esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolution of System Embedded Optical Interconnect in Sub-Top-of-Rack Data Center Systems
## 8 Copackaged Optical Assembly--The Ultimate Migration of System-Level Optical Interconnect
In order to accommodate a 51.2 Tbps switch with four times the I/O bandwidth of a  
Figure 16: Copackaged optical assembly: (**a**) schematic view of copackaged optical module comprising an ASIC (blue) and transceiver chiplets (red) on common carrier (grey) mounted on PCB (green) of a host card [31], (**b**) optical fiber connections from the copackaged optical module to (**c**) passive optical connector at front face-plate and/or backplane/midplane interface, (**d**) example of transceiver chiplet (courtesy of AIO Core Ltd.) [31; 32], (**e**) 1 st generation silicon photonics transceiver from H2020 COSMICC, (**f**) 2 nd generation of silicon photonics transceiver from H2020 COSMICC project [33], (**g**) projected timeline for data center switch ASICs with increasing aggregate bandwidths. 12.8 Tbps switch, it is envisaged that sixteen 3.2 Tbps CPO engines [35] will be required with potentially four times the number of fibers at the front face-plate. In addition, most CPO solutions will use an external light source (ELS) to provide the source of continuous-wave light to the modulators in the chiplets, and this ELS module will preferably also be a pluggable module on the face-plate, which will further reduce the available space on the face-plate. The media by which optical signals are conveyed between the on-board CPO module and the passive optical connectors on the front face-plate will at first be fiber due to its maturity and availability. In applications where the number of fibers becomes prohibitive, sophisticated fiber management solutions will be introduced, such as under-board fiber shuffles. The media on the CPO module itself to provide the possibly hundreds of optical connections between the chiplets and the main board outside the CPO module will also be fiber-based in the short term; however, other media such as polymer waveguides [36] and planar glass waveguides [37; 38] have also been investigated.
## 3 Methodology
### Accessibility Score Computation
#### 3.2.2 TAW Accessibility Score  
The accessibility score computation by TAW is based on four components, \(\kappa\), \(\lambda\), \(\mu\) and \(\chi\). These four components refer to the number of passed, failed, not-tested, and not-decided guidelines, similar to the returned component by Mauve. Like the accessibility-barrier computation via Mauve, TAW-based accessibility-barrier computation considers variable weight and severity factors. The four variable weights are \(\omega_{\kappa}\), \(\omega_{\lambda}\), \(\omega_{\mu}\), \(\omega_{\chi}\), and their severity factor is \(\epsilon\). The weight coefficients \(\omega_{\kappa}\), \(\omega_{\lambda}\), \(\omega_{\mu}\), and \(\omega_{\chi}\) are scaled by their severity factors \(\epsilon\), as shown in Algorithm 2. The pass, fail, not-tested, and not-decided ratio computations are shown in Equations (11)-(14).  
\[\rho 1=[\kappa*\omega_{\kappa}*\epsilon] \tag{11}\] \[\rho 2=[\lambda*\omega_{\lambda}] \tag{12}\]\[\rho 3 = \left[\mu*\omega_{n}\right] \tag{13}\] \[\rho 4 = \left[\chi*\omega_{\chi}\right] \tag{14}\]  
The score of TAW, \(\rho_{(Taw)}\), is computed through Equation (15). The _ComputeAccessibility.TAW_ function represents the full procedure, as shown in Algorithm 2.  
\[\text{Taw score, }\rho_{(Taw)}=\rho 1-\sum(\rho 2+\rho 3+\rho 4) \tag{15}\]  
```
Input: URL (\(\alpha\)) Output: Accessibility score count \(\rho_{(Taw)}\) FunctionComputeAccessibility.TAW 0
1.  dataTaw = invoke Taw (\(\alpha\))
2.  if <dataTaw *> then
3.  do preprocessing (dataTaw)
4.  Initialize:
5. \(\epsilon\) = 0.2//set severity factor
6. \(\kappa\) = dataTaw.count (pass)//fetch pass result count
7. \(\lambda\) = dataTaw.count (fail)//fetch fail result count
8. \(\mu\) = dataTaw.count (not-tested)//fetch not-tested result count
9. \(\chi\) = dataTaw.count (not-decided)//fetch not-decided result count
10.  if \(\kappa\) == 0, then
11.  if \(\lambda\) == 0, then
12.  if \(\mu\) == 0, then
13.  if \(\chi\) == 0, then
14. \(\rho_{(Taw)}\) = 0
15.  else
16.  else
17.  else
18.  else
19. \(\rho_{1}\) = \(\kappa^{*}\omega_{\chi}\) *\(\epsilon\)
20. \(\rho_{2}\) = \(\lambda^{*}\omega_{\chi}\)//where \(\omega_{1}\) - \(\omega_{\chi}\)*\(\epsilon\)
21. \(\rho_{3}\) = \(\mu^{*}\omega_{\chi}\)//where \(\omega_{\mu^{*}}\) - \(\omega_{1}\)*\(\epsilon\)
22. \(\rho_{4}\) = \(\chi^{*}\omega_{\chi}\)//where \(\omega_{\chi^{*}}\) - \(\omega_{\mu}\)*\(\epsilon\)
23. \(\rho_{(Taw)}\) = \((\rho_{1}-\sum\left(\rho_{2}+\rho_{3}+\rho_{4}\right))\)
24.  return\(\rho_{(Taw)}\)
