esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Silicon Photonics Micro-Transceiver with Multimode Interface
### Multimode Optical Interface Performance Challenges at High Temperature  
In the transmitter section, the modulated light is coupled through a vertical grating coupler into a tilted waveguide called an optical pin (Figure 15a). The emission angle of the grating coupler varies with wavelength (Figure 15b), and quantum dot lasers exhibit a wavelength shift with temperature (Figure 15c); this in turn results in a change in the direction of optical output over the temperature range. In the case of Fabry-Perot lasers, the wavelength has a temperature dependence of \(\sim\)0.56 nm/\({}^{\circ}\)C, so when the temperature rises from 25 to 105 \({}^{\circ}\)C, the emission angle of the grating coupler changes by about 5\({}^{\circ}\) due to the corresponding variation in wavelength.  
If the multimode optical fiber were directly connected to the grating coupler, the coupling loss would change more strongly over the temperature range in response to the change in emission angle. However, the optical pin has a higher NA of 0.4 and a core diameter of 35 \(\upmu\)m, which confines the light more strongly in the optical pin core than a multimode fiber would and can thus better accommodate this variation in emission angle.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Ta 40 \({}^{\circ}\)C (Tj 60 \({}^{\circ}\)C)** & **Ta 105 \({}^{\circ}\)C (Tj 125 \({}^{\circ}\)C)** \\ \hline w/ FEC & \(-\)8.2 dBm & \(-\)7.8 dBm \\ w/o FEC & \(-\)5.6 dBm & \(-\)5.0 dBm \\ \hline \end{tabular}
\end{table}
Table 1: Reception sensitivity.  
Figure 15: Optical coupling interface performance over temperature. (**a**) Schematic view of optical pin on Tx side accommodating variation in directional angle from the grating coupler over temperature; (**b**) variation of angle with wavelength; (**c**) variation of wavelength with temperature.  
This intermediary waveguide then couples light into a multimode fiber with little variation in coupling loss over the temperature range.
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 3 Background in Programmable Data Plane and 5G and Beyond
### 5G and Beyond Technology and Architecture  
#### 3.2.1 5G Technology  
5G technology has been developed by the 3 rd Generation Partnership Project (3 GPP), which has set out to define and finalize its specification over Releases 15, 16, 17, and 18 [7]. 5G aims to deliver enhanced data rates exceeding the capabilities of existing 4G LTE networks. Moreover, it offers wider network coverage, more reliable connections, reduced latency times, lower power consumption, and improved scalability [30]. These capabilities allow the implementation of new applications, also known as verticals, such as: eHealth, autonomous vehicles (V2X), virtual/augmented reality (VR/AR), smart cities, smart homes, and Industry 4.0. Accordingly, the IMT-2020 (International Mobile Telecommunications--2020), issued by the ITU-T [8], has defined three categories to classify 5G applications, as can be observed in Figure 3:  
* Enhanced mobile broadband (eMBB): Designed to deliver high data rates (up to >> 1 Gbps), allowing users to experience new levels of mobile broadband connectivity. It supports services like virtual reality, ultra-high-definition video streaming, or immersive gaming.
* Ultra-reliable and low-latency communications (uRLLC): Enables devices to communicate with each other in \"real time\". This category of service is applied in scenarios  
Figure 3: Categories for 5G applications [8].  
where data loss must be avoided, low latency is crucial, and a high level of reliability is required. Applications such as V2X, distribution automation in a smart grid, or remote medical surgery are supported.
* Massive machine type communications (mMTC): Enables massive numbers of devices to be connected. Usually, these devices transmit relatively low volumes of non-delay-sensitive data. Backed services include IoT applications: Smart cities, smart homes, or some industrial IoT scenarios.  
The aforementioned usage scenarios are associated to a number of KPIs defined for IMT-2020 in [10]. In this manner, the KPIs have minimum technical demands to fulfill in order to be 5G complaint, e.g., user plane latency of 4 ms for eMBB and 1 ms for uRLLC. Table 1 reports a complete list of these requirements.
