esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review
## 3 Overview of the Application of Machine Learning in Construction Productivity Publications
### Vision-based  
As the deployment of cameras is becoming more common at construction sites, images and videos as sources of information have been made more accessible and trustworthy [59]. In addition, the emergence of computer vision and ML technologies has allowed automated analysis of construction activities. Computer vision algorithms utilize images and videos from one or more cameras to recognize and classify events and have been heavily studied for both the purpose of safety monitoring and construction worker productivity analysis [46; 59].  
Numerous pieces of research in vision-based areas can be categorized into three levels (i.e., object detection, object tracking, and activity/action recognition) [21; 36]. However, recognizing action is crucial for the capturing of spatiotemporal information, which can be used to track progress and calculate quantities of accomplished work [70; 71]. The work conducted in the field of computer vision techniques has produced strong recognition models, which have generally resulted in extremely well-performing models. For instance, an improved convolutional neural network (CNN), a three-dimensional (3D) CNN, was developed to accommodate complex activities of workers capturing temporal and spatial information [46]. In addition, an improved version of You Only Watch Once (YOWO), YOWO53, was developed, with at least a 16% improvement of accuracy in activity classification for the computation of the percentage of workers' value-adding and non-value adding activities [36]. The majority and recent researchers adopted and improvised DL algorithms (YOWO, CNN, Faster Region-Proposal Convolutional Neural Network (Faster R-CNN), and You Only Look Once (YOLO)) are proven to be more promising compared to traditional ML algorithms (e.g., SVM and k-NN) in this approach due to the complex and changing conditions (e.g., background of workers, viewpoint changes, illumination variance, and human variability) in the recorded image or videos [36; 46; 59; 72].  
The significant advantage of computer-vision algorithms over kinematic-based approaches is that they do not require workers to wear sensors on their bodies. However, computer-vision methods can have certain drawbacks as well. Using cameras is a fixed approach that requires a well-lit environment and the possibly of experiences of occlusions of practically all configurations, especially in situations involving dynamic work. Additionally, because camera files (pictures or videos) require a large storage space, keeping the data required for the supervised ML algorithms could potentially be expensive [21]. Ultimately, similar to all supervised DL-based approaches, a large dataset is needed for this approach to be feasible. Despite the convenience, as compared to the kinematic approach, much processing is needed after the images or videos are gathered, opening this approach to more future developments.
## 3 Results and Discussions
### Energy Capture Properties of C-TENG
#### 3.2.2 Influence of Resistance  
Different from the traditional electromagnetic induction generator, it is capacitive. It has very high internal impedance, so the load resistance has a great influence on the output performance, and there is an optimal resistance matching problem. Therefore, in COMSOL software, we design the models with the resistance of 100 M\(\Omega\), 500 M\(\Omega\), 1000 M\(\Omega\), 5000 M\(\Omega\), 104 M\(\Omega\). Through the simulation results obtained, we can draw the chart of the peak voltage, current and power under different resistances. The results curve is shown in Figure 7. The influence of external load resistance on the output power was studied. The changes of current, voltage and power under different load resistances are obtained through the simulation. The results show that the peak value of current decreases with the increase of load resistance, while the peak value of voltage increases with the increase of load resistance. The output power increases first and then decreases with the resistance increasing, and reaches the maximum when the resistance is 5000 M\(\Omega\). It can be seen from Figure 7 that the maximum peak power is about 1.716 mW. According to the designed model parameters, the inner radius of the cylinder is 0.03 m, the length is 0.3 m, and the contact area of the two electrodes of the C-TENG is 0.0565 m\({}^{2}\). Therefore, the power density is about 30 mW/m\({}^{2}\).
