esa-sceva/satcom-chunk-collection huggingface dataset:
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 1 Introduction  
Sensors and the Internet of Things (IoT) can be used to implement various kinds of applications in remote areas, such as collecting environment data and monitoring borders [1]. Many remote areas in developing countries [2] have no access to the internet, which limits the vision of a truly global connectivity of IoT.  
Because satellite communications provide ubiquitous coverage, they play an important role in providing internet connectivity in remote or marginalized areas. Studies have shown [3] that using optical fiber or telephone lines to form a network in those areas are problematic. As concluded in another study [4], satellite broadband reaching rural areas is more cost-effective than other technologies. In Bolivia, the government has chosen satellites to fill the gap in universal Internet access. Satellites help some Bolivians in remote areas use the internet. While using fiber optics in Bolivia costs about USD $3000 per 5 km, satellite methods are more affordable and simpler [4].  
This article aims to propose a satellite-based internet access point suitable for remote areas with the following objectives:  
* To be solar-powered, so it can operate outside the grid. This is particularly important because electricity supply in remote rural areas is often volatile;
* To provide a wireless hotspot so that sensors, IoT, and devices can connect to satellite networks with ease;
* To use low-cost equipment to make the system affordable;
* To be easy to maintain, set up, and use.  
Currently, personal satellite terminals are not designed to support multiple sensor or devices. In our design, a satellite phone is connected to a Wi-Fi hotspot module to provide Wi-Fi coverage for the sensors or devices. On the other hand, the cost of satellite communication is not low, and most satellite data plans are charged per megabyte. In this paper, we design an access point with cost in mind and make it affordable.  
The work in [5] is similar to our design, i.e., using wireless local network and satellite internet. However, unlike our design, the design in [5] requires an electricity supply. On the other hand, the design in [6] uses solar energy to power satellite terminals, but the work is mainly to demonstrate how the system works and does not cover practical design issues. Unlike previous works, this study provides a practical approach for designing a solar-supplied satellite access point, covering methods to determine the required solar panel size and calculate the number of days to fully charge the battery.
## 4 The Evidence in Humans
### Diabetes Mellitus  
Oxidative stress plays an important role in diabetes mellitus (DM) pathogenesis and its complications. A plethora of medicinal plants have increasingly been tested to achieve glycemic control, specifically via antioxidant mechanisms of action [128].  
Green tea has been extensively investigated at a pre-clinical level, but just one clinical trial has focused on its anti-diabetic potential via a redox effect. Indeed, in type-2 DM patients, green tea reduced DNA damage and enhanced DNA repair through increasing plasma 8-oxoguanine glycosylase (hOGG1) activity and HO-1 protein levels, which are recognized protective factors against pro-oxidant changes [157].  
Ginger (_Zingiber officinal_) is one functional food which contains biological compounds, including gingerol, shogaol, paradol, and zingerone, and its effect, as a 3-month supplementation, has been shown to improve glycemic indices, TAC, and paraoxonase-1 (PON-1) activity in type-2 DM patients [158]. A similar finding was reported for _Nigella sativa_[159], and, in healthy volunteers, _Grewia asatica fruit_[160].  
Conversely, the antioxidant effect of Korean red ginseng in postmenopausal women was not associated with improved fasting glucose and insulin levels and insulin resistance in a double-blind randomized controlled trial [161].
## 3 IoT Traffic Analysis with Two-Stage Clustering
### Two-Stage Clustering  
In the proposed method, we focus on two-stage clustering, which is the basis of the proposed method. Two-stage clustering is a traffic analysis method that aims to obtain dynamic changes in P2 PTV traffic as proposed in [15; 16]. As a preliminary preparation, the traffic data when viewing each P2 PTV video content were divided into predetermined unit time lengths. In the first-stage clustering, the set of divided traffic data was clustered using \(k\)-means clustering for each P2 PTV content. The representative values were extracted from each cluster obtained from the first clustering. Representative values corresponding to all the first-stage clusters were gathered and clustered again using \(k\)-means clustering. These clustering steps possibly equalize the biases of the occurrence frequency of traffic patterns, because every cluster is described as one value, regardless of the cluster size. Hence, two-stage clustering extracts traffic patterns irrespective of the occurrence frequency. In [15], the patterns obtained by two-stage clustering were used to illustrate the transition characteristics of P2 PTV traffic. In [16], comparisons of one- and two-stage clustering confirmed the effectiveness of two-stage clustering in P2 PTV traffic analysis.  
