esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 EBCA Applications  
The main application of Equal Baseline Camera Array is robotics. In particular, it can be applied to autonomous robots equipped with a robotic arm. Such robots can be used for manufacturing, quality assurance [13], agricultural applications or any operations requiring picking up objects in 3D space. EBCA can be even applied to robots designed for construction works such as pairing walls in building [64].  
Another major application of EBCA is providing 3D vision for autonomous cars [65]. Such vehicles operate in outdoor environment causing significant limitations in possibilities of using various kinds of 3D vision systems because intensive sunlight may interfere with measurements performed by these equipment.  
EBCA can also be used for making 3D scans of objects covered with a semi-transparent material or made from this kind of substance [66]. [PERSON] et al. presented a survey on this topic inducing making scans of smoke and objects made from glass [67]. The authors of this paper also performed experiments with 3D scanning of semi-transparent objects which were amber fossils with inclusions such as leaves [68].  
A problem similar to 3D scanning of semi-transparent objects is enabling 3D vision for underwater operations [69]. Commonly used equipment for 3D scanning, such as structured light scanners, faces significant limitations when it is used for such a purpose. The array presented in this paper can be in particular applied to unmanned underwater vehicles and provide these devices with 3D vision. UUVs are remotely controlled and 3D vision of these devices supports their operators in performing expected tasks.  
Drones are another kind of devices that can benefit from using EBCA [70]. Drones have a view over long distances in comparison to their size. Moreover, it is preferable to provide vision in high resolution. Obtaining high resolution for long distances cannot be achieved with LIDAR scanners or structured light scanners. However, cameras are suitable for such an application.  
There is also an area of application of EBCA in a form of a 3D camera for general use. Users of such a camera can record videos similarly as they do using 2D cameras or their mobile phones. Nevertheless, popularizing such an application would have to be related with the growth of popularity and further development of 3D displays.
# Investigation of the Environmentally-Friendly Refrigerant R152a for Air Conditioning Purposes
## 4 Conclusions  
The objective of this paper is the detailed investigation of a heat pump operating with the environmentally friendly refrigerant, the R152a. The analysis is conducted with a developed model in EES which is validated with literature data. The system is examined parametrically and regression models for its performance are suggested. The most important conclusions of this work are summarized below:  
* In the nominal operating conditions, the examined heat pump has 5 kW cooling capacity and a COP equal to 6.46.
* The increase of the rotational speed leads to higher cooling capacity and to lower COP.
* The increase of the ambient temperature leads to lower cooling capacity and to lower COP.
* The increase of the indoor temperature leads to higher cooling capacity and to higher COP.
* It is found that the rotational speed is the most important parameter for the cooling capacity value, while the ambient temperature is the most important parameter for the COP value.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(T_{amb}\) (\({}^{\circ}\)C)} & \multicolumn{6}{c}{_COP_} \\ \cline{2-9}  & **R152a** & **R600a** & **R134a** & **R290** & **R1234 yf** & **R32** & **R404a** \\ \hline  
*Mean COP** & 5.342 & 5.2815 & 5.119 & 5.063 & 4.8784 & 4.917 & 4.444 \\
**Enhancement** & & 1.14\% & 4.36\% & 5.51\% & 9.50\% & 8.65\% & 20.20\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the different working fluids with R152a for \(Q_{e}\) = 5 kW and \(T_{ind}\) = 25 \({}^{\circ}\)C.  
The R152a is found to be the most efficient refrigerant compared to six other refrigerants. The mean enhancement with the R600a is found to be 1.14%, while with R134a 4.36%, while with the R404a 20.20%.  
The final results of this work can be utilized for the proper modeling of a cooling system with a heat pump with R152a. The developed models can be utilized for the simulation of this system in different operating conditions. Moreover, the advantages of the R152a are discussed and explained in this work and finally, this refrigerant is suggested as a reliable choice.  
To conclude, the refrigerant R152a is a highly effective choice for the cooling vapor compression cycles. This refrigerant is non-toxic and it has a low GWP, while it has a reasonable cost. The only issue is the increased flammability which is not so great. All the factors can make this refrigerant a reliable choice for future cooling systems.  
The authors had the same contribution in all the paper.  
This research received funding from \"Bodossaki Foundation\" for the postdoctoral research of [PERSON].  
The authors declare no conflict of interest.  
The authors had the same contribution in all the paper.  
