esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Observational Plan and Intended Measurements in Orbit
### Astrophysical Goals and Issues
The BATSE, RHESSI, Fermi, and AGILE [37; 38] satellites were able to detect TGFs on energy interval ranging from 10 keV to 100 MeV (Figure 13). According to the AGILE catalog, with typical times of 10 ms to a few milliseconds. Due to the capability considerations of a CubeSat, the energy range of 1 keV to 3 MeV was found suitable for the proposed mission. This is consistent with the payload energy range. TGF show a strong correlation with thunderstorm activity. An annual production is of at least 400,000 TGF, or one in 10,000 lightning [7]. A flux of up to 1 photon cm\({}^{-2}\) can be expected at the altitude of the satellite. BATSE detectors gathered from TGFs that have entered the Compton Gamma Ray Observatory CGRO, within the energy range of 1 keV-3 MeV, the photon fluency of order of 10\({}^{-2}\) ph cm\({}^{-2}\)[39]. A detected spectrum that can be explained by a Bremsstrahlung emission altered by the atmosphere. A production altitude is probably between 10 and 20 km [39]. It should be noted that space agencies ESA and NASA declared in their guidelines for high-energy astrophysics that in future decades there is a need for novel instruments covering the spectral region from keV to the MeV, as in these passbands important discoveries are expected [40]. They have declared the following priorities for these efforts, namely: (a) imaging telescopes (e.g., those with multilayer mirrors) for hard X-rays (1-100 keV) and (b) imaging instrumentation with Laue lens covering the energy range 60 keV up few MeV. Apart from imaging elements, i.e., multilayer hard X ray mirrors and Laue lenses, detectors with high efficiency and able to provide fine spectroscopy (e.g., a few % FWHM at 60 keV) along with a moderate spatial resolution (between 0.5 and 2 mm) are needed. An energy threshold close to 1 keV will be essential to allow the investigation of Fe lines e.g., by X-ray focal detectors and wide field monitors for gamma ray burst (GRB) investigation. Such a threshold could also enable synergy with astronomical space based soft X-ray telescopes. In addition to that, the high segmentation of the detector elements and the required spectroscopic properties could have the potential to provide sensitive measurements of the light polarization for a wide variety of celestial sources.
# Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants
## 4 Results and Discussion
### Evaluation Measure
#### 4.1.1 Accuracy  
Accuracy is the most commonly used measure in the ML and DL domains for classification. It can briefly be described as truly predicted instances over total instances, including wrong and right predictions. In terms of the four types used above, the equation for accuracy can be written as follows:  
\[Accuracy=\frac{(TP+TN)}{(TP+TN+FP+FN)} \tag{10}\]  
The equation can be described as the ratio of the summation of _TP_ and _TN_ over the summation of _TP_, _TN_, _FP_, and _FN_.
## 3 Materials and Methods
### The CNN Model  
CNNs [40] are a class of artificial neural networks used to detect, extract, and analyze image features. It consists of neurons composed of a set of layers characterized to accept and process images with high dimensional vectors as input. The CNN general constructor has several hyperparameters that play an essential role in achieving model performance. These hyperparameters can be divided into two types:  
(a) Hyperparameters that determine the network structure are:  
* _Kernel Size_ is the size of the filter,
* _Kernel Type_ shows values of the actual filter (e.g., edge detection, sharpen),
* _Stride_ is the rate at which the kernel passes over the input image,
* _Padding_ adds layers of 0's to make sure the kernel passes over the image edges,
* _Hidden layer_ is layers between input and output layers,
* _Number of neurons_ is the number of neurons in each hidden layers,
* _Activation function_ is used to learn nonlinear prediction boundaries.  
(b) Hyperparameters that determine the network trained, which are:  
* _Learning rate_ regulates the update of the weight at the end of each batch,
* _Momentum_ updates current weights using the previous values,
* _The number of epochs_ is the overall number of training iteration,
* _Batch size_ is the number of patterns shown to the network before the weights are updated.  
