esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Prospects for Further Research
#### 5.3.3 Evaluation of the LRA Model  
At present, there is a serious lack of reasonable quantitative methods for the analysis of the performance of LRA models. On the basis of the calculation results regarding the landslide hazard, LRA studies also need to further systematically analyze the attributes of the disaster-bearing bodies. Some studies adopt the ROC curve, using only the landslide inventory with disaster attributes to evaluate the LRA model. These studies do not take into account the attributes of the disaster-bearing bodies, which is contrary to the calculation principles of the landslide risk. We qualitatively analyzed the generated landslide risk results of the study area based on the spatial distribution characteristics of landslide risk indices. In the next step of the research, we will carry out the quantitative validation of the LRA model according to the principles of the LRA study and effective statistical methods. Moreover, it is indispensable to establish a validation set based on the high-risk sites obtained from on-site investigations.
# Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing
###### Abstract  
In this paper, we present the results of a study on the development of a system to support medical diagnoses based on voice-based medical interviews. The main objective is to develop a tool that improves the process of collecting information from patients, using natural language analysis to identify key diagnostic information. The system processes the collected data to create information vectors for a selected group of diseases, allowing for an initial assessment of possible disease entities. An analysis of data mining and selected machine learning methods was carried out to develop an effective diagnosis algorithm. The system is designed to optimise patient care by automating the initial phase of the medical interview, which can lead to a reduction in errors due to subjective assessments and reduce the workload on doctors. The solution presented in this paper is part of a broader research project to develop a Medical Interview system, and this paper is the first article in a series that describes the experiences and challenges of implementing this system. Further work is planned to develop the model into advanced medical decision support techniques and validate it in a clinical setting.  
v +
Footnote †: journal: Applied sciences
# Visible Spectrum and Infra-Red Image Matching: A New Method
## 4 Experimental Setup and Results
### Image Matching  
Image matching is used as a test problem in this paper to evaluate the performance of Reg-SIFT and to present a comparison of Reg-SIFT with respect to state of the art feature point descriptors. Image matching is widely used as a test problem for the performance evaluation of feature points [15; 34; 35; 36]. In this paper, an image matching framework of [PERSON] et al. [18] is used which is based on projection error. Image matching is carried out in three steps [18]: (i) Feature point detection (ii) Feature point description and (iii) Feature point matching. Image matching is performed between pairs of images of the same scene under scale, rotation, affine, and common type of deformations and transformations. In this paper, the image matching is performed between VS and IR images, which possess high textural and intensity changes.
## 5 Methodology Used in This Study
In addition to geometry discretization, the motion (rotation) is also discretized to define the incremental sliding distance. The full rotation of the bushing is divided into incremental rotations corresponding to incremental sliding distances, \(\Delta s\). For each step of rotation, a contact analysis (either EFM or FEA) is conducted to estimate the contact pressure, and the wear depth for each cycle is then calculated using Equation (16), which is an updated version of Equation (8) incorporating rotation discretization. In Equation (16), \(n\) represents the surface element number, \(i\) denotes the step number (incremental rotation), and \(j\) refers to the number of cycles (complete bushing rotations). \[h_{i,j}^{n}=h_{i-1,j}^{n}+kp_{i}^{n}\Delta s_{i}. \tag{16}\]  
The procedure involves computing the incremental wear depth (\(kp_{i}^{n}\Delta s_{i}\)) for a specific step of bushing rotation across the entire contact region. The geometry is then updated by shifting the contact boundary in a direction normal to the contact surface by an amount equal to the incremental wear. The bushing is subsequently rotated by an angle corresponding to the sliding distance \(\Delta s\), and the wear depth is recalculated. The geometry is updated again, and this process is repeated until one complete cycle is finished. A new cycle then begins, and the procedure continues for the desired number of cycles. The simulation process is summarized in the flowchart presented in Figure 2.