The authors declare no conflict of interest.  
The authors had the same contribution in all the paper.  
The authors declare no conflict of interest.  
The authors had the same
###### Abstract  
The first new beamline of the BRIGHT project--involving the construction of eight new beamlines at the Australian Synchrotron--is the Micro-Computed Tomography (MCT) beamline. MCT will extend the facility's capability for higher spatial resolution X-ray-computed tomographic imaging allowing for commensurately smaller samples in comparison with the existing Imaging and Medical Beamline (IMBL). The source is a bending-magnet and it is operating in the X-ray energy range from 8 to 40 keV. The beamline provides important new capability for a range of biological and material-science applications. Several imaging modes will be offered such as various X-ray phase-contrast modalities (propagation-based, grating-based, and speckle-based), in addition to conventional absorption contrast. The unique properties of synchrotron radiation sources (high coherence, energy tunability, and high brightness) are predominantly well-suited for producing phase contrast data. An update on the progress of the MCT project in delivering high-spatial-resolution imaging (in the order of micron size) of mm-scale objects will be presented in detail with some imaging results from the hot-commissioning stage.  
X-ray micro-CT; absorption contrast; phase-contrast; computed tomography; synchrotron Article
# A Survey of Techniques for Discovering, Using, and Paying for Third-Party IoT Sensors
## 4 Gap Analysis and Directions for Novel Research
### Scalability  
Using a blockchain-based structure for the global sharing of IoT devices supports IoT-ownership, but it may not be scalable to managing the data of a large number of IoT devices and IoT applications because it requires some time and effort to generate blocks (i.e., contain data). Therefore, there is a need for the following:  
1. Limiting the size of information stored in the blockchain to ensure scalability for dealing with a large number of IoT devices. The authors in [71] have proposed a self-managed marketplace for sharing IoT devices using a blockchain. The authors limited the information stored in the blockchain by preventing IoT devices from storing their data in the blockchain and by only storing the IoT devices and data descriptions that are required for discovery, use, and pay. However, there is a need to reduce the size of this information by compressing it or storing part of it in off-chain storage.
2. Developing a new consensus mechanism for blockchain that ensures (1) scalability, (2) suitability to be run by IoT devices, and (3) the majority of nodes that generate blocks belong to the Internet of Things (e.g., IoT devices and gateways).
# GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation
## 1 Introduction
In this paper, a jointly performed estimation-cancellation approach is proposed for spoofing detection and navigation solution recovery based on the technique. Firstly, a composite signal is reconstructed based on the estimated PVT parameters and subtracted from the original signal. Then, a test statistic is calculated with the residual signal to indicate whether there are evil signals such as spoofing and multipath signals. If evil signals are detected, a validation procedure is performed by inspecting the consistency of the signals to determine whether the evil signals are spoofing ones or not. Unlike the RAIM technique, the proposed method can detect spoofing attacks when all of the channels of the victim receivers are taken over by the spoofing signals, and it can even work when the code phase differences between the authentic and spoofing signals are much less than one code chip. The outline of the paper is as follows. In Section 2, the GNSS baseband signal model is introduced. In Section 3, the maximum likelihood estimation for the GNSS PVT solution is reviewed. It is the foundation of the proposed method in the paper. In Section 4, an estimation-cancellation algorithm based on MLE is described, and a statistic for evil signal detection is derived based on GLRT. Furthermore, a metric is proposed for spoofing validation. The theoretical performance of the spoofing countermeasure is also derived in this section. In Section 5, both numerical and simulation results are presented to investigate the factors that affect the performance of the proposed method. In Section 6, the countermeasure is evaluated with the Texas Spoofing Test Battery (TEXBAT), which consists of recorded data in different real spoofing scenarios and has been widely used for GNSS anti-spoofing tests in the world. Finally, we conclude our work in Section 7.
# Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
## 3 Big Data Tools  
There are already some preliminary trials of applying the latest big data tools for traffic big data, as traditional big data tools are not optimized for big data in the transportation domain. Some preliminary efforts have been proposed for expanding the off-the-shelf tools for a better support of spatiotemporal big data, e.g., Hadoop has been expanded with the capacities of spatiotemporal indexing [80] and trajectory analytics [81]. Based on PostgreSQL and PostGIS, an open-source mobility database named MobilityDB [82] was proposed for moving object geospatial trajectories, e.g., GPS traces [83].  
