esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Detailed Implementation of Layer-1 BoD Services
### Interface between Layer-1 BoD Server and L1-OPS  
The interface between the layer-1 BoD server and L1-OPS is a CORBA interface compliant with TMF-814 [18], and it can set up and release layer-1 paths for layer-1 BoD services and allocate the bandwidths of the layer-1 paths to layer-2/3 services (Figure 5). The interface between L1-OPS and the layer-1 switches is TL1.  
The BoD server uses _createSNC_ to request L1-OPS to set up a layer-1 path [Figure 5(a)]. The parameters of _createSNC_ include path information such as the path name managed by the BoD server, route information calculated by the BoD server, and the bandwidth requested by the user. Upon receipt of the request (_createSNC_ REQ), L1-OPS registers the path information in the source layer-1 switch, requests the switch to set up a GMPLS-based layer-1 path, and sends the BoD server the response (_createSNC_ RESP). When the layer-1 path is established, L1-OPS receives a completion notice from the source layer-1 switch, retrieves the path information for confirmation, and notifies the BoD server of the completion of the path setup by sending a notification message [_Notification_ (create CMPLD)]. The BoD server then checks the end-to-end path status, such as the J1 path trace result, by using _getSNC_. To set up a layer-1 path composed of diverse sub-paths corresponding to parallel links and multiple routes, the BoD server establishes sub-paths by using more than one _createSNC_, each of which includes the same path name. For path deletion, the BoD server uses _deleteSNC_, whose parameters include the specified path name. The BoD server sends a _deleteSNC_ REQ to L1-OPS and receives a _deleteSNC_ RESP followed by a _Notification_ (delete CMPLD) from L1-OPS.  
The BoD server uses _changeSNCBandwidth_ to request L1-OPS to change the bandwidth of a layer-1 path for layer-2/3 services [Figure 5(b)]. The parameters of _changeSNCBandwidth_ include the path name and the new bandwidth. For a path bandwidth decrease, upon receipt of the request (_changeSNCBandwidth_ REQ), L1-OPS enables the path to be operated by LCAS and then changes the bandwidth by using VCAT. Because a layer-1 path for layer-2/3 services is established as a permanent non-GMPLS path, L1-OPS converts [or activates in Figure 5(b)] the non-GMPLS resource released from the path into a GMPLS-based resource. L1-OPS then sends the BoD server the response(_changeSNCBandwidth_ RESP). The sequence of operations between L1-OPS and layer-1 switches for a path bandwidth increase starts by converting the GMPLS-based resource into a non-GMPLS-based resource before the LCAS and VCAT control operations begin.
## 2 Mathematical Model
### Thermophysical Properties with Magnetic Field  
\"Magnetorheological heat transfer nanofluids are smart materials made of magnetic particles that can enhance flow characteristics in the presence of a magnetic field. However, the field-dependent thermal conductivity of magnetorheological fluids plays an important role in heat transfer and dissipation in potential new applications. Reference [54] reported that magnetic nanofluids with a low concentration of nanoparticles can significantly enhance their thermal energy\". This research considered that magnetic metallic solids under different magnetic Gauss forces improve thermal and thermophysical properties compared to fluids, and nanofluids exhibit significantly better thermal properties compared to conventional heat transfer fluids. In the following, we present the formulas developed for predicting the thermophysical properties of nanofluids [54]. The magnetic data published in the literature [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31] were used to take into account the impact of the magnetic field, as outlined in Table 1.  
Where \"b\" represents the nanofluid specific property and \"a\" is the magnetic field force in Gauss. \(C_{pnf}\), \(K_{nf}\), and \(h\) are the specific heat, thermal conductivity, and heat transfer coefficients of nanofluids.  
Equation (17) can be used to determine other thermophysical properties, where \(\alpha\) is the thermal diffusivity and \(\lambda\) and \(\rho\) represent the thermal conductivity and density as a function of the properties outlined in Table 1.
## 2 Design, Materials, and Methods
### Silver Ink Preparation  
Silver nitrate (AgNO\({}_{3}\)), sodium citrate, and polyvinylpyrrolidone (PVP\({}_{\text{MW}}\) = 40,000) powder used for this study were all purchased from Nanjing Chemical Reagent Co. Ltd (Nanjing, People's Republic of China). All these reagents, in analytical grade, were used without further purification.  
The silver ink was prepared by following the procedure reported by [PERSON] et al. [18]. In a typical synthesis, 1.5 g sodium citrate and 125 mg PVP were dissolved into 50 mL deionized water. Next, 5.2 g AgNO\({}_{3}\) was dissolved into 40 mL deionized water. After that, the AgNO\({}_{3}\) solution was slowly added into the mixed PVP and sodium citrate aqueous solution under magnetic stirring. The mixture was stirred for 2 h to obtain a white silver ink.
