esa-sceva/satcom-chunk-collection huggingface dataset:
# Biological Production of 3-Hydroxpropionic Acid: An Update on the Current Status
## 2 Production of 3 HP from Glycerol
### Engineering of K. pneumoniae Cells for the Conversion of Glycerol to 3 HP
Among the promoters tested (_tac_ and _lac_), the IPTG-induced _tac_ was found to be the most efficient for overexpression of _PuuC_. Moreover, 3 HP production significantly increased when the synthesis of lactic acid and acetic acid was blocked. Finally, optimization of cultivation parameters, such as aeration (microaerobic conditions), pH (7.0), and IPTG concentration (0.02 mM) improved the production of 3 HP even further, resulting in a concentration of 83.8 g/L with a yield 52 g/g after 72 h of cultivation. To the best of our knowledge, this concentration is the highest reported from glycerol. Although addition of IPTG is not optimal from an economic point of view, the amount required during this work was relatively low compared to what is commonly used (0.5-2 mM IPTG was used for _tac_-driven gene expression in _E. coli_, and up to 5 mM IPTG was used for _Zymomonas mobilis_ or _Pseudomonas putida_) [29]. The high concentration of 3 HP demonstrated is an important step towards industrial application. The most frequent approach for the genetic engineering of novel strains is plasmid insertion (of the required genes) into the host strain. However, as strains containing plasmids are not genetically stable and they require inducers and antibiotics to maintain the selection pressure during cultivation, [PERSON] and [PERSON] [25] tried a different approach for construction of the host strain. More specifically, they constructed a plasmid-free _K. pneumoniae_ strain through chromosomal engineering by replacing the ISI region in the chromosome with the AD DNA cassette containing the _aldH_ gene from _E. coli_ through homologous recombination. This strain was able to produce 3 HP at 0.9 g/L (Table 1) in flask cultures when glycerol, at 40 g/L, was added. Although the concentration of the 3 HP produced was low, this work demonstrated a new approach for the construction of host strains without any need for inducers and antibiotics, which could be very useful for the development of new strains in the future. Finally, a totally different approach to production of 3 HP from glycerol was studied by [PERSON] et al. [30]. In their process, they used a two-step approach, where glycerol was first converted to 1,3-propanediol by _K. pneumoniae_, followed by conversion of 1,3-propanediol to 3 HP by _Gluconobacter oxydans_--a bacterium that incompletely oxidizes a wide range of ketones, organic acids,and aldehydes. The final concentration of 3 HP was 60.5 g/L, and the conversion rate of glycerol to 3 HP was 0.5 g/g. Moreover, it was the first time that acrylic acid production was reported as a by-product of 3 HP production from 1,3-propanediol, at a concentration of approximately 1 g/L--another high added-value chemical with several applications, such as plastics, adhesives, and coatings.
## 3 Load Forecasting Based on GRU and TCN Hybrid Network
### Temporal Convolutional Network  
Temporal convolutional network (TCN) is a variant of convolutional neural network (CNN). The core of TCN is atrous convolution and residual block. TCN has a larger causal convolution than traditional causal convolution with the same network depth, and it can flexibly adjust the size of the receptive field. The TCN model solves the gradient explosion and gradient disappearance problem by initializing weight parameters and regularization layers in time-series data prediction. The residual module is used to make the network information directly and deeply transmitted through the cross-layer form in TCN.  
The convolution process of standard convolution and atrous convolution is shown in Figure 4. By injecting holes into the convolution, atrous convolution allows interval sampling in the input data, which increases the receptive field without going through the pooling layer. The convolution output contains a large range of information to ensure the authenticity of the information.  
Figure 3: Basic structure of GRU.  
