esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Future Research Directions
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Design Aspect** & & **Guidelines Based on Existing Literature** & **Related Research Questions** \\ \hline Communicating Automation State & \(\bullet\) & It should be assumed that drivers are disengaged from the driving task during highly automated driving [42]. & \(\bullet\) & How should information regarding automation state be presented to a driver? \\ Automatic State & \(\bullet\) & Indicate status of vehicle control to driver using effective cues that allow appropriate response times (i.e., whether the vehicle is in control of specific functions or not) [86,122]. & \(\bullet\) & What information does a driver need regarding the state of vehicle automation? \\ \hline Displaying Information & \(\bullet\) & HUDs should be leveraged to reorient drivers to roadway information [128]. & \(\bullet\) & Should the presentation of safety-critical vs. non-safety critical information be different? \\ \multirow{2}{*}{Driver Interaction} & \(\bullet\) & Displays should optimize information to be informative with minimal display clutter [54]. & \(\bullet\) & Given the potential to increase visual clutter, should non-safety-related information be presented via perceptually augmented displays? \\ \hline Driver Interaction & \(\bullet\) & Display content should minimize driver off-road glance time such that messages are easily interpreted without requiring the driver to look away from the road for extended periods of time [7]. & \(\bullet\) & Does non-safety-related information significantly impact driver off-road glance time? \\ \multirow{2}{*}{Driver Interaction} & \(\bullet\) & Visual messages and menus should also allow for auditory navigation to facilitate interaction [122]. & \(\bullet\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._automated driving can result in driver disengagement due to passive monitoring of the roadway. As such, it is necessary to investigate how drivers behave during automated vehicle control and how their capability to perceive and attend to in-vehicle messages is affected by disengagement from the driving task. Furthermore, future work should explore whether the current standards of road signage can be translated to in-vehicle display design or whether new formats of exit information could improve driver performance. The structure of the current road signage should be evaluated for effective in-vehicle display, particularly when the driver is not in control of the vehicle and not attending to information external to the vehicle. The timing of takeover notifications, relative to non-safety-related messaging, should also be explored to determine the minimum presentation time of each message for drivers to perceive and respond effectively without compromising driving safety.
## 3 Results
### Economic Optimum Systems of the Five Cases
#### 3.1.3. Costs (NPCs) of the Economic Optimum Systems
On the contrary, in four of the five \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Battery Type**} & **System** & **Battery** & **PV Generator** & **Diesel NPC** \\  & & **NPC (t)** & **NPC (t)** & **NPC (t)** & **NPC (t)** & **(t)** \\ \hline \multirow{2}{*}{Winery} & Lead-acid \({}^{1}\) & 353,233 & 79,278 & 106,218 & 97,688 \\  & Li-ion & 365,817 & 70,252 & 94,975 & 130,200 \\ \hline \multirow{2}{*}{Irrigation} & Lead-acid \({}^{1}\) & 350,180 & 82,788 & 118,667 & 76,717 \\  & Li-ion & 353,574 & 14,377 & 118,667 & 132,072 \\ \hline \multirow{2}{*}{Pig farm} & Lead-acid \({}^{1}\) & 726,707 & 252,836 & 239,935 & 111,629 \\  & Li-ion & 769,218 & 277,890 & 229,494 & 153,535 \\ \hline \multirow{2}{*}{Single-family home} & Lead-acid \({}^{1}\) & 46,070 & 16,927 & 13,862 & 7953 \\  & Li-ion & 42,893 & 10,926 & 13,862 & 10,653 \\ \hline \multirow{2}{*}{Second home} & Lead-acid & 25,189 & 5465 & 10,650 & 2951 \\  & Li-ion \({}^{1}\) & 23,386 & 3706 & 10,650 & 2867 \\ \hline \hline \end{tabular} \({}^{1}\)Absolute optimum.  
\end{table} 
Table 11: NPCs of the economic optimum hybrid systems. Figure 9: Variations of NPC of the systems and their components in the economic optimum systems with Li-ion in relation to lead-acid battery: (**a**) PV systems; (**b**) hybrid. cases, the higher fuel consumption by diesel genset caused their NPCs to increase, between +33% and +72%. As an exception, for the reasons already explained, in the second home the diesel NPC decreased slightly (\(-2.8\%\)). Finally, in two of the five cases (single-family home and second home), the absolute optimal hybrid system included a Li-ion battery.
# Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks +
## 4 Proposed Solution
### Addition of the DMRS Symbol  
By reducing the beam diameter, the residual Doppler shift of 2100 Hz is reduced to 950 Hz. However, this method also has its problems. The required SNR increases because residual Doppler shift is still present, resulting in lower link performance than without the Doppler shift. Cellular NB-IoT improves link performance through excessive repetition. However, if the NB-IoT is repeated excessively, the time delay increases and the data rate decreases. Therefore, we try to solve this problem by adding more _DMRS_ symbols instead of increasing the number of repetitions. The _DMRS_ symbol addition per slot suppresses link performance degradation even with residual Doppler shifts.  
We suggest increasing the number of _DMRS_ symbols (\(N_{darts}\)) per slot to two or three. Figure 10a shows the NB-IoT resource grid with two _DMRS_ symbols per slot, where the _DMRS_ symbols are arranged in the fourth and seventh symbols. This arrangement is because the CP length of the first symbol is longer than that of the other symbols. Therefore, placing the _DMRS_ symbol on the seventh symbol gives better results in terms of link performance. Figure 10b shows a resource grid of three _DMRS_ symbols per slot. Adding this shows that the performance is suitable only when the number of _DMRS_ symbols is appropriately increased.  
Figure 9: Doppler shift and maximum coverage size compensable by NB\(-\)IoT receivers. (**a**) Residual Doppler shift, (**b**) Target position
# De Novo Transcriptome Identifies Olfactory Genes in _Diachassimomorpha Ionigicaudata_ (Ashmead)
## 5 Conclusions  
In conclusion, we assembled the first transcriptome for _D. longicaudata_ using transcriptome sequencing and identified 43 OBPs, 69 CSPs, 60 GRs, 689 ORs, 26 IRs, and 14 SNMPs. Further, 9 OBPs, 13 CSPs, 3 GRs, 4 IRs, 25 ORs, and 4 SNMPs were differentially expressed in the insects in response to fruit flies with different scents. Our findings provide a basis towards understanding the molecular mechanisms of _D. longicaudata_ in the detection of chemosensory cues. Additionally, the sequences including olfactory genes, noncoding genes, and miRNAs identified in this study can be used in the future and benefit other researchers in this field.  
The following are available online at [[http://www.mdpi.com/2073-4425/11/2/144/s1](http://www.mdpi.com/2073-4425/11/2/144/s1)]([http://www.mdpi.com/2073-4425/11/2/144/s1](http://www.mdpi.com/2073-4425/11/2/144/s1)), Figure S1: Multiple sequence alignment of OBP sequences identified in this study. It showed the conserved six Cys (C1-C6) in these sequences (highlighted in yellow and marked with *). Table S1: miRNA precursors identified in the parasitoid wasp transcriptome. Table S2: Expression profile of transcripts encoding olfactory proteins. Table S3: OBP and CSP sequences for the phylogenetic analysis. Table S4: Differentially expressed transcripts in the parasitoid wasps of fruit flies fed with different fruits.  
Conceptualization, H.Z. and W.L.; methodology, L.T. and J.L.; software, Y.Y.; validation, L.T., J.L. and H.Z.; formal analysis, L.T.; investigation, H.Z.; resources, J.L. and L.L.; data curation, J.L. and Y.Y.; writing--original draft preparation, L.T. and J.L.; writing--review and editing, H.Z. and W.L.; visualization, J.L.; supervision, H.Z.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (31560531) and Guangxi Innovation-driven Projects (AA17202017-2).  
