esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Mass Losses of Torrefraction Process  
As a first evaluation for the torrefraction process, the mass loss of all samples was calculated. The variation in the mass losses for the treated samples for different periods and temperatures can be observed in Figure 3. The regression curves that best highlight this are the polynomial ones. As can be seen, the mass loses present higher values for poplar PW compared to MDF, and this is due to its layered structure. An exception is observed the T\({}_{2}\)t\({}_{2}\) treatment regime, where the mass loss for MDF is almost similar, with a slight increase, compared to poplar PW. The maximum mass loss for poplar PW was 7.65% for a torrefraction treatment at 170 \({}^{\circ}\)C and 2 h, and 11.50% for a torrefraction treatment at 190 \({}^{\circ}\)C and 2 h. The maximum mass loss for MDF was 6.86% for the treatment at 170 \({}^{\circ}\)C and 2 h, and 13.85% for the treatment at 190 \({}^{\circ}\)C and 2 h.
# A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera
## 3 Methods
### Evaluation Metric  
In order to validate the performance of forecasting, various common evaluation metrics have been used to calculate the accuracy of the model, including mean bias difference (MBD), root mean square difference (RMSD), relative root means square difference (rRMSD), and relative mean bias difference (rMBD). We prefer to use differences rather than errors as the measurement data by nature include uncertainty; hence, the true values are unknown [24]. The evaluation metrics are given in the following equations:  
\[MBD=\frac{1}{n}\sum\limits_{i=1}^{n}(P_{e}-P_{m}) \tag{17}\]  
\[RMSD=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}(P_{e}-P_{m})^{2}} \tag{18}\]  
\[rMBD=\frac{\frac{\frac{1}{n}\sum\limits_{i=1}^{n}(P_{e}-P_{m})}{\frac{1}{n} \sum\limits_{i=1}^{n}P_{m}}}{rRMSD=\frac{\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n} (P_{e}-P_{m})^{2}}}{\frac{1}{n}\sum\limits_{i=1}^{n}P_{m}}}\cdot 100 \tag{19}\]  
where \(P_{e}\) is the estimated values at each time, \(P_{m}\) is the measured values at each time, and \(n\) is the number of sample data for the period.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **Searching for Optimal Value** & **Optimal Value** \\ \hline Epoch & [50,100,200,300,400,500] & 300 \\ Activation function & ReLU & ReLU \\ Batch size & [64,128,256] & 128 \\ Optimization & Adam & Adam \\ Learning rate & 0.001 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The hyperparameters used in designing the LSTM model.  
The RMSD explains the deviation from the measurement and it always generates a positive value. This value measures how close the prediction is to the measurement; thus, a smaller value is deemed better. The MBD shows the average bias of the prediction, and it also provides the long-term performance of the model [25], where the positive and negative values represent overprediction and underprediction, respectively. Furthermore, in cases where the data vary with location and time scale, relative metrics such as rRMSD and rMBD are more useful, as they provide the percentage difference.
# Highly Controlled Integration of Graphene Oxide into PAN Nanofiber Membranes
## 2 Materials and Methods
### Preparation of Bare PAN and PAN/GO Nanofiber Membranes via Electrospinning  
GO was prepared by a modified Hummer's method which is described in the previous papers [8; 9]. To modify the surface of GO with surfactants, the prepared GO (0.5 wt %) was suspended in CTAC (6 wt % solution) by sonication. The mixture solution was filtered through a dead-end cell to form CTAC-modified GO sheet, followed by drying in a vacuum oven [10]. Varying amounts of GO was suspended in \(N\),\(N^{\prime}\)-dimethyl formamide (DMF) by sonication, followed by the addition of PAN (MW = 150 kDa) powder. The resulting homogenous solution was electrospun for 6 h under the following conditions (voltage of 15 kV, ejection speed at 0.8 mL/h, and a tip-to-collector distance of 15 cm). Such detailed information of GO including fabrication, size, thickness, and etc., were described in a previous paper [11]. As the electrospun, nanofiber sheet exhibited a low mechanical property, these fiber mats were converted to membrane sheets by a hot-press treatment [1; 12].
