esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Classification of Mushrooms Using a Smartphone Application
## 3 Results
### Inter-Platform Comparison  
One of the questions that arise when using models trained on different platforms is the transferability of the result. Most wild mushroom collection occurs in the remote field, where cellular signal strength can be weak or nonexistent. Therefore, we compared the classification accuracy of server-based testing and smartphone-based testing results to determine the degree of consistency. Figure 6 shows the comparison of three reported performance measures (sensitivity, specificity, and accuracy). Variation was defined as a ratio of the server-based performance measure to the smartphone-based measures. Figure 6A shows a \(\pm 5\%\) discrepancy between the two methods for the three-class model, while Figure 6B demonstrates a wider gap (maximum of \(+15\%\) and a minimum of \(-10\%\)) between the two platforms. For the three-class classification task, _Clavulina_ showed the most stable testing results from the inter-platform comparison with the lowest variation rates of sensitivity, specificity, and accuracy, whereas _Marasmius_ recorded the highest variation rates  
Figure 4: Comparison of sensitivity and specificity for the five classes of _Agaricus_, _Amanita_, _Cantharelus_, _Pleutrotus_, and _Tricholoma_. (**A**) Classification rate from laptop training and testing. (**B**) Test results from importing the trained model to the smartphone handset.  
of the performance measures. Overall, inter-platform testing in the three-class classification showed stable variation results. The five-class classification showed less stability. _Agaricus_ recorded the highest variation rates in sensitivity, about 118%. In addition, _Tricholoma_ species recorded about 90% variation in specificity. Except for those two types, the other three mushrooms recorded stable variation rates. In particular, _Pleurotus_ recorded the lowest variation rate among the five classes. Furthermore, while accuracy showed minimal variations, other performance measures revealed larger variations between the two platforms. For example, smartphone-based sensitivity results reported higher rates than server-based results and vice-versa for specificity.  
Figure 5: Screenshot of PUMA. (**A**) starting screen (**B**) initial menu where the user can select the app functions. (**C**) Results screen where the predicted class with respective probability is shown. Collection ID can be typed as well. (**D**) A snapshot of the in-phone database with thumbnail images and predicted classes. (**E**) Foraging map; when the user clicks on a result, the application automatically transmits the image data’s coordinates to mapping software so that the collection’s location can be displayed.  
Figure 6: Comparison of statistical outputs when two different platforms are used: Smartphone vs. server-based classification. (**A**) For 3-class model (**B**) 5-class model.
## 3 Results: A Standardized Protocol in Five Steps
### Generation of Digital Twins  
As we have indicated, the development of this digital twin has been carried out by combining point clouds obtained by SfM (with images acquired from UAV) and clouds obtained by TLS digital instruments; the result is a 3D digital model [40; 41]. It is a high-quality model of the city canvas, and we can say that it has been obtained through a low-cost and precise \"smart work\" flow [13]. The steps to obtain this physical model are summarized in Figure 8[1; 7; 25; 42; 43; 44]:  
Figure 7: Five steps of the standardized protocol.  
* Orientation of photographs: this orientation allows the scattered point cloud to be obtained.
* Generation of dense point cloud and depth maps.
* Dense point cloud optimization by point filtering.
* Generation of the triangulated mesh.
* Generation of the texture from the images taken by the drone.
* This processing gives rise to digital twins made up of huge meshes, which need to be reviewed and refined to obtain three-dimensional models that can be used in metaverges [13; 45; 46; 47; 48].  
In the case of the model obtained with TLS, we obtain a second virtual twin, which will be built from the documentation using TLS. This is cabinet work (see Figure 9).  
After the study is carried out, it has been decided to use the software of the laser scanner Cyclone 360 register, which is software for photogrammetric processing of digital images and generation of 3D spatial data. Its main functions include: automatic alignment of scans, manual alignment of the scans according to the point clouds, allowing the link with other point clouds through the use of targets, and texturing options through the images generated in the scans (see Figure 10):  
* Step 1: Importing Scans
* Step 2: Aligning Scans  
Figure 8: Photogrammetric processing of the data obtained by UAV.  
