esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Antimicrobial Resistance--Mechanisms and Mitigation Strategies
### Limiting Drug Uptake  
Depending on where in the bacterial cell the antimicrobial exerts its effect, for most antibiotics, the drug needs to be taken up by the bacteria as an initial step. However, this process can be hindered intrinsically, as is the case with certain Gram-negative lipidic cell membranes that constitutively do not allow the diffusion of hydrophilic drugs [10] or by acquiring a thickened cell wall in Gram-positive bacteria [9].  
For drugs that are not chemically capable of diffusing through the bacterial outer membrane, small porin channels are required for drug uptake and this is especially true for betalactam antibiotics or fluoroquinolones. Mechanisms of porin-induced resistance seen in Gram-negative bacteria include a down-regulation in the number of porins expressed on the outer membrane as well as chemical or conformational changes in the porin structure that render it incompatible with binding and/or transport of the antimicrobial drug [10].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site of Infection or Study** & **Novel Agent** & **Phase of the Clinical Trial** & **Expected Completion of the Clinical Trials.** & Clinical Trials. \\  & & **Clinical Trial (Year)** & **gov Reference** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Prevention and \\ decolonization of MDR \\ bacteria \\ \end{tabular} } & Probiotics (_Lactobacillus_ & & & \\  & _casei, Lactobacillus_ & & & \\  & _plantarum,_ & & & \\  & _Streptococcus faecalis,_ & & & \\  & _Bifidobacterium brevis_ & & & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Colonization with MDR \\ Gram-negative bacilli \\ \end{tabular} } & Probiotic: 4 _Lactobacillus_ & & & \\  & strains, 3 bifidobacteria & & & \\  & strains, and _Streptococcus_ & & & \\  & _thermophilus_ 24,731 vs._ & Not mentioned & July 2023 & NCT04431934 \\  & capsulized FMT from & & & \\  & healthy donor & & & \\ \hline \multicolumn{4}{c}{Serious infections due CRE} \\  & (complicated UTI, acute & Meropenem + & & \\  & vaborbactam vs. best & & & \\  & bacteria, abdominal & & & \\  & infection) & & & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Colonization with MDR \\ bacteria \\ \end{tabular} } & Colistin sulphate + & & & \\  & neomycin sulphate days & & & \\  & 1–5 then capsulized FMT & & & \\  & days 7 and 8 & & & \\ \hline CRE or KPC infections & Imipenem/cilastatin/relebactam & Phase 4 & August 2022 & NCT04785924 \\ \hline \hline \end{tabular}  
* CRE = carbapenem-resistant Enterobacteriaceae; FMT = fecal microbiota transplantation; MDR = multidrug-resistant; HABP = hospital-acquired bacterial pneumonia; KPC = carbapenem-producing _Klebsiella_ spp.; PK = pharmacokinetics; VABP = ventilator-associated bacterial pneumonia; VRE = vancomycin-resistant enterococci.  
\end{table}
Table 1: Cont.
## 4 Reproducibility and Scalability
### Reproducibility Framework  
Ensuring the reproducibility of our proposed implementation is vital to maintaining consistency and reliability across different applications. Our proposed framework can be easily reproduced by utilizing Haystack, an LLM orchestrator, building production-ready LLM applications. Therefore, we provide a low-code notebook, easily deployed in Google Colab, including configuration files and detailed documentation, and available in a public GitHub repository, including step-by-step guides for setting up the environment, running the code, and reproducing the virtual assistant, even with data as per the user's preference. Detailed specifications of the software and providers, including versions of libraries, dependencies, and alternative providers' options, are provided.  