The receptive field can be written as:  
\[R=k_{s}d_{\text{max}}s \tag{7}\]  
For 1D sequence input \(X\) and filter \(f\), atrous convolution operation \(F\) on the input sequence can be written as:  
\[F(s)=\sum_{i=0}^{k-1}f(i)X_{s-d\cdot i} \tag{8}\]  
where \(R\) is the size of the receptive field, \(k_{s}\) is the size of the convolution kernel, \(d_{\text{max}}\) is the maximum number of hollows, \(s\) is the number of convolutional layers, \(d\) is the hollow coefficient, \(k\) is the filter size, and \(s-d\cdot i\) is the direction of past data. It can be seen that the receptive field is positively correlated with the size of the convolution kernel, the maximum number of holes and the number of convolution layers.  
The residual connection module uses the previous information and converted them as the current input to ensure the training efficiency; the residual connection module can be written as:  
\[o=Activation(x+\chi(x)) \tag{9}\]  
where \(o\) is the output of residual block, _Activation_ is the activation function, \(x\) is previous information, and \(\chi(x)\) is converted information.
# A Simple Experimental Stand for Studying the Network Security Level of Power Electronic Devices
## 8 Discussion
### Disadvantages and Limitations  
The main drawback of the proposed stand is the mandatory use of a switch having the port mirror function. Such switches are significantly more expensive than regular switches, which would lead to additional financial costs. The proposed stand can be implemented with a regular switch, but the use of such a switch would lead to indeterminacy of the monitored traffic--the monitoring station would only monitor its own generated traffic. Thus, more in-depth studies would be impossible.  
Another limitation is that the computational capabilities of the monitoring station must be high. With weaker computational capabilities, real-time monitoring becomes difficult and at times may not be in real-time. The real-time monitoring tools used at the time require a lot of computational resources.  
Another drawback is that using Kali Linux as a virtual machine leads to erroneous results, including port scanning errors, inability to implement the attacks, etc. To avoid such problems, it is imperative that Kali Linux must be installed as a standalone operating system and not as a virtual machine. This means using a dedicated workstation.  
As a possible limitation of the developed experimental setup is that the studied PEDs use old architectures of their embedded web servers--the use of port 80 (HTTP), i.e., when studying PEDs which are using more modern embedded web server architectures, such as TLS (Transport Layer Security), the exchange of information is encrypted because it uses HTTPS (Hypertext Transfer Protocol Secure), respectively, using port 443; therefore, some of the steps in the methodology will need to be revised.  
Table 1 provides a summary of the possibilities, based on the presented constraints, for the developed experimental setup.
## 5 Evaluation of Keypoint Matching
### Result and Discussion
It could be confirmed that image registration between SAR and optical images was performed with high accuracy. A limitation of the proposed method was that it was hard to apply this method to very high spatial resolution images; we confirmed this with 3-m/pixel imagery by Cosmo-SkyMed [44]. That was because layovers in SAR images prevented training datasets from accurate image registration. We confirmed that the proposed method worked well in middle resolution images (about 10 m/pixel). Considering the balance between the size of the observation area and its detail, the dataset of 10 m/pixel used in this evaluation was considered to be an appropriate spatial resolution dataset.
## 3 Algorithmic Errors
### AI-Enhanced Navigation Systems
#### 3.5.2 Collision Detection and Avoidance  
AI systems are essential for real-time collision detection and avoidance, significantly enhancing navigational safety. These systems integrate data from radar, Automatic Identification Systems (AIS), and other advanced sensors to maintain a comprehensive and up-to-date awareness of the vessel's surroundings. Sophisticated machine learning algorithms analyze these data to identify potential collision risks with other vessels or obstacles. Upon detecting a potential collision, the AI system provides immediate alerts and suggests evasive maneuvers to the crew. This capability is particularly critical in congested waterways and during complex maneuvers in ports. By enabling timely and informed decision-making, AI-driven collision avoidance systems help prevent accidents, ensuring the safety of the vessel, its crew, and its cargo [13; 14; 44; 61].  
With the increasing availability of maritime traffic data from the Internet of Things (IoT), deep learning is also being used for vessel trajectory prediction. The analyses address topics such as auxiliary techniques, complexity analysis, benchmarking, performance evaluation, and performance improvement in vessel trajectory prediction research [62; 63; 64].