Figure 9: Results of the point cloud obtained with TLS.
# From Dormancy to Eradication: Strategies for Controlling Bacterial Persisters in Food Settings
## 5 Mechanisms of Persister Cell Formation
### Stringent Response  
The stringent bacterial response is orchestrated via the stress alarmone ppGpp. This alarmone acts as a secondary messenger, modifying gene transcription in response to environmental cues ([PERSON] and [PERSON], 1996; [PERSON] and [PERSON], 1996; [PERSON] and [PERSON], 1996). Specifically, ppGpp is upregulated during amino acid starvation, halting replication and activating survival pathways in _E. coli_ and _Pseudomonas aeruginosa_. The HokB-SokB type I TA module is activated via ppGpp, inducing persistence ([PERSON] and [PERSON], 1996). ppGpp also functions as an intermediate to activate obg-mediated persistence. Obg, a GTPase protein involved in DNA replication, triggers HokB, which disrupts membrane potential and reduces metabolic activity, enhancing persistence. Recent studies have further elucidated the role of obg in persister cell formation ([PERSON] and [PERSON], 1996). Figure 2 illustrates the pathways for ppGpp production and persister formation through HokB-SokB activation.  
As referred to by [PERSON] et al. (2014) in their review, (p)ppGpp levels can accumulate in response to a wide range of signals, including oxygen variation, pH downshifts, osmotic shock, temperature shift, or even exposure to darkness. As such, the stringent response is not only involved in responses to environmental stress but is also used in bacterial pathogenesis, host invasion, antibiotic tolerance, and persistence.
# Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks
## 4 Results and Discussion
### Simulation Results  
#### Effect of Area  
The impact of different terrain sizes on the performance of our proposed scheme is evaluated in the presence of 20% of type-I and type-II attackers with mobility of 10 m/s and a pause time of 60 s.  
* Performance in the presence of type-I attackers: The performance in terms of packet drops is given in Figure 2a. It can be seen that OCEAN reported small packet drops against small areas, however, as the area increases our scheme outperforms OCEAN because the benchmark scheme provides a second chance to selfish nodes to join the network. The end-to-end delay of both schemes is shown in Figure 2b. OCEAN reported less delay as compared to our scheme for all terrain sizes because OCEAN uses a rank-based routing mechanism. Figure 2c shows the evil throughput, in which our scheme outperforms the benchmark scheme and recoded low evil throughput. Figure 2d represents good throughput, almost a similar performance can be seen in terms of good throughput. NRL is depicted in Figure 2e; NRL of our scheme is less as compared to OCEAN. Figure 2f represents packet delivery fraction; our scheme produces a higher PDF than OCEAN.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Area & 500, 1000, 1500, 2000 m\({}^{2}\) \\ Pause Time & 10, 20, 30, 40, 50, 60 s \\ Maximum Speed & 5, 10, 15, 20, 25, 30 m/s \\ Number of Nodes & 50 \\ Number of Connections & 30 \\ Application & CBR \\ Packet Size & 64 B \\ Simulation Time & 100, 200, 300, 400, 500, 600 s \\ Mobility Model & Random Waypoint Model \\ Selfish Population & 0\% to 100\% \\ \hline \hline \end{tabular}  
* Evil throughput—determines the throughput offered to misbehaving nodes: \[Evil\;Throughput=\frac{\sum\left(\textit{Received pkts}\right)}{\sum\left(\textit{ Sent pkts by misbehaving}\right)}\] (5)
* Packet delivery fraction—the percentage between the number of packets originated by CBR sources and the number of packets received by CBR sinks at the destination;
* NRL—normalized routing load (NRL) is the fraction of packets communicated at the network layer to the CBR packets at the application layer by the receiver node;
* End-to-end delay—represents retransmission delay, queuing delay, buffering during route discovery, and propagation delay.
