esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Chemometrics of the DES-Based Extracts
#### 3.2.1 Principal Component Analysis  
Multivariate processing of the AC data obtained for the studied extracts yielded two principal components (PC1 and PC2), with the variables explaining 95.55% of the total variability. PC1 explained 83.77% and PC2 was 11.78% of the total variability. The first principal component (PC1) had the highest eigenvalue of 2.51 and accounted for 83.77% of the variability in the data set, while the remaining two generated PCs (PC2 and PC3) yielded progressively lower eigenvalues (<1; 0.35 and 0.13) and did not explain the variability in the data (<16.22% total). PC1 inversely correlated with all variables: CUPRAC (\(-\)0.9567), ABTS (\(-\)0.8957), and DPPH (\(-\)0.8921). Moreover, PC2 was highly negatively contributed by DPPH (\(-\)0.4261). This suggests that PC1 is generally more correlated with the variables than PC2.  
From the bi-plot of PC1 and PC2, as shown in Figure 3, ABTS and CUPRAC were the variables with negative loadings on PC1 and positive loadings on PC2, while DPPH was the feature with negative loadings on PC1 and PC2.  
The depicted PCA bi-plot revealed that the LB, M, and ChoP extracts obtained using betaine- and glucose-based DESs (DES2-DES6, Table 2) with high antioxidant properties were located to the left in the score graph and had negative values for PC1. In contrast, all choline-chloride extracts (DES1), as well as Cha, N, and BH samp  
Figure 3: Principal component analysis (PCA) bi-plot of scores and loadings for extracts of herbs and agro-food by-products representing various DES mixtures utilized in the UAE procedure before AC analysis by CUPRAC, ABTS, and DPPH assays.  
green solvents (DES2-DES6), had lower AC values and were situated at the right in the diagram and had positive values for PC1.  
As can be seen, extracts with high DPPH (LB and M extracted by DES4, DES5, and DES6 and DES4-based extracts of Cha, N, ChoP, and BH) as well as low ABTS and CUPRAC values (Cha, N, and ChoP obtained by DES2 and DES5) were located under the A1 axis (Figure 3). For this reason, these analyzed extracts fell into three distinct groups, respectively. However, three LB, N, and ChoP extracts obtained using DES3 (Gu:U-30) with the longest distance from others exhibited high ABTS results and similar medium values of CUPRAC and DPPH. In addition, all samples having the lowest antioxidant potential determined by three analytical methods were separated from other tested extracts. Although Cha-DES3, Cha-DES6, M-DES3, and N-DES6 extracts with low DPPH and similar moderate ABTS clustered around, these two green solvents were more suitable for the simultaneous extraction of compounds scavenging ABTS** cation radicals.  
The PCA bi-plot revealed that the samples of herbs and agricultural residues can be clearly separated based on the type of DES mixture used to prepare extracts before AC determination.
## 2 Datasets  
Three types of datasets were used to quantitatively determine the spatial representativeness of the MODIS daily albedo product: (1) Tower-measured surface albedo datasets from AmeriFlux sites were used as field truth to calibrate 30 m albedo data. (2) The MODIS daily albedo product MOD43A3 (V006, 500 m, daily) was assessed, and annual land cover data were downloaded to identify different land cover types. (3) Landsat TM level-one data were used to estimate the 30 m spatial resolution of the albedo, which was then used as the spatial complete high resolution data to evaluate the spatial representativeness of the MODIS 500 m spatial resolution albedo product.
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 6 Evaluation and Results
### Comparison to Previous Works
#### 6.3.3 Comparison  
In this subsection, the results obtained with the three algorithms are compared. Table 4 shows these results in terms of three relevant aspects: the average user rate in the entire scenario (Equation (52)), the fairness (Equation (51)) and the total shared backhaul bandwidth (TSBB), which,  
Figure 5: Position of UAVs and coverage areas using: Spiral Algorithm [12] (**top**); and \(k\)-Means Algorithm [16] (**bottom**).  
according to Equation (25), should be lower than \(B_{BH}^{total}\) (180 MHz). If it is not fulfilled, the algorithm does not fulfill the constraints imposed by the backhaul implementation, which is marked in gray in the tables.  
