components such as scavenging or releasing objects in response to changes in package headspace to extend the shelf-life of the packaged food. AP can be further classified depending on the objects used inside the package into scavengers and releases. Scavenging objects concern the capture of excess moisture, odors, and gases such as moisture absorbers, oxygen scavengers, and odor absorbers [23]. The releasing objects are usually emitters/generators that release objects such as antimicrobials agents, carbon dioxide emitters, and antioxidants [23].
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Stochastic Models
#### 3.2.2 Factorial-Design-Based Stochastic Approach  
The Factorial Design-Based Stochastic Modeling System (FSMS) integrates a mass transfer model, factorial analysis, and Monte Carlo simulations to address single and multiple uncertainties within the mass transfer model. This system employs a factorial approach, which can be viewed as a specialized form of sensitivity analysis, allowing for the simultaneous assessment of multiple parameters' effects on the outcome [461]. In a study by Qin et al. [462], this approach was applied to a laboratory experiment involving a tank reactor filled with heterogeneous materials (clay, sand, and arable soil) contaminated with benzene. Four parameters were identified as uncertain: the mean and variance of permeability and porosity. Subsequently, the Monte Carlo method was employed to simulate the stochastic processes associated with groundwater flow and benzene transport within the heterogeneous medium. A physical model was created on a pilot scale to validate the stochastic method. It was found that the uncertainty of the input parameters, especially the mean porosity, has an influence on the outcome of the model. The results showed that simple statistics such as mean, standard deviation, and percentiles should be considered when analysing the risk of oil spills. Furthermore, factorial design and Monte Carlo simulations are integral parts of the hybrid stochastic design for modelling NAPL contaminated aquifers [463]. This approach is based on the deterministic numerical 3D model BioF&T, and uses stochastic methods for parameterisation.
# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
## 2 Related Work  
### Interference Signal Recognition Based on Deep Learning  
To solve the problem of feature parameters selected artificially and the poor recognition accuracy in traditional jamming recognition methods, many works based on deep learning have been carried out. Since the convolution kernel of CNN is similar to the filter in traditional signal processing, a lot of recognition methods based on deep learning use CNN to extract features [8; 9; 10; 11; 23]. CNN was used to recognize jamming signals [8; 9], and the network input was short-time Fourier transform (STFT) data of jamming signals [8] and time-frequency maps of simulated jamming signals [9], respectively. JRNet was proposed to recognize ten kinds of suppression jamming signals in [10], which was based on power spectrum features. The CNN-based Siamese network realized radar jamming signal recognition, which deals with the issue of limited training samples [11]. The AC-VAEGAN network structure solved the performance deterioration of the communication jamming recognition method in the case of a small sample set [12]. The Auto-Encoder network built by stacking LSTM reconstructed the transmitted signal, and another RNN realized a separated interference signal recognition in [13]. Multilayer perceptron was used to recognize jamming signals whose input is a singular value of the signal matrix [14]. However, these methods are closed-set recognition. Different from the above works, in this paper, the open-set recognition method of interference signals based on convolution prototype learning is presented.
## 2 Problem Formulation
### Airflow in the Room  
The velocity characteristics in the occupied zone were measured during a one-hour period. However, not the whole sample could be used for analysis, as the case was non-isothermal during the whole period of measurements. Fan coils operated in a cooling mode to reduce the initial temperature and to keep the room air temperature at an approximately constant moderate level. Figure 3 presents the air temperature time variation at the four inspected points. The temperature records at all heights can be divided into 2 phases: the transient period and the quasi-steady-state period. The transient phase takes about 1000 s; the air temperature at the inspected locations drops from 32.44 \(\pm\) 0.1 \({}^{\circ}\)C to 25.16 \(\pm\) 0.2 \({}^{\circ}\)C. During the quasi-steady-state period the air temperature at point \(z\) = 0.1 m varies in the interval [24.7, 26.5] \({}^{\circ}\)C; at \(z\) = 0.6 m the interval is [24.6, 26.4] \({}^{\circ}\)C; at \(z\) = 1.1 m it is [24.3, 26.2] \({}^{\circ}\)C, and at \(z\) = 1.7 m it is [23.9, 25.9] \({}^{\circ}\)C. Therefore, the assessment of _Tu_ and _DR_ at the inspected locations was done for the temperature quasi-steady-state phase only, i.e., for the period starting from the instant of 1020 s (marked with the blue dashed line in Figure 3).  
