esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 4 Results
### Ablation Study  
#### 4.8.1 Effects of the Key Modules in DAFEN  
To evaluate the contribution of key modules in DAFEN to the overall performance, we conduct ablation experiments on DAFEB, CSLB, and FFEM, with the specific results shown in Table 7. Firstly, we remove DAFEB and replace it with the Residual Group (RG) from RCAN [14]. To maintain a similar model structure, we set the number of Residual Channel Attention Blocks (RCAB) in RG to 3. By comparing the first and fourth rows in the table, it can be observed that after replacing DAFEB, the model's parameter count and computational load increased to 1.6 times and 1.8 times that of DAFEN, respectively, but the performance remains close to that of DAFEN. Secondly, we remove CSLB and replace it with a residual block. The specific implementation details are as follows: the input feature information is first processed through Conv3 \(\rightarrow\) ReLU \(\rightarrow\) Conv3, then the processed features are connected with the input features via residual connection, and finally processed through ReLU again. From the comparison between the second and fourth rows, it can be seen that the variant with the replaced CSLB has approximately twice the parameter count and computational complexity of DAFEN but shows similar reconstruction accuracy. However, the SSIM value on the RS-T1 dataset and the PSNR value on the RS-T2 dataset are slightly lower than those of DAFEN. Additionally, we remove FFEM and replace it with the attention-based multi-level feature fusion (AMFF) structure from AMFFN [41]. From the comparison between the third and fourth rows, it can be seen that the performance of all metrics significantly decreased, especially on the Urban100 dataset. Through these experiments, we systematically demonstrate the lightweight design and efficiency of DAFEB, CSLB, and FFEM, as well as their exceptional contributions to the performance of DAFEN.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Params** & **Multi-Adds** & **Times** & **RS-T1(\%) PSNR/SSIM** \\ \hline FeNet [46] & 357 K & 35.2 G & 19.46 ms & 31.89/0.8432 \\ DARN [56] & 596 K & 58.4 G & 18.87 ms & 32.08/0.8470 \\ IFIN-S [55] & 459 K & 51.0 G & 143.34 ms & 32.04/0.8448 \\ DAFEN-S & 192 K & 17.1 G & 11.65 ms & 31.93/0.8459 \\ DAFEN & 422 K & 37.7 G & 17.31 ms & 32.13/0.8527 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Quantify how lightweight the model is on RS-T1 dataset with a scaling factor of \(\times\)3. The best and second-best results are highlighted in red and blue, respectively. The Multi-Adds is calculated corresponding to a 1280 \(\times\) 720 HR image.
## 4 Conclusions  
In this paper, a methodology to select the best performing mathematical tool to study PDS, on the basis of its degree of both the linearity and the stationarity, has been proposed. This methodology can be applied in presence or in absence of distributed generators. In particular, different typologies of distribution circuits have been considered (residential, commercial, with or without connected PV plants), in order to verify the goodness of the proposed methodology. The case study has been based on the data of real and operating PDS and not on simulation data. After the preliminary study of the temporal evolution of the current of each distribution circuit, the index PCref has been introduced to test the linearity degree of the electrical signal. A second check is done to define whether the signal is stationary. These verifications help the user to select the most appropriate method to study a generic electrical signal.  
The proposed methodology has been applied to three different PDS, with the following results. The FFT analysis, which is also the most common mathematical tool, is very easy to be implemented, but is not useful to study signals with important nonlinearities. If the signal is not stationary, then the FFT does not take into account this effect. In order to observe the nonstationary characteristic of the signal, the wavelet transform is a better approach than FFT, because it shows the amplitude of the excited frequency modes of the signal as a function of the time evolution.  
Both FFT and WA are based on a linear approach; therefore, they are not able to study the frequency behavior of signals having strongly nonlinear characteristics. In the presence of nonlinear electrical signals, the HHT shows the best performance with respect to both FFT and WA, exploiting the excited modes in the frequency regime.  
