esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 5 Simulation Results Analysis
### Performance of Uplink Communications with Hidden Nodes
#### 5.4.3 Energy Consumption  
Figure 13 shows the average current consumption by transceiver in each device during simulation time. Compared to consumption obtained without hidden nodes in the network, consumption values with hidden nodes remain far superior in almost all load values and all of the scenarios. The largest increase in current consumption with hidden nodes is due to the increase in the number of frames' retransmissions because of the collisions produced.  
Differences between current consumption obtained in the absence and presence of hidden nodes are more obvious when using, as a metric, the values for total consumed current per MAC payload byte transferred on the network; that is, the current drawn by all devices which is needed to successfully receive an application message byte in the coordinator. Table 5 shows results of current consumption per payload byte, for scenarios of four and 16 devices and load values of 50%, 100% and 200%. Values in Table 5 show an increasing consumption per byte in both scenarios as network load increases, both in the absence and presence of hidden nodes. However, with hidden nodes and network loads greater than 100%, current consumption per payload byte reaches values of magnitude  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **N** \\ **(Number of** **Devices**)** \\ \end{tabular} & \begin{tabular}{c} **Successfully** \\ **Network** \\ **Load** \\ \end{tabular} & \begin{tabular}{c} **Channel Access** \\ **Frames** \\ \end{tabular} & \begin{tabular}{c} **Frame** \\ **Channel Access** \\ **Failures** \\ \end{tabular} & \
\begin{tabular}{c} **Frames** \\ **Transmission** \\ **Failures** \\ \end{tabular} &
\begin{tabular}{c} **Collisions** \\ **Failures** \\ \end{tabular} \\ \hline \multirow{3}{*}{4} & **10\%** & 85.9\% & 0\% & 14.1\% & 41\% \\  & **50\%** & 28.61\% & 0.03\% & 71.36\% & 91.2\% \\  & **200\%** & 0.1\% & 0\% & 99.9\% & 99.9\% \\ \hline \multirow{3}{*}{16} & **10\%** & 82\% & 0\% & 18\% & 48.2\% \\  & **50\%** & 22.73\% & 0.02\% & 77.25\% & 93.3\% \\ \cline{1-1}  & **200\%** & 0.02\% & 0\% & 99.98\% & 99.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: CSMA/CA statistics, with hidden nodes.  
Figure 13: Average current consumption per device, with hidden nodes.  
higher than values obtained without hidden nodes. With hidden nodes in the network, the increase in the number of collisions with transmission load has a double effect in current consumption per payload byte. On one hand, current consumption is significantly increased, as Figure 13 shows. On the other hand, the number of received messages in coordinator is drastically reduced, as shown in message loss ratio values displayed in Figure 12.
## 5 Conclusions  
A two-step biometrics method using EMG signals based on the CNN-LSTM network was proposed. Noise in the EMG signal was removed using NF and BPF, and preprocessing was performed with RMS. After preprocessing the EMG signal, the time domain features and LSTM network were used to examine whether the gesture matched, and single biometrics was performed if the gesture matched. In single biometrics, EMG signals were converted into a two-dimensional spectrogram, and training and classification were performed through the CNN-LSTM network. Data fusion of the gesture registration and single biometrics was performed in the form of an AND, which lowered FAR, thus proving that the proposed method was less likely to misclassify unregistered subjects as registered subjects. A publicly available EMG signal dataset (Ninapro DB) was used to evaluate the performance of the proposed two-step biometrics method using EMG signals. As a result of the experiment, seventeen hand gestures were classified with an accuracy of 83.91%, and twenty people were classified with an accuracy of 99.17%. In addition, we found that the fusion of gesture recognition and single biometrics reduced the FAR, which is important in biometrics. A two-step biometrics experiment will be conducted with a greater number of subjects and hand gestures in the future, and the hand gesture recognition performance using EMG signals will be improved. In addition, check the performance according to the spectrogram image size, and the intra- and inter-individual differences depending on each gesture of the EMG signal will be examined.  
Conceptualization, J.-S.K. and S.-B.P.; methodology, [PERSON] and [PERSON]; software, [PERSON]-[PERSON]; validation, [PERSON]-[PERSON], [PERSON] and [PERSON]; formal analysis, [PERSON]-S.K.; investigation, [PERSON]-S.K.; writing--original draft preparation, [PERSON]-[PERSON]; writing--review and editing, [PERSON]-[PERSON], [PERSON] and [PERSON]; supervision, S.-B.P.; project administration, S.-B.P.; funding acquisition, S.-B.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2017R1A6A1A03015496), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2021R1A2C1014033), and the HPC Support Project, supported by the Ministry of Science and ICT and NIPA.  
Not applicable.  
The data supporting the findings of the article are available in the Ninapro DB2 at [29].
