esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Transcript Assembly and Annotation  
Raw sequence data (SRA accession: PRJNA491434) were processed by trimming the adaptor sequences and removing low-quality or empty reads. The clean reads were clustered using the Trinity v2.4.0 program (BroadInstitute, US, [[https://github.com/trinityrnaseq/trinityrnaseq](https://github.com/trinityrnaseq/trinityrnaseq)]([https://github.com/trinityrnaseq/trinityrnaseq](https://github.com/trinityrnaseq/trinityrnaseq))). The resulting contigs were further assembled to produce unigene ([[http://seq.cs.iastate.edu/cap3.html](http://seq.cs.iastate.edu/cap3.html)]([http://seq.cs.iastate.edu/cap3.html](http://seq.cs.iastate.edu/cap3.html))). All these contigs were functionally annotated by searching the Uniprot database using the Blast program [31]. The level of gene expression in each sample was estimated by the number of reads that were assembled into a contig. The expression value was normalized for the length of the contig and for the total number of reads from a cDNA library by the reads per kilobase per million (RPKM). The gene expression data were then used for hierarchical clustering analysis of the samples and contigs using the TM4 MeV software package.
# Comparing the Diagnostic Accuracy of CBCT
## 1 Introduction
It is difficult to specify the position of jaw bone therefore, the DXA is not a suitable approach to measure the jaw bone density [18; 19]. Some researchers demonstrated strong correlations between DXA and CBCT, and suggested that CBCT is useful for the diagnosis of osteoporosis in mandible [20; 21]. However, other researchers concluded that although CBCT has many advantages over DXA, it should not be used for the screening of osteoporosis, due to a lack of evidence in the literature [11]. Nevertheless, CBCT could help during the initial diagnosis of osteoporosis and point out the need for patient referral if an individual suffers another related disease [11]. Moreover, some researchers concluded that CBCT is not adequate for jaw BMD assessment [22; 23; 24; 25]. However, other researchers concluded that CBCT is adequate for jaw BMD assessment [26; 27; 28]. This variation in the literature might owe to the use of different machines, different voxel values, different imaging parameters, different positioning sites, and measuring different bone regions. In addition to that, most of the above aforementioned studies used small sample sizes [25]. This controversy in the literature, as well as the lack of undisputed relationships between CBCT GS values and DXA T or Z scores, make it necessary to conduct further studies in this regard. In addition, the literature does not include investigations on the GS values of jaw bones at different maxillary and mandibular regions among osteoporotic patients. No previous literature established which GS valueswould associate with osteoporosis, especially in the maxilla. Therefore, this study was carried out to provide more acquaintance in this regard. This investigation aimed to measure the bone mineral density (BMD) of anterior maxilla, posterior maxilla, anterior mandible, and posterior mandible using CBCT GS values, and to find the relationship between the measured CBCT GS values and DXA scores among osteoporotic and normal dental implant participants. In addition, it helps to establish the CBCT GS values in different jaw regions that might indicate or associate with osteoporosis. The null hypothesis for this inquiry was set as that no correlation exists between CBCT GS values and DXA scores among normal or osteoporotic participants, and that CBCT GS values are not related to the existence of osteoporosis.
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 3 Proposed ACS Architecture
### Core Network  
In this architecture, the core network is a dedicated network managed by the railway operator. According to the SDN paradigm, there is a clear separation between control and data planes. The SDN controller is the main element of the control plane, which in this case is out-of-band because there is a point-to-point connection from the controller to each SDN-based device--OpenFlow (OF) switches and OF access points (OF-APs)--of the network (see Figure 2). Additionally, OF is set as the southbound interface, and the forwarding is based on an SDN path-computing application that is explained later.  
The data plane consists of several OF switches forming a mesh topology. These switches connect the access network and the control center network, which is a private network formed by the CCE that supports railway services (e.g., telemetry).
