esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple-Diffraction Subtractive Double Monochromator with High Resolution and Low Stray Light
## 4 Results  
### Simulations  
Based on the above theoretical analysis, we performed some simulations. Figure 7 shows the ZEMAX model (ZEMAX OpticStudio 19.4; Ansys, Canonsburg, PA, USA) of the MSDM, where Figure 7a shows the system layout, and Figure 7b shows the dispersed spot diagram at S\({}_{2}\). From the scatter plot, the spectral line dispersion of the MSDM is 1375 \(\upmu\)m/nm. For a focal length of the collimating and focusing lens of 173 mm, the angular resolution is 0.4554\(\lx@math@degree\)/nm, which is very close to the theoretical value of 0.464885 deg/nm, calculated using Equation (4). The simulation results closely matched the theoretical predictions. The width of S\({}_{2}\) used in the simulation is 25 \(\upmu\)m, which means that the resolution of the mount reaches 18 pm.  
LightTools software (LightTools 2022.03; Synopsys, Sunnyvale, CA, USA)was used to explore the stray light in the system. Figure 8a,b show the irradiance distributions at S\({}_{2}\) and S\({}_{3}\), respectively, as well as the intensity distribution at \(x\) = 0, which represents the light intensity distribution along the dispersion direction of the slit. The simulation results demonstrate that at S\({}_{2}\), the signal irradiance reaches approximately 100 W/mm\({}^{2}\), while the stray-light irradiance measures about 10\({}^{-4}\) W/mm\({}^{2}\), yielding a total stray-light ratio of \(\sim\)10\({}^{-6}\). Following stray-light suppression by the MSDM system, the signal irradiance at S\({}_{3}\) remains unchanged at 100 W/mm\({}^{2}\), with stray-light irradiance reduced below 10\({}^{-7}\) W/mm\({}^{2}\) and the total stray-light ratio suppressed to \(<\)10\({}^{-9}\). These numerical results conclusively validate the stray-light suppression efficacy of the subtractive double-monochromator architecture, and the intensity curves obtained using LightTools matched well with those shown in Figure 6b,d.  
Figure 7: System simulations of mount: (**a**) system layout obtained by ZEMAX; (**b**) dispersed spot diagrams at S\({}_{2}\).
## 7 Conclusions  
This study summarises the findings of prior research on the effectiveness of PCMs in concrete and their connection to the overall energy efficiency of buildings. Different forms of PCM utilised in concrete for various purposes, construction features, and climates were discovered and addressed. During this review, several important findings were made, which can be stated as follows:  
1. The PCM layer made from bricks had greater thermal qualities than the PCM capsules, while the latter had better mechanical properties.
2. The results of thermal analysis studies showed that PSC-5% has a higher heat storage capacity, leading to higher indoor thermal comfort.
3. With the maximum PCM integration percentage simulated, corresponding to 40%, the quantity of stored and released thermal energy doubled from its initial value.
4. The postponement of temperature peaks highlighted the improved energy efficiency of PCM wallboards as compared to conventional concrete, particularly in the case of the bilayer wallboard.
5. When compared to a setup without PCM, the peak temperature was delayed by 70.4%, energy usage was cut by 50%/60%, and T/RH amplitude was lowered by 15.3%.
6. Energy piles may be a viable option because of the suggested fibre-reinforced PCM-HSB, which increases concrete's heat capacity and bearing capacity.
7. Adding 15% as well as 30% PCM composite has decreased the maximum indoor temperature by 1.85 \({}^{\circ}\)C and 3.76 \({}^{\circ}\)C in the test room, respectively, while increasing the thermal storage capacity by 105% and 181%, respectively.
8. Increasing the amount of microencapsulated phase change material aggregation in a unique coreshell or choosing a PCM with a more appropriate phase transition temperature might improve performance by facilitating a more effective heat storage and release procedure.
* In the model of the room with the ceramsite-based encapsulated thermal storage aggregate, the highest temperature drop at the centre point was 4.7 \({}^{\circ}\)C, while in the model of the room with the pumice-based encapsulated thermal storage aggregate, it was 8.7 \({}^{\circ}\)C.
* When PCMs are added to concrete, freeze-thaw damage is significantly mitigated.
