esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermostable D-Allulose 3-Epimerase for Long-Term Food-Compatible Continuous Production Systems
## 3 Results and Discussion
### Expression and Purification of Recombinant TcDAEase  
The TcDAEase gene was successfully overexpressed in _E. coli_ BL21 (DE3) and highly purified via Ni\({}^{2+}\)-NTA chromatography. As shown in Figure 3 and S1, the SDS-PAGE analysis revealed a distinct protein band at approximately 33 kDa; this result is consistent with the calculated molecular mass of 33.7 kDa. Although minor faint bands below 30 kDa were observed, the major band indicates high purity and successful expression of the target protein. The enzyme's molecular size is within the range typically associated with favorable stability and diffusion properties, which may facilitate its integration into immobilizedsystems or continuous flow reactors for large-scale D-allulose production. Notably, the purified enzyme retained high catalytic activity (7.9 \(\pm\) 0.3 U\(\cdot\)mg\({}^{-1}\)) in the absence of externally supplemented metal ions, suggesting that essential cofactors may have remained bound during expression and purification. A similar phenomenon has also been observed in DAEases from _Arthobacter globiformis_ M30 [13] and _Clostridium bolteae_[14], which exhibited baseline activity under metal-free conditions, although their maximal activity was achieved upon Co\({}^{2+}\) addition. In comparison with these enzymes, TcDAEase demonstrates a favorable expression yield and robust activity profile, highlighting its practical advantages for industrial application.  
Figure 1: Phylogenetic tree showing evolutionary relationships between TcDAEase and other reported D-allulose and D-tagatose epimerases. The amino acid sequence sources were as follows: _Thermogemmatispora carboxidiororans_ (WP\({}_{0}\)52889376.1); _Agrobacteriumium fabrum_ (AAK88700.1); _Enterocloster bolteae_ (EDP19602.1); _Rumiticalstridium cellulolyticum_ H10 (ACL75304.1); _Treponema primitia_ (ZP\({}_{0}\)971754.1); _Desmospor_ sp. (WP\({}_{0}\)09711885.1); _Clostridium_ sp. (WP\({}_{0}\)41341767.1); _Clostridium scindens_ (WP\({}_{0}\)04607502.1); _Rumitococcus_ sp. (ZP\({}_{0}\)4858451.1); _Cereibacter sphaeroides_ (ACOS9490.1); _Pseudomonas cichorii_ (BAA24429.1); _Agrobacteriumium_ sp. (EGL65884.1); _Flavorificator plautii_ (WP\({}_{0}\)07494289.1); _Paenibacillus senegalensis_ (WP\({}_{0}\)10270828.1); _Caballeronia fortivita_ (WP\({}_{0}\)61137998.1); _Sinorhizobium_ sp. (WP\({}_{0}\)69063284.1); _Agrobacteriumium tumefaciens_ (ANH56792.1); _Dorea_ sp.(WP\({}_{0}\)22318236.1); _DaeM_ (WP\({}_{0}\)01270828.1); and _Blauti produca_ (WP\({}_{2}\)94524766.1).
# A Transformer-Based Approach for Efficient Geometric Feature Extraction from Vector Shape Data
## 2 Methods
### Application of PSRT in Downstream Tasks
#### 2.4.1 Single-Sample Classification  
Many tasks in cartography and geographic information science can be viewed as single-sample classification problems, such as building shape recognition, road-level identification, and waterway recognition. These tasks can be efficiently executed once a high-dimensional embedded expression of the vector shape features is acquired. The network architecture is shown in Figure 6.  
Figure 5: Coordinate sequence rearrangement task pre-training structure diagram.  
In the serialization process, a control point, \(P_{control}^{t,t}\), was added at the beginning of the serialized sequence. This control point represents the overall shape feature and is effective in capturing the global information of the sample. The vectors are then inputted into a transformer encoder block and processed through multiple layers of transformer encoders, resulting in a high-dimensional embedded representation of the shape feature. To enhance classification accuracy, the re-expressed feature encoding of this control point is connected to a linear classification layer. This design enables the network to generate the logits necessary for final classification, thereby facilitating efficient sequence classification suitable for various single-sample classification tasks.
