esa-sceva/satcom-chunk-collection huggingface dataset:
# The Vehicle Routing Problem: State-of-the-Art Classification and Review
## 3 VRP and Its Variants
### VRP Variants  
Practical requirements and new challenges require extensive definitions and formulations of the VRP. For example, distance, driver working hours, time windows, traffic conditions, and so on can all arise in real-world VRPs and enrich the definition and applications of VRPs. This chapter provides an overview of recent research on different models for vehicle routing. The main goal of this chapter is to present an overview of vehicle routing and scheduling areas while discussing several real-world applications. Some features of VRPs are summarized in Table 4 based on the research by [PERSON] et al. [4]. Other variants have also been studied beyond the classical VRP. These variants include the influence of time factors, time windows of customers, maximum operating time of vehicles, differing delivery times caused by varying traffic conditions, varying characteristics of vehicles, varying capacities, varying speeds, and new types of electric vehicles. By referring to the taxonomy of [4], we divided models into three main categories: customer-related, vehicle-related, and depot-related models, which is the most important issue to represent the difference in real delivery problems. These categories have representative model features that are sorted in the tables below according to the year as shown in Tables 5-7. \begin{table}
\begin{tabular}{c c} \hline
**1. Type of Study** & **3.4. Number of Points of Origin** \\ \hline
1.1. theory & 3.4.1 single origin \\ \hline
1.2. applied methods & 3.4.2 multiple origins \\ \hline
1.2.1 exact methods & 3.5. number of points of loading/unloading facilities (depot) \\ \hline
1.2.2 heuristics & 3.5.1 single depot \\ \hline
1.2.3 simulation & 3.5.2 multiple depots \\ \hline
1.2.4 real-time solution methods & 3.6. time window type \\ \hline
1.3. implementation documented & 3.6.1 restriction on customers \\ \hline
1.4. survey, review of meta-research & 3.6.2 restriction on roads \\ \hline
2. scenario characteristics & 3.6.3 restriction on depot/hubs \\ \hline
2.1. number of stops on rout & 3.6.4 restriction on drivers/vehicle \\ \hline \end{tabular}
\end{table} 
Table 4: Taxonomy of VRP literature (adapted from [4]).
## 2 Materials and Methods
### n-Hexadecane Biodegradability Assay Using 2,6-Dichlorophenol(DCPIP)  
The biodegradability of _n_-hexadecane was assessed using the redox indicator 2,6-dichlorophenol(DCPIP) [27]. In yeast cells, C\({}_{15}\)-C\({}_{36}\) hydrocarbons (including the n-hexadecane) are first hydroxylated to fatty alcohols by cytochrome P450 monooxygenase (based on nicotinamide adenine dinucleotide phosphate--NADPH and flavin adenine dinucleotide--PAD system). Fatty alcohols are further oxidized to corresponding fatty aldehydes and fatty acids in the endoplasmic reticulum, thus forming triacylglycerlecrools or, in the peroxisomes, forming acetyl coenzyme A (Ac-CoA) molecules, which enter the glyoxylate and citrate cycles. The process comprises redox reactions and electron transfer to acceptors (O\({}_{2}\), nitrates or sulphates) [19]. The DCPIP added to a mineral medium, in which a hydrocarbon (i.e., n-hexadecane) represents the sole carbon substrate, acts as an electron receptor during the oxidation process, changing from the oxidized form (blue) to the reduced form (colorless) [28].  
The strain _Y. lipolytica_ CMGB32 was cultivated for 72 h at 28 \({}^{\circ}\)C, 150 rpm in two flasks: on a YP medium supplemented with 1% _n_-hexadecane (FLUKA) and on a YPG medium, respectively. The inoculum of _Y. lipolytica_ CMGB32 (0.3 \(\times\) 10\({}^{8}\) cells/mL, determined using the Thoma chamber) obtained from each flask was used in a total volume of 10 mL Bushnell-Hass (BS) mineral medium (g/L: MgSO\({}_{4}\cdot\)7H\({}_{2}\)O 0.2, CaCl\({}_{2}\cdot\) 2 H\({}_{2}\)O 0.02, KH\({}_{2}\)PO\({}_{4}\) 1.0, K\({}_{2}\)HPO\({}_{4}\) 1.0, NH\({}_{4}\)NO\({}_{3}\) 1.0, FeCl\({}_{3}\) 0.05) (pH 7.0 \(\pm\) 0.2) with 1% _n_-hexadecane and 30 ug/mL DCPIP. The test tubes (duplicates) were incubated for 9 days at 120 rpm and 28 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C and observed every day. The substrate control tubes did not contain inoculum, and the inoculum controls did not contain _n_-hexadecane.
