esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Machine learning, especially deep learning, has made significant progress in many research areas and applications such as visual recognition, image classification and image processing, etc. However, to the best of our knowledge, no deep learning approaches have been successfully applied to RDH schemes which require images to be completely restored and secret information to be extracted. This motivates us to apply such approaches to RDH. In this paper, we propose a reversible data hiding scheme based on three-dimensional prediction-error histogram modification and MLP networks. We utilize a trained MLP neural network to predict pixel values and combining with PEE to achieve RDH. In addition, the proposed method of modifying the three-dimensional prediction-error histogram can better utilize the space in the three-dimensional coordinates for data embedding. Evaluation of the quality and embedding capacity of the stego-images shows that the proposed method still maintains a good PSNR and increases the maximum embedding capacity which is 1.9-9.8 times of previous methods. Nevertheless, the proposed method still has its disadvantages. Specifically, training the neural network and predicting pixels bit-by-bit are both time-consuming. Developing methods to enhance the efficiency of the proposed method, such as reducing the training time and predicting multiple bits at once, deserves to be further investigated in future works. Moreover, this work focused on proposing a novel reversible data hiding scheme which trains multilayer perceptrons by utilizing the correlation between image pixel values and their adjacent pixels so that the accurate pixel predictions can be achieved. There should be a trade-off between the performance and the fragility. For a future research direction, it is worthy to discuss the impact of fragility caused by transmission errors.  
Conceptualization, H.-C.W.; methodology, H.-C.W., C.-C.H. and C.-W.L.; software, C.-W.L.; validation, C.-C.H., C.-C.L., H.-C.W. and C.-W.L.; formal analysis, C.-C.L. and C.-W.L.; investigation, C.-C.L. and C.-W.L.; resources, H.-C.W.; data curation, C.-W.L.; writing--original draft preparation, C.-C.H. and C.-W.L.; writing--review and editing, C.-C.H. and C.-C.L.; supervision, H.-C.W.; project administration, H.-C.W.; funding acquisition, C.-C.H. and H.-C.W. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Taiwan Ministry of Science and Technology under grant no. MOST 110-2221-E-005 -045, no. MOST 110-2222-E-032-002-MY2, and no. MOST 110-2221-E-167-002.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicting interest regarding the publication of this work.
## 4 Self-Sensing Geosynthetics  
With the advent and development of smart infrastructures and intelligent cities in recent years, demand has increased for multifunctional elements and materials exhibiting improved mechanical performance while monitoring health and warning about potential risks [90; 91; 92; 93]. Self-diagnosing or self-sensing geosynthetics are multifunctional composites that can sense their own conditions such as strain, stress, deformation, damage, and temperature [94; 95; 96; 97]. In contrast to most smart geosynthetics, self-sensing composites intrinsically exhibit piezoresistivity-based sensing capabilities, which enable more practical, integrated, and real-time infrastructural monitoring [7]. In addition to exhibiting improved mechanical properties and early damage detection capability, self-sensing geosynthetics can provide the information required for instrumenting and managing smart urban infrastructural development such as transportation systems. Self-sensing geosynthetics are composed of conductive phases including various conductive particles and fibres and non-conductive phases or host materials including different polymeric or natural fibres and fabrics [98].  
The conductive phase generates conductive paths between the host materials. Owing to percolation and quantum electron tunnelling, several random conductive paths are generated between nano-/micro-particles and/or -fibres [99; 100; 101; 102]. Conductive path electrical resistivity is affected by external factors such as stress, strain, and temperature, which can be detected by measuring the fractional change in the electrical resistivity [103; 104; 105; 106].  
Despite the many advantages of self-sensing geosynthetics over other smart geosynthetics, few studies have been conducted to develop applicable self-sensing geosynthetics [92]. However, smart textiles, fabrics, and polymeric filaments developed for monitoring structural components could also be used in geotechnical applications. Hence, recent progress in self-sensing geosynthetics and polymeric elements for applications to geosynthetic components is discussed to provide insight into the evolution of smart geosynthetics.
