esa-sceva/satcom-chunk-collection huggingface dataset:
# MapReduce Algorithm for Location Recommendation by Using Area Skyline Query
## 2 Related Works
### Spatial Skyline Query  
[PERSON] et al. [9] first addressed the problem of spatial skyline queries. They proposed two algorithms for static query points said \(B^{2}S^{2}\) and \(VS^{2}\), and one algorithm for dynamic query points said \(VCS^{2}\) which exploited the pattern of change in query points to avoid unnecessary re-computation of the skyline.  
There exists other literature relating to the spatial skyline problem [10; 11; 12; 13]. [PERSON] et al. [10] considered that the candidates should be close to the facilities. In [11], [PERSON] et al. considered non-spatial preferences, facility types, which had an impact on spatial skyline computation. Sometimes the retrieved data points should be far from those unpreferable facilities. Based on this consideration, [PERSON] et al. [12] first studied TFSS algorithm and proposed a new progressive method, called _Branch-and-Bound Farthest Spatial Skyline_ (BBFS) to compute the farthest spatial skyline queries. The results outperformed TFSS by exploiting spatial locality. In [13], [PERSON] et al. considered that the retrieved data objects not only should be close to preferable facilities but also should be far from unpreferable facilities by using EFFN algorithm.  
In some cases, the candidate points may not exist in the real world. [PERSON] et al. [14] proposed _Untifixed-Shape Areas Skyline_ (UASKY) to make location recommendations without candidate points. Specifically, they used a Voronoi Diagram to divide a given region into disjoint subregions. For each subregion, they executed the previous process again by other facility types. Finally, they computed the distance to the nearest surrounding facilities in each sub-subregion. In [5], [PERSON] et al. proposed another location-based skyline query, called _Grid-based Area Skyline_ (GASKY), which outperformed UASKY by partition a given region into \(n\times n\) grids.  
Most of the existing research for skyline computations rely on some centralized indexing structures. However, with the development of Internet technology, the amount of data increases exponentially. The centralized indexing structures cannot work well.
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## 3 Implementation
### Methodology and Implementation Overview  
To ensure the acquisition of reliable energy utilisation data, the experimental methodology involved executing key generation algorithms across a significant number of iterations for each algorithm. Key generation was performed on the Raspberry Pi, with adjustments made for algorithms with longer key generation times to normalise each algorithm's time-on-test. Baseline energy consumption of the Pi platform (excluding key generation) was established through extensive NULL runs, whose times were subsequently subtracted from the experimental results including the key-generation stage.  
A distributed architecture was employed for the experimentation, utilising a Raspberry Pi as the device under test (DUT) and a Windows-based data acquisition system to record electrical parameters from a TC66C energy meter inline with the Pi's power supply. A block diagram of the experimental setup is illustrated in Figure 2, with a marked-up photograph of the TC66C meter and Raspberry Pi in Figure 3.  
Remote STOP/START signalling via network communication facilitated automated data collection on the Windows collector, with specific steps taken to maintain a consistent testing environment on the Pi, such as fixing the CPU clock and running the fan at 100%, as well as operating the testing over a very large number of iterations. The software measurement developed for the Windows machine featured a multi-threaded design to manage network communication and data acquisition asynchronously, to capture accurate results triggered by the signalling sent from the Pi DUT.  
Aggregated full results are provided and analysed in Section 5, with practical examples of an experimental setup, including screenshots of what is visible to the user in (Appendix A). For reference, the source code, input, and results data files are located on  
Figure 1: Combination of four different subject areas: the compute platform (Pi), library (OpenSSL3.5), key exchange (Classical+PQC ML-KEM), energy use (measured via a TC66C meter).  
this paper's specific, static GitHub site [40]. Further details regarding the environment, experimental setup, system components, test procedures, and software architecture are provided in Section 4.
