esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Nevertheless, some photocatalytic antimicrobial surfaces generate oxidizing radicals that do not engender resistance mechanisms [22]. Pathogenic bacteria and antibacterial substances are important targets for identification and detection in various fields, including food safety, security, medicine, forensic medicine, and public health [23]. Conventional laboratory-based methods generally have long processing times (take days or weeks to complete), and require specialized equipment and professional users, hence these methods are costly. Identification of a pathogenic species is typically performed by isolating and culturing on chromogenic agar, followed by Gram staining, microscopic examination, and other biochemical and molecular-biological techniques [24; 25]. Each technique has its strengths and limitations. Staining is simple and provides valuable information about bacterial morphology. Biochemical assays include detection of enzymes specific to a certain bacterium. Immunological tests include agglutination assays [26] and enzyme-linked immunosorbent assays (ELISA) [27]. The polymerase chain reaction (PCR) allows multiple copies of a bacterial genome to be made (this process is sometimes called 'DNA amplification'), hence even just one bacterium could be identified by genome sequencing after PCR [23; 28]. However, contamination and wrong DNA pairing can cause false-positive or -negative results, and genetically mutated strains may escape the correct probe matching [29]. The overall technique is still quite time-consuming and costly. Real-time PCR (RT-PCR) analysis can be performed faster, within several hours, but needs special reagents and equipment [23; 30]. Mass spectrometry-based methods are also currently widely used due to their speed, high-throughput, and sensitive and specific analysis [31]. The development and implementation of real-time and more sensitive diagnostic methods remains extremely important. Biosensors provide a direct way of characterizing the adhesion of whole bacteria or parts of them. These devices integrate a biological recognition element (e.g., whole cells, receptors, enzymes, antibodies) with a signal transduction element. In the most advanced systems, additional labels are not needed for signal generation and the information is provided in real time. Optical and amperometric techniques are the most commonly used for signal transduction [23]. Antibiotic resistance has also been studied with label-free biosensors [32; 33]. In this review, in the first part, we summarize the most successful mechanical, optical, and electrical biosensors in detecting bacteria, and we also discuss monitoring of antibiotic resistance by novel, label-free techniques. After, we provide a brief summary of biological processes potentially interesting for label-free monitoring. From this perspective, we discuss bacterial adhesion, adhesive properties and mechanisms of bacterial adhesion, the role of their glycolacyx in the process, and even their entry into mammalian host cells.
# Path Planning for Localization of Radiation Sources Based on Principal Component Analysis
## 1 Introduction
To enable autonomous radiation source localization, a mobile robot requires the ability to automatically determine the next location for gamma ray measurement instead of following a predefined path. Based on this requirement, we propose a path planning method for the localization of radiation sources using a mobile robot equipped with a gamma-ray detector, which has a field of view (FOV) in all directions. The number of incident events is approximated to be the squared inverse proportional to the distance between the radiation source and the detector. Therefore, the lower the distance to the source, the shorter the time required to obtain the same radiation counts measured by the detector. The proposed method is designed to reduce this distance to a position where a sufficient number of gamma-ray events can be obtained, which then generates a path that surrounds the radiation sources. Figure 1 presents a conceptual image of the measurement path, which is the objective of the proposed method. The proposed method generates the above measurement path by performing principal component analysis (PCA) based on the measurement results obtained, up to the current time. We demonstrate that the proposed method can automatically generate a measurement path and accurately localize radiation sources via simulations and experiments. The remainder of this paper is organized as follows. Section 2 presents the schematic representation and details of the proposed method. Section 3 presents the experiments and results. Here, the results of the simulation under the three conditions and the experiments are provided. Finally, Section 4 presents the conclusions of this study.
