esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reliable Routing Algorithm Based on Path Satisfaction in the Energy Internet
## 3 Network Architecture and System Model
### System Model  
We will first establish the link model combined with the metrics of node grade difference, link availability, and SRLG with link spectrum connectivity. Then, we will present a service model in terms of the service properties and corresponding QoS requirements to differentiate the services.
## 3 Methods
### Quality Assessment of Included Studies  
A qualitative assessment of the studies included in this review was conducted based on the MMAT 2018 criteria for quantitative descriptive studies by two reviewers working independently [64]. The initial screening questions (S1 and S2) were as follows:  
1. Does a research question (RQ) exist?
2. Do the data collected for analysis address that RQ?  
Studies that did not satisfy the criteria in S1 and S2 did not qualify for inclusion. For all the studies (n = 40) included in this work, \"Yes\" was the answer to questions S1 and S2, and further assessment was performed according to the following questionnaire:  
1. Does the sampling methodology relevantly address the research question?
2. Does the sample properly represent the target population?
3. Have the measurements been taken appropriately?
4. Does the risk of non-response bias remain at a low level?
5. Does the statistical analysis appropriately answer the RQ?  
The answers to these questions were classified into 3 categories, \"Yes\", \"No\", and \"Not specified\". A further breakdown of the assessment for Q1-Q5 is elaborated as follows:  
* The sampling methodology had to be relevant to the research question, i.e., if the subjects' status was healthy, that must align with the research question (Q1).
* Subjects' health status must be clarified within the paper, i.e., non-smoking and no history of respiratory illnesses in the case of healthy subjects. Subjects with illnesses must not have suffered from any other conditions that may have produced faulty results (Q2).
* The authors must describe a complete and comprehensive experimentation method (Q3).
* The authors ensured that the dropped-out subjects and faulty data were excluded from the results (Q4).
* If the results did not properly answer the research question, the paper was excluded (Q5). The quality assessment of the selected studies is presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Q1** & **Q2** & **Q3** & **Q4** & **Q5** \\ \hline [PERSON] (2015) [9] & Yes & Yes & Yes & Not specified & Yes \\ \hline [PERSON] (2021) [34] & Yes & Yes & Yes & Yes & Yes \\ \hline [PERSON] (2017) [35] & Yes & Yes & Yes & Yes & Yes \\ \hline [PERSON] (2017) [10] & Yes & Yes & Yes & Yes & Yes \\ \hline [PERSON] (2018) [23] & Yes & Yes & Yes & Yes & Yes \\ \hline [PERSON] (2020) [12] & Yes & Yes & Yes & Yes & Yes \\ \hline [PERSON] (2013) [44] & Yes & Yes & Yes & Not specified & Yes \\ \hline [PERSON] (2020) [11] & Yes & Yes & Yes & Yes & Yes \\ \hline [PERSON] (2012) [45] & Yes & Yes & Yes & Yes & Yes \\ \hline [PERSON] (2017) [27] & Yes & Not specified & Yes & Not specified & Yes \\ \hline [PERSON] (2021) [46] & Yes & Yes & Yes & Yes & Yes \\ \hline [PERSON] (2020) [36] & Yes & Yes & Yes & Not specified & Yes \\ \hline [PERSON] (2013) [57] & Yes & Yes & Yes & Yes & Yes \\ \hline [PERSON] (2015) [52] & Yes & Yes & Yes & Yes & Yes \\ \hline [PERSON] (2016) [47] & Yes & Yes & Yes & Not specified & Yes \\ \hline [PERSON] (2017) [43] & Yes & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quality analysis of included studies.
## 2 Data and Methods
### Application Workflow  
This section presents a summarized overview of the functioning of DSPEB as a complete workflow, going from raw Sentinel-2 imagery and raw bathymetry measurements to model application and the creation of composite estimates.  
Figure 6a shows the first steps of DSPEB, where the exact dates for model training are selected. The first step in our workflow is concerned with the date preselection, where a set of Sentinel-2 images are filtered and only images where wave propagation and activity can be observed. As mentioned in Section 4, our current date selection criteria is based on S2 Shores. Each of the initial images is used with S2 Shores and a resulting bathymetry estimate is compared to the target bathymetry survey. The images are only kept in our pipeline if the correlation between S2 Shores' estimate and the target survey is higher or equal to 0.5. Next, a subtile is extracted from each of these images for each depth measurement point, such that the depth point is situated in the center of the extracted subtiles. These subtiles are then passed through our pre-processing chain, described in Section 2.3, subtiles are either pre-processed or recorded on disk to be used for model training. A test dataset is created following the same methodology using a different set of initial Sentinel-2 images with different dates.  