## 2 Study Area and Geological Settings  
### Study Area  
The Subis build-up is a massive, cliff-forming coralline limestone hill that hosts the famous Niah Great Cave at the northern flank of the build-up (Figure 1). It is located northwest of the Tinjar Province and extends over 30 km\({}^{2}\), with a peak elevation of 394 m. The northeast-southwest-trending Trusan Fault slices through the build-up and divides it into two major blocks. The Great Cave and Painted Cave towers are the most accessible Subis build-up towers. The Great Cave (Figure 1e) is complex, with several wide passages and high chambers with ceiling openings, while the Painted Cave is located southeast of the limestone complex [24]. The Subis Limestone is commonly used as an analogue for Central Luconia carbonates. Previous studies on the Subis Limestone have focused on the biostratigraphy and sedimentology of carbonate rocks [25; 26; 27; 28; 29] and speleology [24; 30; 31; 32] with no mention of the fracturation history of the outcrop.  
Figure 1: **(a)** Location map of the study area, Subis Limestone, in Niah, Sarawak (red square), with emphasis on the tectonic features [1; 33; 34; 35; 36]. **(b)** Subis build-up on SRTM-Digital Elevation Model (DEM) overlay with a satellite image for clearer lineament delineation. The Subis build-up was divided into two sections by an NE–SW-trending Trusan Fault (white solid line) with several identified internal faults (white dotted lines) that separate the mega limestone into isolated build-ups. **(c)** Flat-top geomorphology of Subis Limestone with the upper portion of the catch-up phase. Different elevations of vegetation indicate the third-order sequence boundary due to sea level changes, now represented by three horizons (base-, inter-, and top-carbonate layers). **(d)** Karst topography distribution [29]. The route of the fieldwork began from the Niah National Park headquarters (HQ) to the Painted Cave. **(e)** A detailed route map within the Great Cave Complex and Painted Cave. The karst features and fault/ fracture evidence were observed along this route.
## 2 Literature Review
### CE in the Textile Sector  
Early research on CE barriers under different circumstances provided insight and practical guidance for CE implementation. Studies that are more recent have focused on specific contexts, such as the manufacturing sector [63], the automobile industry in emerging economies [64], and the construction and demolition waste management industry [65]. Except for these studies, studies on which barriers impede the textile sector from transforming toward CE and how they do so have been rare.  
Textile consumption, along with those of food, housing, and transportation, has substantial environmental effects [66]. From a sustainability perspective, this sector's challenges include the reduction of its material and energy intensity, reduction of toxic substance dispersion, enhancement of recycling, maximization of renewable resource use, extension of product durability, and improvement of service intensity [34]. From a circular perspective, the status quo of the common linear flow of materials requires reform. Such reforms should be undertaken through the careful design of products and industrial processes in such a manner that materials are akin to perpetually flowing nutrients that are managed in closed loops [56]. Most recently, issues related to textile reuse and recycling have gained increased attention in the literature [67; 68]. Textile reuse and recycling are generally recognized as superior methods for reducing environmental effects compared with incineration and landfilling [69]. However, textile recycling remains limited due to the sector's many socioeconomic challenges. The CE is the method of converting supply chain (SC) operations from a linear to a circular production/business model, through which used/waste materials and components are reintroduced by the SC through a closed-loop system by reusing, recycling, remanufacturing, reconstruction, and refurbishing as a way of recapturing inventory and reducing negative impacts [70; 71]. With the adoption of CE strategies, waste generation from manufacturing can be reduced by a significant amount, (1) as well as CE implementation barriers to the creation of innovative sustainable business models. (2) In a CE, waste materials are assessed for further use in the fashion industry, including the inability to rethink the design phase for sustainable product development, poor consumer education, low consumer expectations regarding sustainability, and a lack of alignment of values along the supply chain.  
