esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation and Evaluation of Nodal Distribution and Movement in a 5G Mobile Network
## 2 Review of Simulators
### Gns3  
GNS3 is a free graphical network simulator. It is based on the client-server principle. This tool is open source, and it is used for network emulation and virtualization. The simulator platform is based on the Python programming language emulating Cisco software and hardware. GNS3 supports a wide range of virtual network devices from various network equipment vendors [8; 9]. It allows using a convenient template manager, making it easier to add and edit network devices to the network.  
GNS3 is a client/server application. A client application is intended for modeling. A server application provides the correct operation of the simulator integrally. A client application should be deployed on a local computer. A server application may be deployed on the same computer as a client application and represented as a virtual machine or on a separate dedicated server, providing an opportunity for remote access to a client application. The connection between the server and the client needs to configure after installing the required simulation components. As a result of the performed operations, it is possible to create network topologies using the client software that can be processed on the server.  
GNS3 has its own community of developers and users. Feedback created by a group of like-minded people who want to help others learn, work, and fix bugs in software implementations is an essential advantages.  
A definitive advantage of the emulator is detailed documentation with illustrations for novice users. It is available to observe guides for advanced configuration of both individual network devices and the server-side features. Each virtual network device is an independent network object whose starting or stopping does not affect the operation of another unrelated node. The emulator supports Ethernet connections between network devices and also allows serial connections between devices that support the corresponding modules.  
The main disadvantage of GNS3 lies in the fact that the user needs to create your own software images of network devices for emulation, which are optionally paywall. Therefore, the deployment of GNS3 is contingent on personal or commercial use. Additionally, the developers do not consider security issues as one of the main areas. This emulator allows using all implementations present in Cisco Packet Tracer together with it, but requires the availability of the necessary images of operating systems and devices.
## 3 Results
This method renders concordant results with the conventional \(I\)-\(V\) method, as shown in Table 2.  
The high value of \(n\) obtained for the Ag/PFB/p-Si device is possibly due to the presence of interfacial states at the heterojunction, which further implies that the thermionic emission is not the only dominating charge carrier transport mechanism [38]; however, there exist some other conduction mechanisms as well. The charge transport mechanisms in the Ag/PFB/p-Si device are studied from double logarithmic \(I\)-\(V\) (\(\log I\) vs. \(\log V\)) characteristics according to power/Child's law, i.e., \(I\propto V^{m}\), where \(m\) is the slope of a linear portion of the \(\log I\) vs. \(\log V\) curve, as shown in Figure 5. As reported elsewhere [39; 40], there are various regions in the graph that can be explained on the basis of the power law of current. The region, for which \(m=1\), is an ohmic region, \(m=2\) yields the space-charge limited current (SCLC) region and \(m>2\) demonstrates the trapped-charge limited current (TCLC) region [41]. It can be observed from Figure 5 that the Ag/PFB/p-Si device has three distinct regions. At low voltages, region 1 (\(0.38<\log V<0.44\)) has a slope \(m\) equal to 3.8. This region conforms to the TCLC mode of conduction. The carriers injected at the interface cause current to flow in proportion to the square of applied bias, obeying [PERSON]'s law. In this region, the density of injected free charge-carriers is much greater than that of the thermally generated free charge-carrier [42]. Subsequently, in the intermediate voltage range of region 2 (\(0.45<\log V<0.66\)), \(m=1\). This region is governed by an ohmic transport mechanism, where current increases linearly with the applied voltage because effective density of injected carriers might be less than the thermal carrier density [43]. In region 3, at higher voltages (\(0.67<\log V<1\)), the slope almost equals 0.5, indicating that the dominant conduction mechanism in this domain is thermionic emission, where free carriers are thermally generated at the interface of the top electrode, Ag, and organic layer PFB. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**S. No.** & **Device Parameters** & **Conventional \(I\)-\(V\) Method** & **Norde’s Model** \\ \hline
1 & \(n\) & 3.23 & – \\
2 & \(\delta b_{b}\) & \(0.89\pm 0.03\) eV & \(0.93\pm 0.03\) eV \\
3 & \(R_{s}\) & 171 M\(\Omega\) & 173 M\(\Omega\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of various parameters of the Ag/PFB/p-Si device extracted from the conventional \(I\)-\(V\) and Norde’s techniques. Figure 5: The forward \(\log I\) vs. \(\log V\) graph of the Ag/PFB/p-Si device. Overall comparison of the microelectronic parameters of Ag/PFB/p-Si heterojunction and those of other organic semiconductor/p-Si devices reported elsewhere [25] are given in Table 3. The comparison reveals that the Ag/PFB/p-Si device can overtake the existing similar devices in several key microelectronic parameters. However, higher value of \(R_{s}\) might be the reason for the divergence of the diode from the ideal behavior.
