esa-sceva/satcom-chunk-collection huggingface dataset:
# Synthesis of Mesoporous MWCNT/HKUST-1 Composite for Wastewater Treatment
## 3 Results and Discussion
### X-Ray Diffraction and FTIR Analysis  
An X-ray diffraction pattern was used to identify the phase present in both the as-received and acid-treated MWCNTs. Figure 4 shows stack XRD traces of the as-received as well as the functionalized MWCNT samples.  
Similar phases were observed in the samples, and a prominent peak of carbon nanotubes was obtained at a 20 angle of 26.8\({}^{\circ}\). However, it can be noted that the functionalized MWCNT peak at 26.8\({}^{\circ}\) increased in intensity compared to the as-received sample. This could have been because of the fact  
Figure 4: XRD trace of as-received MWCNTs and acid-treated MWCNTs (FMWCNTs).  
Figure 3: (**A**) SEM image of FMWCNT/HKUST-1 composite, (**B**) High resolution image of FMWCNT/HKUST-1 composite, (**C**) EDS image of FMWCNT/HKUST-1 composite.  
that carbon nanotubes loosen after treatment with acid to form well-ordered MWCNTs, and this is evident in Figure 1A,B [40]. The X-ray diffraction pattern of the as-synthesized MOF and that of the composite sample are shown in Figure 5.  
A typical diffraction pattern of Cu\({}_{3}\)(BTC)\({}_{2}\) (H\({}_{2}\)O)\({}_{3}\).xH\({}_{2}\)O was observed in the virgin MOF samples. The 20 position of the prominent peaks of HKUST-1 synthesized in this work matched the high-purity HKUST-1 XRD pattern reported in the literature [35; 37; 38]. The EDS result corroborated the XRD pattern, which gives an indication that no other phase was present in the HKUST-1 sample. On the other hand, HKUST-1 containing functionalized MWCNT samples revealed distinct peaks of HKUST-1. This shows that the carbon nanotube did not prevent the formation of HKUST-1 crystals, but a peak shift was observed in the sample. In addition, the prominent peak of functionalized MWCNTs (observed in Figure 4 at 20 26.8\({}^{\circ}\)) was greatly reduced in the composite XRD pattern. This could have been because of the relatively low intensity of MWCNTs compared to HKUST-1 MOFs [9]. This observation is reasonably consistent with a report on MOF-5/MWCNT composites [41]. The FTIR spectrums of the as-received MWCNTs, FMWCNTs, HKUST-1 MOFs, and FMWCNT/HKUST-1 composites synthesized in this study are shown in Figure 6.  
Figure 5: Stack XRD pattern of HKUST-1 and the FMWCNT/HKUST-1 composite.  
Figure 6: FTIR spectra of the as-received MWCNTs, acid-treated MWCNTs, HKUST-1 MOFs, and FMWCNT/HKUST-1 composites.  
The FTIR spectra of the HKUST-1 revealed a broad peak at 3458 cm\({}^{-1}\) that corresponded to an O-H stretch of the hydroxyl group in the structure. The peaks at 1628 and 1377 cm\({}^{-1}\) corresponded to a -COO group that originated from symmetric stretching of the BTC carboxylate [36]. The band at 1110 cm\({}^{-1}\) was attributed to an C-O-Cu vibration in the MOF structure [14]. The FMWCNT/HKUST-1 composite spectra were similar to those of the HKUST-1 MOFs.
