esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Discriminant Analysis
#### 3.5.3 ATR-FTIR Discriminant Analysis  
A comparable pattern was observed comparing the ATR-FTIR spectra. In particular, the application of PCA revealed a clear grouping tendency of the samples in relation to the fortnight of storage, as illustrated in Figure 8. Thus, the application of the supervised OPLS-DA and the resulting VIP diagram highlighted the regions of the ATR-FTIR spectra that were responsible for this separation (Figure 9). The region of the spectra with the highest abundance during the first fortnight was found to be 1724-1726 cm\({}^{-1}\), while the regions 1460 cm\({}^{-1}\), 2922 cm\({}^{-1}\), 2854 cm\({}^{-1}\) and 1546-1552 cm\({}^{-1}\) exhibited greater abundance during the second fortnight of storage.  
Figure 8: Scores plot of the PCA. Days’ numbers refer to the storage days of the samples. The variance of projection in the studied groups was calculated at 86.4% along the first principal component.  
Figure 7: OPLS-DA analysis for the sample space: (**A**) score plot of OPLS-DA analysis. The red and green dots correspond to Group 1 and Group 2 classification, respectively; (**B**) VIP plot of the studied features; (**C**) validation of the OPLS-DA analysis by permutation test statistics indicates that the extracted model is significantly different from a model built on random data. The permutation tests were carried out with 1000 random permutations, thus providing significance of the model at the 0.001 level (R\({}^{2}\)Y = 0.793 and Q\({}^{2}\) = 0.707).  
Overall, the significant variables identified in each individual discriminant analysis model were found to align with the outcomes of the statistical analysis. Moreover, through discrimination, the tendency of the samples to be grouped according to the fortnight of storage was revealed. In fact, the comparative analysis between the two fortnights highlights the dynamic nature of cucumber quality. The findings of this study reveal significant differences in the quality characteristics of cucumbers across the two fortnights, with notable implications for agricultural practices and market strategies.
## 4 RMSSA Problem Formulation in Spatial Channel Networks
### Routing Path, Conversion Node, and Modulation Format Option  
According to Table 2, DP-32 QAM, DP-16 QAM, DP-8 QAM, DP-QPSK, QPSK, and BPSK are used. The k-shortest paths (KSP) algorithm is used to obtain the routing path \(p(t_{i})\) to establish an optical connection to carry the traffic request \(t_{i}\). For traffic request \(t_{i}\), there are different options of CNs and MFs \(O(t_{i})=\left\{o_{1}(t_{i}),o_{2}(t_{i}),\ldots,o_{|O|}(t_{i})\right\}\), each of which corresponds to a different set of CNs \(V_{c}^{o_{i}(t_{i})}\left(v_{1}^{r},v_{2}^{r},\ldots,v_{|V|}^{r}\right)\subseteq V,s_{i}\
otin V_{c}^{o_{i}(t_{i})},d_{i}\
otin V_{c}^{o_{i}(t_{i})}\). Different CNs divide \(p(t_{i})\) into multiple MFSs, and the set of MFSs is defined as \(\Theta^{o_{i}(t_{i})}(\theta_{1},\theta_{2},\ldots,\theta_{\Theta})\subseteq E\). Each MFS may contain one or more SDM links, and the length of one MFS is equal to the sum of the contained SDM link lengths \(q_{o_{j},\theta_{m}}^{t_{i}}=\sum_{c\in\theta_{m}}q\). The same MF is used on all SDM links in the same MFS.
# Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid
## 2 Preliminaries
### An Additive Honomorphic Variant of the ElGamal Cryptosystem  
The additive ElGamal cryptosystem is a variant of the ElGamal cryptosystem, which contains the following three algorithms.  
* Key generation: Suppose \(Z_{q}^{*}=\langle g\rangle\) is a cyclic group of prime order \(q\). Choose an element \(x\in Z_{q}^{*}\) at random and calculate \(y=g^{x}\). Set the public key as \((y,g,q)\), and the corresponding secret key as \(x\).
* Encryption: For a given message \(m\), randomly select an element \(r\in Z_{q}^{*}\) and compute the ciphertext as \(C=E(m)=(u,v)=(g^{r},y^{r}g^{m})\).
