esa-sceva/satcom-chunk-collection huggingface dataset:
# Robots in Partially Known and Unknown Environments:
## 4 AdaptiveRMP Framework
### OnlineRMP
As the robot moves around an obstacle, it maintains a local goal of target; once it is possible to reach the final goal via a direct path, that path is used, and the local target of the surrounding strategy is discarded. In Figure 11, two cases of detour are shown. The obstacle is represented in gray, the generated path in red, the final objective in green, and the local objective in blue. On the right, the discarding is used, and, on the left, the surround is completed. Figure 10: Use of normal vector concerning the direction for obstacle surrounding in real time. Figure 9: Robot vision range. White cells are visible to the robot, while gray cells are out of sight. Algorithm 2 shows the general structure of the planning approach for an unknown environment using the constructive method. Before traveling the path in real time, an initial route is built considering the known partial obstacles. For this, the SA-based approach for OfflineRMP previously described is used. Since OnlineRMP uses a discretized environment and the path generated by OfflineRMP must be traversed cell by cell, OfflineRMP is used without applying post-processing. The procedure shown in Algorithm 2 starts by constructing an initial solution that defines a direct path between the start and end positions in line 1. Then, the robot starts moving and checking each position in the initial path. From the sensors, the robot can update the information about obstacles in the route in line 3. When a possible collision is detected, re-planning is performed (line 5). Details of the re-planning process are shown in Algorithm 3. Here, when it is possible to reach the final goal via a direct path, then that path is used, and the local goal of the surrounding is discarded. Once a re-planning process is performed, the algorithm resumes the initial path. ```
1:route = direct_route (start, target);
2:for position in route do
3:update_obstacles_map ();
4:if is close to collision then
5: route = Re-planning (position);
6:endif
7:endfor
```  
**Algorithm 2** OnlineRMP  
```
1:path_to_fix = obstacle_length();
2:if direct_route (position, local_target) then
3: route = direct_route (position, local_target);
4: new_route = surround_obstacle (position, path_to_fix);
5:else
6: path_to_fix = obstacle_length ();
7: route = surround_obstacle (position, path_to_fix);
8:endif
```  
**Algorithm 3** Re-planning  
Because OnlineRMP is an algorithm that works in real time, each position the robot moves will be part of the final route. Once the current sub-path is finished, the robot follows the original path. Re-planning is performed as many times as necessary until the target point is reached or when there is no feasible path.
# VOC Removal from Manure Gaseous Emissions with UV Photolysis and UV-TiO\({}_{2}\) Photocatalysis
###### Abstract  
Control of gaseous emissions from livestock operations is needed to ensure compliance with environmental regulations and sustainability of the industry. The focus of this research was to mitigate livestock odor emissions with UV light. Effects of the UV dose, wavelength, TiO\({}_{2}\) catalyst, air temperature, and relative humidity were tested at lab scale on a synthetic mixture of nine odorous volatile organic compounds (VOCs) and real poultry manure offgas. Results show that it was feasible to control odorous VOCs with both photolysis and photocatalysis (synthetic VOCs mixture) and with photocatalysis (manure offgas). The treatment effectiveness \(R\) (defined as % conversion), was proportional to the light intensity for synthetic VOCs mixtures and followed an order of UV\({}_{185+254}\) + TiO\({}_{2}\) \(>\) UV\({}_{254}\) + TiO\({}_{2}\) \(>\) UV\({}_{185+254}\); no catalyst \(>\) UV\({}_{254}\); no catalyst. VOC conversion \(R>80\%\) was achieved when light energy was \(>\)\(\sim\)60 J L\({}^{-1}\). The use of deep UV (UV\({}_{185+254}\)) improved the \(R\), particularly when photolysis was the primary treatment. Odor removal up to \(\sim\)80% was also observed for a synthetic VOCs mixture, and actual poultry manure offgas. Scale-up studies are warranted.  