###### Abstract
Despite the enormous research outputs describing sensitivity and stretchability, sensors built on these strategies still exhibit several limitations related to the detectable pressure range, fabrication process, response time, and biocompatibility. However, in some practical applications such as bionic robots [20; 21; 22], cost-effective production, data processing, and smartness are also major concerns. Hence, new fabrication techniques with intelligent data processing methods are required to develop low-cost smart sensory systems [23; 24]. Advances in AI techniques have leveraged the functionalities of flexible tactile sensors [25]. For instance, machine learning algorithms can enable ways of analyzing modalities of a desired output pattern from touching or grasping objects to gesture recognition in developing human-machine interfaces [26; 27]. Furthermore, the intelligent integration of low-cost flexible materials with stimulus realization and quantification can help accomplish the performance of tactile piezoresistive devices that operate in a similar working principle to the somatosensory system in humans [28]. Recently, various approaches have been reported on the basis of a combination of AI with low-cost materials used in small scales. For instance, [PERSON] et al. [29] developed a smart e-skin based on a capacitive sensing array, and data were processed by applying long short-term memory (LSTM) and neural network. Furthermore, [PERSON] et al. [30] reported a smart sensory e-skin by employing a multiwalled carbon nanotube/polydimethylsiloxane (MWCNT/PDMS) composite film and a deep neural network (DNN) to realize the applied pressure levels, as well as obtain position data by processing changes in the electrical resistance. Therefore, developing fully or semi-automated flexible sensory systems based on machine learning has become a research hotspot in recent years [31]. In this study, an intelligent recognition system was developed by combining a piezoresistive facile and cost-effective tactile sensor with a machine learning model for human-machine interfaces. A microporous PDMS-based sponge was integrated using cost-effective and commercially available sugar cubes. Retrospective studies were analyzed to obtain object manipulation data, and the data recorded by several sensor units were trained with machine learning algorithms to recognize different tactile signals in the hand gestures. The remainder of this paper is structured as follows: Section 2 presents the sensor fabrication process and data measurement procedures; Section 3 includes details of the experimental setup and an analysis of results; Section 4 includes the discussion and conclusion.
## 4 Simulation Results and Discussion
### Comparisons with Other Models
Figure 8: Sensitivity analysis of TKM: (**a**) Concentration ratio sensitivity for parameters detailed in Table 4 and (**b**) segregation sensitivity for the parameters detailed in Table 4.  
* The concentrations of the major species and the segregations of the principal reactions produced by the TKM appear to be relatively insensitive to the specific hydrocarbon mechanism as judged by comparing the CB-4 to the reference case [36]. Significant differences appear in HO\({}_{\rm x}\) species and could be expected in their end products, H\({}_{2}\)O\({}_{2}\) and HNO\({}_{3}\), especially at later times.
# Adaptive Channel-Enhanced Graph Convolution for Skeleton-Based Human Action Recognition
## 3 Method
### Network Architecture  
This study utilizes ACE-GC to develop a graph convolutional network, ACE-GCN, for the purpose of recognizing human skeleton actions. Figure 5 illustrates that the ACE-GCN has ten fundamental network layers. Following the 10 fundamental layers, the network is linked to a global average pooling layer and a Softmax classifier [31]. Each basic network layer is mainly composed of a spatial convolution, a temporal convolution module, and a residual connection. The output of each basic network layer is shown in Equation (14):  
Figure 4: Detailed flowchart of the channel aggregation process in the ACE-GCN.  
\[F_{out}=\sigma(res(X)+TCN(G_{out})),X\in R^{N\times T\times C\times V} \tag{14}\]  
The design of the temporal convolution module in the ACE-GCN basic network layer is based on the design in [31]. The multi-scale temporal convolution module in the basic network layer contains four branches, as shown in Figure 6. Finally, the output of the four branches is aggregated to provide the next network layer with more advanced spatio-temporal features as input.
# A Business-Model-Driven Approach to Task-Planning Knowledge Graph Construction
## 5 Experimentation and Verification of the Proposed Method
### Experimental Preparation  
All the data in this paper come from the Beijing Flight Control Center and are modeled in SysML language and constructed and analyzed with the help of Enterprise Architect 15.2. Space-station mission planning refers to the process of overall planning, resource coordination, and specific arrangements for the various types of tasks to be carried out by the space station during its on-orbit operation; it is a key link in ensuring the orderly and efficient operation of the space station [31]. By analyzing the business processes of space-station mission planning, a business model for standardizing the description of space-station mission-planning business processes is constructed, as shown in Figure 7.  
As can be seen from the figure above, the completion of a space-station mission-planning operation requires the collaborative operation of four departments to realize 10 business operations, such as the reception of the monthly event plan, the acquisition of the elements of the monthly event, and the modeling of the orbital scenario. Each operation generates the domain data that will be utilized in this mission-planning operation, including the monthly event plan, the monthly event components, the list of orbital resources, and so on. These domain data consist of a combination of conceptual and data-based knowledge to provide knowledge support for the construction of ontology and data networks for the space-station mission-planning knowledge graph (hereinafter referred to as SSMPKG).