Finally, the use of the PCsig index has allowed evaluating the penetration of the PV power, on a seasonal basis, thanks to the use of a benchmark passive distribution circuit. Since the proposed approach allows for the feature extraction of the PDS, the future works will be the extension of this procedure for the load forecasting of PDS and for the fault detection issues.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data and wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Feasibility of a Reusable Radiochromic Dosimeter
## 1 Introduction  
For medical physics applications, the dosimetric properties of thermoluminescent dosimeters (TLD) and optically stimulated luminescent dosimeters (OSLD) have shown to be practical, accurate, and precise tools for point dosimetry [1]. An advantage of these inorganic-based detectors is that they are reusable after removing the original signal. The TLD signal is deleted by heating, while OSLDs are cleared by exposure to light. Two-dimensional (2D) thermoluminescence dosimeters are being developed but have not been widely accepted owing to their non-uniformity, low repeatability, and energy dependence [2]. The other formatted 2D dosimeters that have shown clinical applicability are silver halide-based x-ray film (radiographic film) and the more common Gafchromic(r) film containing a diacetylene (e.g., 10,12-pentacosadiynoic acid) active layer; neither format has post-irradiation reusable characteristics [3]. Potentially, a modified polydiacetylene film containing alkali metal salts may have reversible color-changing properties [4, 5].  
In contrast, reusability has been demonstrated in 2D radiochromic dosimeter sheets (3 mm thick) based on a PRESAGE(r) formulation [6] and confirmed to be usable after eightirradiations. However, the PRESAGE(r) 2D formulation described by [PERSON] is not scalable to the volumes required for fabricating a 3D dosimeter equivalent formulation due to heat degradation during the curing phase of the larger volumes (>1 kg). Therefore, this study aims to evaluate in small dosimeters (cuvettes) three additional PRESAGE(r) formulation components from prior studies [7; 8; 9; 10] that are known to be stable at the scales used in 3D manufacturing. The primary components of PRESAGE formulations are a triarymethane leuco dye (TAM) and a radical initiator with the preferred containing carbon halogen bonds. Mechanistically, radicals generated after irradiation with the most labile from the cleavage of the carbon chloride bond, which reacts with the next labile bond (methine bond, central carbon) in the TAM. This reaction creates a colored resonance stabilized free radical and HCl (e.g., ca. > 600 nm) (Figure 1). The chemical structures of numerous TAMs have been evaluated in 3D dosimeters [10; 11]. The density of the TAM radical is primarily on the central carbon with some charge distribution to the nitrogen substituents [10; 11]. Radical stability and sensitivity are primarily due to steric protection of the central carbon and to polar solvents such as dimethyl sulfoxide. These factors influence the equilibrium between the radical (colored TAM) and the colorless starting leuco dye. The three TAMs, along with their formulations, are shown in (Table 1). Structures 1 and 2 were chosen because the methine is structurally hindered, which is reflected in the post-irradiation color stability of over 500 h with 4% solvent in the formulation, while the color is bleached after 320 h when formulated with 15% solvent [10; 11].  
Figure 1: A mechanism for color formation in TAM (LMG) color former.
# Effect of Marine Coolant Additives on Cavitation Erosion-Corrosion of Diesel Engine Cylinder Liner
## 2 Experimental Method  
### Preparation  
The test specimen was selected as ductile cast iron (GCD-500), which is commonly used for the cylinder liners and cylinder blocks that constitute the cooling water jacket system of diesel engines. Its chemical composition and mechanical properties are provided in Tables 1 and 2. Specimens were precisely machined to dimensions of 20 mm \(\times\) 20 mm \(\times\) 10 mm (width \(\times\) length \(\times\) height). The surfaces were polished sequentially with sandpapers (#100 \(\rightarrow\) #200 \(\rightarrow\) #400 \(\rightarrow\) #600), ultrasonically cleaned in distilled water for 1 min, dried with hot air, and stored in a desiccator.  
The experimental solutions were prepared in three types, as shown in Table 3, to evaluate the effects of the presence or absence of coolant additives and chlorides on the corrosion and cavitation erosion of the test specimens. The type of additive selected was a  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{6}{c}{**Composition, wt.\%**} \\ \cline{2-7}  & **C** & **Si** & **Mn** & **P** & **S** & **Mg** & **Fe** \\ \hline GCD-500 & 3.57 & 2.75 & 0.21 & 0.047 & 0.006 & 0.035 & Bal. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of ductile cast iron.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Y.S** & **T.S** & & & \\  & **(N/mm\({}^{2}\))** & **(N/mm\({}^{2}\))** & **E.L (\%)** & **Hardness (HB) 10/3000** \\ \hline GCD-500 & 389 & 563 & 7 & 192–201 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of ductile cast iron.  
nitrite-based additive widely used as a coolant treatment agent in the shipping industry. The additive used in this study was a nitrite-based corrosion inhibitor from Drew Marine, a company specializing in coolant treatment agents for ships. The physical properties of the inhibitor are listed in Table 4. Based on the manufacturer's recommendations, the additive concentration was set to the recommended level of 13,000 ppm, and the chloride concentration was selected to be the maximum upper limit of 100 ppm for the experimental conditions.
