esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In conclusion, by high-intensity pumping, we experimentally demonstrated a high-efficiency SHG laser-pumped CW Ti:sapphire laser in which all components, including a high-loss Ti:sapphire crystal, are easily commercially available. The optical-to-optical conversion efficiency of 32.4% and the slope efficiency of 42.5% were obtained by single-pass pumping at the maximum pump power of 5.0 W. Furthermore, we have succeeded in confirming the reliability of our theoretical analysis by theoretically reproducing the experimental results for further improvement of the efficiency of Ti:sapphire lasers. Based on our reliable theory, even with a high intrinsic residual loss of 4.0%, the highest optical-to-optical conversion efficiency of 55.9% and slope efficiency of 59.6% were obtained by increasing the maximum pump power to 25.0 W and double-pass pumping.  
Even at the higher intrinsic residual loss of 8.0%, the highest optical-to-optical conversion and slope efficiencies were reduced to only approximately 3%. In general, insertion loss of wavelength-selective elements and loss due to mode-locking for ultrashort pulse generation can be easily reduced to a few percent or less. It was also theoretically confirmed that even if the conditions deviated from the optimum conditions for the highest efficiency, the decrease in efficiency would be on the order of a few percent. Therefore, the efficiency of wavelength-tunable, narrowband and ultrashort-pulsed Ti:sapphire lasers, in which all elements are easily commercially available, can be increased to well over half the quantum limit by high-intensity pumping. These results can be used to improve the utility of wavelength-tunable, narrowband and ultrashort pulse lasers and will lead to a transformation in their application fields from basic to practical.  
Conceptualization, S.K.; data curation, T.K.; formal analysis, T.K.; funding acquisition, S.K.; investigation, T.K. and S.K.; methodology, S.K.; project administration, S.K.; resources, S.K.; software, T.K. and S.K.; supervision, S.K.; validation, T.K. and S.K.; visualization, S.K. and T.K.; writing--original draft, S.K. and T.K. All authors have read and agreed to the published version of the manuscript.  
A part of this research was supported by JSPS KAKENHI Grant Numbers JP25390123, JP21K04924; AMADA CO., LTD. (AF-2018223-B3); and the joint research project of the Institute of Laser Engineering, Osaka University (under contract subject \"2016B1-KAWATO-1\").  
Not applicable.  
The authors acknowledge [PERSON] for the experiments, [PERSON] for providing the Ti:sapphire crystal and optical elements, [PERSON], [PERSON], [PERSON], and [PERSON] for the theoretical analysis.  
There are no conflict of interest to declare.
## 4 Conclusions and Prospects
### Comprehensive study of the fully coupled THMC processes:  
While the THMC coupling of rock mechanics has gained significant attention, it is still in the early stages of development. Most current studies focus on waste disposal applications, such as the THMC coupling in landfills and nuclear waste repositories. However, much remains unexplored regarding the coupling mechanisms of THMC in other critical fields, such as underground space development and deep mineral resource extraction. Of particular interest are the chemical effects in deep rock formations, such as those impacted by seawater in coastal mining or offshore oil extraction. These environments present new challenges where chemical interactions cannot be ignored. Therefore, advancing the study of the THMC multi-field coupling in these conditions will be crucial for future research.
# Research on Three-Phase Wireless Power Transfer System
## 3 Experiment and Analysis
### Simulink Simulation
Figure 11 is the Simulink simulation waveform of the voltage and current output by the receiving end with an output frequency of 47.5 kHz. It can be seen from the Simulink simulation that the three-phase bridge arm works alternately, the sine degree of the output current is high, and the time of one cycle is 21 us. By scaling, it can be seen that the output phase voltage waveform is consistent with the principle analysis of control pulse width modulation. The above results show the feasibility and effectiveness of the adopted circuit topology and modulation strategy. In addition, it can also be observed that the phase of the output current is basically the same as the phase of the output voltage, which indicates that the power factor of the system is high. This is very important for wireless power transfer systems because a high-power factor can improve energy transmission efficiency and reduce energy loss. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Power supply phase voltage & \(U_{i}\) & 220 V \\ Switching frequency & \(f\) & 47.5 kHz \\ Load resistance & \(R_{o}\) & 10 \(\Omega\) \\ Coil self-inductance & \(L_{p}\), \(L_{s}\) & 31 uH \\ Resonant capacitor & \(C_{p}\), \(C_{s}\) & 360 nF \\ Sample time & \(t\) & 0.1 us \\ \hline \hline \end{tabular}
\end{table} 
Table 2: WPT Simulink simulation parameters of matrix converter. Figure 9: The inverter circuit controls the pulse width waveform.
# Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager
## 2 Materials and Methods
### Method
#### 2.3.2 Multi-Feature Extraction  
Temperature features that consider heat emissions are the most evident characteristics for detecting IHSPAs. However, it is difficult to clearly distinguish IHSPAs from non-IHSs based on only temperature features, especially on water and road land covers. Optical  
Figure 2: Flow chart of the IHSPA identification model using the SDGSAT-1 TIS and the combination of multi-feature extraction and SVM. It can be divided into four parts: (1) Radiometric calibration of SDGSAT-1 TIS and Landsat-8 data was performed in data prepossessing. (2) Four thermal radiation indices based on three brightness temperatures of SDGSAT-1 TIS data were designed to enlarge the temperature difference between the IHSPAs and background in IHS areas. Then, 10 multi-features, which combined thermal features and optical features, were produced and extracted using SDGSAT-1 TIS and Landsat-8 data. (3) An IHSPA identification model based on SVM with a “small sample advantage” was designed to identify IHSPAs. (4) IHSPAs were detected and validated based on the detection model constructed in part 3.  
features, including color, texture, and spectral features that use the visible and near-infrared spectral range properties of an object or region, can distinguish industrial plants from other land covers [43]. Therefore, thermal features and optical features (as shown in Table 3) were combined to identify IHSPAs precisely. (1) Thermal featuresThermal features include satellite brightness temperatures and thermal radiation indices of the SDGSAT-1 TIS. Satellite brightness temperatures contain three radiation brightness temperatures of each band of the SDGSAT-1 TIS. They can be calculated by Planck's Law as shown in Formula (2):  
\[\mathrm{T_{i}}=\frac{\mathrm{hc}}{\mathrm{\lambda_{i}\times k\times ln\Big{(}1+ \frac{2 hc^{2}}{L_{i}\times w^{3}}\Big{)}}} \tag{2}\]  
where \(\mathrm{T_{i}}\) is the satellite brightness temperature of Band i of the SDGSAT-1 TIS in Kelvin. k is the [PERSON]'s constant (\(1.38\times 10^{-23}\) J/S). h is the [PERSON]'s constant (\(6.626\times 10^{-34}\) J/S). c is the velocity constant of light in a vacuum (\(2.998\times 10^{8}\) m/s). \(\mathrm{\lambda_{i}}\) represents the central wavelength of the emitted radiation band (i) (\(\mathrm{\mu m}\)). To describe the temperature difference between HSPAs and the background, four thermal radiation indices that use brightness temperature values of two different bands of the SDGSAT-1 TIS were designed. They can be calculated by the following formulas:  
\[\mathrm{RI_{1}}=\frac{\mathrm{T_{2}-T_{1}}}{\mathrm{T_{2}+T_{1}}} \tag{3}\]  
\[\mathrm{RI_{2}}=\frac{\mathrm{T_{3}-T_{1}}}{\mathrm{T_{3}+T_{1}}} \tag{4}\]  
\[\mathrm{RI_{3}}=\frac{\mathrm{T_{2}-T_{1}}}{\mathrm{\rho_{NIR}}} \tag{5}\]
## 3 Results and Discussion
### Performance Tests of the Membranes for the Removal of Emulsified Oil
#### 3.5.4 Performance Tests with Different Salty Environments  
The content of produced water and offshore oil spills has high salinity; for this reason, the performance test of TNF/CNF membrane was conducted under different saline environments. Figure 11 presents the outcome of the high salinity emulsion treatment using a TNF/CNF membrane. The percentage of oil removal was above 99% for different salty environments as represented in Figure 11a, and the oil concentration in the permeate was below 11 ppm as shown in Figure 11b, which is lower than the discharge limit of  
Figure 10: (**a**) Performance test (oil rejection rate and water flux (LMH/bar)) of the TNF/CNF membrane using different oil emulsions. (**b**) Oil concentration in the permeate for different types of oil.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Membrane** & **Rejection Rate (\%)** & **Flux (LMH/Bar)** & **Operation Pressure (KPa)** & **Reference** \\ \hline PES/CA \({}^{1}\) & 88 & 6.73 & 400 & [30] \\ Polysulfone & \textgreater{}90 & 1605 & 103.4 & [31] \\ Ceramic ZrO\({}_{2}\) & \textgreater{}90 & \textless{}100 & \textgreater{}100 & [32] \\ TNF/CNF on cellulose paper & \textgreater{}99 & 1520 & 30 & Current paper \\ \hline \end{tabular} \({}^{1}\) PES/CA is polyheresulfone/celluloseacetate/.  
