esa-sceva/satcom-chunk-collection huggingface dataset:
The results indicated that a rise in the temperature of the inlet air and MW power and the use of US and blanching pretreatment can decrease the drying time and SEC compared to the controls. Additionally, [PERSON] et al. [12] examined the effects of US and BL in water on the bioactive features (total phenol, total flavonoids, and quercetin) in onions dried by convective and freeze-drying methods. The results indicated that the US pretreatment performed better in preserving the quality and antioxidant features of the dried onions. Comparing the two drying methods, the use of freeze-drying led to higher quality preservation in the dried onions. Similarly, [PERSON] et al. [17] investigated the bioactive features (antioxidants, total phenol, and flavonoid contents, and vitamin C), drying time, and effective moisture diffusion of cranberry snacks using hot water blanching and US pretreatments in an MW-vacuum dryer. Their results indicated that the dried cranberries with pretreatments required shorter drying times compared to the controls. The antioxidant and total phenol and flavonoid contents of the pretreated samples were also enhanced compared to the controls. Moreover, the color variations and textural damage showed a decrease. In another study, the influence of US energy and blanching on the drying time, color variations, rehydration rate, and bioactive properties of cashew apple dried by hot-air dryer was investigated [18]. The results indicated that the blanching and blanching pretreatment can decrease the drying time as a result of an increment in effective moisture diffusion. Additionally, bioactive properties and rehydration rate were significantly higher in US pretreatment compared to blanching pretreatment and control group. Examination of various references showed that the majority of the studies evaluated the effect of one or two pretreatments considering one of the drying techniques. In the present study, the effects of three pretreatments (ultrasound, blanching, and microwave) on drying kinetics, thermal parameters (moisture diffusion coefficient, SEC, and energy efficiency), qualitative properties (color and rehydration rate), and bioactive properties (total phenol content and flavonoid content) of terephthal is discussed using two different drying methods: convective drying at three levels (50, 60, and 70 \({}^{\circ}\)C) and IR (250, 500, and 750 W).
## 2 Related Works
Furthermore, while the progress of technology and markets influence demand, multiactor interactions within the industry can stimulate innovation and facilitate adaptive transformation. Every stakeholder in the ecosystem contributes different perspectives to improve the value chain. Therefore, the decision maker must develop assessment mechanisms for different business models or applications. Service quality is one of the crucial factors influencing the cooperation among firms. Customers' satisfaction determines the service and product quality. However, reliabilityis considered a time-oriented quality that indicates the ability of a machine or product to perform the intended functions throughout its lifecycle [34]. In terms of service demand in the manufacturing industry, engineering-related departments, such as R&D and quality analysis, should be responsible for reliability assessment and in addressing quality issues [35]. For physical or digital services, defining the specification restriction using domain knowledge gives meaning to the evaluation mechanism. Remote sensing and cloud computing techniques are commonly used in cloud service architectures to monitor real-time applications [36; 37]. Compared to [PERSON] et al. 's study, quality deterioration is also an essential factor that may directly influence the supply chain quality process [38]. Failure time plays an indispensable role in evaluating the reliability and quality issues. In contrast to previous research, [PERSON] et al. (2021) also utilized the customization concept to design a dual-channel supply chain model to help the higher manager to make decisions [39]. The combination of costs and inventory model provides a different perspective on strategy formulation. [PERSON] et al. proposed a hierarchical structure for reliability assessment, in which the reliability of the upper and lower layers will alternately affect the final service quality [12]. Service compositions are one of the methods for allocating resources and discovering new possibilities. Furthermore, cloud services associated with customizing applications are influenced by the reliability and combinations of the system layer. In [PERSON] and [PERSON]'s work (2021), the analysis of service reliability and business process indicates the importance of higher reliability requirements. The web service (UML) can also implement different tasks by applying registry and storage mechanisms and evaluating the service quality [40]. In terms of maintenance, in addition to considering the degree of system matching and mutual support, the life cycle of each layer must be estimated [41]. Several newly developed algorithms can considerably increase the accuracy and diversify analysis despite the difficulty to obtain most of the data in the past. In this research, the proposed framework can assist corporations in handling complex automation issues and expanding their business models. The data modeling approaches combining artificial intelligence can also help higher managers conduct service innovation and process integration.