# Digital Twin-Driven Stability Optimization Framework for Large Underground Caverns
## 3 Practical Implementation and Case Study
### Digital Twin Application for Monitoring and Stability Assessment  
#### 3.2.1 Feasibility Investigation and Laboratory Testing Reports  
The feasibility investigation included extensive geological assessments and laboratory testing to characterize the rock mass conditions for underground powerhouse cavern construction. First, a comprehensive geological survey and site investigation were conducted to understand the regional and site-specific geological conditions. Field mapping identified rock types, stratigraphy, fault zones, and structural discontinuities, while borehole drilling and core logging provided subsurface data, including lithology and rock quality designation (RQD). It established that the underground cavern consists of two kinds of sandstone (SS-1 and SS-2), siltstone, and mudstone. Second, the laboratory testing of rock samples determined key physical and mechanical properties, including uniaxial compressive strength, tensile strength, elastic modulus, Poisson's ratio, cohesion, friction angle, slake durability, and shear strength parameters. The properties of intact rock and the parameters for powerhouse were determined as shown in Table 3. This foundational step ensures accurate deformation monitoring and optimization of rock support design for underground cavern stability during and after construction.
## 3 Indoor Positioning Systems
### Systems Based on Inertial Sensors
Most algorithms designed to identify stage events detect feet in the support phase on the ground, where the strategy is to monitor the lack of activity measured by the IMU (acceleration magnitude) during the support phase [10]. As an alternative, some methods detect repetitive movements to determine the moments when the feet are either on the ground or suspended [10]. Finally, the last point in the fundamental cycle of an inertial system is the estimation of the direction of the person's head at each step. The direction estimation is obtained by integrating the gyroscope signal, which deviates by the errors accumulated during integration [88]. Fortunately, the error growth of the step and heading systems is linear over time, instead of cubic, as in the strapdown systems. The user's location is obtained by a magnetometer, which in indoor environments is influenced by ferromagnetic materials, and may result in a degraded position estimate [74]. The fusion scheme of data obtained from both the magnetometer and the gyroscope shows relatively good accuracy, with the deficiencies of each acting complementarily. That is, the gyroscope produces high-precision measurements in the short term, and the magnetometer provides low-precision measurements that are  
Figure 7: Strapdown navigation system. stable over time [74]. [PERSON] describes an example of a step and direction system, where the authors designed a system for portable smartphones [96]. Likewise, [PERSON] built a system to be used on a smartphone resting in a user's pocket to detect movement using the gyroscope signal [87]. Despite the improvements of inertial systems in reducing drift, it is still present. Therefore, this type of system cannot be applied for a long time without a correction strategy. Inertial systems are fully scalable in terms of size and number of users, as there is no need to deploy infrastructure in the environment. Furthermore, smartphones nowadays already include embedded IMU. Therefore, the cost of the system is minimal. However, as previously described, it is essential to consider the reduced precision produced by inertial drift.
## 1 Introduction  
### On Secure Communications  
Communications systems, standards, and technologies have been developed since ancient times. Today we have the internet, Internet-of-Things (IoT), operating fourth generation wireless networks (LTE), and the expected fifth generation wireless networks. An important requirement of any communication paradigm between these devices is to accomplish secure communication, i.e., to protect the privacy and integrity of users' data that is transferred over the network. To achieve the security of transferred data which can contain sensitive information (e.g., bank account credentials, social security number, etc.) it is of utmost importance to defend against attacks. These attacks might be launched by an eavesdropper ([PERSON]) who has access to the information channel between the communicating parties A ([PERSON]) and [PERSON] ([PERSON]). The attack is passive if it eavesdrops without disturbing the channel. The attack is active (invasive) if [PERSON] disturbs or changes the channel, such as with a man-in-the-middle attack. In the present paper, we introduce a new passive attack against the Kirchhoff-Law-Johnson-Noise (KLJN) secure key exchange scheme.