## 5 Experiments and Discussion
### Comparison and Discussion
Table 4 shows the classification performance of each approach on the degradation detection dataset. Figure 10: The performance comparison of feature extraction. In order to compare the classification performance of the comparison approaches on the degradation detection dataset more intuitively, Figure 11 shows the performance of each approach on different evaluation metrics. As can be seen from Figures 10 and 11, our approach was significantly superior to all other comparison approaches in terms of G-mean, AUC, F1 score, and precision. CSCNN also achieved good results, which can be attributed to its use of cost-sensitive loss functions and the strong feature extraction ability of convolutional networks for sequence data. However, CSCNN still had a significant disadvantage compared to our approach due to the difficulty in determining cost-sensitive factors. The imbalanced data classification methods and anomaly detection methods also showed relatively good performance. However, as can be seen from Figure 11, these methods were significantly less stable than our approach. FST and LTS performed the worst due to the insufficient information provided by the minority class samples, resulting in model bias. From the above experiments, it can be seen that the imbalanced sequence feature extraction neural network proposed in this paper has excellent performance for the detection of LTE-R cells with degraded communication performance. Our approach can achieve an accuracy of about 98% on various imbalanced data classification evaluation metrics, which provides good decision support for the operation and maintenance of the Shuo Huang Railway LTE-R communication network. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **G-Mean** & **AUC** & **F1 Score** & **Precision** \\ \hline SMOTE-SVM & 0.7194 \(\pm\) 0.1048 & 0.7238 \(\pm\) 0.1079 & 0.7041 \(\pm\) 0.1194 & 0.6862 \(\pm\) 0.1028 \\ ADASYM-SVM & 0.7063 \(\pm\) 0.1035 & 0.7095 \(\pm\) 0.1066 & 0.6919 \(\pm\) 0.1195 & 0.6595 \(\pm\) 0.0857 \\ Cosen-SVM & 0.6835 \(\pm\) 0.1360 & 0.6929 \(\pm\) 0.1339 & 0.6658 \(\pm\) 0.1637 & 0.6483 \(\pm\) 0.1385 \\ FST & 0.5231 \(\pm\) 0.0058 & 0.5333 \(\pm\) 0.005 & 0.4788 \(\pm\) 0.0009 & 0.501 \(\pm\) 0.0026 \\ LTS & 0.5294 \(\pm\) 0.0001 & 0.5292 \(\pm\) 0.0001 & 0.5262 \(\pm\) 0.0087 & 0.5195 \(\pm\) 0.0007 \\ CSCNN & 0.8448 \(\pm\) 0.0332 & 0.8564 \(\pm\) 0.0277 & 0.7439 \(\pm\) 0.0263 & 0.7741 \(\pm\) 0.0309 \\ LOF & 0.6217 \(\pm\) 0.1667 & 0.6405 \(\pm\) 0.158 & 0.5911 \(\pm\) 0.19 & 0.6467 \(\pm\) 0.204 \\ VAE & 0.7101 \(\pm\) 0.1215 & 0.719 \(\pm\) 0.1093 & 0.6793 \(\pm\) 0.1499 & 0.7029 \(\pm\) 0.1175 \\ DEDAE & 0.8032 \(\pm\) 0.1034 & 0.8126 \(\pm\) 0.0428 & 0.6824 \(\pm\) 0.0637 & 0.7994 \(\pm\) 0.1132 \\ Our approach & 0.9863 \(\pm\) 0.0092 & 0.9864 \(\pm\) 0.0091 & 0.9799 \(\pm\) 0.0004 & 0.9872 \(\pm\) 0.0181 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The classification results of each approach. Figure 11: Performance of each approach on different evaluation metrics.
# Effect of Transducer Burn-In on Subjective and Objective Parameters of Loudspeakers
## 5 Subjective Measurements
### Analysis of the Third Measurement  
Figure 14 shows that the values are centered around zero. There are no visible differences between the test sequences. Jazz music slightly deviates from the other musical genres, but the differences are minimal.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variable** & **SS Effect** & **df Effect** & **MS Effect** & **SS Error** & **df Error** & **MS Error** & **F** & **_p_ \\ \hline Clarity & 0.423 & 2 & 0.211 & 52.275 & 129 & 0.405 & 0.522 & 0.595 \\ Dynamics & 1.026 & 2 & 0.513 & 44.161 & 129 & 0.342 & 1.499 & 0.227 \\ Brightness & 0.138 & 2 & 0.069 & 54.391 & 129 & 0.427 & 0.163 & 0.850 \\ Overall rating & 0.497 & 2 & 0.248 & 56.775 & 129 & 0.440 & 0.565 & 0.570 \\ \hline \hline \end{tabular}
\end{table}
Table 4: [PERSON]’s test for homogeneity of variance for new and burned-in devices.  