## 3 Results and Discussion
### PHBV Molar Mass Determination  
A variety of parameters in both upstream and downstream processing of PHA production can influence the molar mass of the PHA produced. The upstream parameters include the microorganisms present in the MMC, the medium composition, the state of inoculum, and the fermentation conditions. The downstream processing parameters includes pre-treatment of biomass, extraction method, and subsequent polymer purification procedures [35]. The molar mass (\(M_{w}\) and \(M_{n}\)) and polydispersity index (_PDI_) of purified PHBV were determined by SEC-MALS and the results were from [PERSON] et al. [6] (Table 2). The _PDI_ varied between 1.06 and 1.43. The higher _PDI_ indicates a broader range of distribution of molar mass. The _PDI_ values of the PHBV in this study were lower than those reported for PHBV (_PDI_ of 2.1-2.2) [8]. In an study PHBV extracted with CHCl\({}_{3}\) showed a high _PDI_ of 3.06 [36]. PHBV having low _PDI_ can be used for applications such as controlled drug delivery [37]. Lower _PDI_ values of the PHBV in this study can be attributed to both upstream and downstream parameters. Upstream processing parameters which mentioned earlier will determine the initial HV content and \(M_{w}\) of the cultured PHBV. Downstream processing parameters include some polymer removal in the pretreatment step from lyophilized biomass, solvent type and its atomic interactions with solute, heating energy, and time applied in extraction and purification steps. The combination of these upstream and downstream parameters affects HV content, \(M_{w}\), \(M_{n}\), and as a result _PDI_ of the purified polymer [6; 8; 19; 31; 32; 33; 34].  
It was found that there was a negative linear correlation between \(M_{w}\) (= \(-\)152.0 \(\times\) HV + 42.68) and \(M_{n}\) (= \(-\)134.27 \(\times\) HV + 37.939) with the HV content of the extracted PHBVs (\(R^{2}\geq\) 0.83). Pearson tests were performed between \(M_{w}\) and HV content (\(p\) = 0.031), and \(M_{n}\) and HV content (\(p\) = 0.014). Furthermore, similar correlation was seen between \(M_{w}\) (= \(-\)33.261\(\times\) HV + 17.475) with the HV content of the PHBV in lyophilized biomass (\(R^{2}\) = 0.91). Pearson test was performed between \(M_{w}\) and HV content of the PHBV in lyophilized biomass (\(p\) = 0.012). There was no significant relation between \(M_{n}\) with HV content, and PDI with HV content of the PHBVs in lyophilized biomass.
###### Abstract  
The dry beach length determines the hydraulic boundary of tailings impoundments and significantly impacts the infiltration line, which is crucial for the tailings dam. A deep learning method utilizing satellite images is presented to recognize the dry beach area and accurately measure the length of dry beaches in tailing ponds. Firstly, satellite images of various tailing ponds were gathered and the collection was enlarged to create a dataset of satellite images of tailing ponds. Then, a deep learning method was created using YOLOv5-seg to identify the dry beach area of tailing ponds from satellite images. The mask of the dry beach region was segmented and contour extraction was then carried out. Finally, the beach crest line was fitted based on the extracted contour. The pixel distance between the beach crest line and the dry beach boundary was measured and then translated into real distance by ground resolution. This paper's case study compared the calculated length of dry beach with the real length obtained by field monitoring. The results of the case study showed that the minimum error of the method was 2.10%, the maximum error was 3.46%, and the average error was 2.70%, indicating high precision for calculating dry beach length in tailing ponds.  