The DSPEB model is then trained on the prepared training dataset following the method further described in Section 3.1. After model training, the DSPEB model is used to estimate bathymetry from a Sentinel-2 image using a sliding-window technique (Figure 6b), where each subtile is treated according to the previously described pre-processing scheme (Section 2), resulting in an estimate profile from a single date. Finally, a composite estimate can be created by grouping multiple single-date estimated profiles using a simple point-wise mean (Figure 6c).  
Figure 5: The distributions of wave period (**a**), wavelength (**b**), and direction (**c**) at the different dates included in the training and validation sets in French Guiana and Gironde.
# Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques
## 1 Introduction
These forecasts are achieved based on utilizing deep learning (DL) methods [1; 7; 8; 9; 10; 11; 12]. These related contributions mainly focus on the forecasting aspect while in this paper we attempt to utilize such prediction methods to enhance and rearrange the message content from the communication point of view while investigating the potential challenges that are accompanying this approach. In this paper, since some dissimilar, yet, related topics such as MG control, device-to-device (D2D) communication, and DL based forecasting techniques are combined different approaches were utilized for validation and analysis to achieve the desirable proof of concept. In this regard, link reliability verification and latency analysis were also conducted to show the capabilities and also limitation of the transmission system. On the other hand, short-term and very short-term time-series forecasting was utilized to demonstrate how to predict future time-steps for generation and production. Most importantly, a grid-connected MG model based on the operation of grid-feeding inverters was utilized as a base for the simulation platform so that the aforementioned transmission architecture and the forecasting system can be integrated and the required tests can be conducted. The main contributions of this paper are listed as follows:  
* The detrimental communication link failure effects on the internal energy balancing of MG systems are investigated. The specific MG examined in this study is based on grid-feeding inverters and operates in grid-connected mode. In this configuration, the communication link transmits measurements from the consumption, production, and storage stations to the EMS. Furthermore, it relays power references from the EMS to the ESS so that the storage system can effectively contribute to minimizing the PCC power exchange. * Methodology of short-term load and PV-generation forecastings based on DL methods are described and numerically performed on sample datasets for a residential MG application. * The details of the designed communication infrastructure are explained and evaluated in terms of the dependency of latency values to payload size. * A system architecture that can enhance communication message content is proposed. The effectiveness of this system is evaluated by comparing the PCC power exchange measurements while the communication link is operational using forecasted values following communication link failure. The remainder of the paper is organized as follows. Section 2 introduces the role of communication in MG management. The effect of communication link loss on MG performance is introduced and discussed in Section 3. Section 4 presents DL based methods for time-series forecasting. The description of the selected communication system is provided in Section 5. Section 6 is dedicated to describe the proposed system architecture for enhancing the communication message content. Finally, conclusion and discussions are presented in Section 7.
Nevertheless, the unpredictable characteristics of the terrestrial channels that coherent THz waves traverse can have devastating effects on the integrity of transmitted data. The propagation of coherent THz signals is not only affected by deterministic attenuations such as molecular absorption and path loss but is also affected by random atmospheric turbulence, especially in outdoor coherent THz signal propagation [15]. Among these factors, the random variation of signal irradiance caused by atmospheric turbulence is a main cause of the performance degradation in coherent THz wireless systems. In order to combat and alleviate the problem, researchers have conducted extensive research on terrestrial turbulent channels and proposed different channel models [16]. However, the random behavior of pointing errors between the transmitter and receivers is another major factor affecting the overall performance of wireless THz links, which needs to be considered for channel model [17]. However, there exist few results for coherent THz wireless link performance considering pointing errors. We comment that, as a quasi-optical wave, the pointing error model in free space optical communication can serve as a valuable reference for addressing THz dislocation fading [18; 19; 20]. To bridge the aforementioned research gap, in this work, the bit error rate (BER) performance, asymptotic BER performance, and outage probability of a coherent THz systems with pointing errors are investigated. The high-precision convergent sequence BER for LN fading and the approximate sequence BER for GG channels are obtained. The loss of the signal-to-noise ratio (SNR) caused by pointing error is formulated, and the influence of building swaying on the THz links is theoretically solved. Additional insights into coherent THz wireless links are provided through our asymptotic studies. The rest of this paper is organized as follows. Section 2 formulates the system model for a coherent THz communication link experiencing atmospheric turbulent channels with pointing error. In Section 3, the exact and asymptotic BERs for LN and GG turbulent channels are derived. Section 4 provides an analysis of the outage probability under both weak and strong turbulence conditions. Numerical results and discussions are presented in Section 5. Finally, Section 6 concludes the paper.
