esa-sceva/satcom-chunk-collection huggingface dataset:
# Motion, Static Force, and Efficiency Analysis of Planetary Gear Transmission Based on Graph Theory
## 4 Conclusions  
Multi-speed PGTs improve fuel economy and dynamic performance. In this paper, a novel graph theory method is proposed for the performance analysis of an automatic transmission. Dots are used to represent various components of the planetary gear mechanism (including control elements), and lines are used to represent the connection relationship between the components. According to the research described above, the following conclusions are drawn:  
1. The motion analysis of an automatic transmission can be realized by a motion characteristic matrix, connection matrix, and gear matrix. The moment analysis of an automatic transmission can be realized by a moment characteristic matrix, independent member matrix without external force, and a moment input matrix.
2. From the motion and moment analysis of an automatic transmission, the rotational speed and torque of each component can be obtained, respectively; thus, the power flow of an automatic transmission can be determined. And, the transmission ratio method is used to calculate transmission efficiency.
3. This method is entirely dependent on the matrices, and performance analysis is performed automatically. The automatic analysis of motion, moment, power flow, and efficiency is realized by a computer, which improves the calculation efficiency and accuracy of planetary transmission performance analysis.
4. In contrast to other analysis methods, this proposed method reduces the dependence on the previous design experience and previous work of engineers. It also overcomes the disadvantages of using a traditional analytical method, such as the need to repeatedly derive formulas and the complexity of calculation. This method helps to guide the further design of the PGT, such as the geometrical parameter design of the gears.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**First Gear** & **Second Gear** & **Third Gear** & **Fourth Gear** & **Fifth Gear** & **Sixth Gear** & **Reverse Gear** \\ \hline \(\eta_{1}=0.9188\) & \(\eta_{2}=0.9407\) & \(\eta_{3}=0.9864\) & \(\eta_{4}=0.9869\) & \(\eta_{5}=0.9909\) & \(\eta_{6}=0.9874\) & \(\eta_{R}=0.9472\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: The efficiency of a 6 HP26 automatic transmission at each gear.  
Author Contributions: Writing--original draft preparation, H.X.; fundingacquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (No. 52005181).  
Not applicable.  
The authors declare no conflict of interest.
# Trends in Compliance Monitoring Devices (CMDs) in Ships' Ballast Water Treatment Systems
### Ballast Water Monitoring as a Tool for Compliance  
The parameter of this study is restricted to a few select technological applications in the form of some Compliance Monitoring Devices (CMDs) [7], experiments from scientists who have collected quantitative data on four methods of ballast water monitoring where their findings show challenges in achieving compliance on UV ballast treatment systems, with some technologies also briefly mentioned [8]. It must be noted that there are many excellent inventions and patents that could be discussed under this topic such as a recent patent granted by the Chinese Government to a marine tech entity, Nanjing Yijinuo Environmental Protection, which invented a utility model providing a rapid monitoring device for ballast water, related to the technical field of ballast water monitoring, providing a more reliable, rapid, effective, standardized and portable device and an enrichment device for entry and exit inspection and quarantine departments [9]. There are also green ballast water technologies invented to lower the carbon footprint created by the treatment of millions of gallons of ballast water only to be discharged and so recycling and reusing treated water, which can help with forecasting the environmental safety of the water previously used in ballast tanks [10]. These examples of ballast water monitoring technologies are an illustration of the advancement of marine environmental protection which can only advance with appropriate technologies but since we cannot discuss them all in detail, this study will sample random select examples to study and understand some concerns with the current monitoring practices.  
This study will touch on the rapid results from automated remote ballast water monitoring devices whose application is crucial in certain geographical locations [11]. These technologies validate a result that shows that marine technology investment is the sustaining force in marine environmental protection and furthermore, that, though there may be challenges in the use of some CMDs, it is still ideal for Port States to ensure compliance with the BWM Convention using a few samples of devices for rapid and reliable results for voyage planning as part of marine water technology forecasting, which can only be enhanced by electronic record keeping. Under the new regime, electronic records are easier to input, place on record and, where necessary, amend without having to amend the entire ballast water management plan of the ship first, which means that shortcomings in the system can be addressed as soon as they are monitored and recorded [1; 11]. The legal authority approving the use of those devices is the contextual foundation of this study to provide precision on the ability of nations to apply the relevant international law in marine pollution, aquatic invasions and other preventative measures.