The time variation of air velocity at the inspected locations during the whole period of observation is presented in Figure 4. Besides the instantaneous velocity data (red curves), Figure 4 presents the evolution of the time-averaged air velocity with the averaging period of 1 min, \(<\)_V\(>\)\({}_{60\,\mathrm{s}}\)_ (green curves), and the averaging period of 3 min, \(<\)_V\(>\)\({}_{180\,\mathrm{s}}\)_ (black curves). The blue dashed vertical line marks the start of the temperature quasi steady-state phase of the records.  
The temperature quasi-steady-state period could be divided into two sub-periods according to two different phases of the velocity behavior. At \(z\) = 1.7 m the mean air velocity (180 s long interval) has a plateau of about 0.4 m/s in the time interval [1200, 2700] s. Starting from \(t\) = 2700 s (the dark grey dashed vertical line in Figure 4d) it decreases down to 0.2 m/s during a short period of about 5 min. Relatively low velocities are detected at this point until the end of the sample. Simultaneously, an increase in the velocity is detected at \(z=0.1\) m (Figure 4a). At this point the mean air velocity varies around 0.18 m/s during the time interval [1200, 2580] s, and at the instant \(t=2580\) s a gradual increment of up to 0.3 m/s is visible. Contrary to that, at \(z=0.6\) m (Figure 4b) the plateau is relatively short, and a gradual decrease of the mean air velocity starts at the moment \(t=1680\) s, while at \(z=1.1\) m the plateau is longer again, and velocity reduction starts at \(t=2400\) s. The general conclusion from the measurement results is that in addition to the high-frequency turbulence, low frequency pulsations of the velocity field in the room exist, with a period of ten to twenty minutes or even more.
## 3 Results and Analysis  
### Processes and Data Inputs in the Planning of Road Schedules  
Table 3 presents the processes and data inputs that constitute the planning of road project schedules, identified through a literature review of a final analysis sample of 22 documents. This sample comprised scientific articles and technical manuals from the Scopus search engine and national and international transportation department databases. Identifying the processes and data inputs necessary for schedule planning allows planners and managers to make more informed decisions on allocating resources such as labor, materials, and equipment for schedule development. Detailed knowledge of the planning processes improves project accuracy and control by allowing early identification of potential deviations. With this, planners and the technical team can propose implementing corrective actions to avoid undesirable impacts upon completion. Identifying the processes and the sequence in which they should be developed strengthens risk management by developing robust contingency plans, thus reducing the probability of delays and cost overruns due to poor planning. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Id** & **Process** & **Input Files** & **References** \\ \hline \multirow{4}{*}{P\({}_{1}\)} & & Drawings, design specifications and & \\  & Identify factors affecting & & technical reports, type of contract, \\  & the construction process & & characterization of the area, allocated \\  & & budget, database historical, Traffic & \\  & & Management Plan (TPM) & \\ \hline \multirow{2}{*}{P\({}_{2}\)} & Define procedures for & Project scope and terms of reference, & \\  & incorporating additions & & owner’s requirements & \\ \hline \multirow{2}{*}{P\({}_{3}\)} & & Project scope and terms of reference, & \\  & Define work milestones & & owner’s requirements, drawings, design & \\  & & specifications, and technical reports & \\ \hline \multirow{4}{*}{P\({}_{4}\)} & Define work zones or & & Project scope and terms of reference, & \\  & phases (Major work item) & & owner’s requirements, drawings, design & \\  & & physical requirements and technical reports, & \\  & & characterization of the area & \\ \hline \multirow{4}{*}{P\({}_{5}\)} & & Planning methodologies, tools, database & \\  & Define schedule planning & & historical (KPI), drawings, design & \\  & models & specifications and technical reports, control & \\  & & criteria, rules and regulations & \\ \hline \multirow{2}{*}{P\({}_{6}\)} & Define time units & Project scope and terms of reference & [39; 51; 53] \\  & Define parameters and & Project scope and terms of reference, & \\  & schedule management plan & & owner’s requirements & \\ \hline \multirow{2}{*}{P\({}_{8}\)} & Collect project drawings & Drawings, design specifications, and & \\  & and specifications & technical reports & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Processes and data inputs in the planning of road schedules.