## 3 Classification
### Communication Technologies
#### 3.6.3 Wi-Fi  
Multiple efforts [4; 8; 9; 15; 23; 24; 35] have been made to design Wi-Fi-based V2P safety systems. These systems use a smartphone as a VRU device and typically have 100-150 m of communication range. This range may be enough in urban areas with typical vehicle speeds up to 50 km/h. However, it may not be enough in suburban areas with typical speeds of 100 km/h due to less time available for the driver's reaction to crash warnings. Also, Wi-Fi's association requirement is a challenge due to the mobility of vehicles as it may take too much time before the actual exchange of safety messages happens. Wi-Fi-based V2P systems may be deployed without the help of infrastructure.
# Effect of Ultrasonic Treatment on Taste and Flavor Quality of Japonic Rice
## 3 Results
### Correlation Analysis  
Finally, Pearson correlation analysis was performed to investigate the relationship among rice resistant starch content, texture parameters, and aroma compounds. The correlation of resistant starch content in the cooked rice with textural parameters and aroma compounds is shown in Figure 8. The hardness of rice was significantly and positively correlated with resistant starch content (\(p<0.05\)). The correlations between the textural parameters and aroma compounds of the ultrasound treatment cooked rice were similar to previous studies [34]. Octanal was negatively correlated with the hardness of the cooked rice. Hexanal, heptanal, and 2-pentylfuran showed significant positive correlation (\(p<0.05\)) as they are all products of initial lipid oxidation of linoleic acid [35]. Trans-2-nonenal and trans-2,4-dodecenal also showed a significant positive correlation (\(p<0.05\)) as they are both products of late lipid oxidation of linoleic acid [35]. Therefore, resistant starch has a correlation with the texture and flavor of ultrasound treatment rice.
## 1 Introduction  
Solution-processed thin film solar cells offer the opportunity to decrease the manufacturing cost and the usage of raw materials, which may allow module manufacture to compete with traditional energy production. Organic materials [1], perovskites [2, 3], nanocrystals (NCs) [4, 5], or other semiconductor precursors [6] can be used to fabricate photovoltaic devices by solution processing. Among the solution-processed techniques, CdTe NC solar cells have been well explored due to their high stability and physical properties, tunability, and easy control processes, which permit devices to be built at the atom or molecular level. Furthermore, CdTe NCs have many merits such as being a low complexity compound and having a high optical absorption coefficient (>10\({}^{4}\) cm\({}^{-1}\)) and an ideal bulk band gap of 1.5 eV [7, 8]. By using organic ligands, the sizes of CdTe NCs are well controlled, and they can be dispersed in many organic solvents, such as pyridine, n-propyl alcohol, and even deionized water [9, 10, 11, 12], which permits photovoltaic device fabrication using solution processing. Here, we review the advances in solution-processed CdTe NC solar cells and discuss future innovations that are required to further increase the power conversion efficiency (PCE) to ~15% to allow commercial application. There are four key areas that have led to an increase in CdTe NC solar cell performance in recent years. First, it has been demonstrated that enhanced control over the physical properties of every CdTe NC active layer is crucial to eliminate bulk defects and improve the performance of devices. Secondly, the junction quality is another important issue for CdTe NC solar cells; researchers have realized numerous forms of p-n junctions for the collection of holes and electrons. Thirdly, by introducing a hole transfer layer between CdTe NC and the contact electrode, a dipole layer is formed, which is beneficial for collecting holes and eliminating defects. Finally, the new architecture design and combinations of devices have allowed the development of some forms of graded bandgap design that enable efficient collection of electrons and holes. We review the latest progress in the architecture design of devices and present some perspectives on the further development of CdTe NC solar cells towards \(\sim\)15% PCE [13].