## 5 Experiments and Results  
[PERSON]'s benchmark instances [29; 30] were used for the experiments. At first, the comparison of the original ACO algorithm with this algorithm enhanced by the additional optimization process was conducted; the parameters and settings of the algorithms were the same. The modified optimization criterion was used in experiments. Table 2 shows the results. As can be seen, the AOP process yielded considerably improved solutions. In 21 out of 23 cases, the solutions have been improved. In the remaining cases (p13, p14), the solutions are the same. Since these are the simplest instances with 80 regularly distributed nodes, it can be assumed that these solutions are optimal. In general, the more complex the problem, the higher the level of improvement. The average improvement over all 23 instances is 14.08%. Table 2 also shows the average runtime of one execution of both the original ACO and the ACO with AOP for every instance. All experiments were carried out on a computer with the following configuration: Intel i7 4 GHz CPU with 8 cores, 16 GB RAM, operating system Windows 10. The codes of the algorithms were written in C++ in the Visual Studio IDE. As can be seen, the AOP process requires some extra time, which depends on the complexity of the problem, i.e., the number of nodes. For problems with 360 nodes (p21, p22, p23), the time required to obtain a solution with the algorithm with AOP was about 80% longer compared to the original ACO. Overall, the increase of the runtime on all [PERSON]'s benchmark instances is 39.2%. Table 3 presents the results in the same manner as Table 1. For each instance, numbers of nodes (\(N\)) and depots (\(M\)) are shown. The numbers in bold in the third column record the best known solutions (BKS) taken from NEO Web [31] (available at [[http://neo.lcc.uma.es/vrp/vrp-instances/multiple-depot-vrp-instances/](http://neo.lcc.uma.es/vrp/vrp-instances/multiple-depot-vrp-instances/)]([http://neo.lcc.uma.es/vrp/vrp-instances/multiple-depot-vrp-instances/](http://neo.lcc.uma.es/vrp/vrp-instances/multiple-depot-vrp-instances/))), i.e., the MDVRP optimization criterion (Formula (1)) was used. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Instance** & **Original** & **Orig. ACO** & **ACO with** & **ACO AOP** & **Solution** & **Runtime** \\  & **ACO** & **Runtime** & **AOP** & **Runtime** & **Gap** & **Gap** \\ \hline p01 & 157.31 & 1.5 s & 152.54 & 1.7 s & 3.13\% & 11.11\% \\ p02 & 129.00 & 1.6 s & 125.82 & 1.8 s & 2.53\% & 14.58\% \\ p03 & 139.66 & 3.7  
The ACO algorithm (with AOP process) was executed 100 times on each instance; the best solution is recorded in the fourth column of Table 3 (numbers in bold), with the M-MDVRP optimization criterion applied (Formula (2)). Both BKS and ACO solutions were then recalculated using the opposite
# Saturation Based Nonlinear FOPD Motion Control Algorithm Design for Autonomous Underwater Vehicle
## 2 Modelling of AUV  
In the body coordinate, the kinetic model of an AUV can be described in a matrix form as [6]:  
\[M\,\dot{v}+C(v)v+D(v)v+g(\mathbf{o})=\delta+\omega, \tag{1}\]  
where \(M\in\mathbb{R}^{6\times 6}\) is the inertia matrix including added mass; \(C(v)\in\mathbb{R}^{6\times 6}\) represents the Coriolis centripetal force matrix, which is is skew symmetric and can be neglected if the AUV is moving at a low speed; \(D(v)\in\mathbb{R}^{6\times 6}\) defines the damping matrix which is definite positive; \(g(\mathbf{o})\in\mathbb{R}^{6\times 1}\) is the restoring force vector generated by gravity and buoyancy; \(\delta\in\mathbb{R}^{6\times 1}\) defines the force vector generated by thrusters; and \(\omega\in\mathbb{R}^{6\times 1}\) defines the disturbance vector.  
The dynamic model of an AUV in the earth coordinate can be expressed as:  
\[\dot{\mathbf{\omega}}=J(\mathbf{\omega})v, \tag{2}\]  
where \(v=[\begin{array}{cccccc}u&v&w&p&q&r\end{array}]^{T}\) is the linear velocity and angular velocity in the body coordinate, \(\mathbf{\omega}=[\begin{array}{cccccc}x&y&z&\phi&\theta&\psi\end{array}]^{T}\) represents the position and attitude vector in the earth coordinate, and \(J(\mathbf{\omega})\in\mathbb{R}^{6\times 6}\) is the transformation matrix between the body coordinate and the earth coordinate.