The presented notebook in the GitHub repository ([[https://github.com/gpapageorgiouedu/Enhancing-E-Goverment-Services-through-a-SOTA-Modular-and-Reproducible-Architecture-over-LLMs](https://github.com/gpapageorgiouedu/Enhancing-E-Goverment-Services-through-a-SOTA-Modular-and-Reproducible-Architecture-over-LLMs)]([https://github.com/gpapageorgiouedu/Enhancing-E-Goverment-Services-through-a-SOTA-Modular-and-Reproducible-Architecture-over-LLMs](https://github.com/gpapageorgiouedu/Enhancing-E-Goverment-Services-through-a-SOTA-Modular-and-Reproducible-Architecture-over-LLMs)), accessed on 5 August 2024) is built in this way to be easy to deploy and demonstrate virtual assistant capabilities. Moreover, with slight modifications and refactoring, the proposed framework can be easily transferred into local or cloud-based environments. The documents used for demonstration purposes in our experiments are publicly accessible, including modular components for preprocessing and indexing. Moreover, all the RAG and agent configurations can be easily reproduced under the user's preferences, adding more pipelines, LLM options, and tools.
# Enzymatic Degumming of Soybean Oil for Raw Material Preparation in BioFuel Production
## 3 Results
### The Effect of Temperature on the Enzymatic Degumming Process  
The effect of enzymatic treatment temperature using the Purifine(r) 3G enzyme complex on the efficiency of degumming 1 kg of crude soybean oil was studied. Temperature is a critical factor for ensuring enzyme activity. The temperature range from 50 \({}^{\circ}\)C to 70 \({}^{\circ}\)C was studied. Degumming was carried out for 3 h at fixed pH values of 7.0; Purifine(r) 3G enzyme amount of 100 ppm; 2% H\({}_{2}\)O by weight of oil. The level of phosphorus and free fatty acids in the final product was determined; the results are given in Table 3.  
Purifine(r) 3G enzyme efficiency was maintained in the temperature range of 53-65 \({}^{\circ}\)C. The best results were observed at a temperature range of 56-58 \({}^{\circ}\)C: phosphorus level--10.0 ppm, FFA, acid value--0.86 mg KOH/g oil. Lowering the temperature below 53 \({}^{\circ}\)C led to an increase in phosphorus levels in soybean oil. Raising the temperature above 65 \({}^{\circ}\)C led to an increase in phosphorus levels and increased FFA.  
\begin{table}
\begin{tabular}{c c c} \hline
**Dosage of Purifine® 3G Enzyme Complex** & **Phosphorous Content** & **FFA (Acid Value,** \\
**(ppm)** & **(ppm)** & **mg KOH/g)** \\ \hline
50 & 12.0 & 0.67 \\
100 & 9.5 & 0.85 \\
150 & 8.7 & 1.17 \\
200 & 8.3 & 1.59 \\
250 & 7.8 & 2.15 \\ \hline \end{tabular}
\end{table}
Table 2: Effect of enzyme dose on degumming efficiency.
# Controlling Remotely Operated Vehicles with Deterministic Artificial Intelligence
## 3 Results  
This section provides a concise and precise description of the experimental simulation results, their interpretation as well as the experimental conclusions that can be drawn.  
The controller was tested by applying a series of positive then negative 30-degree step changes to the desired vehicle heading. The trajectory generation was configured to generate a trajectory that would take approximately 2.42 s to complete the 30-degree heading change. Figure 5 shows the response to these inputs and Table 2 summarizes some performance metrics. The rise time in the table is calculated as the amount of time to complete 99.9% of the thirty degree heading change (29.97 degrees). The settling time is calculated as the amount of time required after the heading change was started before the heading stayed within 0.1% (0.03 degrees) of the final value.  
Deterministic artificial intelligence was able to quickly converge to values that provided a stable response with good tracking of the desired trajectory. Even for the first heading change the controller was able to closely track the trajectory, with a root mean  
Figure 5: Yaw step response and the associated trajectory tracking error.  
square (RMS) tracking error of only 0.043 degrees. By the third positive step the RMS error had further decreased by almost a factor of 3, to only 0.016 degrees.  
The simulator was configured such that there were no disturbances, and such that the measurements obtained from the simulator were not corrupted by noise. This represents idealized conditions, but serves to validate that the general algorithm and controller architecture may be viable.