## 2 System Structure and Problem Formulation
The sequence disrupted by the IQD at arm \(f\) is written as [16]:  
\[\overrightarrow{\mathbf{s}}_{\alpha}^{(f)}=\eta^{(f)}\mathbf{s}_{\alpha}^{(f) }+\mu^{(f)}\mathbf{s}_{\alpha}^{(f)*}, \tag{6}\]where \(\eta^{(f)}\) and \(\mu^{(f)}\) are expressed as  
\[\eta^{(f)}=\cos\Bigl{(}\theta^{(f)}_{tx}\Bigr{)}+j\rho^{(f)}_{tx}\sin\Bigl{(} \theta^{(f)}_{tx}\Bigr{)}, \tag{7}\]  
\[\mu^{(f)}=\rho^{(f)}_{tx}\cos\Bigl{(}\theta^{(f)}_{tx}\Bigr{)}+j\sin\Bigl{(} \theta^{(f)}_{tx}\Bigr{)}. \tag{8}\]  
The sequence \(\,\overline{\mathbf{s}}^{(f)}_{\alpha}\) propagates to the destination through a wireless multipath fading channel of \(L\) taps, \(\mathbf{h}^{(f)}=\left[h^{(f)}(0),\cdots,h^{(f)}(L-1)\right]^{T}\). Therefore, the collected sequence at the destination is written as  
\[\overline{\mathbf{r}}=\sum_{f=0}^{F-1}\overline{\mathbf{s}}^{(f)}_{\alpha} \star\mathbf{h}^{(f)}+\mathbf{n}, \tag{9}\]  
where \(\star\) is the convolution action and \(\mathbf{n}\) is the additive white Gaussian noise (AWGN) sequence. The received signal is described here as a combination of the convolution between the signal emitted from antenna \(f\) and the corresponding channel coefficients, for \(f=0,\cdots,F-1\), along with the AWGN contribution. Given the influence of IQDs on the collected signal \(\overline{\mathbf{r}}\), we have  
\[\mathbf{r}=\eta_{R}\overline{\mathbf{r}}+\mu_{R}\overline{\mathbf{r}}^{*}, \tag{10}\]  
where \(\eta_{R}\) and \(\mu_{R}\) adhere to the same pattern as those in (7) and (8). When the IQDs is taken into consideration at the receiver, the collected signal is the sum of the original received signal and its complex conjugate at different scaling factors. Our goal is to detect the kind of SFBC signal using the intercepted signal, \(\mathbf{r}\), in the presence of an IQD over unidentified wireless links.
# Development of a Novel De-NOX Technology for the Aftertreatment of Ship Exhaust Gases
## 1 Introduction
The scope of the present work was to develop and test a pilot system for the efficient elimination of the main pollutants produced by commercial and passenger ships (NO\({}_{\rm x}\), SO\({}_{2}\) and PM). Furthermore, this work aimed to explore the potential of the alternative Hydrogen-Selective Catalytic Reduction (H\({}_{2}\)-SCR) process for NO\({}_{\rm x}\) pollution control on ships. In particular, within the present study, the H\({}_{2}\)-SCR process was combined with conventional technologies for sea-water scrubbing of SO\({}_{2}\) and catalytic oxidation of soot (PM) in order to protect the de-NO\({}_{\rm x}\) catalyst from exposure to SO\({}_{2}\) and particulates. The integrated pilot unit was operated on an active cruise ship. The results obtained indicate that the proposed H\({}_{2}\)-SCR de-NO\({}_{\rm x}\) process can be a suitable alternative technology to urea-SCR for the reduction of NO\({}_{\rm x}\) emissions from ships.
# Analytical Model for the Duty Cycle in Solar-Based EH-WSN for Environmental Monitoring
## 5 Simulations  
In this section we validate the proposed energy model by using the Avrora simulator [9]. For this purpose, we simulate a real sensor network application and compare the energy results provided by Avrora against the results provided by our model. Avrora simulates sensor applications developed in TinyOS 1.x/2.x for platforms that include an AVR microcontroller, as for instance Mica2 and MicaZ sensor nodes. However, we want to highlight the fact that the model proposed in this paper can be applied to any other hardware platform supporting a LPL implementation.  
Avrora enables a complete framework of simulation of a sensor network platform running a particular TinyOS 1.x/2.x application. This simulator manages with high accuracy the time in which the actions on the physical components of the node platform take place, providing evaluators with a better understanding of their behavior. One of the strengths of Avrora is its module for the evaluation of energy consumption, which is based on the AEON energy prediction model [57]. AEON estimates the energy consumed by a node running a particular application on the basis of usage of components, times spent at each state, and their current draws. The Avrora simulator reports the overall energy consumption of each node, breaks it down into the consumptions of individual components and, in turn, into different states, by capturing all low-level events generated by the application. In our evaluations, we will focus on the energy wasted by the radio component, which accounts for the five states shown in Figure 1 (in contrast to our model which ignores transitory states).  