The first skin lesion images are fed to the first layer. Then nine series of combined convolutional operations, Relu activation, and max-pooling are performed on them. Then, the extracted features from the first step are applied to a flattened layer, two dense layers, and a softmax operation to obtain the classification results. The optimized CNN aimed at achieving accurate classification of skin cancer images. The convolution layer is the foundation of the CNN and extracts features from the input images through filters, enablingrecognition of more complex and significant portions of the image. The ReLU transformation is applied to the feature map after each convolution process to introduce nonlinearity to the model. Further convolutional layers or pooling layers are included to perform downsampling, reducing the size of the image maps. Finally, a fully connected layer is responsible for classifying the images based on the features extracted in the previous layers through various filters. The fully connected layer employs a softmax activation function to assign probabilities to each image class, with the class having the highest probability being selected as the final prediction.
# Analysis of a Three-Level Bidirectional ZVS Resonant Converter
## 2 Circuit Schematic of the Developed Converter  
Figure 1a provides the converter schematic of the studied bidirectional converter. There is a three-level diode-clamp circuit topology on the high-voltage side with the benefit of using low voltage rating switches. Clamped diodes \(D_{a}\) and \(D_{b}\) and capacitor \(C_{f}\) are used to balance input voltages \(V_{CH1}\) = \(V_{CH2}\) and reduce the voltage stress on \(S_{1}\)\(\sim\)\(S_{4}\). Full bridge circuit topology is used on the low-voltage side to achieve full-wave rectification. \(S_{ac}\) and \(L_{b}\) are series-connection and connect to points \(a\) and \(b\) in order to achieve _LLC_ circuit operation under backward power flow operation (\(S_{ac}\) is ON). For forward power operation from \(V_{H}\) (high-side voltage) to \(V_{L}\) (low-side voltage), \(S_{ac}\) is OFF and \(L_{b}\) is disconnected on the primary-side. Figure 1b gives the circuit structure under forward power operation. \(S_{1}\)\(\sim\)\(S_{4}\) are main power devices to control output voltage \(V_{L}\). \(L_{tr}\), \(L_{un}\) and \(C_{r}\) are _LLC_ resonant circuit and \(Q_{1}\)\(\sim\)\(Q_{4}\) are activated as synchronous switches. For reverse power operation from the \(V_{L}\) terminal to the \(V_{H}\) terminal, \(S_{ac}\) is ON and Figure 1c provides the circuit diagram of reverse power operation. Switches \(Q_{1}\)\(\sim\)\(Q_{4}\) are major power switches and \(L_{r}\), \(L_{b}\) and \(C_{r}\) are resonant circuit. \(D_{S1}\)\(\sim\)\(D_{S4}\) are operated as a full-wave diode rectifier. Therefore, _LLC_ resonant characteristics for both power flow are achieved and the turn-on switching loss of major power switches is removed.  
Figure 1: \(Cont.\)
# Comparison of Condition Rating Systems for Bridges in Three European Countries
## 5 Discussion  
The paper showed that European countries use different methodologies in the decision-making process regarding existing bridge management. For example, the number of levels  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Alert Situation** & \multicolumn{2}{c}{**Intervention Periodicity**} \\ \hline Structural safety & Urgent intervention & \\ Traffic safety & Urgent intervention & \\ Anti-corrosion protection & Necessary intervention & (2 to 3 years) \\ Protection of the foundations & Necessary intervention & (1 to 2 years) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Intervention periodicity according to the alert situation (adapted from: [25]).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Condition State** & \
\begin{tabular}{c} **Intervention** \\ **Time** \\ \end{tabular} &
\begin{tabular}{c} **Inspection Frequency** \\ \end{tabular} \\ \hline EC \(\leq\) 2 & 2 to 5 years & Normal surveillance (every 6 years) \\ EC = 3 & 3 to 5 years & Enhanced surveillance (every 6 years + every 3 years \\  & & for the components with EC = 3) \\ EC \(\geq\) 4 & 1 to 2 years & High surveillance (every 6 years + every year for the \\  & & components with EC \(\geq\) 4) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Inspection frequency and intervention time according to the IP specifications (adapted from: [25]).  
