esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analysis of Proteomic Data  
Raw LC-MS/MS data were inspected using the Bruker Compass Data Analysis tool (version 4.3, Bruker Daltonik GmbH, Leipzig, Germany). After processing with the Compass Data Analysis tool, the resulting mgf file was used for a \"target-decoy\" search on the Mascot server (version 2.6.2), applying a 1% false discovery rate (FDR) for peptide spectrum matches (PSMs) above the homology threshold. Alternatively, data were processed using the PEAKS Studio 11 software. For PEAKS analysis, the precursor mass error tolerance was set to 1.2 Da and the fragment ion tolerance to 0.6 Da. Trypsin [KR \(\mid\) P] was designated as a cleavage enzyme, allowing for up to 2 missed cleavages. N-terminal protein acetylation and methionine oxidation were selected as variable modifications, while carbamidomethylation of cysteine was used as a fixed modification. Peptide identifications and sequence coverage were further enhanced by selecting N-terminal acetylation, one of the most prevalent modifications in both prokaryotic and eukaryotic proteins [24; 25]. Peptide FDR was set at 1.0%, and proteins with a \(-\)10 log\({}_{10}\)(P) \(\geq\) 15.0 were considered significant. FASTA files for MS2 spectra searches were downloaded from the UniProt database (www.uniprot.org), as follows:  
1. _Homo sapiens_: UP000005640_9606.fasta containing 20,598 entries, corresponding to one protein sequence per gene was used for analysis of HEK293T cell and human plasma samples.
2. _Cricetulus griseus_: UP000001075_10029.fasta containing 23,887 entries, corresponding to one protein sequence per gene was used for analysis of CHO cell samples.  
Escherichia coli_ (strain K12): UP000000625_83333.fasta containing 4403 entries, corresponding to one protein sequence per gene was used for the analysis of _E. coli_ samples. The accession ID parsing rule \(\hat{\ }>\) ((tr\(\backslash\) \(\{\cdot^{\star}\}\)\(\backslash\) \(
## 4 Discussion  
### Impact of Spectral Pre-Processing on Sweetness Prediction  
The spectral reflectance of kiwifruit aligns with the typical reflectance curve of the plant [41]. The shape of the reflectance curve is mainly controlled by the absorption information of the biochemical components present in the sample. But the scatter information related to the complex interaction of the light with the physical structure of the samples also added to the reflectance signals [42]. Solid samples often feature non-uniform surfaces and can scatter light during diffuse reflection, resulting in additive and multiplicative effects and causing a weaker spectral signal. Therefore, the remove of the scatter information from the hyperspectral data is the first step for predicting chemical components in NIR data modeling. Given that kiwifruit spectra are susceptible to various factors, both the MSC and SNV transformation were used to correct the light scattering from the uneven surface distribution of kiwifruit. In SNV, each spectrum was centered and then scaled by the corresponding standard deviation to reduce the multiplicative effects of scattering. MSC corrected the baseline and amplification effects to the reference spectrum for every spectrum [43; 44]. They were used to compensate for additive and/or multiplicative effects in spectral data. Studies on different subjects showed different performances of each scatter-correction technique [45; 46]. Our result showed improved sweetness prediction results after MSC and SNV transformation than the smoothed spectra. By contrast, the performance of MSC was slightly better than SNV under the same regression method, but the difference between two methods was not significant. Some studies have shown that a better predictive performance could be obtained when the information from differently scatter corrected data was jointly used [47; 48]. The fusion of information from different scatter-correction techniques will cause a large amount of data. Whether the fusion is effective for the estimation of kiwifruit sweetness needs to be verified in future work. Overall, MSC or SNV transformation was suggested for the extraction of kiwifruit properties with NIR data.
## 5 Design and Verification of Vertical Steel Damper
### Verification Tests of Steel Damper Performance  
The quasistatic test was performed to verify the hysteretic damping performance of the steel dampers in Table 11. In the verification test, three specimens were used for each steel damper _ID_. Figure 16 is a photograph of the steel damper specimen shapes and test facility with installation of the steel damper specimen. Table 12 shows the specifications of the DC motor-driven testing machine used in this test.  
