esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Contaminated beef is one of the main sources of foodborne STEC infection in humans [56]: about 52% of STEC infections are associated with foodstuffs of bovine origin [57]. The PCR-based methods are widely applied to detect pathogens in food, clinical and environmental samples [58]. The use of PCR and the implementation of surveillance of STEC infections in foodstuffs or animal carcasses could constitute a valid approach to reducing the rate of STEC incidence.  
This study was divided into three steps. The first was focused on identifying the minimum incubation time required in order to detect different concentrations of STEC in artificially contaminated sponges. The results revealed the ability of ddPCR to detect STEC already after sample incubation for 7 h.  
The second step was directed to overcoming the difficulties caused by the saturation condition of the ddPCR due to the prolonged incubation of samples.  
The last phase was, instead, focused on comparing the ability of ddPCR and PCR to detect STEC bacteria in sponges used for inspection.  
On the basis of the data obtained, we can hypothesize that ddPCR is a valid and efficient method for detecting STEC contamination. Moreover, it provides results within one working day. As reported by the EFSA [23], there is a pressing need to develop innovative approaches that can provide the same results in less time, with more sensitivity and specificity, than the official method of analysis.  
In conclusion, this study revealed the reliability, efficiency, and sensitivity of ddPCR, thus confirming that this technique is useful for the detection of STEC. Future studies will be carried out in order to search for additional genes, thereby providing additional information for the identification of STEC contamination.  
Conceptualization: A.M., F.C. and Y.T.R.P.; Methodology: A.M. and Y.T.R.P.; Software: A.M., S.G. and F.C.; Validation: A.M., F.C. and Y.T.R.P.; Formal Analysis: A.M., S.G., O.D.M. and D.C.; Investigation: A.M., A.F., F.C. and Y.T.R.P.; Resources: F.C. and Y.T.R.P.; Data Curaton: [PERSON], A.F., S.G. and F.C.; Writing--Original Draft Preparation: A.M., A.F.; Writing--Review & Editing: A.M., A.F., F.C. and Y.T.R.P.; Visualization: A.M., F.C., Y.T.R.P.; Supervision: F.C. and Y.T.R.P.; Project Administration: F.C. and Y.T.R.P.; Funding Acquisition: F.C. and Y.T.R.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors acknowledge the European Union Reference Laboratory VTEC of the Istituto Superiore di Sanita (Rome, Italy) for providing the specimen of _E. coli_ O157 (code: UERL-VTEC C07; eae+, stk1+, stk2+).  
The authors declare no conflict of interest.
# Performance Comparison of H.264 and H.265 Encoders in a 4K FPV Drone Piloting System
## 4 Experimental Results  
The results presented in this section were obtained in experiments conducted on BURA, using one node for encoding each sequence and 24 nodes for assessing the quality. For this research, we needed raw video from the camera in order to avoid any possibility that previous compression led to degradation, which could influence the comparison results. We filmed several sequences of 4K/60 fps drone flights with a DJI ZENMUSE X55 camera on a DJI Inspire 2 drone.
# Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm
## 2 Related Technology  
### Federated Learning Model  
The standard workflow for federated learning is as follows: A central server initializes a global model and distributes it to each client. Clients then perform multiple rounds of training on the model using their local data, generating locally updated models that are subsequently transmitted back to the central server. The server aggregates the local models it receives to form an updated global model, which is then broadcast to all clients. By repeating this process, the global model is progressively refined. Classical federated learning utilizes a star topology, as depicted in Figure 1, comprising a central server and multiple clients. In this setup, \(\theta_{i}^{k}\) denotes the model parameters of the i-th client during the k-th communication round, while \(\theta^{k+1}\) represents the aggregated global model parameters and the client model parameters during the \((k+1)\)-th communication round.  
