esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards DC Energy Efficient Homes
## 3 Factors for DC Installations
### Availability of DC Household Devices  
As explained in Section 2, home devices are generally DC compatible because most internal components are available in DC versions, i.e., electronics, motors, or lighting. The market for these appliances is focused on off-grid, as well as specific applications, such as recreational vehicles and boats, but there is a lack of available products for grid-connected systems [60; 61]. Manufacturers, such as Dometic, Alphatronics, Unique, or NIWA Solar, develop DC products in order to solve the needs of isolated homes, rural areas, ships, caravans, etc.  
DC appliances can meet quotiian needs with lower voltages than their AC counterparts, usually 12 V DC or 24 V DC rather than 110 V AC or 230 V AC [62; 63]. Therefore, the security levels of DC appliances are much higher than those of their AC counterparts. In addition, these can be connected to batteries or renewables, such as PV systems, in an efficient way because they do not need intermediate conversions.
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 2 Circuit Structure and Operation Analysis
### Description of Operating Range
#### 2.3.2 Stage II [\(t_{2}<t\leq t_{3}\)]  
As shown in Figure 6b, \(Q_{1}\)-\(Q_{4}\) remain at the cut-off state. The intrinsic diodes \(D_{B5}\) and \(D_{B8}\) keep on conducting while \(D_{B6}\) and \(D_{B7}\) remain at the cut-off state. The resonant current \(i_{L71}\) flows via intrinsic diodes \(D_{B1}\) and \(D_{B4}\) to retain the conduction loop. As the power switch turns on at the end at \(t=t_{3}\), the ZVS is realized, whereas due to the magnetizing inductor \(L_{m}\) being clamped by the output voltage \(V_{Bat}\) and the secondary resonant capacitor voltage \(V_{Cr2}\), \(L_{m}\) is absent from resonating, and thus \(i_{Lm}\) keeps on rising linearly. At \(t=t_{3}\), resonant current \(i_{L71}\) approaches 0, and this stage ends.
# Characterization and Optimization of a Conical Corona Reactor for Seed Treatment of Rapeseed
## 2 Materials and Methods
### Seed Characterization  
#### 2.4.1 Temperature  
The temperature at the seed surface was monitored using an infrared (IR) thermal camera (Testo 865, Testo, Titisee-Neustadt, Germany). Temperature assessment was made immediately after treatment on top of a wooden surface to avoid reflection interferences. The measurements were performed in three different seeds after the treatment of a group of 55 seeds.
## 5 Discussion
### Conclusions  
This study compared the emotional responses, enjoyment, and operational performances of design beginners using two techniques, namely parametric CAD modeling and VR free-form modeling. We discovered that the emotional response, enjoyment, and operational performance achieved with these methods were significantly different. The results of this study confirmed that the incorporation of VR into conventional CAD modeling was beneficial because it resulted in more positive and intense emotional experiences as well as higher enjoyment, which led to better operational performance. We formulated recommendations for improving VR free-form modeling on the basis of the obtained statistical and interview results. The conclusions of this study are as follows:  
1. The participants who used VR free-form modeling exhibited higher levels of emotional valence and emotional arousal than those who used parametric CAD modeling. The incorporation of VR into CAD modeling brought a sense of novelty and operational freedom for design beginners, which enabled them to have positive and intense emotional responses.
2. VR free-form modeling was more interesting than conventional parametric CAD modeling; however, the overall flow experience produced by VR free-form modeling was not as high as expected. No significant differences were found in concentration, sense of time distortion, and sense of control between the two modeling techniques. Although VR can bring enjoyment and promote learning, in practice, designers are still faced with challenging problems, such as completing professional modeling work, to attain their goals. Under such circumstances, professional training, such as logical training in map construction and the acquisition of relevant skills, becomes the main factor influencing all operative techniques.
3. We compared the performance of the participants in two modeling tasks, namely basic shape modeling (cube modeling) and free-form surface modeling (mug modeling), and found that VR free-form modeling led to higher operational performance than conventional parametric CAD modeling. The intuitive operation of VR free-form modeling enabled the participants to learn from their mistakes, and this operation was more suitable for shaping free-form surfaces than that of parametric CAD modeling. However, because VR free-form modeling lacked strict logic and precise numerical inputs, the accuracy of cube modeling was relatively low. By comparison, parametric CAD modeling enabled rapid and precise model creation, and the modeling result could easily be edited. Accordingly, in practice, VR free-form modeling is suitable for early ideation, whereas conventional parametric CAD modeling can be used for the 3D digitization of known shapes.