\end{table}
Table 3: Different membranes and their performance for oil/water separation.  
EPA regulations. High oil rejection rate was maintained at high saline environment as presented in Figure 11a. As salts only can be dissolved in water rather than oil and the new membrane has a high separation rate between oil and water as evidenced in Figure 11, therefore there was no significant differences in terms of oil rejection rates between different salty environments. This is an indication that the membrane can function well under high saline environment. Figure S2 shows the water fluxes using TNF/CNF membrane using different loading of TNF and CNF and the percentages of oil rejection using TNF/CNF membrane using different loading of TNF and CNF.
# PPDC: A Privacy-Preserving Distinct Counting Scheme for Mobile Sensing
## 1 Introduction
Through expanding each bit of the hashing values of users' original data added to the FM sketch, PPDC enhances the FM sketch to apply the bitwise XOR homomorphic encryption algorithm as an encryption method, so that users' privacy gets protected even under a known plaintext model. We conduct theoretical analysis and experiments, and the results show that our scheme achieves practical counting accuracy and efficiency. The remainder of this paper is organized as follows. Section 2 defines system and security models and introduces several necessary preliminaries. In Section 3, we present the main idea and essential module of PPDC. Section 3 also analyzes the correctness and security of PPDC. After that, Section 4 provides the experimental results about the evaluations of accuracy rate and efficiency of PPDC. Section 5 discusses the related work. Finally, we conclude the paper in Section 6.
# Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks
## 5 Simulation
### Transmission Range  
Figure 17 depicts that the error distance of TCAT and CTCI decreases, but the error distance of DCTTP increases rapidly as the transmission range increases. Because the location method of DCTTP is centroid positioning, the result of the positioning will be influenced by the position of the active sensor nodes. When the transmission range increases, there are more active sensor nodes that are far from the target. The result of the positioning is far away from the target; therefore, the error distance of DCTTP is larger.  
Figure 16: Impact of the number of sensor nodes on the average energy consumption.  
Figure 15: Impact of the number of sensor nodes on the average data size.  
In TCAT, the cluster size increases as the transmission range increases. For position estimation of TCAT, a larger cluster size may results in a larger number of active sensor nodes in the cluster to which the leader belongs. Therefore, the error distance decreases as the bounding area for sampling is smaller. Similarly, there are more active sensor nodes participating in refining the bounding area in CTCI than in TCAT, so the error distance of CTCI is smaller. Figure 18 depicts that the data size and the energy consumption increase rapidly as the transmission range increases. Figure 19 shows that when the transmission range is larger than 25, the energy consumption of CTCI is larger than that of TCAT and smaller than that of DCTTP.  
Figure 17: Impact of transmission range on the average error distance.  
Figure 18: The impact of the transmission range on the average data size.
# Design and Investigation of Capsule Networks for Sentence Classification
## 3 Proposed Model
### Primary Caps Layer  
This layer is a standard convolution layer which accepts scalar inputs from the previous convolution layers and produces vector outputs called capsules to preserve the instantiate parameters such as the local order and semantic/syntactic representations of words. In this Primary Caps layer of dimension 8D and 32 channels, features detected by the previous convolution layers produce combinations of features grouped into capsules.  
CapsNets replace scalar-output feature detectors with vector-output capsules in this layer and going up to the next layer in the network requires the routing by agreement algorithm. When multiple capsules agree in their vote to a specific capsule in the layer above, that capsule gets activated and results from lower level capsules are propagated to that capsule. This activated capsule will create a cluster of capsules which came from a lower layer and causes the higher-level capsule to output a high probability of observation that an entity is present and a high-dimensional pose.  
Figure 3: Sentence model.  