# Magnetic Properties of NiZn Ferrite Nanofibers Prepared by Electrospinning
###### Abstract  
When the size of a material is decreased to the nanoscale, the effects of forces that are not influential on a macroscopic scale become increasingly important and the electronic structure is improved. The material then exhibits significantly different physical and chemical properties than in the bulk state. The smaller the size of the material, the more exposure it receives to the nano effects, and the physical properties can be changed via size control. In this study, Ni\({}_{0.5}\)Zn\({}_{0.5}\)Fe\({}_{2}\)O\({}_{4}\) ferrite nanofibers were prepared by electrospinning, and the sizes of the prepared samples were controlled to ensure different average diameters by controlling the polymer concentration of the precursor solution. Field emission scanning electron microscope images showed that the samples had average diameters of 224 to 265 nm. The single crystal phase of Ni\({}_{0.5}\)Zn\({}_{0.5}\)Fe\({}_{2}\)O\({}_{4}\) and the different crystallite sizes of 13 to 20 nm were confirmed by X-ray diffraction analysis. The magnetization behavior of the samples was measured using a vibrating sample magnetometer and the result confirmed that the samples had different magnetic properties, according to the diameter and crystallite size of the nanofibers. This study suggests that control of magnetic properties and excellent electrical conductivity in a one-dimensional nanostructure can be positively applied to improve the performance of a filler for the electromagnetic-interference shielding film.  
electrospinning; NiZn ferrite; nanofibers; microstructure; magnetization behavior +
Footnote †: journal: Applied Physics Letters
## 3 Nondestructive Testing Methods  
### X-ray  
High-frequency electromagnetic radiation in the range of \(3\times 10^{16}\) to \(3\times 10^{19}\) Hz is used for X-ray inspection. The high-energy photons are generated by an X-ray tube and are directed toward the specimen as the fanned beam of a point source, penetrate it, and are collected on the other side by a X-ray detector similar to a photo imaging sensor. Frequency and corresponding wavelength can be adjusted by varying the applied voltage [26]. While penetrating, the photons interact with the material of the specimen. Relevant effects are absorption (photon loses its complete energy), scattering (referred to as Compton scattering: photon loses a part of its energy and is diverted in another direction), and pair production (photon disappears and generates an electron-positron pair). All depend on the density and atomic number of the material, where higher densities and higher atomic numbers increase their probability [27]. The X-ray detector collects all unaffected photons in planar resolution and breaks down the volume information of the specimen into a two-dimensional (2D) image. Continuous development over the years has improved the capabilities of X-ray to allow fine resolutions down to 1 \(\upmu\)m, fast measurement times even for in-line inspections in production, and automated and 3D imaging with the latest-generation equipment [28].  
X-ray is often used to detect voids and nonwetted areas in solder joints. The trapped gases and unsoldered volumes have a much lower density compared to the solder and, therefore, appear brighter in the X-ray image [7]. However, X-ray is not able to detect cracks, adhesive defects, and sinter porosities. Cracks are small gaps in a material layer without volume change [27], adhesives have a small density shielded by surrounding high-density materials such as the substrate and heatsink [27], and sintering creates a porous microstructure far too small for the X-ray resolution [8].
# Wake-Up Receiver-Based Routing for Clustered Multihop Wireless Sensor Networks
###### Abstract  
The Wireless Sensor Network (WSN) is one of the most promising solutions for the supervision of multiple phenomena and for the digitisation of the Internet of Things (IoT). The Wake-up Receiver (WuRv) is one of the most trivial and effective solutions for energy-constrained networks. This technology allows energy-autonomous on-demand communication for continuous monitoring instead of the conventional radio. The routing process is one of the most energy and time-consuming processes in WSNs. It is, hence, crucial to conceive an energy-efficient routing process. In this paper, we propose a novel Wake-up Receiver-based routing protocol called Clustered WuRv based on Multicast wake-up (CWM), which ensures energy optimisation and time-efficiency at the same time for indoor scenarios. In our proposed approach, the network is divided into clusters. Each Fog Node maintains the routes from each node in its cluster to it. When a sink requires information from a given node, it's corresponding Fog Node uses a multicast wake-up mechanism to wake up the intended node and all the intermediate nodes that will be used in the routing process simultaneously. Measurement results demonstrate that our proposed approach exhibits higher energy efficiency and has drastic performance improvements in the delivery delay compared with other routing protocols.  