## 7 Performance Evaluation
### Functionality  
The results of a comparison of various functionalities achieved by the protocols are tabulated in Table 3. The \(\surd\) symbol signifies that the protocol achieves a specific functionality. The \(\times\) symbol signifies that the function is not achieved by the protocols. The - symbol means that a specific functionality is not available in the protocol. It is observed that my proposed protocol provides the support for more functionalities and security properties compared with the related works. Only my protocol includes a IoLT-based U-healthcare application and a cross-server E2E communication in the design. The proposed work is also the only one to provide user biometrics update for a three-factor authentication solution with the cost-saving biohash function employed.
## 2 Thermal and Electrochemical Parameters of Interest
### Transference Number  
The transference number is a measure of the mobility of the cation relative to the mobility of the anion or, the number of moles of cation that is transferred per Faraday of charge [9, 61]. This quantity, which ranges between 0 and 1, can be calculated based on ion diffusion data from pulsed field gradient nuclear magnetic resonance (NMR) experiments or via electrochemical methods. Both the NMR method and a commonly used electrochemical technique, the Bruce-Vincent method, work best in dilute systems. The pulse field gradient NMR experiment is not able to account for the motion of poorly dissociated species (ion pairs and aggregates are observed as individual ions) which results in an over estimation of the transference number [62, 63]. The Bruce-Vincent method also tends to over-estimate the transference number because it measures the motion of neutral ions along with the motion of charged species [9, 62]. There are other electrochemical methods that can be used to measure transference numbers in concentrated systems: the Balsara-Newman method and the Hittorf method [59, 63, 64]. However, these methods are more difficult to perform. The Balasara-Newman method requires data from several different types of experiments including the diffusion coefficient, the steady state electrochemical potential and the potential of the concentrated cell while the Hittorf method requires partitioning the electrolyte into sub-sections which can be difficult for polymer electrolytes [63]. All of the transference numbers that are cited in this publication were determined using the Bruce-Vincent method suggesting that they could be over-estimations of the true values. Another effect that must be taken into account when comparing transference numbers is that reactions between the electrolyte and lithium anodes can produce additional mobile ionic species that can reduce the lithium ion transference number [65].  
Lithium ion transport numbers tend to be relatively low in solid polymer electrolytes, below 0.5, as a result of high affinity between the lithium salt and the electrolyte or high anion mobility [66; 67]. However, values as high as 0.66 have been reported in some polymer electrolyte systems [68]. This range of lithium-ion transport numbers aligns fairly well with what has so far been reported for epoxide-based polymer electrolytes. Electrolytes prepared from reagents that contain epoxides only and/or Jeffamines tend to have lithium-ion transport numbers between 0.2 and 0.3 compared to 0.4 for PEO-LiTFSI [10; 18; 33; 34]. Silicone-based reagents have tended to yield polymer electrolytes with higher lithium cation transport numbers, 0.5 to 0.6 [22; 27]. This may be a result of reduced affinity of PDMS-based polymer chains for lithium salts compared to other types of polymer discussed here [69].
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 4 ML Techniques in Energy Prediction
### Supervised ML Solutions
#### 4.1.6 Deep Learning Algorithms  
Deep learning approaches represent an enhanced iteration of artificial neural networks (ANNs) that utilize a complex architecture comprising multiple layers featuring diverse linear and non-linear transformations. One objective of deep learning is to supplant manually selected features with efficient hierarchical feature extraction and unsupervised or semi-supervised feature learning methodologies. The most prevalent deep learning algorithms include the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Deep Boltzmann Machine (DBM), Deep Belief Network (DBN), and Stacked Autoencoders [63]. Deep learning has been effectively utilized in big data analytics across various domains, including natural language processing (NLP) applications, medical diagnostics,stock market trading, network security, and image recognition. Deep learning is widely used by major enterprises and corporations for various applications, including real-time speech translation, voice recognition, and smart home devices [64]. However, due to the large parameters in deep learning, it requires more data for estimation. Two predictive algorithms based on deep learning frameworks are restricted Boltzmann machines and deep belief networks, as well as a hybrid model [63]. A comprehensive deep learning framework is proposed for human activity recognition using convolutional networks and recurrent neural networks, suitable for multimodal wearable sensors like accelerometers, gyroscopes, and magnetic field sensors. A two-phase neural-based deep model is introduced for classifying human activities, with the initial phase autonomously acquiring spatiotemporal characteristics via convolutional neural networks. Convolutional neural networks are also used for the acceleration-based identification of human activity. Wearable sensors, such as accelerometers and gyroscopes, are used as components of deep convolutional neural networks for human activity recognition [65; 66; 67].
