esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods and Modeling
### Traffic Alerting Model  
The traffic alerting model mapped a regression result to a certain warning level. We defined \(H_{\text{Min}}\) and \(H_{\text{Max}}\) equal to the maximum and minimum values of the forecasting traffic incident heat in a normative data sample, respectively. All other values were mapped within the range of 0-1 based on this maximum and minimum value as follows:  
\[H_{i}^{\prime}=\ \frac{H_{i}-H_{\text{Min}}}{H_{\text{Max}}-H_{\text{Min}}} \tag{2}\]  
Note that \(H_{i}\) represents each traffic incident heat and \(H_{i}^{\prime}\) represents the normalized traffic incident heat.  
The traffic incidents at the highest level of early warning rarely occur. If we divided the level equally, the highest warning level will be easily triggered. To reflect differences among these warning levels, we were inspired by the JCR Journal Partition designed by the National Science Library, Chinese Academy of Sciences ([[http://www.fenquiao.com](http://www.fenquiao.com)]([http://www.fenquiao.com](http://www.fenquiao.com))) for which the top academic journals in the first level only account for 5%. The warning levels are defined as follows.  
First, the total number of forecasted city-level traffic incidents was defined as \(n\), and all their forecasting heat values were ranked in reverse order. The traffic incidents with top 5% forecasting heat values were regarded as level 1 warning incidents. The heat value threshold of level 1 warning incidents was defined as \(H_{t_{1}}\) where \(t_{1}=[n\times 0.05]\). Then let \(S\) equal the total number of heat values for the remaining 95% of traffic incidents, which is represented as the following formula:  
\[S=\sum_{i=t_{1}+1}^{n}H_{i}^{\prime} \tag{3}\]  
We then checked the remaining 95% traffic incidents' heat values, denoting the heat values interval of level 2 warning incidents as \([H_{t_{2}},H_{t_{1}})\). Starting from an index of \(t_{1}+1\), the reverse-ranked traffic heat values were added one by one until the cumulative value was greater than or equal to \(S/3\). The low border index of level 2 warning interval was defined as \(t_{2}\), which can be derived as follows:  
\[t_{2}=\underset{m}{\text{argmin}}\left(\sum_{j=t_{1}+1}^{m}H_{j}^{\prime}\geq S /3\right) \tag{4}\]  
The low bonder index of level 3 warning interval was denoted as \(t_{3}\), which can be obtained in the same way of deriving \(t_{2}\). Thus, when a new incident heat value \(h\) arrives, we derive the warning level from the following equation:  
\[\text{{Level }}(h)\ =\ \left\{\begin{array}{ll}1,&h\geq H_{t_{1}}\\ 2,&H_{t_{1}}>h\geq H_{t_{2}}\\ 3,&H_{t_{2}}>h\geq H_{t_{2}}\\ 4,&\text{otherwise}\end{array}\right. \tag{5}\]  
The application of this warning level partition could make different warning level variations within incidents. For better comprehension, we randomly sampled 3200 incidents from the total number of incidents and their warning level distribution is shown in Figure 3.
## 4 Optimal Design of IEPCM  
The optimal design of the IEPCM through finite element simulation can save the volume consumption of the material ahead of the experiment. Here, the local area just between neighboring electrodes is focused on; the finite element design model of the IEPCM is optimized with the voltage on the positive electrode, applying up to 100 V. The wafer width (\(W_{\mathrm{p}}\)) of the IEPCM is fixed at 0.001 m, the \(P\), \(W\) and \(H\) of the IEPCM are supposed as the design variables, and their changing scopes are set as 0.001 m \(\leq P\leq\) 0.015 m, 0.0003 m \(\leq W\leq\) 0.0009 m, 0.001 m \(\leq H\leq\) 0.015 m, respectively. Assuming the maximum stress in the \(x\)-direction does not reach the ultimate stress of the IEPCM, the effect of the stress of the IEPCM can be accordingly neglected. The maximum strain in the \(x\)-direction (\(T_{\mathrm{MAX}}\)) is selected as a state variable, and the objective function is the total volume of the IEPCM (\(V_{\mathrm{TOT}}\)). The scope of \(T_{\mathrm{MAX}}\) is restricted by the condition \(T_{\mathrm{MAX}}\geq 60\)\(\mu\epsilon\) through preliminary simulation. Figure 7 shows the \(V_{\mathrm{TOT}}\) changing trend along with the number of iterations.  
