esa-sceva/satcom-chunk-collection huggingface dataset:
# First Report of Hepatitisis E Virus in Shellfish in Southeast Italy
## 3 Results and Discussion
Similar to other enteric viruses, variations in the prevalence of HEV may result from differences in contamination levels of shellfish harvesting areas. These differences in contamination may be caused by geographical (e.g., distances from the seacoast, rivers, and estuaries) and environmental (e.g., sewage discharge) factors [18]. Because of the small number of positive samples, differences in HEV prevalence among bivalve species could not be calculated. A similar result was obtained in a study conducted in Spain [27]. Further studies are needed to assess possible differences in contamination among bivalve species. In this study, no data on genotype were obtained from the two positive samples. The absence of amplification by nested RT-PCR confirms the lower sensitivity of conventional PCR compared with real-time RT-PCR assays, especially in food samples with low levels of contamination [9]. In particular, the assay utilized in this study [20] has been reported to be more sensitive than other RT-qPCR assays [9]. It is tricky to achieve a helpful sequence from positive real-time RT-PCR food specimens, due to several factors (e.g., failure of identification by the conventional primers, concomitant amplification of various strains, virus concentration below the limit of detection of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample ID** & **City** & \
\begin{tabular}{c} **Sampling Date** \\ **(Day/Month/Year)** \\ \end{tabular} & **Species** & **Cq Value** &
\begin{tabular}{c} **HEV Concentration** \\ **(Genome Copies/g)** \\ \end{tabular} \\ \hline
2882 & Fasano & 27/03/2018 &
\begin{tabular}{c} Mussel \\ _(Mytitus_ \\ _galloproteinis_) \\ \end{tabular} & 35.5 & \(\approx\)2.5 \(\times\) 10\({}^{2}\) \\
5450 & Cagnano Varano & 24/05/2018 &
\begin{tabular}{c} Oyster \\ _(Crassostra gigas)_ \\ \end{tabular} & 38 & \(\approx\)7.0 \(\times\) 10\({}^{1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sampling date, city, Cq values, and HEV concentrations (genome copies/g) of the samples positive for HEV RNA. traditional reverse transcription PCR, not enough extraction of clean RNA suited for sequencing) [32]. Earlier studies carried out in Italy showed that HEV genotype 3 was present in shellfish [13; 17; 28]. Because this genotype is the most common in Italy, it was likely present in our positive samples. However, further investigations are needed to confirm this assumption.
## 3 Data and Preprocessing
### Dataset Splitting and Statistical Visualization  
To appropriately evaluate model performance in both within-source and cross-domain scenarios, we split the FOMC and ECB corpora chronologically. Specifically, within each dataset, documents from the most recent 12 months were reserved as the test set. The remaining corpus was randomly split into training and validation sets using an 80%:20% ratio. This temporal split ensures the novelty of the test data and prevents information leakage during training and validation. Within each subset, we maintained approximately the same distribution for \"up/down/stable\" and \"hawkish/dovich\" labels to ensure that the evaluation results were not biased by class imbalance.  
To intuitively present the data's underlying structure, we generated two important figures. The first is a histogram showing the token length distribution of FOMC statements (Figure 2), illustrating that most documents cluster between 1000 and 4000 tokens, with long-tail documents not exceeding 12,000 tokens. This validates the necessity of our long-text handling strategies. The second is a pie chart displaying the distribution of interest rate change labels (Figure 3), showing \"stable\" accounting for about 54%, \"up\" for about 25%, and \"down\" for about 21%. This aligns with the historically conservative adjustment strategy of the Federal Reserve and provides a non-trivial prior distribution for the multi-class task. These figures not only depict the raw data distribution, but also provide strong evidence for subsequent model architecture design choices, such as weighted loss for minority classes or data augmentation strategies.  
Figure 1: Text preprocessing and structuring workflow. The diagram shows the sequential steps from raw document collection through cleaning, segmentation, and final structuring for model input.