# Accelerated Simulation of Discrete Event Dynamic Systems via a Multi-Fidelity Modeling Framework +
## 2 Problem Definition
### Model Fidelity  
In the simulation world, the level of fidelity is defined as the degree of output similarity between a reference system and a model that abstracts the reference system. Figure 3 illustrates conceptual  
Figure 2: An example of a discrete event system model: Discrete event system specification (DEVS) model.  
examples to show the level of fidelity for the target system model: dynamic and discrete event systems. In Figure 3, the desired result is an output of the reference model, which is the reference system itself or the most accurate model compared with the reference system. Fundamentally, outputs of the high-fidelity models (blue lines in Figure 3) are much more similar to the desired outputs compared to those of the low-fidelity models (red lines in the second rows of Figure 3).  
To evaluate the fidelity quantitatively, we measure an error between the output of the reference model and that of the target system model, which is classified into two aspects (i.e., time and value). In the dynamic system model, a value error is calculated as the ratio between two output values given the same input. On the other hand, in the discrete event system model, a time error is measured by the difference between time intervals from input to output events given the same input event.  
For each aspect, the output error \(\epsilon\) is defined by the following equation:  
\[\epsilon=\sqrt{\frac{\sum_{i=1}^{N}(Error_{i})^{2}}{N-1}} \tag{1}\]  
In Equation (1), \(\epsilon\) as the root mean square error can be the value error for the dynamic system model or the time error for the discrete event system model. \(N\) is the number of samples. For example, \(N\) is the number of time samples for the dynamic system model or the number of output events for the discrete event system. The range of \(\epsilon\) is \([0,+\infty]\). If \(\epsilon\) is close to zero, the outputs of the target model are almost identical with those of the reference model, which means that the target model has a very high fidelity for the samples, \(N\). On the contrary, a large \(\epsilon\) value means that the target model has a low fidelity so that the outputs of the model are distinguished from those of the reference model.  
In the DTSS model for the dynamic system, the output values are influenced by the state variables, and the state variables are updated from the state transition function. Therefore, the state transition is closely connected to determining the fidelity of the model. In the DEVS model for the discrete event system, the time of the output is decided in the time advance function. Thus, the time advance function is important in evaluating the fidelity of the model. These functions will be used for developing lower-fidelity models in Section 4.  
Figure 3: Output errors of high- and low-fidelity models: Value error for the dynamic system model and time error for the discrete event system model.
# AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
## 4 Use Cases
### Use Case 2: AI-Based Nonlinear Noise Monitoring  
As elaborated in Section 3.1, many ML-based methods are proposed to monitor the nonlinear SNR in [66; 67; 72]. To improve the monitoring accuracy, in [5], the AI-based monitoring method combines the analytical models and the monitoring features such as ANC and PNC. As shown in Figure 3a-c, when combining monitoring features with analytical models, the maximum error reduces from 1.2 to 1 dB and 0.8 dB using the IGN and CGN, respectively. Moreover, the comparison of the CDF in Figure 3d also shows that the CGN model outperforms the IGN model to improve the ANN performance by 0.35 dB. In this work, the analytical model provides an approximate estimation. Afterwards, monitoring features are applied to improve the estimation accuracy based on the prior  
\begin{table}
\begin{tabular}{c c} \hline \hline  & 1. \(SNR_{NL}\) from the GN model \\ \hline
2. & Span number \\ \hline
3. & Maximum span length \\ \hline
4. & Average span length \\ \hline
5. & Launch power \\ \hline
6. & Link length \\ \hline
7. & Net CD \\ \hline
8. & Average gamma of fiber spans \\ \hline
9. & Average alpha of fiber spans \\ \hline
10. & Number of WDM channels \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of the modeling input features used in [5].  
Figure 2: **(a)** The structure of the proposed modeling scheme in [5]. **(b)** The comparison of cumulative distribution functions (CDF) [5]. \(SNR_{NL}^{SSM}\) means the nonlinear SNR estimated by the SSFM. \(SNR_{NL}^{EST}\) means the estimation made by the model proposed in [5]. \(\Delta SNR\) means the estimation error between the \(SNR_{NL}^{EST}\) and the \(SNR_{NL}^{SSM}\). **(c)** The simulation setup.  
approximate estimations made by analytical models. Therefore, we can infer that ML can reach a higher accuracy if the input features are selected and processed properly.