Based on the results shown in Figure 7, we conclude that the \(V_{\mathrm{TOT}}\) optimization design for the IEPCM requires eight iteration cycles, and the \(V_{\mathrm{TOT}}\) values have sharp fluctuations for the first five iterations. After that, the \(V_{\mathrm{TOT}}\) comes to a low plateau, and the optimal \(V_{\mathrm{TOT}}\) appears at the eighth iteration. Thus, the optimal values of \(W\), \(P\), and \(H\) of the IEPCM are 0.00073 m, 0.00102 m and 0.00062 m, respectively, and the corresponding state variable \(T_{\mathrm{MAX}}\) is 95.3 \(\mu\epsilon\), whereas the optimized \(V_{\mathrm{TOT}}\) is 10.9 mm\({}^{3}\). It is noted that the maximum strain \(T_{\mathrm{MAX}}\) along \(W_{\mathrm{p}}\) appears in the center line between neighboring electrodes.  
Figure 6: Strain distributions of IEPCM vs. time: (**a**) \(x\)-direction; (**b**) \(y\)-direction.
## 2 Materials and Methods
### Aroma Compound Analysis
#### 2.4.2 Identification of the Aroma Compounds  
The aroma and aroma-active compounds were determined by using a GC system (Shimadzu Nexis GC-2030, Kyoto, Japan), an MS system (Shimadzu GC-MS-QP2020 NX, Kyoto, Japan), and an olfactometer integrated into the system. The column outlet was divided into three equal parts using a special separator: the first part was directed to the flame ionization detector (FID), the second to the mass spectrometer (MS), and the third to the olfactometer (O) for odor analysis. The GC-FID system was utilized to determine the aroma compounds that were separated by using a DB-WAX capillary column (60 m \(\times\) 0.25 mm \(\times\) 0.4 um). The injector and detector temperatures were set to 220 and 250 \({}^{\circ}\)C, respectively. The column temperature program started at 60 \({}^{\circ}\)C (held for 3 min), followed by an increase of 2 \({}^{\circ}\)C/min up to 220 \({}^{\circ}\)C, then by 3 \({}^{\circ}\)C/min up to 245 \({}^{\circ}\)C, where it was maintained for 20 min. A 3 uL sample was injected into the device in a split ratio of 1:10 and helium was used as the carrier gas at a flow rate of 1.5 mL/min [22].  
The aroma compounds were identified by using the MS, which was connected to the same GC. The injector type and temperature program matched those used in the GCanalysis, with helium as the carrier gas at a flow rate of 1.5 mL/min. The ionization energy of the MS was set to 70 eV, the ion source temperature to 250 \({}^{\circ}\)C, and the quadrupole temperature to 120 \({}^{\circ}\)C. Scans were performed at one-second intervals across a mass-to-charge (\(m/z\)) range of 29-350. Peak identification was achieved by injecting standard solutions for compounds with available standards and comparing the mass spectra of the compounds without standards to those in flavor libraries (Wiley 10.0, NIST-11, and Flavor 2L). After the peak identification, the concentrations of the flavor constituents were calculated by using the internal standard method. The retention indices of the aroma compounds were calculated using the n-alkane series (C\({}_{5}\)-C\({}_{30}\)) [22].