Keywords:air quality; air pollution; emissions mitigation; sustainable livestock production; confined animal feeding operations; odor control; advanced oxidation; environmental technologies +
Footnote †: journal: Physics Letters B
# Novel Microfluidics Device for Rapid Antibiotics Susceptibility Screening
## 1 Introduction
The latter provides microfluidic devices with the unique property of absolute control over fluidic flows and reactants supply in different chip regions. Additionally, the decreased reagents volumes, usually in the micro/nanoliters range, can significantly reduce reaction times, energy consumption and overall cost for a certain process [19]. By providing well-defined microenvironments that mimic the natural habitats of bacteria, microfluidic devices have also enabled successful antibiotic susceptibility testing, on-chip cultivation, etc. [20]. Some of the many advantages of microfluidic devices utilized for antibiotic resistance screening include live monitoring of bacterial growth dynamics, rapid readout of resistance test results (usually within a few hours) and high potential for automation [20]. One example was reported in [21], where the susceptibility of _E. coli_ to antibiotics was quantified on the basis of measurements of fluorescence intensity within 4 h. In [22], a microfluidic point-of-care device utilized for rapid antibiotic prescription assistance in cases of urinary tract infections caused by _E. coli_ was presented. The growth rate of individual cells under the influence of antibiotics was determined based on automated phase-contrast microscopy at a single-cell level and subsequent image processing. The results were detectable within 30 min from sample loading to test readout. A detailed review of existing microfluidic devices for AST is described in [23]. The aforementioned devices indicate that the utilization of microfluidics can accelerate the AST of bacterial cells even further. The main drawbacks of those techniques remain the necessity of advanced detection devices (e.g., single-cell microscope) and/or the presence of expensive soft lithography machinery for microfluidic chip manufacturing [24]. In this paper, we present a novel microfluidic device for on-chip AST. We describe a novel cost- and time-efficient method for device fabrication without the need for a clean room environment. The distinguishing capacity of polydimethylsiloxane (PDMS) for gas permeability combined with dedicated vacuum channels was utilized in order to engineer a cultivation device that allows for reliable inoculation in a semi-automatic manner. This way, we were able to populate more than 100 independent cultivation chambers with _E. coli_ cells and efficiently monitor their growth in the presence/absence of various antibiotic compounds.
## 1 Introduction
The sunshine-hours models have become increasingly complex, improving their regional applicability and calculation accuracy. [PERSON] et al. [26] used some equations to calculate global solar radiation from sunshine hours for 16 meteorological stations in Spain. These equations involved the original Angstrom-Prescott linear regression and modified functions (quadratic, third degree, logarithmic and exponential functions), it finds that the third degree models performed better than the other models, and the linear model is preferred due to its simplicity and application. In other words, high-order nonlinear sunshine-hours models can be applied in various regions [27]. Meteorological parameter models such as sunshine-hours models, therefore, are primarily used to calculate the amount of radiation [28]. Furthermore, a new method for estimating sunshine hours, like using remote sensing data, is emerging based on data from sunrise to sunset obtained from the Fengyun-2G geostationary meteorological satellite. [PERSON] et al. [29] proposed a method to calculate sunshine hours according to the total hourly cloud data between sunrise and sunset, which can accurately capture the spatial variation of the sunshine hour. As available solar radiation measured data do not meet the needs of Qilou street redesign, by analyzing the sunshine conditions on summer days for Qilou streets in the traditional commercial area of Nanning, this study explores the relationship among street height-to-width ratio, sunshine hours and street orientation, since it greatly influences the thermal comfort and UHI mitigation of urban commercial block herein. Additionally, an optimization strategy for commercial block replanning based on sunshine hours and street orientation is proposed. This study was achieved by (i) analyzing the application of sunshine hour in the thermal environment design of the commercial streets by reviewing the available literatures (Section 1); (ii) describing the characteristics of the thermal environment and sunshine in Qilou commercial Street (Section 2); (iii) establishing a model to predict the street height-to-width ratio based on the sunshine hours and street orientation (Section 3); (iv) discussing the influences of the Qilou street pattern on sunshine hours and proposing optimization strategy to the street height-to-width ratio (Section 4); and (v) making the conclusions of this study.