## 2 Theoretical Background
### Design of Experiment and Response Surface  
Design of Experiment (DOE) is a widely adopted statistical approach used to plan experiments and analyze the relationship between the input and output variables of a system. The aim of this methodology is to maximize the information gain while minimizing the number of experiments required, thereby optimizing the use of resources. In simulations, DOE helps identify the most influential factors, their interactions, and their effects on outputs. Traditional DOE methods require optimal sampling strategies to obtain the maximum information about this system. Full Factorial (FF) [29], Central Composite Design (CCD) [30], and Latin Hypercube Sampling (LHS) are well established sampling algorithms.  
Response Surface Methodology (RSM) [31; 32; 33; 34; 35; 36] is an extension of DOE that focuses on modeling output responses determined by a set of input parameters. It involves using various mathematical techniques to model a response surface that approximates the relationship between input and output variables to create a continuous response over the design space. The main methods used to implement response surfaces include Neural Networks (NN) [31], Polynomial Functions [32], Kriging [33], Genetic Aggregation (GA) [34; 36], Non-Parametric Regression [35], and RBF.  
In this study, the GARS (Genetic Aggregation Response Surface) method was used to find the correlations between input and scalar output. In fact, this method is very robust and accurate, and it is widely used in the literature [34; 36]. The GARS method employs genetic algorithms to determine an optimal combination of multiple response surfaces, ensuring that the aggregated model captures the essential characteristics of the provided data. It iteratively refines parameters to enhance accuracy. Initially, a population of potential solutions is generated and evaluated based on fitness criteria. Through successive generations, individuals with higher fitness are selected and undergo crossover and mutation, producing offspring solutions. The process continues until convergence, yielding optimized weights for the response surface. This iterative approach efficiently navigates the solution space, improving the accuracy of weight evaluation for enhanced model performance.  
Mathematically, the algorithm can be expressed as follows:  
\[G(x)=\sum_{i=1}^{n}\omega_{i}R_{i}(x) \tag{4}\]  
where  
* \(G(x)\) is the aggregated response surface,
* \(Ri(x)\) are the individual response surfaces,
* _wi_ are the optimized weights.  
The weights are evaluated by integrating a Multi-Objective Genetic Algorithm (MOGA) [37] to meet the optimization objective requirements. A specific RS function can take various forms, and its ability to fit the data depends on the type of problem and the simulation model used.
# Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks
## 4 Experimental Results  
### Performance Result of the Proposed Clustering Algorithm  
To visualize the location of all the 62,908 UE, we selected three features, the type of cell, the latitude, and the longitude of each UE, as depicted in Figure 9.  
Figure 8: At a given time: UE ready for Handover are far from the cell center (C1\({}_{UE}\)), and UE not ready for Handover are close to the cell center (C3\({}_{UE}\) and C2\({}_{UE}\)).  
Moreover, the elbow technique shown in Figure 6 explained the number of clusters on the X-axis and the WCSS values on the Y-axis to produce the visual utilizing the WCSS idea.  
The elbow point is at 3, as shown in Figure 6; thus, we selected three as the optimal number of clusters because our dataset indicates three different types of cells. Since we use unlabeled data in unsupervised learning, consequently, models must identify patterns or similarities in the data. In our NTN simulator, we designed the intra-satellite handover architecture with one satellite and three cells (the candidate cell, serving cell, and target cell). Therefore, the satellite-based navigation system provides location information to users with Global Positioning System (GPS) receivers. Figure 10 shows the cluster of the 62908 users according to their location.  