## 3 Simulation Analysis
### Influence of the Supply Pressure  
Because the maximum working pressure of the developed VCM-DHPV is 8 MPa, the numerical simulation was conducted under different supply pressures which are less than 8 MPa (2, 4, 6 and 8 MPa) to investigate its dynamic characteristics. Figure 7 illustrates the displacement of spool versus simulation time of VCM-DHPV under different supply pressures (with the exciting voltage of 240 V). When only the supply pressure of VCM-DHPV is changed in the simulation, it can be seen in Figure 7 that the opening response time increased with the rise of the supply pressure. Here, the opening response times are 5.7, 5.9, 6.1 and 6.2 ms under the supply pressures of 2, 4, 6 and 8 MPa, respectively. This is attributed to the fact that the transient and steady gas flow forces gradually increased with the rise of supply pressure.
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
## 3 Results
### Short Distance Link Whit a Real-Time Video Game  
We evaluated the TCP-NACK performance over a video gaming scenario with two players and one server wirelessly connected by an AP, where players and server were laptops with the modified TCP-NACK Ubuntu Operating System. The game at the server was \"Quake IV\" and the employed topology was Point-to-Multipoint (PtM), as Figure 5 shows.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Bandwidth & 5000 kb/s \\ \(\delta\) & 70 ms \\ Packet error probability & 0.5\%, 1\% and 5\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Wireless Link emulation Parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**TCP Entity** & **Linux Command** \\ \hline Emitter (server) & iperf - tPIdirection -t time \\ Receiver (client) & iperf -s \\ \hline \hline \end{tabular}
\end{table}
Table 2: IPERF Configuration Commands.  
Figure 4: Emulation test topology.  
We perform this experiment over two scenarios. The first one was an in-door scenario with a 7-m distance between user equipment and the Netgear N600 wireless router configured as AP, using IEEE 802.11n as communication standard. The second one was an out-door scenario with 160 m' distance between the user equipment and the Ubiquiti PowerStation2 antenna configured as AP, using IEEE 802.11b/g as communication standard. The experiments were carried out with a 128.8 kb/s bandwidth between the user equipment and the server (uplink), and a 222 kb/s bandwidth between the server and the user equipment (downlink). Additionally, we introduced the OpenVPN tool for the adequacy of the multiplayer communication system. OpenVPN allows to connect multiple remote clients to the server, where any client can communicate with the rest of them [21].
# Optimizing Vehicle Body Cross-Sections Using a Parametric Mathematical Model
## 3 Fully Parametric Mathematical Model of Body Structure Based on the Transfer Matrix Method
The displacement in the loading direction of the loading point is represented by 'u', and the corresponding mathematical equation is Equation (8):  
\[u=G\Big{(}\{C\}_{(1)},\{C\}_{(2)},\cdots\{C\}_{(n)}\Big{)} \tag{8}\]  
The variables of the mathematical model of the body are the area A of each beam section and the moments of inertia \(I_{y}\), \(I_{x}\). Since the body mass is only related to the area A of the beam unit, \(I_{y}\) and \(I_{x}\) have no effect on the body mass. Therefore, under the objective of maximum body stiffness, \(I_{y}\) and \(I_{x}\) will take the upper limit of the constraint interval, so that the optimization results will become meaningless. In order to solve the above problem, the mechanical parameter variables of the beam cell need to be transformed into specific geometric variables. The body structure's beam unit cross section can be approximated as a thin-walled rectangle. The use of a rectangular cross-section is not only beneficial for preliminary studies but can also significantly reduce the cost of manufacturing prototype vehicles, improving manufacturing feasibility. Figure 4 shows a common thin-walled aluminum alloy section with a rectangular cross-section and a cross-sectional shape. Figure 3: Relationship of the skeleton composition of the whole vehicle. For a rectangular cross-section, the dimensions t, h, and b represent the wall thickness, height, and width, respectively, these define the geometric parameters of the beam section: \(X_{i}=\left\{t,h,b\right\}_{i}\). The cross-section area A and moments of inertia \(I_{y}\) and \(I_{z}\):  
\[A=2 tb+2 th-4t^{2} \tag{9}\]  
\[I_{y}=\frac{1}{6}\Big{(}th^{3}+3 tbh-6t^{2}bh-6 ht^{2}+12t^{3}h+4t^{3}b-8t^{4} \Big{)} \tag{10}\]  
\[I_{z}=\frac{1}{6}\Big{(}tb^{3}+3 tbh-6t^{2}bh-6 bt^{2}+12t^{3}b+4t^{3}h-8t^{4} \Big{)} \tag{11}\]  
These Formulas (8)-(11) are used to calculate the deformation \(\Delta Z\) at the loading point:  
\[\Delta Z=f(\left\{X\right\}_{1},\cdots,\left\{X\right\}_{i^{\prime}}\cdots \left\{X\right\}_{n}) \tag{12}\]  
where \(\left\{X\right\}_{i}\) is the set of dimensions for the iii-th cross-section.