## 2 Proposed Method
### Moore-Penrose Inverse  
From Equations (4) and (5), only the matrices \(\mathbf{D}\) and \(\mathbf{C}\) are unknown, and combining Equation (3), \(\mathbf{Z}\) can be solved by solving the matrices \(\mathbf{D}\) and \(\mathbf{C}\). Since the spectral information in HR-HSI is mainly from LR-HSI, the idea that LR-HSI and HR-HSI contain the same spectral basis was proposed and verified in [22]. Therefore, we can perform SVD on LR-HSI to obtain the preliminary spectral basis.  
\[\mathbf{H}=\mathbf{U}\mathbf{\Sigma}\mathbf{V}^{\mathrm{T}} \tag{6}\]  
Since the first few singular values of a matrix can contain more than 90% of the matrix information, we save the first \(q\) singular values in \(\mathbf{\Sigma}\) to obtain the following spectral basis, i.e.,  
\[\mathbf{D}=\mathbf{U}(:,1:q) \tag{7}\]  
In Equation (5), only the matrix \(\mathbf{C}\) is unknown, and the matrix \(\mathbf{C}\) can be obtained by inverting the matrix \(\mathbf{RD}\).  
\[\mathbf{C}=(\mathbf{RD})^{-}\mathbf{M} \tag{8}\]  
Since the matrix \(\mathbf{RD}\) is not necessarily a square matrix, the inverse of the matrix \(\mathbf{RD}\) in Equation (8) is not unique, which will cause errors in the solution results. However, the Moore-Penrose inverse (\"+\" inverse) is unique among all inverse matrices of the matrix,so the fuzzy estimate of the matrix \(\mathbf{C}\) can be obtained by introducing the Moore-Penrose inverse as follows.  
\[\mathbf{C}=(\mathbf{R}\mathbf{D})^{+}\mathbf{M} \tag{9}\]  
According to the definition of the matrix Moore-Penrose inverse, we can solve the Moore-Penrose inverse of the matrix RD through the Algorithm 1.  
```
0: RD \(\left[\mathbf{U}_{1},\mathbf{\Sigma}_{1},\mathbf{V}_{1}\right]=\mathrm{SVD}( \mathbf{R}\mathbf{D})\) \(\left[\mathbf{m},\mathbf{n}\right]=\mathrm{size}(\mathbf{\Sigma}_{1})\) \(K=\min\left(\mathbf{m},\mathbf{n}\right)\) for\(\mathbf{k}=1\):K do if\(\mathbf{\Sigma}_{1}(k,k)!=0\)then \(\mathbf{\Sigma}_{1}(k,k)=1/\mathbf{\Sigma}_{1}(k,k)\) endif endfor \(\mathbf{\Sigma}_{2}=\mathrm{zeros}(\mathbf{n},\mathbf{m})\) for\(\mathbf{k}=1\):K do \(\mathbf{\Sigma}_{2}(k,k)=\mathbf{\Sigma}_{1}(k,k)\) endfor \(\left(\mathbf{R}\mathbf{D}\right)^{+}=\mathbf{V}_{1}\mathbf{\Sigma}_{2}\mathbf{ U}_{1}{}^{T}\) return\(\left(\mathbf{R}\mathbf{D}\right)^{+}\)
```  
**Algorithm 1** Moore-Penrose inverse
## 2 Related Work
### Fog Applications  
This section is devoted to the analysis of applications in Fog Computing or in Cloud to Edge Computing solutions. The focus of this analysis is to identify the main way of designing, composing and deploying applications in these distributed environments.  
In [12], the authors studied the management of application elasticity in Kubernetes. To do so, they divided applications into small and independent microservices. Each of these microservices represented a single functionality encapsulated in a Pod, managed by Kubernetes. The application itself was represented as a Deployment in Kubernetes and they considered it to make runtime adaptation decisions about the elasticity of the microservices.  
[PERSON] [21] focused on modeling the applications and explicitly reflecting the relationship between the services that compose them based on TOSCA concepts. In fact, tools such as Kubernetes do not reflect the connections, and that task requires reverse engineering from the application managers.  
The division of applications in microservices was also treated in [20], leaning on TOSCA to reflect the microservices and their communication relationships. Furthermore, the authors focused on the importance of considering the communications between microservices when scheduling individual Pods in Kubernetes. The approach presented in [19] presented a model-driven, role-based application orchestration approach where applications were designed based on TOSCA templates, transformed into OAM-compatible YAML files and deployed in a OAM-extended Kubernetes. This way, the application design could leverage the capabilities of TOSCA modeling and abstract the designer from the implementation in a deployment platform.  
In [13], the authors presented an Application-Centric Orchestration Architecture (ACOA) focused on the distributed scheduling of applications in the Cloud-Edge continuum. Following the _Workload Model_, applications are made up of components that comprise executable elements. Components are deployed inside containers and the application topology is represented in channel elements that reflect message transmission between pairs of components, representing applications as directed graphs.  