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 3 Identifying Challenges in RSA: Literature Review
### Challenges in Obstacle and Surrounding Information Acquirement and Detection
#### 3.2.2 Limitation of Using Video Feed  
The quality of the video feed that matters to the RSA is the steadiness and clearness. The video stream is easily affected by the motion of the camera (e.g., handheld mobile device or glasses) and becomes unstable. It is reported that agents are more likely to experience motion sickness when users are not holding the camera (e.g., smartphone hanging around the user's neck) [43]. To mitigate the challenges of reading signages and texts in the user's camera feed, researchers [9] demonstrated the necessity of enhancing the quality of the video stream. A smooth frame rate and high resolution are essential when agents read signs, numbers, or names. The quality of the video stream can affect the performance of RSA in hazard recognition [109; 110], and thus, it is considered as one of the main factors determining the safety of blind users [10]. This explains why the task of an intersection crossing was recognized as one of the most challenging situations for agents in a navigation aid. RSA agents find it very challenging because it is difficult to identify traffic flow through the narrow camera view, poor video quality, and the high speed of vehicles [9; 13; 43].
## 4 Experiments
### Comparison with a Single Network Model
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**UP-DETR**} & \multicolumn{2}{c}{**Yolo**} & \multicolumn{2}{c}{**EfficientNet**} & \multicolumn{2}{c}{**ResNet**} & \multicolumn{2}{c}{**WideResNet**} \\ \cline{2-10}  & **Top-1** & **Top-3** & **Top-1** & **Top-3** & **Top-1** & **Top-3** & **Top-1** & **Top-3** & **Top-1** & **Top-3** \\ \hline (2,21) AB & 98.930 & 99.896 & 90.683 & 96.756 & 96.687 & 99.965 & 96.515 & 99.965 & 94.237 & 99.482 \\ (3,32) ABC & 98.370 & 99.502 & 87.228 & 95.267 & 96.445 & 99.728 & 95.245 & 99.343 & 93.524 & 98.777 \\ (4,42) ABCD & 98.447 & 99.551 & 84.972 & 93.099 & 96.377 & 99.551 & 94.910 & 98.982 & 93.841 & 98.602 \\ (5,53) ABCDE & 98.059 & 99.385 & 83.443 & 92.508 & 94.750 & 99.152 & 94.217 & 98.728 & 92.931 & 98.496 \\ (6,63) ABCDEF & 98.447 & 99.195 & 83.506 & 91.534 & 95.480 & 98.930 & 94.134 & 98.401 & 93.214 & 98.217 \\ (7,74) ABCDEFG & 97.993 & 99.128 & 83.500 & 91.324 & 95.554 & 98.796 & 94.036 & 98.110 & 93.400 & 97.983 \\ (8,84) ABCDEFGH & 98.275 & 99.249 & 83.791 & 91.442 & 95.980 & 88.896 & 94.229 & 98.137 & 93.599 & 97.800 \\ (4,84) IJKL & 98.878 & 99.672 & 88.708 & 96.368 & 95.385 & 99.603 & 95.057 & 99.094 & 93.703 & 98.913 \\ (2,84) MN & 98.404 & 99.189 & 93.444 & 98.490 & 96.946 & 99.965 & 96.912 & 99.784 & 96.006 & 99.862 \\ (1,84) O & 99.784 & 99.957 & 90.709 & 96.299 & 99.905 & 100.000 & 99.569 & 99.983 & 99.681 & 99.983 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of accuracy (%) according to the number of model separations. The accuracy saturation of the single neural network model is inevitable if there are thousands of products to classify. The tipping point of the single neural network model, given a much larger set of design rights, is a subject for subsequent studies. However, note that it is highly costly and time-consuming to obtain the photos of proprietary products following the design rights. Moreover, cooperation from the design right owner is needed to take the images of their products in various settings. Addressing the issues with data acquisition is another research topic to be studied in the future.