Observing the results, it can be concluded that the average rate per user is very similar for the three algorithms. Note, however, that the proposed algorithm achieves much better fairness, which means that all users will perceive almost the same quality of service (QoS). This would not be the case for the other two algorithms, where there can be high differences between the rates assigned to the best and the worst users, which is a negative effect to be avoided, if possible. The reason the Spiral and \(k\)-Means algorithms do not achieve a fairness as high as our proposal is because they locate the UAVs using only geometrical criteria but without taking into account the quantity of traffic generated by the set of users assigned to each single UAV. In addition, the strategy proposed in this paper fulfills the constraints related to the total bandwidth/capacity of the backhaul link. In other words, the proposed solution achieves a higher level of QoS while not exceeding the backhaul capacity.
# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 3 Materials and Methods
### Field Experiment  
As has been discussed in Section 1, the aim of this research is to implement and analyse the performance of the HSP to determine the potential benefits of such a system to improve the data quality and reduce the cost of the data collection process in PA. In order to evaluate the HSP's performance, a field experiment has been conducted, which focuses on aerial data validation and calibration to improve the aerial data quality. To conduct field experiments regarding aerial data validation and calibration, we collected data from a lemongrass farm during the spring season. However, the HSP can be used to analyse the health status of different plants and farms of various sizes. Therefore, the plant type and farm size are not important for presenting and evaluating the contribution of this paper. The data collection was performed for four consecutive days using aerial and ground-based nodes. Although larger data sets will result in better training data for the validation and calibration process, the approach and techniques for data collection and data analysis will be the same. The ground-based data were obtained continuously for the entire experimental duration. The ground-based optical sensors were pointing directly to the target plant. A two-pad variable resistor soil moisture sensor was also installed next to the target plant at 5 cm depth to analyse the soil moisture in different conditions based on soil conductivity between the two pads. The weather station was also used to collect environmental data including temperature, humidity, solar radiation, wind speed, wind gust, and rainfall. The aerial data were collected in three cycles per day (morning, afternoon, evening). Each aerial cycle data collection was captured during a 10 min flight. The UAV was flown at different altitudes and over the entire field of the experiment. In addition, the aerial data were captured in different weather conditions, including sunny, cloudy, and windy conditions, to analyse the impact of such environmental conditions on the data. In total, 6 experiments were conducted for validation and calibration purposes. The results of these experiments are discussed in Section 4.
# Fault Prediction of Mechanical Equipment Based on Hilbert-Full-Vector Spectrum and TCDAN
## 3 Construction of Prediction Model
### TCDAN Model  
The temporal convolution double-layer attention network (TCDAN) proposed in this paper introduces a two-layer attention mechanism based on the TCN residual module, as shown in Figure 6. First, the input time-series is preliminarized through multiple TCN residual modules, and the convolution operation is used to capture the high and low-dimensional hidden features of the context sequence in the input data. The output of the TCN residual module maintains the same temporal dimension as the input data, and the feature dimension is increased by multiple convolution kernels. According to the input and output of the TCN residual module, Attention-1 mainly mines the correlation between the features corresponding to different input variables and the predictor variables. The Dense layer with SoftMax activation function is used to calculate the weight of each input, and the Multiply layer outputs the input and the corresponding assigned weight. Then, the influence of key features on the prediction results is highlighted. Compared to Attention-1, Attention-2 adds a Permute layer to adjust the dimensions of the input and output in the hidden layer. Therefore, the Dense layer in Attention-2 is applied to the time dimension to mine the influence of different time step data on the prediction results, and adjust the corresponding weights according to the contribution. After the Attention layer, the Flatten layer is used to flatten the multi-dimensional output in the Attention-2 layer. Finally, the prediction results were output by the fully connected layer and the predicted value was output after the anti-normalization layer processing.