Figure 13: Mean ratings with 75% confidence intervals for new and burned-in audio devices.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variable** & **SS Effect** & **df Effect** & **MS Effect** & **SS Error** & **df Error** & **MS Error** & **F** & **_p_ \\ \hline Clarity & 0.197 & 2 & 0.098 & 91.773 & 129 & 0.711 & 0.138 & 0.871 \\ Dynamics & 1.470 & 2 & 0.735 & 96.409 & 129 & 0.747 & 0.983 & 0.377 \\ Brightness & 0.106 & 2 & 0.053 & 92.523 & 129 & 0.717 & 0.074 & 0.929 \\ Overall rating & 0.652 & 2 & 0.326 & 124.341 & 129 & 0.964 & 0.338 & 0.714 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analysis of variance (ANOVA) for new and burned-in devices.  
The statistical tests conducted for the third measurement, shown in Tables 6 and 7, revealed no statistically significant effects. [PERSON]'s test confirmed the assumption of homogeneity of variances, indicating that the variability within groups (used vs. conditioned devices) is consistent across all perceptual features. Similarly, the ANOVA results showed no significant differences in mean ratings between the devices.  
The statistical analysis indicates that, across all tests, the loudspeaker devices were evaluated consistently, with mean values remaining nearly identical. The plotted means are centered around zero and do not exceed a difference of 0.5. No significant differences were found between musical genres--any variations observed were minimal or negligible.  
These slight discrepancies may stem from a subconscious desire to detect differences, and the observed deviations could be the result of illusory perception rather than actual acoustic differences.
# A Fitting Recognition Approach Combining Depth-Attention YOLOv5 and Prior Synthetic Dataset
## 3 Hardware and Data
### System Integration  
The load capacity and installation space of FPTLIR are considered in the system integration, and the key components are listed in Table 2. The FPTLIR's size is 1.76 m \(\times\) 1.76 m \(\times\) 1.1 m, the weight is 38 kg, the rated lift is 42 kg, and the maximum lift is 90 kg. The onboard computer uses NVIDIA jetson NX to run various algorithms and communicates with PX4 and STM32, where PX4 controls the flying mechanism and STM32 controls the walking mechanism. The FPTLIR has two D-RGB cameras and one inspection camera, D435i, ZED, and SJ4000, respectively. Figure 2 shows the installation position of these cameras. The direction of ZED is the same as the X-axis, estimating the position relationship between the transmission line and the machine body when FPTLIR flies up the line. The SJ4000 is installed on the three-axis stabilization platform underneath FPTLIR for inspection targets.  
Figure 1: Schematic diagram of robot structure. (**a**) complete machine structure; (**b**) flying mechanism; (**c**) walking mechanism.  
The direction of D435i is the same as the Y-axis for recognizing and locating targets. D435i has a size of 90 mm \(\times\) 25 mm \(\times\) 25 mm, a depth field of view angle of 87\({}^{\circ}\)\(\times\) 58\({}^{\circ}\), a depth resolution of 1280 \(\times\) 720, and binocular infrared depth technology.  
RGB images and depth maps are collected by D435i (there are official modules for the alignment of both types of images), and they are input into a YOLO model trained using a synthetic dataset, where the depth maps are used as input to an attention mechanism to improve the accuracy of detecting the fixtures.
## 5 Experiment
### Comparative Experiments of New Algorithms in Recent Years
#### 5.5.1 Compared with IABC  
We tested SGPVEFA using the same environment maps from [25], comparing the results with those from [25]. The IABC parameters were set as follows: 200 maximum iterations, 50 initial population size, and 100 maximum population size. The four environments were configured as follows: a simple environment with regular obstacles (Environment 1, obstacle rate 0.19), a complex environment with regular obstacles (Environment 2, obstacle rate 0.26), a simple environment with random obstacles (Environment 3, obstacle rate 0.17), and a complex environment with random obstacles (Environment 4, obstacle rate 0.22). The paths found by the two algorithms in different environments are shown in Figure 20. Table 4 provides the mean path lengths and node counts, along with the optimal values and standard deviations for both algorithms. SGPVEFA is an exact algorithm, and its results are consistent across all runs.  
In the comparative experiment of Environment 4, the optimal path found by IABC slightly outperformed the algorithm proposed in this paper. This is because in Environment 4, IABC frequently encountered paths that crossed through the common point of two obstacle grids, a scenario that the proposed algorithm in this paper avoids for safety reasons (It has been stated in Section 2). Under the same conditions, SGPVEFA still outperforms IABC.  
Figure 20: Path comparison under different environments.  
Experiments show that, whether in simple or complex obstacle environments, the paths found by SGPVEFA are smoother compared to those found by IABC, with fewer turns and nodes, and shorter path lengths. In complex obstacle environments, IABC often encounters walls, whereas SGPVEFA avoids such issues, making it safer in real-world applications.