# Potato Leaf Disease Segmentation Method Based on Improved UNet
## 3 Results
### Experimental Results and Analysis  
This study employed an improved UNet network model to train on the potato dataset, yielding a well-trained model. To validate the effectiveness of this model, a batch size of 1 and 300 iterations were selected for the training process. This accelerated the model's training speed and significantly improved its data-fitting capabilities, contributing to enhanced accuracy in potato lesion segmentation on a limited dataset. Ultimately, the model achieved an impressive Dice coefficient of 88.86% in the segmentation task, which is a notable result. During training, the model was saved every five iterations, and the stochastic gradient descent (SGD) algorithm was utilized for optimization, aiding in the gradual fine-tuning of network parameters to optimize model performance. Through these steps, a trained and optimized model was established, capable of accurate potato leaf lesion segmentation in practical applications.
## 5 Conclusions  
This study employs a graded loading approach combined with similarity theory to design stepwise creep experiments for gangue-roof composites with varying particle sizes and moisture contents, establishing both a fractional-order creep constitutive model and a three-dimensional creep constitutive model for the gangue-roof composite system, with parameter identification conducted for the fractional-order model. Key conclusions are as follows:  
1. Gangue from Xinjuolong Mine exhibits a bulk density of 2.11 g/mL and a bulking factor of 1.203, with continuously graded particle sizes (0-5 mm fraction dominant at 40.1%). Its water absorption progressively saturates over time (reaching 4.51% at 48 h), while mineralogical composition is dominated by quartz and kaolinite, and chemical components primarily consist of SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) (combined 85.58%).
2. Creep deformation of the gangue-roof composite progresses through initial and steady-state stages, where both instantaneous and creep strains decrease with increasing particle size. Smaller particles exhibit greater deformation due to enhanced fluidity and crushability under instantaneous and long-term loading, whereas larger particles demonstrate superior structural stability and deformation resistance, particularly under high-stress conditions. Higher moisture content amplifies material fluidity and softening effects, rendering gangue more prone to fragmentation under instantaneous pressure (resulting in increased instantaneous deformation) while inducing nonlinear evolution of creep strain during long-term loading.
3. The established fractional-order creep constitutive model effectively characterizes the composite's rheological behavior. Parameter identification reveals that particle size governs both elastic modulus and fractional-order coefficient, with larger particles enhancing elastic components while diminishing viscoplastic responses.  
Conceptualization, P.H. and Y.W.; methodology, P.H. and Y.W.; software, Y.W.; validation, P.H., Y.W. and E.T.; formal analysis, P.H., Y.W. and G.R.; investigation, Y.W., S.M., B.W. and Q.L.; resources, P.H. and Y.W.; data curation, P.H. and Y.W.; writing--original draft preparation, P.H. and Y.W.; writing--review and editing, P.H., Y.W., G.R., E.T., S.M., B.W. and Q.L.; visualization, Y.W.; supervision, P.H.; project administration, P.H.; funding acquisition, P.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (project No. 73521064), the Discipline Innovation and Talent Introduction Program for Higher Education Institutions (project No. B21016), and the Jiangsu Province Graduate Practice Innovation Program (project No. SJCX25_1367).  
Not applicable.  
The data presented in this study are available from the corresponding author upon request.  
Author [PERSON] was employed by the company China Energy Jinjie Energy Company. 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.
# Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain
## 5 Discussion of the Results  
The objective of this section is the empirical analysis of multiple-seasonal Holt-Winters models applied to hourly electricity demand in Spain.  
The literature found that analyses the parameters of the models tries to give a solution to the theoretical stability analysis, with the determination of the concept of predictability. However, its application to Holt-Winters models is not direct. It is necessary to carry out an empirical analysis of the models and their forecasts, and from there to draw conclusions about predictability.  
A framework was established consisting of the usual process of adjustment and forecasting using a Spanish hourly electricity demand data set provided by REE. The forecasts obtained are then analyzed.  
The double seasonal models with an 8 week adjustment period are shown to be robust with respect to predictions. Two different periods, with different characteristics, were used and 24-hour prediction MAPEs of around 2% to 2.6% were obtained. In the models with the best behaviour, the parameters were analyzed, and a direct relationship was found between variability and high values of the smoothing parameters associated with seasonality, and in the periods when a greater number of holidays occur.  