Other works have proposed the division of applications in functions using the serverless pattern [14; 24]. Functions are executed in containers and managed by orchestrators.  
It is clearly noticeable that microservices are the leading paradigm when designing applications in distributed environments. Furthermore, the design of applications and their deployment are usually considered separate processes and decoupling the design of applications from the development and deployment of the components that compose them is a requirement in the works identified.
## 4 Problem Space for DL Applications
### Device Deployment with DL
#### 4.2.2 DL Applied in Wearable Devices  
The value of the global wearable technology market has been projected to grow dramatically. The value increase from USD157.30 billion in 2024 to USD1695.46 billion by 2032. Active wearables include smart glasses, VR headsets, and smartwatches. Passive wearables include wristbands, rings, key rings, brooches, and clothing [174].  
DL applications in healthcare monitoring include DL use in wearable devices with sensors for tracking patient vitals and movements to enhance care [122]. The rise of wearable technology, such as smartwatches and ECG monitors, is driven by an aging population. These devices collect substantial health data that can be analyzed with DL for condition monitoring and prediction. Studies often employ CNNs [175] to process ECG signals for arrhythmia detection and RNNs to analyze heart rate data for predicting cardiac events.  
DL models, especially CNNs, embedded in wearable devices to predict and record activities (e.g., sitting, walking, lying down, climbing stairs, jogging, running, falling) can achieve human activity recognition (HAR). CNNs are widely used in image recognition, with excellent short-distance signal processing [176].
## 5 Results and Discussion
### Throughput  
The amount of data determines the throughput transmitted to other nodes in the network. In Figure 5, the throughput is displayed for various network sizes, including 100, 200, and 300. The number of data packets received by the sink node from the CH in the network size 100 scenario is 130,000, 140,000, 170,000, and 180,000 for EECHS-ARO, EECHS-ISSADE, PDU-SLno, and EACH-COA, respectively. The number of data packets received by the sink node from the CH in the network size 200 scenario is 220,000, 200,000, 240,000, and 260,000 for EECHS-ARO, EECHS-ISSADE, PDU-SLno, and EACH-COA, respectively. The number of data packets received by the sink node from the CH in the network size 300 scenario: 310,000, 300,000, 360,000, and 400,000 for EECHS-ARO, EECHS-ISSADE, PDU-SLno, and EACH-COA, respectively. It is clear that the COA transmitted more packets to the sink node in the three different scenarios compared to other similar algorithms: EECHS-ARO, EECHS-ISSADE, and PDU-SLno. This is also a reason for the enhanced throughput and network lifetime compared to other algorithms.  
Table 4 lists the throughput of the proposed EACH-COA with respect to the various node numbers. The sink node received a more significant number of packets in comparison to EECHS-ARO, EECHS-ISSADE, and PDU-SLno. The proposed COA requires less time to converge during CH rotation within the network.
# Quality Characteristics and Antioxidant Activities of Six Types of Korean White Wine
## 2 Materials and Methods
### Evaluation of Antioxidant Activities of Korean White Wine  
The diphenylpicrylhydrazyl (DPPH) radical scavenging activity was assessed based on a method described by [PERSON] et al. (2011) [PERSON] et al. (2011), with minor modifications. Briefly, 100 \(\upmu\)mol DPPH was dissolved in 96% ethanol to prepare the radical stock solution immediately prior to experimentation. Subsequently, 1 mL of DPPH was mixed with 1 mL of the wine sample and 3 mL of 96% ethanol. After thorough agitation, the mixture was stored at room temperature under dark conditions for 30 min. The decrease in the absorbance of the resulting solution was monitored at 517 nm after 30 min. The obtained results were corrected for dilution and expressed in \(\upmu\)M Trolox/mL of Korean white wine. The absorbance was measured using a spectrophotometer (UV-1601, Shimadzu Co.).  