## 2 The Design of the Control System
### Fuzzy Logic  
Traditional control methods require the establishment of a mathematical model of the control system, but for some control objects with complex structures and unknown mechanisms, traditional control methods cannot be controlled. Fuzzy control is a control technique that uses fuzzy logic to deal with uncertainties and inaccuracies in control systems. Fuzzy logic reasoning is a method based on empirical summaries whose rules can be expressed in natural language without the need to know the mathematical model of the control object. Therefore, fuzzy logic reasoning is particularly suitable for control objects where it is difficult to obtain mathematical models and dynamic characteristics and has the advantages of robustness and adaptability. In MPC, the use of fuzzy logic reasoning can improve the accuracy of the models used for prediction and control. A driver's driving style can be thought of as a set of preferences and rules that govern the way the driver operates the vehicle. Incorporating these preferences and rules into the control system through fuzzy control allows the control system to adapt to the driver's style, improving driving comfort and safety.  
The basic idea of fuzzy logic reasoning in MPC is to improve the accuracy of the predictive model by adjusting the subordination function used in fuzzy logic to deal with the relationship between the various variables in the control system, and thus predict and control the actions more accurately. Traditional predictive control models with fixed parameters do not respond quickly enough to the driver's actions, which can affect the smoothness of the driving operation. In addition, road slope conditions affect driver performance and fuzzy logic reasoning can improve the adaptability of the model predictive control model to the environment. Therefore, this paper uses a fuzzy control approach to adaptively adjust the weighting factors with changing operating conditions and uses the Fuzzy Logic Designer tool in MATLAB to complete the construction of the model.
# Energy Bus-Based Matrix Modeling and Optimal Scheduling for Integrated Energy Systems
## 1 Introduction
The contributions of this study are listed below. * An energy bus-based matrix-modeling method is proposed, which can be used to formulate steady- and transient-state models of the IES. The modeling process and results are relatively simple. * A transient-state model that can constitutionally express the multi-time-scale characteristics of the different energy flows is presented. * Rolling optimization based on the system's transient-state model is used to optimally schedule the multi-energy flows of the IES and compensate for the prediction errors of the RESs and loads. It can accurately track the day-ahead schedule and satisfy supply-demand balances of multi-energy flows to minimize the total operation cost of the IES. The remainder of this paper is organized as follows. Section 2 describes the matrix-modeling method. Section 3 provides the model parameters. Section 4 describes the coordinated scheduling method. Section 5 provides the case study, and the conclusion is provided in Section 6.
# Congestion-Free Ant Traffic: Jam Absorption Mechanism in Multiple Platoons
## 6 Concluding Discussion  
We presented an agent-based model inspired by ants' chemotaxis behavior on a unidirectional single-lane ant trail to understand the jam-free AT presented in [14]. Our model was an improvement over the model in [9], which was a general model of ant traffic. Our modeling assumptions were based on previous physiological studies, and our model was in basic agreement with the experiment from [14]: (1) the absence of jamming for a wide range of densities, (2) platoon formations, and (3) a decrease in velocity fluctuations with an increase in density. We also presented a computer-simulated analysis of different platoon scenarios to understand the platoon-related phenomena. The primary objectives of these analyses were to verify the marching-platoon hypothesis and to understand the mechanism of jam-free AT.  
Similar to previous studies, our study also mimicked the platooning and constant average velocity of AT for a wide range of densities (\(d=0\)-\(\approx 0.75\)). However, contrary to previous studies, the intra-platoon analysis presented in this paper showed that ants in a platoon (other than the leader) might be experiencing stop-and-go motion. It was also interesting to note that different agents in a platoon had different variations in their velocities. This difference means that contrary to the previous perception, ants in a platoon might not be marching synchronously.  
Meanwhile, the inter-platoon analysis of ATM presented in this paper shows that ants' chemotaxis behavior leads to a jam absorption mechanism, which helps to avoid jamming. In the jam absorption mechanism of ATM, the inter-platoon distance (i.e., the JAB) provided enough time for the system to dissipate the queuing effect due to leading platoons. Thus, the following platoons did not get involved in stop-and-go motion due to those leading platoons.  