# Automated Cart with VIS/NIR Hyperspectral Reflectance and Fluorescence Imaging Capabilities
## 2 Materials and Methods  
### Imaging  
Images are acquired using a gated, intensified, camera (iStar DH334T-18U-73; Andor Technologies, Belfast, UK). Camera specifications include a USB interface, a minimum gate width of less than two ns, 1024 \(\times\) 1024 pixels, 16-bit resolution, and responsivity in the visible wavelengths through the very near infrared. The camera is thermoelectrically cooled to \(-20\)\({}^{\circ}\)C and incorporates a digital delay generator that is used to control image acquisitions. After enabling an image acquisition, an image is acquired after a programmed delay following input of a pre-laser trigger signal. A spectral adapter (ImSpector V9; Specium, Oulu, Finland) inserted between the camera and lens allows the acquisition of hyperspectral, line-scan, images. Lens selection is based on desired operational specifications such as the area to be imaged; tests were conducted using a 16 mm lens (M1614-MP2 2/3\" 16 mm F1.4; Computar, Tokyo, Japan). Images are acquired using a program written in Visual Basic Version 6 (Microsoft, Seattle, WA, USA) and an SDK (Andor Technologies, Belfast, UK), are corrected for dark current, and are saved in either 16-bit TIF or ENVI formats [10].  
Illumination for reflectance images is provided by ambient solar radiation. Illumination for fluorescence excitation is provided by a frequency-tripled Nd:YAG pulsed laser (\(\sim\)6 ns pulse width; Ultra 100 THG WS MVAT, Quantel USA, Bozeman, MT, USA). The laser power supply (ICE450) generates a TTL signal pulse 100 ns prior to the Q-switch trigger. The 355-nm laser has a maximum pulse rate of 20 Hz and a maximum energy of \(\sim\)31 mJ per pulse, and includes an attenuation module programmed using a serial interface. A [PERSON] lens with a fan angle of 30\({}^{\circ}\) ([PERSON], 1987; 8.9 mm lens dia. for 4 mm beam dia., BK7 glass, Laserline Optics Canada, Osoyoos, BC, Canada) is used to convert the Gaussian energy profile of the laser beam into a line illumination source more appropriate for use with line-scan imaging. To address spillover from the 532-nm doublet wavelength and possible back-reflection from the expansion optics, a 4 mm iris and a 355-nm laser window tilted 5\({}^{\circ}\) from the optical axis (Techspec 1/20\(\lambda\) high-power laser-line window, Edmund Optics Inc., Barrington, NJ, USA) are inserted between the laser and the [PERSON] lens. The expansion optics are mounted in standard 1\" lens tubes that are connected to the laser using a fabricated mounting plate with SM1 threading. The Powell lens is mounted in a rotation mount (CRM05, Thorlabs, NJ, USA) after first inserting the lens into a hole drilled into the center of a threaded plug (SM05 PL, Thorlabs). The rotation mount allows the angle of the illumination profile to be adjusted to match the field of view of the hyperspectral adapter. Offsets are addressed by adjusting the position of the laser relative to the camera.
## 5 Discussion
### Indication of Excess Topography for the Re-Occurrence of New Landslides on GALs  
In this research, the concept of excess topography is introduced to study the destructive effect of the occurrence of GALs on slopes. Based on the calculated threshold hillslope of 30\({}^{\circ}\), quantitative analysis of the relationship between the cataloged surface data of GALs in the Lengqu River section and the excess topography is conducted. The result displays that 77.75% of GALs are still on excess topography, which means 77.75% of the threshold hillslope at the original location where the GALs occurred has not been changed. The results are consistent with the results of the quantitative relationship between landslides and excess topography obtained by [PERSON] et al., [PERSON] et al. and others based on other different types of landslides; that is to say, there is a similar pattern for GALs in the Lengqu River section. This means that there is still a volume of objects on the threshold interface under the threshold hillslope of 30\({}^{\circ}\), and when the trigger of a certain external condition such as rainfall or earthquake happens, there may occur new landslides or secondary  
Figure 9: Quantitative relationship between resurgent GALs and excess topography in Lengqu River section: (**a**) Quantitative relationship between six resurgent GALs monitored by InSAR and excess topography; (**b**) Quantitative relationship between five resurgent GALs and excess topography; (**c**) Quantitative relationship between inactive GAL depositions and excess topography.  