The 2,2'-azino-bis-3-ethylbenzothazoline-6-sulfonic acid (ABTS) radical scavenging activity was evaluated according to a method described by [PERSON] et al. (2011) [PERSON] et al. (2011). Briefly, ABTS was dissolved in water to prepare a 7 \(\upmu\)M concentration. The ABTS radical cation (ABTS) was generated via the reaction of ABTS stock solution with 2.45 \(\upmu\)M potassium persulfate (final concentration) and was stored at room temperature under dark conditions for 12-16 h prior to utilization. This radical remained stable in this state for \(>\)2 days. Samples containing the ABTS solution were diluted with redistilled water to reach an absorbance of 0.700 \(\pm\) 0.02 at 734 nm and were equilibrated at 30 \({}^{\circ}\)C. Subsequently, after adding 3.0 mL of diluted ABTS solution (A\({}_{73\upmu\mathrm{m}}\) = 0.700 \(\pm\) 0.02) to 30 \(\upmu\)L of a wine sample, the absorbance was measured exactly 6 min after initial mixing. The results were corrected for dilution and expressed in \(\upmu\)M Trolox/mL of Korean white wine. The absorbance was measured using a spectrophotometer (UV-1601, Shimadzu Co.).  
The ferric ion-reducing antioxidant power (FRAP) was measured according to a method described by [PERSON] et al. (2011) [PERSON] et al. (2011). This assay was based on the reducing power of a compound (an antioxidant). A potential antioxidant can reduce ferric ions (Fe\({}^{3+}\)) to ferrous ions (Fe\({}^{2+}\)), whereby the latter forms a blue complex (Fe\({}^{2+}\)/TPTZ), leading to an increase in absorbance at 593 nm. The FRAP reagent was prepared by mixing 300 \(\upmu\)M acetate buffer (pH 3.6), 10 \(\upmu\)M TPTZ solution in 40 \(\upmu\)M HCl, and 20 \(\upmu\)M FeCl in a ratio of 10:1:1 (\(v/v/v\)). Then, 300 \(\upmu\)L of FRAP reagent and 10 \(\upmu\)L of wine samples were added to 96-well plates and thoroughly mixed. The absorbance was measured at 593 nm after 10 min using a spectrophotometer (UV-1601, Shimadzu Co.). A standard curve was constructed using different Trolox concentrations. All solutions were prepared on the same day of the experiment. The results were corrected for dilution and expressed in \(\upmu\)M Trolox/mL of Korean white wine.
# Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
###### Abstract  
This paper focuses on predicting road passenger probability and optimizing taxi driving routes based on trajectory big data. By utilizing clustering algorithms to identify key passenger points, a method for calculating and predicting road passenger probability is proposed. This method calculates the passenger probability for each road segment during different time periods and uses a _BiLSTM_ neural network for prediction. A passenger-seeking recommendation model is then constructed with the goal of maximizing passenger probability, and it is solved using the NSGA-II algorithm. Experiments are conducted on the Chengdu taxi trajectory dataset, using _MSE_ as the metric for model prediction accuracy. The results show that the _BiLSTM_ prediction model improves prediction accuracy by 9.67% compared to the BP neural network and by 6.45% compared to the _LSTM_ neural network. The proposed taxi driver passenger-seeking route selection method increases the average passenger probability by 18.95% compared to common methods. The proposed passenger-seeking recommendation framework, which includes passenger probability prediction and route optimization, maximizes road passenger efficiency and holds significant academic and practical value.  
trajectory data; taxi passenger-seeking; road passenger probability; _BiLSTM_ prediction; route optimization; NSGA-II algorithm
## 2 Materials and Methods
### Statistical Analysis  
The normality of the data distribution was assessed using the Shapiro-Wilk test and by visually inspecting Q-Q and box plot graphs. The paired \(t\)-test was employed to evaluate potential differences between the right and left sides concerning spatiotemporal and dynamic parameters measured in each leg. One-way repeated measures ANOVA was performed to detect possible differences between walking conditions for step length, width and time, cadence, and the duration of stance and swing phase. Differences between walking conditions (within-subjects factor) and foot areas (rearfoot, midfoot, and forefoot; between-subjects factor), as well as the walking condition-by-foot area interaction for vGRFs, were assessed using a mixed-design two-way ANOVA. The sphericity of the data was determined based on Mauchly's Test, where significant Greenhouse-Geisser correction was used. Pairwise comparisons were performed using the Bonferroni adjustment. The statistical analysis of the data was performed with SPSS 29.0 (IBM Corp, Armonk, NY, USA), while the significance level was set at the level of \(p\leq 0.05\).