# Matched Filtering for MIMO Coherent Optical Communications with Mode-Dependent Loss Channels
## 1 Introduction
We show that the generalized linear MIMO MMSE receiver, for channels with negligible MDL, can be simplified to a matched filter MIMO receiver, which completely eliminates the ISI and crosstalk introduced by the channel. When the optical channel exhibits a significant MDL, we include linear equalization and carry out numerical simulations to get the performance of a system that consists of: A PAM transmitter with square-root raised cosine pulses; a complete long-haul optical channel with SDM; and a MIMO receiver based on the FSE approach with oversampling of two. To this end, an ensemble of thousands of random optical channels has been generated and the system performance is evaluated by means of the signal to noise and distortion ratio (SNDR) loss at the receiver output w.r.t., the one of an optimal equivalent system without ISI and crosstalk. These results are presented for a configuration with a set of parameters for a fiber, transmitter, and receiver, which is representative of current technology. The paper is structured as follows. After a short section of defining the notation used (Section 2), we begin by describing the optical channel model for a long-haul communication system using SDM, including CD, MD, and MDL impairments (Section 3). Next, a communication system based on a generalized PAM transmitter with square-root raised cosine pulses, and a linear MIMO receiver designed under the MMSE optimization criterion are discussed in Section 4. In Section 5, the numerical simulations are presented and SNDR loss metrics are given for the optical communication system with a FSE MIMO receiver for different values of channel dispersion (including MDL) and roll-off factor of the square-root raised cosine pulses. Finally, conclusions are summarized in Section 6.
# Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information
## 3 Methods
### CNN Model
#### 3.1.1 Convolutional Layer  
The convolution layer occupies a central position in convolutional neural networks, which performs the convolution operation via the convolution kernel. In particular, its essence is the discrete convolution operation of two two-dimensional matrices. To predict traffic flow, the convolution kernel extracts additional traffic data features to yield the output, and the activation function of each convolution layer processes the nonlinear mapping relation between the output and input of the traffic flow, which serves as the subsequent input of the convolution layer. Comprehensively, its operation function is as follows:  
\[G(x,y)=\sum_{u}^{m}\sum_{v}^{m}M(u,v)N(x-u,y-v) \tag{1}\]  
where \(m\) denotes the size of the convolution kernel, \(M\) represents the parameter matrix of the convolution kernel, and \(u\) and \(v\) indicate the step size of the convolution kernel in the input data dimension \((x,y)\).
# Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor
## 3 System Model
### Receiver  
(1) CP Removal  
The receiver removes the cyclic prefix from the received signal.  
\[\mathbf{y}_{\text{rec}}=\mathbf{y}[L_{\text{CP}}:L_{\text{CP}}+N-1]. \tag{15}\]  
This step restores the original structure of the GFDM block and ensures that subsequent demodulation can assume circular convolution. This is necessary for matched filtering using the GFDM modulation matrix, which relies on circular signal alignment.  
(2) GFDM Demodulation  
\[\mathbf{\hat{d}}=\mathbf{A}^{H}\cdot\mathbf{y}_{\text{rec}}. \tag{16}\]  
Demodulation is performed using the Hermitian transpose of the modulation matrix, implementing a matched filter receiver. This maps the received signal onto the known transmit basis functions and suppresses uncorrelated noise. While the process seems simple, the matched filter may allow residual self-interference in GFDM systems due to the non-orthogonality of the modulation basis.  
(3) BPSK Demapping  
\[\hat{b}_{i}=\begin{cases}0,&\Re(\hat{d}_{i})<0\\ 1,&\Re(\hat{d}_{i})\geq 0.\end{cases} \tag{17}\]  
Each demodulated symbol is thresholded to its nearest BPSK constellation point. Since BPSK symbols are real-valued, the decision boundary is at zero. This slicing operation reverts the continuous-valued detector outputs to binary decisions, providing an estimated bitstream for decoding.  
(4) Channel Decoding  
\[\mathbf{\hat{b}}=\mathcal{E}^{-1}(\mathbf{\hat{b}}_{c}). \tag{18}\]  
The decoder inverts the FEC encoding operation using the added redundancy to detect and correct bit errors. This step is crucial in recovering the original message when operating under noisy or fading conditions. The quality of decoding depends on the SNR(whose values range from \(-5\) to 30 dB) and the strength of the channel coding process (see Table 1).