## 3 The Analysis
### Meeting the Delay Requirement
: _If the URLLC traffic follows a periodical process with an arrival curve of \(\alpha_{u}(t)\), and it is transmitted through a wireless channel with a service curve of \(\beta_{ch}(t)\), the delay \(D(t)\) of any URLLC packet at time \(t\) is upper bounded by_  
\[D(t)\leq h(\alpha_{u}(t),\beta_{ch}(t))=\frac{k}{R_{2}(n,P, \epsilon,M)\cdot W}, \tag{13}\] \[\text{Subject to: }\frac{k}{\tau}\leq R_{2}(n,P,\epsilon,M)\cdot W.\]  
_where \(h(x(t),y(t))\) is the maximum horizontal distance between functions of \(x(t)\) and \(y(t)\), defined as \(h(x(t),y(t))=\sup_{s\geq 0}\{\inf\{\tau\geq 0:x(s)\leq y(s+\tau)\}\}\)._  
It could be decomposed that the delay is mainly determined by three parts: (1) how an information message is processed by coding and modulation scheme, indicated by codeword length \(n\) and modulation order \(M\); (2) how the channel condition is when it is transmitted, indicated by SNR \(P\) and error probability \(\epsilon\); (3) how much system resource is allocated denoted by system bandwidth \(W\). Therefore, optimization among these factors is needed, which are further discussed below. Figure 2: Equivalent Analysis Model for the Considered Wireless Communication System.
# Surgical Tool Detection in Open Surgery Videos
## 2 Related Work  
### Surgical Tool Detection in Mis and Cataract Surgery  
Surgical tool detection has been worked on using various approaches. These include sensor-based methods such as radiofrequency identification (RFID) [29] or endoscopic image-based methods. The most conventional image-based methods rely on the defined handcrafted features, such as color [30; 31], gradient [32], and texture [33].  
With the rapid development of convolutional neural networks (CNN), most surgical tool detection algorithms started to utilize deep learning techniques. [PERSON] et al. [18] introduced a baseline model called EndoNet that performs both tool presence detection and phase recognition tasks in a multi-task manner. [PERSON] et al. [14] employed the YOLO architecture [34], which can directly predict the location of tools as a representation of the boundary box in real-time but has no outstanding accuracy. [PERSON] et al. [15] utilized a region proposal network and a multi-modal two-stream convolutional network for the surgical tool detector. [PERSON] et al. [9] proposed a U-Net architecture-based model performing tool detection and 2D pose estimation jointly. [PERSON] et al. [13] achieved high detection accuracy using a region-based CNNs (R-CNNs) [27].  
Figure 1: An example of surgical tool detection in an open surgery video.  
Moreover, many approaches that consider the long-term temporal information and model the temporal dependency between the frames have been proposed. Particularly, a method combining CNN with a recurrent neural network (RNN) has been a trend [6; 35; 36]. [PERSON] et al. [36] applied a CNN-RNN model to detect a tool used in the surgical videos; they employed a boosting mechanism instead of end-to-end training. [PERSON] et al. [6] developed an end-to-end approach composed of CNN + convolutional LSTM (ConvLSTM) neural networks that can perform tool presence detection and tool tracking using tool binary labels. [PERSON] et al. [37] proposed taking advantage of both 3D CNNs and graph convolutional networks (GCNs) for tool presence detection, thereby considering the relationship between tools. [PERSON] et al. [38] presented a multi-task recurrent convolutional network with correlation loss (MTRCNet-CL) for tool presence detection and surgical phase recognition.
## 2 Fault Analysis of Load Mutation Caused by Electrical Interference  
The generator set's automatic gain control (AGC) is the basis for the unit to achieve a stable power output or variable load capability. The control performance of the generator set deteriorates and is very likely to cause sudden load change failure when working in a disturbed environment, aging equipment, or grid disturbances. Generation, the key indicator of a unit's generating capacity, can affect its life and economy when a load-mutation fault occurs. Load mutations caused by electrical interference can be categorized into pulses and oscillations [18]. This fault is related only to the generator and electrical system rather than its thermal system. Despite the lack of such failure data for single-shaft CCPPs, many domestic coal-fired units with long operating lives could provide a lot of historical data to extract fault features of load mutation. We can draw on the fault data of the electrical interference of coal-fired units with the same capacity to analyze the fault characteristics of such faults in detail.