# An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks
## 4 Simulation Results and Evaluations
### Simulation Results
#### 4.2.3 Network Coverage  
One of the most important factors of the quality of service (QoS) of WSNs is network coverage. The selection of the best set of CHs requires maximum coverage. The maximum area coverage detected by WSN in forest fire application is necessary, because these areas might not be appropriate for the nodes due to their uniform random distribution. In other words, as mentioned before, the goal of this measure is to reduce the number of non-CH nodes. In order to evaluate the network coverage properly, an environment of \(100\times 200\) is graded in squares of \(10\times 10\) (dividing the area into smaller rectangles), as shown in Figure 11. Thus, the maximum number of graded points is 231, and the number of grids is 200. The goal of this division is to minimize the number of uncovered grids.  
Figure 11: Dividing the area into smaller rectangles for coverage.  
Figure 10: The number of live nodes for sink locations (**a**) (100, 100), (**b**) (0, 100), (**c**) (50, 150), and (**d**) (50, 200).  
As can be seen in Figure 12, inspired by Figure 11, the proposed method outperforms PSO-HSA by about 1.08-1.32 times and TPSO-CR by about 1.18-1.34 times for the different sink locations. It is observed that the LEACH algorithm in round 16 has a network coverage of zero, which is much lower than the other methods. In general, the numerical results in Figure 12 demonstrate that the proposed method can cover the number of non-CHs more efficiently than PSO-HSA and TPSO-CR.
###### Abstract  
In this paper, 1 \(\times\) 2 MIMO of Palm Tree Coplanar Vivaldi Antenna is presented that simulated at 0.5-4.5 GHz. Some GPR applications require wideband antennas starting from a frequency below 1 GHz to overcome high material loss and achieve deeper penetration. However, to boost the gain, antennas are set up in MIMO and this is costly due to the large size of the antenna. When configuring MIMO antenna in the E-plane, there is occasionally uncertainty over which antenna model may provide the optimum performance in terms of return loss, mutual coupling, directivity, beam squint, beam width, and surface current using a given substrate size. However, the configuration of E-plane antenna in MIMO has an issue of mutual coupling if the distance between elements is less than 0.5a. Furthermore, it produces grating lobes at high frequencies.We implement several types of patch structures by incorporating the truncated, tilt shape, Hilbert and Koch Fractal, Exponential slot, Wave slot, the lens with clips, and metamaterial slot to the radiator by keeping the width of the substrate and the shape of the feeder. The return loss, mutual coupling, directivity, beam squint, beamwidth, and surface current of the antenna are compared for 1 \(\times\) 2 MIMO CVA. A continuous patch MIMO has a spacing of 0.458A at 0.5 GHz, which is equivalent to its element width. From the simulation, we found that Back Cut Palm Tree (BCPT) and Horizontale Wave Structure Palm Tree (HWSPT) got the best performance of return loss and mutual scattering at low-end frequency respectively. The improvement of directivity got for Metamaterial Lens Palm Tree (MLPT) of 4.453 dBi if compared with Regular Palm Tree-Coplanar Vivaldi Antenna (RPT) at 4 GHz. Elips Lens Palm Tree (ELPT) has the best beam squint performance across all frequencies of 0\({}^{\circ}\). It also gets the best beamwidth at 4.5 GHz of 3.320. In addition, we incorporate the MLPT into the radar application.  
Keywords:bandwith; Coplanar Vivaldi Antenna; mutual coupling; radiation pattern +
Footnote †: journal: _electronics_
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 3 Related Work
### Adaptive Approaches
#### 3.2.3. Atas  
The authors of [11] propose an autonomous traffic-aware scheduler (ATAS). Through their approach, cells are allocated dynamically based on the traffic load. If the number of remaining packets in the queue is high enough, N consecutive transmitting cells areallocated. N is computed based on the size of the slotframe. Since transmit and receive cells should be synchronized, the value of N is sent in the footer of the next packet. The node that receives the packet adjusts the number of receive cells accordingly. In this approach, the traffic is also differentiated based on priority by adding the low priority packets at the tail of the queue and the high priority packets at the head of the queue. The authors evaluate their implementation in simulation and in real, based on three performance metrics: the average end-to-end delay, the average PDR, and the average duty cycle. Compared to RBS Orchestra, this approach improves the end-to-end delay and prioritizes the critical traffic.
# Role of Restored Underwater Images in Underwater Imaging Applications
## 4 Results
### Results for Edge-Detection Application  
Sobel edge-detection operator is used to detect the edges in an image. Both the raw and restored underwater images shown in Figure 3 are passed through the Sobel edge-detector operator, with results given in Figure 4. Results of edges detected from the raw underwater images of Figure 4a-e, are obtained from passing the raw underwater images in Figure 3a-e, through the Sobel edge-detection operator, respectively, whilst results of edges detected from restored images of Figure 4a-e are obtained from passing the restored images in Figure 3a-e through the Sobel edge-detection operator, respectively.  