```  
**Algorithm 2**ComputeAccessibility.TAW
# represents a blank space denoting the sentence's beginning and end.
## 4 Discussion
### Experimentation with the Ensemble Method
#### 4.2.2 DL Algorithm Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **AUC (\%)** & **TPR (\%)** & **TNR (\%)** \\ \hline Passive Aggressive & 0.99 & 0.99 & 0.99 \\ \hline Logistic Regression & 0.991 & 0.99 & 0.99 \\ \hline Multinomial Naive Bayes & 0.968 & 0.99 & 0.95 \\ \hline Bernoulli’s Naive Bayes & 0.979 & 0.98 & 0.98 \\ \hline Gaussian Naive Bayes & 0.906 & 0.85 & 0.96 \\ \hline LSTM & 0.934 & 0.94 & 0.93 \\ \hline BI-LSTM & 0.941 & 0.99 & 0.89 \\ \hline BERT & 0.961 & 0.96 & 0.92 \\ \hline RoBERTa & 0.969 & 0.98 & 0.94 \\ \hline CNN & 0.910 & 0.84 & 0.97 \\ \hline CNN-LSTM & 0.89 & 0.86 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: AUC of the ensemble and DL models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Year** & **Model** & **Performance Evaluation Matrix** \\ \hline
[20] & 2020 & RF + XG Boost & \(F1\) score: 92.00 \\ \hline
[21] & 2021 & SVM & Accuracy: 91.70 \\ \hline
[22] & 2022 & GA & Accuracy: 80.50 \\ \hline
[23] & 2022 & RF & Accuracy: 90.40 \\ \hline
[24] & 2020 & Semi-Supervised LR & Accuracy: 87.91 \\ \hline
[25] & 2022 & Random subspace (SVM) & Accuracy: 89.76 \\ \hline
[26] & 2022 & PA + NB & Accuracy 93.00 \\ \hline
[27] & 2023 & Bagging + SVM & Accuracy: 91.40 \\ \hline
[28] & 2023 & Boosting + Add Boost & Accuracy: 94.92 \\ \hline
[29] & 2017 & RNN + BioIE & Accuracy: 91.00 \\ \hline
[30] & 2022 & Attention + CNN & Accuracy: 89.00 \\ \hline
[32] & 2023 & MTTV + Transformers & Accuracy: 92.36 \\ \hline
[38] & 2020 & DC-CNN & Accuracy: 90.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results comparison of the proposed methodology with previous studies.
## 5 Validation Procedures and Results
### Simulation Settings  
Proposed secondary authentication mechanisms in this research are composed of two schemes: initial secondary authentication and re-authentication. In Figure 2, steps 1 to 5 show the detailed process of initialization and steps 6 to 8 means the primary authentication process. Detailed procedures of secondary authentication are illustrated in steps 9 to 22 of Figure 2. Since the processes of primary authentication are already standardized and are out of our research scope, we evaluated the performances about the initialization and secondary authentication schemes. Likewise, in Figure 3, steps 1 to 3 mean the pre-procedures, and steps 4 to 15 mean the re-authentication. In the experiment, we evaluated the performance of re-authentications. According to the cost terms explained in Table 3, we analyzed the costs of each communication in the authentication process. Table 4 shows the analyzed costs of each communication.  
Figure 4: Types of Communications for Authentication in 5G.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Term** & **Explanation** \\ \hline \(n(T_{AS})\) & The number of packet transmission between the UE and SN node \\ \(n(T_{SN})\) & The number of packet transmission between SN nodes \\ \(n(T_{NAS})\) & The number of packet transmission between the SN node and HN node \\ \(n(T_{HN})\) & The number of packet transmission between HN nodes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cost Terms on Communication Types of 5G Authentication.  
