esa-sceva/satcom-chunk-collection huggingface dataset:
# Intra-Domain Transfer Learning for Fault Diagnosis with Small Samples
###### Abstract  
The concept of deep transfer learning has spawned broad research into fault diagnosis with small samples. A considerable covariate shift between the source and target domains, however, could result in negative transfer and lower fault diagnosis task accuracy. To alleviate the adverse impacts of negative transfer, this research proposes an intra-domain transfer learning strategy that makes use of knowledge from a data-abundant source domain that is akin to the target domain. Concretely, a pre-trained model in the source domain is built via a vanilla transfer from an off-the-shelf inter-domain deep neural network. The model is then transferred to the target domain using shallow-layer freezing and finetuning with those small samples. In a case study involving rotating machinery, where we tested the proposed strategy, we saw improved performance in both training efficiency and prediction accuracy. To demystify the learned neural network, we propose a heat map visualization method using a channel-wise average over the final convolutional layer and up-sampling with interpolation. The findings revealed that the most active neurons coincide with the corresponding fault characteristics.  
fault diagnosis; transfer learning; time-frequency spectrum; small samples; heat map Article
## 2 ITS Architecture and Applications  
With the technological advancements in the areas of mobile computing, wireless communications and remote sensing, intelligent transport systems (ITS) have recently emerged as a promising technology that will enable the deployment of diverse applications related to road safety, traffic efficiency and infotainment. This section provides a high level overview of the ITS architecture, characteristics, challenges and target applications.
## 1 Introduction
The incorporation of CF was found to contribute to improving flexural performance while greatly reducing electrical resistivity [32; 33; 34]. In the case of CF, which has excellent conductivity due to its low electrical resistivity among carbon-based materials, it is imperative to consider its heating properties according to the variation in the amount of supplied current. However, information regarding research and development using CF as a heating element in cement-based composites is still somewhat lacking. In order for a construction material to be applied to actual sites for snow melting and deicing of pavements, not only electrical and heating performance but also flexural strength of design must satisfy a certain level at the same time. In the Republic of Korea, the standard specification for road construction (cement concrete pavement work) stipulates that \"the design standard flexural strength of cement concrete for pavement should be 4.5 MPa or more\" [35]. Accordingly, the ultimate goal of this study is to develop an electrically conductive mortar (ECM) that can simultaneously satisfy the heating performance and flexural strength requirements of mortar mixed with conductive fibers. Its purpose is to provide basic data for field application as an eco-friendly construction material for snow melting as well as deicing on the surface of pavements in the future. Therefore, this study purports to fabricate ECM mixed with conductive fibers such as CF and SF and to investigate the effects on the heating performance and flexural strength properties. To this end, fiber volume fractions were varied from 0.25% to 1.25% at 0.25% intervals to prepare specimens. Then, their performance was compared and reviewed with plain mortar (PM). At the same time, the surface temperature of each specimen was measured using an infrared thermal camera (T630 sc, FLIR, Wilsonville, USA) at various applied voltages (DC 10 V, 20 V, 30 V) and different electrode spacings (40 mm, 120 mm). Furthermore, in order to examine the internal microstructure of the ECM under repetitive electrical heating, images analysis using a scanning electron microscope (SEM) as well as thermogravimetric analysis using a thermogravimetric analyzer (TGA) were performed.
# Study on Denoising Method of Weld Defect Signal Based on SSA-VMD-WPD
## 2 Materials and Methods
### Analytical Methods and Statistical Analysis  
The Sparrow Search Algorithm (SSA), proposed by [PERSON] in 2020 [35], was an optimization algorithm that introduced an abstract model based on seeking, tracking, and warning. This model simulated the foraging behavior of sparrows to find solutions to optimization problems. The SSA demonstrated strong stability and convergence capabilities. Variational Mode Decomposition (VMD) employed non-recursive mode decomposition to simultaneously estimate modes with different central frequencies. Wavelet Packet Decomposition (WPD) extended the wavelet transform theory by allowing the simultaneous decomposition and processing of both low-frequency and high-frequency components, unlike the wavelet transform, which only decomposed low-frequency components. Consequently, for non-stationary signals, WPD had stronger denoising capabilities.  
