esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### DisA and CdaA Are Required for SAM in B. subtilis
Three portions of agar (plugs) were taken from free colonies sites of MM Spizizen plates, every third day, the plugs were extracted and resuspended in 1X SSMM, subjected to serial dilutions and plated on LB media to determine viable counts as described in Materials and Methods. Data represent the average CFU per plug of agar of three determinations \(\pm\) standard deviations. No significant differences were found. Disruption of disA and/or cdaA Promotes the Appearance of Triple His\({}^{+}\) Met\({}^{+}\) Leu\({}^{+}\) Prototrophs  
The strain _B. subtilis_ YB955, which is auxotrophic to His, Met and Leu due to the chromosomal nonsense _his_C952 (amber), _met_B5 (ochre) and missense _leu_C427 mutations, has been widely employed to study mutagenesis in amino-acid-starved cells [18; 19]. It has been shown that in this strain, a high percentage of the Met\({}^{+}\) prototrophs obtained, are derived from tRNAs suppressor mutations [18]. To investigate if DACs influences the generation of suppressor mutations, the percentage of suppressor mutations in the parental and mutant _B. subtilis_ strains used in this study was determined. Our results revealed that a fraction of the His\({}^{+}\) colonies obtained of the _cdaA_ and _disA/cdaA_ strains on SAM assays were also Met\({}^{+}\) prototrophs, suggesting that suppressor mutations on tRNAs gave rise to these revertants (Table 1). This result is in agreement with a previous report for parental strain YB955 in which 20% of His\({}^{+}\) revertants also exhibited a Met\({}^{+}\) phenotype and were indeed suppressor mutations [18]. Regarding the _disA_ strain, in reference to the parental strain YB955, an increase of \(\sim\)two times in the percentage of His\({}^{+}\) colonies with the Met\({}^{+}\) phenotype was observed, suggesting that disruption of _disA_ promotes the formation of ochere tRNA mutations that suppress the His' phenotype. In the case of Met\({}^{+}\) revertants, in the parental strain YB955 and in all mutant strains, more than 80% of colonies also exhibited a His\({}^{+}\) phenotype, suggesting that they were generated by ochere tRNA suppressor mutations (Table 1). Of note, these results paralleled those previously reported for the strain YB955 [18]. Interestingly, our suppressor analysis revealed that disruption of _disA_, _cdaA_ or both genes promoted the production of colonies exhibiting a triple His\({}^{+}\) Met\({}^{+}\) Leu\({}^{+}\) phenotype, as some of the His\({}^{+}\) Met\({}^{+}\) revertants, also grew on SSMM lacking leucine (Table 1). As previously shown [18], none of the Leu\({}^{+}\) prototrophs produced by the strain YB955 grew in MM selective for His\({}^{+}\) or Met\({}^{+}\), strongly suggesting that these revertants resulted from intragenic mutations. Altogether, these results suggest that fluctuations in the c-di-AMP levels elicit a hypermutagenic state that positively impacts the survival of nutritionally stressed _B. subtilis_ cells.
# Sparse Indoor Camera Positioning with Fiducial Markers
## 4 Experiments
### Entire Building Dataset  
This experiment presents our last dataset, which extends our methodology to an entire building comprising two floors connected by a stairwell section (see Figure 6). This setup allowed us to evaluate the robustness of our camera positioning approach over large vertical distances and across multiple interconnected levels. By testing these configurations, we aimed to assess the method's accuracy in maintaining spatial continuity and precise camera alignment over extended and complex structures. The fixed and moving camera models were the same as in the previous experiments.  
The experiment involved 42 fixed cameras distributed uniformly across the floors, ensuring no overlap in their fields of view. In addition, each floor and stair section included pathways recorded using a moving camera and 50 ArUco markers, obtaining 23 different groups. We performed ablation studies on the dataset to examine the effects of different optimization term combinations on the reconstruction quality. An overview summarizing the datasets used in this work, including the entire building configuration, can be found in Table 1.  