# From Analogue to Digital Photogrammetry: Documentation of Padise Abbey in Two Different Time Stages
## 3 Re-Processing of Original Images
### Digitization  
The glass plate negatives were digitized on a flatbed scanner Epson Perfection V700 Photo. The glass plate negatives were scanned in 1200 dpi and 2400 dpi scanning resolution. After the comparison between the different scanning resolutions, we decided to work with 2400 dpi scanning resolution. We assumed that scanning under 1200 dpi scanning resolution was not sufficient because geometric value of the negatives was assessed as higher. Images scanned under 2400 dpi were demanding in terms of data size. Even these data were not easy to operate during pre-processing due to the size and scanning glass plate negatives under a higher scanning resolution than 2400 dpi would cause difficulties. Additionally, scanning under too high of a scanning resolution does not necessary increase geometric value of the digitized images. After digitization, knowing approximate image scale M = 1:124, we were able to calculate a ground sample distance (GSD), so 1 pixel in-image is equivalent to approximate 1.3 mm on the wall.
## 2 Related Works  
The concept of opportunistic networks derives from early intermittent connected networks (ICNs) [16] and delay tolerant networks (DTNs) [17]. In recent years, the research on opportunity networks mainly focuses on the routing algorithm [5; 6; 10; 12; 13], and several innovative routing algorithms are proposed for different application scenarios in opportunistic networks. Consequently, in this section, we will give a brief introduction to the state-of-the-art of both two-phase and profile-based routing algorithms in opportunistic networks.
## 3 Influence Metrics
### Quality of Service Metrics Analysis
#### 4.3.3 Routing Decision on Contact  
The main objective of Algorithm 3 is to determine whether node \(A\), given the connection between nodes \(A\) and \(B\), should send a copy of the messages it carries to node \(B\). This decision aims to minimize overhead. Instead of sending copies to every encountered node \(B\), it is preferable to choose a node with better connectivity. Such a node is more likely to have greater access to a larger network.  
```
Input:\(N1\) and \(N2\) (new contact between two nodes), \(R\) (best nodes ranking), \(N1\)\(LNF\) Output:\(N2\) messages queue
1 begin
2 foreachmessage in \(N1\) queue do
3\(N3\) = obtain message destination;
4 is_one_of_best_nodes = (\(N2\) in \(R\));
5 are_friends = (\(N3\) in \(N1\)\(LNF\));
6 arrived_to_destination = (\(N2\) = \(N3\));
7 ifis_one_of_best_nodes or are_friends or arrived_to_destination then
8 forward message to \(N2\) queue;
9 endfor
10 return\(N2\) messages queue
```  
**Algorithm 3** Routing decision on contact  
This algorithm requires the use of different data structures to determine whether node N1 forwards the messages in its queue to \(N2\). Therefore, these data structures are accessed in a single loop, where the processing of each one is decided. The time complexity is O(N). In terms of spatial complexity, we need a list M for each node N to act as a message buffer, and a unique list to store the ranking of nodes. Consequently, the spatial complexity is O(N \(\times\) M).  
This approach restricts the generation of message copies by node \(A\) (a finite number in the case of S&W or unlimited in the case of Epidemic) to specific connections where node \(B\) exhibits one of the following three characteristics:  
* Node \(B\) ranks among the top positions in the ranking obtained through Algorithm 1. Being a node with good connectivity, it is more likely to successfully deliver the packet to the intended recipient or another node that can assist in reaching the message's destination;
* The source and destination nodes of the message are friends. This indicates that they have previously connected and are likely to reconnect. Therefore, node \(A\), carrying the message, is allowed to deliver a copy to node \(B\);
* Node \(B\) is the intended destination of the message. In this scenario, it is logical for node \(A\) to deliver the message to node \(B\).