The denoising process of SSA-VMD-WPD is illustrated in Figure 5.  
Using envelope entropy as the fitness function and applying the Sparrow Search Algorithm (SSA), the optimal values for the decomposed mode number (\(K\)) and penalty factor \(\alpha\) were obtained. Prior to optimization, the initialization of parameters for the sparrow algorithm was conducted, setting the minimum and maximum values for (\(K\)) and \(\alpha\), population size, and maximum number of iterations. Subsequently, the optimization algorithm was executed to obtain the optimized parameters ([\(K\),\(\alpha\)]) along with the corresponding minimum envelope entropy value.  
Then, the optimized parameters were applied to perform VMD decomposition on the signal. The correlation coefficients and variance contribution rates of each decomposed mode component were calculated, followed by filtering of the noisy mode components.  
Next, varying wavelet basis functions and decomposition levels were explored. The decomposition levels ranged from 2 to 5, and there were a total of 54 wavelet basis functions, including haar, db series, and sym series, among others. The indicator \(T\) was utilized to determine the optimal wavelet basis function and decomposition level. Wavelet packet decomposition and reconstruction were performed to obtain the denoized mode components for the noisy mode components.  
Finally, reconstruction processing was carried out by combining the denoized mode components with the mode components unaffected by noise, resulting in a denoized signal.  
Figure 4: Schematic diagram of the detection experiment.
# Highway Microgrid Project Evaluation under Energy Transportation Integration
## 4 Case Study
### Example Introduction  
In this paper, the microgrid of a highway demonstration project is taken as the research object. With a general length of 178 km, the highway is rich in solar irradiation resources and can use more land. Its installed photovoltaic capacity is 123.99 MW, the installed capacity of the wind power system is 8 kW, there are 32 charging piles of 60 kW, and there is a set of 15 MW/30 MWh battery energy storage systems. On the basis of the above data, three planning schemes are proposed.  
Scheme 1 is the scheme to be adopted by the project, which realizes the \"Source-Network-Load-Storage\" interaction of the highway microgrid.  
Scheme 2 reduces the investment in distributed photovoltaics on the basis of Scheme 1 to verify the feasibility of distributed photovoltaics in highway microgrid projects.  
Scheme 3 eliminates the configuration of charging piles on the basis of Scheme 1 to verify the comprehensive income of the \"Source-Network-Load-Storage\" integrated operation mode of the microgrid project.
## 4 Results and Discussion
### Results of Demodulation with/without Compensation  
To further verify the performance of the DE estimator of the TVCFO, the demodulation results with and without CFO compensation and with DE estimation value compensation are given in Figure 6. In this figure, the black solid line with a circle represents the demodulation results for the OFDM system model without compensation. The blue dashed line which is marked as \"OFDM without TVCFO\" shows the demodulation results in the absence of the CFO, which is equivalent to compensation with real CFO values. And the red squares marked as \"OFDM with TVCFO compensation\" show the demodulation results when using the DE estimation values. The BER without compensation was much higher than that with compensation, which was predicted. Surprisingly, the results of the compensation with the estimated values were highly coincident with the results when there were no CFO values. Moreover, the demodulation results for the CFO compensation with DE estimation were better than those without the CFO when the SNR is equal to 20 dB. The only possible reason for this was the influence of noise. According to (7) and (8), the reconstructed signal could be used to directly calculate the cost function with the actual received signal without considering the noise. Therefore, the effect of noise on the demodulation performance was converted into the CFO on each OFDM block by the DE estimator and taken into account in the simulation values.  
Figure 7 shows a constellation map after demodulation in the three cases: without the CFO compensation, with the actual CFO compensation, and with the DE estimation value compensation. The CFO values that varied as a sinusoidal function were adopted in these simulations. The received data without CFO compensation were randomly distributed,  
Figure 5: Bit error rate statistics of three estimators on the improved model with CFO values varying as a sinusoidal function.  
Figure 6: Bit error rate statistics of demodulations in three cases: without CFO compensation, with actual CFO compensation, and with DE estimation values compensation.  
which resulted in difficulties in making the correct decision. In the case of the compensation with the DE estimation values, the data surrounding the correct constellation points were well organized. Importantly, the data boundaries were very clear, because valid decisions could be easily made. In addition, these data were highly coincident with the compensation results with the actual CFO values. Specifically, there is significant overlapping between the red square and blue star data in the figure. This phenomenon demonstrates the validity of the proposed scheme for estimating TVCFO values.