# Wireless Remote Control for Underwater Vehicles
## 4 Scenario Description and Simulation Setup
### Routing Protocols  
All traffic types that cannot be transmitted through the multihop acoustic network due to the high throughput requirements, such as images, video streams, and joystick position control, were transmitted with the direct link from the control station and the HROV without the need for any routing layer: to simulate this in DESERT, we used a static routing (SR) layer where the address of the next hop and the destination were the same. Monitoring and way-point packets, instead, were transmitted through the underwater acoustic network. The packet routes in this case depended only on the HROV position; hence, for this types of traffic, we employed the Estimate-position based routing (EPBR) protocol, first presented in [15]. In this protocol, all static nodes (e.g., relays and control station) collected information about the position of the HROV and its speed and direction of movement from the packets received from the other nodes. The node topology and the position of relays and control station were assumed to be known by each node: in this way, both the relays and the control station could decide the best route of a packet for the HROV depending on their estimate of the HROV's position while the HROV could decide whether to transmit a packet to a relay or to convey the packet directly to the control station observing its current position. Once a relay node received a packet sent by the HROV, it forwarded it to the control station via static routing. The HROV position estimate can be performed, for instance, through a Kalman filter or with the algorithm presented in [15].
## 1 Introduction
### A Brief Background on the Use of FTIR in Material Characterization  
Infrared (IR) light belongs to the spectrum whose frequency range lies between 4-140,000 cm\({}^{-1}\). It is bounded by microwaves and visible light. Generally, different parts of the electromagnetic spectrum are characterized by radiation velocity as well as the frequency which is inversely related to the wavelength [8]. There have been several applications of FTIR in different fields, such as the study of healthy and pathological blood samples and deducing molecular bond differences.  
Furthermore, spectrometry has been used to study healthy and pathological blood samples in order to deduce molecular bond differences [9], organic chemistry [10], biomedical applications [11], and building diagnostics [12] in order to conserve energy and achieve a sustainable environment. In recent times, this technique is used for nano-particle detection, as reported by [PERSON] et al. [13], through its ability to identify bonds existing within the main compositions such as S-O bonds in gypsum, C-O in carbonates, hydroxyl (H-OH) groups, and aluminosilicates (Si-O-T; T is Si or Al) [14]. The purpose of FTIR is to provide additional information on X-ray diffraction (XRD) by relating bond characteristics to mineral characteristics. It remains a versatile tool for bond characterization of aluminosilicate and hydroxyl-based compounds. It could also be used to differentiate between amorphous and crystalline products with linear or cross-link chain products [12; 15].
# Influence of Inflow Turbulence on the Flow Characteristics around a Circular Cylinder
## 1 Introduction
This was initially reported by [PERSON] and [PERSON] [18], [PERSON] and [PERSON] [19] used an experimental setup in a wind tunnel and at subcritical Reynolds numbers and found that the drag reduction and a delay in the separation angle were caused by turbulence. [PERSON] [20] studied the flow around a circular cylinder over a Reynolds number regime from \(1\times 10^{5}\) to \(7.5\times 10^{5}\) and observed narrow-band vortex shedding up to a Reynolds number of \(5.5\times 10^{5}\). [PERSON] [21] experimentally studied the effect of high-intensity, large-scale, free-stream turbulence on the flow past a rigid circular cylinder at subcritical Reynolds numbers, finding that the turbulent intensity of the flow mainly increased vortex frequency in the circular cylinder's wake but did not weaken the vortex shedding of the wake. [PERSON] and [PERSON] [22] conducted wind tunnel tests of circular cylinders with two different diameters under different turbulence intensities and obtained the variation curve of aerodynamic coefficients with Reynolds numbers under different turbulence intensities. They found that the critical Reynolds number under turbulence appeared earlier than the smooth flow and that the area of the critical Reynolds number in the turbulent flow was also not obvious. [PERSON] and [PERSON] [23] studied the steady and unsteady forces induced by a cross flow over a smooth circular cylinder and found that turbulence will cause the critical Reynolds number regime to move to a smaller Reynolds number regime. [PERSON] and [PERSON] [24] conducted a wind tunnel test on a smooth circular cylinder at subcritical Reynolds numbers from \(5.2\times 10^{4}\) to \(2.09\times 10^{5}\); measurements showed that turbulence induces an aft shift in the separation point ranging from \(5^{\circ}\) to \(50^{\circ}\) beyond the laminar separation angle of \(80^{\circ}\). Based on the above analysis, the previous research has mainly discussed the variation of the drag coefficient of circular cylinders with the Reynolds number; however, research on the aerodynamic coefficient and the wind pressure distribution of the smooth circular cylinder surface in the turbulent flow is deficient. Except some review articles [25], there have been few recent studies on this problem. In this paper, a smooth flow and three different turbulent flows for different wind speeds were measured for a smooth circular cylinder with a diameter of 0.3 m. The characteristic parameters of the mean wind pressure distribution curves in different turbulent flows were studied. The mean and fluctuating values of the drag coefficient and lift coefficient in different turbulent flows were obtained and then compared with the results obtained by other researchers. Furthermore, the correlation characteristics of the wind pressure coefficients on the circular cylinder surface in different turbulent flows were analyzed. The results in this paper can serve as a reference for related basic research and engineering applications.