In general, transforming the textile sector toward a new CE requires system-level changes with an unprecedented degree of commitment, collaboration, and innovation [72]. Additionally, the speed and scale of the transition depend on the knowledge, awareness, and engagement of all market participants. Although various barriers have been revealed by these prior studies, scholars have provided few suggestions for transforming textile systems [73]. Our study thus aims to contribute to this gap by providing prescriptions for the development of CE intervention strategies for the textile industry.
## 3 Results and Discussion
### Correlation Analysis between Aroma Compounds and Bacteria at Genus Level
Spearman correlation with hierarchical clustering among the volatile compounds was applied. * \(p\) < 0.05; **_p_ < 0.01. Almost all of the acids, including acetic acid (sour), propanoic acid (rancid), butanoic acid, pentanoic acid, hexanoic acid, heptanoic acid, and octanoic acid (cheese), were classified in the second cluster. _Lactobacillus_ in fermented dough was positively correlated with these acids in CSB, while _Pediococcus_ and _Weissella_ were negatively correlated with them. This can be explained in two ways: First, in the dough ecosystem, _Lactobacillus_ had higher acid-producing capacity and better tolerance to acid stress than _Pediococcus_ or _Weissella_[41]. Secondly, _Lactobacillus_ might promote the formation of organic acids (mainly C3-C6 acids), in dough co-fermented by LAB and yeast [31].
# Physicochemical and Sensory Evaluation of Yanggaeng Treated with Roasted Guateman Coffee Extracts
## 3 Results and Discussion
### pH and Brix Values of Yanggaeng  
Table 5 presents the pH and Brix values of Yanggaeng prepared with varying concentrations of G at different degrees of roasting. Group L exhibited the lowest pH value of 6.29. As the concentration of G increased, the pH increased in a dose-dependent manner, reaching 6.61 for 2%, 6.67 for 4%, and 6.75 for 6% concentrations (\(p<0.05\)). Conversely, in the M group, pH values decreased with higher concentrations of G, declining to 6.19 in M4 and 5.99 in M6 compared to the CON value of 6.26 (\(p<0.05\)). Group D had relatively stable pH values, with a CON value of 6.28. However, the addition of G significantly increased the pH initially, although higher concentrations led to a subsequent decrease, resulting in the lowest pH in the D6 sample within the D group (\(p<0.05\)). Across all roasting degrees, group M consistently showed the lowest pH values compared to groups L and D.  
Table 5 lists the Brix values of Yanggaeng samples. In the L group, Brix values increased with higher concentrations of G. For CON, the value was 3.03; for L2, it was 3.30; for L6, it was 3.50; and for L4, it reached the highest value of 3.70 (\(p<0.05\)). The M group exhibited an initial increase in Brix values from 3.07 in CON to 3.40 in M2, followed by a slight decrease to 3.20 in M4, and a subsequent rise to 3.57 in M6. In group D, Brix values consistently increased with increasing concentrations of G, ascending from 3.07 in CON to 3.50 in D2, 3.70 in D4, and peaking at 3.90 in D6. When comparing roasting degrees, group D achieved the highest Brix values, followed by groups L and M (\(p<0.05\)). The observed pH variations indicate that the addition of G influences the acidity of Yanggaeng differently, based on the degree of roasting. Light roasting generally preserves more natural acids, resulting in higher pH values, whereas medium roasting slightly lowers the pH owing to the breakdown of organic acids. Dark roasting further reduces acidity levels as chlorogenic acid degrades into compounds such as quinic acid, affecting overall acidity [32; 33; 34]. Chlorogenic acid is most prominent during the early stages of roasting but diminishes as the roasting intensity increases. Consistent with these findings, [PERSON] et al. reported an increase in pH with a stronger roasting of both espresso and drip coffee [35]. Additionally, [PERSON] et al. demonstrated that dark-roasted coffee beans had increased acetic acid levels; however, the perception of acidity did not increase, because bitter compounds mask the acidic taste [36]. The pH values of Yanggaeng with added coffee extract revealed significant differences based on the degree of roasting. The Yanggaeng samples with coffee extract D exhibited the highest pH values, whereas those with coffee extract M showed the lowest pH values. In group L, the addition of G increased the pH, suggesting a buffering effect that reduced acidity. Conversely, in the M group, higher concentrations of G significantly decreased