# Hybrid Wavelet-CNN Fault Diagnosis Method for Ships' Power Systems
## 6 Conclusions  
The related works mentioned in Section 2 involved only a few proposed methods related to wavelet techniques in coordination with DL. Additionally, there is a strong interest to diagnose faults adequately early when the current exceeds the rated amperage capacity of the circuit of the connected equipment since, in cases of failure faults, the power system could be damaged, and the health of human operators could be threatened. The present work introduced a hybrid wavelet-CNN and a corresponding method for current analysis to diagnose stator winding faults of 3-phase IMs; its main contributions are as follows:  
1. It can automatically and effectively classify faults related to short circuits even in indistinguishable cases where white noise and load changes occur.
2. It can drastically reduce both the training time and the data volume employed for training the neural network while maintaining competitive accuracy. Therefore, the proposed method could be considered a data compression method as well.  
In contemporary sensor network applications that often include mist and edge computing systems with limited processing power and storage capabilities, reductions in model training time and data volume are crucial.  
Future work will be conducted to test the hybrid method with experimental data of IM operation to verify the system's accuracy. We are also interested in testing the method for fault detection in a variety of other applications and classifying several types of faults. An example is the thermographic fault diagnosis of motor shafts. Lastly, possible future work could involve its implementation to other power systems, including power transformers, power generators, and other types of motors, such as brushless DC electric and permanent-magnet motors.  
Conceptualization, C.S.; methodology, C.S. and D.P.; software, D.P.; validation, C.S. and D.P.; formal analysis, C.S.; investigation, D.P.; resources, D.P.; data curation, D.P. and F.G.; writing--original draft preparation, D.P.; writing--review and editing, F.G.; visualization, D.P. and C.S.; supervision, C.S.; project administration, C.S. All authors have read and agreed to the published version of the manuscript.  
This research was cofinanced by the European Union and Greek national funds through the Operational Program of Eastern Macedonian-Thrace Administration titled: \"Innovation, Research and Development Investment Plans Enterprises of the Electronics Production sectors and Electrical Equipment\" (project title: DIAKIVOSI, code: AM@P7-0067180).  
Not applicable.
# Monte Carlo Simulations in Aviation Contrail Study: A Review
## 2 Contrails in Aviation: Generalities
It has been found that, at the microscopical level, contrail cirrus clouds are distinguishable from their natural counterparts, as evidenced by the bimodal spectrum in the particle dimension distribution occurring when both are observed [15; 16]. In ice clouds, sedimentation, deposition growth, and, in particular, radiative properties, depend on the ice crystals' habits [17], and references therein, since ice aggregates scatter shortwave radiation more strongly than pure ice crystals of the same mass. In accordance with the considerations above, the experimental observation of microphysical properties could be crucial for quantitatively determining the environmental impact, since their statistical distributions could, in principle, be used for developing a Monte Carlo calculation (or eventually as a benchmark for ab initio calculations). Unfortunately, experimental findings available in the literature do not build up a statistically significant sample, and there are still aspects of contrail formation and evolution that need to be further observed [6; 18]. In the last three decades, however, an increasing number of articles have been devoted to the discussion of measurement campaign focusing on condensation trails. Some authors [18] distinguish between persistent contrails, which maintain their linear shape, and proper cirrus, which originates from contrails. In general, both these phenomena can be classified as Aircraft-Induced Cloudiness (AIC).
# Scenario-Based Configuration Refinement for High-Load Cellular Networks: An Operator View
## 3 High Population Density Scenario
### Scheduling Request and Channel Quality Indicator  
The parameters related to the scheduling request (SR) and the channel quality indicator (CQI) directly determine the maximum number of online users for a single cell [12]. We take several cells deployed in this university campus using ZTE's devices as an example because only ZTE devices support the adjustment of values of these parameters related to SR and CQI. The other vendors, such as Huawei, Ericsson, Bell, and DTT do not support the customization of these parameters.  