resurgence, and it can be assumed that the generation of a single landslide usually did not cause the threshold hillslope to suffer complete damage.
## 4 Discussion
### Validation of Damage Intrinsic Model  
The appearance of interlayer dip angle causes the misalignment of the upper and lower parts of the filler, so it is necessary to verify whether the layered cemented filler with different dip angles conforms to the intrinsic damage model. The mechanical properties of the filling specimens tend to stabilize after 28 d of maintenance age, so in order to reduce the test error, the test data with 28 d of age and interlayer inclination angles of \(0^{\circ},10^{\circ},20^{\circ}\), and \(30^{\circ}\) were selected. Table 3 shows each specific principal structure model.  
The stress-strain curves obtained from the intrinsic damage model and the test are plotted in Figure 9, and it is not difficult to find that the intrinsic model and the test curve maintain the same trend. Among these Figure 9a,b, the test curves overlap with the intrinsic model in the pre-peak stress phase. However, the two overlap poorly in the post-peak phase, while Figure 9c,d show the opposite state, with a poor overlap in the pre-peak phase and a good overlap in the post-peak phase. This situation before the peak stress is since the initial pore compacting phase inside the filler, i.e., the \"upward concave\" phase shown in the first part of the stress-strain curve, is not considered in constructing the intrinsic model. The phenomenon that the curve of the principal model is lower than the test curve after the peak stress is due to the idealized assumption of damage of the principal model, i.e., the cavity inside the material that cannot carry the load. In contrast, the specimen in the actual test can still bear the load after the damage, showing post-peak ductility. Overall, the damage intrinsic model can predict the stress-strain curve of the tailing sand colluvium filling body more accurately and has high reliability for different interlayer dips, providing a theoretical basis and technical guidance for the actual filling construction.  
Figure 8: Damage forms of layered fillers with different interlayer dip angles; (**a**) natural delamination and (**b**) sandblasting delamination.  
\begin{table}
\begin{tabular}{c c c} \hline
**Specimen Number** & **Interlayer Dip Angle** & **Principal Structure Model** \\ \hline a & \(0^{\circ}\) & \(\sigma=223.74\exp[-\ln(1.375)\big{(}\frac{\varepsilon}{(0.023)}\big{)}^{3.14}]\varepsilon\) \\ b & \(10^{\circ}\) & \(\sigma=204.56\exp[-\ln(1.509)\big{(}\frac{\varepsilon}{(0.027)}\big{)}^{2.43}]\varepsilon\) \\ c & \(20^{\circ}\) & \(\sigma=124.74\exp[-\ln(1.252)\big{(}\frac{\varepsilon}{(0.027)}\big{)}^{4.45}]\varepsilon\) \\ d & \(30^{\circ}\) & \(\sigma=190.31\exp[-\ln(1.376)\big{(}\frac{\varepsilon}{(0.023)}\big{)}^{3.13}]\varepsilon\) \\ \hline \end{tabular}
\end{table}
Table 3: Damage ontology model for specimens with different interlayer dip angles.