One of the issues in [11] is the signaling storm brought on by moving all of the UE in a cell to a different cell. As a result, it envisaged that every few seconds, all users in a cell will switch to another cell due to handover. UE location is a crucial factor to consider while executing handover because 3 GPP has defined location (UE and Satellite) triggering as one of the conditional handovers; this is why we developed the UE-based clustering approach by using the centroid-based method.  
The clusters in the centroid-based technique are created by how close the UE is to the centroid. Here, the centroid of the cluster is calculated with the least possible distance from  
Figure 10: UE clustered based on their location: Cell type, latitude, and longitude.  
Figure 9: Location of User Equipment based on their latitude and longitude.  
the UE; therefore, we calculated the centroid of each cell, as shown in Table 2, so that we can determine the distance between all the users in a cell and its center point. In addition, Figure 11 shows the clustering of 49,000 users.
## 2 Materials and Methods
### Development of the Project with Parallel Activities  
From a technological standpoint, the project can be divided into multiple components that need to be integrated with each other. Consequently, the implementation of the Smart Drifter Cluster was executed through various activities, each with distinct sub-objectives, while considering the shared overarching goal. The list of the different activities is given hereafter:  
1. Research and development (R&D) of an electronic circuit incorporating GPS, GSM, WiFi, and LPWAN communication functionalities aimed at enhancing the long-range capabilities and energy efficiency of cutting-edge, cost-effective wireless communication technologies. The circuit is designed to be optimized for the specific use case.
2. IT for intelligent management of cluster elements and drifter data processing; design of the software that implements the communication strategies among the elements of the drifter cluster and also manages the communication modes (intensity, frequency, synchronizations, data type, etc.).
3. Testing of different physical housing for the drifter made with materials with a low environmental impact and with the appropriate mechanical characteristics appropriate for the specific use of the drifter (semi-submerged, with floating buoy drag, etc.).
4. Design of a propulsion system for the few primary elements to provide a light navigation capacity to maximize data collection from the secondary elements and to maximize long-range communication capabilities.
5. IT (with digital twin technology) for the primary elements to forecast long-term motion and future cluster fragmentation and to develop an optimization policy based on the quality and quantity of collected data.
6. Energy efficiency and energy harvesting to make the smart drifters virtually energy autonomous.
# Targeted Lipid-Based Drug Delivery Systems for Lung Cancer Therapy
## 3 Drug Delivery Systems
### Niosomes/Proniosomes  
Niosomes (Figure 1d) were introduced by L'Oreal in 1989 [63] and they consist of neutral lipids, for example, cholesterol and surfactants [64]. They have proven more successful for skin penetration and are more stable due to the surfactant used instead of a phospholipid used in liposomes, as they can be easily oxidised [65]. The presence of cholesterol also increases the stability of the encapsulated drug, which in turn increases the entrapment efficiency [66]. Even though niosomes exhibit very good chemical stability, they lack physical stability and, like liposomes, there might be issues like vesicle aggregation, vesicle fusion and hydrolysis of the encapsulated drug, which in turn could lead to shelf-life shortening [67]. For this reason, proniosomes offer a better, more stable alternative. Some drugs which have been entrapped within niosomes for lung cancer therapy as shown in Table 1 are vinblastine [68] and co-delivery of gemcitabine and cisplatin [69]. Intravenous niosomal delivery of ofloxacin demonstrated higher Cmax, AUC and t1/2 values compared to the commercially available intravenous ofloxacin product, suggesting that the investigated floxacin niosomes could be a potential carrier system to improve patient compliance and reduce side effects [70]. Other routes of administration for niosomes include transdermal [71], oral [72] and ocular [73].
# A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles
## 6 Comparative Analysis and Future Directions
### Comparisons of Roll Stability Control Between Road and Off-Road Vehicles
Their control systems are more reliant on environmental perception and terrain adaptability, employing multi-sensor fusion and active leveling technologies to improve stability and safety under extreme conditions.