dry beach length; deep learning; tailings pond; YOLOv5; satellite image Article
## 2 Materials and Methods
### Optimization of Electric Fields  
As a first step, we tested the triple-GEM detector with a 3 TBq Cs-137 source to optimize the electric fields before coupling it to the optical matrix. The optimal selection of the electric fields is important to maximize the total charge gain (defined as the ratio of the detected charges in the anode plane per incident primary charge), and consequently achieve a high signal-to-noise ratio [23]. The ratio of the number of emitted scintillation photons to the number of secondary electrons produced during the avalanche is called the light yield. As mentioned above, the Ar/CF4 (90/10) gas mixture offers a high secondary scintillation light yield of up to 0.5 photons per secondary electron. Hence, it is expected that the number of photons that reaches the photodiode layer is proportional to the induced current in the anode [24]. Based on this consideration, the electric field selection performed by maximizing the anode current is also optimal to maximize the number of detected photons.  
The measurements were performed at the Calibration Laboratory of CERN Radiation Protection group [25]. The detector was placed on a support table at 0.82 cm from the source. A 6157 Keithley electrometer [26] was connected to the ITO anode to measure the induced current. The transfer, drift and induction fields were optimized to maximize the absolute value of the current at the anode. On the other hand, based on the well  
Figure 3: External Quantum Efficiency (EQE) (black curve and left axis) and Spectral Response (SR) (red curve and right axis) of the OPD layer deposited on the readout matrix. The broad emission band of Ar/CF4 with a peak at around 630 nm is represented by the orange region.  
known exponential dependence of the effective gain on the GEM voltages, the electric fields between the top and bottom copper layers in each GEM foil were kept constant for a total voltage of 960 V [27]. The fields were optimized one after the other by keeping all but the field under investigation constant.
# Research on Out-of-Plane Bending Test of PVB Laminated Glass Plate with Different Number of Layers
## 3 Results and Discussion
### The Influence of the Number of Layers of Glass on Stiffness  
Figure 12b compares the stiffness of PVB LG with different numbers of layers. The stiffness \(D_{t}\), represented as the average slope of the load-deflection curve from Figure 11, increases with the number of layers. The average bending stiffness values for three-layer, two-layer, and single-layer glass plates are 2.77 kN/mm, 1.71 kN/mm, and 1.21 kN/mm,  
Figure 12: Ultimate load, stiffness of LG of different layers.  
Figure 13: The ultimate bearing capacity per unit thickness–number of layers curve.  
respectively, with a ratio of 1:0.62:0.44. As depicted in Figure 14, the stiffness of LG increases nonlinearly with the number of glass layers, a trend that deviates from expected outcomes. To understand the cause, the bending calculation theory for PVB LG must be considered.  
Typically, three assumptions are made when calculating the bending of PVB laminated glass: (a) treating the glass as a simple stack, completely ignoring the PVB and only considering the normal compression between glass layers; (b) assuming the PVB's elastic modulus is sufficiently high, treating each glass layer as a single unit subject to deformation; (c) the PVB is soft but has adequate bonding strength to participate in deformation coordination. In reality, laminated glass is most likely in the third state, a hybrid of the first two conditions [39]. These three states are illustrated in Figure 15.  
If the PVB's presence is completely disregarded, as portrayed in Figure 15a, the complete calculation method is provided in Chapter 9 of JGJ 113-2015 [10]. For \(n\)-layer PVB LG with equal thickness, the deflection calculation is formulated into the following functions:  
\[d_{t}=\frac{0.00406\;qa^{4}}{D} \tag{3}\]  
\[D=\frac{Et_{c}^{3}}{12(1-v^{2})} \tag{4}\]  
Figure 14: Stiffness–number of layers curve.  
Figure 15: Three working states of PVB LG.  
\[t_{e}=\sqrt[3]{nt^{3}} \tag{5}\]  
Among them: \(E\) is the elastic modulus of glass (MPa); \(q\) is load (MPa); \(v\) is Poisson's ratio for glass (MPa); \(d_{t}\) is center deflection (mm); \(t\) is thickness of single-layer glass (mm); \(D\) is flexural rigidity for classical theory (Nmm); \(t_{e}\) is the effective thickness (mm); \(a\) is the edge length of the board (mm).  