* Decryption: For a given ciphertext \(C\), use the private key to compute \(g^{m}=\frac{v}{(u)^{x}}\). Finally, apply the Pollard lambda method [35] to obtain \(m\).  
The additive ElGamal cryptosystem has additive homomorphic properties and constant multiplicative homomorphic properties [34], i.e., for given ciphertexts \(E(m_{1})\) and \(E(m_{2})\), we have \(E(m_{1}+m_{2})=E(m_{1})\oplus E(m_{2})\), \(E(m)^{n}=(u,v)^{n}=(g^{nr},y^{nr}g^{nm})\), where \(n\) is a constant.
# Current Trends on Green Wireless Sensor Networks
## 1 Introduction
This transfer can be done wirelessly from a node specialized in energy harvesting or a node with enough energy resources to a node in need of energy in the same network. [PERSON] et al. [7] review several mechanisms for energy transfer such as inductive and magnetic coupling, or electromagnetic radiation. Usually, many energy balance approaches involve using a combination of several schemes to address the efficient use of energy of the WSN [7]. This paper aims to survey the main contributions of these three types of techniques for extending the lifetime of WSNs (conservation, harvesting and energy transfer), also identifying the main research topics that are trending presently. There are many surveys addressing one of these three categories, but there are only a few surveys that covers all the types, one published in 2017 by [PERSON] et al. [8] and another one published in 2018 by [PERSON] et al. [7]. In this survey, we will intend to classify and summarize the different techniques proposed in the literature, since at least 2018, for conservation, harvesting and transfer of energy in WSNs, focusing on the most recent surveys and, mainly, trying to identify the trending research topics and the most active areas at present, commenting the most relevant and interesting works in each case. The bibliographical revision was closed on November 2020. The paper is organized as follows: After introducing the subject and the objectives of this survey, in Section 2 we present the mechanisms--and the latest research--that seek to extend the life of the sensor network by reducing the energy expenditure associated with some of the usual processes in which a sensor is immersed. In Section 3 we address the issue of energy harvesting using natural energy sources. In Section 4 we review the work that would allow the use of radio frequency signals for the transfer of energy between network elements. There is a discussion and hints for future directions in Section 5. Finally, in Section 6, we present the conclusions.
## 2 Materials and Methods
### Global Analysis with Google Earth Engine and Python Geospatial Libraries  
GEE is a highly scalable cloud computing platform tailored for geospatial analysis on planetary scales, harnessing access to a multi-petabyte dataset of EO products. It includes an interactive application programming interface (API) that enables users to easily handle and visualize results in the interactive development environment (IDE). Users can perform spatial calculations with access to the database and make requests via the JavaScript or Python APIs that are sent to Google via JSON objects by the client [53]. This investigation greatly benefited from the availability of various datasets on the platform, e.g., ERA5 Land surface variables and the access to these products in the cloud, saved cumbersome programming and computational work. Furthermore, additional TROPOSIF and CGLS products were uploaded and consequently handled in GEE. To aid batch processing and to harvest the complex computational libraries of Python, GEE's data were accessed via the REST API (Representational State Transfer Application Programming Interface) and converted into an _array_ format. The global GC maps, along with their error metrics, were calculated as _arrays_ before conversion back into GEE's _image_ type for further processing within the platform. Both ELVs and VPs were resampled to the same (50 km) spatial resolution. This resolution allows for reduced memory usage and faster processing (around 1 h for an ELV\(\rightarrow\)VP GC map processed with 2 vCPU @ 2.2 GHz an 13 GB RAM). Consequently, this allowed for a global biome scale analysis while still being computationally feasible. To assess the five biomes' response characteristics to ELVs, the 50 km distribution proved to be a robust trade-off between accurate assessment and computational efficiency. The coarse resolution is further justified given that various datasets operate at a coarse spatial footprint (ERA5 11 km, TROPOSIF 7 km). For visualization purposes, additional regional analyses at an 11 km (nominal ERA5) resolution are shown in Figure 14. Processing the globe at this resolution would have taken up to a roughly \(\sim\)20-fold increase in computing time.