## 2 Experimental Program and Setup
### Design and Fabrication of Bridge Piers  
In this study, eight specimens of cantilever bridge piers constructed with PP-ECC were fabricated and investigated experimentally. All specimens were designed with the same dimensions. Figure 2 illustrates the geometry and reinforcement details of bridge piers. The total height of bridge piers was 3.1 m, and the effective height was 2.1 m (from the top of the foundation block to the center of loading block). The shear-span ratio is 7.0 to ensure a flexural failure dominated response. At the bottom of the pier, a rigid foundation block with dimensions of \(1.4\times 1.4\times 0.7\) m is cast and mounted to the laboratory floor with four anchor bolts. The cross-section of piers is \(0.3\times 0.3\) m. The dimensions of the loading block are \(0.6\times 0.6\times 0.6\) m for better setup with the MTS actuator.  
Table 3 summarizes the design parameters and test variables of eight concrete bridge piers. The main design parameters includes: (a) transverse reinforcement bar (stirrup) (0%, 0.46%, and 0.79% by volume), (b) axial loading ratio (0.1 and 0.3), and (c) height of the PP-ECC (0, 250, and 500 mm), details are presented in Table 3. In each bridge pier, twelve steel rebars with a diameter of 12 mm were used as the longitudinal reinforcement bars. They were symmetrically distributed in the cross-section. The resulting longitudinal reinforcing ratio \(\rho_{\text{l}}\) is 1.51%. The yield strength and ultimate strength of the longitudinal reinforcement bars were 442.5 and 616.8 MPa, respectively. The steel bar with a diameter of 6 mm is selected for the transverse reinforcement. To study the effect of transverse reinforcement ratio at the plastic hinge region on the seismic behavior of specimens, the bar spacing of transverse reinforcement bars was set to 500, 140, 70 mm within the height of 0.5 m from the top of foundation  
Figure 2: Geometry and reinforcing details of test piers (unit: mm).  
block. The corresponding volume ratios of transverse reinforcement bars \(\rho_{\text{si}}\) were 0%, 0.46%, and 0.79%. The transverse reinforcement bars with a spacing of 70 mm were maintained the same for the upper portion of piers, that is, the volume ratio \(\rho_{\text{so}}\) is 0.79% for all specimens. The yield strength and ultimate strength of the transverse reinforcement bar were determined as 440.6 and 612.4 MPa, respectively. The concrete cover was 20 mm for all bridge piers.
## 2 Previous Studies
### Waste Monitoring  
This category of studies can be subdivided into \"Real-time monitoring systems\", \"Predictive analysis\", \"Data-driven decision making\", and \"IoT and sensor networks\". Real-time monitoring systems employ AI technologies for digestible and indigestible waste classification and real-time monitoring [26]. Studies on predictive analysis analyze historical or real-time data to predict future waste generation trends, such as medical waste [27] and municipal solid waste [28]. Data-driven decision-making is also a significant area that focuses on enhancing decision-making by analyzing large volumes of data [29] or providing effective modeling and optimization tools [30]. IoT and sensor networks leverage smart bins and smartphone applications to enable citizens' participation in and interaction with the recycling process [31], ensure timely interventions by waste management centers [32], and so on. Figure 2 shows the classification of these related research fields.  
This study is categorized into two groups: image recognition and machine vision. Initially, it focuses on identifying the items that are causing the fire using static photos of small-sized products that have been used. After the detector has been trained, the paper also employs a real-time waste monitoring system to detect target items. Although the above-mentioned study is slightly similar to previous research, its distinct advantage lies in its focus on specific products containing LIB and endangering waste treatment facilities. Since municipal waste treatment is critical to daily life, preventing accidents in these facilities is highly important. Contamination of the deteriorated LIBs in municipal waste should be detected, and the necessary countermeasures should be implemented. Previous studies [17; 33] were consulted regarding the AI algorithm, and various versions of You Only Look Once (YOLO) [34] were selected as detection algorithms. Then, the criteria to evaluate the performance of the developed detector were determined.
## 2 Atmospheric Oxygen Transmittance Effects at Tower Scale
### At-Sensor and At-Canopy Solar Irradiance  
For practical reasons, solar irradiance is commonly measured at a distance from the vegetated target location. Therefore, the relationship between the solar irradiance (\(E\)) measured at sensor level (e.g., on a tower or UAV), and the solar irradiance reaching the surface at TOC (\(\overline{E}\)) is driven by Equation (1):  
\[\overline{E}\sim E\cdot t^{\downarrow} \tag{1}\]  
where \(t^{\downarrow}\) is the downward total (direct and diffuse) transmittance from the sensor level to the TOC level, for a given illumination geometry. Note that hereafter the over-line symbol on \(\overline{E}\) (or over \(L\)) refers to magnitudes measured at the surface/canopy level. For the sake of readability, the spectral dependency of each term is omitted in the formulation presented in this section.  