# An Analytical Model for Interference Alignment in Broadcast Assisted VANETs
## 3 Model of Broadcast Assisted Transmission Based IA Application in VANETs
### Model of Interference Alignment between Vehicles and Base Station  
According to IA technology, when vehicle communicates with base station, the interference caused by other vehicles could be aligned together in the same direction to save network freedom. Since interference from the same CSI cluster cannot be aligned in the same direction [9], then for a cluster \(m\), we have following equation:  
\[\sum\limits_{i\in\mathbb{C}(m)}al_{i,j}(t)=0,\ (j=St) \tag{12}\]  
Since interference from vehicles in the same cluster cannot be aligned to each other, the number of valid signals and interference caused by vehicles in all areas should be less than the number of antennas of base station. Then, we have  
\[\sum\limits_{i\in\mathbb{C}(m)}\lambda_{\ i,j}(t)+\sum\limits_{i\in\mathbb{C} (m)}il_{i,j}(t)\leq N_{a}(j),\ (j=St) \tag{13}\]The number of interference can be aligned in same cluster cannot outnumber the total number of interference invoked in same area, we get the first constraint in (14). And the actual number of degrees of freedom consumed at base station cannot exceed its number of antennas, the second constraint of (14) should be met:  
\[\left\{\begin{array}{l}\sum\limits_{i\in C(m)}al_{i,j}(t)\leq\sum\limits_{i\
otin C (m)}il_{i,j}(t)\\ N_{a}(j)\geq\sum\limits_{i=1:N}(\lambda_{\ i,j}(t)+il_{i,j}(t)-al_{i,j}(t))\\ \end{array}\right.,(j=St) \tag{14}\]  
Symbol \(il_{i,j}(t)\) is the number of interference caused by vehicle \(i\) to station \(j\) and \(al_{i,j}(t)\) is the number of aligned interference. Then we get  
\[al_{i,j}(t)\leq il_{i,j}(t),(IL(i,j)=1,j=St) \tag{15}\]  
According to the meaning of \(il_{i,j}(t)\), when vehicle is the off state that not transmits nor receives, it will not cause or receive any interference, we get  
\[il_{i,j}(t)=0,(\delta_{\ R}^{\ j}=0,j=St) \tag{16}\]  
If vehicle is outside interference range of others, it will not be interfered by them.  
\[il_{i,j}(t)=0,(j=St,IL(i,j)=0) \tag{17}\]  
For base station, signals are treated as interference if not intended for it. Thus, we have following constraint  
\[il_{i,j}(t)\leq\delta_{\ T}^{\ i}(t)\cdot\delta_{\ R}^{\ j}(j=St,\lambda_{\ i,j}(t)=0) \tag{18}\]
## 3 Finding Models to Predict Energy Consumption
### Deep Learning Models  
As mentioned previously, we propose three deep-learning techniques for STLF: simple RNN, LSTM, and GRU. RNNs are a type of Artificial Neural Network (ANN) designed to recognise patterns in sequential data streams. In RNNs, the decision, classification, or learning done at a given moment \(t-1\) influence the decision, classification, or learning at a subsequent time \(t\) in the time series. RNNs contain two sources of input: the present and the recent past. These data are combined to determine how new data is predicted. RNNs have a memory that, for example, multi-layer perceptrons (MLP) and Convolutional Neural Network (CNN) do not. As such, RNNs use information in the sequence itself to perform tasks that other ANNs are unable to do. RNNs have limitations, the most significant of which are difficulties in training RNNs to capture long-term dependencies due to vanishing and exploding gradient problems [24; 25]. LSTM and GRU are variations of RNN that overcome such problems.  
LSTM [26] is a variation of RNN that overcomes gradient problems through the use of a chain structure containing four neural networks and different blocks of memory [27]. LSTM updates its unit states using three gates: a forget gate, an input gate, and an output gate. The forget gate deletes information that is no longer useful in the unit [27]. The current input \(x_{t}\) and the output from the previous unit \(h_{t-1}\) are multiplied by the weight matrix. The result is passed through an activation function that provides a binary output that causes the data to be forgotten. The input gate performs addition of useful information to the unit's status. First, the information is adjusted using a sigmoid function. Then, the _tanh_ function is used to create a vector that produces \(-1\) to \(+1\). Finally, the output gate completes the extraction of useful information from the current state of the unit to be displayed as an output. In order to do so, a vector is generated by applying a _tanh_ function to a cell. Due to its structure, LSTMs can predict time series with time intervals of unknown duration [28], a significant advantage over traditional RNNs. Notwithstanding this, long training times are a significant limitation [29].  