The simulation environment consisted of 10 UEs, and the other nodes, such as SAF, ADMF, DN-AAA, etc. were configured one by one. In the experiment, we simulated the random actions of each UE repeatedly and calculated the number of packets generated in each step on communication types. For each step of the simulation, each UE repeats the action such as inter-network physical moving or using service, that can trigger the request of authentication, re-authentication, and the expiration of authentication. If the DN-AAA does not authenticate a UE, UE continuously requests a connection for secondary authentication with a specific probability (we had set the probability on authentication request to 0.8). If a UE is authenticated, the UE continuously take some actions that could trigger the expiration or re-authentication (we had set the probability of expiration to 0.2 and the probability of re-authentication to 0.8). Moreover, each success rate of the re-authentication had been set to 0.8. We performed the simulation repeatedly for 1000 steps.
# Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach
## 1 Introduction
Our simulation results show the benefits of having two separate control components that autonomously adapt the deployment of DSP applications on a dynamic set of computing resources. They demonstrate the flexibility of the proposed infrastructure-level policy based on RL, which can be tuned to optimize different deployment objectives while still supporting the application-level elasticity. The results also show that the combination of RL-based solutions for the ICS as well as for the ACS represents the most flexible solution: if conveniently configured, it reduces the average number of used computing resources with respect to the other policies, while also limiting the number of application reconfigurations (thus increasing the application availability). The rest of this paper is organized as follows. In Section 2. we review related work; in Section 3, we describe the system model and the problem under investigation; and, in Section 4, we present E2 DF and its hierarchical control architecture for self-adaptation. To control DSP applications and computing resources, E2 DF exploits hierarchical policies (Section 5). For the ICS, we propose novel policies resorting on a simple heuristic and on a RL-based approach (Section 6). To show the multi-level adaptation capabilities of E2 DF, we also consider some elasticity policies already proposed in literature, which can conveniently scale the number of operator replicas at run-time (Section 7). Then, by means of simulations, we extensively evaluate the proposed RL-based control policy for the ICS and the resulting self-adaptation capabilities of E2 DF (Section 8). Finally, we conclude in Section 9.
## 1 Introduction
For example, _CogMed_ and _RehaCom_--the two most widely studied cognitive training game platforms--leverage a keyboard and a mouse [31; 32; 33] and a custom-designed keyboard-like interface [34; 35; 36; 37], respectively. These interfaces require bi-manual and/or fine-hand motor skills [34; 35; 36; 37], which could be especially challenging for stroke survivors who often have hemiparesis and limited distal upper-limb motor function [38]. Furthermore, these games have been primarily developed for their use in clinical settings, where therapists' supervision is readily available. For instance, individuals with acquired brain injury who used _RehaCom_ had to rely on their therapists to customize and administer game-based therapies [39]. Our research team has recently developed _Neuro-World_--a set of six serious games for cognitive training on mobile devices (e.g., smartphones and tablet computers)--that provides simple and user-friendly touchscreen interfaces so that stroke survivors, who often exhibit hemiparesis, can self-operate the technology [40]. In the current work, we examine the therapeutic effectiveness of our serious game-based training on improving cognitive function via a randomized controlled trial in chronic-stage stroke survivors with mild-to-moderate cognitive and motor impairments. The study was conducted in a clinical setting (i.e., nursing home associated with a rehabilitation hospital), where participants were reminded of the training, although they were asked to self-operate the games without any assistance from caregivers or research staff. Our underlying hypotheses included that (1) stroke survivors with hemiparesis could effectively self-operate _Neuro-World_ and (2) chronic-stage stroke survivors with mild-to-moderate cognitive impairment would significantly improve their cognitive function after using _Neuro-World_ for a 12-week period.
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 3 Game Theory-Based Routing Protocols in WSNs
### Routing Protocols Based on Noncooperative Games
#### 3.2.4 Cooperation Optimal Protocol for Multi-rate Opportunistic (COMO) Routing and Forwarding  
Multi-rate opportunistic routing can achieve high throughput in WSNs. However, the performance of a multi-rate opportunistic routing cannot be ensured when the nodes participating in routing have selfish behaviors. In [38], [PERSON] et al. designed a cooperative-optimal protocol for multi-rate opportunistic routing (COMO) that can guarantee the faithfulness of each player.  