## 4 CRISPR/Cas Mechanism
### Adaptation via Spacer Acquisition  
The least characterised stage of CRISPR/Cas defense is adaptation, whereby new spacers are acquired from phages and plasmids and, with duplication of the repeat, are added to the leader proximal end of the CRISPR array (Figure 2). A number of metagenomic studies of phage-bacterial dynamics in environmental [50,51] and human [52] niches have provided clear evidence that CRISPR/Cas systems actively acquire new spacers from mobile genetic elements over short time-scales. The first demonstration of spacer incorporation from phages and plasmids in the laboratory was in the type II-A system of _S. thermophilus_[26,53]. Very recently, studies have observed spacer acquisition in the _E. coli_ type I-E [43,54,55], _P. aeruginosa_ type I-F [56], _Streptococcus agalactiae_ type II-A [57] and _Sulfobolus solfataricus_ type I and III-B [58] systems. The most well characterized system is the _E. coli_ type I-E, so this will be the focus of the following discussion.  
Figure 2: **Model of CRISPR/Cas adaptation** (based on type I-E systems). **(a)** Naive acquisition. Cas1 and Cas2 are required for acquisition of new spacers. The first repeat at the leader end of the CRISPR array is duplicated and incorporates a new spacer sequence from a protospacer. The final nucleotide of the repeat is not duplicated but is provided by the PAM nucleotide immediately adjacent to the protospacer; **(b)** Priming acquisition. Expression of the CRISPR array and generation of the crRNAs against the foreign DNA results in binding / targeting, which is hypothesized to aid in the generation of precursors for integration (possibly single- or double-stranded). Productive interference is not required for priming. Priming acquisition requires Cascade-crRNA, Cas3 and Cas1 and Cas2 and results in new spacers derived from the same strand as the initial spacer.
# A Low Power IoT Sensor Node Architecture for Waste Management Within Smart Cities Context
## 5 Energy-Saving Policy and Consumption Estimation
### Estimation of the Lifetime of the Batteries
Then, it is possible to evaluate the node consumption per cycle (\(c_{t}\)) in this configuration as:  
\[c_{t}=\frac{c_{1}t_{1}+[(c_{2}+c_{Arduino})t_{2}]+c_{4}t_{3}+[(c_{5}+c_{6})t_{ 4}]}{T_{m}}\simeq 0.53\,\mathrm{mA}, \tag{10}\]  
that leads to the following hourly node consumption (\(c\)):  
\[c=\frac{3600c_{t}}{T_{m}}\simeq 0.546\,\mathrm{mA}. \tag{11}\]  
Assuming the use of the same four AA 1.5 V 350 mAh lithium batteries, the lifetime becomes:  
\[L=\frac{3500}{c}\simeq 6206\,\mathrm{h}, \tag{12}\]  
which approximately corresponds to 259 days, roughly half the value obtained with the proposed solution. Moreover, the average cost of an Arduino UNO board is around 20 euros, while the ATmega328 microcontroller costs around 2 euros: this means that, besides the doubled life time, the overall cost of the sensor node is one order of magnitude lower than the Arduino-based one. This estimate strongly confirms the effectiveness of the energy-saving policy even because further improvements could be adopted. One of these could be lowering the clock frequency of the microcontroller down to 8 MHz. Another one could be the employment of more efficient hardware components. However, this would require additional studies and researches to be carried out and at the same time it could mean higher costs to be afforded for the implementation of the IoT node.
# Learning-Free Text Line Segmentation for Historical Handwritten Documents
## 3 Scientific Background
### Scale-Space Representation with Automatic Scale Selection  
The notion of scale is important when processing unknown image structures by automatic methods. This problem can be approached by representing image structures at different scales, so called scale-space representation [41]. However, scale-space representation does not address the problem of how to select local appropriate scales. Therefore, we use automatic scale selection [42] that adapts to the local scales of image structures.  
Scale-space representation together with automatic scale selection apply to a large class of differential image descriptors. We adapt this for detecting blobs, regions brighter than their surroundings, and from text lines and term this procedure as blob line detection with automatic scale selection.  
Given \(n\) scale values \((\sigma_{1},\sigma_{2},\cdots,\sigma_{n})\), scale-space representation is a stack of \(n\) layers where the \(i\)th layer is the convolution of image by Laplacian with the scale of \(\sigma_{i}\) (Figure 2). Spatially, the Laplacian response will be maximum at the center of blob when the scale is matched with the scale of the text line. However, Laplacian response decreases as the scale \(\sigma\) increases. To eliminate this decay, Laplacian response is multiplied by \(\sigma^{2}\) and is called scale-normalized Laplacian. Then, automatic scale selection assigns the maximum scale-normalized convolution response to each pixel of the image.  