## 4 Conclusions  
In this work an optical device based on an SPR excitation in a POF platform has been described for the measurement of very low levels (\(<\)0.15 ppm) of furfural (2-FAL) in transformer oils. The sensing layer in the developed POF-MIP sensor is a molecularly imprinted polymer specific for 2-FAL. It has been demonstrated to work properly in the oil medium in which 2-FAL must be detected, even better than in water solution, for example, as far as the affinity constant is concerned. Selective and rapid responses, for which small amounts of sample are required, are obtained. Moreover the POF-MIP sensor is robust, cheap and suitable for frequent measurements, possibly by housing the sensor body in a flow cell, through which the insulating oil could be forced to perform on-line measurements on transformers. Evidence of the feasibility of this approach emerged from tests carried out on samples of ex-service  
Figure 4: Normalized SPR spectra acquired by the POF-MIP sensor P2 for different furfural (2-FAL) concentrations in ppm in fresh oil and in two serviced transformer oils (CT.134 and CT.729). Inset: zoom of resonance wavelengths.  
insulating oil. No interference from the usual components of used transformer oils, either coming from the oil or cellulose degradation, could be detected.  
Notice that the proposed measurement procedure is very convenient and only requires a small amount of oil. Even if it was here applied to used oil from ex-service transformers, it could be used to the determination of 2-FAL in oil inside the transformer, due to the very small oil volume required for the determination. This could be drawn out of the transformer by a flowing circuit similar to that commonly used for the determination of gaseous degradation products. Alternatively the measurement could be done by placing the sensor directly inside the flowing circuit.  
This work has been financed by the Research Fund for the Italian Electrical System under the Contract Agreement between ERSE and the Ministry of Economic Development--General Directorate for Energy and Mining Resources stipulated on 29 July 2009 in compliance with the Decree of 19 March 2009. The work was partially supported by POR Campania FSE 2007/2013.  
[PERSON] and [PERSON] of the Department of Chemistry (University of Pavia) realized the MIP receptor and elaborated the data. [PERSON] and [PERSON] of the Department of Industrial and Information Engineering (Second University of Naples) realized the optical sensor platform. [PERSON] of the RSE together with [PERSON] and [PERSON] performed the experimental analysis.  
The authors declare no conflict of interest.