## 2 Materials and Methods
### ANN Development and Implementation  
This section describes the development and implementation of an artificial neural network (ANN) model designed to detect starch adulteration in milk. It covers the ANN's architectural setup, the training processes, evaluation metrics, and optimization techniques  
Figure 4: Normalized Spectral Data of Milk Samples with Different Starch Concentrations.  
employed to enhance model performance. Each step is critical in ensuring the accuracy and reliability of the ANN in real-world applications.  
ANN Structure:  
The ANN model used in this study consists of an input layer, three hidden layers, and an output layer:  
Input Layer: This layer receives the scaled spectroscopic data, containing neurons equal to the number of features in the training dataset. The scaling equation used is  
\[X_{scaled}=\frac{X-\mu}{\sigma} \tag{1}\]  
where \(X\) is the feature matrix, \(\mu\) is the mean of the features, and \(\sigma\) is the standard deviation of the features.  
Hidden Layers: The three hidden layers contain 128, 64, and 32 neurons, respectively, each using the 'ReLU' activation function, defined as  
\[f(x)=max(0,x) \tag{2}\]  
Output Layer: The output layer, which performs the classification task, contains five neurons using the'softmax' activation function.  
\[P(y=j\mid X)=\frac{e^{z_{j}}}{\sum_{k=1}^{K}e^{z_{k}}} \tag{3}\]  
Here, \(P(y=j\mid X)\) represents the probability of class \(j\) given the features \(X\), and \(z_{j}\) represents the logits.  
A visual representation of the ANN architecture is provided in Figure 5, illustrating the configuration of the input, hidden, and output layers along with their respective neuron counts and activation functions.  
Model Training:  
During model training, the 'adam' optimizer and'sparse_categorical_crossentropy' loss function were used. Training was conducted with 5-fold cross-validation, with early stopping implemented for each fold, monitoring the validation loss ('val_loss') with a patience parameter set to five.  
Model Evaluation:  
Predictions were generated for each fold, and the model's performance was evaluated using a classification report, which includes accuracy and other performance metrics.  
Model Optimization:  
The model training process used a batch size of 32, 50 epochs, and 20% of the training set as the validation set. The model was trained and evaluated for each fold, and the obtained accuracy values were averaged to evaluate the overall performance of the model.
_Appl. Sci._**2012**, \(2\), 465-478; doi:10.3390/app2020465  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Communication_  
**A Dual PET/MR Imaging Nanoprobe: \({}^{124}\)I Labeled Gd\({}_{3}\)N(\(\alpha\)C\({}_{80}\)**  
**[PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 1, [PERSON] 2, [PERSON] 1 and [PERSON] 3,**  
* Department of Radiology, Virginia Commonwealth University, 1101 East Marshall Street, Richmond, VA 23298, USA; E-Mails: [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (J.D.W.); [EMAIL_ADDRESS] (J.I.H.); [EMAIL_ADDRESS] (P.P.F.)
* Department of Chemistry, Virginia Tech, 1109 Hahn Hall, Blacksburg, VA 24061, USA; E-Mails: [EMAIL_ADDRESS] (J.Z.); [EMAIL_ADDRESS] (H.C.D.)
* Department of Biochemistry and Molecular Biology, Virginia Commonwealth University, 1101 East Marshall Street, Richmond, VA 23298, USA
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-804-828-7943; Fax: +1-804-828-1473.  