## 3 ML and Data-Driven Models for Remanufacturing
These algorithms can optimize workflows, resource allocation, and adaptive quality control processes, learning from continuous feedback to improve remanufacturing strategies over time [55; 56; 57]. **Data-Driven and Optimization Models**: Data-driven and optimization models utilize quantitative algorithms to enhance decision-making. These models include: (i) **Graph-based models**, such as AND/OR Graphs [58; 59] and Petri Nets [60], which provide a framework for modeling complex remanufacturing systems and processes, especially in HRC. These models are particularly effective in optimizing disassembly sequences, modeling production workflows, and task allocation within humans and robots; (ii) **Mathematical Programming Models**, including Convex Optimization [61], Linear Programming (LP) [62] and Nonlinear Integer Programming (NLIP) [63; 64], offer robust solutions for complex planning and scheduling problems; (iii) **Meta-Heuristics** are optimization methods designed to generate or select heuristics that provide sufficiently good solutions to complex optimization problems [65; 66]. In remanufacturing, meta-heuristics, such as the Genetic Algorithm (GA), Bees Algorithm (BA), and Particle Swarm Optimization (PSO), can be used to solve intricate problems related to scheduling, resource allocation, and process optimization [67]. These techniques are particularly useful when dealing with multiple objectives [68] and dynamic remanufacturing environments [69]; (iv) **Probabilistic Models**, including Monte Carlo simulation [70] and Markov chains [71], play a crucial role in modeling uncertainty and stochastic processes inherent in remanufacturing. These models assist in assessing risks, and optimizing decision-making under uncertainty, which is essential given the variable nature of returned products in remanufacturing. It is important to highlight advanced manufacturing technologies that can provide additional opportunities that could be synergized with the aforementioned algorithms to further enhance remanufacturing processes. Immersive technologies like VR and AR could enhance remanufacturing by providing real-time, detailed visualization and simulation, thereby improving training, design, and troubleshooting processes. VR simulates complex scenarios, while AR assists in assembly, disassembly, and maintenance activities by overlaying digital information onto the physical world, thus enhancing accuracy and reducing training time [72]. Cobots work alongside humans to increase productivity, precision, and safety by handling automated disassembly, cleaning, and reassembly tasks. Cobots are versatile and can be programmed for various tasks, thereby reducing injury risk and ensuring consistent quality in remanufacturing operations [73].
## 3 Samples Expansion of ITF in PMSM through Cyclic GAN  
### Steps in Building a Cycle GAN for Sample Expansions  
Cycle GAN is used to expand the PMSM sample set, and the expanded sample set is used to train the feature extraction network DAE and the fault classifier. The validity of Cycle GAN to sample expansion is verified by analyzing the correlation between generated and actual samples [22,23]. The improvement effect of this sample expansion scheme on fault feature classification is verified by analyzing the accuracy of the DAE feature extraction network and fault classifier to fault classification. The PMSM ITF fault diagnostic steps are as follows:  
1. Pre-process the collected three-phase stator current data of the PMSM in a healthy state and ITF of different degrees, unify the size of each sample, and avoid missing data during the acquisition.
2. Set up the Cycle GAN and DAE networks.
3. Perform correlation analysis between the generated PMSM samples and the actual PMSM samples.
4. Input the expanded sample set to the DAE for feature extraction and use the Softmax classifier to classify the fault features.
###### Abstract  
Group decision support systems (GDSSs) have been widely studied over the recent decades. The Web-based group decision support systems appeared to support the group decision-making process by creating the conditions for it to be effective, allowing the management and participation in the process to be carried out from any place and at any time. In GDSS, argumentation is ideal, since it makes it easier to use justifications and explanations in interactions between decision-makers so they can sustain their opinions. Aspect-based sentiment analysis (ABSA) intends to classify opinions at the aspect level and identify the elements of an opinion. Intelligent reports for GDSS provide decision makers with accurate information about each decision-making round. Applying ABSA techniques to group decision making context results in the automatic identification of alternatives and criteria, for instance. This automatic identification is essential to reduce the time decision makers take to step themselves up on group decision support systems and to offer them various insights and knowledge on the discussion they are participating in. In this work, we propose and implement a methodology that uses an unsupervised technique and clustering to group arguments on topics around a specific alternative, for example, or a discussion comparing two alternatives. We experimented with several combinations of word embedding, dimensionality reduction techniques, and different clustering algorithms to achieve the best approach. The best method consisted of applying the KMeans++ clustering technique, using SBERT as a word embedder with UMAP dimensionality reduction. These experiments achieved a silhouette score of 0.63 with eight clusters on the baseball dataset, which yielded good cluster results based on their manual review and word clouds. We obtained a silhouette score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset. With the results of this work, intelligent reports for GDSS become even more helpful, since they can dynamically organize the conversations taking place by grouping them on the arguments used.  
group decision making; dynamic clustering; natural language processing; argumentation
## 3 Results and Discussion
### Elastic Properties  
The apparent elastic properties are related to the SVE size. The mean, minimum, and maximum values for elastic properties (\(\kappa\), \(G\)) are shown in Figure 5 for each material. The smaller SVE sizes have a greater range of values than larger ones. Interestingly, mean values exhibit minimal variation across all SVE sizes of the same material. The mean values for \(\kappa\) are approximately 194 GPa, 255 GPa, and 335 GPa for M1, M2, and M3, respectively. The fact that M3 has a higher mean for homogenized properties can be explained by the fact that M3 has the highest volume fraction of the stiffest martensitic phase. Figure 6 and Table 3 show the power relation results corresponding to Equation (6). Overall, bulk modulus has similar convergence powers, \(\alpha_{f}\), across all \(v_{f}\) (Table 2). The shear modulus corresponds to a smaller \(\alpha_{f}\) (and large RVE size) than the bulk modulus. This trend is in agreement with the results in [16
# SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation
## 4 Experimental Results
### Results
#### 4.5.1 Backbone  
We conducted an ablation study to compare the performance of various backbone networks. Along with EfficientNet-B0, the backbone used in this study, we experimented with MobileNetV2 and MobileNetV3. Tables 4 and 5 reveal that for the MPII and COCO datasets, the FLOPs of EfficientNet-B0 and MobileNetV2 were nearly identical. MobileNetV2 had slightly fewer parameters; however, the model using EfficientNet-B0 as the backbone had slightly better accuracy.