Normally, the differential descriptor for forming a blob is the Laplacian of an isotropic Gaussian (LoG) [42]. However, we define a scale-space representation using Laplacian of anisotropic Gaussians, because a text line is elongated and has different scales along the two coordinate directions. In the following sections we define the mathematical formulas for scale-space of Laplacian of an anisotropic Gaussians and automatic scale selection using scale-normalized Laplacian.
# Correlation of Gravity and Magnetic Field Changes Preceding Strong Earthquakes in Yunnan Province
## 2 Data Processing and Accuracy
### Calculation Method
#### 2.3.2 Normalization Processing and Correlation Coefficient Calculation  
The grid gravity change value and the total geomagnetism value are normalized and converted into a dimensionless scalar with a value range of [\(-\)1,1]. To avoid changing the positive or negative sign of the change value, the following method was used in the normalization process.  
\[X_{norm}=\frac{X}{\left|X\right|_{\max}} \tag{3}\]  
In Formula (3), \(X\) is the change value and \(\left|X\right|_{\max}\) is the maximum absolute value of the interannual change value.  
To accurately show the relationship between the gravity change value and the total geomagnetism value, the normalized result of the two is used as a variable, and the linear correlation coefficient between the two was calculated by Formula (4).  
\[r(X_{Gra},X_{Geo})=\frac{Cov(X_{Gra},X_{Geo})}{\sqrt{Var[X_{Gra}]Var[X_{Geo}]}} \tag{4}\]  
In the formula, \(r\) is the correlation coefficient, \(X_{Gra}\) is the normalized gravity value, \(X_{Geo}\) is the normalized total geomagnetic intensity, \(Cov(X_{Gra},X_{Geo})\) is the covariance of the gravity value and the total strength of the magnetic field, \(Var[X_{Gra}]\) is the variance of the gravity value, and \(Var[X_{Geo}]\) is the total geomagnetism variance.
# Real-Time 3D Reconstruction Pipeline for Room-Scale, Immersive, Medical Teleconsultation
## 2 Related Work
### Mesh Simplification with Dual Contouring  
Since the DC method nicely operates on sparse data structures with mixed resolution, [PERSON] et al. [17] demonstrated simplification via octree-based bottom-up vertex clustering with quadratic error functions [59] before mesh extraction, which is much more efficient than other mesh simplification techniques that generally require the high-resolution mesh in memory [60]. However, this approach limits simplification opportunities to the octree structure, which is addressed by enhancing the number of representative vertices per cell using a more advanced encoding as performed by [PERSON] et al. [61], who increased the limit to two vertices per cell. [PERSON] et al. [55] later completely removed this limitation by performing the top-down dual mesh traversal before bottom-up simplification, allowing a list of representative vertices for different surfaces to be contained in each cell with accompanying conditions for manifold preservation. However, their procedure is no longer tail recursive because the results obtained by top-down traversal are required during bottom-up traversal, causing significantly more data dependencies that reduce opportunities for parallelization.  
Since performance is our most important requirement and we aimed to implement simplification in GPU kernels [62], we used the octree-based vertex clustering with a single representative vertex, similar to the original work of [PERSON] et al. [17] due to the simpler encoding as a proof of concept.
# Integrated Hydrological Analysis of Little Akaki Watershed Using SWAT-MODFLOW, Ethiopia
## 2 Materials and Methods
### Groundwater Modelling Method and Procedures  
MODFLOW-2005 was used to develop a 3-dimensional model grid used to characterize a two-dimensional groundwater flow through a single aquifer layer. For the use of finite difference calculations, a grid is overlaid over the 30 m \(\times\) 30 m digital elevation model (DEM) of the study area, and aquifer hydraulic parameters necessary to solve the flow equation are been in an average of over the area of cell and given at a node at the center of the block. In the block-centered preparation, the nodes for which water levels are simulated placed at the midpoint of the grid cells. These cells are the smallest volumetric units over which the hydraulic properties are assumed constant.  
The extent of the model area comprises the boundaries of the regional flow system of the Little Akaki River above the Little Akaki river gauging station. The model covers an area of about 134 km squares.  
The model conceptualization process is an essential process to control the modeling technique to be used. The next important step, after building the necessary record of the database required for the modeling, is the development of a conceptual model. This is the most fundamental part of the modeling procedure [21; 22]. The important data necessities in the course of conceptualization are the hydraulic characteristics of the aquifer, available surface water bodies, recharge value and zones, and area of outflow zones. The conceptual model for the study area was developed using GMS software (Aquaveo 2016). The coverages for the model boundary, source and sinks, recharge rate, and hydraulic conductivity zones were created.