As shown in Figure 6, we represent the traffic feature from campuses covered by ZTE devices. We observe that when the number of online users is larger 250, the wireless connection rate success rate is significantly deteriorated. Meanwhile, the practical cell capacity is much lower than nominal capacity. After studying the configurations related with SR and CQI, 'SRTrCHNum' and 'CQIRpTTINum' are two key parameters directly related with network performance. 'SRTrCHNum' and 'CQIRpTTINum' represent the number of allocated channels corresponding to the SR period of user equipment (UE) and the number of time division subframes in the shortest CQI periods. After reconfiguring 'SRTrCHNum' and 'CQIRpTTINum', the capacity of the corresponding cell can support 400 online users. In addition, we can give the first priority of user access using Huawei's device, which can increase the number of online users to achieve the maximum device capacity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Bell**} & \multicolumn{2}{c}{**Huawei**} \\ \hline
**Downlink PRB** & **Average Uplink PRB** & **Average Downlink** & **Average Uplink PRB** & **Average Downlink** \\
**Utilization Ratio** & **Utilization Ratio** & **PRB Utilization Ratio** & **Utilization Ratio** & **PRB Utilization Ratio** \\ \hline
0\(\sim\)10\% & 2\% & 1\% & 8\% & 2\% \\
10\(\sim\)15\% & 16\% & 12\% & 34\% & 12\% \\
15\(\sim\)20\% & 21\% & 17\% & 31\% & 18\% \\
20\(\sim\)30\% & 25\% & 23\% & 34\% & 23\% \\ \(\geq\)30\% & 30\% & 32\% & 40\% & 42\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: PRB utilization ratio balance between uplink and downlink in dormitories of the university.  
Figure 5: Downlink and uplink traffic and the corresponding PRB utilization ratio in different areas of university campus.
## 2 Materials and Methods
### In Situ Hydrogen Permeation Monitoring Test
#### 2.2.2 In Situ Hydrogen Permeation Monitoring Test in the Outdoor Marine Environments  
The in situ hydrogen permeation monitoring test was carried out to investigate the hydrogen permeation behavior into steel in outdoor marine environments. During the test, sensors were individually suspended on an insulated wire rope and fixed at different positions in the vertical direction of the sea by clamps. The hydrogen permeation sensor was connected to a multi-channel Potentiostat (made in Japan) via copper wires. Meanwhile, a temperature sensor was installed near the hydrogen permeation sensor to monitor temperature changes. According to the tidal record of the experimental sea area, the position distribution of the sensors in the vertical direction of the sea was determined, including the marine atmospheric zone, the splash zone, the tidal zone and the immersion zone. Details of the position distribution of the sensors along the vertical direction of the sea are shown in Figure 3.  
As a blank control, the completely sealed sensor was suspended alone on another insulated wire rope and placed side by side with the sensor exposed to the marine atmospheric zone. Tide heights were monitored in real time through a pressure sensor placed at the bottom of the immersion zone. Both the tidal data and above temperature data were recorded by a HIOKI MEMORY HILOGER. In addition, due to the important influence of humidity on the corrosion of steel in the atmospheric zone, the changes in marine atmospheric humidity were also monitored and recorded by a hygrothermograph. The in situ hydrogen permeation monitoring test started on 6 July 2020, and ended on 12 October 2020, a period encompassing the summer and early autumn of the Northern hemisphere.  
Figure 2: Schematic diagram of the hydrogen permeation sensor.
# Cross-Sectional Shape Optimization of Cylindrical Elastomer Spring for Sensitive Cargo Container
## 3 Discussion  
This study is an experimental and analytical study on the development of an optimal shape of a polyurethane spring instead of natural rubber for anti-vibration spring materials inside a sensitive cargo container.  
A compression displacement control test was performed on the polyurethane spring to verify the effectiveness of the optimized cross-section. Figure 17 shows the results of the compressive load-displacement test and analysis for the original and optimal shapes. Figure 17a shows a compressive load-displacement test using a tester rig. As shown in Figure 17b, the correlation coefficient between the analysis of the original shape and the test result was 0.911, and the correlation coefficient of the optimal shape was 0.928.  
The correlation coefficient of the optimal shape was higher than that of the original shape, so the shape optimization effect could be confirmed.  
Figure 16: Design sensitivity analysis.  
Figure 15: Comparison of cross-section shape for cylindrical polyurethane spring.  
In order to improve the stiffness and strength of the spring, nonlinear transition analysis, optimal design, and spring tests and a comparison was performed in this study.  
Since the test and analysis were performed during the spring development stage, the effect of reducing vibration when loading cargo after applying the spring inside the actual container was not confirmed.  
The vibration optimization of the elastomer spring should take into account the weight or material of the load. Therefore, it will also be necessary to classify several cargoes and optimize vibration by category.  