# Survey on Wireless Technology Trade-Offs for the Industrial Internet of Things
## 3 Trade-offs in Wireless Network Design
### Scalability  
Scalability is primarily determined by MAC design. Scheduling, contention resolution and other MAC mechanisms work together to provide maximum network capacity. In single-hop networks, the network capacity upon reaching the upper limit can only be extended by deploying more base stations. However, in practice the density of such base stations is limited. Multi-hop networks address this issue by allowing for wireless data forwarding, at the expense of overall throughput. In TDMA-based protocols, network density is limited by the need for synchronization and time division in combination with QoS requirements.
# Mapping of the Upper Limb Work-Space: Benchmarking Four Wrist Smoothness Metrics
## 2 Materials and Methods
### Outcome Measures and Statistical Analysis  
First, the smoothness metrics were compared between directions in the same sector and between sectors, in order to evaluate movement in the entire workspace and the average values across subjects were reported in spider plots.  
Then, the smoothness metrics were analyzed with respect to the shoulder plane of elevation and elevation angle (that define most of the limb orientation) in a compact colormap representation so that the smoothness of the movement was reported also in function of the intrinsic articular angles. Furthermore, a smoothness 3D map was reconstructed in which the smoothness metric was reported in the position of the wrist at the end of the movement with respect to the initial shoulder position in order to represent smoothness in the 3D workspace with respect to the subject.  
Statistical analyses were performed between sectors for each smoothness metric to discriminate smoothness differences in the workspace. The distributions were tested for normality through the Kolmogorov-Smirnov test and the ANOVA test was performed, setting the alpha level of significance to 0.05. Then, the Tukey-Kramer post-hoc test was used to identify the significant differences between sectors.  
Finally, the correlations between all the smoothness metric pairs were performed and the regression line was reported in scatter plots. All the analyses were performed for P2P and EXP movement separately.
# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 2 Approaches to Modeling, Determining, and Mitigating Errors in MLS Point Clouds
### Classification of Our Contribution  
Many scientists and practitioners conducted field studies and published their findings on MLS data quality. We build upon existing work but view the problem from the end user's perspective. In other words, we assume that we obtain the point cloud and the trajectory without being involved in data collection and processing. Thus, the focus of our work is on quality control and error minimization. Before going into the details of the method, we point out the overlap between our work and existing research:  
* The idea behind our approach is similar to strip adjustment. Similar to [33], we attempt to compensate for discrepancies between overlapping scan strips. Unlike [18], we do not attribute the differences between multiple runs of land-based MLS point clouds to mounting parameters for the reasons mentioned earlier.
* Similar to the work of [18], our approach extracts profiles from the MLS point cloud but in a different way.
* Instead of performing profile matching [18], we use the nearest neighbor approach as in the ICP algorithm or as in [27] to establish point correspondences.
* While [27] calculates translations to the reference point clouds, we estimate the six degrees of freedom similar to the methods in [25; 32]. However, we do not have to make assumptions about planarity, parallelism, and orthogonality in our least-squares adjustment.
* We also employ the estimated transformation parameters to adjust the point cloud, similar to [31]. However, before transforming profiles, we first smooth the parameters.
# Development of a Social DTN for Message Communication between SNS Group Members
## 2 Related Studies
### DTN Routing  
Well-known DTN routing methods include epidemic routing [4], Spray and Wait [5], location-based routing [6], and the motion vector scheme (MoVe) [6]. Epidemic routing attempts to send a message to the destination node by propagating copies of a bundle to surrounding nodes unconditionally and without restrictions. In contrast, Spray and Wait suppresses resource depletion by setting an upper limit to the number of copies that can be created. Location-based routing assumes that all nodes involved in message relaying are informed of the location of the destination node. It makes each of these nodes forward a message to an adjacent node that is located closer to the destination node than itself until the message reaches the destination. Although this routing principle of selecting the next relay node based on location information is the simplest, there is no guarantee that the bundle-relaying node moves close to the destination node. MoVe uses motion vectors (i.e., moving speed vectors) instead of location information in order to raise the probability at which the relaying node moves towards the destination node. In these methods, no node holds a routing table, a table which contains information about the relaying nodes that forward messages between the source node and destination node.  
Existing routing methods that use dedicated relay nodes include message ferry [7] and routing based on traveling route information [8]. In the former, \"ferry nodes\", which are dedicated relay nodes, travel through clusters, are places where some or all nodes are likely to come together. Ferry nodes have routing tables for all nodes or all clusters. In routing based on traveling route information, dedicated relay nodes have routing tables, but no routing tables for routing inside a local area because epidemic routing is applied there.  