## 5 Experimentation, Results, and Analysis
### Analysis
#### 5.4.3 Visual Explanation Using Grad-CAM
###### Abstract  
A total of 12 evaluators participated in the human evaluation process. These included:  
* **Four Linguistic Experts:** Professionals with academic backgrounds in Tamil linguistics and natural language processing.
* **Five Native Tamil Speakers:** Individuals fluent in colloquial and literary Tamil, ensuring cultural and grammatical relevance.
* **Three Image Processing Experts:** Researchers and graduate students with prior experience in multimodal AI systems, ensuring the alignment of captions with visual semantics.  
All evaluators underwent a brief training session to familiarize themselves with the scoring criteria and examples of both high- and low-quality captions to calibrate consistency across evaluations.
## 2 Materials and Methods
### Data Processing and Growing Season Determination  
Monthly NDVI, GPP and radiation data covering a three-year period from 2009 to 2011 were used in this study. The temporal range was determined according to time (the end of 2011) of the latest publicly-available MPI-BGC GPP product. VI and radiation products were resampled to the spatial resolution of GPP product. MOD13C2 products were resampled by mean of regional averaging and CERES SWR products were resample by mean of bilinear interpolation. Then single-pixel-based temporal analyses were conducted at the spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\).  
To determine the growing season, we used a method proposed by [PERSON] et al. [(22)]. This method calculates the dates for SOS and EOS using a threshold of annual maximum of the selected indicators which are NDVI, NDVI \(\times\) PAR, NDVI\({}^{2}\)\(\times\) PAR and GPP for this study. Seasonal values were firstly normalized to the range [0, 1] using annual maximum and minimum, then a 6-degree polynomial equation was used to fit the seasonal cycle curve and finally the spring and fall dates were calculated according to the selected threshold. In this study we used a threshold of 0.5.
## 5 Simulation Results and Discussion
### Test Systems and Results  
#### 5.1.1 Test System 1  
This system contained 13 thermal units with a fuel cost function having a valve-point effect is used. The power demand, in this case, it is assumed to be 2520 MW, including transmission losses. The detailed parameters of the system were adopted from [24]. The solution obtained from the proposed RLGWO is compared with oppositional real coded chemical reaction optimization (ORCCRO) [29], a different version of PSO [19], shuffled differential evolution (SDE) [39], biogeography-based optimization (BBO) [13], DE/BBO [22], and OIWO [24], where Table 1 shows their best results. The convergence characteristic of the 13-unit test systems with the original GWO and RLGWO algorithms for fitness value are presented in Figure 4. This shows that the result of RLGWO is lower than the result of GWO.  
The statistical results of RLGWO and other published algorithms are listed in Table 2, showing the maximum, minimum, and average cost obtained using different methods over 50 trials. Figure 5 shows a visualization of the results presented in Table 2 of the different algorithms. The box graphs in Figure 5a show that the RLGWO always produces one of the lowest results for this problem. The ICA-PSO, on the other hand, has result statistics that are scattered over a wider range of cost. Figure 5b shows the time and number of hits  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Unit Number**} & \multicolumn{4}{c}{**Methods**} \\ \cline{2-5}  & **RLGWO** & **SDE** & **OIWO** & **ORCCRO** \\ \hline
1 & 628.3112 & 628.32 & 628.3185 & 628.32 \\
2 & 299.1788 & 299.2 & 299.1989 & 299.2 \\
3 & 299.1798 & 299.2 & 299.1991 & 299.2 \\
4 & 159.7297 & 159.73 & 159.7331 & 159.73 \\
5 & 159.7297 & 159.73 & 159.7331 & 159.73 \\
6 & 159.7297 & 159.73 & 159.7331 & 159.73 \\
7 & 159.7284 & 159.73 & 159.733 & 159.73 \\
8 & 159.7297 & 159.73 & 159.7331 & 159.73 \\
9 & 159.7284 & 159.73 & 159.733 & 159.73 \\
10 & 77.3839 & 77.4 & 77.3953 & 77.4 \\
11 & 113.1011 & 113.12 & 113.1079 & 112.14 \\
12 & 92.3254 & 92.4 & 92.3594 & 92.4 \\
13 & 92.3891 & 92.4 & 92.3911 & 92.4 \\ Total generating output & 2560.2446 & 2560.44 & 2560.3686 & 2559.44 \\ Power loss & 40.2446 & 40.43 & 40.3686 & 39.43 \\ Fuel cost & 24,514.78 & 24,514.95 & 24,514.83 & 24,513.99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Best simulation results of the 13-unit system with loss (\(P_{D}=2520\) MW).  
of the minimum result. The RLGWO computational time is one of the fastest, with one of the highest number of hits to its minimum solution.