There are three reasons for applying the two-stage clustering in the proposed method. First, the two-stage processing can realize load distribution, which is one of the considerations when assuming implementation in actual environments. For instance, the first clustering step is performed at a gateway in each smart home, and the clustering results are subsequently transferred to a cloud server and clustered in the second stage. Thus, the entire clustering process is distributed in the network. Second, the concept that every cluster is described as one value regardless of the cluster size can reduce the impact on IoT traffic analysis from the biases of traffic data, which is similar to P2 PTV traffic analysis. Moreover, this representation mechanism can reduce communication traffic when combined with the distributed implementation described above. Only representative values of the clusters are transferred from each smart home to a cloud server. Third, the objective of two-stage clustering [15; 16] to obtain dynamic changes in P2 PTV traffic coincides with the need to determine whether an IoT device functions normally or appropriately, thereby addressing the concerns raised in the context of conventional methods. This is because two-stage clustering primarily divides traffic data into a series of data pieces and thus realizes time series data analysis. Therefore, we decided to use a two-stage clustering in the proposed method.
# Metachrylate-Based Polymeric Sorbents for Recovery of Metals from Aqueous Solutions
## 1 Introduction  
Due to the rapid technological development, large amounts of heavy, precious, and rare metals cause contamination of water resources [1; 2]. Major pollutants of marine and ground waters are toxic heavy metals such as nickel, chromium, lead, zinc, arsenic, cadmium, selenium, and uranium, originating from mining, metal processing, pesticides, pharmaceuticals, etc. [3]. Moreover, since they exist in a dissolved ion state in wastewaters, heavy metals accumulate in living organisms, causing serious pollution and health problems [3; 4].  
Precious metals (primarily gold, silver, platinum, palladium, and rhodium) find their applications in specialized fields (catalysis, electronic devices, and jewelry) [5]. Rare earth elements (REEs) signify a group of 17 chemically similar elements representing about 17% of the total quantity of all naturally occurring elements [6]. They have wide applications in electronics, optics, catalysis, green energy technologies, etc. The growing demand for these metals, key ingredients of modern technologies, is a source of numerous and severe ecological and economic issues. Thus, the efficient removal and recovery of these metals prior to their discharge into the environment is nowadays the main focus of scientific research [3; 7; 8; 9].  
Various conventional techniques for the removal and recovery of metal ions, as well as environmental detoxification, are adsorption, chemical precipitation, electrochemical extractions, membrane filtration, biosorption, ion exchange, evaporation, flotation, and oxidation [3; 8; 10; 11].  
The most popular among them is adsorption, which is proven to be economical and effective. A great variety of articles published on the usage of different adsorbents can be found in the literature, such as: agro-industrial waste [12; 13; 14]; polymers and polymer membranes [15; 16; 17; 18]; organic-inorganic hybrid polymers [19; 20; 21]; bioadsorbents [22]; chitosan; modified chitosan and chitosan-based hybrid composites [23; 24; 25; 26]; biogenic iron compounds [27]; polymer hydrogels [28; 29]; acrylamide-based microgels [30; 31]; geopolymers [32; 33]; silica [34]; biochar [35]; carbon nanotubes and graphene [7]; green activated magnetic graphitic carbon oxide [36]; magnetic nanoparticles [1]; nanomaterials [37], etc.
# Improving Eight-State Continuous Variable Quantum Key Distribution by Applying Photon Subtraction
###### Abstract  
We propose a new method to effectively improve the performance of a quantum key distribution with eight-state continuous variables by the photon subtraction method. This operation is effective in increasing and distilling Gaussian entanglement between quantum states, and can be easily realized by existing technology. Simulation results show that the channel-loss tolerance of the eight-state continuous variable quantum key distribution (CVQKD) protocol can be extended by the appropriate photon subtraction algorithm; namely, single-photon subtraction.  