Accordingly to Equation (1), the difference between solar irradiance measured by the tower-mounted calibration panel (or sensor) and the actual solar irradiance reaching the surface at TOC will increase as either the distance between them, or the SZA, increase. Making use of MODTRAN, Figure 1 shows the relative difference in per cent (RD%) of the total solar irradiance, \(E\), acquired at different sensor elevations for the O\({}_{2}\)-B and O\({}_{2}\)-A bands, and covering a range in SZAs. Details about inputs configured in MODTRAN are described in Table A1 of Appendix A.
# Testbed for Experimental Characterization of Indoor Visible Light Communication Channels
## 5 Experimental Results
Therefore, we confirm that the experimental testbed provides realistic results affected by many practical impairments and slight variations among similar devices, thus offering certain new information for the evaluation of indoor VLC scenarios that is not possible or easy to model in studies based on simulations. Figure 16: Experimental results for mockup with walls when the four lamps transmit the same data considering vertical orientation upward of the receiver and orientation given by Equation (14): (**a**) Measured throughput for vertical orientation (\(R_{\max}=47.5\) Mbps, \(R_{\text{mean}}=46.1\) Mbps, \(R_{\min}=43.9\) Mbps). (**b**) Measured throughput for receiver orientation given by Equation (14) (\(R_{\max}=47.2\) Mbps, \(R_{\text{mean}}=46.3\) Mbps, \(R_{\min}=45.2\) Mbps). (**c**) Measured mean SNR for vertical orientation (\(\overline{\text{SNR}}_{\max}=21.8\) dB, \(\overline{\text{SNR}}_{\text{mean}}=21.5\) dB, \(\overline{\text{SNR}}_{\min}=21.1\) dB). (**d**) Measured mean SNR for receiver orientation given by Equation (14) (\(\overline{\text{SNR}}_{\max}=21.8\) dB, \(\overline{\text{SNR}}_{\text{mean}}=21.5\) dB, \(\overline{\text{SNR}}_{\min}=21.2\) dB). (**e**) Measured BER for vertical orientation (\(\text{BER}_{\max}=1.2\times 10^{-3}\), \(\text{BER}_{\text{mean}}=1.1\times 10^{-3}\), \(\text{BER}_{\min}=0.7\times 10^{-3}\)). (**f**) BER for receiver orientation given by Equation (14) (\(\text{BER}_{\max}=1.2\times 10^{-3}\), \(\text{BER}_{\text{mean}}=1.1\times 10^{-3}\), \(\text{BER}_{\min}=0.7\times 10^{-3}\)).
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 3 Basics
### Named Data Networking  
_Named Data Networking (NDN)_ is a novel network technology developed on top of Information-Centric Networking (ICN) [24]. NDN was first presented in [3] in 2014.  
The _Internet Protocol (IP)_ is based on the notion that every host has a globally unique address, known as an IP address. The prefix of this address gives away a host's approximate location, enabling routers to forward packets to the correct subnetworks.  
In contrast, NDN does not have such a concept of a host-specific address: Instead, every packet has a _name_ unique for the network. This name is independent of the host's location. Thus, with NDN, the focus shifts from location-centric to data-centric networking.  
In an NDN network, a _consumer_ is interested in data from the network produced by one or more _producers_. To express interest in some data, a consumer can send an _Interest packet_, which is uniquely identified by its name. Network nodes between the consumer and some producer forward the packet until a producer that retains the data has been found. The producer then returns a _Data packet_ with the same name and the corresponding payload.  