**Acknowledgments:** We acknowledge all the members from the Key Laboratory of Intergrated Pest Management on Tropical Crops, Ministry of Agriculture, Environment and Plant Protection Institute, Chinese Academy of Tropical Agricultural Sciences for their helpful discussions and kindness opinions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
The Copernicus Climate Change Service (C3S) includes estimates of Essential Climate Variables (ECVs) as a series of Climate Data Records (CDRs) derived from satellite data. The C3S Surface Albedo (SA) v1.0 CDR is composed of observations from National Oceanic and Atmospheric Administration (NOAA) Very High Resolution Radiometers (AVHRR) (1981-2005), and VEGETATION sensors onboard Satellites for the Observation of the Earth (SPOT/VGT) (1998-2014) and Project for Onboard Autonomy satellite (PROBA-V) (2014-2020), and will continue with Sentinel-3 (from 2020 onwards). The goal of this study is to assess the uncertainties associated with the C3S PROBA-V SA v1.0 product, with a focus on the transition from SPOT/VGT to PROBA-V. The methodology followed the good practices recommended by the Land Product Validation sub-group (LPV) of the Working Group on Calibration and Validation (WGCV) of the Committee on Earth Observing Satellites (CEOS) for the validation of satellite-derived global albedo products. Several performance criteria were evaluated, including an intercomparison with National Aeronautics and Space Agency (NASA) MCD43A3 C6 products. C3S PROBA-V SA v1.0 and MCD43A3 C6 showed similar completeness but had higher fractions of missing data than C3S SPOT/VGT SA v1.0. C3S PROBA-V SA v1.0 showed similar precision (\(\sim\)1%) to MCD43A3 C6, improving the results of SPOT/VGT SA v1.0 (2-3%), but C3S PROBA-V SA v1.0 provided residual noise in the near-infrared (NIR). Good spatio-temporal continuity between C3S PROBA-V and SPOT/VGT SA v1.0 products was found with a mean bias between \(\pm\)2%. The comparison with MCD43A3 C6 showed positive mean biases (5%, 8%, and 12% for visible, NIR and total shortwave, respectively). The accuracy assessment with ground measurements showed a median error of 18.4% with systematic overestimation (positive bias of 11.5%). The percentage of PROBA-V retrievals complying with the C3S target requirements was 28.6%.  
surface albedo; validation; PROBA-V; C3S; CEOS LPV; SPOT/VGT; MCD43
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 2 Communication Network Architecture for Smart Metering
#### Smart Metering Application and Communication Requirements  
According to previous studies [5; 12; 14], mainly NB-PLC has been used in smart metering roll-out in Europe.  
According to other studies [11; 15], Smart Metering applications in NAN and FAN include demand response, load management, prepaid services, user information, real-time pricing and control, outage management, remote meter reading, firmware update, prepayment, TOU pricing, and service switch operation. Network requirements for Smart Metering vary according to application type: on-demand, scheduled (e.g., 1 min, 15 min), or bulk meter reading. The article [15] also analyzed typical data sizes, latency, and reliability of these Smart Metering applications.  
For these Smart Metering applications, the communication requirements are:  
* Reliability >98%.
* Latency: seconds.
* Typical data size: 100-2400 bytes/per meter with sampling 1 min or 15 min.
* Data rate 100 kbps-10 Mbps.
* Transmission from a large number of smart meters.
* Low cost.
* Simplicity.
* Secure communication and data privacy.  
If we consider only scheduled remote meter reading for Smart Metering requirements [3; 5], a NB-PLC provides adequate data rates. On the contrary, BB-PLC provides solution for broad-bandwidth requirements to fulfil the evolution of all Smart Metering applications (real-time). Therefore, this article focuses on the communication potential of BB-PLC technology for fulfilling all Smart Metering requirements useable for real-time Smart Metering applications.