For the simulation purpose, we have first developed a data-gathering application for TinyOS 2.x/MicaZ where every node periodically samples several sensors, composes a message from the measurements, and transmits such message towards the base station. As stated previously, MicaZ integrates a CC2420 radio [56], which is compliant with the 802.15.4 standard. Unless the developer explicitly modifies the network implementation, any application is built on top of the default network protocols, i.e., Collection Tree Protocol (CTP) and BoX-MAC-2 as routing and MAC protocols, respectively.
## 3 Test Bench Layout
### Transducers  
The transducers adopted in the setup are two current transducers and one voltage transducer. The selected sensors are the CAS 15-NP current transducer and the LV 25-P voltage transducer, both manufactured by LEM (LEM International SA, Meyrin, Switzerland). The transducers' main properties are reported in Table 5. The current transducer generates a voltage output signal that is directly read by the Arduino. As for the voltage transducer, it is connected to the positive and negative terminals with a predetermined resistance and generates a current signal with a nominal value of 25 mA. To read this signal, the Arduino monitors the voltage drop caused by the generated current over a 150 \(\Omega\) resistance. As for the experimental setup, the current transducers are supplied directly by the Arduino through its 5 V pin, while for the voltage transducer, an additional dedicated power supply is required. As for the monitored parameters, the voltage transducer measures the DC bus voltage, while the current transducers measure the battery currents and the load current.  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **CAS 15-NP** \\ \hline Measurement & Current \\ Measuring range & \(-51\div 51\) A \\ Supply voltage & 5 V \\ Accuracy & 0.8\% \\ Output signal & 0.375 \(\div 4.625\) V \\ \hline \hline  & **LV 25-P** \\ \hline Measurement & Voltage \\ Measuring range & \(10\div 500\) V \\ Supply voltage & \(\pm 12\) V \\ Accuracy & 0.9\% \\ Nominal output signal & 25 mA \\ \hline \hline \end{tabular}
\end{table}
Table 5: Transducers’ main properties.
# Emergency Planning for UAV-Controlled Crowd Evacuations
## 4 Results and Discussion
### Mortality  
Figure 6 demonstrates the mortality rate when the evacuation process depends on UAVs as the main form of guidance for all pedestrians. In general, as with the results obtained for the partial guidance model, the positioning of exit doors opposite each other led to lower mortality compared with the adjacent exit doors. Comparing the no-UAVapproach with the full guidance model, it was found that placing UAVs in the middle, along with exit doors in opposite positions decreased the mortality to approximately 0%, even when the hall was full (CD = 1); however, mortality exceeded 20% with adjacent exit doors.  
Figure 6: Mortality for full guidance model.  
Overall, using the full guidance approach was found to enhance the evacuation process, as evidenced by reduced AET and mortality rates. Although placing UAVs in the middle of the space with opposite exit doors led to high AET, the mortality percentage was still the lowest compared with using randomly placed or no UAVs. Table 4 summarizes the results of all experiments conducted on the partial and full guidance models.
## 4 Training and Performance Evaluation of the CCBA Model
### Two-Stage Training Strategy
#### 4.2.2 Stage II: Fine-Tuning and Knowledge Consolidation  
In the second stage, the focus shifts to refining the student model's performance on classifying sound from the UrbanSound8K dataset only. The CNN-Audio layers, trained for generalization in the previous stage, are frozen to preserve their generalized feature extraction capability. The model is then fine-tuned using only UrbanSound8K data, emphasizing knowledge distillation and ground truth labels. Here, the loss function is balanced to emphasize knowledge consolidation, with knowledge distillation and ground truth losses weighted equally at a loss ratio of 0.5:0.5, as described in Equation (4). The temperature \(\tau\) remains set to 4 in this stage. This ensures that the student model not only refines its output but also solidifies the knowledge learned from the teacher model during the first stage. By integrating ground truth with knowledge distillation, the student model strikes a balance between aligning with the teacher's behavior and optimizing for the specific task at hand.