(or classes) used for the description of the structural state of the bridge varies, but in general, they describe a similar evolution of deterioration. Therefore, this should not significantly influence the final decision. Nevertheless, if the number of levels in condition rating systems is higher, it can be more difficult to describe the differences in the structural states because the border between them is not so significant. All mentioned countries use the multilevel approach in the bridge management system.  
The comparison of presented condition rating systems for bridges in three countries is listed in Table 10. Some differences are in the applicability of listed condition rating systems as the Italian methodology can be used in the case of both road and railway bridges. On the other hand, engineers in Slovakia and Portugal use different approaches on roadways and railways.  
Regarding the periodicity of inspections, the differences are much more significant. For example, in Slovakia, the administrator should visit and visually inspect the bridge every spring and more precisely every four years during so-called main inspections. In Portugal, for bridges in EC \(\leq\) 2, the inspections are performed every six years. And finally, in Italy, the inspections are decided on the basis of the \(CoA\) and, in any case, at least every two years.  
The final assessment in the decision-making process is based on different factors. In Italy, the conclusion is proposed considering the \(CoA\). The Slovaks consider the \(LCC\) factor as the most important information for the administrator, and the Portuguese propose the next steps according to the outputs of inspections.
## 3. Designed Scheme
### Image Protection
#### 3.1.2 Watermark Embedding  
The embedding process is described in Pseudocode 2, where \(wr\), \(wg\), \(wb\) and an authentication watermark \(autr\), \(auto\) or \(auth\) are embedded in a selected channel of \(Ih\). Firstly, a random permutation of the reconstruction watermarks utilizes a seed of the user key, which must be different for each processed RGB channel or bit plane.  
The watermark embedding process employs the 1-LSB method to embed the watermarks for reconstruction and a single watermark for authentication. This is possible due to the size of each one of the reconstruction watermarks, which is \(5 AB/16\), and because a single authentication watermark is composed of \(AB/16\) bits.  
Finally, a bit adjustment process is applied, where each pixel in the \(ij\)-th position of each RGB channel of the host image, denoted as _Ihw_, is compared with the pixel in the same position of _Ih_. This comparison has the objective to modify the intensity value of the pixels in _Ihw_ to enhance its objective quality. This process depends on the total number of marked bit planes. For each watermarked pixel, the following equation is used:  
\[\textit{Ihw}_{i,j}=\left\{\begin{array}{ll}\textit{Ih}_{i,j}-1,&\textbf{if }v\textbf{ is }(2^{N}-1)\textbf{ and }\textbf{ LSB}_{N+1}(\textit{Ih}_{i,j})\textbf{ is }1\\ \textit{Ih}_{i,j}+1,&\textbf{if }v\textbf{ is }-(2^{N}-1)\textbf{ and }\textbf{ LSB}_{N+1}(\textit{Ih}_{i,j})\textbf{ is }0\end{array}\right., \tag{1}\]  
where \(N\) represents the watermarked bit plane and \(v=\textit{Ihw}_{i,j}-\textit{Ih}_{i,j}\) for \(1\leq i\leq A\), and \(1\leq j\leq B\). This equation can be used only for values \(N>2\).  
**Pseudocode 2** Watermark embedding  
**Input:** Image (single channel) to process **Ih**; Seed **S**; Bit plane **bit Plane**; Recovery watermarks **wr**, **wg** and **wb**; Authentication watermark **aut**  
\([A,B]=\textit{size}(\textit{Ih})\)  
\(\textit{rng}(S)\)\(\rightarrow\)_Control random number generator_  
\(\textit{num}Rand=\textit{random}(AB/16)\)\(\rightarrow\)_Random permutation of integers in range \([1,\textit{AB}/16]\)_  
\(\textit{num}=1\)  
**For \(i=1\) step 4 to \(A-3\)do**  
\(\textit{For}j=1\) step 4 to \(B-3\)do  
\(\textit{subindex}=5*numRand[num]\)  
\(\textit{index}=subindex-4:subindex\)\(\rightarrow\)\(numbers\)\(from\)\((subindex-4)\) to subindex  
\(\textit{tmpW}=concat(w_{index},wg_{index},wb_{index},aut_{num})\)\(\rightarrow\)concatenation tmpW contains \(16\) bits  
\(\textit{Ihw}_{i:i+3,j+3}=embed\big{(}\textit{Ih}_{i:i+3,j+3},\textit{bitPlane}, \textit{tmpW}\big{)}\)\(\rightarrow\)\(LSB\) embedding of \(tmpW\) in _Ih_ by bitPlane  
\(\textit{num}=\textit{num}+1\)  
**End for**  
Equation \((\ref{eq:1})\), **where \(N=\textit{bit Plane}\)**  
**Output:** Watermarked image **Ihw**
# Carbon Nanocomposites in Aerospace Technology: A Way to Protect Low-Orbit Satellites
## 2 Corrosion Factors and Mechanisms in Low Orbit Environment  
The low orbit space is a dynamic environment that varies with different factors such as altitude, orbital position and solar activity. The effects of low orbit environment on spacecraft materials are highly important to determine their functionality and durability. These effects were studied in various in-flight and simulated space environments. Such experiments and their key findings are briefly described in this section.
