esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Monitoring and Control Systems Based on Internet of Things
## 3 Monitoring and Control Systems Based on IoT
### Monitoring and Control Systems for Smart Agriculture Based on IoT
In [51], the authors discuss advanced strategies for monitoring and managing electricity consumption, introducing modern approaches based on IoT technology within intelligent energy grids. Traditional energy transmission systems relied on one-way communication, which hindered the user's ability to generate energy. The solution to these challenges is microgrids and intelligent energy grids that use the latest technologies, such as smart energy meters and the IoT, to enable more efficient, flexible and transparent monitoring and control of energy transactions. Intelligent grids include hybrid power generation, integrating renewable sources such as solar, wind and fuel cells, and using advanced algorithms such as Perturb and Observe for maximum power point tracking (MPPT). The article details the architecture of a power monitoring and control system for solar power generation and how various components, such as Arduino and Raspberry Pi microcontrollers, communicate. Experiments conducted as part of the research confirm the effectiveness of the proposed solutions in monitoring and controlling energy consumption in intelligent energy networks, which contributes to increasing energy efficiency and sustainability.
# Context-Driven Service Deployment Using Likelihood-Based Approach for Internet of Things Scenarios
## 4 Proposed Technique
### Improvement in Service Discovery/Search Time  
Service search time depends on the size of the search space. In our approach, the use of combined contexts reduces the necessary search space, as discussed earlier. Furthermore, the inclusion of the temporal service consumption patterns of users/devices reduces the search choices; the options for searching for a service are therefore reduced. This helps to improve the service discovery time.
## 2 Protocols for Synchrotron X-Ray Experiments
### Xrd
#### 2.4.2 Data Analysis Protocol  
The acquired data are recorded as diffraction intensity profiles, 20-transformed data, and integrated datasets for analysis. Rietveld refinement is performed using Z-Rietveld (version 1.1.3), part of the Z-Code software package [21], to determine crystal structure parameters, including lattice constants, atomic displacement parameters, occupancy, and reliability factors (\(R_{\text{wp}}\), \(S\)) [22; 23].  
Crystallite size is primarily evaluated using the Scherrer equation, which relates the FWHM (\(\beta\)) of specific diffraction peaks and the Bragg angle (\(\theta\)) as follows:  
\[D=\frac{K\,\lambda}{\beta\text{cos}\,\theta} \tag{6}\]  
Here, \(D\) is the crystallite size, \(K\) is the Scherrer constant (typically 0.9), \(\lambda\) is the X-ray wavelength, \(\beta\) is the FWHM in radians, and \(\theta\) is the Bragg angle [24]. This method provides an initial estimate of the crystallite size under the assumption that strain effects are negligible.  
Figure 9: XRD spectra of the platinum catalyst as a standard sample (TEC10V50E, red line), supplied by TANAKA Precious Metals; background of an empty Lindemann glass capillary (green line) and cerium oxide (black line).  
To account for lattice strain (\(\epsilon\)) and its influence on diffraction line broadening, the Williamson-Hall method is also employed [25], and the combined contributions of the crystallite size and strain are employed using the following relationship:  
\[\beta\text{cos}\,\theta=4\epsilon\text{sin}\,\theta+\frac{K\lambda}{D} \tag{7}\]  
Here, the slope of the linear plot of \(\beta\text{cos}\,\theta\) versus \(\sin\theta\) yields the lattice strain (\(\epsilon\)), while the intercept allows for refinement of the crystallite size (\(D\)) in Figure 10. By combining the Scherrer equation with Williamson-Hall analysis, both the crystallite size and strain contributions are quantitatively evaluated, providing comprehensive insights into the microstructural characteristics of the samples.
# Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs
## 5 Conclusions and Future Work  
Our work reported in this article aims to provide a starting point in Arabian camel breed image classification. We started by gathering and annotating a dataset of Arabian camel breed images which consists of 1073 images of 6 Arabian camel breeds: Waddeh, Majaheem, Honor, Sofor, Shaele, and Shageh. We proposed a CNN model for Arabian camel breed classification and prediction based on extensive empirical studies on popular pre-trained CNN models and selected the best-performing model, i.e., NASNet-Large, as the basis in our model. We pre-trained and experimented with seven popular CNN architectures including InceptionV3, NASNet-Large, PNASNet-5-Large, MobileNetV3-Large, and EfficientNetV2 (with three variants: small, medium, and large) on our Arabian camel breed dataset using transfer learning to achieve the best classification accuracy; NASNet-Large turned out to perform the best on our curated Arabian camel dataset with an accuracy of 85.80% and an F1 score of 86%. Furthermore, we integrated our proposed model into a mobile application for ease of access, further validation, and practical application in real-world scenarios. As future work, we plan to enhance our curated Arabian camel dataset in terms of both scale (number of images) and quality as well as expand the scope of our research by incorporating object detection and seeking better robustness and higher accuracy of camel classification. Additionally, we plan to experiment with a variety of more aggressive data augmentation techniques and new state-of-the-art CNN architectures to further improve the performance of our model. Based on the encouraging result we haveachieved with Arabian camel classification, we plan also to extend the capabilities of our model and system to classify Arabian camels along other properties, such as age, gender, disease, and degree of beauty.  
Conceptualization, S.A.; Methodology, S.A.; Data curation, S.A. and A.A.; Software, S.A.; Validation, S.A., M.S., M.A. and D.C.; Formal analysis, S.A.; Investigation, S.A., M.S. and M.A.; Resources, S.A.; Writing--original draft, S.A. and A.A.; Writing--review and editing, S.A., A.A., M.S., M.A. and D.C.; Visualization, S.A.; Supervision, S.A.; Project administration, S.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deputyship for Research and Innovation \"Ministry of Education\" in Saudi Arabia (IFKSUOR3-031-1).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors extend their appreciation to the Deputyship for Research and Innovation \"Ministry of Education\" in Saudi Arabia for funding this research (IFKSUOR3-031-1).  
The authors declare no conflict of interest.
# QiOi: Performance Isolation for Hyperledger Fabric
## 5 Evaluation
### Support for Practical Co-Located Tasks  
This subsection evaluates whether QiOi can provide performance isolation when Fabric components run with a practical task other than sysbench. We chose Spark [26] as it is a widely-used cloud service, which processes data analytic operations in a distributed manner to enable fast big data processing. There are map and reduce phases in Spark: the map phase involves processing the input data and creating several small chunks of data, while the reduce phase involves processing the data that come from the map phase and producing a new set of outputs. Spark uses a master-worker architecture. A master is the worker cluster manager that accepts jobs to run and schedules resources among workers. Each worker executes the jobs. For experiments, we utilize eight workers running as containers with two threads (i.e., Spark containers) and _Terasort_ job with a 47 GB input file for Spark. _Terasort_ job sorts some number of data as quickly as possible.  
Figure 12 illustrates the impact of QiOi when Fabric runs with eight Spark containers on a host server. Figure 12 shows Fabric throughput (left y-axis as bars) and latency (right y-axis as a solid line) compared between _w/o QiOi_ and _w/QiOi_. When QiOi runs with Fabric, Fabric throughput increases by 16%, and latency decreases by 2.4 times. On the other hand, the throughput change of Spark containers (right y-axis as a solid line) is negligible, which  
Figure 11: Impact of QiOi on Fabric using marbles chaincode.  
is only 3% of a decrease as in Figure 11(b). Because Spark containers perform the compute-intensive map phase [35; 41; 42], the Fabric components can experience scheduling delay similar to the case with sysbench containers. Note that CPU utilization of Fabric increases by 17%, and that of Spark containers decreases by 5%.  
To sum up, QiOi dynamically controls the _CPU share_ of the orderers and peers, it can improve Fabric performance running with Spark containers simultaneously.