The number of edges detected on the raw image in Figure 4a is less than the edges detected on the restored image, with edges of the coral reefs not properly detected on the raw image. This is in comparison to the clear detection of the edges of coral reefs in the restored underwater image in Figure 4a. The clear number of edges detected when using the restored underwater images points to the necessity of the proposed restoration method for underwater image processing.  
In the raw image of Figure 4b, only the edges of the front part of the ship are detected, whilst after processing with the proposed method, the number of edges detected increases. The edges of the front as well as the back part of the ship can be detected using the processed image; showing the efficiency of the proposed method in improving the feature details of the objects in the image by reducing blurriness, as shown in the restored result of Figure 4b.  
The edges of raw image are almost not detected in Figure 4c, due to the blurriness of the raw image. Since the proposed method has the ability to reduce blurriness and improve texture details, the edges detected in the recovered image of Figure 4c are remarkably improved; the coral reefs as well as the fishes can be clearly detected in the restored underwater image.  
For images from Figure 4d, it can be seen from the raw image that only a limited number of edges has been detected; particularly, the small sand and stone particles cannot be detected due to poor clarity of the raw image. On the other hand, the edges of the fish, sand, and small particles are clearly detected on the restored image, as can be seen in edges detected in Figure 4d, proving that the proposed method can be used in underwater feature detection applications.  
The raw image of Figure 4e, which is heavily affected by backscattering effect, has a very limited number of edges detected; with the middle part of the image containing the turtle, which is almost not detected at all. On the other hand, the edge detection is  
Figure 3: Test images taken with (**a**) Naturally lit, (**b**) Bluish nature (**c**) Greenish nature (**d**) Artificial light source, and (**e**) Backscattered image.  
very good in the restored image, and consequently, it can be said that proposed method is efficient and can be used for underwater edge detection applications.
## 3 Results
### Sampling Points Crossing the Significant Rayleigh's \(Z\) Value  
Next, we investigated which of the sampling points in the 800 ms interval per individual subject crossed the significant Rayleigh's \(Z\) value (>2.9957). When the prediction length was increased to 800 ms, the performance of both methods declined (Table 1). Amongst the 21 participants, 15 subjects showed higher crossing values with the LMS-based AR model than with the AR model. A significant difference was observed between the two methods for time points <400 (Table 2). At 64 ms, the AR model surpassed the LMS-based AR model, whereas for the 128 ms, 256 ms, and 340 ms, the LMS-based AR model outperformed the AR model. For time points greater than 400 ms, there was no significant difference between the two methods, and their performances declined.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Subjects**} & \multicolumn{3}{c}{**Results of Channel O1 800 ms**} \\ \cline{2-3}  & **Autoregressive (AR) Model** & **Least Mean Square (LMS)-based AR Model** \\ \cline{2-3}  & **Time Points** & **Time Points** \\ \hline
1. & 424 & 446 \\ \hline
2. & 368 & 392 \\ \hline
3. & 536 & 550 \\ \hline
4. & 492 & 484 \\ \hline
5. & 382 & 416 \\ \hline
6. & 420 & 578 \\ \hline
7. & 452 & 456 \\ \hline
8. & 678 & 752 \\ \hline
9. & 392 & 370 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time points crossing the significant [PERSON]’s PLV for channel O1 for both the autoregressive (AR) model and the least mean square (LMS)-based AR model. Asterisk (*) means that out of 800 ms, the particular channel did not cross the significant value (>2.9957).  
Figure 7: Statistical Comparison of the AR model and the LMS-based AR model for the twice prediction length (256 ms). Asterisk (*) shows significant p-value. (**A**) Mean PLVrz at 128 ms; (**B**) Mean PLVrz at 256 ms. Error bars indicate the standard deviation of mean.
# Improving the Efficiency of Energy Consumption in Buildings: Simulation of Alternative EnPC Models
## 2 State of the Art
### Energy Service Companies (ESCOs)  
As mentioned earlier, EnPCs are signed between customers and providers, with the former consisting of public or private entities, and the latter typically consisting of private companies that are titled Energy Service Companies (ESCOs) [16].  
According to the European Commission (2012), an ESCO is: \"A natural or legal person that provides energy services and/or other energy efficiency improvement measures in a users' facility or facilities, and accepts some degree of financial risk in doing so. Payment for the service provided is based (in whole or in part) on the achievement of energy efficiency improvements and compliance with other agreed performance criteria\". The type of service provided by ESCOs has been identified by several experts and scholars as a high-potential alternative for meeting consumers' energy needs in a more sustainable way than is currently the case [15].  