Further inter-platoon analysis of ATM from a JAB-density perspective showed that the JAB in the free-flow phase increased with density, thereby canceling out the increase in queues due to density. Finally, we also observed that near a critical density of \(d\approx 0.75\), the multi-platoon structure collapsed to form a single platoon (infinite cluster). This caused the stop-and-go motion of one ant to propagate to the following ant without being absorbed. Consequently, the average velocity decreased with density, triggering the jamming phase of the fundamental diagrams.  
Our model provided a detailed understanding of the dynamics of ants on an ant trail. The new findings of our study need to be verified and validated by using different methods. Nevertheless, our results may have significant implications for swarm intelligence and intelligent transportation systems. In future work, we intend to understand the JAB by analyzing different related phenomena.  
Conceptualization, P.K. and H.N.; modeling, simulation, and validation, P.K.; writing, original draft preparation, P.K.; supervision and critical review, H.N.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Methods
### Slip Detection
#### 2.3.2 Farneback Optical Flow  
To estimate the motion field of a scene from two consecutive images, one approach is to make the assumption that the pixel intensities do not change from one frame to the other (Equation (1)).  
\[I(x,y,t)=i(x+dx,y+dy,t+dt) \tag{1}\]  
Another assumption made is that neighboring pixels have the same motion, indicating that they belong to the same object. To capture motion at different scales, an image pyramid of downsampled versions of the original image is created. Through polynomial expansion,  
Figure 4: The resulting binary mask (**left**) and the original RGB image with the ellipse estimating its size (**right**). The ellipse results from fitting it to the largest boundary of the binary mask. For this specific setup, since the gripper is at the very bottom of the frame, the assumption can be made that the object is also always at the bottom. Hence, a half-dome shape was used by point rotation of the mask by 180\({}^{\circ}\) and fitting the ellipse to it.  
the algorithm fits a quadratic function to the pixel intensities between the two frames and calculates coefficients that minimize the difference for each layer. The coarser layers aid in estimating the optical flow of the less coarse layers. After upsampling the result to the original image size, the flow direction can be estimated for each pixel, resulting in a good approximation of the actual motion field of the scene. However, this method has some limitations. Changes in lighting can refute the first assumption, as they may cause optical flow to be detected even when there is no actual motion. Conversely, when objects with smooth surfaces are moved (e.g., a ball is rotated), the pixel intensities do not change, and no optical flow is detected, despite actual motion occurring.
# Porous Electrodeposited Cu as a Potential Electrode for Electrochemical Reduction Reactions of CO2
## 1 Introduction
Of the primary uses of Cu electro-catalysts for CO\({}_{2}\) conversion applications are those that produce hydrocarbon or oxygenase products [10; 25], as exemplified by the cathodic electrochemical reactions in Equations (1) and (2). The anodic reactions, on the other hand, involve the oxygen evolution reaction (OER), whereby oxygen molecules and electrons are generated at the anodes. \[\text{CO}_{2}+8\text{H}^{+}+8\text{e}^{-}\rightarrow\text{CH}_{4(\text{g})}+ 2\text{H}_{2}\text{O} \tag{1}\]  
\[\text{CO}_{2}+6\text{H}^{+}+6\text{e}^{-}\rightarrow\text{CH}_{3}\text{OH}_{ 4(\text{aq})}+\text{H}_{2}\text{O} \tag{2}\]  
Compared to monolithic Cu electrodes, porous Cu electrodes could potentially contribute to improvements of product selectivity, catalytic activity, and energy consumption of CO\({}_{2}\)RR. Nevertheless, a study of porous Cu or porous metals in general for the CO\({}_{2}\)RR application is still limited. [PERSON] et al., for example, demonstrated that introduction of porosity with a certain pore size to Ag deposits results in the increase of faradaic efficiency for CO\({}_{2}\) conversion to CO [35]. [PERSON] et al. introduced DHBT Cu for CO\({}_{2}\)RR and showed that the porous Cu that was deposited with a certain duration (i.e., 60 s) preferably enhances faradaic efficiency of hydrocarbons (methane, ethylene, and propene) [10]. [PERSON] et al. recently studied the influence of pore morphology of Cu electrodes, particularly the width and depth of pores, on CO\({}_{2}\)RR. The group showed that it largely affects product distributions of hydrogen (H\({}_{2}\)), ethylene (CH\({}_{4}\)), and ethane (CH\({}_{6}\)) [22]. Likewise, [PERSON] et al. demonstrated that surface modification of porous Cu electrodes could further influence CO\({}_{2}\)RR product distribution [2].