## 5 Experimental Results
### Results and Discussions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Dataset/** \\ **Measure/** \\ **Methods/** \\ \end{tabular} } & \multirow{2}{*}{**Experimental**} & \multicolumn{6}{c}{**Distance Error (\(Err_{d}\) (m)) of**} \\ \cline{3-10}  & & & \multicolumn{3}{c}{**the TQU-SLAM Benchmark Dataset**} & \\ \cline{3-10}  & & & **1 st** & \multicolumn{2}{c}{**2 nd**} & \multicolumn{2}{c}{**3 rd**} & \multicolumn{2}{c}{**4 th**} \\ \cline{3-10}  & & & **FO-D** & **FO-D** & **FO-D** & **FO-D** & **FO-D** & **FO-D** & **FO-D** \\ \hline \multirow{6}{*}{DPVO} & L1 & 13.42 & 14.68 & 16.70 & 14.69 & 15.62 & 16.46 & 15.78 & 15.81 \\ \cline{2-10}  & L2 & 18.14 & 17.20 & 15.83 & 15.25 & 18.08 & 17.15 & 15.52 & 14.51 \\ \cline{2-10}  & L3 & 16.72 & 15.35 & 15.44 & 15.65 & 17.41 & 16.48 & 15.87 & 15.25 \\ \cline{2-10}  & Average & 16.09 & 15.74 & 15.99 & 15.20 & 17.04 & 16.70 & 15.72 & 15.19 \\ \hline \multirow{6}{*}{TartanVO} & L1 & 19.24 & 17.14 & 17.79 & 15.39 & 15.60 & 15.31 & 19.30 & 19.92 \\ \cline{2-10}  & L2 & 19.24 & 17.14 & 17.79 & 15.39 & 15.60 & 15.31 & 19.30 & 19.92 \\ \cline{1-1} \cline{2-10}  & L3 & 19.24 & 17.14 & 17.79 & 15.39 & 15.60 & 15.32 & 19.29 & 19.92 \\ \cline{1-1} \cline{2-10}  & Average & 19.24 & 17.14 & 17.79 & 15.39 & 15.60 & 15.31 & 19.30 & 19.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The results of the camera motion trajectory estimation error (\(ATE\)) based on the DPVO and TartanVO networks on the TQU-SLAM benchmark dataset. Figure 14 shows the results of estimating the camera's motion trajectory based on the DPVO model when experimenting with \(L1\) of the TQU-SLAM benchmark dataset. Figure 15 shows the results of estimating the camera's motion trajectory based on the TartanVO model when experimenting with \(L1\) of the TQU-SLAM benchmark dataset.
# Axial Compression Behavior of Steel Angle-Corrugated Steel Plate-Confined Concrete Columns
###### Abstract  
This paper proposes a novel type of steel angle-corrugated steel plate-confined concrete column (SA-CS column). The axial compression behavior of the SA-CS column was investigated by load experiments and finite element (FE) simulation. The study also compared the load-displacement curves, core concrete strength index, and specimens' ductility indices between the SA-CS columns and concrete-filled steel tube (CFST) columns. The results indicated that the mechanical properties of the SA-CS column were better than those of the CFST column with a similar steel ratio. The rationality and reliability of the finite element model were confirmed by comparing the experimental results' load-displacement curves and failure modes. Based on the FE model, a supplementary parametric analysis of the effects of the compressive strength of core concrete, thickness, yield strength, and wave height of the corrugated steel plate, thickness, and yield strength of the steel angles was conducted. Moreover, some existing design codes were assessed based on the numerical results available in this study. A new calculating model for the SA-CS column was proposed, which is capable of reasonably predicting the numerical results.  
corrugated steel plate; axial load experiment; finite element simulation; parametric analysis; bearing capacity calculation +
Footnote †: journal: Applied Sciences  
1
Footnote 1: School of Environmental & Civil Engineering, Jiangnan University, Wuxi 214122, China 1  
Footnote 2: College of Engineering, Zhejiang University City College, Hangzhou 310015, China  
Footnote 2: Correspondence: [EMAIL_ADDRESS]
# Numerical Integration Schemes Based on Composition of Adjoint Multistep Methods
## 2 Preliminary  
### Basic Definitions  
Let us consider the initial value problem (IVP) as a system of first-order ordinary differential equations (ODE) with corresponding initial value:  
\[\dot{y}=f(x,y(x))\,, \tag{1}\] \[y_{0}=y(x_{0})\,,\]  
where \(f:\mathbb{R}\times\mathbb{R}^{N}\rightarrow\mathbb{R}^{N}\) is a right-hand-side function, \(x\) is a independent variable often assumed as time, \(y_{0}\) is an initial conditions (IC) and \(\dot{y}=\frac{dy}{dx}\). The function \(y(x)\) satisfying (1) is called a solution of IVP.  
