esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Fabrication Process  
The fabrication process of Si\({}_{3}\)N\({}_{4}\) PICs initiates with a precise wafer surface precleaning procedure. To achieve this, we employ established CMOS technology processes--SC-1 and SC-2 [50]. These processes play a pivotal role in eliminating any organic and metallic contamination adhering to the wafer surface, ensuring a pristine starting point for subsequent fabrication stages. Following the precleaning, we embark on two critical processes for material layer formation--wet thermal oxidation and low-pressure chemical vapor deposition (LPCVD). These processes are meticulously designed to yield a 2.3 \(\upmu\)m thick SiO\({}_{2}\) layer as the initial foundation, succeeded by a 400 nm Si\({}_{x}\)N\({}_{y}\) layer. This layering is fundamental for the subsequent stages of the fabrication process.  
The subsequent step involves transferring the layout onto a resist, utilizing a 175 nm thick e-beam resist AR-P 6200 from Allresist, employing electron beam lithography (EBL). The exposure process is executed with precision, incorporating proximity effect correction and strategies to minimize stitching errors. The pattern obtained in the resist is then faithfully transferred onto the Si\({}_{x}\)N\({}_{y}\) layer using dry etching--specifically, reactive ion etching (RIE) with CHF\({}_{3}\)/O\({}_{2}\) gas. Any remaining residue on the resist is diligently removed using oxygen plasma. The final fabrication step entails cleaving the wafers into individual chips, culminating in a detailed scanning electron microscope (SEM) inspection. Representative photos illustrating the successful fabrication process are presented in Figure 6. For a more in-depth understanding of the technological processes employed, readers are encouraged to refer to our comprehensive previous work [51]. This work provides a detailed exploration of the intricacies involved in each fabrication step, offering valuable insights into the methodology employed for silicon nitride PIC production.  
Figure 6: SEM image of the RR structure with R = 50 \(\upmu\)m. The gap (g) between the bus waveguide and the ring is 200 nm. Yellow dashed box provides a magnified image of coupling section.  
The inspection unveiled a commendable level of fidelity in reproducing the Si\({}_{3}\)N\({}_{y}\) shapes concerning the intended layout. The apparent roughness observed on the upper surface of the waveguides is a consequence of the extended etching process. Importantly, this textural irregularity does not impede the optical parameters of the RRs, except for marginally increased optical power losses. The overall integrity of the fabricated components remains intact, underscoring the robustness of the production process despite the surface roughness encountered during etching.
# Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting
## 3 Methodologies
### Singular Spectrum Analysis  
Decomposition  
The process of decomposition comprises two steps: embedding and singular value decomposition (SVD). This decomposition is the main result of the SSA algorithm. Decomposition is important when each restored subsection can be categorized as either a trend pattern, or as an oscillation or aspect of noise.  
Step 1: Embedding  
The embedding step is the initial step in the SSA algorithm. This method converts the observed time series to a multi-dimensional vector sequence.  
The embedding technique maps the original time series by shaping \(K=N-L+1\) lagged vectors \(X_{i}=\left\{x_{i},\ldots,x_{i+L-1}\right\}^{\text{T}},\ i=1,\ldots.K-L+1\) of the series f as:  
\[X=\left[X_{1},\ldots,X_{K}\right]=\left[\begin{array}{ccccc}x_{1}&x_{2}&x_{ 3}&\ldots&x_{K}\\ x_{2}&x_{3}&x_{4}&\ldots&x_{K+1}\\ x_{3}&x_{4}&x_{5}&\ldots&x_{K+2}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ x_{L}&x_{L+1}&x_{L+2}&\ldots&x_{N}\end{array}\right] \tag{9}\]  
In Equation (9) we notice that \(X\) has the same elements on the anti-diagonals. This type of matrix is called a Hankel matrix.  
According to [90], the size of the window \(L\) should be sufficiently large, but less than half of the time series. Larger values of \(L\) allow longer period oscillations to be detected, but a too-high value of L may require a large number of eigentriples and skip some essential major components with high contributions. Ref [62] selected window length \(L\) from experience, [91] repeatedly tried with varying window length, and another study [92] used proportional data length, such as \(\frac{\text{N}}{3}\), \(\frac{\text{N}}{4}\). In the present study, the method of proportional data length proposed by [92] is adopted.  