# Energy Efficiency for Green Internet of Things (IoT) Networks: A Survey +
###### Abstract
There were 25 billion devices connected to the Internet (i.e., 3.47 connected devices per person), in the world population of 7.3 billion in 2015 [6, 7]. In addition, an estimated of 50 billion connected devices around the world are deployed by 2020, which means 6.41 connected devices per person and a world population of 7.8 billion in 2020 [8, 9]. Another study [10] reported the number of smart objects will reach to 80 billion in 2025 with 9.8 connected devices per person. Based on these studies. Wireless sensor networks (WSNs) is an essential part of the IoT technology as it helps in combining heterogeneous systems, data and applications. In such systems, sensor nodes capable of detecting the required information, performing some processing and communicating with other connected nodes are the main component of these networks. However, the life of these nodes is often restricted by being powered by a battery with a limited life, constraining processing ability, memory, and radio communications [11, 12]. Energy efficiency is one of the most crucial issues for WSNs. Most of the energy is consumed in data processing and transmissions [13]. This means it is not rational to waste energy on protocol overheads, the transmission of unneeded data or non-optimized transmission of data packets, especially retransmissions, due to inefficient scheduling and routing algorithms. Thus, it would be prudent to design and implement efficient load balancing schemes of energy gauge nodes to maximize the lifespan of constraint-oriented networks [14]. The paper is arranged as follows: Section 2 highlights the research motivation of the paper. Section 3 introduces an overview of IoT enabling technologies introduces in Section 3.1. A brief description of the WSNs technology that enabled the IoT revolution is provided in Section 3.2. Various sources of energy wastage and different solutions have been mentioned in the literature and are presented in Section 3.5. Section 5 identifies the gaps in the existing literature in terms of energy conservation measures that could be considered in future works. Finally, Section 6 is summarized the paper.
# Arithmetic Study about Efficiency in Network Topologies for Data Centers
## 4 Coefficient Proposed to Obtain Balance between Performance and Simplicity  
The coefficient proposed to measure efficiency of the different network topologies proposed for data centers needs to take into account both performance and simplicity of use along with maintenance in a way to achieve a trade-off between them. Regarding the former, it is measured by means of the average distance among nodes, whereas the latter it is stated as the average number of links per node (its degree) for graph-like designs, or otherwise, the average number of links per device (considering both nodes and switches) for tree-like designs.  
Anyway, both averages should be as small as possible in order to obtain both performance and simplicity, such that the smaller the coefficient, the better. Regarding the average distance among nodes, it is going to be measured as the average number of hops between all pairs of nodes and denoted as \(\alpha\). With respect to the average number of links per device, it is going to be measured as stated above and described as \(\beta\).  
Putting everything together, the coefficient proposed is going to be obtained by multiplying both averages, namely, \(\alpha\times\beta\). In this sense, the lower the result obtained, the better the combination of both factors; hence, better balance between performance and simplicity will be attained. That is why the coefficient is branded \(\eta\), which is the Greek letter used in physics and engineering for efficiency. Therefore, it may be said that (1) defines \(\eta\).  
\[\eta=\textit{AvgDistance}\times\textit{AvgLinks}=\alpha\times\beta \tag{1}\]  
The main motivation of this paper is to calculate a coefficient involving a compromise between performance and simplicity--defined in the terms exposed in the introduction--in a way that such a value may help decide which data center network topology is more suitable, thus acting as a sort of tie-breaker to select among diverse interesting options.  
Therefore, this coefficient is just another tool for making the decision as to which data center is more convenient among diverse options along with other tests related to throughput, latency, cost or robustness.  
As a practical-use case, let us focus on the situation exposed in the first two paragraphs of Section 3, which is devoted to related work. In the first one, it is stated that after the analysis carried out on reliability and survivability, BCube is the most robust design regarding link failures, whereas DCell isthe most robust regarding switch failures. Likewise, in the second one, it is claimed that after the tests were undertaken, BCube provided better security and data integrity, whilst DCellprovided better speed in data transmission. Hence, the coefficient proposed may act as a tie-breaker for the selection of the data center network architecture, as it presents a compromise between performance and simplicity for each of the possible options available.