## 3 Methodology
### GNSS Inversion Method
Equation (11) is the same as the following inequality:  
\[|Ax|\leq e \tag{12}\]  
where \(A\) represents the constraint matrix. The above inequality is transformed into an equality below, and then a non-linear programming approach is utilized to solve it by introducing the vector \(y\ (0\leq y\leq 2e)\). \[Ax+y=e \tag{13}\]  
By combining Equations (5) and (13), the following equation for TWS inversion under spatial constraints is established:  
\[\left\|Pv\right\|^{2}\to min\,\text{s.t.}\ Ax+y=e \tag{14}\]  
where \(x\) and \(y\) are the parameters that need to be estimated. Equation (14) can be written as follows:  
\[\left\|P\bigg{(} \begin{bmatrix}G&0\end{bmatrix} \cdot \begin{bmatrix}x\\ y\end{bmatrix} -L\bigg{)}\right\|^{2}\to min\,\text{s.t. }\ \begin{bmatrix}A&I \end{bmatrix} \cdot \begin{bmatrix}x\\ y\end{bmatrix} =e \tag{15}\]  
Figure 6: The relation between the smoothing factors and CVSS for conventional constraints (**a**) and Laplacian constraints (**b**). The red circles represent the optimal smoothing factors. We use MATLAB's lsqlin routine to solve Equation (15) following [PERSON] et al. (2016), which represents a constrained least squares adjustment. We choose the 'interior-point' algorithm in the lsqlin routine, where the lower and upper bounds of \(x\) are set to be \(-0.5\) m and \(0.5\) m, respectively, the lower and upper bounds of \(y\) are set to be \(0\) and \(2\)-e, respectively, and the initial points of \(x\) and \(y\) are set to be \(0\). Then, the two unknown parameters \(x\) and \(y\) are solved.
# Saturation, Allowed Transitions and Quantum Interference in Laser Cooling of Solids
## 2 Theory
### Electric-Dipole-Allowed Transitions for Rapid Cooling
The Gaussian-shaped curves illustrate the distribution of energy due to the optical interaction. In the context of laser cooling, one thing which differentiates optical from acoustic phonons is their higher frequency and the limited number of allowed modes determined by crystal symmetry. Transitions involving optical modes have narrow linewidths and are well-defined in frequency. Hence, they exhibit quantum effects more readily than acoustic modes, which have a continuous density of states versus frequency. One consequence of this is illustrated in Figure 5, where the red arrow indicates excitation from the lowest sub-level of a vibrational ladder in the ground state (v = 1) to the lowest sub-level of the excited state (v' = 0). Such a transition avoids configuration relaxation because it takes place at a wavelength longer than the mean fluorescence wavelength. When this is the case, the quanta of the incident field have less energy than required to generate both the electronic transition and a phonon. Absorption is followed by anti-Stokes emission, shown as the downward blue arrow connecting v' = 0 to v = 0. The red and blue transitions jointly remove a vibrational quantum of the mode in question, thereby contributing to refrigeration. Other quantized absorption transitions are possible at longer wavelengths from the occupied sub-levels of other modes and other electronic sub-levels of the ground state. Figure 6 depicts a more detailed picture of the vibrational and electronic sub-levels of the \({}^{2}T_{2}\) ground state of Ti\({}^{3+}\):Al\({}_{2}\)O\({}_{3}\)[21; 22]. The absorptive transitions that contribute to cooling were all assumed to terminate in the lowest v' = 0 sub-level of the electronic excited state. This is necessary to avoid the generation of optical phonons. Because of the common final state, the cooling resonances are distinguishable by their initial states alone and form a discrete spectrum as a function of wavelength because all the electronic and optical mode energies exceed \(\sim\)400 cm\({}^{-1}\) and have low damping parameters [21]. Figure 5: Quantized anti-Stokes luminescence in a 2-level system with electric-dipole-allowed transitions between ground and excited state vibrational sub-levels. The red arrow is a v = 1 to v’ = 0 absorption transition. The blue arrow is a v’ = 0 to v = 0 emission transition that contributes to cooling by avoiding configuration relaxation.