# Mechanism and Equivalence of Single Event Effects Induced by 14 MeV Neutrons in High-Speed QDR SRAM
## 3 Results and Analysis
### SEU Cross-Section
#### 3.1.2 Description of High-Altitude Test  
A high-altitude real atmosphere test was completed at the Yangbajing International Cosmic Ray Observatory, Lasa, China, at an altitude of 4300 m [10]. There were four test boards, which include 72 QDR SRAMs, were placed on a water-level platform. Over the course of the 153-day experiment, 39 SEUs were observed, of which SBU accounted for 76.9% and MCU accounted for 23.1%.  
The above SEUs were induced by high-energy neutrons (E > 1 MeV), thermal neutrons, and \(\alpha\) particles from the device package together. To obtain the SEU cross-section of high energy neutron, the thermal neutron and \(\alpha\) particle sensitivity of the DUT must be investigated. First, we found that thermal neutrons have no influence on the DUT [11]. Second, \(\alpha\) particle induced soft error rate, \(SER_{\alpha}\), of the DUT was 303 FIT/Mb. All the SEUs were SBU events [12]. Therefore, \(\alpha\) particle induced SEU number during the high-altitude test, \(N_{SEUL,\alpha}\), can be calculated:  
\[N_{SEUL,\alpha}=SER_{\alpha}\times T\times N_{bit}/(1024\times 1024\times 10^{9}) \tag{1}\]  
where \(T\) and \(N_{bit}\) are the effective test time (unit: hr) and capacity (unit: bit), respectively.  
Furthermore, the SEU cross-section of high energy neutron during the high-altitude test, \(\sigma_{high\,altitude}\), can be calculated:  
\[\sigma_{high\,altitude}=(N_{SEUL}-N_{SEUL,\alpha})/(flux\times T\times N_{bit}) \tag{2}\]  
where \(N_{SEUL}\) denotes the total SEU number during the high-altitude test, _flux_ means the high energy neutron flux at the test site (unit: n/(cm\({}^{2}\cdot\)hr)).
# Dynamic Load Test and Contact Force Analysis of the AERORail Structure
## 2 AERORail Test Line with Modal Test
### Modal Test  
Modal analysis is the premise and foundation of the dynamic analysis of linear elastic structures. Although in most cases the modal parameters calculated by using the finite element model can meet the needs of engineering applications, for the study of new structures the structural modal parameters (modal frequency, modal shape and modal damping, etc.) verification research methods are fundamental work and very necessary [19; 20; 21]. The modal test and analysis of the five-pole supported AERORail structure with a span of 30 m are used to verify the correctness of the finite element modal analysis method and obtain more real modal parameters.  
The 30 m span and five-pole supported AERORail selected for the model test are provided with a set of supports at 1/6, 1/3, 1/2, 2/3 and 5/6 of the span, and the five-pole support heights are 21.5 cm, 52 cm and 62 cm, 52 cm and 21.5 cm, respectively (see Figure 6).  
Since the modal frequency of the AERORail structure is directly related to the cable force, the cable strain is also measured in the modal test and finally used to calculate the actual cable force. After measurement and calculation, the cable force on the north and south sides is 30.2 kN.  
Figure 5: AERORail structure and support picture.  
Figure 6: Schematic diagram of the 30 m span AERORail supported by 5 poles.  
The modal analysis uses frequency domain decomposition to calculate modal frequencies and modal shapes [8; 22], the modal damping ratio is determined by the half-power bandwidth method, and the excitation method is hammer excitation [21]. After analysis and calculation, the modal parameters of the 30 m-span five-pole supported AERORail structure are shown in Table 2.  
According to the actual structure of the 30-m-span structure, calculated by the finite element model, the transverse modal frequencies are 0.49 Hz, 1.15 Hz and 2.09 Hz, and the vertical modal frequencies are 1.17 Hz, 1.90 Hz and 4.26 Hz.  