# Voltage Optimization in PV-Rich Distribution Networks--A Review
## 2 PV Prosumers in Distribution Network
### PV Inverter Impact on Distribution Feeder Voltage Profile  
To provide a better insight into the voltage problem in DN caused by PVs, a case study is carried out. Two PV inverter control modes are chosen to clarify its capabilities for voltage optimization. The DN model, presented in Figure 4[40], consists of three radial  
Figure 3: Power capability curve of PV inverter.  
Figure 2: Volt–VAR curve of the PV inverter. Inductive reactive power reduces voltage value while capacitive reactive power increases the voltage value.  
feeders supplied by a 10/0.4 kV substation. Each feeder supplies 20 residential consumers. More information about the network model can be found in [40]. It is assumed that half of the residential consumers have PV systems on their rooftops. The nominal power of each PV plant is 5 kW. The case studied in the simulations corresponds to maximum production and consumption of 0.2 kW with an inductive power factor of 0.9.  
DIgSILENT PowerFactory [41] software is used for case study implementation and the conventional power flow is analyzed. Two modes of PV inverter operation are used in simulations: fixed power factor mode and volt-VAR control mode. The results for the fixed power factor mode are presented in Figure 5. The power factor range is taken from a real-life example of an inverter [42]. The voltage profiles at different power factor values are compared. There is an increase in the voltage profile at the unit power factor. The voltage profile is corrected by changing the power factor.  
The comparison of voltage profiles at the unit power factor and the applied volt-VAR control mode is shown in Figure 6. In the case without voltage control (unit power factor), there is a voltage rise in the distribution feeder caused by PVs. The voltage values are in the range of 1.005 p.u. to 1.05 p.u. In the volt-VAR control mode, the voltage values are lower than the unit power factor and are in the narrower range of 0.992 p.u. to 1.005 p.u. These values are more acceptable for the operation of DN, i.e., voltage deviation is smaller.  
Figure 4: Model of DN used in the case study.  
The obtained results show an improvement in the voltage profile of DN compared to the case without voltage control (unit power factor of the PV inverter). Voltage control in PV prosumer-rich DNs has a positive impact. The case study shows the possibilities of PV inverters regarding voltage control and the situation when there are lots of inverters placed at different positions in the DN. Determining the optimal operating point of the PV inverters imposes using optimization algorithms from which the OPF are imposed as a logical solution.
# Robust and Secure Data Transmission Using Artificial Intelligence Techniques in Ad-Hoc Networks
## 4 Proposed Work  
In this research, an enhanced routing protocol (i.e., AODV) is proposed and utilized in the detection of BHA nodes. The entire flow is provided in Figure 2. The proposed work is mainly partitioned among three sub-parts.  
The route is first established using the AODV routing protocol, then optimized using the Artificial Bee Colony algorithm's fitness function before classifying the nodes based on their attributes. Table 2 lists the parameters that were considered for the system.
## 4 Evaluation
### Qualitative Comparison
Consequently, since our approach does not require strict real-time identification, simple optimization should suffice to yield acceptable performance for practical operation. The importance of encrypted communication in networks has spurred efforts to standardize protocols such as DNS over TLS (DoT) [50], which encapsulates DNS queries and their responses inside TLS communications to prevent eavesdropping and tampering. Since the proposed approach relies solely on the character strings of domain names, it works unaffected by DoT encryption under the condition that the query logs can be retrieved from the RDNSs. Meanwhile, it is powerless when DGA malware directs queries to the external RDNSs, such as Google and Cloudflare Public DNSs. A guideline published by National Security Agency [51] recommends blocking name resolution via such unauthenticated RDNSs because of security and privacy concerns. Consequently, the condition does not compromise the practicality of our approach in typical network operation. Our approach, similarly to all ML- and DL-based methods, requires the preparation of large datasets to achieve high performance. Several projects have released datasets as part of their results, and several studies have focused on the accumulation of datasets themselves. For example, [PERSON] et al. [52] implemented a system called Thales, which creates massive amounts of malicious domain names by distilling multiple freely available sources, while [PERSON] et al. [53] developed a scalable, accurate, and ethical system, called Iris, which measures global name resolution and uses active manipulation to track the trends of domain names that evolve over time. In addition, [PERSON] et al. [54] referred to SysTaint, which facilitates reverse engineering of malware communications. Therefore, the increasing importance of ML and DL techniques for security applications may be expected to further stimulate activities associated with expanding the datasets on which such methods rely. The above discussion demonstrates the superiority of our proposed approach for detecting dict-DGA malware. Specifically, our approach allows the elimination of infected computers without restrictions due to encrypted communication or network scale, thereby helping to ensure secure network operations.