## 3 Objective Measures Overview
### STOI and ESTOI  
In the field of measuring the intelligibility of speech, several algorithms have been proposed with the aim of replacing expensive subjective listening tests. Among them, STOI [42] has proven to be good for evaluating intelligibility in signals to which time-frequency weighting is applied. The method requires both the signal to be evaluated and a clean time-aligned reference. It calculates the time frequency (TF) representation of both signals with a discrete Fourier transform (DFT) of the windowed frames and, using a one-third octave analysis, it groups the bins of the DFT into 15 bands and computes the norm of each one, which is called the TF-unit. It uses an intermediate measure of intelligibility for each TF-unit, which depends on \(N\) consecutive TF-units of both the signal to be evaluated and the reference. Typically the value of \(N\) is such that the intermediate measure depends on speech information from the last \(\approx\) 400 ms. To calculate the global intelligibility measure, the average of the intermediate intelligibility measurements between frames and frequency bands is computed. This operation implies an independent contribution to the global measure of each band.  
One evolution of STOI is ESTOI [43], which unlike STOI does not assume independence between frequency bands. This feature allows to better capture the effect of time-modulated noise maskers. Both STOI and ESTOI evaluate the signals between 0 and 1.  
The success of these measures has led to propose their use in several areas. STOI has proven to predict intelligibility quite accurately in mobile phone output [46]; noisy speech processed by ideal time-frequency masking and single channel speech enhancement algorithms [47]; and speech processed by cochlear implants [48]. Additionally, it is also robust against different types of languages, such as Mandarin [49], Danish [47] or Dutch [50]. STOI has been also used to evaluate synthetic speech, showing high correlation with MOS [51, 52]. In Ref. [53], it was used to evaluate the intelligibility of dysarthric speech. In this case, as a time-aligned reference signal was not available, so an utterance-dependent reference signal was generated from several healthy speakers and then dynamic time warping (DTW) was used to align the pathological signal and the reference signal.
# Bridging the Gap: Enhancing Maritime Vessel Cyber Resilience through Security Operation Centers
## 1 Introduction
### Vessel Connectivity Cyber Risks
These examples demonstrate how a seemingly harmless aspect of maritime digitalization, improved connectivity, can result in a significant increase in the threat attack surface and in some cases, lead to crew safety being severely compromised. This turns the focus to the role of regulation. The maritime domain has been noted to be one heavily dependent on regulation for the improvement of safety [25]. The downside to this approach is a situation where the cyber maturity of the domain lagged due to there being no enforceable regulation until 2017. This left it vulnerable to crippling attacks as was seen in the 2017 NotPetya ransomware attack that significantly impacted the operations of Maersk, a shipping giant [16]. However, the International Maritime Organization (IMO) did pass new cyber requirements that came into force in 2021 [26; 27]. The goal of this regulation was to rapidly improve the lagging cybersecurity maturity of the industry. This regulation has been backed by strong industry support from other key maritime stakeholders such as BIMCO [28], DNV [29], Bureau Veritas [30], and the International Association of Classification Societies (IACS) [31; 32]. Among other things, the maritime cyber security guidelines passed by the IMO call upon stakeholders to implement measures that would enable the timely detection of cyberthreats [26; 27]. However, there is a gap in knowledge on the current state of this implementation. This study therefore aims to fill that gap by investigating the industry response to timely cyber threat detection from the perspective of Maritime Security Operation Centers (M-SOCs). Specifically, this study will seek to answer the following research question:  
What are the enabling factors and challenges that M-SOCs have faced in their quest to facilitate the timely detection of cyber events? The findings of the above research question will then be related to a future where autonomous vessel Remote Operation Centers (ROCs) will be more prevalent.