## 4 Experiments
### Experimental Environments
#### 4.1.2 Open Datasets  
The comparative experiment is based on the open dataset nuScenes [15], which contains urban road scene perception data from IMUs, cameras, and Lidars. To evaluate the effectiveness of our proposed algorithm, we extracted parking lot and on-street parking data from this dataset for testing. The ground truth number of parking spaces in the dataset is shown in Table 4. Since the dataset does not specifically focus on collecting parking lot data, and the data is collected while the vehicle is in motion, we count the number of parking slots based on the point cloud projection occupying more than 50% of the vehicle projection area. Parked cars and parking spaces far from the driving direction are not included in the ground truth. For instance, taking the parking lot in Singapore's Queenstown as an example, the garages in the dataset are in the form of perpendicular parking spaces, as shown in Figure 15a. For parallel parking spaces, consider a section of Boston Seaport, as shown in Figure 15b. These data were collected while the vehicle was in motion, allowing for motion estimation, SLAM mapping, and parking space detection based on these data. This meets the computational requirements of both our algorithm and the comparative algorithm. It should be noted that there is no angled parking data in this dataset. However, from the perspective of coordinate transformation, the difference between angled parking and perpendicular parking is merely a rotation angle. Therefore, detecting angled parking involves transforming the coordinates of the fitting straight line representing the angled parking to align with the perpendicular parking scene. Consequently, the experiment on perpendicular parking can represent the detection results of angled parking, as Section 3.3.2 illustrated. The hardware used to collect data in the open dataset nuScenes includes a Lidar, an IMU, and six surround-view cameras. The Lidar system has 32 lines, an operating frequency of 20 Hz, a 360\({}^{\circ}\) horizontal FOV, a \(-30^{\circ}\) to \(10^{\circ}\) vertical FOV, a detection range of 70 m, a detection accuracy of 2 cm, and captures 1.4 million point clouds per second. The IMU outputs high-frequency pose data at 100 Hz. The resolution of the six surround-view cameras is 1600 \(\times\) 900, compressed in JPEG format, with a sampling frequency of 12 Hz. The nuScenes experimental vehicle is a Renault Zoe mini electric car, measuring 3.45 m in length, 1.68 m in width, and 1.42 m in height. Considering the safety distance, the \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experimental Site**} & \multirow{2}{*}{**Number of Parking Slots**} & \multicolumn{2}{c}{**Type of Parking Slots**} \\ \cline{3-4}  & & **Parallel** & **Perpendicular** \\ \hline Site 1 (Parking Lot) & 135 & 0 & 135 \\ Site 2 (On-street & 32 & 32 & 0 \\ Parking) & Total & 167 & 32 & 135 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parking space statistics of nuScenes.
# Quadratic Curve Fitting-Based Image Edge Line Segment Detection: A Novel Methodology
## 1 Introduction
Hence, prior to employing these methodologies on distorted images, the pre-processing of these images would be required in addition to the standard denoising procedures [18]. This preliminary processing step not only amplifies the visual workload, especially for cameras with wide-angle lenses (such as fisheye lenses and spherical lenses), but also fails to effectively ensure the preservation of image information in wide-angle views. Consequently, detecting edge line segments and performing feature correction on the original distorted images presents a more effective approach for enhancing the speed of distortion correction, particularly for methods requiring high efficiency, such as the SLAM methods (e.g., ORB-SLAM [19] and VINS-MONO [20]). In response to these challenges, this paper presents a novel image edge line segment detection technique based on quadratic fitting, termed the QF-LSD method. Experimental outcomes demonstrate that, in comparison to mainstream line segment detection algorithms, the proposed method is capable of not only detecting edge lines in undistorted (or slightly distorted) images, but can also accurately and reliably identifying edge curve segments in highly distorted images. Additionally, the experiments demonstrate that the algorithm's computational efficiency is 27 times faster than those in traditional approaches. The main contributions of this paper are as follows:  
(1) This methodology leverages the property that a 3D straight line's projection onto a distorted 2D image forms a quadratic curve. It iteratively estimates the quadratic curve model's optimal parameters through hypothesis verification and uses this optimal model to find the longest and continuous pixel sequence as the edge line segment within the edge contour. (2) To reliably extract the multiple edge line segments that potentially exist within the edge contour, each identified line segment within the contour is removed, and the remaining pixels in the contour form a new edge contour. The technique then re-estimates the quadratic curve model's optimal parameters through hypothesis verification to extract new edge line segments, and this process is continued until no valid edge line segments can be extracted from the edge contour. (3) Due to the edge detection method used in this article being independent of the camera's distortion parameters, there is no need to preprocess the distorted images. This article conducts tests using image data from normal lenses, wide-angle lenses, and fisheye lenses, respectively. The subsequent sections of this paper are structured as follows: Section 2 elucidates the mathematical derivations of the edge line segment model; Section 3 provides a detailed description of the edge line segment extraction algorithm based on quadratic fitting; Section 4 presents a performance comparison with the classical LSD algorithm using image data from normal lenses, wide-angle lenses, and fisheye lenses; and Section 5 concludes the paper.