# GIS-Based Solar Radiation Mapping, Site Evaluation, and Potential Assessment: A Review
## 3 Solar Site Evaluation Using GISs
### Fuzzy AHP (FAHP)  
Although the AHP is broadly utilized to deal with the complexity of many problems by prioritizing the alternatives, the AHP does not consider the uncertainty associated with the process. To alleviate this issue, fuzzy set theory has been combined with the AHP. The FAHP considers the vagueness, imprecision, and uncertainty associated with the process.  
The FAHP has been widely applied in Asia to locate the most appropriate sites for PV or CSP power plants [46; 47; 48]. In these studies, various criteria for climatology, location, environment, and meteorology related to solar power plant installation were considered. In addition, the annual electricity production for solar energy plant was estimated in some studies. The FAHP has also been applied to the PV plant installation suitability analysis for Ulleung Island, Korea [49]. PV solar farm criteria were evaluated for an island-based case region having complex topographic and regulatory criteria (Figure 3a), along with high demand for low-carbon local electricity production. Six factor variables (solar radiation, sunshine hours, average temperature in summer, proximity to transmission lines, proximity to roads, and slope) (Figure 4) were normalized via a fuzzy theory to calculate an on-site suitability index (Figure 3b).  
Figure 4: Factor layer inputs to identify a suitable area for PV installation on Ulleung Island ([PERSON] and [PERSON] [49]).  
Figure 3: PV solar farm evaluation for Ulleung Island, Korea using the FAHP method: (**a**) Distribution of constraint areas; (**b**) Suitability index for a PV solar farm ([PERSON] and [PERSON] [49]).
## 3 Results and Discussion
### Sensory and Physicochemical Analysis of Fruit Juice Formulations
rose-apple juices) (_p_ < 0.05). Therefore, the mixed juices with the highest concentrations of rose juice presented the highest TSS contents (_p_ < 0.05). Diversified values of colour parameters for fruit juices are caused by the method of juices obtaining the degree of ripeness and the date of fruit harvest [40]. The obtained juices differed significantly in terms of brightness (L*), as well as the share of red (a*) and yellow (b*) (Table 4). The apple juice was the brightest among the single-component juices, while the rose juice was characterized by the highest share of yellow colour. Strawberry juice had the lowest brightness and the highest share of red colour. In the study published by [PERSON] and [PERSON] [41], the colour of apple juice was characterized by brightness at a level very similar to that obtained in this study. On the other hand, the share of yellow colour was four times higher compared to the tested juice. For strawberry juice, [PERSON] [42] obtained its brightness as lower by more than a half, and red and yellow colours two and three times higher, respectively, compared to the strawberry juice described in this paper. In another paper, [PERSON] and [PERSON] [43] studied strawberry juice reconstituted from concentrated juice. It was characterized by a brightness lower by almost five times (19.65) and a slightly higher share of red (44.56) and yellow (33.71). In the study of [PERSON] et al. [35], the parameters L*, a*, and b* for the juice of the _Rosa rugosa_ rose were 76.50, 10.54, and 58.39, respectively. Two-factor analysis of variance showed a significant effect of the addition of strawberry or apple juice on the value of all colour parameters (Table 5). The proportion of rosa juice added also had a significant impact on all colour parameters. The proportions of added juices significantly changed individual colour parameters, which influenced the final colour assessment. The average value of the brightness parameter was lower for strawberry juice compared to apple juice. In the case of the share of red and yellow colours, strawberry juices had a higher average. These values did not depend on the amount of rose juice added. In rose-strawberry juices, with the increase in the share of rose juice, the brightness and the share of red colour decreased, while the share of yellow colour increased. In rose-apple juices, however, a decrease in brightness was observed, and an increase in the share of yellow and red colours with an increase in the addition of rose juice. Similarly, [PERSON] and [PERSON] [41] studied apple juices with the addition of rose nectars. The addition of rose nectars to apple juices resulted in the darkening of the colour of the products. With a 25% share of rose nectars, the value of the brightness parameter
# Multi-Objective Optimisation in Multi-QoS Routing Strategy for Software-Defined Satellite Network
## 4 Multi-QoS Routing Model
### Routing Model  
We assume that the LEO satellite constellation is the iridium constellation, which consists of M*N satellites, where M denotes the number of LEO satellites in each plane, N denotes the number of LEO satellites planes and the radians between two adjacent satellites is \(2\pi/\)N. For each LEO satellite, there are four ISLs except the polar or reverse seam according to satellite visible light communications [30]. If the number of satellites is more than four, that would be better. But for simplicity, we assume there are only four ISLs. Each LEO satellite repeater has a send buffer and has no receive buffer.  