The results of these evaluations, as detailed in Table 4, demonstrate the significant role of control points in enhancing pose estimation across the entire building, achieving an optimal outcome with an approximate error of 42 cm. The inclusion of control points  
Figure 6: Entire building dataset. Representations of camera positions and orientations are depicted using blue and purple pyramids.  
proved especially critical in this large-scale dataset, contributing markedly to the accuracy of the results due to the high-dimensional nature of the environment.
## 3 Selected Results of Optical Metrology Techniques
### Techniques Based on Geometrical Optics Suitable for In-Process and In Situ Applications
#### 3.2.2 In-Process Measurement and Evaluation of Laser Added Manufacturing Processes for Metals
Therefore, they have no relation to the color scale of Figure 16.  
It is important to note that no powder features can be seen in the camera image (Figure 14). This means that a simple 2D photographic detection and evaluation does not provide any results here. In contrast, evaluation with height values is a good way of assessing the quality of the powder distribution. Since the assessment with surface parameters such as Sa or RMS is only based on one value, the application of the structure function provides a wealth of information. In Figure 17, the calculated SF of both areas is documented. While the SF of area 1 runs relatively smoothly, the SF of area 2 shows the ripples of the powder distribution very clearly. In the high to mid spatial frequency range, both SFs show almost the same ordinate values. This confirms the similar roughness values presented in Figure 11:0 One can notice further that the two SFs differ a little from a separation value of around 150. This may be due to a slight slope in the powder distribution. Figure 11:1 Presentation of the height distribution of layer 10 before powder recoating takes place from right to left. The measured sample has a size of about 150 \(\times\) 150 mm (x, y). Two fields of the powder bed are chosen for investigation. For area 1 (**right**): Sa = 21 \(\upmu\)m, RMS = 26 \(\upmu\)m, powder appears rather smooth. For area 2 (**left**) : Sa = 22 \(\upmu\)m, RMS = 28 \(\upmu\)m, suggests slight ripples in the gray values. For each area, approximately 90,000 height values are obtained, and the lateral dimension is approximately 25 \(\times\) 25 mm. In summary, it can be stated that an evaluation of the PBF-LB/M AM technology using a non-contact measurement of (x, y, z) coordinates provides useful knowledge with regard to quality. The assessment of the consolidated material has shown that the mean height of a printed layer depends on its porosity. This result should now further be exploited and correlated with experiments using a wide range of parameters from other PBF-LB/M processes. As a second result, the uniformity of the powder application can be assessed down to the single-digit micrometer range. The statistical method of the structure function provides very precise and instructive results here.
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 4 Workbench Description and Test Conditions  
### Implementation of the ITU-T G.9904 (PRIME) Layer Used for the Simulation  
For the above mentioned purposes, a complete ITU-T G.9904 (PRIME) device, which works in real HW and inside a Personal Computer (PC), has been carried out. It is the same physical layer included in the PRIME devices from ZIV (as shown in [18]).  
The implementation of this physical layer has been performed following PRIME specifications as in [15; 16; 17]. It has been certified by independent laboratories (Tecnalia, ITE, Kema) following the Certification Test Book of the PRIME Alliance [19]. Several million of PRIME meters are currently installed on field with this implementation. Regarding the applicability of the results presented in this paper, tests carried out by the PRIME Alliance Members assure that PRIME certified products have similar performance and as such, the obtained results will also apply to other HW implementations.  
The implementation of the PRIME physical layer has been done mostly in C but optimized in assembler for some platforms. The simulation environment is executed with the same modern in a PC environment in which PLC modems communicate to each other using Inter Process Communication (IPC) sockets. The propagation channel simulator is performed in an additional computer process which takes the representation of the analog signal, processes it and interchanges it with the modems.  