# An Effective Wide-Bandwidth Channel Access in Next-Generation WLAN-Based V2X Communications
## 3 Proposed Scheme and Its Variants
### Proposed Channel Access Scheme for 20-MHz Transmission
#### 3.1.2 Proposed Channel Access Scheme (All Back-Off AIFS)  
Based on the proposed device architecture as in Figure 4, each 10-MHz channel is able to perform carrier sensing (e.g., PD and ED) independently. Such independent carrier sensing capability enables the back-off of the entire 20-MHz channel, as depicted in Figure 5, which guarantees fair contention with other 10-MHz frame transmissions in each 10-MHz channel. In the proposed 20-MHz frame transmission scheme, the 20-MHz channel is determined to be idle when both 10-MHz channels are sensed as idle because of PD and ED sensing in each 10-MHz band. Channel sensing of the primary channel is performed by an IEEE 802.11 transceiver, and channel sensing of the secondary channel is performed by the previously described newly added receiver with simplified MAC layer and PHY layer. When the back-off duration expires, the station is able to transmit a 20-MHz frame without secondary-channel idle status checking for the AIFS duration as in the conventional AIFS scheme.  
Figure 4: Proposed station architecture for 20-MHz channel access.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### Qelr  
DQELR [57] is a latency- and energy-aware routing scheme that aims to prolong network lifespan. It implements a deep Q network (DQN) algorithm using both on-policy (i.e., training the neural network online), and off-policy (i.e., training the neural network offline) techniques to route adaptively according to network conditions (Figure 14). In order to reduce overhead, the authors use a hybrid approach, where unicast and broadcast communication are combined. With stringent latency constraint and less energy consumption, the lifespan of the network is prolonged by picking forwarders having the highest Q value. Moreover, DQELR can handle changes in topology by implementing an on-policy technique to find new routes upon disruption of the current route. DQELR achieves improved network lifespan with low latency and low energy consumption.
# Resilient Green Cellular IoT for Landslide Monitoring Using Voice Channels
## 4 Evaluation of VRTHI
### Test Setup  
The nodes are connected directly to the cellular network in the field. They periodically send sensor data to the data management center (DMC). The nodes have ultra-low resources, and, when a DoS attack compromises a node, it stops transmitting data to DMC. It also stops responding to incoming messages. This discontinuity in periodic transmission of data to DMC could be due to various reasons, such as (i) lack of node power, (ii) lack of cellular signal strength, (iii) absence of data communications while only the voice connection is available, or (iv) due to DoS (denial of service) attack. We log the nodes health (power), GSM signal strength, the IP address of the peer node establishing a connection with the current node, and the timestamp of the incoming IP address. These are stored in  
Figure 7: Characteristics of tone transfer. (**a**) Data rate in Control Channel; (**b**) Latency in Control Channel.  
non-volatile EEPROM memory on the node. This metadata is also sent to DMC, along with sensor data.  
If there is no incoming data in DMC from a node, we first analyze the above metadata sent by the node. When a DoS attack happens, we observe that reasons (i) and (ii) are not the primary cause, but nodes still fail abruptly to send data to DMC. Hence, we start to analyze the logged IP addresses in the EEPROM. We find that the nodes have been hit by IP addresses with different octets in Most Significant Bit (MSB) than the IP addresses of the subscribed local cellular network domain. Hence, we concluded that these IP addresses are not in the subscribed local cellular network domain. By tracking these IP addresses, we derive the intensity of DoS attacks. The analysis is done using three IoT devices connected to public network as these nodes engage in mutual communication. The deployment is done using ten such devices deployed in the field at strategically selected locations.
## 6 Conclusions and Future Work  
This paper studied the topology control problem in a satellite cluster network with EO missions by applying the link and network reliability metric. The motivation was to make the network more reliable by analyzing the proposed EO missions' and ISLs' reliability model. Then, this paper proposed the RAT algorithm and its corresponding topology control strategy to maximize the stability of data transmission in the satellite cluster network. Compared with most existing approaches for satellite cluster networks, where neither the periodicity of SCNs nor the predictability is adopted, the RAT uses both. The simulation results validated the theoretical analysis and effectiveness of the RAT algorithm. It showed that the RAT algorithm behaves the best of 50% average packet loss rate, compared to \(\text{FIYG}_{2\pi/3}\) of 80% and MST of 90%.  