The routing by agreement mechanism used here is dynamic routing and this mechanism provides a means to connect the Primary Caps Layer with the next layer--Class Caps Layer. If we assume the output of a single capsule (pose vector) is \(u_{i}\), it is multiplied by a translation matrix \(w_{ij}\) to produce a vector \(\hat{u}_{j|i}=w_{ij}\cdot u_{i}\). Here, capsule '\(i\)' is in the current layer (Primary Caps Layer in this case) and capsule '\(j\)' is a capsule in the higher level. A capsule in the higher level is fed the sum of the outputs in the lower level (Primary Caps Layer) multiplied with a coupling coefficient \(c_{ij}\) that is calculated during the dynamic routing process:  
\[s_{j}=\sum_{i}c_{ij}\cdot\hat{u}_{j|i}. \tag{2}\]  
These coefficients \(c_{ij}\) are calculated using a softmax function and they all add up to one:  
\[c_{ij}=\frac{exp(b_{ij})}{\sum_{k}exp(b_{ik})}, \tag{3}\]  
where \(b_{ij}=b_{ij}+\hat{u}_{j|i}\cdot v_{j}\). \(b_{ij}\) is the initial logits (prior probabilities that capsule \(i\) should be coupled to capsule \(j\) ) and \(v_{j}\) is the output vector limited to [0, 1] using a squashing function as given below:  
\[v_{j}=\frac{\left\|s_{j}\right\|^{2}}{1+\left\|s_{j}\right\|^{2}}\frac{s_{j}}{ \left\|s_{j}\right\|}. \tag{4}\]  
The length of the instantiation vector represents the probability that a capsule's entity is present in the scene. For details of the dynamic routing algorithm, look at [3].
# Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes
## 6 Discussion  
Smart home IoT devices are exposed to numerous attacks due to their vulnerable environment, low computation power, the tendency of users to utilize default device credentials, and insecure networks. Therefore, many security schemes have been developed to secure the smart home network environment. However, most of these schemes still have numerous flaws that render them vulnerable to attacks. Performance degradation occasioned by highly extensive cryptographic operations is another major issue in smart home networks. To this end, highly efficient technologies such as NB-IoT and LoRaWAN have been developed. However, many vulnerabilities exist in these LPWAN technologies that can be exploited to bring the entire network down. For instance, LoRaWAN uses three keys, _AppKey_, _NwbSKey_, and _AppSKey_, that are exposed to risks during message transmissions, key management procedures, key generation phase at the onset of communication sessions, and in storage at the end devices. If these keys are compromised through side-channeling, then other keys derived using them as the basis can be compromised as well. For instance, the compromise of _NwbSKey_ can expose the entire LoRaWAN to attacks. This is because the adversaries are now able to decrypt the exchanged messages. In addition, the LoRaWAN  
Figure 8: End-to-end latency.  
Figure 9: Network throughput.  
gateway periodically transmits beacons to the network server, and therefore these beacons can be intercepted to launch numerous attacks.  
Another serious issue in smart homes is that much attention has been paid to mutually authenticating the remote user to the gateway node, while the mutual authentication between this gateway node and the IoT sensors is largely ignored. In addition, some application layer protocols in IoT, such as the Message Queue Telemetry Transport (MQTT) and Constrained Application Protocol (COAP), do not have adequate security protection. As such, they have to depend on the underlying transport layer security protocols. Unfortunately, these protocols exhibit high computation overheads. To this end, a highly efficient protocol has been developed in this paper, which has been demonstrated to offer sufficient privacy and security protection at the lowest computation, storage, and communication costs. Considering the lowest overheads attained in other related schemes, Table 14 presents the percentage improvements achieved by the proposed protocol.  
As shown in Table 14, the proposed protocol reduced the computation and communication costs by 57.7% and 16.7%, respectively. Considering the security comparative evaluation in Table 14 above, then it is clear that the proposed protocol records the best performance and also offers salient security features. It is also demonstrated to resist the highest number of attacks. It is, therefore, the most suitable for deployment in the smart home network, where devices are not only resource-limited but also exposed to numerous attacks.