The presented routing protocol achieved social efficiency and Pareto-efficient Nash equilibrium with faithfulness as a given property. The term social efficiency implied the maximum end-to-end throughput in the COMO. In the Pareto-efficient Nash equilibrium, no node can improve its utility without reducing the utility of at least one player. This routing scheme was the first scheme to integrate game theory with opportunistic routing. The authors of [38] modeled the problem of multi-rate opportunistic routing as a strategic game. Each player (node) in the routing game belonged to a set \(N\). S was the set of strategies in the COMO where \(S=\{S_{1},\ S_{2},S_{3},\ldots,S_{n}\}\). The utility function (\(u_{i}\)) was expressed as the difference between payment \(p_{i}\) and cost \(c_{i}\) for forwarding data packets:  
\[u_{i}=p_{i}-c_{i}. \tag{22}\]  
It was assumed that the players (nodes) were rational and they aimed to increase their net utilities. Technical strengths:  
* Considerable amount of throughput is gained in presence of the selfish nodes. Technical weaknesses:* No comparison with the existing state-of-art protocols was demonstrated. Applications:
* Applicable for high throughput multimedia data transmission. Probable future improvements:
* The only routing metric that COMO [38] covers is the end-to-end throughput. This metric is defined as the number of packets transmitted per second. Certain other important routing metrics for the performance comparison could have been considered to prove the robustness of the designed protocol.
# Dynamic Line Scan Thermography Parameter Design via Gaussian Process Emulation
## 2 Materials and Methods  
### Data Generation  
The simulated data used in this manuscript are provided by a finite element simulation. The simulation consists of a flat bottom hole plate and a line heater. The flat bottom hole plate has the following dimensions: 330 \(\times\) 170 \(\times\) 10 mm. The material linked to the plate is PVC, and the circular pocket is located in the center of the sample. A representation of the simulation can be found in Figure 1.  
The line heater translates above the flat bottom hole plate, and the thermal response of the sample is examined. The sample to be inspected is a PVC flat bottom hole plate with defects varying in size and depth. The simulation uses the following variables: movement velocity, distance between the heat source and the camera, heating power, start depth of the defect, diameter of the defect, height of the camera and ambient temperature. This allows for a variety of scenarios to be mimicked. The result of the simulation is the temperature difference between a position on the surface above a defect and a position that is not above a defect. The result of each simulation is used to generate a response surface (see Figure 2). For more details, we refer to [2].  
Figure 1: Visualization of the finite element simulation consisting of a flat bottom hole plate (blue) and a line heater (yellow). The line heater moves above the sample in a linear motion. For a more detailed figure and explanation, we refer the reader to [2].  
Figure 2: Response surface as generated in [2]. The surface is generated from 1000 finite element simulations, using eight input parameters. The simplified response surface has all input parameters fixed, except for the heat load and the source velocity. The fixed parameters are: \(d_{heat-cam}\) = 425 mm, \(d_{start}\) = 5.8 mm, \(D_{hole}\) = 9 mm, \(d_{height}\) = 430 mm, \(T_{ambient}\) = 48 \({}^{\circ}\)C. Using this response surface, one can find the best temperature difference as a valley or top.  
Running a simulation is much faster and cheaper than performing actual measurements since there is no need for cooling time between consecutive simulations. However, as thoroughly described in [2], every simulation still requires solving time. The data provided by these simulations serve as input for the training of the underlying model in the emulation. In this work, the model is a Gaussian process.
# Blade Shape Optimization and Analysis of a Propeller for VTOL Based on an Inverse Method
## 2 Modeling
### Non-Cruising Stage Trimming  
The trimming revolutionary speed and/or collective pitch on non-cruising stages including hover and takeoff should be solved to obtain the energy consumption for the whole mission profile. For a VPP, the trimming procedure is to solve the minimum power optimization problem with the revolutionary speed \(\omega_{i}\) and the collective pitch \(d\theta_{i}\) as the design variables, which is expressed as:  
\[\begin{array}{l}d.v.y=(d\theta_{i},\omega_{i})\\ \min\ P_{i}^{BLDC}(y)\end{array} \tag{5}\]  
The other objective is the maximum thrust-to-weight ratio \(\kappa\) at hover S\({}_{1}\), which is expressed as:  
\[\kappa=N_{p}T_{1}^{max}/W_{to}g \tag{6}\]  
where \(g\) is gravity acceleration, \(W_{to}\) is the takeoff weight and \(N_{p}\) is the number of propellers. Meanwhile, to complete the maximum velocity vertical flight stage S\({}_{6}\), the maximum thrust in S\({}_{6}\) should satisfy the following relationship:  
\[N_{p}T_{6}^{max}\geq W_{to}g \tag{7}\]  
The maximum thrust at S\({}_{1}\) and S\({}_{6}\) are solved by the following optimization problem:  
\[\begin{array}{l}d.v.y=(d\theta_{i},\omega_{i})\ (i=1,6)\\ \max\ T_{i}(d\theta_{i},\omega_{i})\\ s.t.\ P_{i}^{BLDC}\leq P_{limited}^{BLDC}\\ Q_{i}^{BLDC}\leq Q_{limited}^{BLDC}\end{array} \tag{8}\]  
Here, \(P_{limited}^{BLDC}\) and \(Q_{limited}^{BLDC}\) are the limited input power and limited output torque of the BLDC motor, respectively.  