Figure 6: MAPE of the forecasts for 24-hours ahead according to the size of the dataset used to obtain the \(AMC_{24,168,8766}\) model.  
The size of the observed data set influences the stability. The MAPE of the \(AMC_{24,168}\) model has a variability of 0.2%, which from about 20,000 h is reduced to 0.1%. The \(NMC_{24,168}\) model is much more stable--the series did not show a trend either--achieving this stability in sets longer than 5000 h. As the number of observations increases, the smoothing parameters show a stabilization.  
In the case of triple seasonal models, a large number of observations are necessary to adjust the model. Therefore, parameter values are stabilized from the beginning. It can be seen how the predictions get worse when the set of values starts at significant dates in the autumn, a season with many public holidays. Forecasters need to use a dataset avoiding to start in autumn. If no other solution is possible, it is interesting to reduce the dataset size as it is large enough, but not including autumn.  
In short, it can be seen that the parameters need a large data set to stabilize, and that the triple seasonal models are not able to improve the double seasonal forecasts due to the calendar effect. As a consequence, it is necessary to develop models that are able to reduce this variability by including the calendar effect in the model.
# Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder
## 3 State Recognition Process of MCPs Based on the SSAE  
The MCP status identification process based on the SSAE is shown in Figure 3.  
Figure 2: SSAE structure.  
then, the frequency domain signal is normalized, and the normalized frequency domain amplitude signal is obtained.
2. Dataset partitioning: The normalized frequency domain amplitude signals of the MCP in different states are divided into training sets and test sets, which are convenient for subsequent SSAE feature extraction.
3. Depth feature extraction: Taking the training set as the input of the SSAE model, unsupervised depth feature extraction is carried out to extract the hidden features of the vibration signals of the MCP under different states.
4. Classification training: The feature parameters extracted using the SSAE are input into the classifier for classification training.
5. Model verification: After the test set is input into the trained SSAE model for feature extraction, the test set is input into the classifier for state recognition, and the model is judged according to the state recognition accuracy.
## 8 Future Directions
### Hybrid VLC Systems  
Combining LEDs and lasers in a hybrid VLC system can harness the broad coverage of LEDs and the high data rates of lasers. LEDs can provide general illumination and broad coverage, while lasers are used for high-speed data transmission over longer distances. A system where LEDs handle ambient lighting and data broadcasting, while laser diodes are used for point-to-point high-speed communication within the same environment, optimizes overall system performance.
## 4 Postprocessing
### Filtering
The colors represent filter activity and soundings passing all filters are shown in white. Numbers in brackets represent filter throughputs. Above land (Figure 8, left), the potential outliers filter reduces the variance of \(\Delta\)XCO\({}_{2}\) from 2.05 ppm\({}^{2}\) to 1.68 ppm\({}^{2}\). The Angstrom exponent \(\hat{\text{A}}\) is the dominant parameter, contributing 38% to the variance reduction. All parameter thresholds found for the potential outliers filter above land are listed in Table 1 (left). Above sea (Figure 8, right), this filter reduces the variance of \(\Delta\)XCO\({}_{2}\) from 1.22 ppm\({}^{2}\) to 1.09 ppm\({}^{2}\). In glint geometry, scattering is less important and the dominant parameter is the wavelength squeeze in the wCO\({}_{2}\) fit window \(\lambda_{\text{sq}}^{\text{wCO}^{2}}\), contributing 32% to the variance reduction. All parameter thresholds found for the potential outliers filter above land are listed in Table 1 (right). The combined throughput of all three post-filters (convergence, residual, and potential outliers) is about 42%.
# Seismic Effectiveness of Multiple Seismic Measures on a Continuous Gider Bridge
## 5 Calculation Analysis and Discussion
### Internal Force  
#### 5.3.1 Impact Force at Expansion Joints  
The bounding at bridge expansion joints under the action of the earthquake is an important control object in seismic design. Seismic measures greatly influence the impact force of expansion joints. Figure 12 shows the impact force acting on the expansion joint under the action of a large earthquake in different cases. Figure 12a shows the time history of the impact force at the expansion joint without seismic measures in Case 2. In the whole process of seismic excitation, the expansion device collides many times, with a maximum impact force of 30,301 kN.  