The primary data structures in NDN are the Content Store, Pending Interest Table, Forwarding Information Base, and the forwarding strategy. The _Content Store (CS)_ is an in-network cache, allowing each node to make a local copy of a Data packet if appropriate. If a node cannot find a packet name in its CS, the _Pending Interest Table (PIT)_ stores all the pending Interests that a node has forwarded to other nodes. The _Forwarding Information Base (FIB)_ keeps track of the name prefixes of incoming Interest packets. The _forwarding strategy_ fetches the longest matching prefix from the FIB and decides when, where, or even whether to forward an Interest packet. For a more detailed review of these data structures, one may refer to [3].
# Effect of Ozonized Water against Pathogenic Bacteria and Filamentous Fungi on Stainless Steel
## 2 Materials and Methods
### Ozonized Water Treatments  
The ozone generator used was a waterproof unit model with a generation capacity of 8 g/h (230 VAC, 50 Hz, 750 W) (MET SrI, Bologna, Italy). The generator was supplied with purified oxygen obtained from a Pressure Swing Adsorption (PSA) apparatus that separates oxygen from atmospheric air, providing the generator with a higher oxygen concentration in order to increase its efficiency. The ozone produced was then delivered at the bottom of a conical filter funnel Robu (Borosilicate Glass 3.3, diameter = 120 mm, capacity = 2 L) equipped with a sintered disc filter of porosity 3. The conical filter funnel was filled with sterile water that was ozonized due to the microbubbles produced by the ozone flow passing through the disc filter. In this way, stable ozonized water was generated. Its concentration was measured by means of an Ozone Vacu-vials\({}^{\circledR}\) Kit (K-7423, CHEMetrics Inc., Midland, VA, USA). The spectrophotometric measurement and the determination of the ozone concentration in the water solution were performed using a V-2000 Photometer (CHEMetrics Inc., Midland, VA, USA). From the stock solution of ozonized water, solutions at the desired ozone concentrations were obtained by dilution. The final concentration of the diluted solution was measured again before starting the treatment.  
SS tiles (15 \(\times\) 20 \(\times\) 1 mm) were utilized as germ carriers. They were previously sterilized at 121 \({}^{\circ}\)C for 15 min, inoculated with one microbial suspension at a time, and dried for one hour in sterile conditions under a laminar flow hood. Some of the inoculated tiles, marked as \"positive controls,\" were analyzed without being treated (see the correspondence with the \"time 0\" bars in each figure) to consent to a comparison with those that were treated. Treatments were carried out just after the drying of the inoculum in order to avoid the loss of viable cells (Figure 1).
## 3 Results
### Comparison with Decision Tree Technique  
In order to further evaluate the results obtained, as we explain in the methodology section, we used a Decision Tree as another technique using the same data.  
To implement the Decision Trees, we used the Knime application [30], an open-source data analytics tool. This application implements a wide set of techniques with good results, including Decision Trees. The Knime Decision Tree implementation output is categorical, so we adapted patients' data to this circumstance. We grouped the Average Total Charge column in eight categories.  
In Table 4, the list of categories can be seen.  
\begin{table}
\begin{tabular}{c c} \hline
**Category** & **Billing Range Category** \\ \hline
1 & value \(<\) 3500 \\ \hline
2 & 3500 \(<\) value \(<\) 5000 \\ \hline
3 & 5000 \(<\) value \(<\) 7000 \\ \hline
4 & 7000 \(<\) value \(<\) 9000 \\ \hline
5 & 9000 \(<\) value \(<\) 10,500 \\ \hline
6 & 10,500 \(<\) value \(<\) 15,000 \\ \hline
7 & 15,000 \(<\) value \(<\) 30,000 \\ \hline
8 & \multicolumn{1}{c}{value \(>\) 30,000} \\ \hline \end{tabular}
\end{table}
Table 4: Range of predicted values of the ATC variable.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**DRG** & \begin{tabular}{c} **Perceptron** \\ **Time** \\ \end{tabular} & \begin{tabular}{c} **DNN** \\ **Time** \\ \end{tabular} & \
\begin{tabular}{c} **Perceptron** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **DNN** \\ **Accuracy** \\ \end{tabular} \\ \hline
39 & 3.957 s & 4.490 s & 85.04\% & 85.08\% \\ \hline
64 & 4.344 s & 4.439 s & 79.48\% & 79.48\% \\ \hline
178 & 4.250 s & 4.415 s & 83.68\% & 83.68\% \\ \hline
473 & 4.480 s & 4.405 s & 83.55\% & 83.60\% \\ \hline
536 & 4.105 s & 4.415 s & 83.75\% & 83.75\% \\ \hline
690 & 4.105 s & 4.467 s & 83.04\% & 83.04\% \\ \hline
811 & 4.169 s & 4.379 s & 80.14\% & 80.14\% \\ \hline \end{tabular}
\end{table}
Table 3: Perceptron Neural Network results vs. DNN results.  