The modal test and analysis results (Figure 7) show that the vertical vibration mode obtained by the finite element analysis is basically the same as the vibration mode of the AERORail structure, but there is a certain error in the modal frequency, which is approximately 10%-20%. Transverse modes have large errors (over 100%) in both mode shape and frequency. This is because the real structure is a multispan structure transversely, and there is no transverse constraint. Therefore, under the support of the current research results, the modal parameters of the finite element model can be used for the vertical vibration analysis of AERORail, while the lateral vibration needs to be further studied.
## 2 Fundamental Theories
### Semi-Supervised Fuzzy C-Means Clustering  
Semi-supervised fuzzy C-means clustering (SSFCM) [18] is an semi-supervised clustering algorithm based on the fuzzy C-means clustering (FCM) algorithm [42]. Specifically, supposing that the sample to be clustered is \(S=[s_{1},s_{2},\ldots,s_{n}]\) and the number of clusters is \(c\) (\(2\leq c\leq n\)), then the objective function of SSFCM is given as:  
\[J_{w,\alpha}(U,V;S,F)=\sum\limits_{i=1}^{c}\sum\limits_{j=1}^{n}u_{ij}^{\alpha }d_{ij}^{2}+\rho\sum\limits_{i=1}^{c}\sum\limits_{j=1}^{n}\left(u_{ij}-f_{i}b_ {j}\right)^{\alpha}d_{ij}^{2} \tag{13}\]  
where \(U_{c\alpha n}=[u_{ij}]\), \(u_{ij}\) represents the fuzzy membership of the \(j\)th sample belonging to the \(i\)th cluster, and the matrix formed by cluster centers is \(V=[v_{1},v_{2},\ldots,v_{n}]^{T}\). \(F_{c\alpha n}=[f_{ij}].f_{ij}\) denotes the membership value of labeled samples, and \(b_{j}\) is a two-valued (Boolean) indicator to distinguish labeled and unlabeled samples. Moreover, \(d_{ij}=||s_{j}-v_{i}||\) is the Euclidean distance between the \(j\)th sample and the \(i\)th cluster center, while \(w\) (\(w\geq 1\)) is weighting parameter, and \(\rho\) (\(\rho\geq 0\)) is the balance coefficient.  
With Lagrange multipliers introduced, the optimization problem of SSFCM can be converted into the following constrained minimization problem:  
\[J_{w,\alpha}(U,V,\lambda;S,F)=\sum\limits_{i=1}^{c}\sum\limits_{j=1}^{n}u_{ij }^{\alpha}d_{ij}^{2}+\rho\sum\limits_{j=1}^{n}\sum\limits_{i=1}^{c}\left(u_{ ij}-f_{i}b_{j}\right)^{w}d_{ij}^{2}-\sum\limits_{j=1}^{n}\lambda_{j}[\sum\limits_{i=1} ^{c}\left(u_{ij}-1\right)] \tag{14}\]  
where \(\lambda_{j}\), \(j=1\), \(2\), \(\ldots\), \(n\), is the Lagrange multiplier. In the sequel, fuzzy membership \(u_{ij}\) and cluster center \(v_{i}\) can be iterated as:  
\[u_{ij}=\frac{1}{1+\rho}[\frac{1+\rho(1-b_{j}\sum\limits_{k=1}^{c}f_{kj})}{\sum \limits_{k=1}^{c}d_{ij}^{2}/d_{kj}^{2}}+\rho f_{ij}b_{j}] \tag{15}\]  
\[v_{i}=\sum\limits_{j=1}^{n}u_{ij}^{2}s_{j}/\sum\limits_{j=1}^{n}d_{ij}^{2} \tag{16}\]  
where \(i=1\), \(2\), \(\ldots\), \(c\), \(j=1\), \(2\), \(\ldots\), \(n\).  
On the whole, primary procedures of SSFCM can be summarized as follows:Step 1: Initialize membership matrix \(U\) and give values of \(c\), \(b\), \(F\); Step 2: Calculate prototypical cluster center \(V\); Step 3: Update \(U\) based on Equation (15); Step 4: Compare \(U^{\prime}\) to \(U\), if \(||U\)-\(U^{\prime}||<\delta\) (\(U^{\prime}\) is the last iteration result and \(\delta\) is a certain tolerance) then loop end, else go to step 2.