# The Challenges of Advanced CMOS Process from 2D to 3D
## 5 The Etching Challenges and Solution of 3D  
### Depth Loading Control of Fin Etching  
Generally, only a small part of process gases is ionized into plasma under the radio frequency power. Most of molecules exist in the chamber as neutrals, which induce deposition during etching process. Neutrals are easy to stick on to the surfaces before entering trench bottom and they induce taper profile that will block ions to reach to the bottom. On the other side, new stored ions are formed at trench bottom to react upon the incoming ions. As a result, as etching reaction is on going and etching depth increases, flux ratio of Ion/Neutral will decrease and etching bombardment is weakened as shown in Figure 11a. Within CD sizes, smaller CD induced weaker bottom reaction. Therefore, the etching depth depends on the opening CD size: bigger opening CD size induce deeper depth as shown in Figure 11b.  
It is possible to fine-tune the gas ratio and bias power to improve the Ion/Neutral flux ratio and improve the CD size dependency to etch rate. However, this traditional tuning is less effective to 14 nm FinFET due to the extreme small size limit the function and cannot meet the requirement of 5 nm depth loading. Using higher bias power will induce higher electron temperature that will damage under layer in gate or spacer etching process. To solve this problem, the concept of bias pulsing can be introduced. Since bias power is the main source to provide ion bombardment, bias pulsing can change Ion/Neutral flux ratio momentarily as bias power turn on/off. However, the flux ratio at bias-on is greater than that at bias-off. Figure 12a,b shows how the bias pulsing affects the etching process during both bias-on and bias-off. When the bias power is on, plasma etching dominates the process. While the neutral deposition dominates during the bias-off. In bias opening moments, ions may crush the bottom of trench in a short time and react with silicon in the bottom. In this case, when the biaspower is turned off suddenly, it will induce enough reacting before the back potential force forms. This kind of time-split to bias on/off results smaller depth loading effect that are caused by different opening CD size [122; 123].  
For example, using bias power pulsing, the intra-cell loading can be reduced from 20 nm to 5 nm under 5 nm CD difference structure as shown in Figure 13.  
Figure 11: (**a**) Depth reduced Ion/Neutral flux ratio and (**b**) Opening CD size dominates the depth loading. Reproduced with permission from [121]. Copyright IEEE, 2015.  
Figure 12: (**a**) Bias pulsing power effect and (**b**) Bias pulsing process. Reproduced with permission from [121]. Copyright IEEE, 2015.  
Figure 13: Bias pulsing improve intra-cell depth loading (**a**) loading without pulsing and (**b**) loading with pulsing. Reproduced with permission from [121]. Copyright IEEE, 2015.
## 2 Real-Time Monitoring of Power Distribution Systems
### Decentralized DSSEs  
The objective of this paper is to provide conclusive evidence for the advantageousness and feasibility of decentralized monitoring of PDSs, focusing on the development and implementation, on a bench, of one of the decentralized architectures proposed in [7]. This architecture allows for the use of decentralized DSSEs, for instance, the multiarea SE approaches that have already been proposed in the literature. Therefore, this paper does not propose any decentralized DSSE.  
Multiarea SEs seek computational performance gains (processing time, memory allocation, processing capacity), by exploring the fact that measurements are obtained from a wide area spread across the electrical network. Such estimation process consists of the separation of the electrical network into sub-areas, in which a local state estimation is formulated for the internal nodes of such sub-areas and with a special treatment for frontier regions (boundaries of each area). Different architectures of multiarea SEs perform the state estimation process separately for each area, and the results refine the estimation of the frontier nodes.  
Although the first multiarea state estimation approaches for PDSs presented a large number of iterations to converge and had high coordination latency [18; 19], more recent approaches show significant gains in computational performance [20; 21; 22].