## 3 Results
### Intelligent Vehicle Platoons Scenario Experiment
#### 3.2.3 Mixed Convoys of Gasoline and Electric Vehicle  
In the current scenario of mixed gasoline and electric traffic flows adopting a convoy driving strategy, with electric vehicles accounting for 40% and gasoline vehicles accounting for 60% of the total, the impact of convoy size on carbon emissions from traffic flow is shown in the following graph:  
As shown in Figure 10, when both gasoline and electric vehicles adopt the convoy driving strategy, the total road carbon emissions decrease, which is followed by an increase as the maximum convoy length increases. Minimum carbon dioxide emissions were achieved when \(N\) was approximately 2-3, and the specific value varied with the desired headway times of the vehicles. Furthermore, the outer lane has the highest road carbon emissions, whereas the middle lane has the lowest. The carbon emissions trend of the three lanes aligns with the overall trend of the total carbon emissions.  
A combination of fuel vehicles and electric vehicles constitutes a blended fleet, which is guided by the CACC system for their following and lane-changing actions. Consequently, the patterns demonstrated by the mixed fleet in relation to convoy size align closely with those observed in electric vehicle convoys. Since fuel vehicles are integrated into the convoy composition, the collective carbon emissions of the fleet are lower compared to a convoy exclusively comprising electric vehicles.  
Figure 9: Impact of different desired headway settings in CACC.
## 4 Results
### Energy Consumption  
Figure 7 shows the evolution of the available battery capacity for the whole network. The battery capacity for each node is added to the total for each iteration of the control system, and them the resulting value is calculated in relation to the first aggregated value for each experiment. The graphs have been restricted to the shortest experiment, so we can easily compare the results from one experiment to the others.  
We can measure the performance of the system by performing the numerical integration of the curve obtained in each experiment, as expressed in Equation (1). The network consumes less energy when \(J_{e}\) is lower.  
\[J_{e}=\sum|C_{e}| \tag{1}\]  
A first look at the graphs also shows a linear trend like the one expressed in Equation (2).  
\[C_{e}=\alpha_{e}-\beta_{e}x \tag{2}\]  
Figure 7: Evolution of the total charge per experiment, relative to the first value of each one.  
The slope determined by \(\beta_{e}\) is obviously related to the discharge rate, as it represents the direct proportionality between the charge and the time, in this case expressed as the round, or the iteration of the control system. Figure 8 shows the different values of \(\beta_{e}\) obtained in the experiments with the control system running compared to the values obtained in the experiments with fixed transmission power (\(P_{TX}\)), while Figure 9 shows the comparison of the \(J_{e}\) values. The values for \(\beta_{e}\) and \(J_{e}\) are shown in Table 3.  
Figure 8: Slope (\(\beta_{e}\)) comparison between experiments.  
Figure 9: \(J_{e}\) comparison between experiments.  
The results show an improvement in the energy consumption relative to the experiment with a fixed transmission power set to the available maximum value. The percentage changes relative to \(J_{e}\) and \(\beta_{e}\) are also shown in Table 3. As can be observed, although there is a clear improvement with respect to the use of a fixed maximum transmission power, all the adaptive experiments resulted in percentage changes lower than the one using a fixed transmission power set to a medium value.