By applying the numerical integration method one can obtain values of function \(y(x)\) in times \(x_{1},x_{2},\ldots,x_{n}\) with some accuracy. The simplest example of a numerical integration method is an explicit Euler method with a constant integration step:  
\[y_{n}=y_{n-1}+hf_{n-1}, \tag{2}\]  
where  
\[h=x_{i}-x_{i-1}\] \[f_{i}=f(x_{i},y_{i})\,,\]  
and \(y_{i}\) is a numerical approximation of \(y(x_{i})\).  
The linear k-step numerical integration method can be described by the following formula:  
\[\sum_{i=0}^{k}a_{i}y_{n-i}=h\sum_{i=0}^{k}b_{i}f_{n-i}, \tag{3}\]  
where \(a_{i},b_{i}\in\mathbb{R}\), \(a_{0}\
eq 0\) and \(k\) is a number of steps. A linear multistep method is explicit if \(b_{0}=0\) and implicit otherwise. In the latter case, there is dependence \(y_{n}\) from \(f_{n}\) which leads to the application of the [PERSON] method, or another technique for resolving implicitness. There is another representation of multistep methods which is more common for software implementation:  
\[y_{n}=\sum_{i=1}^{k}\overline{a}_{i}y_{n-i}+h\sum_{i=0}^{k}\overline{b}_{i}f_ {n-i},\]where \(\overline{a}_{i}=\frac{a_{i}}{a_{0}}\) and \(\overline{b}_{i}=\frac{b_{i}}{a_{0}}\). There is plenty of linear multistep methods, but the most common examples are explicit Adams-Bashforth methods and implicit Adams-Moulton methods which are often used within the common predictor-corrector scheme as well as implicit backward differentiation formulas. General forms and equations for coefficients of these methods are thoroughly given in [20]. Thus, we will further consider only some particular linear multistep methods.  
To analyze linear multistep methods one can use the following generating polynomials:  
\[\rho(\xi) =\sum_{i=0}^{n}a_{i}\zeta^{n-i}\] \[\sigma(\xi) =\sum_{i=0}^{n}b_{i}\zeta^{n-i}\]  
According to the classical [PERSON] theory of stability, the linear multistep method is called stable or zero-stable if roots of \(\rho(\xi)\) lie on the unit circle and there should be no multiple roots of modulus one. Linear multistep method is called weakly stable if it is stable and have more than one root of modulus one, which leads to an empty stability region. Known examples of weakly stable methods are explicit midpoint rule and Milne-Simpson method. Linear multistep method is called strongly stable if it is stable and have a single root of modulus one. Examples of strongly stable method are Adams-Bashforth and Adams-Moulton method.
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.14 Spatial Modulation (SM)  
Multiple antennas at the transmitter and receiver end effectively boost spectral efficiency while reducing hardware complexity. Addressing inter-cell interference, in contrast, makes implementation more difficult because signal processing at the BS demands a large amount of energy. Spatial Modulation (SM), one of the most effective methods for reducing the complexity of an MIMO network, views the antenna arrays as a spatial constellation diagram, with each antenna in the system carrying a sequence of information bits [29]. After a successful match of the incoming symbol with the existing symbol on the antenna, whose spatial position is encoded with the data needed to be broadcast, this method allows broadcasting from a single antenna of the antenna array at once.  
Spatial modulation boosts communication network performance greatly and takes advantage of huge MIMO benefits for enhanced energy economy in wireless communication systems [110]. SM, on the other hand, sacrifices a spatial degree of freedom due to the logarithmic increase in multiplexing gain with an increasing number of antennas at constant energy dissipation (which induces a capacity gain with no supplemental bandwidth requirements as a result of the transmission of different data streams using the same resources in a spatial domain). SM is regarded as an important underlying technology for 5G networks in satisfying the expectations of emerging data-hungry technologies because of its several advantages [110].