## 2 Sequential Modelling Strategy
### Spacecraft Dynamics with Structural Considerations
#### 2.4.3 Two Rigid-Body Dynamics  
Given the first order approximation presented, the space debris object is now divided into two parts: the main body and solar array(s), each assumed to be rigid bodies themselves connected by a hinge connection. Only for the solar array dynamics is the structure modelled as an equivalent thin plate with the same dimensions and inertial properties of the original array (see Figure 4). As in reality, the solar array will rotate and add to the angular momentum of the overall system, and there exists a coupling between the rotational motion of the satellite and its large solar array(s).  
The two bodies are assumed to be joined by a joint connection, here modelled as a torsional-damping system, which allows the solar array to rotate only in one direction by an angle \(\theta_{s}\) (see Figure 4). In order to model the dynamics of the solar array, the methodology presented in [25] is used as a starting point, in which the EoMs of the main body and solar array are coupled to each other.  
In this work, the coupling between the rotational and translational dynamics is removed by using the CG instead of the origin of the body-fixed frame \(\mathcal{B}\). This removes the unnecessary complexity related to the translational motion.  
By performing the aforementioned transformation, the main body rotational dynamics can be written in the same manner as Equation (10), leading to [18; 25]:  
\[\mathbf{I}_{\mathrm{d}}\dot{\mathbf{\omega}}_{\mathrm{d}}+\mathbf{\omega }_{\mathrm{d}}\times\mathbf{I}_{\mathrm{d}}\mathbf{\omega}_{\mathrm{d}}+\frac{ \mathrm{d}}{\mathrm{d}t}(\mathbf{I}_{\mathrm{d}})|_{\mathcal{B}}\mathbf{\omega}_{ \mathrm{d}}\\ +(I_{h_{2}}\mathbf{\hat{h}}_{2}+m_{SA}\ d\mathbf{r}_{\mathrm{S}_{c}/C} \times\mathbf{\hat{h}}_{3})\bar{\theta}_{s}\\ +\dot{\theta}_{s}\mathbf{\omega}_{\mathrm{d}}\times(I_{h_{2}}\mathbf{\hat {h}}_{2}+m_{SA}\ d\mathbf{r}_{\mathrm{S}_{c}/C}\times\mathbf{\hat{h}}_{3})\\ +\dot{\theta}_{s}^{2}\ m_{SA}\ d\mathbf{r}_{\mathrm{S}_{c}/C}\times\mathbf{ \hat{h}}_{1}=\sum_{i}\mathbf{M}_{\mathrm{CG},i}, \tag{12}\]  
where \(I_{h_{j}}\) represents the moment of inertia of the solar array about \(\mathbf{\hat{h}}_{j}\), \(m_{SA}\) denotes the mass of the solar array, \(d\) stands for the moment arm of the solar array and \(\mathbf{r}_{\mathrm{S}_{c}/C}\) signifies the relative position of the solar array centre of gravity with respect to the spacecraft centre of gravity. Lastly, \(\dot{\theta}_{s}\) and \(\bar{\theta}_{s}\) correspond to the solar array angular velocity and acceleration, respectively (for further details see [25]). It is noteworthy that the previously assumed null term \(\mathrm{d}(\mathbf{I}_{\mathrm{d}})/\mathrm{d}t|_{\mathcal{B}}\) is now present, representing the local derivative of the moment of inertia caused by the relative motion of the solar array.
# Concrete with Organic Waste Materials as Aggregate Replacement
## 3 Results
### Mechanical Tests' Results
#### 3.1.3 Flexural Strength Test  
The average 14-day flexural strength test result is presented in Figure 4b. A consistent trend was observed: the rice husk ash and wood ash concrete exhibited comparable flexural strength to traditional concrete. The two ash-based concretes showed even closer alignment with the performance of the traditional concrete samples compared to the results obtained in the compression tests. Rice husk ash performed the best in the mechanical property testing out of any of the concrete mixtures with replaced aggregate. This was evidenced  
Figure 4: (**a**) Compressive strength test results and (**b**) flexural strength test results for the concrete sample.  
\begin{table}
\begin{tabular}{c c} \hline
**Concte Samples** & **Slump Test Results (mm)** \\ \hline Traditional concrete & 110 \\ Rice husk ash & 90 \\ Wood ash & \textgreater{}200 (collapsed slump) \\ Wheat straw & 105 \\ Corncob & 75 \\ \hline \end{tabular}
\end{table}
Table 3: Slump test results of the concrete samples.  
by its average compressive strength at 14 days of 20.7 MPa and average flexural strength of 1.8 MPa. The mechanical properties of the concrete samples made with wood ash were relatively strong; they were only marginally below that of rice husk ash concrete.