GRUs reduce the complexity of LSTMs by only utilising an update gate and a reset gate to determine how values in the hidden states are computed [25]. In GRUs, only one hidden state is transferred between the time steps [25]. This state is capable of maintaining long- and short-term dependencies at the same time. GRU gates are trained to selectively filter out any irrelevant information while maintaining what is useful. These gates are vectors containing binary values, as in LSTM, and determine the importance of the information. Crucially, research suggests that GRUs have significantly faster training times with comparable performance to LSTM [25; 29].
## 8 Conclusions  
In this paper, a low-complexity NN-based detector has been proposed to be implemented in an OFDM system. The segmented channel responses and received symbols are input to the single-layered network, which directly outputs the detected symbols. By associating the network with the assist of coherence bandwidth of the estimated channel, an algorithm is built to find optimal hyperparameters. The networks are all trained offline, which are applied for both simulations and sea trials with fixed parameters. The quantitative simulations have compared the ZF, MMSE equalizers, and the ELM-based detector with the proposed NN-based detector, and the results show that the proposed detector reaches the lowest BER in the tested UWA channels. The same results can be found in the sea trials. With the best performance among the tested equalizers and detectors, the proposed detector has lower computational complexity than MMSE and ELM-based detectors. The proposed NN-based detector needs accurate estimated channels for better performance, which leads future research to focus on the construction of the optimization of the channel estimator.  
**Author Contributions:** Conceptualization, M.Z. and J.W.; methodology, M.Z. and H.S.; software, M.Z.; validation, M.Z., X.F. and R.L.; formal analysis, M.Z.; investigation, M.Z.; resources, M.Z.; data curation, H.S.; writing--original draft preparation, M.Z.; writing--review and editing, J.W. and J.Q.; visualization, M.Z.; supervision, H.S.; project administration, H.S.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Block-Type Pilot**} & \multicolumn{2}{c}{**Comb-Type Pilot**} \\ \hline  & **BPSK** & **QPSK** & **BPSK** & **QPSK** \\ \hline Proposed NN & 0.1602 & 0.2902 & 0.0970 & 0.1196 \\ ZF & 0.1867 & 0.3009 & 0.0988 & 0.1250 \\ MMSE & 0.1865 & 0.3010 & 0.0988 & 0.1249 \\ ELM & 0.4465 & 0.4905 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 8: BER of systems tested in the shallow sea.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Subcarriers** & **Proposed NN** & **MMSE** \\ \hline
128 & 0.006185 & 0.006673 \\
512 & 0.007568 & 0.007894 \\ \hline \hline \end{tabular}
\end{table}
Table 9: BERs of different detectors with data in June 2018.  
**Funding:** This research was funded by the Key Program of Marine Economy Development Special Foundation of Department of Natural Resources of Guangdong Province (GDNRC [2023]24), the Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application, MNR, NO. 220202, National Natural Science Foundation of China (NSFC) 62271426 and the Natural Resources Science and Technology Innovation Project of Fujian KY-080000-04-2022-025.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Case Study
### IEEE RTS-24 Node Case Study System
#### 4.2.5 Impact of Transmission Line Capacity
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**0.9**} & \multicolumn{2}{c}{**0.8**} \\ \cline{3-6}  & \multicolumn{1}{c}{**Transmission Capacity**} & \multicolumn{1}{c}{**Case 2**} & \multicolumn{1}{c}{**Case 3**} & \multicolumn{1}{c}{**Case 2**} & \multicolumn{1}{c}{**Case 3**} \\ \hline  & Transmission Line Planning Scheme: Corridor & 10(1), 11(1), & 5(1), 11(1), & 5(1), 11(1), & 5(1), 11(1), \\  & (Number of New Lines) & 17(1), 18(1), 21(1) & 33(1) & 18(1), 21(1), 33(1) & 18(1), 21(1), 33(1) \\ Stage 1 & Energy Storage Planning Scheme: Node & - & 4(60.44/361.96) & - & 4(18.25/108.37) \\ Stage 1 & (MW/MWh) & 2.82 & 2.12 & 3.96 & 3.96 \\  & Transmission Line Investment Cost (\(\times 10^{8}\) CNT) & - & 3.17 & - & 0.96 \\  & Wind and Solar Curtailment Cost (\(\times 10^{8}\) CNT) & 1.65 & 0.91 & 1.67 & 1.37 \\ \hline  & Transmission Line Planning Scheme: Corridor & 4(1), 8(1), & 4(1), 8(1), & 4(1), 8(1), & 4(1), 8(1), \\  & (Number of New Lines) & 33(1) & 21(1) & 10(1), 11(1), & 22(1) \\ Stage 2 & Energy Storage Planning Scheme: Node & - & 0 & - & 4(43.98/261.15) \\ Stage 2 & (MW/MWh) & - & 0 & - & 6(14.04/100) \\  & Transmission Line Investment Cost (\(\times 10^{8}\) CNT) & 0.74 & 1.60 & 1.75 & 1.49 \\  & Energy Storage Investment Cost (\(\times 10^{8}\) CNT) & - & 0 & - & 2.32 \\  & Wind and Solar Curtailment Cost (\(\times 10^{8}\) CNT) & 2.59 & 1.49 & 2.60 & 1.31 \\ \hline  & Transmission Line Planning Scheme: Corridor & 4(1), 5(1), & 4(1), 5(1), & 4(1), 6(1), & 4(1), 8(1), \\  & (Number of New Lines) & 8(1), 15(1), & 15(1), & 15(1), 21(1), & 8(1), 9(1), & 17(1), 21(1), \\  & 21(1), 22(1) & 22(1) & 22(1) & 22(1) & 22(1) \\ Stage 3 & Energy Storage Planning Scheme: Node & - & 2(15.18/108.18) & - & 2(14.04/100.0) \\  & (MW/MWh) & - & 4(120.0/855.0) & - & 4(120/826.10) \\  & Transmission Line Investment Cost (\(\times 10^{8}\) CNT) & 2.57 & 2.26 & 2.60 & 2.26 \\  & Energy Storage Investment Cost (\(\times 10^{8}\) CNT) & - & 3.78 & - & 4.11 \\  & Wind and Solar Curtailment Cost (\(\times 10^{8}\) CNT) & 13.82 & 8.00 & 13.91 & 7.85 \\ \hline  & Total Cost (\(\times 10^{8}\) CNT) & 25.38 & 23.35 & 28.43 & 25.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of Case 2 and Case 3 planning scenarios and costs for different transmission capacities. energy storage improve compared to the multi-stage planning of a single transmission grid. This enhancement demonstrates better synergy between transmission grid planning and optimized energy storage configuration, enabling increased integration of renewable energy and meeting the low-carbon goals of the system, aligning more closely with the requirements of power system planning under the \"dual carbon\" targets.
## 5 Conclusions  
This study was conducted to establish the impact on emissions, and to a lesser extent energy consumption, of variably performing heat pump technology as observed in recent field trial data. The context for the study was the UK Government's ambitious targets for heat pump penetration in the English housing stock where all types of heating would be displaced, including relatively efficient main gas central heating boilers. In this context, heat pumps performing at efficiency levels observed in the field trial, or even at BREDEM model assumed levels, could actually lead to a rise in emissions levels due to the relatively high emissions intensity of the electricity grid. There are however modest savings to be realised from heat pumps operating at efficiencies of 450%.  
Given the aspirations for 2030, the only way to realise significant emissions reductions using heat pumps is to concurrently decarbonise the grid. However further analysis similar to that used by [PERSON] [31] is required to understand the efficacy, scale and scope of each strategy. Although outside the scope of this work, the adoption of a suitable probabilistic approach would be beneficial, such as Bayesian inference modelling used in previous studies [32,33]. In the mean time, a phased approach targeting  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline  & & & \multicolumn{3}{c}{**Reduction in CO\({}_{2}\) Emissions**} & \multicolumn{3}{c}{**Relative Reduction in Emissions**} \\  & **Grid** & **Heating** & & **From English Households (Mt)** & & **Compared** & **with Baseline Total** \\ \cline{3-8}
**Description** & **Emission** & **Technology** & **HT \&** & **HTT, Not** & & **HTT \&** & **HTT, Not** \\  & **Intensity \({}^{\text{a}}\)** & **Mix \({}^{\text{b}}\)** & **Fuel** & **Fuel** & **Total** & **Fuel** & **Fuel** & **Total** \\  & & & **Poverty** & **Poverty** & & **Poverty** & **Poverty** & \\ \hline HP COP 2.0 & Current & 100\%HP2 & 2.2 & 3.3 & 5.5 & 2.0\% & 3.0\% & 4.9\% \\ HP COP 2.5 & Current & 100\%HP2.5 & 2.8 & 4.2 & 7.0 & 2.5\% & 3.7\% & 6.3\% \\ HP COP 4.5 & Current & 100\%HP4.5 & 3.8 & 5.6 & 9.4 & 3.4\% & 5.0\% & 8.4\% \\ HP COP 2.0 & 2030 & 100\%HP2 & 5.3 & 8.0 & 13.3 & 4.8\% & 7.1\% & 11.9\% \\ HP COP 2.5 & 2030 & 100\%HP2.5 & 5.4 & 8.1 & 13.4 & 4.8\% & 7.2\% & 12.0\% \\ HP COP 4.5 & 2030 & 100\%HP4.5 & 5.5 & 8.2 & 13.6 & 4.9\% & 7.3\% & 12.2\% \\ \hline \end{tabular}  
* See Table 2 for grid emissions intensity; \({}^{\text{b}}\) HP2 = Heat Pump at COP of 2 (200% efficiency), HP2.5 = Heat Pump at COP of 2.5 (250% efficiency), _etc._  
\end{table}
Table 9: Annual emission reductions resulting from a phased heat pump installation strategy for all electrical resistive heated homes—phase 1 in fuel poverty, phase 2 the remainder.  