## 2 Materials and Methods
### Statistical Analysis  
All data are presented as mean \(\pm\) standard deviation. Statistical analyses were performed by the SPSS 11 program (Statistical Package for Social Science, SPSS Inc., Chicago, IL, USA). Statistical significance in each group was verified with the analysis of one-way ANOVA followed by [PERSON]'s test of \(p<0.05\). Additionally, statistical significances in these animal studies were evaluated by Student's \(t\)-test (\({}^{*}\)\(p<0.05\); \({}^{**}\)\(p<0.01\), and \({}^{***}\)\(p<0.001\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Genes**} & \multicolumn{3}{c}{**Primer Sequences**} \\ \cline{2-3}  & **Forward (5\({}^{\prime}\)-3\({}^{\prime}\))** & **Reverse (5\({}^{\prime}\)-3\({}^{\prime}\))** \\ \hline GAPDH & CCACCAGAAGACTGTGGTGTGC & CATGTAGGCCATGAGGGTCCACCAC \\ \hline IL-1\(\alpha\) & ACATATCTCAGCCACCACTTG & CTGGAAGCTCTGCATAGAG \\ \hline IL-1\(\beta\) & CCGTGGACCTTCCAGGATG & GATCCACACTCTCAGCTGC \\ \hline IL-2 & TCCACCACAGTTGCTCCTC & CCTGCACTTAGAGGCTGCC \\ \hline IL-4 & CGAAGAACCACCAGAGAGGTGAGT & GACTCATCATGTGGCAGCTTTACG \\ \hline IL-6 & AGAGGACTTCACAGAGGA & ATCTCTCTGAAGGACTCTG \\ \hline IL-12 & CCTGCCATCAGAGGCCTGTC & CATCTTCTCTACGGCGGTGTCA \\ \hline TNF-\(\alpha\) & GGCAGGTCTACTTTGAGTCATTGC & ACATTCAGAGGTCCCAGGTGAATTCCA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design of primers for quantitative real-time PCR.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**(\(\upmu\)L)**} & **Assay Medium** & **Target Cell** & **Effector Cell** & **Triton X-100** \\ \hline Background control & 200 & & & \\ Low control (LC) & 100 & 100 & & \\ High control (HC) & 100 & 100 & & 10 \\ Effector, Target mix & & 100 & 100 & \\ \hline \hline \end{tabular}  
NK cell activity (%) = (Effector, target cell mix \(-\) Low control)/(High control \(-\) Low control).  
\end{table}
Table 2: Scheme for control and test samples.
# The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing
## 2 Preliminaries
### Fusion Techniques  
Fusion techniques combine data from multiple sensors to achieve improved accuracy and more specific inferences than can be achieved by the use of only a single sensor. They have been employed on multi-sensor environments with the aim of fusing and aggregating data from different sensors. The goal of using fusion in multi-sensor environments is to obtain a lower detection error probability and an improved reliability using data from multiple distributed sources [32].  
Fusion techniques have shown to be effective in problems aligned with AR. For example, [PERSON] et al. [33] presented a method to enable dense embedding using kernel similarities. [PERSON] et al. [34] proposed a classifier-fusion schema using learning algorithms, in which feature extractors and classifier combinations were used to achieve higher accuracy. [PERSON] et al. [35] presented a comparison of the performance achieved by single- and multi-sensor fusion for AR using inertial data. The results of these studies showed the significant impact of multi-sensor fusion for recognising human activities, demonstrating the feasibility of fusion techniques for addressing AR problems.
# Drought Propagation in Brazilian Biomes Revealed by Remote Sensing
## 2 Materials and Methods
### Datasets
#### 2.2.1 Land Surface Temperature  
_LST_ is the radiative skin temperature of the land surface, and hydrologic and ecological processes are sensitive to variations in _LST_[70]. _LST_ estimates are available from the MODIS MOD11A2 product and were derived here with a spatial resolution of 1 km and an average 8-day compositing period, consisting of an average from two to eight days of the MOD11A1 daily LST product [63].