Step 2: Singular Value Decomposition (SVD)  
The SVD of the trajectory matrix is the primary component of the decomposition process. From matrix \(X\), define the covariance matrix \(XX^{\text{T}}\). The SVD of \(XX^{\text{T}}\) provides a set of L eigenvalues in descending order of magnitude \(\left(\lambda_{1}\geq\lambda_{2}\geq\ldots\geq\lambda_{L}\geq 0\right)\) and the corresponding eigenvectors \(U_{1},U_{2},\ldots,U_{L}\). The SVD of the trajectory matrix can then be formulated as:  
\[X=X_{1}+\ldots+X_{L}, \tag{10}\]where \(X_{i}=\sqrt{\lambda_{i}}U_{i}V_{i}^{\rm T}\) and \(V_{i}=X_{i}^{\rm T}U_{i}\sqrt{\lambda_{i}},\ i=1,\ldots,L\), (equivalent to the \(i\)th column of \(XX^{\rm T}\)). The matrices \(X_{1}\) have rank 1; thus, they are elementary matrices. The collection \(\left(\sqrt{\lambda_{i}},U_{i},V_{i}\right)\) denotes the \(i\)th eigentriple.
# DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients
## 3 Experiments
### Experiments
#### 3.2.2 Isometric Contraction Tests  
In the treadmill tests, sufficient rest time was given between test sessions to eliminate the effect of muscle fatigue on the experimental results. Another issue associated with muscle fatigue is safety. treadmill walking with exhausted muscles can cause accidents, such as trips and falls, which may lead to injury. For safety reasons, we conducted isometric contraction tests, rather than treadmill tests, to compare the effects of FES envelopes on muscle fatigue.  
For the isometric contraction tests, a customized device was built for this study, as shown in Figure 9. This device restricted the motion of the ankle joint and measured the joint torque in both plantar flexion and dorsiflexion. The joint torque could be calculated by the product of the force, measured by the load cell, and the distance between the load cell and ankle joint.  
Figure 8: Experimental setup for the treadmill test.  
Figure 9: Data acquisition device for isometric contraction tests.  
Furthermore, one foot of the subject was strapped firmly to the device for isometric conditions, while the subject was sitting on a chair positioned in front of the device. The soleus of the strapped leg was stimulated by two different FES envelopes. Both FES envelopes were set to correspond to those for a gait of 3 km/h. The duration of the stimulation for isometric contraction was set to 5 min, which was long enough to cause FES-induced muscle fatigue.  
To estimate muscle fatigue, surface EMG at the soleus was measured before and after FES stimulation. For the EMG measurement, each subject was asked to sustain 90% of their maximum voluntary isometric contraction (MVIC), where the MVIC for each subject was obtained before the isometric contraction tests. During 30 s of voluntary isometric contraction, the force was monitored by a loadcell. The EMG data were collected only when the force was maintained at a constant level.  
FES-induced muscle fatigue could be estimated based on the changes in EMG before and after FES stimulation. As an index to assess muscle fatigue, changes in the median frequency (MDF) of EMG signals are commonly used [41]. A decrease in the MDF of the EMG signal indicated the amount of muscle fatigue. Performance of the DBE and TE were compared based on the decrease in MDF of the EMG signal before and after FES application.
# Blind Channel Estimation Method Using CNN-Based Resource Grouping
This paper overcomes the limitations of the methods proposed in [13] by introducing resource grouping patterns for K-means-based blind channel estimation and proposing a two-step learning method using CNN to select optimal patterns. Unlike existing methods, this study focuses on improving the efficiency of radio resource usage by effectively adapting to fine-grained and dynamically changing channel characteristics. The proposed two-step learning structure identifies the optimal number of subcarriers under each channel condition in the first step and refines the number of OFDM symbols based on time-domain variations in the second step. By designing dynamic resource grouping patterns adaptable to various channel conditions, such as signal-to-noise ratio (SNR), UE velocity, and delay spread, this study distinguishes itself from existing K-means algorithm-based approaches. Performance analysis evaluates the channel estimation accuracy of the proposed method and compares it to existing methods in terms of mean squared error (MSE) and resource efficiency. This study provides a foundation for robust and resource-efficient channel estimation in dynamic wireless communication systems.
# Application of Internet of Things in a Kitchen Fire Prevention System
## 1 Introduction
### Research Motivation and Objectives
#### 1.2.2 Remote Monitoring and Gas Shutoff  
1. An Internet protocol (IP) camera is installed in the kitchen at a location and height that allows users to see the gas stove.