The local loss function of the local client \(i\) is  
\[\min_{\theta}J_{i}(\theta)=\frac{1}{|D_{i}|}\sum_{j=1}^{|D_{i}|}F(\theta,\xi_{ j}) \tag{1}\]  
where \(\theta\) represents the model parameters, \(\xi_{j}\in D_{i}\) is a data sample, \(D_{i}\) is the client's dataset, and \(F\) is the loss function. During the model training process, it is necessary to minimize the global loss function \(J(\theta)\), as defined in Equation (2):  
\[\min_{\theta}J(\theta)=\sum_{i=1}^{|S|}\frac{|D_{i}|}{\sum_{i=1}^{|S|}|D_{i}|}J _{i}(\theta) \tag{2}\]  
where S represents the set of clients selected to participate in the training. The client updates during the training process are provided by Equation (3):  
\[\Delta\theta_{i}^{k+1}=\text{SGD}(\theta^{k},D_{i},\eta_{i})-\theta^{k} \tag{3}\]  
Figure 1: Star topology.  
where \(\eta_{l}\) denotes the learning rate for the local model and SGD refers to the stochastic gradient descent method. Equation (4) illustrates a single update applied by the server to the global model:  
\[\theta^{k+1}=\theta^{k}+\sum_{i=1}^{|S|}\frac{|D_{i}|}{\sum_{i=1}^{|S|}|D_{i}|} \Delta\theta_{i}^{k} \tag{4}\]  
Without loss of generality, we assume that the number of clients is \(n\). The local dataset of client \(i\in[n]\), denoted as \(D_{i}=\left\{\xi_{1}^{i},\ldots,\xi_{q}^{i}\right\}\), consists of \(q\) data points.
## 3 Data and Methods
### Earthquake Monitoring Catalog  
The Seismic Digital Telemetry Network of the Yangtze River Three Gorges Project Reservoir Earthquake Monitoring System was built in 1997 and was officially put into operation in October 2001. It has 24 seismic digital high-gain telemetry stations located between the Sandouping Dam site and Nannmuyuan of Badong County from east to west. (Figure 1). From January 2001 to December 2009, 3986 seismic events were observed in the key monitoring area of the TGR's head area (Figures 4 and 5), with the magnitude of completeness between M0.1 and M0.2 calculated by using Zmap [22] (Figure 3b), which can ensure that the earthquakes in time and space are basically complete. It can be used  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Area A** & **Area B** \\ \hline Kriging & 5.34\% & 17.12\% \\ RBF & 5.73\% & 17.86\% \\ TLI & 6.69\% & 18.88\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The percentage of relative error between different interpolation results and the observed value.  
Figure 3: Data accuracy and integrity: (**a**) Mean-squared error of the gravity observation network from 2002 to 2009. (**b**) Magnitude of completeness calculated by using Zmap.  
to analyze and study the correlation between seismic activity and reservoir water level. To quantitatively discuss the time-domain correlation between seismic activity and water impounding, we tried to use the cross-correlation and reshuffling tests [23; 24] to calculate the correlation between the monthly seismic frequency of M \(\geq\) 0.2 seismic activity and the reservoir water level and give the cross-correlation coefficient and the confidence threshold under different time lag parameters.
# Computation of Optimized Electrode Arrays for 3-D Electrical Resistivity Tomography Surveys
## Appendix A Calculation of Jacobian Matrix  
A fundamental key in solving the 3-D resistivity inversion problem comprises the determination of a relationship that links the variation of the potential (\(V_{\rm i}\)) or the apparent resistivity data (\(\rho_{\rm ai}\)) in relation to a change in the subsurface resistivity (\(\rho_{\rm j}\)) or conductivity (\(\sigma_{\rm j}\)) [33]. The MxN two-dimensional matrix known as Jacobian (Sensitivity) matrix \(\mathbf{J}_{ij}\) expresses this link:  
\[\mathbf{J}_{ij}=\left[\begin{array}{ccc}\frac{\partial\log\rho_{\rm ai}}{ \partial\log p_{1}}&\ldots&\frac{\partial\log\rho_{\rm ai}}{\partial\log p_{N} }\\ \vdots&\ddots&\vdots\\ \frac{\partial\log\rho_{\rm aiM}}{\partial\log p_{1}}&\ldots&\frac{\partial \log\rho_{\rm aiM}}{\partial\log p_{N}}\end{array}\right] \tag{1}\]  
where \(M,N\) are the number of the apparent resistivity measurements and the resistivity model parameters, respectively. Notice that since the resistivities and apparent resistivities often span several orders of magnitude, their logarithms have been used in Equation (1). In this work, the Jacobian matrix values were estimated using the adjoint equation approach.
