esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Super-Resolution Based on CNN Using Multilabel Gene Expression Programming
## 2 Related Work
### SRCNN/Fast SRCNN (FSRCNN)  
[PERSON] et al. [5] first proposed the use of a convolutional neural network for image super-resolution reconstruction. An LR image was first enlarged to the target size using Bicubic interpolation, and then a nonlinear mapping was performed through a three-layer convolutional network. The obtained results were output as high-resolution images, and good results were obtained. As shown in Figure 1, the network structure design of this method is simple. Compared with previous learning algorithms, it saves a lot of artificial feature extraction steps and post-integration, thus opening up the era of deep CNN super-resolution image processing problems.  
After that, [PERSON] et al. [6] improved upon the SRCNN, and the fast SRCNN (FSRCNN) was proposed, which increases the depth of the network and introduces a deconvolution layer to restore features. The deconvolution layer can realize the conversion from low-resolution space to high-resolution space. This feature allows the FSRCNN to directly use the low-resolution image instead of the interpolation result as the network input. Directly using LR images as input can not only  
Figure 1: Network architecture of the super-resolution convolution neural network (SRCNN).  
reduce the calculation amount of the model, but also avoid the obvious artificial traces introduced by interpolation. FSRCNN offers a great improvement in speed, without any preprocessing, to achieve end-to-end input and output of the network, but the restoration accuracy is somewhat insufficient.
# Research on Path and Sequence Planning for Multi-Cabin Onboard Transportation of Large Cruise Ships
## 6 Conclusions  
Addressing the issues of push-pull sequence and path planning for large cruise ships, an improved A-Star algorithm has been proposed. Based on this algorithm, one deck of a large cruise ship was taken as an example to study the maximum push-pull capacity under the constraints of the ship's structure and construction space. Furthermore, considering the limits on the number of pushers and installers, the optimal push-pull plan under human resource allocation was obtained. The main conclusions are as follows:  
Figure 18: Single cabin (16,1) pushing path diagram.  
The improved A-Star algorithm can handle the path planning problem for the transport of multiple cabins simultaneously. When a cabin reaches its destination, the algorithm updates the map in real time by adding the cabin as an obstacle, thus achieving multi-target destination path planning. The improved A-Star algorithm not only recognizes grid obstacles, but also identifies line obstacles.  
The reverse sequencing strategy method used to compile the project schedule addresses the problem of optimizing the transport sequence. This approach considers the inter-relation between transport sequences and their respective paths, avoiding congestion at entry points. It prioritizes cabins that can be transported using optimal paths, allowing for more efficient transport.  
Conceptualization, L.J. and X.W.; methodology, L.J.; validation, K.L. and L.J.; formal analysis; investigation, K.L.; resources, Y.Z.; data curation; writing--original draft preparation, L.J.; writing--review and editing, L.J. and X.W.; supervision, [PERSON] and Y.Z.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is unavailable due to privacy.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company Shanghai Waigaoqiao Shipbuilding Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Discussion and Implication
However, similar research on GNSS acceleration data [36] has shown that following the application of a 1-Hz low-pass Butterworth filter on the raw speed data of different GNSS devices, the derived acceleration variables had decreased differences, suggesting that one smoothing technique used across different GNSS devices might be appropriate, although further research is required to confirm this. To compare the GNSS and 3D motion analysis time-series acceleration, both datasets were synchronised and aligned to achieve the lowest RMSE and highest correlation. Minor alignment inaccuracies may arise when the data are synchronised without an electronic synchronisation point. Including an electronic trigger to create a synchronisation point in both datasets during data collection would enable precise synchronisation. However, to the authors' knowledge, such a solution is currently unavailable. Future research should explore methods to establish a synchronisation point between GNSS and 3D motion analysis data. Data collection was performed outside in optimal conditions on an empty synthetic soccer pitch that was not surrounded by stands. While these conditions are applicable to most field-based team sports, competition usually takes place in stadia surrounded by stands that obstruct the GNSS signal, decreasing the accuracy of the GNSS devices, so the results of this study may therefore not apply to these conditions. Conceptualisation, S.E., D.L.C., P.B.G. and M.C.V. ; Methodology, S.E. and M.C.V. ; Software, S.E. ; Formal analysis, S.E. ; Investigation, S.E. ; Writing--original draft preparation, S.E. ; Writing--review and editing, D.L.C., P.B.G. and M.C.V. ; Visualisation, S.E. ; Supervision, D.L.C., P.B.G. and M.C.V. ; Project administration, S.E. All authors have read and agreed to the published version of the manuscript. This research received no external funding. All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of La Trobe University (reference number: HEC22066). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical restrictions. The authors report no conflicts of interest.