WSN; wake-up based sensors; clusters; multicasting; IoT; routing; latency-minimisation; energy-efficiency +
Footnote †: journal: _Sensors_**2022**, 22, 3254. [[https://doi.org/10.3390/s22093254](https://doi.org/10.3390/s22093254)]([https://doi.org/10.3390/s22093254](https://doi.org/10.3390/s22093254))
## 9 Conclusions  
Clustering techniques are proposed to provide a platform for network topology management to extend network lifetime. Since most existing clustering algorithms overlook the importance of network coverage when evaluating the performance, firstly we have introduced a new metric to measure network lifetime--coverage sensitive longevity. Then we have proposed a Balanced Energy-Efficiency clustering algorithm (BEE) to extend coverage sensitive longevity. Its multihop version supporting mutihop inter-cluster communication, called multihop BEE (BEEM) is also provided. It can maintain coverage notably comparing with classic clustering algorithms--LEACH and HEED.  
Secondly, we have also presented the development trend from traditional WSNs to advanced IoT systems, especially in future network scenarios. In order to cater for dynamic IoT systems deployed in 5G communication environment, we have further enhanced BEEM algorithm with smart intra-cluster routing scheme and named it as Smart-BEEM. Smart-BEEM can adapt to the MIMO communication feature to further improve QoS and QoE. It can facilitate advanced IoT devices to select communication interfaces and CHs based on the network context, including characteristics of the network access interfaces, the current user usage and the details of the available CHs. The experimental results have shown that Smart-BEEM can better maintain the number of alive nodes and the network coverage comparing with BEEM.  
At the end, we have delivered a discussion on the future work that can further improve our algorithm in order to be deployed in more realistic systems from the following perspectives: (1) Diverse energy storage situation. (2) Heterogeneous communication interfaces on nodes. (3) Financial cost and (4) Balancing between energy efficiency and QoE.  
[PERSON] conceived and designed the algorithms. [PERSON], [PERSON] and [PERSON] designed the experiments. [PERSON] performed the experiments. [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 6 Experimentation Results and Discussion
### Cost-Benefit Analysis for Realistic Applications of the Proposed Approach
From Table 7, it can be seen that the normalized cost values of PL-NB are less than 1 for the KC1, V40, V192, V236, and V318 datasets. This proves that, by applying the PL-NB approach for SDP, overall testing costs and efforts can be reduced on these datasets. Similarly, PL-RF showed improvements in costs for the jm1, KC1, V40, V85, V192, V236, and V318 datasets.
## 3 Results and Discussion
### Microbial Communities Diversity in Sediments and on the Surfaces of Electrodes
Chitinophagaceae and Cytophagaceae were found on the surface of cathode both from site TH1 and TH2, and they are the more abundant of the two microbes found on cathodes from site TH1. Some bacteria often included in the Cytophagales are alternatively regarded as apochlorotic cyanobacteria, and the Chitinophagaceae species are capable of degrading chitin and cellulose. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Samples** & & **Number of Sequences a** & **OTUs b** & **Shannon c** & **Coverage d** \\ \hline Site TH1 & original & 12,503 & 1329 & 7.94 & 92.1\% \\  & anode & 5671 & 567 & 5.63 & 90.2\% \\  & cathode & 3257 & 351 & 4.36 & 88.5\% \\  & control & 10,276 & 1026 & 7.68 & 91.3\% \\  & SMFC & 9876 & 756 & 6.65 & 90.9\% \\ \hline Site TH2 & original & 11,165 & 1021 & 7.78 & 91.5\% \\  & anode & 3524 & 452 & 5.21 & 89.3\% \\  & cathode & 2367 & 254 & 4.02 & 89.1\% \\  & control & 9056 & 956 & 7.25 & 88.6\% \\  & SMFC & 7862 & 636 & 6.01 & 90.2\% \\ \hline \hline \end{tabular}  
* Number of sequences indicates high quality sequences that passed quality trimming, denoising, and chumeria checking; b OTUs, calculated with QIIME at the 3% cutoff level; c Shannon, shannon diversity index, indicates both species abundance and evenness; d Coverage, Good’s coverage.  
\end{table} 
Table 1: Observed bacterial richness and diversity estimated based on 97% OTUs clusters for sediments under different treatments. For the past few years, the terrestrialization of shallow freshwater lakes has become increasingly serious because of the deposition of abundant of organic matter. The accumulation of decayed macrophyte litter as the slowly decomposition rate has a direct influence on accelerating the swamping process. From this study, sediments formed different sources confirming that the enhanced litter degradation under sediments with SMFCs can slow down the lake swamping process. So, SMFC has promising potential implications in terrestialized lakes for multiple functions.