# Designing Pedagogically Effective Haptic Systems for Learning: A Review
## 3 Haptics Applications for Learning
### Medicine
#### 3.2.3 Veterinary Medicine  
To keep up with modern concerns and requirements, canonical methods of teaching anatomy to veterinary students must adapt [38]. Ethical, resource, and time constraints are a few problems with traditional dissection and anatomy classes, which typically use live animals or cadavers [38]. Haptic technology allows for realistic training with a virtual model, thus providing a reusable method of instruction while reducing ethical and welfare concerns [39]. A study conducted by [PERSON] et al. [38], investigated the practicality and usefulness of a haptic simulator for teaching basic bovine abdominal anatomy. A total of 184 students provided feedback about their experience by answering a questionnaire. The questionnaire results were markedly positive; the students felt that the simulator was \"useful\", \"helped with their understanding of bovine abdominal anatomy\", and was \"enjoyable\" [38]. The hardware used in the study was the PHANToM Premium (3D Systems, Inc., Rock Hill, SC, USA), which had a thimble attachment at the end of the mechanical arm for the student to place their middle finger in to experience the force feedback with. While the majority of the subjective feedback received from the students was positive, some students expressed the desire for the ability to use the whole hand to experience the haptic simulation as to experience a more immersive haptic simulation. Palpation is a widely used technique in veterinary medicine, especially in relatively smaller animals such as cats or dogs; in small animals, abdominal contents can be assessed directly by palpation [39]. Plenty of hands-on experience is necessary for developing palpation skills, however certain small animals, such as cats, exhibit a low tolerance for such examinations. This can make it difficult for novices to gain necessary skill [39]. To address this problem, seven veterinarians consulted on the design of this mixed-reality simulator which was comprised of a stuffed cat modified with PHANToM premium haptic devices. The consulting veterinarians were confident in the ability of the mixed-reality simulator to teach feline abdominal palpation and specified it could have an effective role in instruction. The feedback was majorly positive and hopeful that the simulator could address training issues, such as insufficient opportunities for hands-on training [39].
## 2 Experiments on a Reduced-Scale Laboratory Bench
### Adaptation for Electrical Measurements  
Finally, the device was adapted to perform contact resistance measurements at both wheel-rail contacts using the four-probe method. The rails were electrically insulated from the frame by interposing a Kapton film. For each side, current supply and voltage measurements were performed using copper brushes pressed against the inside of the wheel and rail, as schematically shown in Figure 2. For the experiments reported in this paper, we used a low-frequency generator associated with a voltage supply (20 V max). Current intensity was determined through the measurement of the voltage drop at a 1 \(\Omega\) resistor terminals; its maximum value was about 6 A.
# Curved Path Following Control for a Small Fixed-Wing UAV with Parameters Adaptation
## 1 Introduction
The main contributions of this study are summarized as follows: First, a Lyapunov-stability-based guidance law for following a class of horizontal paths is presented by using [PERSON]'s lemma, where the ground speed of the UAV is time-varying and bounded. Second, both of the control parameters of the guidance law for following the straight-line and circular paths are designed adaptively. The rules of the time-varying P-like parameter are designed to make it adapt to this different initial flight states, whereas the rules of the time-varying D-like parameter are designed based on the fuzzy logic technique. In this case, the stability of corresponding nonautonomous nonlinear system is also guaranteed. To the best of our knowledge, this the first time that all of the control parameters of the guidance law used are time-varying and adaptive. Third, the control results were compared with the methods when the parameter was fixed, which illustrates the effectiveness and better performance of the proposed control strategies. The remainder of this paper is organized as follows. The equations of the problem description are listed in Section 2. In Section 3, a guidance strategy for a class of horizontal path following is derived. The influence of the parameters on the dynamic characteristics of the relative nonlinear system is also analyzed. The detailed rules of the time-varying P-like parameter and the fuzzy logic controller for optimizing the D-like parameter are also presented. The simulation results obtained using an Aerosonde UAV in Matlab/Simulink are given in Section 4, and some concluding remarks are summarized in Section 5.
# Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks
## 3 IEMACO Routing Algorithm  
The AntHocMMP routing algorithm can achieve higher packet delivery rates and lower end-to-end delays by combining the three methods. However, in the AntHocMMP routing algorithm flow, the convergence speed of the algorithm and the influence of energy consumption on the generation and update of the pheromone are not considered. In order to speed up the route discovery and prolong the network survival time, this paper proposes an IEMACO routing algorithm, which is designed from three aspects: algorithm convergence, pheromone generation and pheromone update.