those properties with electric resistive heating, beginning with those in fuel poverty, could realise reasonable emissions savings, in addition to corresponding savings in electricity consumption and hence related reductions in fuel poverty.
## 1 Introduction
### Scope of This Work and Research Questions  
This study investigates the pivotal role of Moodle in online assessments, particularly focusing on how the platform's capabilities have evolved in response to the demands of the COVID-19 pandemic. This work seeks to emphasise the necessity of continuous professional development for educators and the importance of integrating advanced technological tools in teaching methods. Moreover, as stated in [2], this work aims at prioritising student well-being and academic success in digital environments.  
To do so, first, this work introduced a method to automatically evaluate and help students' learning by developing FTP for the Moodle framework. Second, this method was presented to and tested by several educators from different universities and disciplines. Surveys have been carried out before and after the attendance to the courses to train educators on FTP. The research examines the efficacy of various question formats and import methods--such as GIFT, AIKEN, and Moodle XML--for large-scale question imports. Also, by analysing trends before and after adopting FTP, the study aims to illuminate how educators have adapted their assessment strategies amidst technological advancements. Given so, the key aspects of this research include the following:  
* Evolution of assessment practices: the study explores how the COVID-19 pandemic has reshaped educators' assessment methods, emphasising the transition to online platforms and the adoption of new technologies like FTP.
* Question formats and import methods: examining different question formats and import methods provides insights into their effectiveness and ease of use for educators. This includes a comparative analysis of GIFT, AIKEN, and Moodle XML formats.
* Impact of FTP: the research focuses on the influence of FTP on educators' ability to create and utilise diverse question types, as well as its effect on the frequency of multimedia use in assessments. FTP also makes it easy to use images, which can be attractive to students.
* STEM vs. Non-STEM Practices: the study also assesses whether there are differences in assessment question types used by educators from STEM and non-STEM disciplines.  
This approach provides a comprehensive overview of current LMS trends and Moodle's evolving role in educational technology. It emphasises the impact of technological advancements, such as FTP, on assessment practices and aims to offer actionable insights for enhancing online assessments. Considering the key points, the work has addressed the following research questions (RQs):  
* RQ1: How have educators' assessment methods changed before and after COVID-19?
* RQ2: Are there differences in the assessment question types used by STEM and non-STEM educators?
* RQ3: Have the assessment question types used by educators changed before and after adopting FTP?
* RQ4: Do educators use images more frequently in different question types after implementing FTP?
###### Abstract  
Adjusting implant abutment for crown delivery is a common practice during implant installation. The purpose of this study was to compare the fracture resistance and stress distribution of zirconia specimens on four occlusal surface areas of implant abutment. Four implant abutment designs [occlusal surface area (SA) SA100, SA75, SA50, and SA25] with 15 zirconia prostheses over the molar area per group were prepared for cyclic loading with 5 Hz, 300 N in a servo-hydraulic testing machine until fracture or automatic stoppage after 30,000 counts. The minimum occlusal thickness of all specimens was 0.5 mm. Four finite element models were simulated under vertical or oblique 10-degree loading to analyze the stress distribution and peak value of zirconia specimens. Data were statistically analyzed, and fracture patterns were observed under a scanning electron microscope. Cyclic loading tests revealed that specimen breakage had moderately strong correlation with the abutment occlusal area (\(r=0.475\)). Specim breakage differed significantly among the four groups (P = 0.001). The lowest von Mises stress value was measured for prosthesis with a smallest abutment occlusal surface area (SA25) and the thickest zirconia crown. Thicker zirconia specimens (SA25) had higher fracture resistance and lowest stress values under 300 N loading.  
monolithic zirconia crown; dental implant abutment; cyclic loading; finite element analysis Article