# Research Trends in Collaborative Drones
## 5 Navigating under Uncertainty
### Problem Description  
When drones are navigating an unknown environment, they may have to find a flight path from a source to a destination in a GPS-denied environment using only clues obtained from landmarks visited. In particular, when hovering over an area, they can acquire data through their camera(s) and other sensors, which may be visual, acoustic, etc. Data collected are being used to interpret landmarks. A priori, drones may be given clues and specific characteristics about landmarks that they can use throughout their route discovery. For example, they may be seeking a green door or a tall building. Our main question here is the following:  
How can drones navigate through an environment of landmarks without using GPS information?
Sensors_**2013**, _13_, 2051-2075; doi:10.3390/s130202051  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Cognitive Mobile BTS Solution with Software-Defined Radioelectric Sensing**  
**[PERSON]**\({}^{1}\)**, [PERSON]**\({}^{2}\)**, [PERSON]**\({}^{3}\)**, [PERSON]**\({}^{3}\)**, [PERSON]**\({}^{3}\)**, [PERSON]**\({}^{1,3,\ast}\)**, [PERSON]**\({}^{3}\)**, [PERSON]**\({}^{3}\) **and [PERSON]**\({}^{3}\)  
\({}^{1}\) Gradiant, CITEXVI, Campus Universitario, Vigo 36310, Spain; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Communications and Information Technologies, Universidad Politecnica de Cartagena, Cartagena 30202, Spain; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) AtlantTIC, University of Vigo, Campus Universitario, Vigo 36310, Spain;  
E-Mails: [EMAIL_ADDRESS] (F.Q.G.); [EMAIL_ADDRESS] (S.C.);  
[EMAIL_ADDRESS] (M.P.); [EMAIL_ADDRESS] (M.G.S.);  
[EMAIL_ADDRESS] (R.L.V.); [EMAIL_ADDRESS] (C.L.B.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 7 November 2012; in revised form: 23 January 2013 / Accepted: 30 January 2013 / Published: 5 February 2013_  
**Abstract:** Private communications inside large vehicles such as ships may be effectively provided using standard cellular systems. In this paper we propose a new solution based on software-defined radio with electromagnetic sensing support. Software-defined radio allows low-cost developments and, potentially, added-value services not available in commercial cellular networks. The platform of reference, OpenBTS, only supports single-channel cells. Our proposal, however, has the ability of changing BTS channel frequency without disrupting ongoing communications. This ability should be mandatory in vehicular environments, where neighbouring cell configurations may change rapidly, so a moving cell must be reconfigured in real-time to avoid interferences. Full details about frequency occupancy sensing and the channel reselection procedure are provided in this paper. Moreover, a procedure for fast terminal detection is proposed. This may be decisive in emergency situations, e.g., if someone falls overboard. Different tests confirm the feasibility of our proposal and its compatibility with commercial GSM terminals.  
**Keywords:** SDR; cognitive radio; mobile communications; vehicular communications; BTS; cellular communications
# The Impact of Distributed Energy Storage on Distribution and Transmission Networks' Power Quality
## 2 Methodology
### Empirical Analysis  
The electric power system analysis feature of the NEPLAN software was used to investigate the impact of an established ESS integration design or topology on the power quality of transmission and distribution networks. NEPLAN is an industry-leading intuitive graphical user interface-oriented platform that is widely used to design, simulate, and conduct analyses ranging from basic functions like load flow and short circuit analysis to more dynamic analyses like simulating the intermittency challenges of renewable energy generation systems [10].  
In order to generate results for further study, the following tasks were carried out:  
1. Load flow analysis for a benchmark network in order to acquire voltage magnitude and voltage angle power quality characteristics.
2. The benchmark network was modified to reflect centralized and distributed ESS integration architecture, and the corresponding power quality characteristics were determined.
3. Power quality characteristics were determined for variations of the benchmark network that represented the integration of ESS within the transmission and distribution topologies.  
After deriving the power quality parameters for the variants, their values were compared to the benchmark network for nodes that performed below the tolerance limits for voltage magnitude specified by the UK National Grid Code.
5. In order to get answers for the research topics, various cross-comparisons of the variants' performances were conducted based on their adopted ESS integration architecture or ESS network location topology.