# Madeira Wine Volatile Profile. A Platform to Establish Madeira Wine Aroma Descriptors
## 3 Materials and Methods
### Sensory Analysis  
A descriptive sensory analysis of Madeira wines samples used in this study was conducted by a panelist of eleven expert judges (seven females, four males) with an average age of 35 (\(\pm\) 5.1). The eleven members of the panel are winemakers belonging to different wine companies, such Madeira Wines, Barbeito, Borges, Justinos, and some employees from Instituto do Vinho Madeira. The panelists were trained over a period of 70 days to assess wine aroma using a \"Le Nez du Vin\" aroma kit (supplied by Ease Scent Company, Beijing, China). The \"Le Nez du Vin\" is an aroma kit composed by 54 vials, where each vial contains one typical aroma character in wine, such as blackcurrant, honey, caramel, coffee, chocolate, green pepper, smoke, wood, mint, among others. The training was carried out three times each week for 60-90 min. Each wine (30 mL) was presented to panelists in standard wine tasting glasses coded with three-digit numbers, covered with a Petri dish (to minimize the escape of VOCs), at 19-22 \({}^{\circ}\)C, in isolated booths under daylight-type lighting, with randomized presentation order. Cold water was used as palate cleansing. All wines were evaluated in triplicate in three formal sessions that were held on different days.  
Headspace Solid-Phase Microextraction Tandem with Gas Chromatography-Mass Spectrometry (HS-SPME) methodology  
The HS-SPME experimental parameters were previously established [15]. Briefly, aliquots of 4 mL of the wine sample were placed into an 8 mL glass vial. After the addition of 0.5 g of NaCl, 10 \(\upmu\)L of 4-methyl-2-pentanol (internal standard, 250 \(\upmu\)g/L) and stirring (0.5 \(\times\) 0.1 mm bar) at 400 rpm, the vial was capped with a polytetrafluoroethylene (PTFE) septum and an aluminum cap (Chromacol, Hertfordshire, UK). The vial was placed in a thermostatted bath adjusted to 40.0 \(\pm\) 0.1 \({}^{\circ}\)C for 5 min, and then the DVB/CAR/PDMS fiber was inserted into the headspace for 30 min. Three independent aliquots of each sample were analyzed in triplicate. Blanks, corresponding to the analysis of the coating fiber not submitted to any extraction procedure, were run between sets of three analysis.
## 7 Networks
### Synchronization
#### 7.1.5 Chaotic Synchronization
Coelho and Bernert evaluated the classical Tribes, MTribes (a modified Tribes optimization algorithm based on truncated chaotic Zaslavskii map), and PSO approaches (see Section 4.1.2) to tune PID controller gains in a synchronization application of two Lozi systems with different initial conditions. The principles of Tribes are [139]:  
1. _The swarm is divided in tribes, and each particle belongs to a single tribe_
2. _Initially, the swarm is composed of only one particle_
3. _Particles are added or removed according to tribes' behaviors_
4. _Displacement strategies are adapted according to the performance of the particles_  
\"Good\" tribes may benefit from removal of their weakest member, as they already possess good problem solutions and may be able to afford to reduce their population; \"bad\" tribes, on the other hand, may benefit from the addition of a new member, as this increases the possibility of improvement. New particles are randomly generated. MTribes uses a truncated chaotic Zaslavskii map  
\[\left\{ \begin{array}{l}w_{n}=mod[w_{n-1}+v+az_{n}]\quad(1),\\ z_{n}=cos(2\pi w_{n-1})+e^{-\tau}z_{n-1},\end{array} \right. \tag{111}\]  
where \(n\) is the sample and \(mod\) is the modulus after division by 1. The Zaslavskii map shows a strange attractor with the largest Lyapunov exponent for \(v=400\), \(r=3\), and \(a=12.6695\). In this case, the values of \(z_{n}\in[-1.0512,1.0512]\). The choice of the chaotic Zaslavskii map in the MTribes design is justified theoretically by its unpredictability, i.e., by its spread-spectrum characteristic and large Lyapunov exponent. Simulation results based on synchronization of two Lozi systems (with \(a=1.7\) and \(b=0.5\)) with different initial value conditions have demonstrated the effectiveness and efficiency of the proposed MTribes method.