Figure 9: Results from three topology control algorithms (RAT, \(\text{FIYG}_{2\pi/3}\), MST) showing total received packets (**a**) and average packet loss rate (**b**).  
Although the assumptions stated in Section 2 are widely used in existing topology algorithms, some of them may not be practical. Our future work will focus on how to relax these constraints (e.g., satellites are homogeneous) for RAT algorithm so as to improve its practicality in actual applications. Furthermore, to make inter-satellite data transmission more practical in the future, it not only requires research on the topology control strategy, but also on the routing algorithms for the SCNs.  
The work in this paper was supported by the Major Program of the National Natural Science Foundation of China (Grant No. 91438202) and the Natural Science Foundation of Heilongjiang Province of China (Grant No. ZD201414). [PERSON] and [PERSON] conceived of the main proposal of SCNs architecture. [PERSON] put forward the improved link cost metric and designed the experiment. [PERSON] performed the experiment, analyzed the data and plotted the figures. [PERSON] designed the topology control strategy with reliability insurance, wrote and revised the article.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Distinction of Field Parts  
In Figure 12, the main part as well as the two headlands can be distinguished for the examined field. After the extraction of passes with fewer than 49 in-work points, which was the Savitzky-Golay filtering frame length, 55 passes remained. Of these, 41 corresponded to the main part and seven to each of Headland 1 and Headland 2. It must be noted that the algorithm for the distinction contains simplifying assumptions and thus can only be applied to well-structured fields such as the examined ones. There is an enormous variety of fields with more complex geometries and constellations of main  
Figure 11: (a) D-GNSS positions of the lowering, lifting and in-work points with deposited RHP values; and (b) a zoomed area.  
parts and headlands. The thresholds for the angle between the pass vectors could be adapted but the algorithm hardly contains any scope for further adaptions.  
The distinction between the two headlands for every field using k-means clustering was a prerequisite for a differentiated analysis of the overlapping and it enabled making assumptions for the rotation scheme of the plow separately for every headland. Here again, simplifying assumptions were made. For more complex field geometries and fieldworks, it is possible to have some more areas that can be defined as headlands or to even have no headland at all. Even though the number of headlands could be adjusted for every field separately, the developed logic contains hardly any scope for further adaptions. Another aspect is the inability of the algorithm to clearly identify an individual headland. Due to the used k-means clustering, the determination, if a headland is the \"Headland 1\" or \"Headland 2\" of a specific field, has a random character.
## 3 Classification of Organic Matter in Shale and Its Morphological Characteristics  
This study, utilizing high-resolution organic matter imaging after argon ion polishing and considering micro-scale features such as source, morphology, distribution, and maceral types, proposes a \"two major, six minor\" classification scheme for organic matter in highly to over-mature marine shales. Organic matter is first divided into two major categories: depositional organic matter and migrated organic matter, based on whether the organic matter has undergone migration. According to the microscopic occurrence characteristics of organic matter, depositional organic matter is further divided into four types: bioclasts, compacted kerogen, in situ remnants from post-hydrocarbon generation, and migrated organic matter into three types: migrated organic matter in intragranular pore, migrated organic matter in intragranular pore, and bitumen in microfractures. Based on macerals, bioclasts can be subdivided into alginite, zooclasts, acritarchs and encapsulated organic matter.