###### Abstract  
Five countries in the Lancang-Mekong region, including Myanmar, Laos, Thailand, Cambodia, and Vietnam, are facing the threat of deforestation, despite having a high level of forest coverage. Quantitatively assessing the forest ecosystem status and its variations based on remote sensing products for vegetation parameters is a crucial prerequisite for the ongoing phase of our future project. In this study, we analyzed forest health in the year 2020 using four vegetation indicators: forest coverage index (FCI), leaf area index (LAI), fraction of green vegetation cover (FVC), and gross primary productivity (GPP). Additionally, we introduced an ecosystem quality index (EQI) to assess the quality of forest health. To understand the long-term trends in the vegetation indicators and EQI, we also performed a linear regression analysis from 2010 to 2020. The results revealed that Laos ranked as the top-performing country for forest ecosystem status in the Lancang-Mekong region in 2020. However, the long-term trend analysis results showed that Cambodia experienced the most significant decline across all indicators, while Vietnam and Thailand demonstrated varying degrees of improvement. This study provides a quality assessment of forest health and its variations in the Lancang-Mekong region, which is crucial for implementing effective conservation strategies.  
forest ecosystem; Lancang-Mekong region; FCI; LAI; FVC; GPP; EQI +
Footnote †: journal: Sensors
## 4 Experimental Results
A similar situation where MDIS identifies more MVs than DS arises due to the extended search range facilitated by search probabilities. With DS, blocks located near the edges of frames often end up without assigned MVs. However, MDIS more frequently succeeds in finding MVs for such blocks due to its extended search range capability. The final search directions file was generated using a Pythonversion 3.9.13 script. The script is designed to process the input files and generate an output file containing the final search directions for the MDIS algorithm. The four input files follow the scheme explained earlier, and each file corresponds to one joystick movement. The script checks which joystick had the highest push intensity at that moment, and writes only that information in a separate file. Later, that newly created file is processed in the following way. If the push intensity is below 0.1, write a '0' to the final directions file. Otherwise, compare the intensity and movement category with the previous value, and follow the logic described earlier in this research. For example, if the movement category is forward motion, and high acceleration is detected, then still write a '0' in the output file because the drone is currently leaning forward, and the classical forward motion model can not yet be applied. Only once the high acceleration stops, start applying the forward motion model, or in other words, start writing the corresponding number to the output file.
# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 5 Results
### Sick Leave Prediction
#### 5.2.4 Comparison of Prediction Approaches  
To predict sick leave one month ahead, we can either use single-target models to predict sick leave for each week separately or build a multi-target model to predict all weeks together. To determine the better strategy, we compared the performance of both approaches.  
Figure 12 presents the RMSE measure results for all targets, comparing single-target and multi-target models (detailed results are available in Table S9 in the Supplementary Materials). Across the board, Bagging emerges as the best-performing method with the lowest errors. The results also demonstrate that multi-target models generally provide slightly better performance across methods, reflecting their ability to capture correlations between targets.  
The results also reveal a pattern where predictions for week W1 have the least errors, while errors progressively increase in subsequent weeks. This finding suggests that the models are best suited for short-term forecasting.  
Figure 10: Results for two-week-ahead sick leave prediction for all four evaluation metrics. The evaluation metrics are shown separately for each week (W1 and W2), with the “Average” line representing the mean value of the metric across both weeks.  
Figure 13 presents the results for the CC measure. Again, Bagging demonstrates the highest performance overall, with the highest correlation coefficients (detailed results are available in Table S10 in the Supplementary Materials). The figure also indicates that multi-target models tend to provide slightly better correlation coefficients than single-target models.  
Similarly to the RMSE results, the CC values are highest for W1 and then progressively decrease with each subsequent week, underscoring the difficulty of accurately forecasting over longer time frames. These results highlight that while Bagging remains the best prediction method for this task, all models perform better for short-term predictions compared to longer horizons.