## 4 Discussion
### Future Prospects for Capturing Phenology Metrics in Pan-Arctic regions
Satellite chlorophyll fluorescence has shown potential for improving the accuracy of monitoring phenology at a global scale. It can be used to track the onset of spring and the end of the season in forest ecosystems [(20)], while the VI and models based on reflectance often overestimate the length of the photosynthetic cycle. SIF could lead to an improved assessment of photosynthetic phenology, particularly for evergreen coniferous forests that have been difficult to measure from satellites using more conventional vegetation indices. In pan-Arctic regions where the influence of snow, soil moisture, and other factors are relatively large, the development of advanced instruments such as OCO-2 [(67; 68)] suggests that SIF may play a more important role in monitoring phenology. To better estimate the phenological metrics in pan-Arctic regions, other possibilities would be combining vegetation indices and indices that illustrate the information of snow or soil moisture (e.g. NDWI and NDSI). Or to take into account spatio-temporal variations of soil moisture through monitoring with SAR data [(69)].
# SPinDP: A High-Speed Distributed Processing Platform for Sampling and Filtering Data Streams
## 2 Background and Related Work
### Sampling and Filtering Algorithms  
Sampling is a statistical technique that extracts some of the data representing the population. As shown in Figure 3, sampling algorithms can be divided into batch-specific sampling and data-stream-specific sampling [14]. First, batch-specific sampling includes systematic sampling, stratified sampling, and cluster sampling [15]. Second, data-stream-specific sampling includes reservoir sampling, priority sampling, hash sampling, KSample, and binary Bernoulli sampling [16; 17; 18]. A brief explanation of these representative sampling methods is provided as follows:  
Figure 2: An example structure of Apache Storm’s programming model.  
Systematic sampling randomly selects the first entry, and then selects the \(k\)-th entry every time.
* Stratified sampling divides the population into nonoverlapping layers, and then extracts samples from each layer.
* Cluster sampling divides the population into several subclusters, and then selects some subclusters as samples.
* Reservoir sampling selects the first \(k\) samples of the data stream, and probabilistically replaces those previous samples using the subsequent input stream.
* Priority sampling works similarly to reservoir sampling, but priorities are assigned according to the frequency of data occurrence. That is, the more frequent data, the more that are selected.
* Hash sampling applies a hash function to a specific field of data records, and selects a record as a sample if its hash value is greater than (or smaller than) the specified value.
* KSample [19] is a random sampling method that dynamically increases the sample size to keep the sampling rate constant for the input data stream.
* Binary Bernoulli sampling [20] extracts samples at the same probability when data streams come from multiple sources.  
Filtering is a technique for extracting only data satisfying a given condition (or predicate) to improve data quality or analysis speed [21]. Representative examples include query filtering [22] to extract only data satisfying the given query, Bloom filtering [15] to stochastically calculate whether the data belong to the set, and Kalman filtering [23; 24] to estimate the original data from data containing errors, such as sensor data. The applications of these filtering techniques to the data stream can be divided into two major categories. First, the standing queries method evaluates the predefined query condition for the input data, and stores or passes the data when the condition is satisfied. Second, the ad hoc method stores input data for a predetermined time or size and evaluates the query on the stored data.