## 7 Results and Comparisons
The SES works in the light mode of conversion of data bits because one stream of the data bits is converted and the rest of the data stream is forwarded in the shadow form of the previous data bitstream. It is observed that, as frequency increases, the PSD utilization is reduced, and with a decrease in frequency, PSD utilization increases. Some digitizing schemes waste the most useful resource, network bandwidth while transmitting the data: for instance, RZ uses double bandwidth for each bit transmitted over the network. However, the SES uses each Tb for each bit of data. Double bandwidth is not required in the case of SES. All bits of data are sent over the network in each Tb per bit. It is shown in Figure 11 that SES performed better than other digitizing schemes in terms of bandwidth utilization and synchronization problems. The SES presents that each bit has one Tb, which is mapped for the signal. The slices of Tb help the receiver to understand the starting and ending position of the signal. Figure 11 shows that there is no tight variation in the signal, as with RZ. The strict variation in the signal is bound to the receiver, which cannot lose its timing in any case. The SES defense against the bandwidth is very descriptive and is less bound to the receiver. Figure 12 presents the bit error rates between SES and CSS. It has been observed that, as the number of data frames increases in the network, the bit error rate increases. The results show that SES modulation performs better than the CSS modulation technique. The bit error rate is calculated based on audio, video, and text messages. The SES provides support to modulate three formats (audio, video, and text messages) of data, but, on the other hand, CSS is purely designed for text messages for IoT communication. Figure 13 illustrates the difference in SNR between SES and CSS. It seems that there are few variations in SNR as the amplitude of the input signal increases. Similar modulation behaviors can be observed for SES and CSS in the calculation of SNR. When the modulators are in the stable region of operation, we observed a close match of the approximated SNR results during the simulation between SES and CSS. Figure 10: SES vs. Various line codes. Figure 11: PSD and bandwidth utilization between SES and other digital encoding schemes. Figure 12: Bit error rate of SES vs. CSS.
# CF-CNN: Coarse-to-Fine Convolutional Neural Network
###### Abstract
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**CF-PyramidNet**} \\ \hline \multirow{2}{*}{**Layer Name**} & \multirow{2}{*}{**Output Size**} & \multirow{2}{*}{**Block Structure**} & \multicolumn{3}{c}{**Block Numbers 200-Layer**} \\ \cline{5-8}  & & & & **Baseline** & **Main** & **Coarse1** & **Coarse2** \\ \hline Conv1 & \(112\times 112\) & \(3\times 3,64\) & \(1\) & \(1\) & & \\ \hline Average pooling & \(56\times 56\) & \(3\times 3\) & \(1\) & \(1\) & & \\ \hline \multirow{3}{*}{Conv2} & \multirow{3}{*}{\(56\times 56\)} & \(1\times 1,D_{k}\) & \(\alpha=300\) & \(\alpha=300\) & \multirow{3}{*}{\(N=66\)} \\  & & \(3\times 3,D_{k}\) & \(N=66\) & \(N=66\) & & \\ \cline{1-1} \cline{5-8}  & & \(1\times 1,D_{k}\times 4\) & \(3\) & \(3\) & & \\ \hline \multirow{3}{*}{Conv3} & \multirow{3}{*}{\(28\times 28\)} & \(1\times 1,D_{k}\) & \(\alpha=300\) & \(\alpha=300\) & \(\alpha=286\) & \\  & & \(3\times 3,D_{k}\) & \(N=66\) & \(N=66\) & \(N=13\) & \\ \cline{1-1} \cline{5-8}  & & \(1\times 1,D_{k}\times 4\) & \(24\) & \(24\) & \(4\) & \\ \hline \multirow{3}{*}{Conv4} & \multirow{3}{*}{\(14\times 14\)} & \(1\times 1,D_{k}\) & \(\alpha=300\) & \(\alpha=300\) & \(\alpha=286\) & \(\alpha=177\) \\  & & \(3\times 