# The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather
## 6 Case Study  
### Photovoltaic Output and Load Forecasts under F-H  
According to the methods mentioned in Section 2, the data of the AQI value, temperature (minimum and maximum), weather type, day type, photovoltaic output and load of a certain region in Baoding, China, during the period from 1 December 2013-26 March 2014 were gathered to conduct photovoltaic output and load forecasts. To verify the analysis of the influence of F-H, the prediction results of 27 March 2014 (AQI = 305, overcast) are shown as follows (both considering and ignoring F-H).  
As shown in Figure 9, the red curve (with AQI) is closer to the black curve (real value) than the blue curve (without AQI); in Table 2, the MAPE and the MSE with AQI are much smaller than those without AQI, which indicates that the prediction accuracy of the photovoltaic output is higher if F-H is considered.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **MAPE (\%)** & **MSE** \\ \hline With AQI & 4.4181 & 2.9696 \\ Without AQI & 8.4941 & 8.5712 \\ \hline \hline \end{tabular}
\end{table}
Table 2: MAPE and MSE of the photovoltaic output forecast.  
Figure 9: Result comparison of the photovoltaic output forecast with and without AQI.  
As shown in Figure 10, the blue curve (without AQI) is higher on average than the black curve (real value), especially in the period of peak load, while the red curve (with AQI) is closer to the black curve; in Table 3, the MAPE and the MSE with the AQI are also smaller than those without AQI, which indicates that the prediction accuracy of the load is higher if F-H is considered.  
In other words, the prediction accuracy is higher because photovoltaic outputs and loads of similar days are much closer to those of the day being predicted when the AQI factor is included. During the similar days of F-H, the atmosphere factors, the operating states of PV panels, the electric power consumption of the whole society and the macroscopic readjustment of the Chinese government are similar, as well. The WNN model with better learning ability is able to catch the change pattern of photovoltaic outputs and loads to form more accurate predictions.
# A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery
## 4 Results and Discussion  
### BC Mapping in the Sample Area  
Using the Google Earth Engine (GEE) platform, high-quality Landsat imagery with less than 10% cloud cover for the period of July to September in three study areas was obtained. Based on the BCI, we mapped the BC areas in the three study areas. The detection results underwent mathematical morphology processing for boundary smoothing, retaining patches larger than four pixels, resulting in a distribution map of bare coal areas. Figure 8 shows the BC areas after applying the lower limit (0.8), where pixels with a bare coal fraction greater than or equal to 90% are considered bare coal, and all other pixels are considered non-bare coal. As seen in Figure 8, the resulting distribution of bare coal closely matches the actual ground reference area. To assess the quality of the BCI identification results, part of the study area was selected as a validation area, and BC information within this area was manually extracted as real data. The classification accuracy was evaluated using the \"BCs/non-BCs\" criterion and compared with the actual ground area. The results for the three study areas showed an average overall accuracy of 97%, an omission rate of 16.93%, a commission of 3.87%, and a Kappa coefficient of 0.87, indicating that the proposed method can accurately identify bare coal pixels.  
However, Figure 8 reveals a large missed area in the Shengli coalfield detection results. Comparing the BC fraction and spectral values of this area, it was found that the average bare coal fraction is 70%, with the reflectance of the SWIR2 band being lower than that of the SWIR1 band. Spectral analysis using images from multiple years and calculations based on other bare coal detection methods yielded similar results. Currently, detailed ground information for this area is lacking, and future research should conduct an in-depth analysis to understand the reasons behind this phenomenon. In addition, to evaluate the broader applicability of the proposed method, we applied a threshold of 0.8 to identify bare coal distribution in additional open-pit mining areas across Inner Mongolia. The results demonstrate a high degree of consistency between the extracted bare coal extent and the actual mining areas, further validating the reliability and accuracy of this approach (Figure 9).  