# A Baseband Wireless Spectrum Hypervisor for Multiplexing Concurrent OFDM Signals
## 7 Simulation Results
In the case of vPHY#1, the useful subcarriers are mapped into the Hypervisor's subcarriers every 2 points, resulting in a subcarrier frequency spacing of 7.5 [KHz]. In the case of vPHY#2, the useful subcarriers are mapped consecutively into the Hypervisor's subcarriers, which results in a subcarrier frequency spacing of 3.75 [KHz]. The receiver side for receiving the signal of vPHY#0 works as before, since we respect the  
Figure 14: BER curves for a setup where 12 vPHYs concurrently have their signals multiplexed by the hypervisor and use: (**a**) QPSK, (**b**) 16 QAM and (**c**) 64 QAM modulation schemes. subcarrier frequency spacing of 15 [KHz]. For receiving the signal of the other 2 vPHYs the receiver would need to use FFT with lengths of 512 and 2048 and assume subcarrier frequency spacing of 7.5 and 3.75 [KHz], respectively. Figure 16 shows uncoded BER results for the setup used for plotting Figure 15 where the 3 vPHYs with different but correlated numerologies concurrently have their signals multiplexed by the hypervisor. These 3 vPHYs employ QPSK, 16 QAM, and 64 QAM modulation schemes for each one of the simulation results presented in the figure. In each one of the sub-figures, we compare the achieved BER of the 3 vPHYs against the BER achieved by a single PHY in AWGN channel. The single PHY and each one of the 3 vPHYs modulate 72 subcarriers by using QPSK, 16 QAM, and 64 QAM modulation schemes. For each SNR point, \(1\times 10^{6}\) iterations were run, where the total number of wrongly decoded bits and transmitted bits were calculated for the BER calculation. As expected, the vPHY BER curves exactly match the single PHY BER curve, meaning that there is no interference between the current transmissions. Additionally, it is important to highlight that even though the vPHYs have different numerologies the Hypervisor is able to multiplex the signals so that the different receivers can successfully decode the signals. Moreover, it is also worth mentioning that all curves match the theoretical BER curves, which are approximated by (15). Again, as expected, the results presented in Figure 16, show that the proposed architecture provides perfect isolation among all the signals being multiplexed, even if they have different but correlated numerologies. This perfect isolation is due to the orthogonality provided by the IFFT processing in the hypervisor, which guarantees that every single subcarrier, spaced of \(\Delta_{f}=3.75\) [KHz], is mutually orthogonal to all other ones. Figure 15: Frequency-domain representation of the wide baseband signal generated by the hypervisor for 3 vPHYs with uncorrelated numerologies.
## 4 Demanding Applications of Blockchain Technology and the Motivation behind Them
### Increasing Cross-Border Remittances and Reducing Trans-Border Transaction Costs  
A major problem facing banks and other organizations is the high cost of cross-border transactions. In most cases, models take three days or longer to complete these transactions. Cryptocurrencies and blockchain technology are now used by companies such as Ripple to overcome these barriers. There are now over 40 countries on six continents on Ripple's network. Blockchain technology allows cross-border transactions to be completed at near-real-time, at a fraction of the cost.
# Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest
## 5 Results and Discussion
### Feature Importance Analysis Using RF  
Basically, the RF algorithm allows evaluating the importance of input parameters. The importance of each predictor variable was measured as the change in the prediction accuracy (by an increase in mean square error--MSE), computed by permuting (value randomly shuffled) the variable with out-of-bag data in the random forests validation approach [72]. A more significant percentage of increase in mean  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**RF Model**} & \multirow{2}{*}{**MVR**} & \multirow{2}{*}{\begin{tabular}{c} **[PERSON]** \\ **(1976)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **[PERSON] and** \\ **[PERSON]** \\ **(1982)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **[PERSON]** \\ **(1995)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **[PERSON]** \\ **(2008)** \\ \end{tabular} } & \multirow{2}{*}{**AJJ (2004)**} \\ \hline R\({}^{2}\) & 0.866 & 0.702 & 0.467 & 0.485 & 0.334 & 0.391 & 0.611 \\ MAE (kN) & 2.924 & 94.267 & 77.830 & 280.727 & 312.297 & 340.668 & 205.721 \\ RMSE(kN) & 98.161 & 183.046 & 224.500 & 340.606 & 483.335 & 400.470 & 265.531 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Summary of different pile bearing capacity prediction methods  
Figure 9: Comparisons of pile bearing capacity prediction methods.  
square error indicates higher importance of a given variable in the prediction process [73]. The total sum percentage of the increase in the MSE of all variables is equal to 100%.  
Amongst the 10 variables used to predict the bearing capacity of pile with RF, the average SPT blow along the embedded length of the pile (N\({}_{\text{s}}\)) was the most important variable, as an increase in MSE of 33% was noticed (Figure 10). Indeed, N\({}_{\text{s}}\) is an important indicator of predicting pile-bearing capacity, and it is related to the ultimate friction along the pile shaft. The pile tip elevation \(Z_{\text{m}}\) was the second important variable, confirmed by an increase in MSE of 25%. From a soil mechanic point of view, it meant that with a little change in the soil properties, the pile tip elevation played an important role in the bearing capacity of pile; such a variable is involved in the pile tip resistance. The variables L\({}_{2}\), L\({}_{1}\), and E\({}_{\text{t}}\) were ranked as the third to the fifth important predictors, with an increase in MSE, ranging from 5% to 19% (Figure 10). Other predictor variables included in the model (\(Z_{\text{g}}\), N\({}_{\text{t}}\), Z\({}_{\text{p}}\), L\({}_{3}\), and D) had lower than 5% of the increase in MSE. This observation was also in good agreement with MVR results, where the coefficients of input variables L\({}_{3}\) and Z\({}_{\text{p}}\) were equal to 0, while D had a relatively small coefficient of 2.33 (see Table 8).