# Performance of SIMO FSO Links over Mixture Composite Irradiance Channels
###### Abstract  
Free space optics (FSO) technology has demonstrated an increasingly scientific and commercial interest over the past few years. However, due to signal propagation in the atmosphere, the operation depends strongly on the atmospheric conditions and some random impairments, including turbulence and pointing error (PE) effects. In the present study, a single-input multiple-output FSO system with wavelength, spatial, or time diversity over the turbulence and non-zero boresight PE effects is thoroughly investigated. A versatile mixture composite model which accurately describes both impairments is employed for the performance evaluation. Novel mathematical expressions of the outage probability and the average bit-error rate assuming intensity modulation/direct detection and optimal combining at the reception are provided.  
Keywords:free space optical communications (FSO); receiver diversity; atmospheric turbulence; pointing errors +
Footnote †: journal: Applied Sciences
## 5 The Agent-Based Model
### Configuration Phase
#### 5.1.2 The Social Network  
As discussed previously, the behavior of a household-agent is significantly impacted by its social network. For this reason, during the configuration phase the social links between agents are created, namely each family has a set of friends (other households). Since previous research has shown (see [53; 54; 55; 56]) that a small-world topology maps well the real network of relationships that exists between people, the social network adopted in this ABM has small-world properties. Small-world networks are characterised by a shortest-path distance between nodes that increases relatively slowly as a function of the number of nodes in the network [57].  
The extended version of the rank-based model proposed by [PERSON] et al. [58] was used to get the small-world properties. The probability that a link between node \(u\) and node \(v\) existed was proportional to a ranking function which depended both on the geographical proximity of the nodes (physical neighbours) and on the attribute proximity of the nodes (how the nodes are similar w.r.t.  
their attributes). After a network was built using the extended rank-based method, randomness was added through long-range links. These links drastically reduced the average path length because they connect distant parts of the network. The randomization process takes every edge and rewires it with an empirically obtained probability \(p\).
# A New Low-Cost Compact Antenna for the 2.45 and 5.8 GHz ISM Bands
## 1 Introduction
To improve the performance of these antennas, notably, in terms of bandwidth, gain, and efficiency, slots can be inserted in the radiating element in the case of microstrip antennas with a large radiating surface [20]. Several types of monopole [21; 22] and large-area patch antennas [23; 24] operating in the 2.45 GHz and 5.8 GHz bands are presented in the literature. As far as slot geometry is concerned, there are several shapes, such as L, T [19], rectangular [23], or fractal geometry [23; 24], that enable multiband antenna operation. The dielectric substrate can also influence the performance parameters of patch antennas. Indeed, patch antennas with thick, low-dielectric-constant substrates offer higher efficiency, greater bandwidth, and fields that are less restrictive to radiation in space. On the other hand, antennas with thin substrates and high dielectric constants result in high losses and low bandwidth. Several types of substrates are often used to design patch antennas: Rogers [25], Epoxy Flame Retardant-4 (FR-4) [26], PEC [27], Teflon [28], Textile [29], and RT Duroid [30]. Flexible substrates such as polydimethylsiloxane (PDMS) and PF4-foam materials [18] are also used. However, the most commonly used substrate is FR-4 epoxy (despite its high loss tangents) due to its low cost, ease of manufacturing, suitability for mass production, and relatively high performance. Additionally, FR-4 is highly water-resistant and does not absorb moisture. The aim of the study presented here is to design a dual-band antenna operating at ISM frequencies 2.45 GHz and 5.8 GHz, targeting, for example, common WLAN standards such as Bluetooth, Wi-Fi, and Wi-MAX. More specifically, in addition to an antenna operating at these two frequencies, we are looking for a good compromise between performance, size, and cost. Section 2 describes the initial choices made, and the step-by-step methodology followed, with particular emphasis on optimization. Section 3 focuses on the impact of the initially chosen rectangular patch topology (including the effect of inserting slots and notches on the patch antenna) at the various stages of antenna design, with the antenna's reflection coefficient as the main criterion. In Section 4, the manufactured prototype is experimentally characterized, showing good agreement with the simulations presented. A comparative overview of antennas of the same type in the literature is also provided. Section 5 concludes this study and opens up a few perspectives.