## 2 Materials and Methods
### Parameter Optimization and Performance Evaluation  
The Random Forest (RF) was chosen for classification due to its robustness and suitability for noisy data. To optimize performance, two parameters were considered: the ECG segment lengths; and overlap. Four metrics were calculated, namely the Ac, precision, Se, and F1 score [26], with TPs, TNs, FPs, and FNs representing true positives, true negatives, false positives, and false negatives, respectively (Equations (3)-(6)).  
\[\text{Accuracy}=\frac{TP+TN}{TP+FN+FP+TN} \tag{3}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{4}\]  
\[\text{Recall}=\text{Sensitivity}=\frac{TP}{TP+FN} \tag{5}\]  
\[\text{F1 score}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{6}\]  
The study employed an RF classifier for stratified 5-fold cross-validation, as proposed in [27].
# Buckling-Restrained Bracing System with Ultra-High-Performance Fiber Concrete
## 9 Conclusions  
This study demonstrated the performance of BRB using UHPFRC as filler material. A design procedure and working mechanism were provided for the BRB. To achieve the objectives, numerical analysis and experimental testing were conducted to assess the resistance and performance capability of the BRB when UHPFRC was used as the filler material. Subsequently, a new design for a BRB device, named RBRB, was developed by incorporating hyperelastic rubber components to serve as an additional load-bearing mechanism to enhance the vibration dissipation capacity. Finite element models were built for both BRB and RBRB to facilitate a comprehensive comparison between them. Based on the results obtained in this research, the following conclusions can be drawn:  
1. The numerical comparison between a BRB filled with conventional concrete and one filled with UHPFRC concrete shows the advantages of using UHPFRC to increase stress and resistance force. Moreover, the finite element results indicated significant agreement with the experimental test.
2. It is revealed that using concrete with higher compressive strength results in an increase in resistance force and improves the confinement.
3. The results of the parametric study indicate that the effect of the concrete compressive strength exceeding 50 MPa on the BRB performance is negligible, and it may not justify the use of concrete with very high compressive strengths due to the associated costs.
4. The results of the study indicate that the proposed RBRB has the ability to enhance the conventional BRB performance by increasing the resistance force caused through the addition of rubber components.
5. Based on numerical results, the proposed RBRB is capable of dissipating more energy due to the greater hysteresis curve resulting from the added resistance of rubber component inserted inside the RBRB device.
6. The results demonstrate that the RBRB has the capability to overcome the drawbacks of conventional BRB systems and improve the structure's resistance against the adverse effects of seismic hazards.  
Conceptualization, N.O. and F.H.; Methodology, N.O. and F.H.; Software, N.O.; Formal analysis, N.O.; Investigation, N.O. and F.H.; Resources, F.H.; Data curation, N.O.; Writing--original draft, N.O.; Writing--review and editing, F.H.; Visualization, N.O. and F.H.; Supervision, F.H.; Project administration, F.H.; Funding acquisition, F.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All new data created through this study are already included in this paper. Therefore, data sharing is not applicable to this article.
## 3 Research Approach
### Application in Shipping Notes  
The shipping notes that the text recognition algorithm can currently process are two: sales account documents and work account documents. This choice was made for the simple fact that they are the documents of greatest volume, both in terms of documents that arrive per day and for the number of lines to be processed for each individual document. The sales account shipping notes are those delivered by raw material suppliers, while the work account shipping notes are those provided by companies that subcontract the company's work. Consequently, in this case only shipping notes containing semi-finished or finished products will arrive. Firstly, it was necessary to define what the fundamental data are that Kapto must read and process for correct loading, and they are: supplier code, transport document number, order, item code and quantity. As for the supplier code, there is a database that associates each supplier or companies that subcontract work. To detect it, it is only necessary for text recognition algorithms to recognize the header of the document. As for supplier or subcontractor orders, they must always be present in the document but are coded differently. Supplier orders are composed of six digits (e.g., 475896), while subcontractor orders are composed of a code that always starts with \"U\" and followed by another five digits (e.g., U25698). As for the item codes, raw materials are identified by a six-digit code, which always starts with \"1\" (e.g., 135682). Semi-finished products are identified by a six-digit code, which always starts with \"4\", indicating the semi-finished product, with the addition of an asterisk and a further four-digit code, with the aim of indicating the specific configuration of the semi-finished product (e.g., 458265\"0001). Finally, with regard to finished products, they have a code structured like semi-finished products, with the difference that they start with \"5\" (e.g., 526457\"0010).  