# Enhancing Robotic-Assisted Lower Limb Rehabilitation Using Augmented Reality and Serious Gaming
## 4 Proposed Methodology for the Lower Limb Rehabilitation Treatment
### Integration Strategy in the Rehabilitation System
Long-Term Tracking and Adaptation has the role of ensuring that the rehabilitation process evolves as the patient progresses, making long-term adjustments to the treatment plan based on performance trends by the following:  
* Trend Analysis--use machine learning algorithms to analyze long-term trends in patient data, identifying patterns such as consistent improvement, plateauing, or regression. * Goal Reassessment--periodically reassess and update the patient's rehabilitation goals based on their progress. For example, if a patient has reached 75% of their range of motion goal, the system may suggest adjusting the target upwards or focusing on strength-building. * Therapy Plan Modification--automatically modify the treatment plan based on the long-term analysis. For example, introduce more advanced serious gaming scenarios, reduce assistance from the robot as strength improves, or shift focus to more challenging motor skills. * System Integration--the system uses AI-driven analytics to recommend these long-term adjustments to both the patient and therapist, ensuring the therapy remains aligned with recovery goals. By using Customizable Rehabilitation Protocols, a flexible system may be developed, where rehabilitation protocols can be customized based on the patient's progress, preferences, and clinical condition. This flexible system can be obtained by the following:  
* Modular Therapy Design--design therapy modules (e.g., serious gaming levels, robotic-assisted movements) that can be swapped or modified depending on the patient's needs. * Condition-Specific Protocols--tailor therapy protocols based on the patient's specific condition (e.g., stroke, Parkinson's disease) and adjust these protocols dynamically as the patient's condition evolves. * Remote Monitoring--if applicable, integrate remote therapy sessions where patients can continue their rehabilitation at home, while the system remotely tracks their progress and updates the therapy plan. * System Integration--the system should offer flexible customization features, allowing therapists to choose or modify therapy modules while providing an overarching, adaptive structure to ensure continuous patient progress.
## 4 Discussion
showed an increase in ISQ values in implants treated with SMF during the 90-day observation period, whereas in the control group, ISQ values decreased in the first 30 days and increased up to 90 days [25]. The mechanism by which the effect of SMF on the osseointegration process occurs is not well understood, although some studies have attempted to clarify it [26]. The in vitro study by [PERSON] et al. showed that SMF stimulates bone growth by promoting the differentiation and/or activation of osteoblasts [27], which is also claimed by [PERSON] et al. [28]. Bone formation around implants seems to be accelerated under the influence of SMF compared to control [29,30]. [PERSON] et al. found that SMF did not affect the adsorption of fibronectin on the titanium surface but promoted cell adhesion and proliferation [31]. Additionally, the in vivo study by [PERSON] et al. suggests that the use of SMF may promote angiogenesis [32]. The effects of SMF seem to be most evident in the early stages of the osseointegration process of dental implants [33]. The use of Supercharged(r) Sc offers a unique advantage, as Sc are temporary devices used in the early stages of healing. Additionally, the improved stability of the implant allows a reduction in the time required for fixed prosthetic rehabilitation. It can be assumed that regenerative procedures can also benefit from SMF [34, 35, 36, 37]. The major limitation of this study is represented by the small sample size. However, it should be noted that this is the first study conducted on humans.