# Optomechanical Analysis and Design of Polygon Mirror-Based Laser Scanners
## 2 Characteristic Functions and Parameters of PMs versus GSs
### Duty Cycle  
The duty cycle of a scanner is defined as its time efficiency, i.e., the ratio of the useful time (for the scanning process) and the total considered time interval. Essentially, this parameter is related to the scan angles above, but also to the specific structure (and application) of the scanner:  
* For a GS, the duty cycle cannot be, for example, \(\eta=2\theta_{a}/2\theta_{m}\) (as one may consider at a rapid glance), as the extreme angular intervals (i.e., the stop-and-turn portions between \(\pi/2-\theta_{a}\) and \(\pi/2-\theta_{m}\), as well as between \(\theta_{a}\) and \(\theta_{m}\)) are not scanned with a constant velocity, even for a triangular input signal, as shown in the example in Figure 1c. Therefore, for such a signal, Equation (17) in Table 1 is the appropriate one for low \(f_{\rm s}\), while Equation (18) in Table 1 is appropriate for high \(f_{\rm s}\), as studied in detail in [9; 10]. For sawtooth input signals, the discussion is similar, and one must differentiate between the theoretical duty cycle \(\eta_{t}\) (Equation (13) in Table 1) and the effective one \(\eta\) (Equation (14) in Table 1) with regard to \(f_{\rm s}\), as discussed in [7; 8; 9]. These values of the duty cycle can be improved with the use of (rather expensive) F-theta lenses, to linearize the scan, but we have shown that even a well-calculated (and cost-effective) achromatic doublet can be utilized for certain GS-based setups [54].  
This discussion often assumes the small-angle approximation for applications such as OCT, which is commonly \(\theta_{m}<5^{\circ}\), to allow for obtaining high-resolution images (as the aquisition time is limited, especially for in vivo imaging, therefore \(f_{\rm s}\) must be high). However, for larger scan angles \(\theta_{m}\), this approximation produces errors that must be calculated or minimized. The latter solution is addressed further in Section 4.  
* For PM scanning heads, this problem of the duty cycle is less complicated, because the rotational velocity \(\omega\) of the PM is constant. Therefore \(\eta=(\theta_{2}-\theta_{1})/(\theta_{max}-\theta_{min})\), and this gives the final Equation (19) in Table 1. One must point out that this \(\eta\) is \"\(n\)\" times higher than for monogon scanners (i.e., pyramidal PMs with a single facet), which seemed to make a comeback in the context of developing fast rotational scanning heads that may avoid the use of an objective lens [61; 62].
* For both GSs and PMs, due to the finite diameter of the beam, \(\eta\) is also affected by vignetting at the margins of a facet/mirror [26]--an effect minimized by placing the incidence point in (for GSs) or around the object focal point (for PMs).
## 3 Results
### Comparative Experiments  
To validate the effectiveness of the improved VGG-16 model in anomaly detection, three classical convolutional neural networks, AlexNet [44], ResNet-18 and ResNet-34 [45], are also used as the backbone network model to conduct numerical experiments on the same dataset. The detection results of four network models are listed in Table 11. From Table 11, we can see that the improved VGG-16 model obtains the highest accuracy and F1-Score for all three cases. Moreover, the other evaluation indicators (Precision and Recall) of the improved VGG-16 model are also at the same level as the other three models.