## 4 EDETA Protocol Description
### Fault Tolerant Mechanisms  
To guarantee the operation of the whole WSN it is important to provide it with some fault tolerant mechanisms. There are three main failing points: sinks, CHs and leaf nodes.  
If a sink fails in a single-sink network, there is no possible immediate solution. But, as mentioned above, the protocol allows for the existence of multiple sinks in the network. In this scenario, if a sink fails, its tree becomes root-less. In this case, the CHs in the root-less tree will detect this situation because they do not receive information from its parent. They will then wake-up the surrounding CHs using a radio-triggered wake-up signal which is propagated by the other CHs in order to reconfigure the whole network. This mechanism requires extra energy and it is only implemented at CH level, not at LF node level. This radio-triggered wake up capability is proposed in [10].  
Another important failure point exists at CH level. If a CH fails, all the nodes in the cluster are unable to send their data. In addition, its children CH's in the tree will never reach the sink. To reduce the impact of this failure, the substitute node role has been introduced. A substitute node is a node within the cluster that shares the schedule with the CH and takes its place if it fails.  
The substitute node is selected by the CH in the initialization phase, based on node's energy and the proximity between them, but, in order to allow leaf nodes to remain in a low power state as long as possible, the leaf node will be notified during the first round of the normal operation phase.  
This substitute node keeps monitoring the CH's operation. This way, if a CH fails, its substitute node would detect that the CH is no longer responding to its messages, and it would take its place, acting as the new CH.  
Another failure point exists at the leaf nodes level. If a leaf node temporarily malfunctions and performs a reset, it will become an orphan and must remain in \"sniffer\" receiving mode waiting for polling messages from other nodes in order to synchronize its clock and to receive information on the next initialization phase. Then the leaf node will remain in sleep mode until the initialization phase starts.  
Depending on the configuration of the network, this solution can recover lost leaf nodes at the expense of its remaining energy.
# Holographic Mixed Reality System for Air Traffic Control and Management
## 5 Design and Development Process
### Calculation of Scaling Factor
#### 5.1.2 Map View  
The 3D height map for this view was generated using the 2D image and the height map from Google Maps. While taking the 2D image from Google Maps, the information was calculated about the distance between the control tower and GPS coordinates of one corner of the bounding box selected for taking the 2D image for the generation of the 3D model of the map view area, as shown with the arrows in Figure 9. For calculating the scaling factor, that distance was substituted at D\({}_{2}\) in Equation (1), while D\({}_{1}\) was a one scale unit of the mixed reality world.  
Figure 8: The execution order for process of mapping an airplane’s location in a mixed reality world.
## 3 Load Distribution Model
### Loaded Tooth Contact Analysis  
As shown in Figure 8, the compatibility conditions should be satisfied to calculate the number of contacting tooth pairs, which can be represented as follows:  
\[\begin{array}{l}\text{contact}:\qquad\Delta\phi_{c}>\phi_{bil},\alpha_{i}= \Delta\phi_{c}-\phi_{bil}\\ \text{no contact}:\qquad\quad\Delta\phi_{c}<\phi_{bil},\alpha_{i}=0\end{array} \tag{24}\]  
where \(\Delta\phi_{c}\) is the increment of the rotational angle of the cycloid disc, and \(\alpha_{i}\) is a small angular displacement of the cycloid gear tooth.  
Then, the force and moment static equilibrium equation can be expressed as  
\[T=\sum_{i=1}^{n_{1}}\alpha_{i}l_{i}^{2}/C_{ni}, \tag{25}\]  
where the arm of force \(l_{i}\) can be represented geometrically as  
\[l_{i}=\frac{-e\tan\theta_{ri}\cos\phi_{in}-e\sin\phi_{in}+a\sin i\psi_{r}+a\tan \theta_{ri}\cos i\psi_{r}}{\sqrt{\tan^{2}\theta_{ri}+1}}. \tag{26}\]  
An iterative process is repeated to find the solutions, as shown in Figure 5. Firstly, two initial values of load and angular displacement are chosen to solve the compatibility and equilibrium Equations (24) and (25) and to calculate the tooth compliance according to Equations (15)-(23) in the meantime, after the backlash is calculated by unloaded TCA. All the values are updated each time the load and angular displacement enter into the system of equations. When the variations of the force and angular displacement converge within 1%, the iterative procedure is terminated to output the loads, contact stress, contact pattern, loaded transmission error (LTE), and gear ratio (GR).