3,D_{k}\) & \(N=66\) & \(N=66\) & \(N=13\) & \(N=9\) \\ \cline{1-1} \cline{5-8}  & & \(1\times 1,D_{k}\times 4\) & \(3\) & \(3\) & \(3\) & \(3\) \\ \hline \multirow{3}{*}{Conv5} & \multirow{3}{*}{\(7\times 7\)} & \(1\times 1,D_{k}\) & \multirow{3}{*}{\(\alpha=300\)} & \multirow{3}{*}{\(\alpha=300\)} & \multirow{3}{*}{\(\alpha=286\)} & \multirow{3}{*}{\(\alpha=177\)} \\  & & \(3\times 3,D_{k}\) & \(N=66\) & \(N=66\) & \(N=13\) & \(N=9\) \\ \cline{1-1} \cline{5-8}  & & \(1\times 1,D_{k}\times 4\) & \(3\) & \(3\) & \(3\) & \(3\) \\ \hline \multirow{3}{*}{Refine layer} & \multirow{3}{*}{\(7\times 7\)} & \(1\times 1,D_{k}\) & \multirow{3}{*}{\(3\times 3,D_{k}\)} & \multirow{3}{*}{\(2\)} & \multirow{3}{*}{\(1\)} & \multirow{3}{*}{\(1\)} \\  & & \(1\times 1,D_{k}\times 4\) & & & & \\ \hline Average pooling & \(1\times 1\) & \(7\times 7\) & \(1\) & \(1\) & \(1\) & \(1\) \\ \hline \multirow{2}{*}{fc} & Classes & \multirow{2}{*}{\(1\)} & \multirow{2}{*}{\(1\)} & \multirow{2}{*}{\(1\)} & \multirow{2}{*}{\(1\)} & \multirow{2}{*}{\(1\)} \\  & number & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Structure of the CF-PyramidNet model for ILSVRC 2012 dataset.
## 2 Results and Discussion
### Solar Performance of SANP-S and SANP-T Devices  
The photovoltaic performances of DSCs based on SANP photoanodes (10.0 \(\pm\) 0.5 \(\upmu\)m) were compared, with the current-voltage responses (J-V) and incident photon to charge carrier efficiency (IPCE) results shown in Figure 6a,b and (Figure S3, highlighted scattering effects on IPCE responses, Supplementary Materials) and Table 2 and (Table S1, Supplementary Materials). In brief, devices based on SANP-S were more efficient than those using SANP-T films. The differences were more significant with the lower absorbing N719 (peak \(\epsilon\)\(\sim\) 14700 M\({}^{-1}\cdot\)cm\({}^{-1}\)) than with D149 (\(\epsilon\)\(\sim\) 68700 M\({}^{-1}\cdot\)cm\({}^{-1}\)). Differences in short-circuit current densities (\(J_{sc}\)) were the primary reason for these discrepencies and occurred in spite of SANP-T having a slightly higher dye loading compared to SANP-S, while the lower \(J_{sc}\) seen in SANP-S\(\,\)\(\mid\)N719 was reflected as differences in both magnitude and breadth of IPCE (Figure 6b). The differences in dye loading shown above can be used to estimate a lower limit of light harvesting (negating the scattering effects). When accounting for fluorine doped tin oxide (FTO) coated glass absorption, all devices will have an LHE very close to unity at the peak wavelength, and as such, this is not ascribed to be the cause of the discrepancy in the IPCE peak. It should also be noted that the fill factors (FF) of the devices using SANP-T\(\,\mid\)D149 were also lower than those of the devices made with SANP-S\(\,\mid\)D149, while for N719-sensitised devices, the difference in FF was within experimental uncertainty.
## 2 Materials and Methods
### Characterization  
The morphological and structural properties of the PbS Qdots and rGO were measured using transmission electron microscopy (TEM), high-resolution TEM (HR-TEM) (Field Emission Transmission Electron Microscope; Model: FEI company) and X-ray diffraction (XRD) (Model: PANalytical X' pert PRO). The topography and three-dimensionality (3D) of the surface roughness morphology (RMS) were analyzed using the atomic force microscopy (AFM) technique (Model: Park NX10). The device's parameters, such as (FF) fill factor (%), (J\({}_{\text{sc}}\)) short-circuit current density (mA/cm\({}^{2}\)), (V\({}_{\text{oc}}\)) open-circuit voltaic (V) and (PCE) power conversion efficiency (%) were measured using a solar simulator (Keithley 69911) (Beaverton, OR, USA) under AM 1.5 illumination at 100 mW/cm\({}^{2}\).