In order to verify the hysteretic energy dissipation performance, the test was carried out in five cycles for two ranges of maximum \(\pm\)24 mm and \(\pm\)30 mm, considering the target design displacement limit with quasistatic displacement control. The test speed applied to the quasistatic displacement control test is 5 mm/min. Figure 17 shows a photograph of the deformed shape under the maximum displacement load during the cyclic tests on the steel damper. Through visual inspection after the test, it was confirmed that there were no surface cracks on all steel damper specimens.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Item** & **Performance** \\ \hline Facility model name & INSTRON 5982 \\ Max. loading (kN) & 100 \\ Footprint dimensions (mm) & \(1130\times 777\) \\ Height (mm) & 2273 \\ Control axes & 1 \\ Max. displacement (mm) & 1330 \\ Min. speed (mm/min) & 0.0001 \\ Max. speed (mm/min) & 1016 \\ Excitation mechanism & Brush DC Motor \\ Control software & INSTRON Bluehill 3 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Performance characteristics of test facility.  
Figure 16: Photos of steel damper specimen and test facility: (**a**) Steel damper specimen; (**b**) test facility with specimen.  
Figures 18-20 present a comparison of the results of the hysteretic behavior of the steel dampers between the tests and inelastic analyses.  
As shown in the figures above, we can see that the hysteretic behavior of the steel dampers by tests are in good agreement with the analysis results. In addition, the initial yield behavior and cyclic hardening characteristics are well matched, and all steel dampers are rapidly stabilized after three cycles. Therefore, it is expected that there is no significant change in the energy dissipation performance due to the increase in the yield strength according to the cyclic load. From the comparison results of these tests and analysis results, it is confirmed that the design of the steel damper presented in this study for application to the vertical seismic isolation device of the 3D seismic isolator can ensure the damping performance of 30% or more, which is the design target in terms of energy dissipation performance.  
Figure 17: Deformed shapes of steel dampers after the maximum displacement loads.  
Figure 18: Comparison results of hysteretic behavior between tests and analyses for steel damper, SD1: (a) for \(\pm\)24 mm stroke; (b) for \(\pm\)30 mm stroke.
# Compact and Accurate Scene Text Detector
## 3 Proposed Method
### Loss Function  
The total loss function \(L\) consists of two loss functions: \(L_{s}\) and \(L_{g}\). \(L_{s}\) is a loss for the score map, and is defined using the dice-coefficient loss function described in Equation (1). Throughout this paper, the notation \(\hat{X}\) means a predicted value for the corresponding variable \(X\); for example, \(\hat{S}(p)\) is a predicted score for the pixel \(p\) and \(S(p)\) is a ground truth score for the pixel. The \(\lambda_{L_{s}}\) is used to regularize the scale, which we set to 0.01 in this paper.  
\[L_{s}=\lambda_{L_{s}}(1-2\sum_{p}\frac{\hat{S}(p)S(p)}{\hat{S}(p)+S(p)}) \tag{1}\]  
\(L_{g}\) is a loss function for the geometry and is defined as follows. \(L_{AABB}\) indicates the loss function for an axis-aligned bounding box (AABB); specifically it represents a loss for the coordinates of a word box. The \(L_{\theta}\) is a loss function for a ration angle \(\theta\). In this paper, we set \(\lambda_{L_{\theta}}\) = 20, making the approach more sensitive to rotations of word boxes.  
\[L_{g}=L_{AABB}+\lambda_{L_{\theta}}L_{\theta} \tag{2}\]  
The definition of \(L_{AABB}\) is as follows, where \(R\) is the size of a bounding box. Here \(w_{p}\) and \(h_{p}\) are the width and height intersected in ground truth word box between predicted word box for the corresponding pixel \(p\), respectively. We define the four variables \(\hat{d}_{top}\), \(\hat{d}_{right}\), \(\hat{d}_{bottom}\), and \(\hat{d}_{left}\), which represent the distances from \(p\) to the top, right, bottom and left edges, respectively, as described in Equation (4). The \(w_{p}\) and \(h_{p}\) indicate the width and height of the overlapped area between the predicted region and the ground-truth region, respectively.  
\[L_{AABB} =-log(IoU) \tag{3}\] \[=-\sum_{p}log(\frac{w_{p}h_{p}}{\hat{R}(p)+R(p)-w_{p}h_{p}})\]  
\[w_{p} =min(\hat{d}_{right},d_{right})+min(\hat{d}_{left},d_{left}) \tag{4}\] \[h_{p} =min(\hat{d}_{top},d_{top})+min(\hat{d}_{bottom},d_{bottom})\]  
The definition of \(L_{\theta}\) is as follows. \(L_{\theta}\) ranges from 0 to 2, where greater a value implies a greater difference in the rotation angle.  