Non-cruising state trimming problems for an FPP can be generated by eliminating the collective pitch \(d\theta_{i}\) and leaving the speed revolutionary \(\omega_{i}\) as the only design variable. All the optimization problems in this section could be rapidly solved by a gradient-based optimization method such as sequential quadratic programming (SQP) algorithm.
# Biosynthesis of Silver Nanoparticles Using _Tabernaemontana ventricosa_ Extracts
## 3 Results and Discussion
### UV-Visible Spectroscopy  
UV-visible absorption spectra are often used to confirm and characterize (the formation, distribution, and stability) synthesized AgNPs in a solution [33; 39]. In the present study, the production of biologically synthesized AgNPs using the various leaf and stem extracts (methanol, fresh, and powder) of _T. ventricosa_ was followed by determining the surface plasmon resonance (SPR) of the various solutions at a wavelength ranging from 300 to 700 nm [40]. Characteristic SPR bands for silver (Ag) are usually detected around 400-460 nm [15]. The UV absorption spectra of the synthesized AgNPs are displayed in Figure 2. Variable absorption bands were observed for all AgNPs. The results of the current study observed SPR bands at 418, 418, 478, 364, 446, and 414 nm for synthesized AgNPs using leaf methanol (LM), stem methanol (SM), fresh leaf (FL), fresh stem (FS), powdered leaf (PL), and powdered stem (PS), with the highest maximum absorbance of 1.824 for synthesized AgNPs using leaf methanol (LM) followed by stem methanol (1.599), powdered leaf (1.036), powdered stem (0.832), and fresh leaf (0.521) and the lowest absorbance produced by the fresh stems (0.123). The wavelength of all synthesized AgNPs except fresh leaves fell within the category of SPR bands for AgNPs. Previous studies using extracts of _T. divaricata_ for the synthesis of AgNPs showed contrasting results, as these extracts resulted in peaks at 420 nm [2], compared to the current study, which observed various other peaks (Figure 2). In addition, a recent study by [PERSON] et al. [41] also investigated _T. divaricata_ extracts, which showed an extreme absorption band at 426 nm signifying the creation of AgNPs. Moreover, the nature of lengthy tails as displayed in Figure 2 suggests deviations in the size distribution of the synthesized NPs [42]. [PERSON] et al. [2] observed broad peaks indicative of well-dispersed particles in the solutions.
# Extended Spatial-Index LED Modulation for Optical MIMO-OFDM Wireless Communication
## 2 System Model  
In this section, the proposed ESI-LED system structure and its maximum a posteriori probability (MAP) estimator are presented as well as the indoor optical wireless channel model.
## 4 Discussion
### Between PI in Soil and Contents in Crops  
The \(PI\) values of heavy metals in soils and their contents in crops in the 95 corresponding samples in the Xinping area are illustrated in Figure 5.  
For Hg and Pb, all crops are unpolluted and grew in the soils with background-level pollution. For As, Cd, and Cr, most crops are unpolluted and grew in the risk-free-level soils with percentages of about 93.7%, 82.1%, and 77.8%, respectively, in the 95 crop samples. Meanwhile, two polluted crops grew in the screening-level soils (accounting for about 2.1%) for Cd. Therefore, the consistency of pollution status between crops and soils for Hg, Pb, As, Cd, and Cr in the Xinping area are 100%, 100%, 93.7%, 84.2%, and 77.8%, respectively.  
Figure 4: Geochemical map of the \(PI_{5}\) of heavy metals in surface soils from the Xinping area along with its geological map. Notes: the triangle symbol in black (rice1) represents the uncontaminated rice samples, while that in red (rice2) represents contaminated rice samples.  
However, one polluted crop grew in the background-level soils (accounting for about 1.1%) for Cr. This inconsistency in pollution status between crop and soil is called Type I inconsistency here, while a few unpolluted crops grew in the screening- and intervention-level soils for As (about 5.3% and 1.0%, respectively), Cd (about 14.7% and 1.1%, respectively), and Cr (about 11.6% and 9.5%, respectively). This inconsistency in pollution status between crop and soil is called Type II inconsistency here. These inconsistencies are new challenges between criteria of crops and soils.