## 6 On Control Messages Protection
### FLOOD: The Routing Protocol for UAN
#### 6.1.1 The Network Discovery Phase  
The FLOOD Network Discovery Phase is based on the _flooding_ principle. The protocol is based on the _flooding mechanism_. Upon receiving a control discovery message, a node re-broadcasts it unless the node has not already done it. To fix ideas, consider Figure 5 which shows an execution instance of the FLOOD protocol. In the figure, we show that the network is composed of three nodes. Furthermore, assume that the transmission range of node \(u_{1}\) (the gateway) only covers node \(u_{2}\), whereas node \(u_{2}\) is able to reach both \(u_{1}\) and \(u_{3}\), and, finally, node \(u_{3}\) is able to reach only \(u_{2}\).  
The protocol is initiated by the GW which broadcasts a HELLO(\(u_{1}\)), including its identifier and the time \(t_{i}\) at which the message is sent. Upon receiving the hello message, the node \(u_{2}\) adds to the message its identifier and other information, including the quality of the link (e.g., signal attenuation). Then \(u_{2}\) relays the message HELLO(\(u_{1}\),\(u_{2}\)) by broadcasting it after a randomized delay. When \(u_{1}\) receives the message, it re-broadcasts it. When \(u_{2}\) receives the new message, it sends to \(u_{1}\) a REPORT(\(u_{1}\),\(u_{2}\)) unicast message. The GW acknowledges \(u_{2}\) by sending it a ACK(\(u_{1}\),\(u_{2}\)) unicast message. With the ack message, the protocol between \(u_{1}\) and \(u_{2}\) is completed. However, also the node \(u_{3}\) receives the HELLO(\(u_{1}\),\(u_{2}\)) message. Suppose that \(u_{3}\) receives it after that \(u_{2}\) receives ACK(\(u_{1}\),\(u_{2}\)). The node \(u_{3}\) adds to the message its identifier and information about link quality. Then it broadcasts HELLO(\(u_{1}\),\(u_{2}\),\(u_{3}\)). Upon receiving this message, the node \(u_{2}\) re-broadcasts it. The message is received both by the GW \(u_{1}\) and the node \(u_{3}\). Upon receiving the message, the node \(u_{3}\) builds a report message REPORT(\(u_{1}\),\(u_{2}\),\(u_{3}\)). When \(u_{2}\) receives the report message from \(u_{3}\), it sends it the ACK(\(u_{1}\),\(u_{2}\),\(u_{3}\)) unicast message so that the protocol between \(u_{2}\) and \(u_{3}\) is complete. When the GW receives the message HELLO(\(u_{1}\),\(u_{2}\),\(u_{3}\)) it re-broadcasts it. Upon receiving the message the node \(u_{2}\) builds a report message REPORT(\(u_{1}\),\(u_{2}\),\(u_{3}\)). When \(u_{1}\) receives the report message from \(u_{2}\), it sends it the ACK(\(u_{1}\),\(u_{2}\),\(u_{3}\)) unicast message so that the protocol between \(u_{1}\) and \(u_{2}\) is complete.  
Figure 5: An execution instance of FLOOD.
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 4 I/O Detection Scheme
### Data Collection
#### 4.1.1 Data Collection
I/O detection scheme can provide information of places where the navigation system required an enhancement method, such as the combination of Wi-Fi signal [15] or other GPS systems [20; 21] to improve its accuracy.
# Transaction Mechanism Based on Two-Dimensional Energy and Reliability Pricing for Energy Prosumers
## 4 Case Study
### Performance Evaluation  
For a transaction mechanism to work well, the benefits for both the prosumer and the utility should be preserved. The revenue for the prosumer and the reliability for the utility were considered to verify the performance of the proposed mechanism.