# Influence of Welding Parameters on the Weld Pool Dimensions and Shape in a TIG Configuration
## 3 Results
The ratio between \(E_{fusion}\) and \(E_{applied}\) leads to an indication of the efficiency (\(\gamma\)) of the experimental parameters. Their values are indexed in Table 3.  
\[\gamma=\frac{E_{fusion}}{E_{applied}} \tag{3}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Area1.37, Current 100 A, Arc Length 5 mm, Tip Angle \(\alpha\)**} \\ \hline  & & **Case n\({}^{\circ}\): t. \(\alpha\) = 60\({}^{\circ}\)** & & & **Case n\({}^{\circ}\): \(\alpha\) = 30\({}^{\circ}\)** \\ \hline  & **t = 1 s** & **t = 4 s** & **t = 8 s** & **t = 12 s** & **t = 16 s** & **t = 20 s** \\ \hline  & **Method 1 (\(\pm\)0.20)** & - & 6.11 & 6.58 & 6.90 & 6.92 & 7.12 \\
**Width (mm)** & **Method 2 (\(\pm\)0.40)** & - & - & - & - & - & 7.12 \\  & **Method 3 (\(\pm\)0.02)** & - & - & - & - & - & 7.32 \\
**Depth (mm)** & **Method 3 (\(\pm\)0.02)** & - & - & - & - & - & 3.32 \\
**Depth-to-Width ratio** & - & - & - & - & - & - & 0.45 \\ \hline  & \multicolumn{2}{c}{Case n\({}^{\circ}\): Helium, Current 100 A, Arc Length 3 mm, Tip Angle 30\({}^{\circ}\)} & & & & & \\ \hline  & & **t = 1 s** & **t = 4 s** & **t = 8 s** & **t = 12 s** & **t = 16 s** & **t = 20 s** \\ \hline  & **Method 1 (\(\pm\)0.20)** & - & 7.30 & 7.57 & 7.69 & 7.70 & 7.65 \\
**Width (mm)** & **Method 2 (\(\pm\)0.40)** & - & - & - & - & - & 7.65 \\  & **Method 3 (\(\pm\)0.02)** & - & - & - & - & - & 7.74 \\
**Depth (mm)** & **Method 3 (\(\pm\)0.02)** & - & - & - & - & - & 5.12 \\
**Depth-to-Width ratio** & - & - & - & - & - & - & 0.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Area1.37, Current 100 A, Arc Length 5 mm, Tip Angle \(\alpha\)**} \\ \hline  & & **Case n\({}^{\circ}\): t. \(\alpha\) = 60\({}^{\circ}\)** & & & **Case n\({}^{\circ}\): \(\alpha\) = 30\({}^{\circ}\)** \\ \hline  & **t = 1 s** & **t = 4 s** & **t = 8 s** & **t = 12 s** & **t = 16 s** & **t = 20 s** \\ \hline \(E_{fusion}\) **(k)** & 0.097 & 0.361 & 0.462 & 0.752 & 0.690 & 0.854 & 1.365 \\ \(E_{applied}\) **(k)** & 1.35 & 6.12 & 12.70 & 19.40 & 24.42 & 29.18 & 31.70 \\ \(\gamma\) & 7.18\% & 5.89\% & 3.63\% & 3.87\% & 2.82\% & 2.92\% & 4.30\% \\ \hline  & \multicolumn{6}{c}{**Area1.37, Current 150 A, Arc Length 5 mm, Tip Angle \(\alpha\)**} \\ \hline  & **t = 1 s** & **t = 4 s** & **t = 8 s** & **t = 12 s** & **t = 16 s** & **t = 20 s** & **t = 20 s** \\ \hline \(E_{fusion}\) **(k)** & 0.150 & 0.630 & 1.270 & 2.070 & 2.905 & 3.030 & - \\ \(E_{applied}\) **(k)** & 2.40 & 10.01 & 19.40 & 28.73 & 37.02 & 45.70 & - \\ \(\gamma\) & 6.25\% & 6.29\% & 6.54\% & 7.20\% & 7.84\% & 6.63\% & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The table indexes the applied energy (\(E_{applied}\)) estimated with measurements of voltage and current intensity during the interaction and the energy (\(E_{fusion}\)) involved to fuse the amount of metal related to the weld pool [1].