Some control measurements have been performed to check that the provided simulation data match with the results obtained with real HW devices in laboratory conditions. The tests have been performed for control points of \(FER=0.1\) and \(FER=0.01\) for the Additive White Gaussian Noise (AWGN) noise, synchronous impulsive noise and frequency selective channel for the same modulation schemes evaluated in Section 6. These measurements confirmed that, as the modulation is a low frequency digital modulation, results with HW devices and SW simulations match properly with differences smaller than 1 dB in the range in which the real HW is linear (avoiding HW saturation limit and equipment noise floor).
# Multiple Kernel Stein Spatial Patterns for the Multiclass Discrimination of Motor Imagery Tasks
###### Abstract  
Brain-computer interface (BCI) systems communicate the human brain and computers by converting electrical activity into commands to use external devices. Such kind of system has become an alternative for interaction with the environment for people suffering from motor disabilities through the motor imagery (MI) paradigm. Despite being the most widespread, electroencephalography (EEG)-based MI systems are highly sensitive to noise and artifacts. Further, spatially close brain activity sources and variability among subjects hampers the system performance. This work proposes a methodology for the classification of EEG signals, termed Multiple Kernel Stein Spatial Patterns (MKSSP) dealing with noise, raveled brain activity, and subject variability issues. Firstly, a bank of bandpass filters decomposes brain activity into spectrally independent multichannel signals. Then, Multi-Kernel Stein Spatial Patterns (MKSSP) maps each signal into low-dimensional covariance matrices preserving the nonlinear channel relationships. The Stein kernel provides a parameterized similarity metric for covariance matrices that belong to a Riemannian manifold. Lastly, the multiple kernel learning assembles the similarities from each spectral decomposition into a single kernel matrix that feeds the classifier. Experimental evaluations in the well-known four-class MI dataset 2a BCI competition IV proves that the methodology significantly improves state-of-the-art approaches. Further, the proposal is interpretable in terms of data distribution, spectral relevance, and spatial patterns. Such interpretability demonstrates that MKSSP encodes features from different spectral bands into a single representation improving the discrimination of mental tasks.  
Keywords:brain-computer interface; motor imagery; electroencephalography; multiple kernel learning +
Footnote †: journal: Applied Sciences
# Research Progress in Methods for the Analysis of the Internal Stability of Landslide Dam Soils
## 2 Qualitative Identification of Internal Stability Based on Physical Properties of Soils  
The physical properties that affect the internal stability of soil under seepage include the gradation, particle shape, and pore distribution. Some scholars have proposed identification methods to qualitatively evaluate the internal stability of soils through experiments [22; 23; 24; 25]. The gradation of soils is a key factor in assessing their internal stability [26; 27], and it is usually characterized by the characteristic particle size. The fine particle content, as an important indicator for evaluating gradation, directly determines the degree of pore filling within the soil skeleton and is another crucial factor affecting the internal stability [28; 29]. Therefore, the establishment of internal stability determination methods for soils often starts from these two aspects.
## 2 Materials and Methods
### Statistical Analysis  
In this study, various statistical methods were employed to analyze the trends and correlations of variables such as air temperature, precipitation, and carbon budget indicators. Specifically, linear regression was used to determine the overall trend of these variables. The Sen's slope estimator, originally proposed by [PERSON] in 1968 [48], has been utilized to detect trends in carbon budget indicators at the pixel scale. Concurrently, the nonparametric Mann-Kendall (MK) test [49; 50] was employed to test the statistical significance of these trends. Furthermore, the partial correlation method was applied to test the correlation between two variables. To investigate the differences in NEP among different grassland categories, the one-way analysis of variance (ANOVA) was employed. The significance in statistical analysis of non-raster data was determined through T-tests or F-tests, and the degree of significance was reflected by the \(p\) value. Prior to conducting statistical analysis, all non-raster data were subjected to normality testing using the Shapiro-Wilk test and homogeneity of variance testing using the Bartlett test. In addition, the sliding window method was utilized to analyze trend changes, with a sliding step of 1 year. To identify the optimal window lengths that yielded the most significant changes in trends, all window lengths ranging from 3 to 18 years were tested. It is noteworthy that the selection of window length settings was based on specific application scenarios and was not uniform across all analyses.  