## 4 Communication Technologies for Seamless Connectivity in Smart Farming  
Seamless connectivity is a vital part of IoT-based smart farming. The provision of connectivity and value-added services have a large effect on the entire chain. The connectivity services that are offered by cellular operators only indicate the small portion of the entire intelligent farming market. Telecom operators should provide a new range of services, in rural regions in particular, due to increasing the demands.
# Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy
## 3 Results and Discussion
### Identification of Potential Rainfed Fields
The mean monthly NDVI was also compared with the mean soil moisture retrieved over the two groups of fields. The comparison does not show any appreciable difference between the maize fields cultivated within or outside the irrigation districts.
## 3 Preliminary Foundation
### System Model  
The system model is comprised of three different entities, as illustrated in Figure 1; namely, data owner, data user and cloud server. The data owner has a collection of documents which will be uploaded to the cloud server. An index is generated for each document in order to efficiently search the documents by keywords which are raised later by data users. To protect the information in the documents from the peep of the cloud server, the documents and the corresponding indexes are encrypted before outsourcing to the cloud server. Finally, the data owner uploads the encrypted documents and index to the cloud server.  
When a data user wants to search the documents of his/her own interest, the data user has to ask the authorization from the data owner for the search. Based on the query keywords provided by the data user, the data owner creates the trapdoor, which is a secret key generated by encrypting the query keywords, to the data user. The data user sends the trapdoor to the cloud server where a pre-defined search procedure is executed. Since the trapdoor is also encrypted, the cloud server cannot know the search keywords and, thus, the privacy of the data user is also secured. The pre-defined procedure essentially computes the similarity between query keywords and the document indexes. The cloud server will return the corresponding encrypted documents with high similarity scores to the data user.
# The Impact of Optogenetics on Regenerative Medicine
## 3 Biomedical Applications of Optogenetics
### Applications in Neurological Diseases  
Useful applications of optogenetics also involve the study of the peripheral nervous system (PNS). Researchers have assessed that the expression of opsins can be induced through adequate viral concentration; on the other hand, the use of ATR (all-trans-retinal) acid has been used to increase the photosensitivity of tissues releasing ChR2 in the PNS. This strategy allowed an optogenetic stimulation, followed by an electrophysiological response of PNS [41]. The vestibular system is controlled by neurons within the vestibular nucleus (VN), stimulating the auditory, visual, and motor centers [42]. Generally, through fMRI (functional magnetic resonance), brain functions are mapped in response to different sensory or cognitive tasks; however, fMRI does not work well in the studies on VN. Optogenetics overcomes these limitations by stimulating VN neurons with light that activates neurons of the associated thalamic nuclei (auditory, visual, somatosensory, and motor). Combining optogenetics with fMRI, the researchers have answered the main questions related to the visual cortex, in the thalamus, for more precise medical diagnoses [43].  
In recent years, many studies have investigated the usefulness of optogenetics in pain therapy [44]. Chronic pain is often related to neuronal damage, immunological impairment, and inflammatory conditions: it is characterized by the production of molecules correlated with specific ion channels [45]. The connection between the modulation of ionic channels activating the nociceptors was observed. In the priming of immune cells, the receptors that couple to the G proteins (GPCR) are very important [45]. Light-mediated manipulation of G protein subunits of ChR2 and NpHR proteins can activate or inhibit the receptors of injured neurons. The sequencing of the RNA can promote novel research targeting such protein chains able to control the pain with optogenetic tools [46].  
The optogenetic stimulation on ChR2 proteins expressed by spinal astrocytes leads to reversible and time-depending hypersensitivity to pain. Therefore, pain can be treated through optogenetics working on spinal astrocytes [47]. Cortico-trigeminal pathways can modulate nociceptive input from the spinal trigeminal caudal nucleus (SpVc) and regulate pain perception. Through optogenetic stimulation on ChR2, there is a suppression of the SpVc responses to noxious stimuli, leading to a pain modulation [48].