Other routing methods intended to reduce flooding of message copies include a method that creates routing tables from encounter history [9], a method that calculates the probability at which messages reach their destination nodes based on encounter probability and uses routing tables that hold this information [10], and a method that creates routing tables based on the profiles and behavior history of individuals (called \"social-context-awareness\" [11] in this paper).  
However, these methods do not use node identification information to select nodes that participate in a DTN and thus permit all nodes to participate. In the method proposed in this paper, only those nodes that hold a community token are permitted to participate in a DTN and these nodes have routing tables.
# The Rapid Detection Technology of Lamb Wave for Microcracks in Thin-Walled Tubes
## 1 Introduction
### The State of the Art
#### 1.2.4 Defects Identification Method  
[PERSON] et al. [42] studied the interaction between the Lamb wave of the \(S_{0}\) mode and the damage in an aluminum plate and used an artificial neural network (ANN) to locate the damage. The results showed that the ANN was an effective tool for damage location in a plate structure. The local wavenumber method of Lamb waves was used by [PERSON] et al. [43] to characterize the flat bottom defects in isotropic thin plates. The simulation and experimental results showed that the average wavenumbercould be used to not only identify the shape, position, and size of the damage but also quantify the depth of the damage.
# Development of Digital Device Using ZigBee for Environmental Monitoring in Underground Mines
## 3 Methods
### Development of Dust Measurement Module  
To measure the dust density in the mine, PMSA003A, a sensor that optically detects dust using a laser, was used. The sensor uses the laser-scattering principle. After irradiating a laser with suspended particles in the air, a certain amount of scattered light was collected, and fine dust was measured with scattered light that varied according to the diameter per unit volume of the suspended particles. In Figure 4a, the shape and pin arrangement of the sensor can be confirmed, the Vcc pin supplies power to the dust sensor itself, and the GND pin is used to ground the current. The Rx and Tx pins were connected to the microcontroller and transmitted the measured value of the dust sensor. This sensor can measure up to approximately 1 mg/m\({}^{3}\) of particulate matter (PM) 2.5 and PM 10. However,  
Figure 3: (**a**) ADXL335 accelerometer, (**b**) schematic, and (**c**) product of vibration measurement module.  
it is difficult to obtain high accuracy owing to the limitations of low-cost sensors; therefore, this study aimed to understand the overall dust density change and distribution in the mine rather than obtaining accurate dust density.  
To measure the dust density in an underground mine and transmit data, we developed a portable dust measurement module. Figure 4b shows the schematic of the developed module. Figure 4c shows the actual developed module. To read data from the PMSA003A sensor, software serial communication was used for programming and connecting the Arduino Pro Micro microcontroller. As the dust sensor was optimized for a voltage of 5 V, the power supplied from the battery was boosted to 5 V using a regulator. Because the dust measurement module uses more power than the vibration measurement module, we connected D-type batteries with relatively large capacities. The measured dust density value was input to the Arduino through the Rx and Tx pins of the Arduino Pro Micro, and the result was transmitted to other modules through Xbee. The dust measurement module must continuously measure and transmit dust values and act as a router for connecting the Xbee modules.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 4 Centralized Protocols
### Fd-Mac  
The challenge of efficient scheduling in a mmWave WPAN was investigated in [40]. The proposed frame-based scheduling directional MAC (FD-MAC) is a collision-free transmission scheme. In this system, a PNC is deployed along with several DEVs, thus making it a centralized MAC protocol. The nodes and DEVs in the network direct their beams toward the PNC when they are idle. Moreover, the PNC also decides and controls the scheduling by managing the traffic requests from the DEVs and the nodes. In a 60 GHz FD-MAC, the network operation time is divided into non-overlapping frames, which further include a scheduling phase. The non-overlapping intervals are divided in such a way where each interval have equal lengths. These steps are executed by the PNC.  
Specifically, each frame has scheduling and transmission phases. The operation of the above FD-MAC protocol is portrayed in Figure 6. A polling method [41] is employed in this protocol to correctly schedule, queue, and transmit packets. The PNC receives traffic patterns from DEVs and determines specific transmission schedules during the scheduling phase. Each schedule has sequences of topologies and time intervals, respectively. Afterwards, in the transmission phase, depending on the instructions of the scheduling phase, the DEVs perform their transmission. In addition, the PNC and the DEVs store the packets that arrive in the current time frame before re-transmitting them to the next frame.  