Figure 2: Comparison between the simulated carbon budget (net ecosystem productivity (NEP), gross primary productivity (GPP), and ecosystem respiration (ER)) and eddy covariance (EC)-based carbon budget. Confidence bands in the figure represent 95% confidence.
# Integrating a Blockchain-Based Governance Framework for Responsible AI
## 2 Programming Smart Contracts for Responsible AI
### Algorithms and Frameworks  
There are several algorithms [69] that can be used to prevent bias and ensure accountability via smart contracts, including:  
1. **Fairness Algorithms:** These algorithms can be used to detect and correct bias in AI models by identifying and adjusting for factors that may be causing discrimination. For example, algorithms such as the Zafar-Valera-Gummadi (ZVG) algorithm [70] or the Prejudice Remover algorithm can be used to identify and remove bias in training data [71].
2. **Explainable AI (XAI) Algorithms:** These algorithms can be used to provide transparency and accountability for AI decision-making by generating human-understandable explanations for the decisions made by AI models [72]. For example, the Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) algorithms can be used to generate explanations for individual predictions made by a model [73].
3. **Auditing Algorithms:** These algorithms can be used to monitor and evaluate the performance of AI models over time, including detecting and reporting any issues such as bias or drift. For example, the Fairness and Accountability Audit (FAA) algorithm [74] can be used to evaluate the performance of an AI model with respect to a set of fairness metrics.
4. **Decentralized AI Algorithms:** These algorithms can be used to enable decentralized decision-making for AI systems and ensure the distribution of power between stakeholders; for example, the Federated Learning and Multi-Party Computation (MPC) algorithms can be used for decentralized training of AI models, and blockchain-based consensus algorithms can be used for decentralized decision-making [75; 76].
## 2 Foundations on RISs
### RIS-Assisted Communication System Model
The main approaches for RIS beamforming are:  
* Active beamforming: requires additional _active_ components or circuits to manipulate the amplitude and phase of the RIS-impinging signals. Although the signal adjustments are implemented in real time, the power consumption and system complexity increase. * Passive beamforming: The system relies on exploiting the properties of RISs by software rather than adding additional electronic circuits. These systems are less complex than active beamforming and consume less power. * Hybrid beamforming: Several approaches combine the active and passive designs [38; 44]. In practice, the RIS is usually designed to have many reflecting elements. In this case, it will be more efficient to implement discrete amplitude/phase-shift levels, which require only a small number of control bits for each element. As expected, the cost of phase-shift control is higher than amplitude control since the optimal phase-shift solutions should align all the signals reflected by the RIS despite their strength with the signal transmitted from the BS [38]. The alignment produces a coherent combined signal that maximizes the received signal power at the receiver.
# Study on the Optimization of Pile Length of Micropiles in Soil Landslides
## 1 Introduction
proposed a new multi-objective comprehensive optimization method for the optimization problem of anti-slip pile reinforced slopes and analyzed the effects of pile position, pile length, and pile spacing on the reinforcing effect of homogeneous slopes through numerical simulation. A large number of scholars have conducted research in pile length optimization. Most of them study the pile length optimization problem of anti-slip piles, multi-row piles, and bridge piles; although some scholars have also studied the pile length [22] optimization problem of micropiles, the pile length optimization problem of micropiles must still be further researched, and this study improves the pile length optimization problem of micropiles, so that small landslides can be managed economically and quickly, and the loss of life and property caused by landslides can be prevented and reduced. When dealing with landslides, it is important to clarify the load-sharing mechanism of each row of micropiles and the load-carrying mechanism of individual micropiles. Based on this, this chapter summarizes and derives the design calculation method of minipiles in order to obtain a more convenient and practical engineering design calculation method. At the same time, the pile length design problem that was not directly solved using the previous model experiments and numerical simulations was optimized using engineering structural optimization ideas so as to truly solve the practical design problem of minipiles from an engineering perspective.