The two ends of inter-satellite link can transmit data to each other and four directions of the four links are regarded as the four interfaces of the router. When a satellite receives a packet, the packet is placed in the sending buffer based on the routing algorithm and the packet that stores the forwarding is waiting for the buffer.  
As we can see in Figure 2, the sender satellite is LEO11 and receiver satellite is LEO55. When a packet is transmitted to LEO11, LEO11 puts it to a QoS queue in send buffer according to the application type. According to the proposed routing algorithm, LEO11 selects the next hop and sends the packet. Until the packet arrives in LEO55, receiver satellite(LEO55) gets the packet.  
Figure 1: SDSN architecture.  
In order to meet the requirements of multi-QoS routing targets, the queues in sender buffer are classified according to the application type. In this paper, the application service type is set to four kinds, including time-delay sensitive application, video application, packet loss sensitive application and no special QoS requirements application. The congestion level of each interface depends on the sending of packets from all QoS queues. Figure 3 depicts the queue model.
# A Service-Caching Strategy Assisted by Double DQN in LEO Satellite Networks
## 4 Simulation  
In the simulation, we constructed a satellite network consisting of 66 satellites distributed across 6 orbital planes. On the ground, we employed Monte Carlo to generate 200 users following a Poisson-point-process (PPP) distribution for simulation settings. As shown in Table 1, the other parameters in our experiments were set according to references [34].  
Firstly, we conducted training in the environment of Pytorch. Figure 5 illustrates one instance of the training process, showing that, as the training epochs increase, the decision outcomes of the DRL agent improve progressively. Around the tenth episode, the agent's performance remains stable within a certain range of fluctuations. The latency stabilizes  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Description** \\ \hline LEO satellite bandwidth & 500 MHz \\ LEO satellite altitude & 1000 km \\ LEO satellite antenna gain & 40 dBi \\ UE antenna gain & 30 dBi \\ Number of UEs & 50–400 \\ Number of Sats & 66 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variable description.  
Figure 4: Flowcharts.  
at around 66.75 ms, and the cache hit rate stabilizes at around 24%, demonstrating the stability and effectiveness of the algorithm.  
Subsequently, as depicted in Figures 6 and 7, we compared the proposed method with the performance benchmark introduced in Section 3.3. We varied the cache capacity from 50 to 400 to obtain cache hit rates and content transmission delays under different cache capacities. With the increase in cache capacity, except for the random algorithm exhibiting the worst performance, the cache hit rates of the other three algorithms continuously increased, while the average content transmission delay continuously decreased. When the cache capacity was sufficiently large, the \(\varepsilon\)-greedy algorithm and Thompson algorithm showed similar performance, while our proposed method consistently demonstrated optimal performance.  
Figure 5: Cache hit radio and content transmission delay of each episode in training.  
Figure 6: Cache hit radio with different cache capacities.  
To demonstrate the distinctiveness and superiority of the proposed method over existing methods, we compared it with the most advanced method, CAFR, in the current edge-caching domain. The content-caching delay was primarily used as the evaluation metric. Figure 8 presents the comparative results, from which it can be inferred that the proposed method effectively addresses the satellite edge-caching issue.