* Advantages: As the frames are divided into two groups, the core scheduling algorithm of FD-MAC can compute a schedule for certain traffic requirements while maintaining overall transmission time. Virtual queue helps to schedule the backlogged packet arrival in the next frame. Moreover, the scheduling phase is dedicated to represent the overhead, hence no additional overhead is needed at the transmission phase.
* Disadvantages: Although the GC algorithm reduces the computation complexity, with the increase of network size, the execution time increases multiple fold when comparing a small network to a large network.
## 5 Proposed Solutions and Policy Recommendations
### Policy Recommendations
#### 5.1.2 Clarifying Legal Definitions and Governance of Mega-Constellations  
Secondly, because there is no definitive, legally binding definition of mega-constellations, the concept is legally ambiguous; this calls for discussions in order to clarify what mega-constellations are, as well as limitations on their operation, in order to close the gap that the lack of governance has created and that mega-constellation operators are taking advantage of.  
Furthermore, it is necessary to clarify the parameters of space objects to cater for space debris, as well as the liability for damage incurred in the event of claims.
# Discrete-Time Position Control for Autonomous Underwater Vehicle under Noisy Conditions
## 4 Simulation
### The Proposed Controller under Different Noise Conditions  
The proposed controller in trajectory tracking scenarios under three different noise conditions is tested. For each trial, the noise is considered to be independent in all directions of motion. The mean of random Gaussian noises \(\xi(k)\) and \(\mathbf{\zeta}(k)\) are set as zero. For simplicity, the covariance matrices of noise are set to constant matrices. So, \(\mathbf{Q}(k)=[Q_{1}\ Q_{2}\ Q_{3}]^{T}\) and \(\mathbf{R}(k)=[R_{1}\ R_{2}\ R_{3}]^{T}\). For practical reasons, all elements of the covariance matrices are  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** & **Value** \\ \hline \(m\) & Weight in air & 219.8 kg \\ \(m_{u}\) & Mass in surge & 391.5 kg \\ \(k_{u}\) & Linear drag coefficient in surge & 16 \\ \(k_{u|u}\) & Quadratic drag coefficient in surge & 229.4 \\ \(m_{p}\) & Mass in sway & 639.6 kg \\ \(k_{p}\) & Linear drag coefficient in sway & 131.8 \\ \(k_{0|v|}\) & Quadratic drag coefficient in sway & 328.3 \\ \(m_{u}\) & Mass in heave & 639.6 kg \\ \(k_{w}\) & Linear drag coefficient in heave & 65.6 \\ \(k_{w|w}\) & Quadratic drag coefficient in heave & 296.8 \\ \(W(t)\) & Buoyancy force in heave & \(-5\) N \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameter values.  
assumed to be less than or equal to 1. For the sake of the trials, three cases are randomly generated as follows:  
\[Case\ 1:\mathbf{Q}(k)=\begin{bmatrix}0.16\\ 0.04\\ 0.09\end{bmatrix}\]  
\[\mathbf{R}(k)=\begin{bmatrix}0.64\\ 0.04\\ 1\end{bmatrix}\]  
\[Case\ 2:\mathbf{Q}(k)=\begin{bmatrix}0.04\\ 1\\ 0.81\end{bmatrix}\]  
\[\mathbf{R}(k)=\begin{bmatrix}0.25\\ 1\\ 0.04\end{bmatrix}\]  
\[Case\ 3:\mathbf{Q}(k)=\begin{bmatrix}0.01\\ 0.36\\ 0.49\end{bmatrix}\]  
\[\mathbf{R}(k)=\begin{bmatrix}0.09\\ 1\\ 0.81\end{bmatrix}\]  
Let the ensemble size N = 100 and the sampling time T = 0.01 s. The proposed controller is tested for cases 1-3 as presented above. The reference positions and the actual tracked positions for cases 1-3 are presented in Figures 4-6. All figures highlight that the AUV effectively follows the desired positions with high precision in surge, sway, and heave. Hence, it is verified that the proposed controller is effective in position tracking for AUV under various noise conditions.