## 3 Experiment and Analysis
### Sensitivity Analysis of the Proposed Model's Parameters
#### 3.2.3 Sensitivity Analysis of the Merging Distance Threshold  
The sensitivity of the proposed model to the merging distance threshold \(\lambda\) is evaluated using \(\varepsilon_{x}=0.220\) m, \(\varepsilon_{y}=0.110\) m, \(\delta=0.110\) m, \(\alpha=0.043\) m, and \(\tau=0.001\). As shown in Figure 21, the model achieves its highest recall of 91.5% when \(\lambda\geq 0.220\) m.  
When \(\lambda\geq 0.220\) m, the model delivers a precision of 98.0%. The highest precision of 99.1% is observed for all \(\lambda\leq 0.120\) m, with recall ranging between 84.1% and 87.4%. In such scenarios, the F1 score provides a more balanced evaluation, with the highest value of 94.6% observed when \(\lambda\geq 0.220\) m.
## 3 Results and Discussion
### In Vitro Drug Release Kinetics
Another parameter that might influence the release rate of the drug is the structural rigidity of the carrier, i.e., the branching density (\(b_{\rm d}\)). As pointed out earlier, the branching density increased in the series G2 PBG\({}_{65}\) < G2 PBG\({}_{29}\) < G2 PBG\({}_{15}\). Interestingly, the amount of drug released after 50 h was inversely related to the branching density of the PBG cores, namely G2 PBG\({}_{15}\) < G2 PBG\({}_{29}\) < G2 PBG\({}_{65}\). The significant differences in release rate observed within this series of micelles are attributed to their different core \(b_{\rm d}\). Very compact and dense cores (G2 PBG\({}_{15}\), \(b_{\rm d}\) = 0.80) would indeed be expected to prevent the diffusion of the drug molecules out of the core more strongly, thus resulting in a slower release. In contrast, a more porous core structure (e.g., G2 PBG\({}_{65}\), \(b_{\rm d}\) = 0.11) should facilitate the diffusion process. The results obtained therefore strongly suggest that the release rate of DOX can be controlled through variations in the branching density of the core for the randomly grafted arborescent copolymers. An investigation of drug release over an extended time period (up to 30 days) was also carried out (Figure S8, Supplementary Materials). It revealed that in all cases, the DOX release profiles displayed a biphasic release pattern at both low and high pH, with an initial burst release followed by sustained release of the drug. At neutral pH (7.4) for example, 7% of the loaded DOX was released from G2 PBG\({}_{65}\)-\(g\)-PEO/DOX over the first 6 h and only 19.7% after 50 h. Due to the random grafting of the PEO chains onto the PBG substrate, the burst release is attributed to the drug solubilized in the interfacial region of the micelles, which can diffuse more easily and faster into the release medium. The sustained release behavior of the unimolecular micelles again correlates with the branching density, with a larger cumulative release observed for the micelles with the lowest branching density (G2 PBG\({}_{65}\)-\(g\)-PEO/DOX). Even after one month the release of DOX from the micelles was incomplete (Figure S8, Supplementary Materials). This could be due in part to strong hydrophobic interactions between the PBG core and the DOX molecules, which was indeed confirmed by analysis of the micelles left in the dialysis bag. A significant amount of DOX may also be physically trapped deeper within the hydrophobic PBG core of the micelles, which would make its diffusion out of the highly dense and branched PBG core more difficult. Such a hydrophobic environment can prevent the protonation of DOX, and thus decrease the diffusion ability of DOX. A similar explanation was suggested by [PERSON] et al. [26] for the incomplete release of indomethacin from highly branched arborescent polystyrene-\(g\)-poly(2-vinylpyridine) copolymers, serving as non-biocompatible model systems with a structure similar to the unimolecular micelles used in the current investigation.