# Transformer-Based Cascading Reconstruction Network for Video Snapshot Compressive Imaging
###### Abstract  
Video Snapshot Compressive Imaging (SCI) is a new imaging method based on compressive sensing. It encodes image sequences into a single snapshot measurement and then recovers the original high-speed video through reconstruction algorithms, which has the advantages of a low hardware cost and high imaging efficiency. How to construct an efficient algorithm is the key problem of video SCI. Although the current mainstream deep convolution network reconstruction methods can directly learn the inverse reconstruction mapping, they still have shortcomings in the representation of the complex spatiotemporal content of video scenes and the modeling of long-range contextual correlation. The quality of reconstruction still needs to be improved. To solve this problem, we propose a Transformer-based Cascading Reconstruction Network for Video Snapshot Compressive Imaging. In terms of the long-range correlation matching in the Transformer, the proposed network can effectively capture the spatiotemporal correlation of video frames for reconstruction. Specifically, according to the residual measurement mechanism, the reconstruction network is configured as a cascade of two stages: overall structure reconstruction and incremental details reconstruction. In the first stage, a multi-scale Transformer module is designed to extract the long-range multi-scale spatiotemporal features and reconstruct the overall structure. The second stage takes the measurement of the first stage as the input and employs a dynamic fusion module to adaptively fuse the output features of the two stages so that the cascading network can effectively represent the content of complex video scenes and reconstruct more incremental details. Experiments on simulation and real datasets show that the proposed method can effectively improve the reconstruction accuracy, and ablation experiments also verify the validity of the constructed network modules.  
video compressive sensing; convolutional neural network; transformer; cascading reconstruction; residual measurement Article  
16 March 2023  
1J.-cB, [PERSON]  
1 Jiangsu Big Data Analysis Technology Laboratory, Digital Forensics Engineering Research Center of the Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China; [EMAIL_ADDRESS] (H.J.); [EMAIL_ADDRESS] (K.H.); 2021 College of Automation, Southeast University, Nanjing 210096, China; [EMAIL_ADDRESS]
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 3 Threats and Challenges in Underwater Wireless Sensor Networks (UWSNs)
### Wormhole Attack  
In a wormhole attack, two distant malicious nodes are commonly involved, which constitute a wormhole attack tunnel. As illustrated in Figure 8a, A malicious attacker (labeled as W1) captures packets from source node N1 and uses a faster tunnel to send the packets to another attacker node (labeled as W2) which delivers the packets to the destination node N4.  
Figure 8: Wormhole attack; (a) normal route; (b) wormhole route.  
Figure 7: Sinkhole attack.  
As shown in Figure 9, the wormhole tunnel can be a RF link (above the water surface) or wired link, which is much faster than underwater acoustic channel [45]. This attack can prevent the source node from discovering and selecting other legitimate routes and thus disrupts network functionality. Similar to the black hole attack, a wormhole attack can attract packets forwarding and severely disturb normal routing. Wormhole attack detection and countermeasures have attracted many research studies. In [46], a wormhole attack was simulated in NS 2. The work selected the existing wired object link in the NS 2 wired simulation package to simulate the \"tunnel\" by copying the local packets and directly sending them to the wormhole object installed in a remote node. A secure localization algorithm for UWSNs is brought out in [47], which is based on the constraints of propagation distance and reputation values. The anchor nodes evaluated the reputation of paths to other anchor nodes and broadcast these reputation values to the network, and unknown nodes select credible anchor nodes with a high reputation. In [48], a two-tier localization scheme is proposed to identify short-range wormholes instantly, and long-haul wormholes within a limited latency. A distributed approach to detect and mitigate wormhole attacks is identified in [49], and an analytical model is provided to capture the interactions between various contributing parameters. In [50], a distributed visualization of wormhole is proposed to detect wormhole attacks. However, it is not feasible for large-scale or high-density UWSNs, since the reconstruction of the network would cause large energy consumption.