2. When users are unsure whether they have turned off the gas stove, they can check the live video of their cameras from their phones. When necessary, they can activate the gas shutoff device to shut off the gas supply by using voice control or clicking on an activation button on their phones, thereby ensuring home safety.
# Scalar-on-Function Relative Error Regression for Weak Dependent Case
## 4 Smoothing Parameter Selection
### Bootstrap Approach  
In addition to the leave-one-out cross-validation rule, the bootstrap method constitutes another important selection method. The principle of the latter is based on the plug-in estimation of the quadratic error. In the rest of this subsection, we describe the principal steps of this selection procedure.  
**Step 1.**: We choose an arbitrary bandwidth \(h_{0}\) (resp. \(k_{0}\)), and we calculate \(\widetilde{R}_{h_{0}}(x)\) (resp. \(\widehat{R}_{k_{0}}(x)\)).
**Step 2.**: We estimate \(\widetilde{\epsilon}=Y-\widetilde{R}_{h_{0}}(x)\) (resp. \(\widehat{\epsilon}=Y-\widehat{R}_{k_{0}}(x)\)).  
Step 3.We create a sample of residual \(\epsilon^{*}\) (resp. \(\epsilon^{**}\) ) from the distribution  
\[G^{*}=((\sqrt{5}+1)/2\sqrt{5})\delta_{\tilde{e}(1-\sqrt{5})/2}-((1-\sqrt{5})/2 \sqrt{5})\delta_{\tilde{e}(\sqrt{5}+1)/2^{\prime}}\]  
\[(\text{resp. }\ G^{*}=((\sqrt{5}+1)/2\sqrt{5})\delta_{\tilde{e}(1-\sqrt{5})/2}-((1- \sqrt{5})/2\sqrt{5})\delta_{\tilde{e}(\sqrt{5}+1)/2}),\]  
where \(\delta\) is the Dirac measure (see Hardle and Marron [19] for more details).  
Step 4.We reconstruct the sample \((Y_{i}^{*},X_{i}^{*})_{i}=(\epsilon^{*}-\widetilde{R}_{h_{0}}(X_{i}),X_{i})\), (resp. \((Y_{i}^{**},X_{i}^{**})_{i}=(\epsilon^{**}-\widehat{R}_{k0}(X_{i}),X_{i})\),  
Step 5.We use the sample \((Y_{i}^{*},X_{i}^{*})_{i}\) to calculate \(\widetilde{R}_{h_{0}}(X_{i})\) and \((Y_{i}^{**},X_{i}^{**})_{i}\) to calculate \(\widehat{R}_{k_{0}}(X_{i})\).  
Step 6.We repeat the previous steps \(N_{B}\) times and put \(\widetilde{R}_{h_{0}}^{r}(X_{i})\) (resp. \(\widehat{R}_{k_{0}}^{r}(X_{i})\)), the estimators, at the replication \(r\).  
Step 7.We select \(h\) (resp. \(k\)) according to the criteria  
\[h_{opt^{\text{Boo}}}=\arg\min_{h\in H_{n}}\sum_{r=1}^{N_{B}}\sum_{i=1}^{n}( \widetilde{R}_{h}^{r}(X_{i})-\widetilde{R}_{h_{0}}^{r}(X_{i}))^{2}, \tag{8}\]  
and  
\[k_{opt^{\text{Boo}}}=\arg\min_{k\in K_{n}}\sum_{r=1}^{N_{B}}\sum_{i=1}^{n}( \widetilde{R}_{k}^{r}(X_{i})-\widetilde{R}_{k_{0}}^{r}(X_{i}))^{2}.\]  
Once again, the choice of the subset \(H_{n}\) (resp. \(K_{n}\)) and the pilot bandwidth \(h_{0}\) (resp. \(k_{0}\)) have a significant impact on the performance of the estimator. It will be very interesting to combine both approaches in order to benefit from the advantage of both selections. However, the time cost of this idea is very important.