## 3 Testing Framework
### Testing Environments  
Transactions for all four platforms (Quorum, Tendermint, Hyperledger Fabric and Apache Kafka) are sent using slave machines. These are virtual machines (VMs) (see details in Section 3.2.1) deployed in the Microsoft Azure cloud and controlled by a master testing machine, also deployed on Microsoft Azure. Each slave machine runs a given test script distributed from the master to the slaves (see Figure 2). All test scripts used in this study contain a method for randomising the order of node addresses, to ensure that transactions are randomly distributed among the 28 nodes for the blockchain platforms, namely not following the same order for every user sending transactions. For the Apache Kafka test, each transaction is sent to one of the five brokers and automatically distributed from there to a partition. The test scripts communicate through web calls to each of the nodes/brokers of the four platforms. Logs of transmission data (time-stamped when the transaction is sent, success status and transaction hash) are collected and stored on the master virtual machine. For the blockchain platforms, after all the transactions have been sent and processed from the slave VMs, a Python script extracts information (time-of-mining, block number and block size) about the specific transactions using the transaction hash from the transmission data log. These data points make it possible to calculate transactions per second (tps) and further provide the foundation for the visualisations shown in Section 4 (see Figures 3-5).  
Figure 3: Quorum throughput over time in both KB per second (**top**) and tps (**bottom**). All three data storage methods (Calldata, Memory and State) are compared using both 2 KB and 10 KB transactions. Each marker represents a new block.  
Figure 2: Overview of the testing environment.
## 5 Conclusions  
Novel wide-passband filters (with or without a triple-passband) based on SIW and SSPP structures are proposed, along with the design details, methodology and procedures. First, a single-passband filter operating at 24.25-29.5 GHz is realized by designing an SIW-SSPP unit cell with novel SSPP structure, where the upper and lower cutoff frequencies can be independently adjusted. Subsequently, loading an Archimedean spiral structure significantly enhances the filter performance, achieving lower insertion loss while maintaining compact dimensions. Moreover, the filter exhibits high out-of-band rejection above the upper cutoff frequency. To suppress unwanted signals in wireless communication systems, CSRR structures are incorporated to achieve dual-sub-bandpass and ultimately triple-bandpass filtering. The SIW-SSPP filter, loaded with both Archimedean spiral and CSRR structures, demonstrates significant advantages over existing designs in the literature. The final filter operates across 24.25-29.5 GHz, covering the 5G NR n257, n258, and n261 bands, exhibiting strong practical relevance and application potential.  
C.L.: conceptualization, supervision, project administration, analysis data, writing, and manuscript editing; Q.L.: investigation and design, data analysis and experimentation, and writing and editing the manuscript. G.T.: literature search and figures. T.X.: literature search and data collection. L.H.: data collection and literature search. H.W.: supervision and formal analysis. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (grant number 11973034).  
The original contributions presented in the study are included in the article text; further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Type**} & **Passband** & **Insertion** & **Rejection** & **Size** & **High** \\  & & **Range** & **Loss** & **Loss** & **(\(\lambda_{g}^{2}\))** & **Frequency** \\  & & **(GHz)** & **(dB)** & **(dB)** & **(\(\lambda_{g}^{2}\))** & **RL (dB)** \\ \hline
[22] & SIW & 8.24–8.76 & 1.7 & 10 & 1.155 & 35 \\
[24] & SSPP & 3.85–7.125 & 1.2 & 10 & 2.61 & 30 \\
[26] & SIW + & 8.1–12.1 & 3.5 & 10 & 1.005 & 35 \\
[28] & SSPP + & 10.25–11.3 & 1.4 & 15 & 11.9 & 40 \\
[29] & SSPP + & 51.5–58.4 & 2.6 & 10 & 0.16 & 65 \\  & CSRR & 7.4–8.8 & & & & \\
[30] & SIPP & 9.0–9.72 & 1 & 10 & 1.83 & 53 \\  & & 10.01–12.5 & & & & \\  & Filter 1 & 24–30 & 0.5 & 12 & 1.68 & 70 \\  & Filter 2 & 24–25.5 & 0.5 & 12 & 1.68 & 70 \\ Pro. & & 26.3–30 & 0.5 & 13.5 & & \\  & & 24–25.3 & 0.5 & 13 & & \\  & Filter 3 & 26–27.9 & 0.7 & 13.5 & 1.68 & 70 \\  & & 28.6–30 & 0.8 & 12 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the designed performances.