# A Study on Analyzing Travel Characteristics of Micro Electric Vehicles by Using GPS Data
## 4 Results
### Summary of Results  
This study analyzed the advantages of micro-EVs and performed three analyses to select the most suitable service type for promoting micro-EV use. First, the travel characteristics of micro-EVs across different service types were compared and analyzed. The results showed that micro-EVs used for the delivery service had the longest daily usage time and travel distance (3.5 h/day and 38.5 km/day, respectively), as well as the highest average number of trips (24.1 trips/day), average number of trip chains (2.6 trip chains/day), average travel distance per trip chain (17.7 km/trip chain), and number of trips per trip chain (9.4 trips/trip chain) compared to other service types. This suggests that micro-EVs were used most frequently for the delivery service. Second, the driving performances of micro-EVs and a conventional vehicle on narrow roads were compared. The analysis revealed that micro-EVs had a higher average speed on narrow roads than the conventional vehicle. In particular, Section C showed the largest speed difference of 8.3 km/h. This suggests that micro-EVs have an advantage over conventional vehicles when driving on narrow roads. Finally, the characteristics of the roads on which micro-EVs were driven were analyzed. The results indicated that micro-EVs traveled on roads with a high proportion of narrow road links. Specifically, 57% of the total road length around the roads where micro-EVs were exposed had road links with a width of less than 5 m.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Road Width**} & \multicolumn{3}{c}{**Road Length of the Areas Traversed by Road Width (km)**} \\ \cline{2-5}  & **Shared Transport Service** & **Delivery Service** & **Public Service** & **Total** \\ \hline \textless{}5 m & 1811 (42\%) & 6676 (68\%) & 2488 (47\%) & 10,975 (57\%) \\ \hline \textless{}5–10 m & 1099 (26\%) & 877 (9\%) & 521 (10\%) & 2497 (13\%) \\ \hline \textless{}10–15 m & 385 (9\%) & 365 (4\%) & 400 (7\%) & 1150 (6\%) \\ \hline \textless{}20 m & 73 (2\%) & 31 (0\%) & 57 (1\%) & 161 (1\%) \\ \hline \textgreater{}20 m & 901 (21\%) & 1830 (19\%) & 1882 (35\%) & 4613 (24\%) \\ \hline Total & 4269 (100\%) & 9779 (100\%) & 5348 (100\%) & 19,396 (100\%) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Road length of the areas traversed by the micro-EVs by service type.  
Based on the above analyses, it can be concluded that micro-EVs were most actively used for the delivery service. Micro-EVs are expected to exhibit the greatest advantages when used for delivery services, which often involve narrow roads leading to residential areas.
## 2 Operational Principle  
Among fixed-wing UAVs in a conflict, which are called participants, the algorithm coordinates available maneuvers, generates and shares responding planned trajectories, decides the combination of planned trajectories to provide the best separation, and activates the preferred maneuvers when a collision is extremely imminent. The structure of the algorithm is shown in Figure 1. Based on the host condition, navigation solution, radar target location, data link and other information, each host aircraft determines the severity of the risk posed by the threat aircraft. Through a special datalink, UAVs that are participants in a conflict communicate with each other. According to the relative position and motion between participants, each participant chooses three maneuvers from a pool of nine available maneuvers that may be performed to avoid collision. Three trajectories responding to the three maneuvers are generated through the trajectory prediction module and then are shared among the participants via a data link. The evaluation module compares the combinations of the planned trajectories and determines the optimal combination of maneuvers with the maximal separation. In the optimal combination of maneuvers, each participant has a preferred maneuver. The system updates and repeats the above procedure, thus generating new maneuvers and evaluating a combination of maneuvers at regular intervals. Meanwhile, the system will activate the preferred maneuver when the planned trajectories converge in time and space.  
The five modules of the algorithm are as follows:  
1. Module of maneuver generation: the module chooses three maneuvers from a pre-set pool of nine available maneuvers according to the relative situation and motion between the host aircraft and threats.
2. Module of trajectory prediction: the module predicts the three planned trajectories responding to the three maneuvers. The trajectories are predicted based upon the current status and control parameters.
3. Module of threat management: the module of threat management determines if a UAV poses a threat to the host aircraft and evaluates the risk degree of threats.
4. Module of maneuver evaluation: the module evaluates combinations of three planned trajectories of each participant and determines which combination can delay the activation of the maneuver as far as possible. Through the maneuver evaluation, the three maneuvers are tagged as one preferred maneuver and two alternative maneuvers separately. Once tagged, the determination is shared among each participant
5. Module of maneuver activation: the module judged whether the planned trajectories of participants will converge in time and space. If so, the preferred maneuver is activated, and the aircraft will maneuver to avoid collision.