## 6 Conclusions  
WSNs have unique characteristics such as resource limitations, communication flow from many to one, and deployment in error prone environments. Many routing protocols have been developed to address these characteristics. These routing protocols mainly contributed to prolonging network survivality by saving energy during communication, but most research studies do not provide reliable communication in an error-prone environment. Lossy WSNs results from contention and interference among nodes, node mobility, fading by object, or node failure from battery discharge and physical damage.  
We proposed a radio-aware routing algorithm to improve the reliability of DD in lossy WSNs. DD is well-suited for WSNs' routing because it uses local interactions among distributed nodes. DD also provides data-centric, robust, and scalable models, but previous research did not consider the unstable network. The data delivery rate of DD decreased very quickly in an error prone environment.  
In this paper, to handle the lossy environment of WSNs properly, RA algorithm which uses radio information was proposed. RA algorithm is a data centric routing protocol like the original DD, but RA algorithm can be aware of the network's status by adopting a cross-layer approach between MAC and network layers. Furthermore, a fast re-route scheme was also applied to provide consistent and quick re-transmission for reliability. The radio-aware reliable routing algorithm contributed to providing QoS, reliability, and reduction of DD's overheads for lossy WSNs including scalability and interoperability.  
To prove the effectiveness of the proposed algorithm, the simulation was performed with _ns_-2 under various conditions. Lossy WSNs were emulated by randomly blinking sensor nodes to create random node failures. According to the simulation results, the radio-aware routing algorithm showed better performance for each error rate. The error model for five simulations contained grid and random topologies with diverse MCP including multiple sources and different sink position at error rates of 0%, 5%, 10%, 15%, and 20%. As the error rate increases, the proposed solution showed higher data delivery rate, lower protocol overhead, and lower average transmission delay as compared to DD regardless of the error rates and topologies.  
The simulation results demonstrate the possibility of providing a reliable transmission method for QoS requests in lossy WSNs by using radio-awareness. However, when a node chooses its forwarding node based on our algorithm, the re-route scheme consumes more energy due to packet retransmission. The energy consideration and mobility will be addressed in future work.
# The Microverse: A Task-Oriented Edge-Scale Metaverese
## 4 Case Study: A Smart Public Safety Surveillance Microverse for Smart Communities
### Workflow and System Settings  
The SPSS microverse prototype incorporates a range of key technologies. Table 1 outlines the system's configurations, while the workflow of our experimental setup is depicted in Figure 3. In this setup, operators pilot drones in the building's parking area, utilizing the same wireless local area network (WLAN) that connects to the surveillance room inside the building. A specially developed Android application enables the smartphone to relay real-time video and data from various sensors to the digital twin (DT) in the virtual environment. As demonstrated in Figure 4a, vital parameters such as longitude, latitude, altitude, and the drone's rotation (yaw spin) are transmitted as JavaScript Object Notation (JSON) packets, ensuring the virtual replica's location in the microverse is synchronized. Furthermore, the camera's live feed is streamed to the server via Wi-Fi, employing the Real-Time Streaming Protocol (RTSP).  
Figure 2: SPSS microverse prototype architecture.  
In the surveillance room, a desktop personal computer (PC) operates as the server for the microverse system, which is developed using Unreal Engine 5 (UE5). This system processes incoming data, encompassing sensor readings and camera footage, to ensure synchronization of DTs and support advanced analytical capabilities. Utilizing live feeds from surveillance drones in RTSP, learning methodologies are employed to execute various algorithms, including object detection, tracking, and trespassing alerts. In the SPSS prototype, the lightweight YOLOv8 model is integrated for object detection on the video stream. Figure 4b showcases a real-time demonstration of object detection using YOLOv8, which is integrated into the UE system. Furthermore, to enhance the user experience with immersive interactivity, a VR device is incorporated to enable real-time first-person perspectives from the drones.