## 1 Introduction
From the previous research's conclusions, the gap clearly lies in optimally sizing and placing large-scale energy storage systems in a transmission system including renewable energies such as wind energy generation. Optimal sizing and placement should be achieved for the optimal operation of the transmission system in terms of minimum operation cost, losses and voltage deviation in addition to generation adequacy. Nowadays, pharmacophore-based techniques have become an essential part of many computer-aided drug design workflows and have been successfully used for many tasks such as virtual screening, lead optimization and de novo design. Pharmacophores are easy to comprehend and intuitive, which makes them also useful as a tool for medicinal chemists to describe, explain and visualize ligand-target binding modes. Virtual screening is considered the most common application of pharmacophore modeling; it is used as a query for the virtual screening of large compound libraries. The main target of virtual screening is the discovery of novel compounds that exhibit a set of desired pharmacophoric features that are considered crucial for biological activity toward a particular target of interest. The generation of a ligand-based pharmacophore model has become a key for facilitating drug discovery in the absence of a macromolecular target structure. It is usually performed by selecting the common chemical features from the 3D structures of a set of known ligands representative of essential interactions between the ligands and a specific macromolecular target. In general, pharmacophore model development relies on multiple ligands (usually called training set compounds). Pharmacophore model generation comprises two main steps: first, creating the conformational space for each ligand in the training set to represent the conformational flexibility of ligands and, second, aligning the multiple ligands in the training set to select the essential common chemical features to develop pharmacophore models [16,17]. The main contributions of this paper are as follows:  
* Application of optimal placement and sizing of large-scale energy storage stations in a power system (transmission level) including wind farms while achieving optimal power system operation. * Applying pharmacophore modeling as an optimization technique outside the pharmacy discipline. * Comparison between weighting factor and normalization methods for building the multi-objective function. * Employing energy storage system sizing and placement to achieve optimal operation of a power system in terms of minimum cost and generation adequacy. The rest of this paper is presented as follows: Section 2 illustrates the modeling of the energy storage system(s), while Section 3 illustrates the optimization process to optimally size and place the energy storage systems in transmission power systems. Section 4 illustrates the pharmacophore modeling as an optimization technique applied in this research. Section 5 presents the main simulation results, while Section 6 presents the discussions. Section 7 presents the main conclusions.
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 6 Evaluation
### Results of the Simulation
#### 6.2.3 Data Packet Size  
As explained before, the variable parameter with the most significant influence on the rank and on energy consumption is the _data packet size_. The deviation from the average is very clear across all graph sizes. So the smaller the packets are, the less energy is consumed: Packets of size 128 bytes have a rank deviation of approx. 125 to 150, while 8192-byte packets have a deviation of approx. \(-125\) to \(-150\). This is true across graph sizes, topologies, and request types.
## 5 Proposed Architecture
Similarly, spectral decoding is performed at the respective receivers by employing two optical bandpass filters (OBPFs) tuned at the intended wavelengths in accordance with the respective code sequence and balanced photodetection. After balanced photodetection, the electrical signal is lowpass filtered and sent to the BER estimator for BER measurement. A summary of the major simulation parameters for this setup is shown in Table 4.0  
Table 4.1 Block diagram of the proposed setup and code implementation, \(\mathbf{L_{1-8}}\): CW lasers, \(\mathbf{S_{1-8}}\): Power splitters, \(\mathbf{U}\): Sensors, \(\mathbf{C_{1-4}}\): Star couplers, \(\mathbf{OA}\): Optical amplifier, \(\mathbf{S^{{}^{\prime}}_{1-8}}\): Power splitters, \(\mathbf{R}\): Receivers, \(\mathbf{PRBS}\): Pseudo-random bit sequence generator, \(\mathbf{NRZ}\): Non-return to zero pulse generator, \(\mathbf{MZM}\): Mach–Zehnder modulator, \(\mathbf{OBPF}\): Optical bandpass filter, \(\mathbf{PD}\): Photodetector, \(\mathbf{ELPF}\): Electrical lowpass filter, \(\mathbf{BER}\): Bit error rate estimator.