Figure 4: Structures of four comparable decoders, where \(OS\) is the output stride, \(c\) is the number of channels, and convolutions of kernel size \(3\times 3\) are denoted by _Conv_. (**a**) decoder of EAST, (**b**) decoder of CRAFT, (**c**) decoder using IRB for all stages, and (**d**) a different decoder using IRB for all stages. To ensure decoder (**d**) has a similar complexity, its expansion is set to be 3.  
\[L_{\theta}=1-cos(\hat{\theta}-\theta) \tag{5}\]  
Finally, the total loss is a weighted sum of the two loss functions, as described in Equation (6). \(\lambda_{L_{g}}\) ranges between 0 and 1, and can be used to adjust relative importance of \(L_{g}\). We set \(\lambda_{L_{g}}=1\) in this paper.  
\[L=L_{s}+\lambda_{L_{g}}L_{g} \tag{6}\]
## 3 Results
### Cost-Carbon Emission Comparison Analysis  
Table 1 presents a comparative analysis of cement and guar gum as soil-stabilising agents, highlighting their economic and environmental implications when used to treat 1 m\({}^{3}\) of soil with a target dry density of 1.6 g/cm\({}^{3}\) (1600 kg/m\({}^{3}\)). Cement was applied at a dosage of 6%, corresponding to 96 kg/m\({}^{3}\) of soil, whereas guar gum was used at a lower dosage of 1%, requiring 16 kg/m\({}^{3}\). Despite the higher unit cost of guar gum (GBP 1500/ton) compared to cement (GBP 150/ton), the total material cost for treating 1 m\({}^{3}\) of soil with guar gum (GBP 24) exceeded that of cement (GBP 15), due to the significantly higher price per unit mass [33; 34].  
Figure 10: X-ray computed tomography (CT) grayscale images illustrate expansive soil samples’ internal microstructural evolution under cyclic wetting–drying (WD) conditions. The images compare untreated, cement-treated, and guar gum-treated samples after 1 and 10 WD cycles. All images represent horizontal cross-sections at mid-height of cylindrical samples, and cracks are marked with red arrows.  
Regarding environmental impact, cement production produces substantial CO\({}_{2}\) emissions of 0.9 tons per ton of material, translating to 86.4 kg CO\({}_{2}\) per m\({}^{3}\) of treated soil [35]. In contrast, guar gum exhibits a markedly lower carbon footprint of only 0.1 tons of CO\({}_{2}\) per ton, resulting in a minimal emission of 1.6 kg CO\({}_{2}\) per m\({}^{3}\) of soil [36]. This comparison underscores guar gum's potential as a more environmentally sustainable, albeit costly, alternative to cement for soil improvement applications.
## 2 Background
### Missions and Requirements
Due to possible intra-network radio interference and limited centralized radio resources, there exist limits on the ability to utilize multiple MAVs during a mission. Autonomous MAV operation, on the other hand, significantly increases on-board sensing and processing requirements, but reduces required sustained data rates. Autonomous operation can also enable multiple MAVs in a single network, which increases complexity of the network routing and general network resource allocation. Given such tradeoffs, there are a number of important parameters to consider when evaluating a MAV system. The potential capability of the system is directly related to the mission duration, which in turn depends on power consumption. The efficacy at which the MAV can accomplish its goals is directly related to the data transfer capabilities within the system, which can be measured both by data throughput and network latency.
# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 2 Types of Common LPBF Part Defects
### Mechanical Defects  
In LPBF, thermal-induced stresses during part printing are due to temperature gradients. Increased thermal stress can lead to distortion, cracking (liquidation and solidification), and delamination in extreme cases to the processed part. Thermal stress induces mechanical defects that may exceed material yield stress, causing plastic deformations or distortions of the part's top layer(s) [19]. Figure 3 displays the three thermally induced mechanical defect types commonly found in LPBF-printed parts.  
Liquation cracking is formed due to the melted liquid film's stability and stress [20]. High levels of beam power and metallic particle thermal conductivity cause liquid instability and high-frequency temperature fluctuations, inducing stress and leading to cracks. Introducing selected elements to the main composition can control the melt pool's composition and solidification and prevent liquidation cracking [21].  