# Steam Explosion-Based Method for the Extraction of Cellulose and Lignin from Rice Straw Waste
###### Abstract
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Biomass** & **Extraction Method** & **Conditions** & **Yield** & **Strategic Advancements** & **Reference** \\ \hline \multirow{8}{*}{Rice straw} & Sequential & & & & Advanced a significant leap in biomass valorisation through \\  & execution of alkaline & & & & biomass valorisation through \\  & pretreatment, & on the steam & Selective extraction & a novel, optimised steam \\  & steam explosion & explosion process & of cellulose at & explosion process, \\  & with and without & & and oxidation & 70\% efficiency & significantly enhancing \\  & supercritical CO\({}_{2}\) & treatment; trying & with a 20\% yield; & cellulose extraction efficiency & research \\  & integration, & to eliminate & significant & and sustainability, and \\  & oxidation & alkaline & recovery of lignin & providing a holistic approach & \\  & treatment, and & pretreatment & & to agricultural & \\  & bleaching & & & & waste management \\ \hline \multirow{8}{*}{Phoenix canariensis palm leaves oxidation, and bleaching} & Sequential & Steam explosion times between & & Cellulose yield & \\  & processes & 15–33 min; & & significantly & Highlighted the optimised combination of steam \\  & including alkaline & temperature up to & & processing time, & explosion with alkaline and \\  & treatment, steam & 200 \({}^{\circ}\)C; alkaline & & with notable & oxidation treatments for \\  & explosion, & and oxidation & & efficiency in lignin & improved cellulose fibre \\  & oxidation, and & conditions for & and hemicellulose & extraction, suitable for \\  & bleaching & effective & & removal, obtaining & high-value \\  & & delignification & & 56\% cellulose & material applications \\  & & & content & & \\ \hline \multirow{8}{*}{Wheat straw} & \multirow{4}{*}{Steam explosion followed by ethanol extraction} & Steam explosion at & & & Demonstrated an effective \\  & & 1.6 MPa for & & & multilevel composition \\  & Steam explosion & 5.2 min; ethanol & & fractionation yield & fractionation process, \\  & followed by & for lignin & of 73\%; lignin & enabling the high-value \\  & ethanol extraction at & fractionation yield & utilisation of wheat straw for & \\  & & 160 \({}^{\circ}\)C for 2 h with & of 90\%; cellulose & cellulose extraction and \\  & & 60\% ethanol (\(v/v\)) & & yield of 93.2\% & regenerated cellulose \\ \cline{1-1} \cline{2-6}  & & & & film production \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A concise comparative assessment of biomass valorisation methods.
## 1 Introduction
This highlights the importance of developing robust prediction models based on past data to enable the detection of the industrialized construction practitioners' technology utilization level. Predictive analysis is commonly applied in construction-related research, such as contractor performance prediction [16], construction company failure prediction [17], and sustainability performance prediction [18]. However, similar research often requests the users to evaluate dozens of factors to obtain the prediction results, which might be too sophisticated to be understood and utilized by users [17]. In addition, some of the factors, such as organization skills or cultures, are difficult to collect and quantify. The rating process of these determinants could be affected by respondents' selection preferences and subjectivity [17]. Without a benchmarking system, the organization or individuals cannot get a consistent evaluation of these determinants. These limitations mentioned above might decrease the generalization of the predictive model, and a more efficient and objective method is needed. Our preliminary research [8; 13; 19] used Analysis of Variance (ANOVA) to find that company background and personal career profiles can significantly affect the practitioners' technology utilization level in the industrialized construction projects. The data about company background and personal career profiles can be easier to access compared to other types of determinants that need to be evaluated subjectively by respondents. Thus, four variables--company size, company type, working experience, and working position--are regarded as viable predictors to predict the practitioners' technology utilization level. The primary objective of this article is to describe a comprehensive effort to predict the emerging technology utilization level of industrialized industry practitioners using practitioners' company backgrounds and career profiles. An external test dataset has been used to evaluate the efficiency of the prediction model. Correlating historical data allows the development of a model to assist the planning and implementation of emerging technologies into the industrialized construction industry and the collaboration between academia and industry. Although the scope covers any developed economy, this article uses case studies from the United States and Chinese industry. The findings of this article contribute to the body of knowledge and practice by offering an efficient and accurate method to the predict practitioners' technology utilization level in the industrialized construction.
# Enhanced Multicast Repair Fast Reroute Mechanism for Smart Sensors IoT and Network Infrastructure
## 5 Enhancements for M-Rep  
Although the M-REP mechanism represents a new approach to addressing the IPFRR, this mechanism contains some limitations, which we removed with the proposed extensions. In this section we present the results of further research on M-REP mechanism enhancements. We have primarily focused on the treatment of multiple failures and the enhancement of specific deployments, i.e., the Area Border Router extension.