# A Review of Vehicle to Vehicle Communication Protocols for VANETs in the Urban Environment
###### Abstract  
Vehicular Ad-hoc Networks (VANETs) have been gaining significant attention from the research community due to their increasing importance for building an intelligent transportation system. The characteristics of VANETs, such as high mobility, network partitioning, intermittent connectivity and obstacles in city environments, make routing a challenging task. Due to these characteristics of VANETs, the performance of a routing protocol is degraded. The position-based routing is considered to be the most significant approach in VANETs. In this paper, we present a brief review of most significant position based unicast routing protocols designed for vehicle to vehicle communications in the urban environment. We provide them with their working features for exchanging information between vehicular nodes. We describe their pros and cons. This study also provides a comparison of the vehicle to vehicle communication based routing protocols. The comparative study is based on some significant factors such as mobility, traffic density, forwarding techniques and method of junction selection mechanism, and strategy used to handle a local optimum situation. It also provides the simulation based study of existing dynamic junction selection routing protocols and a static junction selection routing protocol. It provides a profound insight into the routing techniques suggested in this area and the most valuable solutions to advance VANETs. More importantly, it can be used as a source of references to other researchers in finding literature that is relevant to routing in VANETs.  
position; routing; VANETs; urban scenario; protocols; traffic density +
Footnote †: journal: _Future Internet_  
**2018**, _10_, 14; doi:10.3390/f10020014  
[PERSON] is currently a senior lecturer at Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak. He has completed his Postdoctoral, Ph.D. and Masters in Networks in 2013, 2012, 2008 respectively from Universiti Technology Malaysia, Johor Bahru, Malaysia and BSc (Hons) Computer Sciences in 2005 from University of Punjab, Lahore Pakistan. His research interest includes Network and Network Security in Wireless communication. He has published more than 70 papers in peer reviewed conferences and journals. He is also acting as a reviewer of many journals.
## 4 Results
### Ultrasonic Scanning Data for Pristine and Delamination Cases  
The ultrasonic scanning data for the pristine and delamination cases generated using the numerical simulations are shown in Figure 6a,b, respectively. The pristine concrete case shown in Figure 6a reveals that the dominant wave components in the full-depth concrete (250 mm thick) are Rayleigh waves. No other wave modes are observed. However, the delamination case (Figure 6b) shows that Lamb waves are generated owing to the reduced concrete depth (60 mm thick). In particular, the fundamental symmetric (S0) and anti-symmetric modes are observed in the delamination case.  
Figure 6: Ultrasonic scanning data: (**a**) pristine concrete case (numerical simulation), (**b**) delamination case (numerical simulation), (**c**) pristine concrete case (experiments), and (**d**) delamination case (experiments).  
The ultrasonic scanning data for the pristine and delamination cases obtained using the noncontact sensor array shown in Figure 4 from the tested concrete sample are displayed in Figure 6c,d, respectively. The experimental data show more complicated responses than the numerical simulation data: each wave component is mixed in the time-space (_t-x_) domain, and individual wave components are not clearly separated. In addition, direct acoustic waves are observed in the experimental data owing to the fully air-coupled excitation and sensing setup. Rayleigh and Lamb waves are presumably present in the pristine and delamination cases, respectively. However, the experimental data in the frequency-wavenumber (_f-k_) domain need to be analyzed further to verify this assumption.  
The _f-k_ domain magnitude spectra for the data obtained from the numerical simulations and experiments are shown in Figure 7. In each case, the Lamb wave dispersion curves obtained via theoretical analysis are overlaid on the _f-k_ domain magnitude spectrum to identify individual wave components. For the pristine concrete case from the numerical simulation (Figure 7a), it is apparent that the Rayleigh wave is the dominant wave component. However, the delamination case from the numerical simulation (Figure 7b) shows both symmetric and anti-symmetric Lamb wave modes. The experimental data for the pristine and delamination cases (Figure 7c,d, respectively) demonstrate observations similar to the numerical simulation data. The simulation and experimental data shown in Figures 5 and 6 demonstrate that concrete delamination generates multimodal Lamb waves, whereas only Rayleigh waves are observed in the full-depth pristine concrete.  
Figure 7: Frequency–wavenumber domain magnitude spectra: (**a**) pristine concrete case (numerical simulation), (**b**) delamination case (numerical simulation), (**c**) pristine concrete case (experiments), and (**d**) delamination case (experiments). Lamb wave dispersion curves are overlaid on the magnitude spectra, where the red and blue lines indicate symmetric and anti-symmetric Lamb wave modes, respectively.