_Received: 9 February 2012; in revised form: 2 May 2012 / Accepted: 3 May 2012 / Published: 10 May 2012_  
**Abstract:** The current report describes the development of a dual modality tomographic agent for both positron emission tomography and magnetic resonance imaging (PET/MRI). The dual-modality agent in this study was based on a \({}^{124}\)I (PET) radiolabeled tri-gadolinium endohedral metallofullerene Gd\({}_{3}\)N(\(\alpha\)C\({}_{80}\) (MRI) nanofibreble platform. The outer surface of the fullerene cage of the Gd\({}_{3}\)N(\(\alpha\)C\({}_{80}\) metallofullerenes was surface functionalized with carboxyl and hydroxyl groups (f-Gd\({}_{3}\)N(\alpha\)C\({}_{80}\)) using previously developed procedures and subsequently iodinated with \({}^{124}\)I to produce \({}^{124}\)I-f-Gd\({}_{3}\)N(\(\alpha\)C\({}_{80}\) nanofibreble. Orthotopic tumor-bearing rats were infused intratumorally by convection-enhanced delivery (CED) with the \({}^{124}\)I-f-Gd\({}_{3}\)N(\(\alpha\)C\({}_{80}\) agent and imaged by MRI or micro PET. The anatomical positioning and distribution of the \({}^{124}\)I-f-Gd\({}_{3}\)N(\(\alpha\)C\({}_{80}\) agent were comparable between the MRI and PET scans. The \({}^{124}\)I-f-Gd\({}_{3}\)N(\(\alpha\)C\({}_{80}\) dual-agent distribution and infusion site within the tumor was clearly evident in both T\({}_{1}\)- and T\({}_{2}\)-weighted MR images. The results demonstrate the successful preparation of a dual-modality imaging agent, \({}^{124}\)I-f-Gd\({}_{3}\)N(\(\alpha\)C\({}_{80}\), which could ultimately be used for simultaneous PET/MR imaging.  
**Keywords:** PET; MRI; metallofullerene; iodination
# Energy Saving Potential of PCMs in Buildings under Future Climate Conditions
###### Abstract  
Energy consumption reduction under changing climate conditions is a major challenge in buildings design, where excessive energy consumption creates an economic and environmental burden. Improving thermal performance of the buildings through support applying phase change material (PCM) is a promising strategy for reducing building energy consumption under future climate change. Therefore, this study aims to investigate the energy saving potentials in buildings under future climate conditions in the humid and snowy regions in the hot continental and humid subtropical climates of the east Asia (Seoul, Tokyo and Hong Kong) when various PCMs with different phase change temperatures are applied to a lightweight building envelope. Methodology in this work is implemented in two phases: firstly, investigation of energy saving potentials in buildings through inclusion of three types of PCMs with different phase temperatures into the building envelop separately and use weather file in the present (2017); and, secondly, evaluation of the effect of future climate change on the performance of PCMs by analyzing energy saving potentials of PCMs with 2020, 2050 and 2080 weather data. The results show that the inclusion of PCM into the building envelope is a promising strategy to increase the energy performance in buildings during both heating and cooling seasons in Seoul, Tokyo and Hong Kong under future climate conditions. The energy savings achieved by using PCMs in those regions are electricity savings of 4.48-8.21%, 3.81-9.69%, and 1.94-5.15%, and gas savings of 1.65-16.59%, 7.60-61.76%), and 62.07-93.33% in Seoul, Tokyo and Hong Kong, respectively, for the years 2017, 2020, 2050 and 2080. In addition, BioPCM and RUBITHERMPCM are the most efficient for improving thermal performance and saving energy in buildings in the tested regions and years.  
energy saving; EnergyPlus; Phase Change Material (PCM); the future climate change +
Footnote †: journal: Applied Soft 2017
# Study on the Effectiveness of Multi-Dimensional Approaches to Urban Flood Risk Assessment
## 2 Methodology
### Hazard  
Assessing hazard involves evaluating both the potential intensity of natural urban flood in a specific region and the extent of possible damage. Physical indicators such as the straight-line distance to the closest river or road, along with structural damage estimates based on flood depth (Table 2), play a central role in this analysis. To accomplish these measurements, GIS tools were used for calculating direct distances, and damage functions were applied to derive structural damage rates. These parameters are critical for understanding the physical attributes of the area and for projecting how losses might be distributed in the event of an urban flood.