_applied sciences_  
_Article_  
Contributions to the Process of Calibrating Corn Seeds Using a Calibrator with Cylindrical Sieves  
**[PERSON] 1**, [PERSON] 1,**2, **3, **4, **5, **6, **7, **8, **9, **10, **11, **12, **13, **14, **15, **16, **17, **18, **19, **20, 21, **22**  
**[PERSON] 1,**3, **11, **12, **13, **14, **15, **16, **17, **18, **19, **21, **23, **24, **25, **26, **27, **28, **29, **30, 31**  
**and [PERSON] 4**  
**Abstract: This paper presents theoretical and experimental research on the process of calibrating corn seeds using a specialized equipment with cylindrical sieves, studying the influence of process parameters and corn seed particularities on the quality of the calibration work. The research took into consideration corn seed characteristics, namely, their dimensions (length, width, and thickness) and weight, determining the influence of process parameters--the contact point between the seed and the cylindrical sieve, the influence of the length of the sieve, and the sieve's revolution speed on the separation process. The experiments for determining the influence of these parameters on the calibration process were conducted on a calibrating equipment with cylindrical sieves using three different corn hybrids. It was noticed from the experiments that, overall, the revolution speed had the most important effect on the calibration process, with sieve length also affecting the process, but to a lesser degree. Seed calibration efficiency was best at the smallest revolution speed (33 rot/min) and decreased when the revolution speed increased up to 49 rot/min for all corn hybrids tested. The number of calibrated seeds decreased in the second part of the cylindrical sieve. Seed thickness influenced the process, in the respect that seeds smaller than 4.8 mm passed through the first third of the sieve and those with a thickness between 4.8 and 5 mm passed through the other two thirds of the sieve length.**  
**Keywords: corn seed calibration; cylindrical sieve; revolution speed; sieve length; seed thickness**
# LoRa Enabled Smart Inverters for Microgrid Scenarios with Widespread Elements
## 4 LPWAN Systems and MG Control
### MG Communication System Design Considerations  
In MGs, a communication (cyber) system must be operating in parallel with the electrical structure [62]. This concept is illustrated in Figure 4, and similarities can be observed with the smart grid architecture model (SGAM) which provides a five-layer interoperability architecture for smart grids [63; 64]. Local MG control and management only occupies the first two layers of the SGAM framework.  
In order to design a proper communication system for an MG, some key factors are to be considered carefully:  
Figure 3: LoRaWAN architecture.  
The physical scale of the MG: By this, we are referring to how widespread the DGs are in the system. For example, a short-range communication system may be applicable for a residential MG (smart house) architecture, whereas it might not be suitable for a small solar generation plant.
* The location of the MG: As discussed before, the lack of access to popular cellular networks is a deciding factor in considering the utilization of LPWANs.
* The level of sensitivity to latency and reliability: In LPWANs, in order to achieve low-power and long-range characteristics, throughput and robustness are being sacrificed. This should be considered when the application requires a very reliable and high throughput communication.
###### Abstract  
The era of digitization and IoT devices is marked by the constant storage of massive amounts of data. The growing adoption of smart home environments, which use sensors and devices to monitor and control various aspects of daily life, underscores the need for effective privacy and security measures. HE is a technology that enables computations on encrypted data, preserving confidentiality. As a result, researchers have developed methodologies to protect user information, and HE is one of the technologies that make it possible to perform computations directly on encrypted data and produce results using this encrypted information. Thus, this research study compares the performance of three ML models, XGBoost, Random Forest, and Decision Classifier, on a real-world smart home dataset using both with and without FHE. Practical results demonstrate that the Decision Classifier showed remarkable results, maintaining high accuracy with FHE and even surpassing its plaintext performance, suggesting that encryption can enhance model accuracy under certain conditions. Additionally, Random Forest showed efficiency in terms of execution time and low prediction errors with FHE, making it a strong candidate for encrypted data processing in smart homes. These findings highlight the potential of FHE to set new privacy standards, advancing secure and privacy-preserving technologies in smart environments.  
Keywords:fully homomorphic encryption; machine learning; smart homes; IoT; neuronal models; privacy-preserving +
Footnote †: journal: Applied