## 3 Experimental Results
### Results of Dynamic Uniaxial Compression Tests on Hard Ice
#### 3.1.3 Dynamic Deformation Measurement by DSCM
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Temperature** **(\({}^{\circ}\)C)**} & \multirow{2}{*}{**Sample** **Name**} & \multicolumn{2}{c}{**Nominal** **Strain-Rate**} & \multirow{2}{*}{**Sustaining Time (ms)**} & \multirow{2}{*}{**Average True** **Strain-Rate**} & \multirow{2}{*}{**Nominal** **Ultimate**} & \multirow{2}{*}{**True** **Ultimate**} \\  & & **(s\({}^{-1}\))** & & & & (s\({}^{-1}\)) & \\ \hline \multirow{8}{*}{\(-5\)} & HV01 mps3 & 2 & 4.80 & 0.56 & 0.96 & 2.69 \\  & HV02 mps2 & 4 & 2.30 & 1.20 & 0.92 & 2.76 \\  & HV05 mps3 & 10 & 1.05 & 2.20 & 1.05 & 2.31 \\  & HV1 mps3 & 20 & 0.24 & 5.29 & 0.48 & 1.27 \\  & HV2 mps2 & 40 & 0.11 & 10.26 & 0.44 & 1.13 \\  & HH01 mps2 & 2 & 5.20 & 0.47 & 1.04 & 2.44 \\  & HH02 mps2 & 4 & 2.45 & 1.02 & 0.98 & 2.50 \\  & HH05 mps2 & 10 & 1.08 & 2.10 & 1.08 & 2.27 \\  & HH1 mps2 & 20 & 0.45 & 4.65 & 0.90 & 2.09 \\  & HH2 mps2 & 40 & 0.14 & 9.14 & 0.56 & 1.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ultimate strain and strain-rate results of hard ice derived from actuator displacement and DSCM. HH denotes the hard ice sample loaded in the horizontal direction; HV denotes the hard ice sample loaded in the vertical direction. Figure 13: Average true strain-rate versus image NO profiles of sample HH05 mps2 at \(-10\)\({}^{\circ}\)C: (a) \(y\)-direction true strain-rate; and (b) \(x\)-direction true strain-rate. According to Table 2, obviously, there is a significant difference between nominal ultimate strain and true ultimate strain, as well as nominal strain-rate and average true strain-rate, and the formers are greater than the latters. As the loading rate becomes great enough, the ice specimen is more likely to fail in a crushing mode and achieve peak load at a relatively small nominal ultimate strain, as shown in Figure 8 and Table 2. The trend of failure in splitting mode is not as obvious as that at relative lower loading rates.
## 3 Proposed Adaptive KF-Based Tracking  
Inadequate prior knowledge of process noise statistics would lead to suboptimal KF estimations or divergence. In the GNSS signal fading and high dynamic applications, the actual noise statistics are not fixed but variable. Accordingly, the noise statistics determined beforehand in the KF tracking are not sufficient to satisfy the practical situations. To enhance the KF tracking performance, an adaptive approach is proposed to tune the process noise covariance \(\mathbf{Q}_{k}\) to satisfy the variable environments.
## 4 Results
### RQ6: Issues in WSN and IoT Frameworks
#### 4.6.7 Authorization and Authentication  
Nodes are the building blocks of the Internet of Things that must be defined in the network. Transmission between devices and access to the entire network span a wide range in IoT and WSN. IoT devices perform role-based access control, and their devices are allowed to do only what is required [96; 97]. Devices and their data must be protected from physical and logical attacks on the network. Attacks on the DNS cache could affect the overall performance of the network. Authentication is the process by which each node on the network can access data based on a fixed connection to a server or cloud-based server. If the authentication process is not administered properly, it will lead to security issues and questions. An attacker can easily access the network and make it fail temporarily by making too many wrong attempts.  
Authentication is complicated due to the massive proliferation of wireless media and the nature of sensor networks. Authentication is usually done using the credentials of a legitimate user [98; 99]. However, this technique is not secure enough. Therefore, passwords should be changed regularly and computers should not be left unattended to make this technique robust. Both the sender and the recipient should perform authentication to verify the origin of the communication [100; 101].