Keywords:quantum key distribution; eight-state continuous-variable protocol; photon subtraction +
Footnote †: journal: Applied
## 4 Results and Discussion
### Experiment and Analysis
#### 4.2.4 Comparison Experiments  
We did the same tests again on different areas to obtain accurate evaluation results. Overall, applying the trained model in the test sample, the present method achieved an overall accuracy of 97.8% and an overall mIOU of 0.726 on the available dataset. Table 1 shows the performance of the present method on the same dataset with methods such as Pointnet [11], where the present method has a partial improvement in overall accuracy. The present method shows significant improvement in mIOU over PointNet++ [14], with about 30% improvement compared to the Pointnet [11] method; 21.1% improvement in mIOU compared to Pointnet++ [14], and 8.4% improvement in mIOU compared to the graph convolution-based method GACNet [28].  
Streetlights failed to be segmented effectively in Pointnet [11], Pointnet++ [14], and GACNet [28] which is because the streetlights are in the MLS road point clouds data set. The number of point clouds is sparse, which leads to insufficient features and easily confused with features such as signage. The multidimensional features of the data are reduced, which affects the segmentation results. The fusion network model, with the addition of the SRC module and channel attention mechanism, has a much higher segmentation success rate in the streetlights (38.5%) than other methods. Similarly, the segmentation results of this method (98.3%) in signage features with a relatively large number of point clouds compared to streetlights showed a relatively significant improvement compared to other methods (as in Table 2). It can be inferred that the method in this paper is more sensitive to inhomogeneous sparse road point clouds data and easier to learn.
# Modeling and Analysis of a Generic Internal Cargo Airdrop System for a Tandem Helicopter
## 4 Trim Analysis
### Case of Helicopter Flight Operation in the Process of Internal Cargo Airdrops
The airdrop operation significantly decreases the trim position of the longitudinal cyclic control within the helicopter flight velocity envelope flight operation, and significantly decreases the thrust control when the helicopter flies at the velocity of 150 knots. In detail, the negative maximum of the trim deviations from the initial trim state of the longitudinal cyclic and thrust controls reach by about 15% and 25%, respectively. These are because the pitching moment acting on the helicopter would increase gradually when the cargo is moving backward. Furthermore, when the cargo starts to rotate around the tipoff, because the pitching moment acting on the helicopter decreases quickly, the trim positions of the longitudinal cyclic and thrust controls have an obvious trend of returning to the trim state of flight operation without cargo. 2. The increase in flight velocity will evidently increase the influence of the airdrop operation on the thrust control and the pitching attitude when the helicopter flies at a velocity between 0 and 150 knots, and evidently decrease the influence of the airflow operation on the lateral cyclic and pedal controls when the helicopter flies at a velocity of less than 120 knots. 3. The airdrop has a little effect on the rolling attitude. In detail, the maximum of the deviation from the initial trim position of the rolling attitude angle is less than 0.1 deg.
# An Analytical Model for Interference Alignment in Broadcast Assisted VANETs
## 4 The \(\sigma\)-Clustering Algorithm Based on CSI in VANET  
To solve CSI stability problem, which is the key information of IA application, in this section, we design a CSI based clustering algorithm. Reasonable CSI clustering algorithm would improve accuracy of CSI estimation and IA efficiency though avoiding vehicles with similar CSI allocated the same time slot. In this paper, we take RSSI value as CSI parameter.  
\(C=\{C_{1},C_{2},...,C_{\eta}\}\) is the clustered vehicle set. \(\eta\,\,=\mid\,C\mid\) is the number of clusters. \(p_{i}\) is RSSI value between vehicle \(i\) and base station. \(\varepsilon_{i}\) is variance of each cluster which is calculated as follows  
\[\varepsilon_{j}=\sum\limits_{i\in C_{j}}||p_{i}-\frac{\sum\limits_{m\in C_{j} }p_{m}}{\left\|C_{j}\right\|}||^{2},(1\leq j\leq\eta) \tag{32}\]Traditional clustering algorithms is set with fixed \(\eta\). In our clustering algorithm, we impose requirement on \(\varepsilon_{i}\). Define upper limit of \(\varepsilon_{i}\) as \(\sigma\). Then, we have  
\[\varepsilon_{i}\leq\sigma\;,\;(\varepsilon_{i}\in\varepsilon) \tag{33}\]  
According to the definition of \(\varepsilon_{i}\), we can conclude that the number of clusters increases while the value of \(\sigma\) decreases. A reasonable value of \(\sigma\) should be determined.  