# Resilient Green Cellular IoT for Landslide Monitoring Using Voice Channels
## 4 Evaluation of VRTHI
### Reduced Energy Consumption  
The nodes in VRITHI are in stand-by mode with only voice network enabled. Before a peer-to-peer connection, the control channel handshake is initiated with the receiver through voice channel. It saves a considerable amount of power. Figure 10(a) shows the power consumption by sim5300E modem for the data channel and control channel averaged on a hourly basis for a node in a single tier. It is measured using Agilent Source measuring unit B2902A. Since all the three tiers use the same hardware and same carrier networks, their power consumption on voice channel and data channel are the same for a given payload. It can be observed that the control channel has less than half the power of the data channel. This is a significant power saving in Green IoTs. Secondly, whenever a disaster happens, the voice channel has the highest guaranteed bit rate in the mobile switching center of the Telecom network. It happens due to higher congestion in the  
Figure 10: Real-time communication characteristics using VRITHI. (**a**) Latency in Real-time Communications; (**b**) Dynamic IP in WAN.  
incoming calls and the need to provide uninterrupted connectivity at crisis time. Hence, having the control channel for signaling helps significantly.  
Figure 10(b) compares the energy consumption of VRTTHI with that of other IoT protocols. In this experiment, observations are taken over one hour, and the y-axis shows the total energy consumption during that interval. MQTT is in always-listening mode; hence, it has the maximum power consumption. For polling-based mechanisms, the polling period is taken the same as in Figure 10(a), and, at every polling cycle, we measure the power consumption and then obtain the sum up over 1 h. VRTTHI, being interrupt-based, incurs power consumption only while doing the data communications, and, during our experiment, it happens only once. Hence, VRTTHI has the lowest energy consumption, and energy saving increases from 41% to 97.2%.
## 6 Conclusions  
By taking into account the advantages and disadvantages of the pre-existing protocols described in the literature, we suggested a unique mobile sink routing protocol in this study, called modified ring routing. A logical ring is built around the sink node, which often advertises sinks, in this hierarchical heterogeneous network protocol. It is designed to make the information delivery much faster to the cluster head, about the mobile sink that mitigates the hotspot problem, observed in the static sink, and minimizes the data reporting delays, while taking the different mobile sink mobility characteristics into account. Again, this approach makes the data delivery much faster, with the help of clustering, especially for the smart monitoring applications. The extensive simulations are carried out to evaluate the performance of the modified ring routing and is implemented for a range of scenarios with different network capacities and sink speeds. The results indicate that the modified ring routing performs better than the ring, Mob M-LEACH, LBDD, and MSRP, in terms of energy consumption, extending the network's lifespan, minimizing the control overhead, and enhancing the throughput.  
Figure 14: Energy consumption analysis; (**a**) variation in the network density; (**b**) variation in the sink speed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analysis** & **LBDD** & **MSRP** & **Mob M-LEACH** & **Ring** & **Modified Ring** \\ \hline End-to-end Delay Analysis & High & High & Moderate & Low & Low \\ \hline Throughput & \textless{}60 Kbps & \textless{}75 Kbps & \textless{}80 Kbps & \textless{}143 Kbps & \textless{}233 Kbps \\ \hline Control Overhead & High & High & Low & Low & Limited \\ \hline Residual Energy & 28\% & 8\% & 16\% & 54\% & 76\% \\ \hline Mobility &
\begin{tabular}{c} Sink and Sensor \\ Node \\ \end{tabular} & Sink Node & Sensor Node & Sink Node & Sink Node \\ \hline Computation Time & 3.43 s & 3.34 s & 2.67 s & 1.44 s & 0.521 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average complexity analysis for the dynamic sensor network.  
In the future, we will modify the ring construction strategy, by applying the face routing concept and use multiple mobile sinks. It merely justifies the easy and fast data delivery. Therefore, the extensions and modifications to the modified ring routing needs to be justified against the extra costs and other effective mobility pattern utilization, to achieve a much better quality of service in the different application perspectives.  
Conceptualization, A.G. and S.K.P.; Data curation, A.K.; Formal analysis, K.U.S.; Investigation, S.K.P.; Methodology, P.K.S., A.K. and C.S.; Project administration, P.K.B. Visualization, M.K.O. and S.K.P.; Writing--original draft, A.K. and S.M.; Writing--review and editing, S.M. and M.K.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 MAC Design Issues in CRBANs
### Cross-Layer Design  
Spectrum sensing is carried out by the PHY and MAC layers, but spectrum management (such as spectrum decision making, scheduling and spectrum handover) can be related to all other network layers. Therefore, different layers of the protocol stack should be coordinated. As in other wireless networks, network performance and reliability can be significantly improved in CRBANs by considering the cross-layer design and optimization.