Solidification cracking forms during the final stage of the melt pool cooling down and solid zone formation. At low cooling rates, the liquid region forms weak spots that create propagating microcracks [22]. Using alloy elements with a wide range of solidification temperatures has more potential to form solidification cracks [23]. Depending on the  
Figure 2: Microstructural defect SEM imagery of (**a**) LOF pores [9], (**b**) gas pores [2], and (**c**) keyhole pores [5]. All included figures are modified from the originals.  
crack size and location, a re-melting strategy or a series of surface machining for the part minimizes or eliminates solidification cracks. Avoiding a higher cooling rate of the part is the most efficient approach to reduce the chances of forming solidification cracking.  
Delamination is a very detrimental type of cracking in AM parts. It is caused by a high-temperature gradient within the part and residual stresses [24]. The change in part dimensions during fabrication induces plastic deformation, which leads to delamination [25]. Delamination appears between layers, often resulting in complete part failure.
## 3 Results  
### Spatiotemporal Variation Characteristics of Drought in Xinjiang  
#### 3.1.1 Overall Trend Analysis of Drought in the Study Region  
This study analyzed the spatial and temporal characteristics of drought in Xinjiang from 2000 to 2020 using SPEI data at 3-, 6-, 12- and 24-month timescales. Long-term trends were assessed using a linear regression model and the Mann-Kendall test. Figure 3 illustrates the SPEI variations in the study region at each timescale. Shorter timescales (e.g., 3 months) exhibit more frequent fluctuations, whereas longer timescales (e.g., 12 and 24 months) display longer and more pronounced oscillation periods. Despite the differences in the fluctuation frequency across timescales, the overall trend remained consistent. SPEI-3 captured the dynamics of short-term droughts, showing the most frequent variability, whereas the 12-month and 24-month timescales represented long-term drought dynamics with more pronounced periodicity. According to the Mann-Kendall test results, all timescales exhibit slope values less than \(-0.0005,\ 1Z1\) below 1.96, and \(p\)-values above 0.05, indicating that the downward trend is not statistically significant, with a slight overall deterioration in drought severity. A drought event was indicated when the SPEI descended below \(-0.5\). Over the past 20 years, Xinjiang has experienced three severe drought events, with the drought from 2006 to 2010 being the longest and most severe. Since 2016, the extent of drought has eased.
## 3 Results and Discussion
### Ion TOF Spectroscopy  
Figure 5 shows ion TOF spectra of Xe clusters taken under different conditions for high FEL fluences (\(\sim\)8 \(\upmu\)J \(\upmu\)m\({}^{-2}\)) in panels (a)-(c) and low FEL fluences (\(\sim\)3 \(\upmu\)J \(\upmu\)m\({}^{-2}\)) in panels (d)-(f), respectively. The charge distributions of the detected ion yields extracted from each TOF spectrum are shown in the insets of each ion TOF spectrum. Substantial TOF intensity of singly charged Xe ions was observed at a lower XFEL fluence irrespective of cluster size. On the other hand, singly charged ions were suppressed and more highly charged ions were generated at higher XFEL fluence. To investigate the dependence of the charge state on other observables, the maximum charge state and the average charge state were extracted from each TOF spectrum.  
Figure 6a,b show the XFEL fluence dependence of the maximum charge state (\(Z_{max}\)) and the apparent averaged charge state (\(Z_{ave}\)) of Xe ions. \(Z_{max}\) increased steeply and saturated at around 12 as the XFEL fluence increased. \(Z_{ave}\) also increased with the XFEL fluence. In the current data set, both \(Z_{max}\) and \(Z_{ave}\) seemed to show a slight dependence on the cluster size, but further discussion regarding the size dependence is difficult because there are limited data. Elucidation of the size dependence is an important theme for furthering our understanding of the laser-matter interaction.  
Figure 5: Ion TOF spectra of Xe clusters with sizes of approximately (**a**,**d**) 40 nm, (**b**,**e**) 60 nm, and (**c**,**f**) 80 nm. TOF spectra observed for high FEL fluences (\(\sim\)8 \(\upmu\)J \(\upmu\)m\({}^{-2}\)) are shown in (**a**–**c**), and those observed for low FEL fluences (\(\sim\)3 \(\upmu\)J \(\upmu\)m\({}^{-2}\)) are shown in (**d**–**f**). Ion charge states of each peak were determined by a simulation using SIMION [47]. The charge distributions of the detected Xe ions extracted from each TOF spectrum are shown in the insets. The yields of doubly charged ions and singly charged ions are less reliable due to the lowering of detection efficiency and the saturation of the signal, which were caused by the detection of a large number of highly charged Xe ions. The baseline of spectra is corrected because it was modulated due to the lowering of detection efficiency and the detection of a large number of ions.  