# A New Click-Through Rates Prediction Model Based on Deep&Cross Network
## 2 Preliminaries
### Optimization for DNN
#### 2.3.4 Training Techniques  
In the task of CTR prediction based on DNN, the training process of neural network model is essentially to minimize the fitting loss. After defining the loss function, the key of learning is to use an optimization algorithm to minimize the loss function. Therefore, the optimization techniques play an important role, using iterations to get the optimal solution as close as possible. Especially in the complex CTR prediction task, it is very important to choose the optimization algorithm that matches the network model structure and the loss function [42,43].  
In deep learning, the classical optimization algorithms are Stochastic Gradient Descent (SGD) [44], Momentum [45], AdaGrad [46], Adam [47], and Conjugate Gradient [48], and more complex ones are Newton method [49], FTRL-Proximal [8].  
In general, each optimization algorithm has its own strength and weakness. In this paper, when training the deep CTR prediction model, we have tried each optimization algorithm above and found that FTRL-Proximal and Adam are more suitable for the our proposed model and the baselines.
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 4 Our Proposed Slice-Based QoS Manager for IoT
### Slice-Based QoS Manager Solution
#### 4.6.2 Linux Traffic Control  
Linux TC is a handy utility that has a wide range of applicability and is available in standard Linux distributions. The technology has traffic control related features [55], which rely on shaping, scheduling, policing and dropping. The Qdiscs Linux TC implementation is the basis of traffic control. There are several types of both classless and classful Qdiscs. On the classless side, we have First In-First Out (FIFO), Random Early Detection (RED), Stochastic Fair Queuing (SFQ) and Token Bucket Filter (TBF). Classful Qdiscs are Class-Based Queuing (CBQ), Hierarchical Token Bucket (HTB) and Priority Queuing (PRIO).  
In our solution, we considered the HTB, which has shaping capabilities based on TBF and has the ability of classes prioritisation. TBS is the right choice when it comes to limiting traffic to a configured maximum rate, dealing exceptionally well with significant amounts of bandwidth. HTB works by implementing a well-composed link sharing hierarchy of classes with an emphasis on conforming to existing practices. It facilitates guaranteeing bandwidth to classes, while also allowing the specification of upper limits to inter-class sharing.
## 3 Model Solution
### Optimization and Management
#### 3.3.1 Sensitivity Design  
Having obtained the dynamic predicted measures, we could calculate the sensitivity between the input factors and the predicted measures.  
We determined \(\Delta\left(t\right)\), which denotes a change of input or output state, and obtained the values using the following formulae:  
\[\Delta\hat{\alpha}(t)=\hat{\alpha}(t)-\hat{\alpha}(t-1) \tag{6}\]  
\[\Delta\hat{\beta}(t)=\hat{\beta}(t)-\hat{\beta}(t-1) \tag{7}\]  
\[\Delta\hat{\gamma}(t)=\hat{\gamma}(t)-\hat{\gamma}(t-1) \tag{8}\]  
\[\Delta\hat{\delta}(t)=\hat{\delta}(t)-\hat{\delta}(t-1) \tag{9}\]  
\[\Delta\varphi(t)=\varphi(t)-\varphi(t-1) \tag{10}\]\[\Delta\mu(t)=\mu(t)-\mu(t-1). \tag{11}\]  
We could calculate the sensitivity between the predicted measure and the estimated input factors using the following formula:  
\(S(y,x)=\frac{dy}{dx}\times\frac{x}{y}\), which could take the approximated format:  
\[S(y,x)=\frac{\Delta y}{\Delta x}\times\frac{x}{y} \tag{12}\]  
We would get the following results:  
\[S(\varphi(t),\alpha(t))=\frac{\Delta\varphi(t)}{\Delta\alpha(t)}\times\frac{ \alpha(t)}{\varphi(t)} \tag{13}\]  
\[S(\varphi(t),\beta(t))=\frac{\Delta\varphi(t)}{\Delta\beta(t)}\times\frac{ \beta(t)}{\varphi(t)} \tag{14}\]  
\[S(\varphi(t),\gamma(t))=\frac{\Delta\varphi(t)}{\Delta\gamma(t)}\times\frac{ \gamma(t)}{\varphi(t)} \tag{15}\]  
\[S(\varphi(t),\delta(t))=\frac{\Delta\varphi(t)}{\Delta\delta(t)}\times\frac{ \delta(t)}{\varphi(t)} \tag{16}\]  
\[S(\mu(t),\alpha(t))=\frac{\Delta\mu(t)}{\Delta\alpha(t)}\times\frac{\alpha(t )}{\mu(t)} \tag{17}\]  
\[S(\mu(t),\beta(t))=\frac{\Delta\mu(t)}{\Delta\beta(t)}\times\frac{\beta(t)}{ \mu(t)} \tag{18}\]  
\[S(\mu(t),\gamma(t))=\frac{\Delta\mu(t)}{\Delta\gamma(t)}\times\frac{\gamma(t )}{\mu(t)} \tag{19}\]  
\[S(\mu(t),\delta(t))=\frac{\Delta\mu(t)}{\Delta\delta(t)}\times\frac{\delta(t )}{\mu(t)}. \tag{20}\]  
If \(S(\cdot,\cdot)<0\), it denotes a negative influence. The predicted measures would decrease, with the input factor increasing.  