For analyzing and visualizing spatiotemporal big data, new data processing algorithms and methods should be implemented as new or extensions of existing commercial or open-source platforms, for ease of use and a better integration, e.g., the latest SuperMap GIS platform provides full support to the Spark computing framework [84]. Offline and online trajectory analyses are often separated in existing systems.  
To overcome this shortcoming, a Spark-based hybrid and efficient framework named Dragoon [85] was proposed for both offline and online trajectory data analytics. Dragoon manages to decrease storage overhead up to doubled compared with the state-the-art big trajectory data management system Ultraman [86], while maintaining a similar offline trajectory query performance. Dragoon also manages to achieve at least 40% improvement of scalability over Flink and Spark Streaming and achieves an average doubled performance improvement for online trajectory data analytics.  
Based on cellular data (\(2G/3G/4G\)), base station data, user information, and road network data, a real-time urban mobility monitoring and traffic management system was proposed in [87], leveraging big data tools, including Hive, Spark, and Hbase. The system proposed a total of 600 TB cellphone data collected over 3 million people daily for 3 years in a field case study in Guiyang, China.  
In this section, we collect and summarize the off-the-shelf big data storage and computing tools that are already used or can be further leveraged in traffic estimation and prediction tasks. While these tools have not been widely used yet, there is already the trend of more and more data being collected and used in traffic problems, e.g., the large volume of GPS data may exceed the level of TB for a metropolitan area. The storage and computation resources of traditional tools may not be sufficient in the near future.
# Image Transmission over Cognitive Radio Networks for Smart Grid Applications
## 3 Proposed MAC Layer for Image Streaming over CRSNs
### Defining the Average Effective Transmission Time  
In the following, we intend to estimate the average Effective Transmission Time (ETT), \(E(\tau_{ETT}^{i})\). For this purpose, we first define \(\tau_{ETT}^{i}\) in the same way according to the Equation (12):  
\[\tau_{ETT}^{i}=(X^{i}\tau_{P}^{i})P_{Ar}^{PU,X}(X^{i})+(X^{i}-1)(\tau_{P}^{i})( 1-P_{Ar}^{PU,X}(X^{i})) \tag{12}\]  
With a little simplicity we can write the Equation (13):  
\[\begin{split}&\tau_{ETT}^{i}=(X^{i}\tau_{P}^{i})P_{Ar}^{PU,X}(X^{i})+X ^{i}\tau_{P}^{i}-X^{i}\tau_{P}^{i}P_{Ar}^{PU,X}(X^{i})\\ &-\tau_{P}^{i}+\tau_{P}^{i}P_{Ar}^{PU,X}(X^{i})=X^{i}\tau_{P}^{i} -\tau_{P}^{i}(1-P_{Ar}^{PU,X}(X^{i}))\end{split} \tag{13}\]  
The average packet transmission time \(E(\tau_{ETT}^{i})\) should now be computed based on the Equation (14):  
\[E(\tau_{ETT}^{i})=E(X^{i})\tau_{P}^{i}-\tau_{P}^{i}(1-E(g(\tau_{P}^{i},X^{i}, \lambda_{i},P_{fa,T_{S}}))) \tag{14}\]  
Using the extracted Equations (9), (10), (13) and (14) we can note the Equation (15) as below:  
\[\begin{split} E(\tau_{ETT}^{i})&=L_{i}\frac{(1-L_{i }^{X})}{(1-L_{i})}\tau_{P}^{i}-\tau_{P}^{i}(1-\frac{(1-L_{i}^{2X})}{(1+L_{i})} )\\ &=\tau_{P}^{i}(\frac{(1-L_{i}^{X})}{(1-L_{i})}-\frac{(L_{i}-L_{i }^{2X})}{1+L_{i}})\end{split} \tag{15}\]
## 2 Methods
### Determination of Mapping Function
In this study, to balance computational accuracy and efficiency, \(m=16\) was chosen. At this point, the mapped shape is almost identical to the original shape, and the mapping function error is nearly zero. The expression is as follows:\[\omega=\left\{ \begin{array}{l}0.5797x-0.0151-0.0372x^{-1}+0.0346x^{-2}-0.0629x^{ -3}+0.0178x^{-4}\\ +0.0093x^{-5}-0.005x^{-6}+0.0034x^{-7}-0.002x^{-8}-0.001x^{-9}\\ +0.0021x^{-10}-0.0012x^{-11}-0.0003x^{-12}+0.0004x^{-13}-0.0004x^{-14}\end{array} \right\} \tag{13}\]  
Figure 3: Comparison of mapped shape and original shape of the opening: (**a**) \(m=4\); (**b**) \(m=8\); (**c**) \(m=16\); (**d**) \(m=24\). Figure 2: Evolution curve of the mapping function error with increasing \(m\).