Figure 6: DNN predictions and errors for DRG 178: (**a**) results of the predictions (**b**) errors count.  
The tests were implemented by building a Decision Tree with the same training data used to train the Neural Networks and later by applying this Decision Tree to the same patient DRGs. In Table 5, the accuracy obtained is shown compared with the Neural Networks.  
In a first approach, both Neural Networks and the Decision Tree achieved admissible results. For these data, the Neural Network accuracy was around 80%, and that of the Decision Tree was around 70%. The main goal of this comparison was not to make a thorough performance evaluation or create a competition between different AI methods; the goal was to make an assessment of whether applying machine learning to hospital financial management data is possible, whether more than one technique can be used and whether their results provide more confidence in these techniques.
###### Abstract  
The objective of this research was to isolate and identify lactic acid bacteria living in the digestive tract of the queen scallop _Aequipecten opercularis_ and further test it as a probiotic culture on the growth of the bivalve during one month in captivity. Classic microbiological techniques (API and MALDI TOF) were used and four different strains of _Lactiplantiobacillus_ and one _Lactococcus_ were identified and further analyzed, namely: _Lactiplantiobacillus_ _plantarum_ I, _Lactiplantiobacillus_ _plantarum_ 1, _Lactiplantiobacillus_ _plantarum_ 2, _Lactococcus lactis_, and _Lactiplantiobacillus__brevis_. The isolated cultures were further tested for the highest antimicrobial activity towards the most common marine pathogens and survival at different temperatures and pH levels. The strain _Lactiplantiobacillus_ _plantarum_ I had the best results, with the highest antimicrobial activity (77-85% pathogen growth inhibition) and the best safety standards showing no antibiotic resistance, and no ability to synthesize biogenic amine and degrade red blood cells. Considering all the abovementioned characteristics, the strain _Lpb.__plantarum_ I was further tested on the growth and health status of the bivalve _Aequipecten opercularis_ during a month of cultivation in captivity. The results showed that the incorporation of _Lpb.__plantarum_ I led to a decrease in the quantity of test microorganisms in the bivalve and an increase in both the growth rate weight and growth rate length of the queen scallop _A.__opercularis_. Furthermore, the scallops fed with the algae culture + _Lpb.__plantarum_ I gained significantly higher meat yield (33.15 \(\pm\) 2.63%) compared with the control scalings fed only with the algae culture (29.66 \(\pm\) 2.87%). To conclude, the results from this research indicate that including _Lpb.__plantarum_ I as a dietary supplement could enhance growth performance and serve as a feasible approach to reduce pathogen levels while cultivating _A.__opercularis_ in captivity.  
_Lactiplantiobacillus__plantarum_ I; growth performance; pathogens; scallops
# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
## 4 Experimental Results and Discussion
### Comparison of PSO Variant Algorithms for Benchmark Functions  
#### 4.2.1 Benchmark Functions and Parameter Settings for PSO Variants  
To evaluate the optimization performance of the proposed MMPSO, the classical CEC 2005 test suite [44] was selected as the experimental benchmark. The test set consisted of four types of functions: F01-F05 (unimodal (UN)), F06-F12 (multimodal (MN)), F10 and F11 (rotation), F13 and F14 (expanded (EF)), and F15-F25 (hybrid composition (CF)). The test suite can comprehensively and objectively reflect the optimization performance of an algorithm. For a more comprehensive evaluation and analysis, we compared the MMPSO algorithm with seven well-known PSO variants: HIDMSPSO, HCLPSO, HCLDM-SPSO, CLPSO, DMSPOS, EPSO, and FDRPSO. The PSO variants' parameter settings were established as indicated in Table 4.