## 2 Islanding Detection Methods
### Active Methods
#### 2.2.2 Sandia Frequency Shift Method (SFS)  
This technique, which applies positive feed-back to the frequency of the inverter voltage, is seen as an extension of the AFD technique [17]. When islanding happens, the frequency change introduces an inverter phase angle error, which persists until it exceeds the threshold value. In this method, the islanding detection effectiveness depends on the selection of parameters, such as chopping frequency \(\mathrm{Cf}_{\mathrm{o}}\) and accelerating gain \(\mathrm{k}\). The SFS parameters can be given as:  
\[\mathrm{\theta}_{\mathrm{inv}}=\mathrm{\pi}(\mathrm{Cf}_{\mathrm{o}}+\mathrm{ k}(\mathrm{f}-\mathrm{f}_{\mathrm{n}})), \tag{6}\]  
where \(\mathrm{\theta}_{\mathrm{inv}}\) is the inverter phase angle, \(\mathrm{f}\) is the islanding frequency, \(\mathrm{f}_{\mathrm{n}}\) is the nominal frequency, and \(\mathrm{Cf}_{\mathrm{o}}\) and \(\mathrm{k}\) are the SFS parameters.  
This method has the ability to detect the islanding within 6 cycles [18]. When compared to other active approaches, it has the smallest NDZ. Moreover, SFS efficiently reduces the impact of system transient response indetecting efficiency and power quality.
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 3 SatScope Model Framework
### Network Simulator
#### 3.3.1 Access Satellite  
According to relevant studies [39] and FCC documentation [46], Starlink's ground nodes prefer satellites that are located to the north, have a high elevation angle, and are at a short distance as their access satellites. The rules for selecting an access satellite from among visible satellites are as follows:  
* Azimuth \(\theta\): Satellites located to the north are preferred with a selection probability of 82%.
* Elevation \(\phi\): The probability of selecting satellites with high elevation angles (\(445^{\circ}\leq\phi\leq 90^{\circ}\)) is 80%, while for those with lower elevation angles (\(25^{\circ}\leq\phi<45^{\circ}\)), it is 20%.
* Distance \(h\): Although no explicit selection probability is assigned to distance \(h\), preference in the weighting calculation leans towards choosing the nearest satellite to enhance the stability and performance of the communication link.  
The calculation of weights incorporates the selection probabilities based on azimuth and elevation angles, with distance serving as a reference factor. The formula for calculating the weight is as follows:  
\[W=P(\theta)\cdot P(\phi)\cdot f(h), \tag{2}\]  
where  
\[P(\theta)=0.82, \tag{3a}\] \[P(\phi)=\begin{cases}0.8,&45^{\circ}\leq\phi\leq 90^{\circ}\\ 0.2,&25^{\circ}\leq\phi<45^{\circ}\end{cases},\] (3b) \[f(h)=1-\frac{h-300}{500},\quad 300\leq h\leq 800. \tag{3c}\]  
The weight of each visible satellite is calculated based on its azimuth, elevation angle, and distance [39]. The satellite with the highest weight is then selected as the access satellite.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 3 Preliminaries
### System Model  
Figure 1 presents the system model for the proposed protocol in an IoD environment. The proposed system model comprises three entities: a drone \((D_{j})\), ground control station (GCS), and mobile device \((MD_{i})\). Detailed information about these three entities is provided below:  
* Ground control station (GCS): The GCS is considered a fully trusted entity and controls communication between mobile users and drones. The GCS assigns secret values to  
Figure 1: Proposed system model.  
each entity and registers mobile users and drones. The GCS database is considered secure and cannot be compromised by a malicious attacker.
* Drone \((D_{j})\): The drone is registered with the GCS and assigned secret values before deployment in a flight area. Afterward, the drone performs mutual authentication and key agreement with mobile users and communicates with them. Drones deployed in specific flight areas collect and transmit data through their sensors to mobile users.
* Mobile device \((MD_{i})\): In an IoD environment, the mobile user registers the device with the GCS and is assigned secret values. Afterward, the mobile user performs mutual authentication and key agreement with the drones and communicates with them to collect the necessary information.