The text recognition algorithm is capable of receiving any type of document, regardless of its structure, classifying documents based on their type, extracting relevant information, and validating data using external sources. Moreover, the text recognition algorithm operates autonomously, searching for data to complete missing information and uploading data into systems, performing tasks in accordance with established business rules. The text recognition algorithm employs various levels of competing artificial intelligence models, aiming to maximize the interpretive capabilities of document content, requiring human intervention only in extreme circumstances. Regarding the training of artificial intelligence, continuous coaching approaches are adopted that allow for an acceleration of the training phase.
# Incentive Price-Based Demand Response in Active Distribution Grids
## 3 Mathematical Modeling and Simulation Setup
### Model Predictive Control Algorithm of the BESS  
This paper assumes that each BESS is controlled to maximize the self-consumption of the power at the node to which it is connected. A residential load and a PV is assumed to be connected along with each BESS. A sophisticated controller is assumed to be responsible for charging/discharging control of each BESS. Let \(\mathcal{N}_{f}\subseteq\mathcal{N}\) be the nodes where, the BESS are connected. Let the total number of BESS be denoted as \(N_{f}\), hence \(\mathcal{N}_{f}=\{1,\cdots,N_{f}\}\). Let the control horizon be denoted as \(N_{c}\) which is assumed to be equal to the prediction horizon \(N_{p}\) and let the set \(\mathcal{N}_{c}=\{1,\cdots,N_{c}\}\) contains the discrete-time instants of the control horizon. The net power consumption at each node and at each time instant \(\kappa\) is calculated from the difference between net power demand which is the sum of power consumption of the load \((p_{d})\) and the BESS power \((p_{f})\), and the PV power generation \((p_{pv})\) as provided in (10).  
\[p_{n}^{\kappa}=p_{d}^{\kappa}+p_{f}^{\kappa}-p_{pv}^{\kappa} \tag{10}\]  
It is to be noted that a typical load profile for the power consumption and a typical power generation profile for the PV on a clear sky day are considered in this paper.  
The MPC of the BESS involves solving the below optimization problem to compute the active power setpoints of each BESS denoted as \(p_{f}\).  
\[\min_{p_{f}^{\kappa}}J_{f}=\sum_{\kappa\in\mathcal{N}_{c}}(p_{n}^{\kappa})^{2}\] (11a) subject to ( 10 ) and the following constraints, where \[\kappa\] is the time instant. The above objective function tries to compute the power from BESS that will minimize the costs of power consumption at each node over the control horizon, thereby maximizing the self-power consumption over time.  
The constraint that specifies the state evolution of the BESS over a control horizon using the simplified linear model expressed in (9) is provided below.  
\[x_{f}^{\kappa+1}=x_{f}^{\kappa}+\Gamma_{f}p_{f}^{\kappa}\quad\kappa\in \mathcal{N}_{c} \tag{11b}\]  
where \(\Gamma_{f}=-\frac{\eta_{f}\tau_{s}}{E_{f}}\). The limits of the battery SOC are stated in the below constraint.  
\[x_{f\,\mathrm{min}}\leq x_{fk}^{\kappa}\leq x_{f\,\mathrm{max}}\quad\kappa\in \mathcal{N}_{c} \tag{11c}\]  
The values of the BESS parameters are chosen to be \(E_{f}=20\) kWh, \(\eta_{f}=0.91\) pu and \(\tau_{s}=10\) min. The active power limits of the BESS is enforced in the below iteration. As we assume the BESS to be operating in unity pf mode, the maximum limit of the BESS's active power is equal to its apparent power rating.  
\[p_{f\,\mathrm{min}}\leq p_{fk}^{\kappa}\leq p_{f\,\mathrm{max}}\quad\kappa\in \mathcal{N}_{c} \tag{11d}\]