## 1 Introduction
It has also been shown that microalgae can remove pathogenic microorganisms, such as _Escherichia coli_, from WW contributing to effluent disinfection [35; 36]. Different microalgal strains can have different responses to the environmental and culturing conditions, so it is crucial to choose the correct microcalga or microalgal consortium, with some studies reporting better and more promising results using native strains from the WW [37; 38; 39; 40]. Moreover, to properly implement microalgae inside ASP in a cost-effective way, the cultivation system and procedures must be chosen carefully to balance phytoremediation efficiency and energy consumption [18; 30; 41; 42; 43]. Open-pond systems, for example, have the advantage of low maintenance costs but they can occupy more land surface compared to other systems [18; 30; 44]. The same reactor can also be run in batch or continuous mode, increasing the possible combinations [42; 45; 46]. Lastly, to achieve better phytoremediation using microalgae, abiotic factors, e.g., light and temperature, should be monitored [37; 47; 48; 49]. These factors can affect both microalgae growth and nutrient depletion and synergistic effects have been recorded, meaning that fine-tuning of environmental parameters is needed [50; 51; 52; 53]. Unfortunately, these parameters are difficult to control when performing outdoor tests, meaning that seasonal variations in photoperiod and light quality, along with daily and seasonal temperature changes, are major challenges for large-scale applications. During winter, low temperatures can slow down microalgae metabolism, reducing biomass production and nutrient recovery [37; 49; 54; 55]. For example, [PERSON] and colleagues, who monitored a greenhouse phytoremediation plant for a whole year, reported a net P increase in the effluent during winter and linked it to decaying biomass and low algal production [56]. Unfortunately, limited literature has been produced regarding prototype or large-scale applications of microalgae in ASP during the winter season, and environmental conditions drastically change due to geography, so site-specific tests should be performed [57; 58; 59]. Table 1 offers examples reported in the literature of different phytoremediation trials conducted in the winter season, highlighting the differences in microalgae species, cultivation systems and procedures, and geographical positions. In this paper, we performed two prototype-scale phytoremediation tests, one outdoor and one indoor, as a follow-up study of that in [67]. Both tests were performed in open pond-like systems operated in batches during the winter season using the effluent of the sludge thickening process to navigate the possibility of the integration of a microalgae-based process in an ASP-based WWTP even during adverse environmental conditions.
# Mars to Earth Data Downloading: A Directory Synchronization Approach
## 6 Performance Analysis
### TM-EXP Data Flow  
In Figure 8 we have plotted two series, \"TM-EXP generation\" and \"TM-EXP delivery\". For each bundle we have two markers on the same horizontal line, as the ordinate is the bundle number. The interval between these two markers denotes the time elapsed from generation to delivery, i.e., the delivery time. Bundle generation is performed at regular intervals, while delivery is in bursts, due to intermittent connectivity of space links. No bundles are delivered before about 470 s, as the first opportunity to leave the Lander is offered by the first contact with the Orbiter, at 440 s (see Figure 6). Bundles generated before this time must be stored by the bundle protocol and wait for the contact to open. When this happens, all bundles are almost immediately passed to the Orbiter (the propagation delay between Lander and Orbiter is negligible), and then from the Orbiter to ESA-MCC. Here the propagation delay is longer (23 s) but connectivity is very good, as contacts from the Orbiter to the three GSs or EDRS are many and with small gaps. The best route is dynamically selected, bundle-by-bundle by CGR [15; 23]. The same happens for bundles generated after closure of the first contact to the Orbiter, which have to be stored on board until the second Lander-to-Orbiter contact opens (bundles 5 and 6) and then for those generated after its closes and before the direct contacts from Lander to the three GSs open. After the burst of old bundles (7-10) waiting for this opening, new bundles are delivered either via direct contact to GSs or via Orbiter 1 at regular intervals.  
Figure 8: Urgent Telemetry data flow (TM-EXP, expedited priority) from Lander to ESA-MCC: time sequences of bundle generation and delivery.