The strong dependence of the charge state of Xe ions on the fluence of the XFEL pulses is in agreement with the preceding studies using soft XFELs with 91 eV [18] and 800 eV [22]. Moreover, the suppression of singly charged ions and the steep increase in charge state with higher fluence also agrees well with the results using 800 eV XFEL pulses [22], suggesting the efficient suppression of electron-ion recombination. Notably, the saturated charge state observed in this study was also lower compared to the preceding studies. This difference may be due to the different excitation energy, deposition energy, and ionization efficiency of the incident photons.
## 2 Materials and Methods
### EONND: Energy Operator Detector with Neural Network Architecture
#### 2.3.2 Pooling Layer  
The pooling layer is responsible for fusing the multiple possible \"energy\" features estimated by CONV for sampling point \(n\) into an \"optimal\" estimate, completing the sample-level filtering. Usually, the pooling functions are max pooling, linear softmax, exp. softmax, and average pooling. If they are interpreted as the weighted average, the four pooling functions exist in the relationship shown in Figure 4 [53].  
As shown in Figure 4, max pooling and average pooling are the two extremes. When pooling the \"energy\" features, the max pooling function gives the maximum \"energy\" all the weight, while the average pooling function gives all \"energy\" features equal weight. In between, the weight distribution of the linear softmax pooling function and exp. softmax pooling function are assigned in a relatively soft way. The performance differences of the four pooling functions are discussed in Section 3.
# A Dual Stage Low Power Converter Driving for Piezoelectric Actuator Applied in Micro Mobile Robot
## 2 Driving Requirements and Strategies of Piezoelectric Actuator
### Drive Requirements  
Piezoelectric bimorph actuators can be driven with bipolar or unipolar signals. In bipolar driving, the driving voltage varies between a positive voltage and a negative voltage, which causes the piezoelectric element to expand and contract, due to the converse piezoelectric effect. In unipolar driving, the voltage is only positive, which causes contraction. In general, piezoelectric actuators require considerable strain to provide power, and therefore, a high driving voltage is necessary to achieve large wing strokes. The drive signal must be unipolar, because a high-amplitude bipolar driving signal can cause depolarization of the piezoelectric element.  
In the piezoelectric equivalent model, most of the energy is stored in \(C_{0}\) and can be reused, thereby requiring the drive circuit to also complete the task of energy recovery. This energy part can be transferred to another layer of bimorph actuators or power supply to maximize the efficiency of the system.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 3 The Knowledge of Underwater Acoustic Sensor Networks
### The Characteristics of Underwater Acoustic Communication
#### 3.1.1 High propagation delay  
Underwater sensor nodes use acoustic waves rather than RF waves to communicate with each other. As shown in [26], the speed of RF waves in air is about 200,000 times faster than acoustic waves under water, so the propagation time in underwater acoustic sensor networks is appropriately 200,000 times longer than in terrestrial wireless sensor networks, which will cause serious propagation delays. In addition, changing salinity, temperature, and depth all can affect the propagation speed of underwater acoustic waves. The propagation delay in underwater environments is dynamic, which also increases the difficulty of routing protocol design.
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 2 Proposed Frequency-Based Haze and Rain Removal Network (FHRR-Net) Framework
### Analysis of Haze and Rain Models for Creating Synthesized Training Dataset
#### 2.1.1 Haze Model (Atmospheric Scattering Model)  
The physical modeling of haze was done by [PERSON] [16] as a atmospheric scattering model. Many traditional hand-crafted base haze removal methods have been studied based on this model [17; 18; 19]. The single haze image consists of a combination of haze-free images, transmission maps, and global atmospheric light values. The atmospheric scattering model is written as:  
\[I(x)=J(x)t(x)+A(1-t(x)), \tag{1}\]  
where \(x\) is a two-dimensional vector that indicates the position of each pixel. \(J\) is the clean image, \(I\) is the hazy image, \(A\) is the global atmospheric light value, and \(t\) is the transmission which has a value between 0 and 1. The transmission maps are affected by the scattering coefficient and the distance from the object. The transmission maps is expressed as:  
\[t(x)=e^{-\beta t(x)}, \tag{2}\]  
where \(\beta\) is the scattering coefficient. \(d\) indicates distance from the object and the observer in pixel \(x\). As the distance of the object and the atmospheric degradation factor increase, the atmosphere becomes further hazy.