# One-Coil Long-Stroke Permanent Magnetic Actuator Design Applied to Load Breaker Switch for Railway
## 1 Introduction  
In general, switches used to protect the railway system include circuit breakers (CBs), switches (SWs), and disconnecting switches (DSs). While most of these switches are installed at substations or power stations, load breaker switches or DSs are installed on railway tracks to provide and shut down power to the tracks. In the past, load breaker switches or DSs installed on railway tracks adopted the open type, which was exposed. Now, they adopt a solid insulated type to prevent electric shock accidents during work and require no maintenance [1; 2; 3].  
Conventional solid insulated load breaker switches, however, solely rely on the vacuum interrupter (VI) for opening performance, as shown in Figure 0(a). As a result, the gap with the part contacting the VI is short during opening, causing induced voltage and destroying insulation due to decreased insulation performance by the epoxy. To overcome such safety problems with insulation, a load breaker switch with double insulation serially combining the VI and AIR was studied, as shown in Figure 0(b). This serial structure of VI+AIR DS has advantages: it ensures a gap to discourage current leakage, achieves insulation performance in AIR DS in the event of an external surge, maintains insulation performance in AIR DS during decreased VI vacuum, and thereby minimizes concerns for any electric shock [4; 5].  
The novel solid insulated load breaker switch with a VI+AIR DS structure has the advantage of a longer distance through the insulation, but the operating stroke increases by 5 to 10 times compared with the conventional one with a VI structure. As the motor spring charging mechanism generally applied for long-stroke operation has maintenance difficulties due to its complex structure and many parts, the solid insulated load breaker switch, which has a relatively simple structure, uses fewer parts, and shows good durability, is effective for PMAs and is useful for many applications [6].  
In the case of the general PMA method, although it has various advantages, it has the disadvantage of a short stroke [7,8]. To apply a long stroke, an auxiliary opening/closing coil to the typical type of PMA can be applied [9]. However, this PMA method increases the volume of the coil and actuator. Additionally, this PMA's opening and closing holding forces could not be designed independently.  
Nonetheless, the conventional PMA has a stroke of 10 to 30 mm and is not applicable to the new load breaker switch with double insulation presented in this paper. Hence, we designed a 120 mm one-coil long-stroke mechanism to ensure stroke performance in a novel load breaker switch with double insulation and confirmed its operating characteristics and design suitability by comparing FEM analysis values and measured holding force data and producing a simplified prototype [10].
## 5 Conclusions  
The operating performance of an electronic system is closely related to the electromagnetic environment. With the widespread use of pulse signals in various fields, they have become an important part of electromagnetic environment. Therefore, in the development and test stage of electronic system, fully analyzing the impact of pulse signal on equipment EMC and accurately evaluating equipment EMC under pulsed signal interference environment are the keys to reduce equipment performance risk. In this paper, based on the analysis of the interference mechanism of the pulse signal on receiver, we proposed the modelling method for the quantitative characterization of receiver interference effect under pulsed environment. The quantitative relationship between BER and pulse width, repetition period, ISR, duty cycle and signal period is established. The model allows quantitative analysis of the receiver EMS based on the characteristics of different electromagnetic parameters of the pulse signal. A system model is then presented to verify the results of the theoretical analysis through numerical simulations. With the established relationship between the parameters of the pulsed interference and the BER performance of the receiver, a novel EMC evaluation method has been proposed. The quantitative evaluation of the receiver EMC under pulsed interference environment can be achieved. As a practical application example, the evaluation method is verified via a well-designed experiment on BDS. The experiments show that the measured values are in good conformity with the theoretical values, proving the applicability of the method to receiver EMS analysis and EMC evaluation under pulsed interference environment.  
In further work, we will continue to study the modeling method of the receiver EMI effect and EMC evaluation method according to the relationship between typical performance indexes of receiver and the electromagnetic characteristics of pulsed interference signals in different scenarios to make them better for engineering applications.  
Conceptualization, S.C., Y.L. and H.Z.; methodology, S.C., Y.L., H.Z. and D.S.; validation, S.C. and H.Z.; investigation, S.C., Y.L., H.Z. and X.W.; writing--original draft preparation, S.C. and H.Z.; writing--review and editing, S.C., Y.L., H.Z. and X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant number 61427803 and the Science and Technology Research Program of Chongqing Municipal Education Commission under grant number KJQN202000637.  
Not applicable.  
The authors declare no conflict of interest.