# A 77 GHz Transmit Array for In-Package Automotive Radar Applications
## 2 Transmit Array Design
### Transmit Array Unit Cell  
A slot-fed stacked patch antenna [23] was used as a transmit array unit cell, as shown in Figure 5. From previous research [24], it is well known that a slot-fed stacked patch antenna can be used to enhance the directivity of a radiated field if properly dimensioned. Two pairs of stacked patches are separated by a central ground plane. The lower square patches are used as receiving elements while the upper radiators are used as transmitting elements. In the proposed configuration, the receiving patches face the focal source, while the transmitting patches face the free space. An aperture (slot) is used in the central ground plane to couple the receiving elements on their lower side, with the transmitting ones placed on the opposite side of the transmit array. The patches are printed on a 0.2 mm Arlon AD350A with a dielectric constant \(\varepsilon_{r}=3.55\) and a loss tangent of 0.0033. The four patches have the same dimensions. The phase response is controlled by changing the side lengths of all the stacked patches. Unit cells with different patch lengths can be arranged to provide a desired phase distribution across the array surface. The unit cell's periodicity is \(W=1.35\) mm (\(0.35\,\lambda_{0}\) at 77 GHz) for both planes.  
Figure 4: Normalized radiation patterns (E-plane) of the patch antenna with (black line) and without (grey line) mushroom-type EBGs.  
Figure 3: Dispersion diagram of the unit cell EBG structure printed on a Rogers RO3003 substrate. Dp = 1.05 mm; Wp = 0.7 mm; h_sub = 0.25 mm. Blue line: first mode; orange line: second mode.  
In Figure 6, the fields radiated from the unit cell with different angles of incidence are plotted in terms of magnitude and phase. The structure has highly stable behaviour for incidence angles up to 60\({}^{\circ}\). This characteristic can be used to reduce the \(f/D\) of the proposed configuration.  
Figure 5: Transmit array unit cell structure. (a) 3D vies; (b) side view.  
Figure 6: Simulated S21 in terms of phase (a) and magnitude (b) as a function of patch size (Lp) for different angles of incidence.
## 5 Field Tests  
The statistical approach of the proposed bridge health monitoring technique is tested on a healthy concrete slab culvert to assess the repeatability of results under field conditions. In the field analysis, the global change in stiffness (due to temperature change) is studied to investigate the change in statistical properties of BWIM data. The Sentvid bridge (see Figure 11), used for the field testing, is a 6 m long frame/culvert carrying two lanes of traffic and is located in the north of Ljubljana, Slovenia.  
Figure 11: The Sentvid bridge in Slovenia.  
Figure 10: Change in damage indicators with the percentage loss of bridge stiffness: (**a**) standard deviation of inferred GVWs; (**b**) mean 1 st bridge natural frequency.  
Each culvert structure is instrumented with a bridge weigh-in-motion system consisting of four free-of-axle-detector (FAD) transducers [21] and 12 weighing strain transducers which are widely used for a typical BWIM system. Figure 12 shows a plan layout of the transducers on the Sentvid bridge with dimensions in the International System of Units. The FAD transducers (7-8 and 15-16) are located near the quarter points of each culvert and are used for vehicle axle detection and to find the spacing between axles. The weighing strain transducers (1-6 and 9-14) are mounted mid-span of the 6 m long structure [45]. The sampling frequency of all sensors is 512 Hz.  