# Clustering for Lifetime Enhancement in Wireless Sensor Networks
## 3 Advanced Clustering Approach
### Parameters Considered in the LEACH Energy Model  
In this part, we give the leach energy model and extract the main parameters that affect the clustering method. The leach energy model is represented by Equations (1)-(4). Equation (1) represents the process of sending a packet towards either a sink or a neighbor node. Equation (2) is the reception equation. It gives the energy consumed when receiving a packet from a neighbor node. Equation (3) is the aggregation equation, which gives the amount of energy consumed in the aggregation of received packets by a cluster head from its member nodes. The aggregation operation is specified for cluster heads that are responsible for the collection of all packets from their members, aggregating them into one packet that should be sent towards the base station in the case of one-hop communication or towards a neighbor cluster head in the case of multi-hop communication. Equation (4) is used to identify the use of two forms of Equation (1).  
\[E_{Tx}=\begin{cases}L.E_{elec}+L.\varepsilon_{fs}.d^{2},\,if\,d<d_{0}\\ L.E_{elec}+L.\varepsilon_{mp}.d^{4},\,if\,d\geq d_{0}\end{cases} \tag{1}\]  
\[E_{Rx}(n_{p})=n_{p}.L.E_{elec} \tag{2}\]  
\[E_{DA}\big{(}n_{p}\big{)}=(n_{p}+1\big{)}L.E_{a} \tag{3}\]  
\[d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}} \tag{4}\]  
where:  
\(E_{Tx}:\) denotes the energy consumed in the transmission process.  
\(E_{Rx}:\) represents the energy consumed in the reception process.  
\(E_{DA}:\) is the energy consumed in the data aggregation process.  
\(d_{0}:\) is the distance condition value applied to the two equations to obtain the consumed energy in Equation (1).  
The other parameters are given in Table 2:  
Based on these equations, it is obvious that the main considered variable is the distance for the transmission equation. However, the reception and the aggregation equations are linear functions, and their main variable is the number of packets either received or aggregated, respectively. The leach protocol uses clustering as the main approach to reduce the consumed energy, and it utilizes one-hop communication either in the inner cluster (i.e., between a CH and its members) or between CHs and the WSN's BS. For the leach, this is the best approach to obtain better WSN performances.  
This study starts by checking the correctness of these hypotheses. The two fundamental questions we have to answer are, is clustering efficient for all times, or do some variations need to be fixed to obtain better performance? And, is one-hop communication the preferable method to transfer packets either between CMs and their CH or between CHs and their BS?
# Query Optimization in Distributed Database Based on Improved Artificial Bee Colony Algorithm
## 2 Proposed Method
### Time and Space Complexity of the Hybrid Model
```
1:Data sharding-site matrix (DSM) for query statement and the number of output query plans k
2:Top-k query execution plans and their query execution costs
3:Initialize \(X_{i}\); set \(N\), \(P_{select}\), \(P_{\text{\it crossover}}\), and \(P_{\text{\it mutation}}\)\(\triangleright\) Initialize each food source; set the parameters: population size, selection rate, crossover rate, and mutation rate
4:Initialize \(\delta^{\prime}\), \(L\), Limit, \(t=1\); \(\triangleright\) Initialize the perturbation coefficient as well as the maximum number of iterations and the food source abandonment threshold; current number of iterations
5:while\(t<L\)do
6: The leader bee phase:
7:for\(i=0\):\(N\)do
8:\(\eta_{i}\leftarrow\) Equation (3) [\(X_{i}\)] and search \(X_{i}\); \(\triangleright\) Assign a leader bee to food source \(X_{i}\) and calculate the dynamic perturbation factor \(\eta_{i}\) for food source \(X_{i}\) according to Equation (4)
9: Recalculate perturbation coefficient, selection rate, crossover rate, and mutation rate based on dynamic perturbation factor \(\eta_{i}\);
10:\(Y_{i}\leftarrow\) a new food source; \(\triangleright\) Generate a new food source \(Y_{i}\) according to Algorithm 1
11: Determine whether or not to perform cross and mutate operation based on the selection rate; if yes, proceed to step 10, otherwise go directly to step 13;
12:\(X_{i}\leftarrow\) Mutation(\(X_{i}\)); \(\triangleright\) Food source \(X_{i}\) generates a new food source \(Z_{i}\) through a mutation operation
13:\(X_{best}\leftarrow\) best food source; \(\triangleright\) Assign the food source with the best fitness value among all current populations as \(X_{best}\)