# A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data
## 2 Study Area and Data
### Hj-1B Satellite Data  
HJ-1B is a small optical satellite that is part of the \"Environment and Disaster Monitoring and Forecasting with a Small Satellite Constellation (HJ-1)\" project in China. The HJ-1 satellite system is dedicated to the study of ground environment changes and disaster monitoring. HJ-1B carries two charge-coupled device (CCD) cameras and an infrared camera sensor (IRS). The sensor parameters are shown in Table 1[17; 18].  
The source data used in this paper are IRS infrared data, _i.e._, the Near Infrared band (NIR, band 5), Shortwave Infrared band (SWIR, band 6), MWIR band (band 7) and LWIR band (band 8). Compared to the Moderate Resolution Imaging Spectroradiometer (MODIS), IRS in HJ-1B is more appropriate for detecting small and cool fire spots with finer pixel resolutions of 150 m/300 m and a temperature saturation point of 500 K. The revisit frequency of 4 days also meets the time requirement for forest fire monitoring. [PERSON] _et al._[19] statistically analyzed and compared the B  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Satellite** & **Sensor** & **Band** & \begin{tabular}{c} **Spectral** \\ **Range (\(\mu\)m)** \\ \end{tabular} & \begin{tabular}{c} **Centre** \\ **Wavelength** \\ **(\(\mu\)m)** \\ \end{tabular} & \begin{tabular}{c} **Spatial** \\ **Resolution** \\ **(m)** \\ \end{tabular} & \
\begin{tabular}{c} **Amplitude** \\ **Width (km)** \\ \end{tabular} &
\begin{tabular}{c} **Revisit Cycle** \\ **(Days)** \\ \end{tabular} \\ \hline \multirow{6}{*}{HJ-1B} & CCD1 & 1 & 0.43\(-\)0.52 & 0.475 & \multirow{6}{*}{30} & \multirow{6}{*}{720} & \multirow{6}{*}{4} \\  & and & 2 & 0.52\(-\)0.60 & 0.56 & & & \\ \cline{1-1}  & CCD2 & 3 & 0.63\(-\)0.69 & 0.66 & & & \\ \cline{1-1}  & CCD2 & 4 & 0.76\(-\)0.90 & 0.83 & & & \\ \cline{1-1} \cline{2-7}  & & 5 & 0.75\(-\)1.10 & 0.92 & & & \\ \cline{1-1}  & IRS & 6 & 1.55\(-\)1.75 & 1.65 & & & \\ \cline{1-1}  & & 7 & 3.50\(-\)3.90 & 3.70 & & & \\ \cline{1-1}  & & 8 & 10.5\(-\)12.5 & 11.5 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: HJ-1B satellite sensor parameters.  
Figure 1: The location of the study area (**a**); and the 30-m HJ-1B CCD false color image (R: Near Infrared (NIR), G: Red, B: Green) acquired on 11 May 2009 (**b**).  
thermal infrared bands (band 7 _vs._ band 21). They found that the main BT distribution range of HJ-1B is narrower than that of the MODIS data, although it includes more random noise.  
The HJ-1B/IRS data used in this paper include 13 images acquired in 2009. The data were downloaded from the China Centre for Resources Satellite Data and Application website [17]. The temporal distribution of the images is shown in Figure 2, and two BT images from band 7 and band 8 acquired on 22 May 2009 are shown in Figure 3. Although the revisit cycle of HJ-1B is four days, we can obtain two available images over a two-day period because study area is covered in the overlapping region of the two images photographed in different tracks.
# Brushless Field Excitation Scheme for Wound Field Synchronous Machines
###### Abstract  
A new harmonic field excitation technique for the brushless operation of wound field synchronous machines (WFSMs) is proposed in this paper. The proposed scheme involves conventional three-phase and single-phase inverters operating at different frequencies and supply input current to the armature winding simultaneously. This results in a composite output current of the inverters, which contains fundamental as well as the third harmonic current components. The fundamental is utilized to develop the stator field, on the other hand, the third harmonic is used for developing the pulsating magnetomotive force (MMF) in the airgap. This MMF produces a harmonic current in the harmonic winding of the rotor which is later rectified to inject field current to the rotor field winding. The theoretical analysis of the proposed technique is supported using 2-D finite element analysis (FEA).  