The acceleration-based BWIM can be applied to accelerometers or existing strain-based systems. Strain-based WIM (SiWIM) [43] is more common as the accelerometer WIM is still in early development. For the field tests, one year of data of passing trucks with axle-weights calculated from the SiWIM system is used. The mid-span strain responses of the bridge are used to approximate the acceleration responses as the difference of strain differences. It should be noted that these derived accelerations are oriented horizontally (longitudinally) rather than vertically, as would normally be measured using an accelerometer. For the remainder of the paper, acceleration response means derived accelerations from the SiWIM.  
The strain response at a sensor location 6 which is under one of the wheel tracks of the trucks traveling in the slow lane of the bridge is used to derive the accelerations in this way. Figure 13a shows the strain response to a typical 5-axle truck. The peaks on the left side correspond to the passage of the first two axles while the peak on the right corresponds to the tridem axle group. The derived second derivative of the strains is shown in Figure 13b.  
Figure 12: Plan layout of the transducers on the Séntvid bridge (dimensions in m).
# Advances in Random Fiber Lasers and Their Sensing Application
## 2 RFLs Based on Various Optical Fibers
### PCF-Based RFLs  
Light performs nonlinear effects, and pattern properties influenced by the arrangement of pores in the cladding, and birefringence effect--caused by the asymmetric arrangement of pores, while it transmits in the PCF [71; 72; 73; 74]. This concentrates it in a set of tiny pores arranged periodically along the fiber. Thus, an unknown world that RFLs are based on the PCF has opened up.  
In 2007, a quasi-one-dimensional RFL generated by putting suspension TiO\({}_{2}\) rhodamine 6G solution into the hollow-core PCF [30], and the efficiency was at least two orders of magnitude higher than similar systems in bulk format. This work has made researchers concentrate on the RFLs. Moreover, PCF is a promising platform of quasi-two-dimensional the RFLs, which could be convenient for understanding the oscillation mechanism of the RFLs. In 2015, random laser action was observed by filling 4-(dimethylene)-2-methyl-6-(4-dimethyl-styrene)-4h-pyran laser dye solution in PCF [75]. In 2017, [PERSON] et al. found that the threshold of the RFLs pump is related to the excitation polarization when filling PCF holes with dye-doped nematic liquid crystals (NLC). If so, the threshold would be much lower, due to stronger scattering efficiency caused by the high refractive index of the NLC. The cross-section of the PCF is shown in Figure 1a, and the optical setup for probing laser emission is depicted in Figure 1b [76].
# Design and Experimental Verification of a PZT Pump with Streamlined Flow Tubes
## 1 Introduction
It was reported that the channel structure can alleviate the flow instability induced by the single flow trend [31; 32]. Although the pump proposed by [PERSON] et al. can alleviate the flow instability because of the single flow trend, the structure of the pump is still the traditional conical flow tube, which cannot avoid the cone-shaped tube's disadvantages of reflux and vortex. Moreover, the pump by [PERSON] et al. is a plane structure (2D), which would induce some unavoidable disadvantages during fluid delivery, such as bad symmetry, large system disturbance, low efficiency, and necessary consideration of the one-way flow resistance [21]. According to the above analysis, a streamlined flow tube valveless PZT pump (SLFT PZT pump) is proposed in this paper to improve the trend of fluid flow and the flow stability. The pump has a 3D structure, which can realize the special fluid delivery and energy transfer and match the energy consumption between diffuser and nozzle. The flow resistance at each point of the streamlined flow tube produces the same external force, which will minimize the internal consumption of the pump itself. In this study, firstly, the reasons for the change of flow trend caused by streamline structure were analyzed. Then, the expressions of flow resistance and flow rate were established by theoretical analysis. Finally, the performance of the pump was further studied by means of experiment and simulation, and the results obtained are discussed in this paper.