If \(S(\cdot,\cdot)>0\), it means a positive influence. With the input factor increasing, the predicted measures would increase.  
The bigger the absolute value of \(S(\cdot,\cdot)\) is, the more powerful the influence between the input factor and the predicted measures would be. By this means we could obtain the critical factor, which acts on the global influence.
# An Empirical Recommendation Framework to Support Location-Based Services +
## 5 Experimental Results and Evaluation
### Dataset and Implementation  
The information concerning the location contains the name, latitude, longitude, type, and rating are already shown in Table 2. In our work, the number of locations is 55,368 from 2500 different users. We considered 2000 users who checked more than 50 spots. In the DBSCAN clustering algorithm we used around a radius \(\epsilon\) of 0.84 and the value of _MinPts_ of the cluster is 18. In the Cosine similarity formula, we used 200 users profile for similarity calculation. We consider eleven parent categories based on the statistical analysis of the extracted POIs using the google place API. To demonstrate Figure 3 we only show five categories as an example.
# Fractal Metasurfaces and Antennas: An Overview for Advanced Applications in Wireless Communications
## 4 Fractal Metasurfaces
### Future Developments of Fractal Metasurfaces  
As previously discussed, fractal metasurfaces have demonstrated significant potential in various fields, including telecommunications, EMI reduction, energy harvesting, and cloaking devices. However, further exploration is required in these areas, as well as expansion into new areas such as wearable devices and biosensors [92; 93; 94; 95]. Future developments of fractal metamaterials should consider various aspects, including challenges, technological advancements, and potential applications. To tackle the manufacturing challenges associated with the precision and cost of subwavelength fractal elements, it is crucial to concentrate on advancements in fabrication techniques that allow for the creation of intricate and precise fractal structures at smaller scales. Techniques like 3D printing and nanolithography may be essential in achieving finer details and more complex fractal geometries. Additionally, the ability to customize and adjust the properties of fractal metamaterials by incorporating materials or components with adjustable parameters is a crucial aspect. In the future, fractal metasurfaces may become increasingly multifunctional, offering a wider range of capabilities in a single device. The trend towards miniaturization is expected to continue, with fractal metasurfaces finding applications in smaller devices and systems. It is possible that integration with existing technologies, such as consumer electronics, medical devices, and wearable technology, will become more prevalent.  
Figure 23: Fractal metamaterials integrated antennas: (**a**) compact single-feed CP patch antenna based on the combined use of meta-surfaces and meta-resonators (Adapted with permission from [81], IEEE, 2013), (**b**) miniaturized CP microstrip antenna loaded by cross-shaped fractal metamaterial surface [82], (**c**) microstrip antenna integrated with fractal-perturbed mushroom structure (Adapted with permission from [83], IEEE, 2014) (**d**) fractal metamaterial for mutual coupling reduction in microstrip antenna array [84], (**e**) fabricated GRIN slab lens and fractal meta-element (Adapted with permission from [89], Springer Nature, 2014).  
In conclusion, the future development of fractal metamaterials is promising, driven by ongoing advances in fabrication techniques, expanding applications, and integration with other cutting-edge technologies.