## 2 Materials and Methods
### Plasma Treatment  
CAP treatment was performed using an argon-based plasma pipette (Femto Science, Hwaseong, Republic of Korea; Figure 4A) at a power of 4 W, voltage of 10 kV, and frequency of 100 kHz [22]. The output pressure of the regulator was set to 0.02 MPa, with the flow rate being controlled through a dedicated flow controller [22]. In addition, the temperature of the plasma column was maintained at 40 \({}^{\circ}\)C. The CAP jet was applied for 2 min, maintaining a consistent distance of 10 mm between the top of the nozzle of the handpiece and the biofilm (Figure 4B). Biofilms that were not exposed to CAP received 1.5 mL of distilled water (DW) for 1 min as a negative control group and 1.5 mL of 0.12% CHX (Hexamedine; Bukwang Pharm. Co., Ltd., Ansan, Republic of Korea) as a positive control group. All specimens were rinsed thrice with 1.5 mL of phosphate-buffered saline (PBS; Welgene, Gyeongsan, Republic of Korea) post-treatment to eliminate any residual antimicrobial agents. Fresh medium (1.5 mL) was added after completing all antibacterial treatments. All biofilms were stored in a 10% CO\({}_{2}\) environment at 37 \({}^{\circ}\)C following each treatment. The treatments were repeated once daily for 6 days (Figures 1 and 3).  
Figure 4: Argon-based plasma pipette. (**A**) Components of the plasma pipette. (**B**) The 10 mm white dotted line shows the distance from the top of the nozzle of the plasma pipette to the oral microcosm biofilm [22].  
Figure 3: Establishing the oral microcosm biofilm and conducting the treatment protocols (CAP, cold atmospheric plasma; CFUs, colony-forming units; CHX, chlorhexidine gluconate; DW, distilled water; QLF-D, quantitative light-induced fluorescence-digital).
## 2 Materials and Methods
### Experimental Methodology
#### 2.3.3 Micro Analysis Test  
(1) X-ray Diffraction (XRD)  
The stabilized soil and cement soil samples cured for 28 days were processed into powder, and these particles were dried in the oven, and then taken out and tested. The test instrument was a Rigaku-2500 X-ray (Rigaku, Tokyo, Japan) diffractometer. CuK\(\alpha\) radiation was used with a scan rate of 10\({}^{\circ}\)/min at 30 kV and 60 mA for testing.  
(2) Scanning Electron Microscopy (SEM)  
The morphologies of stable soil and cement soil samples cured for 28 days were analyzed using a FEI Scios 2 HiVac (Thermo Fisher Scientific, Waltham, MA, USA) scanning electron microscope. The sample was processed into thin slices and dried. Gold was sprayed on the surface of the sheet, and then it was tested in high vacuum mode; the acceleration voltage was 10 kV and the working distance was about 10 mm.  
(3) Thermogravimetric Analysis (TG)  
The pre-treatment of the sample was the same as described in Section 2.3.2 (1). In total, 25 mg of the powder was weighed and a HCT-1 (HENVEN, Beijing, China) thermal analyzer was used. The powder was heated from 50 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min in a nitrogen environment.  
(4) Fourier-Transform Infrared Spectroscopy (FTIR)  
The pre-treatment of the sample was the same as described in Section 2.3.2 (1). In total, 1-2 mg of the sample powder was mixed with 100-200 mg of spectrally pure KBr powder, ground to about 2 \(\upmu\)m. The mixture was pressed into a transparent disk with a thickness of about 1 mm and a diameter of about 10 mm at a pressure of 6 MPa. A Thermo Nicolet Nexus 670 (Thermo Fisher Scientific, Waltham, MA, USA) FTIR Spectrometer was used for analysis with a resolution of 4 cm\({}^{-1}\), 32 scans, and a range of 4000-400 cm\({}^{-1}\).