# Effects of Propanol on the Performance and Emissions of a Dual-Fuel Industrial Diesel Engine
## 2 Experimental Setup  
The research on the co-combustion of diesel fuel with propyl alcohol was carried out on a compression-ignition engine (Andoria 1 CA90, Poland), air-cooled, equipped with an additional fuel supply system, and an apparatus allowing for operation in the dual-fuel system (dual-fuel engine). Diesel fuel was supplied to the engine by an original direct injection system, while propanol was supplied with an additional injector to the intake manifold (PFI). The test engine was a single-cylinder engine with a displacement of 573 cm\({}^{3}\) and a cylinder bore and a stroke of 90 mm. The rotational speed of the engine was 1500 rpm. The compression ratio was 17:1. The start of the injection was 20\({}^{\circ}\) CA bTDC. The rated power of the engine was 7 kW. The tests included indicating the engine, that is, recording changes in pressure in the engine cylinder and measuring its exhaust emissions. During the experiment, a piezoelectric pressure sensor (Kistler 6061) was placed in the combustion chamber. A crankshaft rotation angle marker (encoder) was installed on the engine crankshaft. A charge amplifier (Kistler 5011) and a digital data acquisition system with an A/D card (Measurement Computing USB-1608 HS) were used. Figure 1 shows a diagram of the test stand for testing a dual-fuel engine powered by diesel and propyl alcohol, while Table 1 shows the technical data of the test engine.  
The engine's emissions were measured using a five-gas exhaust gas analyzer (Bosch BEA 350) [27]. Concentrations of NO, HC, CO, and CO\({}_{2}\) were recorded. Soot concentration was recorded with use of an opacimeter (AVL Smoke meter 415 SE) [28]. The method of measuring HC, CO, and CO\({}_{2}\) emissions was the technology of nondispersive infrared (NDIR) spectrometry, while the measurement of NO emissions was based on electrochemical sensors. The smoke opacity of the exhaust gases was measured using the filter paper method on the FSN scale (Filter Smoke Number) (according to ISO 10054), which enables the mapping of the soot concentration in mg/m\({}^{3}\). Details of the equipment used can be found in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & \\ \hline Engine model & Andoria 1 CA90 \\ \hline Type of engine & Stationary, four stroke \\ \hline Type of ignition & Compression ignition \\ \hline Rated speed (rpm) & 1500 \\ \hline Engine rated power @1500 rpm (kW) & 7 \\ \hline Technique of cooling & Air cooled \\ \hline Intake system & Naturally aspirates \\ \hline Bore/stroke (mm) & 90/90 \\ \hline Displacement (cc) & 573 \\ \hline No. of cylinders & 1 \\ \hline Injection system & Direct injection \\ \hline Compression ratio & 17 \\ \hline Start of injection (SoI) (deg bTDC) & 20 \\ \hline \end{tabular}
\end{table}
Table 1: Main technical specifications of the test engine.  
Figure 1: Andoria 1 CA90 engine on a test stand (**a**) and schematic arrangement of the engine test stand, instrumentation, and data logging system (**b**).
## 4 Performance Evaluation of Millimeter-Wave V2V
### Millimeter-Wave V2V Communications with Antenna Height Diversity
Under the \(h_{\rm n}^{\rm best}\), we defined the valid communication range \(R\) as the range for which the difference of \(\Phi\) was from \(115^{\circ}\) to \(245^{\circ}\), as follows. Figure 13.3 Figure 13.4 (18) Figure 13.5 \[=\frac{144}{23\lambda}h_{\rm t}h_{\rm n headed arrows show the effective communication range in the case of a 10 and a 20 cm antenna space. From Figure 15, we can see that the outage capacity in the effective range was better than the outside. Although there are several solutions to Equation (15) at a 10 cm antenna space in \(d<22\) m and at a 20 cm antenna space in \(d<48\) m, rapid fluctuation of the difference of the phase difference degraded the outage capacity. On the other hand, since the difference became stable at more than \(\pi\) in \(d>48\) m at a 10 cm antenna space and in \(d>95\) m at a 20 cm antenna space, the improvement from no height diversity gradually decreased. From the above analysis, we concluded that the antenna space determined short-range or long-range communication. The statistics show that the average speed on highways is about 70 km/h and on roads is about 30 km/h [37]. Moreover, we adopted the two second rule to decide the average V2V distance. From this rule, we derived the average distance on roads, which was 17 m, and on highways, which was 39 m. Based on these average distances, we proposed two additional receivers for height diversity that supported the above two values. At 60 GHz, we set the first receiver at a 5 cm space whose effective range was from 11 m to 24 m and the second receiver at a 12 cm space whose effective range was from 27 m to 57 m. In the case of 30 GHz, ten centimeters and 24 cm of space were necessary. Finally, in the case of 5 GHz, sixty centimeters and 144 cm of space were necessary. Figure 16 shows the outage capacity at 5, 30, and 60 GHz with height diversity. Note that 5, 30, and 60 GHz used three receivers as proposed in the above discussion. We can see that all outage capacities had no sharp drop and decreased linearly.