Figure 12b shows that Case 3, in which the restrainer is installed, has a maximum impact force of 28,973 kN, which is smaller than Case 1. However, the expansion joint still collides due to the fact that the restrainer has a relatively limited restraining capacity. In Case 4, in which the unseating prevention devices are installed (see Figure 12c), and Case 6, in which the restrainer and unseating prevention devices are installed (as shown in Figure 12d), the impact forces at the expansion joints almost disappear, meaning that the seismic measures can effectively control the bounding between girders under the action of earthquakes.  
The results showed that, under proper configurations, the seismic measures work effectively in reducing the actions of bounding in the longitudinal direction. The restrainers were effective through the entire recording and reduced the maximum hinge opening compared to Case 2. The result of this study is identical to that of [PERSON] and [PERSON] [32; 38].  
Figure 12: Impact force at expansion joints under a large earthquake: (**a**) Case 2, (**b**) Case 3, (**c**) Case 4, and (**d**) Case 6.
# A New Constrained State Estimation Method Based on Unscented Hoo Filtering
## Preliminaries
### State Estimation under Constraints  
The classical Kalman filter (KF) provides optimal state estimates under Gaussian disturbances and linear model assumptions [20]. In practice, however, the system state vector may need to satisfy an inequality or an equality constraint [21]. For example, the liquid height limitation, the mass of conservation and the state of charge of lithium-ion batteries should be in the range of 0 and 1, the species concentrations are nonnegative in a chemical reaction, etc. Constraint handling has become an integral part of any practical state estimation.  
The process model and measurement model of the system are shown in Equations (14) and (15). The state may need to satisfy some equality constraints \(D_{k}x_{k}=d_{k}\), or some inequality constraints \(D_{k}x_{k}\leq d_{k}\), where \(D_{k}\) is a known constraint matrix and \(d_{k}\) is a known constraint vector. For any \(k\geq 0\), this paper assumes that the \(D_{k}\) matrix is fully-ranked, and \(d_{k}\) is a constraint vector. The constrained state estimation problem is to estimate the state in Equations (14) and (15) under the condition that the constraints are satisfied. We utilize the UHF for the constrained state estimation problem to improve the state estimation accuracy by minimizing the error function according to an additional constraint, e.g., \(D_{k}x_{k}=d_{k}\).  
The solution to the estimation of the constrained state \(x_{k}\) can be formulized by [11]  
\[\left\{\begin{array}{l}\min J(x_{k})=(x_{k}-\hat{x}_{k})^{T}W(x_{k}-\hat{x}_{ k})\\ s.t.D_{k}x_{k}=d_{k}\end{array}\right. \tag{34}\]  
where \(W\) is a positive definite weighting matrix, \(x_{k}\) is the state without the constraint, \(\hat{x}_{k}\) is the estimated state with the constraint. The constrained optimization problems can be solved using the Lagrange multiplier method as follows:  
\[x_{k}=\hat{x}_{k}-W^{-1}{D_{k}}^{T}(D_{k}W^{-1}{k_{C}}^{T})^{-1}(D_{k}\hat{x}_ {k}-d_{k}) \tag{35}\]  
Equation (35) is the most general approach to the problem. This method of embedding the constraints into the Kalman filtering framework to correct the state estimation is usually called the projection algorithm, which is applied to project the current state estimation to the constraint subspace in order to satisfy the equality constraints in the system. The projected value will be closer to the true value, as shown in Figure 1. The shading surface denotes the state constraint region. The UHF algorithm generates sigma points according to the unscented transform, and each sigma point corresponds to a possible state trajectory. If the constraint condition is not met, it can be brought into the most probable position in the constraint region according to the projection algorithm [11]. Points that are already within the constraints remain unchanged.  
In summary, the flow chart of the UHF algorithm under constraints is shown in Figure 2.  
Figure 1: Sigma points projection.  
Figure 2: The flowchart of the proposed constrained Düo filtering (UHF) algorithm.