## 2 Conceptual Development of Structural Design  
To come up with a design that solves the aforementioned issues, several design concepts are considered at the beginning of development based on the defined design goals. The CubeSat standard interface developed by California Polytechnic State University is used as a design reference [19]. An example of a design specification document is the JAXA's Japanese Experimental Module (JEM) Payload Accommodation Handbook, for those CubeSats that are released from the JEM Remote Manipulator System using the JEM Small Satellite Orbital Deployer J-SSOD installed in [20]. In addition, constraints from previous heritage subsystems are taken into account during the development of the design concept. Satellite bus subsystems, such as the electrical and power subsystem (EPS), onboard computer (OBC), and communication (COM) boards, have been used for several previous projects. Since design modification of these subsystems costs a significant amount of money, the basic shape and dimensions of these internal PCBs are kept and used as additional design constraints.  
During the development of this new design, Fusion 360 software is used to model the structure. After a series of design iterations and evaluations, a slot-based design concept is chosen for a 3U STM using a total of eight aluminum structural components, as shown in Figure 4. Two parallel mirror slot plates are designed to mount the satellite's internal bus and payload. They are attached to the two bottom and top plates. Additionally, for this STM, two side frames on the Plus and Minus X axis are used to connect the top and bottom plates to give the structure extra stiffness and provide additional support for PC/104 standard subsystems such as the ADCS units.
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 4 Network Traffic Forecasting Based on ARIMA-BPNN
### Design of the EMD-ARIMA-BPNN Hybrid Traffic Forecasting Model  
Through the above analysis, the satellite network traffic described by the heavy-tailed ON/OFF superposition model has both linear and non-linear characteristics. ARIMA has higher accuracy in linear data forecasting, whereas the BPNN has better performance in forecasting non-linear data. In this paper, after decomposing satellite network traffic using EMD, the ARIMA was utilized to extract the linear laws of traffic. Meanwhile, the residual of ARIMA will be utilized as an input for a BPNN to extract the non-linear laws. The overall flow chart of the EMD-ARIMA-BPNN is demonstrated in Figure 8. The concrete steps of the EMD-ARIMA-BPNN hybrid model for satellite network traffic forecasting are as follows.  
**Step 1**: Satellite network traffic generation based on the ON/OFF model. Network traffic is aggregated to the satellite network through gateway stations to generate satellite network traffic data \(D(t)\).  
**Step 2**: Decompose \(D(t)\) into multiple IMFs with short-range dependence using EMD. Batch modelling of multi-order IMFs is implemented according to Algorithm 1 to construct ARIMA(\(p_{i}\), \(d_{i}\), \(q_{i}\))\((i=1,2,\cdot\cdot\cdot,n)\). The final linear sequence \(L^{\prime}(t)\) of the satellite network traffic sequence is obtained.  
**Step 3**: Eliminating the linear sequence \(L^{\prime}(t)\) from the raw satellite network traffic \(D(t)\) to obtain the residual sequence \(R(t)\):  
\[R(t)=D(t)-L^{\prime}(t) \tag{24}\]  
Figure 8: EMD-ARIMA-BPNN hybrid model for satellite network traffic forecasting.  
**Step 4**: The IGWO algorithm is utilized to optimize the hyperparameters of the BPNN, i.e., \((e_{i},n_{i})\). The residual \(R(t)\) is forecasted; \(R^{\prime}(t)\) demonstrates the forecasting result.  
**Step 5**: Combining the linear sequence \(L^{\prime}(t)\) of the satellite network traffic obtained from ARIMA(\(p_{i}\), \(d_{i}\), \(q_{i}\)) and the forecasted residual \(R^{\prime}(t)\), the forecasted satellite network traffic \(D^{\prime}(t)\) is obtained:  
\[D^{\prime}(t)=L^{\prime}(t)+R^{\prime}(t) \tag{25}\]