# Study of the Direct Metal Deposition of AA2024 by ElectroSpark for Coating and Reparation Scopes  
[PERSON]  
1 Innovation Engineering Department, University of Salento, Via per Arnesano s.n., 73100 Lecce, Italy; [EMAIL_ADDRESS] (P.L.); [EMAIL_ADDRESS] (G.R.)  
[PERSON]  
1 Innovation Engineering Department, University of Salento, Via per Arnesano s.n., 73100 Lecce, Italy; [EMAIL_ADDRESS] (P.L.); [EMAIL_ADDRESS] (G.R.)  
[PERSON]  
2 DMMM, Politecnico di Bari, Viale Japigia, 182, 70124 Bari, Italy 2*Correspondence: [EMAIL_ADDRESS]; Tel.: +39-080-596-2753 20 July 20172018 August 2  
The deposition parameters regimes (voltage, capacitance, frequency) depend on the type of electrode and its melting temperature, thermal conductivity, chemical reactivity of the anode elements, diffusivity, density, electrical resistance, thermal inertia, and flowability [19].  
A wide variety of metals and ceramic/metal deposits are produced by ESD [1; 20]. Among them, there are Fe-based alloys [21], superalloys [22; 23; 24], and magnesium alloys [25]. [PERSON] and [PERSON] [26] determined that ESD is a feasible method of producing an aluminum-nickel deposit, consisting of a nano-structured Al\({}_{3}\)Ni phase. [PERSON] and [PERSON] [27] investigated the feasibility of using the ESD process to deposit amorphous electrode material (Zr\({}_{41.2}\)Ti\({}_{13.8}\)Ni\({}_{10}\)Cu\({}_{12.5}\)Be\({}_{22.5}\)) onto an amorphous substrate of the same composition without crystallizing the deposit or the substrate. [PERSON] et al. [2] investigated the deposition of Fe\({}_{48}\)Cr\({}_{18}\)Mo\({}_{7}\)B\({}_{16}\)C\({}_{4}\)Nb\({}_{7}\) crystalline alloy on 316L stainless steel substrate. [PERSON] and [PERSON] investigated the ESD of several Ni-based super-alloy systems [28; 29], assessing the feasibility of repairing nickel based super-alloy substrates with McArIY deposition. Unfortunately, to date, the state of art is very poor of studies regarding the use of ESD technique to deposit thermally sensitive materials such as the 2024 alloy.  
In this paper, thick deposits of 2024 aluminum alloy on similar substrate using different process parameters were produced and analyzed. The surface, the macro and microstructure, micro-hardness, and defectiveness of the samples were characterized and discussed for different process parameters. Moreover, since the investigated alloy specifically susceptible to hot cracking, even the relationship between the processing parameters and the cracks in the substrate was investigated. Finally, the suitable process parameters for reducing both porosity in the deposits and the cracks in the substrate were identified.
# Subsampling Algorithms for Irregularly Spaced Autoregressive Models
## 5 Numerical Results
### Time Complexity  
To evaluate the computational efficiency of the subsampling methods, we repeat the simulation 30 times and record the average computational times for each method. For comparison, we implement the full-data estimator (**full**) using the algorithm described in Section 3 as a benchmark.  
We consider three different full-data sample sizes, \(m=10^{4}\), \(10^{5}\), and \(10^{6}\), and six subsample sizes \(r=500,1000,1500,2000,2500\), and \(3000\). Table 1 reports the average computational times in milliseconds for the case where \(\phi=0.5\) and \(\sigma=1\). The computation times for other values of \(\phi\) and \(\sigma\) follow a similar pattern. These results are based on simulations using a Julia implementation run on an Apple MacBook Pro with an M1 Pro chip.  