# Smart Black Box 2.0: Efficient High-Bandwidth Driving Data Collection Based on Video Anomalies
## 6 Discussion  
While our results indicate that the SBB improves the recording of anomalous data, the massive difference between the ground-truth and the actual performance of the SBB indicates the need for improved video-based anomaly detection and action detection. Furthermore, a key limitation of our study is the extraction of the data value purely from a single video stream. Most autonomous vehicles feature huge sensor suites, including multiple cameras, radar, LiDAR, CANdata, etc. Future research in intelligent event data recorders may tap into this wealth of sensor data to more effectively detect EOIs and assign the data value.  
Additionally, further work towards high-bandwidth vehicular communication networks can serve to ease the onboard memory constraints under which the SBB works. Currently, the SBB is designed to record data over the course of a day or multiple days. However, high-speed vehicle-to-everything communication allowing for rapid data upload in real time would significantly reduce the data recording period of the SBB and possibly transform the SBB into a downstream application to be applied after data upload.  
Finally, although our manuscript focuses on the application of the SBB to autonomous cars, the SBB pipeline can be adapted for intelligent data recording in other domains as well. Autonomous and semi-autonomous systems are being developed for truck, sea, and air transport to accommodate increased volume [41; 42; 43] and to improve safety [44]. As the onboard sensor suites of these mediums continue to increase in complexity and data bandwidth, intelligent event data recorders must be developed to store and manage valuable sensor data.
## 6 Performance Evaluation
### Simulation Results
#### 6.2.3 Average Residual Energy  
We computed the average residual energy in two ways, i.e., (i) against different network sizes while keeping the communication round as constant, (ii) against the number of communication rounds while keeping the network size as constant. Also, we have computed the average residual energy against different network sizes at 1000 rounds and 2000 rounds to check the performance of all clustering schemes. Figure 5 depicts the average residual energy of SEP-E, DEEC-E, EBCS, TEEN, and HFECS for different network sizes at 1000 rounds. It can be observed from Figure 5 that the performance behavior of all clustering schemes is analogous at different network sizes. Furthermore, HFECS outperforms all other clustering schemes including EBCS for larger network sizes. As we increase the network size, the average residual energy starts dropping down for all clustering schemes, which indicates the increase in energy consumption. A noticeable increase in energy consumption can be seen from SEP-E, EBCS, and TEEN but there is a remarkable increase in energy consumption for DEEC-E, due to higher negative slope in average residual energy. Likewise, Figure 6 depicts the average residual energy of SEP-E, DEEC-E, EBCS, TEEN and HFECS for different network sizes at 2000 rounds.  
Figure 7 illustrates the average residual energy of SEP-E, DEEC-E, EBCS, TEEN and HFECS against number of communication rounds while keeping the network size as \(300\times 300\) m\({}^{2}\). The performance behavior of all clustering schemes is analogous at different numbers of communication rounds. However, DEEC-E and HFECS perform slightly better than other clustering schemes after the 1500 th round of communication. DEEC-E functionality depends on an absolute threshold value calculated in every round which do not involve network size, so the average residual energy of DEEC-E is reduced as we increase the communication rounds due to sensor nodes' poor lifetime and the increase in network size, but comparing the remaining energy of sensor nodes with that of an absolute threshold value gives advantage to sensor nodes with energy slightly above the threshold value, which results in better average residual energy than the rest of the clustering schemes. For smaller network sizes, the residual energy of the sensor nodes in HFECS is also consumed by link connectivity calculations for a higher number of sensor nodes per cluster, therefore the average residual energy of DEEC-E for smaller network sizes is better than in HFECS, but, as we increase the network size, the cluster load in HFECS reduces, thus resulting in better average residual energy for HFECS. Furthermore, as HFECS performs lossless data aggregation at the CH node for reducing the information flow and energy consumption, we have better average residual energy for higher values of communication rounds for HFECS than other clustering schemes. The percentage improvement of HFECS in terms of average residual energy is given in Table 11.  