Figure 8: Results of BC mapping in the study areas: **(a)** Chenqi Coalfield, **(b)** Shengli Coalfield, and **(c)** Dongsheng Coalfield (OA: overall accuracy; OE: omission error; CE: commission error).
# Structural Modal Time Domain Identification Method Based on the Bayesian Uncertain Quantification
###### Abstract  
Based on the Bayesian framework, a time domain method is proposed for the uncertain quantification of structural modal identification. First, a theoretical prediction model is constructed from the state space model in modal space and then transformed into physical space using the modal basis. Second, taking into account the uncertainty of the identification results caused by measurement noise and modeling errors, the negative log-likelihood function is constructed using time domain measurement data and a theoretical prediction model based on the Bayesian system identification framework. Finally, an unconstrained quadratic function for the identification parameters is derived through matrix vectorization, and, by mathematically transforming the optimization problem, only the dynamic spectral parameters (the natural frequencies and damping ratios) need to be identified, while the spatial parameters (the mode shapes and modal contribution factors) can be analytically calculated from the spectral parameters, which greatly reduces the dimensionality of the identification parameters. In numerical examples, the identification of the modal parameters for a spring-mass system and high-speed pantograph was studied, and the identified modal parameters based on the simulation response's data were in good agreement with the theoretical values. Moreover, the modal parameters of the actual structure of the pantograph were identified based on the experimental data, and the identifying uncertainties were quantified by the coefficient of variation.  
Keywords:Bayesian approach; modal state space model; uncertainty quantification; experimental modal analysis +
Footnote †: journal: applied sciences
## 4 Deformation Compensation  
The compliance is considered in the compensation algorithm in order to improve the accuracy of the EE, and the assumption is that the force applied to the EE causes pose deviation. Figure 2 shows the block diagram of the deformation compensation scheme. \(P\) is the pose control signal, \(F\) is the force signal, and other elements are explained in equation (45). First, the stiffness model is formulated based on the estimated stiffness parameters. Then, the force sensor mounted on the EE of the industrial robot measures the external forces and torques that are used to calculate the deformation related to the compliance during the contact. [PERSON] analyzed the stiffness properties of serial robots in drilling applications and optimized the trajectory of the robot [32]. A compensation algorithm for robotic application was presented in [PERSON]'s paper, and the algorithm can be modified based on dual quaternion algebra in this paper.  
Figure 2: Block diagram of deformation compensation.  
The dual quaternion error can be defined as  
\[\Delta\hat{q}=\hat{p}*\hat{q}^{-1}, \tag{36}\]  
where \(\hat{p}\) is the nominal dual quaternion, \(\hat{q}\) is the dual quaternion calculated based on the estimated values. \(\Delta\hat{q}\) is a unit dual quaternion.  
The final deformation compensation input to the industrial robot controller is represented in the robot base coordinate frame and is summarized as follows:  
\[J*\Delta\theta=\Delta Q, \tag{37}\]  
\[\Delta Q=\left[\begin{array}{c}\Delta q_{r}\\ \Delta q_{d}\end{array}\right]=\Delta\hat{q}^{T}, \tag{38}\]  
where \(\Delta Q\) is the dual quaternion error represented in the matrix form, \(\Delta p_{r}\) is the real part of \(\Delta Q\), \(\Delta q_{d}\) is the dual part of \(\Delta Q\).  