Since signal fading rate varies due to buildings and other obstacles, the clustering algorithm only based on RSSI is not pragmatic in VANETs. Hence, the location information should also be taken into consideration. After RSSI based clustering algorithm, we introduce kemans++ clustering algorithm based on location to avoid this problem. Pseudocode of our algorithm is listed in Algorithm 1.  
```
1:set the max cluster number \(\eta\)
2:\(Clusters\),\(Clusters2\)
3:\(N=size(A)\)
4:\(PathLosses=CalPathLoss(A,St)\)
5:for\(i=1\) to \(\eta\)do
6:\(\left\{C_{1},C_{2}...C_{i}\right\}\) = Cluster Based on Path loss
7:\(\left\{\varepsilon_{1},\varepsilon_{2},...,\varepsilon_{i}\right\}\) = Calculate \(\varepsilon\) for each cluster
8:for\(j=1\) to \(i\)do
9:if\(\varepsilon_{j}\leq\sigma\)then
10: Add the \(C_{j}\) into \(Clusters\)
11:endif
12:if\(\varepsilon_{j}\geq\sigma\)then
13: continue
14:endif
15:endfor
16:endfor
17:for\(c\) in \(\left\{C_{1},C_{2}...C_{i}\right\}\)do
18: cluster2=Kmeans++(\(c\)) based on location of vehicles
19: Add \(cluster2\) into \(Clusters2\)
20:endfor
21:return\(Clusters2\)
```  
**Algorithm 1** Clustering(\(A,St,\varepsilon,\sigma\))
## 5 Experiments and Results
### Auditive Transparency
#### 5.2.2 Key-Based Additive Strategy  
The additive strategy was evaluated for \(\alpha\) values \(\in[\frac{1}{16}...1]\), in steps of \(\frac{1}{16}\) in descending fashion, until the highest \(\alpha\) value was achieved which approximated the distortion results for the unsecured data-hiding version. The found \(\alpha\) value was \(\frac{1}{8}\); Figure 7 shows the PSNR results and the PSNR distribution for that \(\alpha\) value.  
The PSNR mean and median were calculated as 44.36 and 43.30, respectively.  
The ODG evaluation achieved similar values as the unsecured data-hiding version; Figure 8 shows the ODG results and the ODG distribution. The ODG mean and median were calculated as \(-1.0638\) and \(-0.6409\) respectively.  
On the other hand, statistical tests shown no statistical difference, in terms of PNSR and ODG, for the additive strategy.
## 5 Evaluation
### Results
#### 5.4.3 Results for RQ3
MovePerf delivers the best performance, with a mean of 4.92% and a very small margin of 0.02, showing significant improvements in both its prediction accuracy and stability. Compared to FM and the DFNN, MovePerf achieves a notable enhancement in its overall performance. For Kevin, the prediction MREs of the two components are quite significant, at 28.74% and 25.21%, respectively. Although the prediction MRE of MovePerf has not yet reached the ideal level, its predictive accuracy is superior to that of the individual components. Additionally, MovePerf also demonstrates a notable advantage in terms of stability. In summary, MovePerf consistently outperforms FM and the DFNN in terms of both accuracy and stability across multiple datasets. Integrating the two components significantly improves the prediction precision, reduces fluctuations, and results in more consistent performance, making MovePerf a superior approach compared to its individual components.