Figure 5: Average residual energy at round=1000 against variable network size.
# A Programmable ROADM System for SDM/WDM Networks
## 4 Simulation Results  
Simulation analysis was used to demonstrate that an optimal virtual topology with bypass connection can be found to replace the fully flexible topology in optical network and saving a lot of hardware devices. To assess the optical network performance, Blocking Probability is used to evaluate the performance, including bandwidth blocking probability and traffic blocking probability. Both blocking probabilities are simulated and analyzed. Bypass connection would reduce the wavelength switch in the node so that the bandwidth continuity request will be reduced. To begin with this simulation, the assumed config  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Topology** & **Bypass Core/WDM Core** & **WSS Number** & **Total Ports Number** & **Maximum Port Number** \\ \hline Fully flexible topo & 0/104 & 416 & 10296 & 41 \\ Topo 1 & 15/89 & 38\(-\)7.7\% & 9000\(-\)12.6\% & 33 \\ Topo 2 & 27/77 & 360\(-\)13.5\% & 8028\(-\)22.1\% & 25 \\ Topo 3 & 24/80 & 368\(-\)11.5\% & 8352\(-\)18.9\% & 25 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of hardware request between four topologies.  
Figure 3: Three different Virtual topologies, named topology 1, 2, 3. The number of one-hop and two-hop bypass connection cores is separately for 1:1, 2:1, 2:0.  
uration should be set. The number of cores in one multi-core fibre (MCF) is 13 and the supposed topology is a typical 6-node core optical network as mentioned above. Assuming the whole bandwidth in one core is 2 THz and every slot is 12.5 GHz, there are 160 slots in one core based on every traffic can be randomly generated from 12.5 GHz, 25 GHz, 37.5 GHz, 50 GHz, 100 GHz, and 200 GHz corresponding with 1, 2, 3, 4, 8, and 16 slots. In addition, bidirection has been considered in this simulation, and it appears that the 2 THz bandwidth is only for the unidirection in one core and the 4 THz assumed used to present bidirection bandwidth. Meanwhile, the cross talk and attenuation could not be considered as mentioned above there are several methods to compensate them. Furthermore, the distance between every two nodes is assumed 40 km so that EDFAs should not be used in the whole optical network.  
To evaluate the performance of the proposed network with programmable ROADM combined fibre/core switch with WDM routing/selection node, the traffic blocking probability and bandwidth blocking probability of the same traffic request based on different topology should be given. The blocking probability can be seen as the ratio of the amount of rejected traffic and traffic bandwidth which cannot be transmitted network to the total amount of traffic and the traffic bandwidth in the whole optical network.
## 2 Sensor Platform LSI Network System  
### System Overview  
Figure 2 shows the overview of the network system, in which 48 sensor platform LSIs are implemented on a microstrip-based single-ended bus line. Power supplies provide 3.3 V (VDD33) and 1.2 V (VDD12) powers for each sensor platform LSI. The signal (BUS) and ground (GND) lines are connected to a relay node based on an Field Programmable Gate Array (FPGA).  
The relay node performs some primary data processing, such as data decoding and error check using Cyclic Redundancy Check (CRC) codes, for data packets received from the sensor platform LSIs, and then transmits data packets to the PC-based host. The relay node also processes command packets received from the host, and then transmits the encoded command packets to a specific or all sensor platform LSIs. An 8-bit physical address is assigned to each sensor platform LSI for its identification and specific configuration. The address with all bits at '1' is reserved for broadcast. The relay node communicates with the host through a USB interface (FT2232 HL chip from Future Technology Devices International Ltd., Glasgow, UK).  
The host was programmed using C# language on a Personal Computer (PC) to analyze data packets received from the relay node as well as to send configuration command packets to the sensor platform LSIs through the relay node. The communication between the host and the relay node is based on USB, which is supported by FT2232 HL chip on the relay node and a Dynamic Link Library (DLL) file provided by Future Technology Devices International Ltd. on the PC.