The multiplication operator of two dual quaternions that were formulated in Equation (6) can be expressed in the following:  
\[\hat{p}*\hat{q}=\left[\hat{p}^{+}\right]\hat{q}=\left[\hat{q}^{-}\right]\hat{p}, \tag{39}\]  
where  
\[\left[\hat{p}^{+}\right]=\left[\begin{array}{cc}\left[p^{+}\right]&\left[0 \right]\\ \left[p^{+}\right]&\left[p^{+}\right]\end{array}\right], \tag{40}\]  
\[\left[\hat{q}^{-}\right]=\left[\begin{array}{cc}\left[q^{-}\right]&\left[0 \right]\\ \left[q^{-}\right]&\left[q^{-}\right]\end{array}\right], \tag{41}\]  
\[\left[p^{+}\right]=\left[\begin{array}{cccc}p_{0}&-p_{1}&-p_{2}&-p_{3}\\ p_{1}&p_{0}&-p_{3}&p_{2}\\ p_{2}&p_{3}&p_{0}&-p_{1}\\ p_{3}&-p_{2}&p_{1}&p_{0}\end{array}\right], \tag{42}\]  
and  
\[\left[q^{-}\right]=\left[\begin{array}{cccc}q_{0}&-q_{1}&-q_{2}&-q_{3}\\ q_{1}&q_{0}&q_{3}&-q_{2}\\ q_{2}&-q_{3}&q_{0}&q_{1}\\ q_{3}&q_{2}&-q_{1}&q_{0}\end{array}\right]. \tag{43}\]  
Equation (36) calculates the deformation and can be rewritten as  
\[\Delta Q=\left[\hat{p}^{+}\right]\hat{q}^{-1}. \tag{44}\]  
Then, the compensation \(\Delta Q\) is implemented in the control loop.  
\[\sum Q=Q+\Delta Q, \tag{45}\]  
where \(Q\) is the kinematic input represented by dual quaternions.
## 4 Results
### Evaluation Metrics
#### 4.1.1 Covariance  
Covariance is a consistently additive relationship across the two data samples. This relationship can be summarised between two variables, called the covariance. It is calculated as the average of the product between the values from each sample, where the values have been centred (had their mean subtracted) [63]. Covariance does not imply causation--it only measures the statistical relationship. Understanding covariance can help identify patterns and dependencies when analysing data. A covariance matrix represents the relationships between pairs of variables in a dataset. It quantifies the extent to which two variables vary together.  
\[\text{Cov}\;(\text{X},\text{Y})=(\text{sum}(\text{x}-\text{mean}(\text{X})) \times(\text{y}-\text{mean}(\text{Y})))\times 1/(\text{n}-1) \tag{1}\]  
In this heatmap, each cell corresponds to the covariance between two variables. The diagonal cells (top left to bottom right) represent the covariance of a variable with itself. These values are always positive. The off-diagonal cells (top right and bottom left) represent the covariance between different variables. These values can be positive or negative. The heatmap uses colour intensity to represent covariance values. Dark red indicates a strong positive covariance (close to 1), meaning the variables tend to increase or decrease together. Dark blue indicates a negative covariance (close to \(-1\)), suggesting an inverse relationship. Lighter shades represent a weaker correlation (closer to 0).  
The heatmap above in Figure 4 shows the \(x\)-axis and \(y\)-axis labels correspond to specific variables (e.g., 'var_2663' and 'var_831') var_2663 represents.tINTF_General_Visu.tLaneProtection.tCover3.tMotor4.tMotor.tPT100.tAlarm.bStatus and var_831--.tINTF_General_Visu.tLaneProtection.tCover1.tMotor4.tCB.SignalWithError.tError.bHasError.  
Figure 4: Covariance.  
The top left quadrant has a dark red shade, indicating a robust positive covariance (around 0.25) between var_2663 and itself. The bottom right quadrant also shows a solid positive covariance for var_831. The light blue off-diagonal quadrants suggest a weak inverse relationship (around \(-0.02\)) between var_2663 and var_831, indicating that motor 4 has a positive covariance with the temperature sensor error in the motor four control block.