Mapping procedures are validated by domain experts using the rule engine of open-data platform. Thus, transformed data becomes exportable and downloadable in open such formats as XML, JSON, RDF/XML, RDF/JSON, Turtle, N-triples, Notation 3, HTML, CSV and Excel. The platform draws on the strength of semantic web technologies while generating operations of mapping sensor devices' data with a trait ontology, creating datasets stored in open formats, and performing manipulation on stored data. The objectives of the developed open-data platform are as follows: (i) to collect domain-specific data concerning particular agricultural products through ontology-based data acquisition forms generated by domain stakeholders using crop-specific trait ontologies; (ii) to gather and visualize stream data concerning site-specific parameters of particular agricultural products through WSNs; (iii) to produce domain-specific linked open data using mappingrules constructed by any domain stakeholder using crop-specific trait ontologies; (iv) to store semantically annotated agricultural data within the diverse databases and files such as relational databases, graph databases, XML files, RDF files, etc. ; (v) to provide syntactical interoperability using web services and APIs, which allow stakeholders to share data for a particular agricultural product between different kinds of software applications; (vi) to publish well-defined, well-structured and semantically annotated data concerning a particular agricultural product using open standard in appropriate formats such as RDF/XML, RDF/JSON, N-triples, Notation 3, Turtle, XML, JSON, HTML, CSV and Excel. Considering that all the objectives of the developed open-data platform are constructed based on crop-specific trait ontologies, it would be appropriate to describe them as the main pillars of the platform. The domain stakeholders who want to use the capabilities of the open-data platform can use an existing crop-specific trait ontology or upload a new one to the platform using the user interface represented on Figure 11 in order to achieve their goals. This paper is organized as follows: Section 2 describes the general terms that are focused on within the scope of this paper and addresses some challenges regarding semantic interoperability. The architecture of the proposed approach is detailed in Section 3. Section 4 introduces the implementation of open-data platform which integrates IoT data based on the proposed approach. Section 5 includes the conclusion of the paper and discussion of the future work.
## 4 Experimental Validation
### Uncertainty Discussion  
During the experimental validation process, although we obtained relatively satisfactory results, some uncertainty factors still exist. Firstly, the wear and distribution changes of abrasive particles during the polishing process may affect the polishing effect, and these factors are difficult to fully control in experiments. Secondly, the inherent material differences and microstructures of marble can significantly impact the polishing effect, causing marble from different batches to exhibit varying levels of glossiness and roughness under the same polishing conditions. Additionally, environmental factors such as temperature and humidity during the experimental process may also influence the polishing outcomes to some extent.  
To mitigate the impact of these uncertainty factors, we implemented the following measures: Firstly, before each experiment, we carefully inspected the SG flexible tool to ensure uniform distribution of abrasive particles and the absence of significant wear. Secondly, we selected marble with similar material properties and consistent microstructures for the experiments to reduce the influence of material differences on the results. Lastly, we maintained constant temperature and humidity conditions during the experiments to minimize the effects of environmental factors on the polishing outcomes. Despite these measures, it is still recommended to further explore the specific impacts of these factors on the polishing effect in future research and seek more effective control methods.
## 2 Methodology
### Autoencoders  
Autoencoders are a type of neural network architecture, where the output is the same as the input, which can be used for unsupervised learning tasks, such as data compression, feature extraction, and dimensionality reduction [7; 52]. The main components of an auto-encoder include an encoder network, a decoder network, and a bottleneck layer, as shown in Figure 3.  
The encoder takes the condition monitoring input data \(x\) and maps it to a lower-dimensional latent space representation \(z\). The output of the encoder is  
\[z=f(Wx+b) \tag{10}\]  
Figure 3: Main components of an autoencoder.  
Encoding is typically done through a series of hidden layers that gradually reduce the number of nodes in the network. The bottleneck is the layer in the middle of the network where the input data is compressed into the lower-dimensional latent space representation. The size of the bottleneck layer determines the dimensionality of the latent space representation. The decoder takes the latent space representation \(z\) and maps it back to the reconstructed input data \(x^{\prime}\):  
\[x^{\prime}=f(W^{\prime}z+b^{\prime}) \tag{11}\]  
Like the encoder, the decoder typically consists of a series of hidden layers that gradually increase the number of nodes in the network. The autoencoder is trained to minimize the difference between the original input data and the reconstructed input data by the decoder. A loss function, such as the mean squared error (MSE) or binary cross-entropy, is minimized.  
Autoencoders can capture complex nonlinear relationships between variables, making them suitable for representing high-dimensional data with nonlinear structures [7].