Typically, an ESCO (which must be selected through a public tendering procedure) is responsible for the implementation of measures providing the knowledge required for effective energy efficiency improvements, and also for monitoring the contract during the respective period. If it does not guarantee the energy savings set out in the contract between both parties, the ESCO may not receive payment for the services rendered [17]. The investment made by ESCOs can be made through funds from the companies themselves or through mechanisms provided by a financial institution [17].
## 3 5G HetNet Services, Architecture, and Related Technologies
### 5G Technologies
#### 3.3.5 Full-Duplex Communication in 5G HetNet  
Full-Duplex (FD) communication is a key aspect of 5G HetNet that allows for the simultaneous transmission and reception of data on the same frequency band, delivering a range of benefits that are crucial to achieving in 5G [69]. One of the key advantages of FD communication is increased network capacity, as it eliminates the need for separate frequency bands for transmission and reception. This results in efficient use of the available spectrum and improved SE, leading to higher data rates and better network performance [70]. Additionally, FD communication provides real-time bidirectional communication, which is critical for supporting new 5G use cases such as augmented reality, virtual reality, and IoT applications. Furthermore, FD communication reduces latency, making it an ideal solution for applications that require low-latency communication. It also eliminates time-division multiplexing, which can result in increased reliability and reduced errors in the transmission of data [70; 71].  
FD communication also offers more flexibility and dynamic network configurations, enabling the network to adapt to changing traffic patterns and support new use cases. The bidirectional nature of FD communication also provides an additional layer of security, verifying the identity of the communicating parties in real-time. Interference is reduced as FD communication eliminates the need for frequency division, improving overall network performance. The use of a single frequency band also reduces the need for multiple frequency bands, resulting in cost savings associated with acquiring and managing spectrum licenses. Ultimately, FD communication delivers higher data rates, lower latency, and improved reliability, resulting in an improved user experience for subscribers and better support for new 5G use cases. In conclusion, FD communication is an essential component of 5G HetNet and will play a critical role in shaping the future of wireless communication.
## 4 System Model
### Two-Level Rolling Optimization
Meanwhile Equation (22) also shows that the prediction horizon is two hours, and it is a long-term optimization. Second (Step 2), when \(t_{s}=1\), the condition is Case 1. MPC 2 does not make system decisions, and MPC 1 takes charge of making decisions. The MPC 1 controller obtains information (except for the pumped hydro storage information). MPC 1 then makes optimal decisions (except for pumped hydro storage decisions) and performs actions based on the decisions. Note that the MPC 1 controller cannot be in charge of the pumped hydro storage. The main purpose of the second step is to carry out a short-term and a look-ahead dispatch when the system continues to work. The short-term rolling optimization is used to deal with the turbulences by controlling the fly wheel. In Case 1, the pumped storage participated in scheduling; however, the power value of pumped storage was not decided in  
Figure 7: Two-level MPC performing rolling optimization. Case 1, and it is up to Case 0. In Case 1, the decision process of the pumped storage can be described by the following equation:  
\[\text{Case1}:p_{t=0}^{\text{px}}=p_{t=1}^{\text{px}}=p_{t=2}^{\text{px}}=p_{t=3}^ {\text{px}}=p_{t=4}^{\text{px}}=p_{t_{-}initial}^{\text{px}} \tag{23}\]  
where \(p_{t_{-}initial}^{\text{px}}\) is the pumped hydro storage operating state at the beginning of the half-hour period. \(p_{t=0}^{\text{px}}=p_{t=1}^{\text{px}}=p_{t=2}^{\text{px}}=p_{t=3}^{\text{px }}=p_{t=4}^{\text{px}}=p_{t_{-}initial}^{\text{px}}\) means that, for this instance of rolling optimization, the pumped storage decision variable is constant at the initial time. In Equation (23), the prediction horizon is 25 min, this indicated that the computation burden is significant less than that of Case 0. This is main reason that two-level MPC is adopted to controlling wind farm and hybrid storages. Third (Step 3), when \(t_{s}\) is in the range of 2-5, the conditions are Cases 2-5, respectively, and MPC 1 and MPC 2 operate as in Step 2. Finally, when \(t_{s}=6\), a new 30 min period starts, and MPC1 and MPC 2 repeat Step 1 (Case 0). The relationship between \(t_{s}\) and the cases is expressed by Equation (1). Figure 8 shows the work sequence of MPC1 and MPC2. In Figure 8, when \(t_{s}=0\), \(t_{s}=6\), \(t_{s}=12\ldots\), a new 30 min period has arrived, and at these scheduling intervals, MPC 2 starts to work, while MPC 1 stops working. When \(t_{s}\) is in the range of 1-5, 7-11,..., MPC 2 stops working, and MPC 1 starts to work. Note that at any scheduling interval, only one MPC controller is working.