# Unsupervised Detection of Changes in Usage-Phases of a Mobile App
## 3 Materials and Methods
### Probability Distribution Based Methods
#### 3.5.2 Usage-Phase Model Based Detection  
Once we detect usage-phase changes, we are able to collect screenshots (\(\mathcal{U}_{t}=\{y_{t,1},\cdots,y_{t,2},y_{t,i}\}\); \(y_{t,i}\) denotes the \(i\)th screenshot belonging to the \(t\)th usage-phase, \(\mathcal{U}_{t}\)) deemed to belong to a same usage-phase. With the probability distribution converted screenshots in \(\mathcal{U}_{t}\), we build a model, \(M_{t}\), for \(\mathcal{U}_{t}\):  
\[M_{t}=\left(P(v^{(1)}|\mathcal{U}_{t}),\cdots,P(v^{(n)}|\mathcal{U}_{t}),P(e^{ (1)}|\mathcal{U}_{t}),\cdots,P(e^{(m)}|\mathcal{U}_{t})\right) \tag{8}\]  
where \(P(v^{(k)}|\mathcal{U}_{t})=\frac{1}{Z}\times\sum_{i=1}^{|\mathcal{U}_{t}|}v^{( k)}_{t,i}\) and \(P(e^{(k)}|\mathcal{U}_{t})=\frac{1}{Z}\times\sum_{i=1}^{|\mathcal{U}_{t}|}e^{( k)}_{t,i}\) (\(Z\) is a normalization constant to make the summation of all elements in \(M_{t}\) equal to one; \(v^{(k)}_{t,i}\) and \(e^{(s)}_{t,i}\) denote the number of the \(k\)th UI widget and the \(s\)th edge in the \(i\)th screenshot in \(\mathcal{U}_{t}\), respectively). That is, \(P(v^{(k)}|\mathcal{U}_{t})\) is computed from the number of occurrence of the UI widget, \(v^{(k)}\), in screenshots belong to \(\mathcal{U}_{t}\). From Equation (8), we can compute the likelihood \(L(y_{new}|M_{t})\): the probability of a new screenshot, \(y_{new}\), being sampled from a probability distribution represented by \(M_{t}\). The log-likelihood \(L(y_{new}|M_{t})\) is computed by:  
\[L(y_{new}|M_{t})=logC_{M_{t}}+\sum_{k=1}^{n}logP(v^{(k)}|\mathcal{U}_{t})^{y _{new}^{(k)}}+\sum_{k=1}^{m}logP(e^{(k)}|\mathcal{U}_{t})^{e^{(k)}_{new}}, \tag{9}\]  
where \(C_{M_{t}}=\frac{(\sum_{i}v^{(i)}_{new}+\sum_{j}e^{(j)}_{new})!}{\prod_{k}v^{ (k)}_{new}!\prod_{i}e^{(j)}_{new}}\), \(v^{(k)}_{new}\) and \(e^{(l)}_{new}\) denote the number of the \(k\)th UI widget and the \(l\)th edge in \(y_{new}\), respectively. If the computed likelihood is greater than a threshold, \(y_{new}\) is accepted as a member of \(\mathcal{U}_{t}\), else \(y_{new}\) is regarded as a member of a new usage-phase, \(\mathcal{U}_{t+1}\).
## 3 Model and Methods
### Validation  
In this study, separate model training and depth mapping were conducted for each research area. Due to the lack of in situ depth data that could serve as ground truth for validation, we evaluated the accuracy of the inversion results from our method by isolating a portion of the available ICESat-2 data for accuracy verification. The assessment was carried out using scatter plots, linear regression lines, correlation coefficients (R\({}^{2}\)), root mean square error (RMSE), and mean absolute error (MAE) between the ICESat-2 data and the depth results based on GF-1. The RMSE and MAE were calculated using the following formulas:  
\[RMSE=\sqrt{\sum\limits_{i=1}^{m}\frac{\left(H_{G}-H_{I}\right)^{2}}{m}} \tag{5}\]  
\[MAE=\frac{1}{m}\sum\limits_{i=1}^{m}\left|H_{G}-H_{I}\right| \tag{6}\]  
where \(H_{G}\) represents the water depth data obtained through the proposed method based on GF-1 fusion data, while \(H_{I}\) denotes the ICESat-2 depth measurement results used for validation. The variable \(m\) indicates the number of ICESat-2 data points available for validation in each study area.