# An Improved Bi-LSTM-Based Missing Value Imputation Approach for Pregnancy Examination Data
## 1 Introduction  
### Background  
Hypertensive disorder in pregnancy (HDP) is a common kind of obstetric complication that severely threatens the life safety of pregnant women [1; 2] and has an adverse impact on the growth and development of the fetus. Hence, it is of great significance to discover and cure HDP in advance. Every pregnant woman will have multiple pregnancy examinations during pregnancy, producing examination data capable of reflecting the dynamic changes of health indicators of pregnant women during pregnancy. A huge number of current prediction models make predictions on the basis of multi-source data [3; 4], especially for pregnancy examination data [5; 6; 7]. However, the pregnancy examination data in the real world always contain different kinds of problems. One of the most common issues is missing data. It exists in almost all kinds of data sets, and the size of the missing data can significantly affect the research outcomes. As more and more studies on HDP are turning into data-driven analysis, the missing data problem is becoming more and more critical in this domain.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**Sensitivity**} & **Precision** & **Specificity** & **Accuracy** \\ \hline \multirow{2}{*}{Deep Learning} & ResNet50 & 100 & 74.2 & 69.2 & 83.7 \\ \cline{2-5}  & LiverNet & 100 & 92 & 92.3 & 95.9 \\ \hline \multirow{2}{*}{Hybrid Model} & ResNet50 & 95.7 & 100 & 100 & 97.9 \\ \cline{2-5}  & LiverNet & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between deep learning and the proposed hybrid model. The proposed method is compared with literature that has used the LiTs17 dataset. The performance of the approach achieved the highest amongst all. Table 6 shows the comparison between this study and literature with regards to the area under the curve (AUC), specificity, sensitivity, and accuracy. This paper shows the high level of confidence obtained using LiverNet as an automated feature extractor besides utilizing the benefits of machine learning to discriminate between benign and malignant liver tumors.
###### Abstract  
This study proposes a decision support system that uses a fuzzy logic model to assess the risk level associated with repetitive upper limb movements during work tasks, which can lead to musculoskeletal disorders. The model considers three main sets: biomechanics, anthropometrics, and productivity. Standardised parameters were utilised to determine the risk level associated with movement. To validate the findings, a fuzzy model was applied to assess 123 female workers across three automatic high-speed production lines as a case study. The model quantifies the risks using 54 membership equations and incorporates nine linguistic variables organised into three sets: biomechanical: this includes applied force, moment force, and angle of the torso from vertical; anthropometric: this includes workers' age and height and body mass index; and productivity: this includes working area depth, exposure time, and repetitiveness. The resulting fuzzy model, which is based on fuzzy set theory, utilises only four general fuzzy rules and allows for the evaluation of multiple workers simultaneously, providing a competitive advantage over models that rely on a large number of individual fuzzy rules to assess just one worker. The biomechanical set evaluates applied force and moment force based on productivity factors. Consequently, the behaviour of the group of 123 evaluations changed as the productivity risk value was introduced. For instance, in Test 1, which involves a low-risk task, we observed a biomechanical risk pattern that was solely related to the worker's anthropometry. In Test 2, which presents a medium risk, the pattern of evaluations shifted, revealing behaviours that were more influenced by both anthropometric and biomechanical characteristics. Finally, in Test 3, the impact of anthropometry and biomechanics was clear in the risk assessment patterns, which aligned closely with the anthropometric. The DSS could help improve policies and work conditions.  
ergonomics; risk assessment; biomechanics; fuzzy logic; anthropometrics +
Footnote †: journal: Applied sciences
# A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition
## 2 Research Method
### Construction of Time-Frequency Spectrum Features
#### 2.1.1 Log-Power Spectrogram Features  
Log-power spectrum is a commonly used representation method for processing audio signals, which is based on the logarithmic transformation of the spectrum. The spectrum is the result of the audio signal through the short-time Fourier transform (STFT) to convert the frequency components and amplitudes of the signal from the time domain to the frequency domain [13]. When the logarithm of the spectrum is taken, the result is the logarithmic spectrum, which is widely used in many audio analysis and processing tasks [14]. The formula is as follows (1), where \(y\) is the speech signal. The extracted birdsong power spectrum features are shown in Figure 1a. The relevant description is in Equation (1).  
\[Log-Spectrum\ (t,f)=\log\ \left(|STFT(y)\right)|^{2}+\epsilon\right) \tag{1}\]