In future studies, it is necessary to conduct a study to improve the vibration reduction effect through additional vibration optimization and train operation tests after cargo loading.
# Exploring the Spatiotemporal Alterations in China's GPP Based on the DTEC Model
###### Abstract  
Gross primary productivity (GPP) is a reliable measure of the carbon sink potential of terrestrial ecosystems and is an essential element of terrestrial carbon cycle research. This study employs the diffuse fraction-based two-leaf light-use efficiency (DTEC) model to imitate China's monthly GPP from 2001 to 2020. We studied the trend of GPP, investigated its relationship with climatic factors, and separated the contributions of climate change and human activities. The findings showed that the DTEC model was widely applicable in China. During the study period, China's average GPP increased significantly, by 9.77 g C m\({}^{-2}\) yr\({}^{-1}\) (\(p<0.001\)). The detrimental effect of aerosol optical depth (AOD) on GPP was more widespread than that of total precipitation, temperature, and solar radiation. Areas that benefited from AOD, such as Northwest China, experienced significant increases in GPP. Climate change and human activities had a primary and positive influence on GPP during the study period, accounting for 28% and 72% of the increase, respectively. Human activities, particularly ecological restoration projects and the adoption of advanced agricultural technologies, played a significant role in China's GPP growth. China's afforestation plan was particularly notable, with the GPP increasing in afforestation areas at a rate greater than 10 g C m\({}^{-2}\) yr\({}^{-1}\). This research provides a theoretical foundation for the long-term management of China's terrestrial ecosystems and helps develop adaptive ecological restoration tactics.  
Keywords:diffuse fraction-based two-leaf light-use efficiency model; gross primary productivity; carbon cycle; climate change; ecological restoration projects +
Footnote †: journal:
# Rice Sesquierpene Plays Important Roles in Antixenosis against Brown Planthopper in Rice
## 2 Results and Discussion  
### Expression of OsSTPS2 Is Associated with SNPs at Transcription Factor (TF) Binding Sites  
The association between single-nucleotide polymorphisms (SNPs) in three transcription factor binding elements (TFBEs) and the expression of the _OsSTPS2_ gene was investigated in the 18 rice varieties/lines. The TFBEs were included the _Arabidopsis thaliana_ homeobox protein 1 (ATHB-1; consensus sequence AATTAATTGT), silencer-binding factor 1 (SBE1; consensus sequence GTGTGTTAAATAAT), and maize activator P (P element; consensus sequence ACCAACCAG). The promoter region and 5 UTR of the _OsSTP2_ gene were analyzed in 18 rice varieties/lines (NCBI accession no. KC511030 to KC511047). Four rice varieties, including Pinkaset2 (PK2), Pinkaset3 (PK3), Homchonlasit (HCS), and Riceberry (RBR), have all SNPs in the TFBEs, which silence the _OsSTP2_ in either BPH feeding or control conditions (Figure 1, Table 1, Supplementary Figure S1). In contrast, the other 13 rice varieties/lines which express the _OsSTP2_ gene inducibly do not have SNPs in the TFBEs. Correlation between SNPs in the TFBEs and the _OsSTPS2_ expression suggests the roles of these TFBEs in the BPH inducible expression. Based on SNPs in these three TFBEs, three haplotype patterns (HP) of the _OsSTPS2_ gene were identified in these 18 rice varieties/lines (Table 1). The rice genotypes with the HP1 of the _OsSTPS2_ gene consisted of KD, PK2, PK3, RBR, and HCS. All of them were susceptible to BPH. The rice varieties that have _OsSTP2_ expression were grouped into the HP2, consisted of Jaohomnin (JHN), Nipponbare, TN1, Mudgo, ASD7, ARC10550, and Pokkali. Finally, six rice varieties/lines, including PTB33, RH, IL143, IL162, IL302, and IL308, were grouped into the HP3. The _OsSTPS2_ gene was also expressed in this HP group. Figure 1: Expression pattern of _OsSTPS2_ (Os04g27430) in control (\(-\)) and 2-day brown planthopper (BPH) feeding (\(+\)) conditions; rice varieties with an undetectable expression of the gene are labeled in red. The rice ubiquitin (_OsLIBq_) was used as the housekeeping gene check.