14:\(12\) new food sources \(\leftarrow\) Crossover (\(X_{i}\), \(Y_{i}\), \(Z_{i}\), \(X_{best}\)); \(\triangleright\) Fully connected crossover operations are performed on four food sources, \(X_{i}\), \(Y_{i}\), \(Z_{i}\), and \(X_{best}\), to generate 12 new food sources and calculate their fitness values
15:\(X_{i}\leftarrow\) best food source of 12 new food sources; \(\triangleright\) Assign the food source with best fitness value as \(X_{i}\) according to the greedy selection method
16: The scout bee phase:
17:if The number of times of stalled update for \(X_{i}>Limit\)then
18: Scout bee \(\leftarrow\) Leader bee; \(\triangleright\) The corresponding leader bee become a scout bee, and the scout bee randomly finds a new food source according to Equation (7)
19:else
20: Go to step 21;
21:endif
22:endfor
23: The follower bee phase:
24:for\(i=0\):\(N\)do
25: Calculate the probability that a food source is followed in the population by Equation (6);
26: The follower bee selects a food source according to the roulette method, searches for it in the same way as the leader bee, and finally determines the food source to be retained according to the greedy selection method;
27:\(t=t+1\);
28:endfor
29:endwhile
30:return Top-k optimal solutions
```  
**Algorithm 2** DYABC-GO Logical Pseudo-Code
## 3 System Description
### Ionospheric Sounding System  
The ionospheric sounding system deployed at the ASJI is a VIS system, particularly the Advanced Ionospheric Sounder (AIS) developed by the Istituto Nazionale di Geofisica e Vulcanologia (INGV) of Rome, Italy. The main reasons to choose this system were the limited time of operation, low-cost and simple infrastructure and transport. The VIS might run only when the ASJI is attended, usually from December through early March, and only an inexpensive instrument was allowed for the proposal. The field available for installing research systems was very limited at that moment, being unrealistic to build an array of receiving antennas. The power supply of the ASJI was also limited, and a low consumption system was required. AIS accomplished all the above restrictions compared to other ionospheric systems available in the market. The authors of [26] provide details of the AIS sounder and [27] the signal processing techniques used. In summary, the main parts of the AIS are a frequency synthesizer, a code generator, a power amplifier and a delta type transmitting antenna into the transmission section; and a delta type receiving antenna, a radio receiver, an analog to digital converter and a digital signal processor into the receiving section. In addition, each section is controlled by a personal computer that also stores and displays data. AIS employs a 16 bit complementary phase code and exploits the advanced HF radar techniques, such as pulse compression and phase coherent integration. Figure 4 shows a detail of the transmitting and receiving antennas of the AIS deployed at the ASJI.  
The AIS was upgraded during the surveys 2012-2013 and 2013-2014, with a new PC and operating system, as well as replacing the former PC ISA bus connection by an easier USB interface connection, adapting the system to current technology. Moreover, the system was upgraded with the Autoscala software [28], able to automatically estimate and scale ionospheric characteristics from ionograms and to estimate electron density profiles [29].
# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 4 Evaluation Methods
### Fsim  
The FSIM (feature similarity index) method is based on the human visual system and compares the structural similarity of image features. It can be adjusted to compare two color images, two grayscale images or a combination. For the purposes of this paper, the original color image will be compared with the grayscale image obtained by different decolorization procedures. The adjusted formula for calculating FSIM is given by Expression (17), where \(S_{G}\) denotes the gradient similarity defined in (18), \(S_{L}\) denotes the luminance similarity defined in (19), and \(S_{C}\) denotes the cross-contrast similarity defined in (20). Coefficients \(\alpha\) and \(\beta\) are weighting factors that determine to what extent contrast, gradient, and luminance will be applied.  
\[\text{FSIM }=S_{C}{}^{\alpha}S_{L}{}^{(1-\alpha)}S_{C}{}^{\beta} \tag{17}\]  
\[S_{G}=\frac{1}{M\cdot N}\sum_{x,y}\frac{\min(G_{R},G_{G},G_{B},G_{Y})}{\max(G _{R},G_{G},G_{B},G_{Y})} \tag{18}\]  
\[S_{L}=\frac{1}{3}\sum_{c}\frac{2\,\mu_{c}\,\mu_{Y}+\text{const}}{\mu_{c}^{2}+ \mu_{Y}^{2}+\text{const}} \tag{19}\]  
\[S_{C}=\frac{1}{3}\sum_{c}\frac{2\,\sigma_{c}\,\sigma_{Y}+\text{const}}{\sigma_ {c}^{2}+\sigma_{Y}^{2}+\text{const}} \tag{20}\]