The entries in the table show a substantial reduction in computation times by using the subsampling methods compared to the time taken for full-data estimation. Unlike the uniform subsampling method, which incurs almost no computational overhead during the subsampling process, optimal subsampling algorithms require additional computations to determine the inclusion probability for each data point. The numerical optimization using subsamples has a time complexity of \(\mathcal{O}(r)\), whereas calculating the subsampling probabilities for nonuniform subsampling methods requires a cost of \(\mathcal{O}(m)\).  
Compared to full-data-based estimation, both **opt** and **iboss** demonstrate exceptional efficiency, reducing the computation time by a factor of \(1/40\) on average when the sample size \(m\) is sufficiently large. Although not as fast as these two methods, **thin** still achieves significant computational savings, taking less than one-tenth of the time required for the full-data estimation. Additionally, an advantage of using **thin** is that subsample selection can be performed sequentially. Overall, all three proposed optimal subsampling methods for the IS-AR(1) model offer reliable strategies for reducing the computational burden in large-scale data applications.
## 3 Results
This also enhances the interpretability of the model by separating independent sources within the data, potentially revealing hidden structures in the data and improving the performance of the classifier on new, unseen data by reducing overfitting. Figure 13: Diagram of the two deployed approaches. \begin{table}
\begin{tabular}{c c c c c} \hline  & **PCA** & **LDA** & **ICA** & **UMAP** \\ \hline Fold\_1 & 0.613496 & 0.772727 & 0.819672 & 0.505494 \\ Fold\_2 & 0.674033 & 0.872065 & 0.858823 & 0.532544 \\ Fold\_3 & 0.628930 & 0.780219 & 0.742138 & 0.537514 \\ Fold\_4 & 0.621468 & 0.745945 & 0.802259 & 0.605714 \\ Fold\_5 & 0.625698 & 0.772803 & 0.797687 & 0.522717 \\ \hline Average & 0.632725 & 0.774324 & 0.804116 & 0.540796 \\ \hline \end{tabular}
\end{table} 
Table 5: F1-measure of the GB model using various dimensionality reduction techniques including PCA, LDA, ICA, and UMAP.
# Yeast Extract Affecting the Transformation of Biogenic Tooeleite and Its Stability
## 3 Results and Discussion
### Environmental Implications  
Elevated As concentrations are usually found in AMD so it is important to find a suitable method to control it. Tooeleite (Fe\({}_{6}\)(AsO\({}_{3}\))\({}_{4}\)SO\({}_{4}\)(OH)\({}_{4}\) 4H\({}_{2}\)O), as a naturally formed As-mineral with a high As content of 20-25 wt%, can be considered a temporary scavenger for As presenting in acidic conditions and plays an important role in As geochemical cycling. Among the current treatment processes for As(III) control, immobilization of As(III) by tooeleite may be considered a promising technology. However, few studies focus on the stability of tooeleite, and this is vital for further application. In previous literature, the leached As can be below 5 mg/L at high initial As concentration (up to 3-4.5 g/L) by coating tooleite with silica or oxidizing Fe(II) slowly [8; 15]. This is not inapplicable to AMD system. In our research, yeast extract can improve the stability of solids without affecting As removal efficiency under initial As concentration of 13.3 mM.  
It was suggested that organic matter plays an important role in regulating and inducing the formation of minerals. There is little available information on organic matter influencing the transformation and stability of tooeleite. As shown in the present study, biogenic tooeleite formation by _A. ferrooxidans_ led to over 95% As removal efficiency, and the incorporation of yeast extract into tooeleite improved its stability by 40%. A schematic diagram of yeast extract affecting the formation of biogenic tooeleite is shown in Figure 10. The As retention capacity of tooeleite may be impacted by the transformation process. Different precursors obtained in the presence of yeast extract will affect mineral transformation, and then relatively purer tooeleite with a specific morphology will be formed to enhance its stability. This study demonstrated that yeast extract could enhance tooeleite stability with better removal and fixation ability of As(III) from high-concentration As acid drainage. However, future studies must be carried out to substantiate details of the treatment process.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**YE Concentration (_w/v_)** & **As (_w/w_)** & **Fe (_w/w_)** & **S (_w/w_)** & **Fe/As (Molar Ratio_)** \\ \hline
0.00\% YE & 16.24\% & 26.26\% & 3.79\% & 2.17 \\
0.10\% YE & 21.71\% & 25.97\% & 3.90\% & 1.60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: ICP analysis results of precipitates obtained with or without yeast extract (YE).