brushless operation; harmonic field excitation; synchronous machines; wound field machines
## 4 Experiment
### Results
This overhead is not required with _max/mean_ because the target length \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method (_Length_Position_Value_)**} & \multicolumn{2}{c}{**Execution time (ms)**} \\ \cline{2-3}  & **Without Attention** & **With Attention** \\ \hline _max\_front\_const_ & 2.8073 & 2.9707 \\ _max\_front\_rept_ & 2.6802 & 3.2585 \\ _max\_end\_const_ & 3.0572 & 3.4547 \\ _max\_end\_rept_ & 3.3429 & 3.4 \\ \hline _mean\_front\_const_ & 0.6032 & 0.6113 \\ _mean\_front\_rept_ & 0.5312 & 0.631 \\ _mean\_end\_const_ & 0.5182 & 0.6322 \\ _mean\_end\_rept_ & 0.5762 & 0.6553 \\ \hline _bmax\_front\_const_ & 0.3254 & 0.345 \\ _bmax\_front\_rept_ & 0.3562 & 0.3627 \\ _bmax\_end\_const_ & 0.368 & 0.3195 \\ _bmax\_end\_rept_ & 0.363 & 0.3347 \\ \hline _bmean\_front\_const_ & 0.1926 & 0.2004 \\ _bmean\_front\_rept_ & 0.1869 & 0.2201 \\ _bmean\_end\_const_ & 0.1784 & 0.2084 \\ _bmean\_end\_rept_ & 0.2016 & 0.1916 \\ \hline PackedSequence & 0.316 & 0.3187 \\ \hline Proposed & 0.3961 & 0.4529 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average execution time (ms) for extracting one embedding. is fixed, and therefore, the truncation/padding is applied in advance. In the case that the standard deviation of the number of frames is somewhat large, like in our experiments (i.e., 30.7), reducing the amount of computation using _bmax/bmean_ is more effective to reduce execution time than applying truncation/padding in advance using _max/mean_. On the contrary, in the case that the standard deviation is small enough, applying truncation/padding in advance can be more effective than reducing the amount of computation. Therefore, it is necessary to select proper method depending on the database used. On the other hand, there is no significant difference according to position or padding value. In our experiments, the proposed method is applied to the output sequences obtained by PackedSequence. The proposed method shows longer execution times than PackedSequence (a relative increase about 33.7%). The increased execution time is due to the output truncation and sequential processing. Recall that the female evaluation trial in Table 1 (i.e., without attention) and the development trial in Table 2 (i.e., with attention), where _max_ showed lower EERs than the proposed method. The _max_ reduced relatively lower EERs of approximately from 0.2 to 1.8%. However, the _max_ revealed far longer execution times, increased approximately from 6.7 times to 8.4 times. In real applications, therefore, use of the _max_ is not good choice when considering not only EER but also execution time, even though the max exhibited lower EERs than the proposed method on those trials.
# Absolute Capacitance Measurement by Direct Digital Fitting of Proportional Coefficient
## 2 Measurement Principle and Simulation
### Direct Fit Proportional Coefficient Capacitance Measurements  
#### 2.2.1 Capacitance Measurement Principle  
In the first-order RC circuit, the solution is solved by proportional coefficient fitting, and one end of the circuit is grounded, which makes the circuit design simpler. The same principle as the voltage source circuit shown in Figure 1, after the input signal is a digital voltage source that generates a square wave excitation signal [24], the RC circuit will obtain the corresponding output signal. As shown in Figure 3, in the RC circuit, \(V_{i}\) is the voltage of the input signal, \(R\) is the standard reference resistor, \(C\) is the measured capacitance, \(V_{o}\) is the voltage of the capacitor.  
When \(V_{i}\), \(V_{o}\), and \(R\) are known, the capacitance of the capacitor can be found by Kirchhoff's law.  
It can be obtained from [PERSON]'s law:  
\[C\frac{dV_{o}(t)}{dt}=\frac{V_{i}(t)-V_{o}(t)}{R} \tag{9}\]  
Figure 3: Image of square wave response signal with different capacitance.  
Figure 2: Input and output signal under noise interference of traditional charge–discharge method. (**a**) Overall diagram of the signal. (**b**) Localized enlarged view of U = 2.5 V position.  
After the conversion of Equation (9) to Equation (10), we can observe that the difference between the input signal and the output signal is proportional to the slope of the output signal.  
\[RC=\frac{V_{i}(t)-V_{o}(t)}{\frac{dV_{o}(t)}{dt}} \tag{10}\]