According to Formulas (3)-(5), we can deduce that  
\[q=n\frac{Et^{3}}{12(1-v^{2})\cdot 0.00406\cdot a^{4}}d_{t} \tag{6}\]  
So,  
\[D_{t}=kn \tag{7}\]  
where  
\[k=\frac{Et^{3}}{12(1-v^{2})\cdot 0.00406\cdot a^{4}} \tag{8}\]  
Since the geometric dimensions and material parameters of LG are determined, \(k\) is a constant. So formula (7) indicates a linear relationship between \(D_{t}\) and \(n\) when PVB is excluded. Therefore, the observed nonlinear increase in PVB LG stiffness can be attributed to the influence of PVB as the number of glass layers increases. If an engineering structure requires tight control over deformation, selecting PVB LG with more layers is necessary.
## 4 Conclusions  
Variable selection is often grounded in a foundational understanding of the spectral properties of samples when the components are simple and the characteristic peaks of the spectrum are evident. However, leveraging mathematical strategies for variable selection proves to be more effective in complex samples. In this study, it was found that the commonly used spectral regions could enhance model performance for different analyzed components; however, their effect on simplifying the model was not significant. The spectral region of 600-1800 cm\({}^{-1}\) combined with 2800-3000 cm\({}^{-1}\) improved model performance by 34-64% by selecting 1400 spectral variables. The variables selected by PCC, UVE, and VIP were not reasonable, resulting in poor model performance across all components. MWPLS, GA, and RF demonstrated better applicability, but they should be employed only after selecting the appropriate spectral regions. The combination of two variable selection algorithms was unsatisfactory, yielding a performance similar to that of a single variable selection algorithm. In contrast, the combination of three variable selection  
Figure 7: Monitoring for glucose (**A**), lactate (**B**), viable cell density (**C**), and ammonium ion (**D**).  
algorithms (MWPLS-GA-RF) successfully selected fewer variables for modeling, and the selected variables were deemed reasonable. For Gluc, Lac, VCD, and NH\({}_{4}\)+, the model performance improved by 16-70% when applying MWPLS-GA-RF with the selection of 30-60 variables. With the optimal variable selection strategy, monitoring can be effectively performed, providing guidance for researchers on variable selection and promoting the application of Raman spectroscopy in cell culture processes.  
Conceptualization, X.D.; methodology, X.D.; validation, X.D.; formal analysis, X.D.; investigation, X.D.; resources, X.Y. and H.Q.; data curation, X.D. and X.Y.; writing--original draft preparation, X.D.; writing--review and editing, H.Q.; visualization, X.D.; supervision, X.Y.; project administration, H.Q.; funding acquisition, H.Q. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Key Research and Development Program of Zhejiang Province, China (grant number 2023C03116).  
Not applicable.  
The data presented in this study are available on request from the corresponding author (privacy).  
The authors would like to thank [PERSON], [PERSON], [PERSON], and [PERSON] from Hisun Biopharmaceutical Co., Ltd. for providing resources.  
Author [PERSON] was employed by the company Hisun Biopharmaceutical Co., Ltd. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Background
### Object Detection
#### 2.2.9. LSTM  
Long Short-Term Memory (LSTM) is a recurrent-type cell for regressive NN. LSTMs are explicitly designed to avoid long-term dependency. This architecture consists of three gates, mainly intended to solve gradient disappearance and explosion in the long sequence training process. Put merely, LSTM can perform better on longer sequences than normal RNNs. An example of the LSTM architecture is shown in Figure 12.  
The previous image shows in more detail the structure of an LSTM, which, contrary to the standard RNN, the repeating modules have four neural network layers instead of a single _tanh_ layer. The principal idea behind the LSTMs is to have the ability to add or remove information to the cell state, which is the horizontal line running through the top of the diagram, where the data could flow along with it unchanged. This ability is regulated by structures called gates composed of a sigmoid neural net layer and a pointwise multiplication operation. Depending on the digital output state, the information would be let through. LSTMs have three of these gates. The first one decides which inputs are going to be ignored by the cell state. The second one determines which values will be updated. With the help of a _tanh_ layer, which creates a vector of new candidate values, an update to the state will be produced to decide which further information will be stored in the cell state. Finally, the third decides what parts of the cell state will be the output [34].  
Figure 12: Long Short-Term Memory (LSTM) architecture, with three gates composed of four NN layers (image under creative commons license).