esa-sceva/satcom-chunk-collection huggingface dataset:
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 9 Conclusions  
We proposed an e-voting system based on DLT called PVPBC. The primary advantage of this system is that it preserves voter privacy and verifiability. It achieves this by using DLT to ensure revocable anonymity, and to preserve voter privacy. An additional feature of PVPBC is that it uses advanced cryptographic primitives to support ballot privacy. Voters' verifiability is ensured by using the Selene voting scheme. To demonstrate that the provision of these features is achieved by PVPBC, we provided a detailed security analysis of PVPBC to establish how it preserves privacy, security, verifiability, integrity, fairness, and eligibility. Empirical evidence of the performance characteristics of PVPBC was then provided. We evaluated its performance on a prototype system, by measuring the time and cost required at the registration phase, authentication phase, and voting phase. In summary, the average time required by PVPBC to perform voter registration was 34 s and the associated average cost of gas was 181000 for each contract. During the voter-authorisation phase, the average time to deploy contracts was 33 s and the average cost of gas was 549719. The scalability of the system was then investigated by examining the average authorisation time as the voter population size increased. We provided evidence that the authorisation time was an approximately linear function of the voter population size over the range of population sizes examined. We also reported that access-request transactions incurred an average latency of 6.275 s. The inherent linear relation between various performance metrics and the number of voters and candidates is an appealing property of PVPBC. For example, we suggested that a linear relationship described the cost in GBP for casting and storing an encrypted ballot alongside tracker commitment as a function of the number of candidates. In summation, PVPBC delivers the following key features: privacy, verifiability, integrity, and, finally, eligibility and initial results suggest that the system is scalable.  
M.S.: Conceptualization; Methodology; Software; Validation; Investigation; Resources; Data curation; Writing--original draft preparation; Visualization; Project administration; Writing--review and editing. R.d.F.: Investigation, Conceptualization, Funding acquisition, Project administration Data curation, Writing--original draft preparation, Writing--review & editing. A.M.: Visualization, Data curation, Resources, Writing--original draft preparation, Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This paper has emanated from research supported in part by a Grant from Science Foundation Ireland under Grant numbers 13/RC/2077_P2 and 15/SIRG/3459.  
Not applicable.  
The authors declare no conflict of interest.
# Deep Learning-Based Ground Target Detection and Tracking for Aerial Photography from UAVs
## 3 Detection-Tracking Framework
### Tracking Score Based on Peak to Sidelobe Ratio (PSR)  
MOSSE proposed peak to sidelobe ratio (PSR) to determine tracking failure. MOSSE used an 11 \(\times\) 11 window around the maximum value in the correlation output as the peak. A similar calculation method is used in this paper.  
After calculating the response map \(S_{f}\{x\}\) of all the feature superimpositions, the peak is set to the area of W/16 \(\times\) H/16 around the maximum response point, and the other areas are set as the sidelobe. The ideal response distribution is shown in Figure 4.  
If the filter is correlated with the corresponding target area, target position response is the largest and the sidelobe region response is very small. If the target is disturbed by occlusion, etc., the resulting response map will inevitably destroy this distribution. Therefore the peak sidelobe ratio is calculated as:  
\[\text{P}=\frac{S_{max}-\mu_{SI}}{\sigma_{SI}} \tag{2}\]  
Here, \(S_{max}\) is the maximum response of the peak, \(\mu_{SI}\) is the mean of the sidelobe response, and \(\sigma_{SI}\) is the standard deviation of the sidelobe response. When the target area is adapted to the filter, the target can be tracked. At this time, \(S_{max}\) is larger. \(\mu_{SI}\) and \(\sigma_{SI}\) are smaller. In this case, the peak to sidelobe ratio P is larger. When the target is occluded, current target area is not adapted to the filter. \(S_{max}\) is smaller at this time. \(\mu_{SI}\) and \(\sigma_{SI}\) are larger. In this case, the peak to sidelobe ratio P is smaller. Therefore, whether the target is lost can be judged based on the magnitude of the peak to sidelobe ratio P.
## 2 Materials and Methods
### Gas-Phase Standard Molar Enthalpies of Formation of Alkyl-Quinolines and Alkyl-Pyridines
\begin{table}
\begin{tabular}{c c c c c} \hline
**CAS** & **Compound** & \(\Delta_{t}H_{m}^{o}(\text{g})_{\text{AT}}\)\({}^{\text{a}}\) & \(\Delta_{t}H_{m}^{o}(\text{g})_{\text{exp}}\)[55] & \(\Delta_{t}H_{m}^{o}(\text{g})_{\text{theor}}\)\({}^{\text{b}}\) & \(\Delta\)\({}^{\text{c}}\) \\ \hline
110-86-1 & pyridine & 139.1 & 140.4 & 141.3 & \(-0.9\) \\
109-06-8 & 2-methylpyridine & 97.4 & 99.2 & 100.1 & \(-0.9\) \\
108-99-6 & 3-methylpyridine & 104.5 & 106.4 & 107.1 & \(-0.7\) \\
108-89-4 & 4-methylpyridine & 102.2 & 104.1 & 104.8 & \(-0.7\) \\
583-61-9 & 2,3-dimethylpyridine & 63.2 & 68.3 & 66.2 & 2.1 \\
108-47-4 & 2,4-dimethylpyridine & 60.6 & 63.9 & 63.7 & 0.2 \\
589-93-5 & 2,5-dimethylpyridine & 63.6 & 66.5 & 66.6 & \(-0.1\) \\
108-48-6 & 2,6-dimethylpyridine & 55.8 & 58.7 & 58.9 & \(-0.2\) \\
583-58-4 & 3,4-dimethylpyridine & 66.7 & 70.7 & 69.7 & 1.0 \\
591-22-0 & 3,5-dimethylpyridine & 70.3 & 72.8 & 73.2 & \(-0.4\) \\
91-22-5 & quinoline & 196.5 & 200.5 \({}^{\text{d}}\) & 198.1 & 2.4 \\
91-63-4 & 2-methylquinoline & 153.5 & 156.6 \({}^{\text{d}}\) & 155.6 & 1.0 \\
612-58-8 & 3-methylquinoline & 161.2 & & 163.2 & \\
491-35-0 & 4-methylquinoline & 159.3 & 158.6 \({}^{\text{d}}\) & 161.3 & \(-2.7\) \\
91-62-3 & 6-methylquinoline & 161.3 & & 163.3 & \\
611-32-3 & 8-methylquinoline & 159.4 & & 161.4 & \\
877-43-0 & 2,6-dimethylquinoline & 118.9 & 121.3 \({}^{\text{d}}\) & 121.3 & 0.0 \\
93-37-8 & 2,7-dimethylquinoline & 118.2 & 119.8 \({}^{\text{d}}\) & 120.6 & \(-0.8\) \\
100-71-0 & 2-ethylpyridine & 75.6 & & 78.5 & \\
536-78-7 & 3-ethylpyridine & 82.9 & & 85.7 & \\
536-75-4 & 4-ethylpyridine & 80.6 & & 83.4 & \\
1613-34-9 & 2-ethylquinoline & 131.9 & & 134.2 & \\
1873-54-7 & 3-ethylquinoline & 139.5 & & 141.7 & \\
19020-26-9 & 4-ethylquinoline & 139.6 & & 141.8 & \\ \hline \end{tabular} \({}^{\text{a}}\) Calculated by the G4 method according to the standard atomization procedure. \({}^{\text{b}}\) Results from atomization reaction were corrected with help of following equation: \(\Delta_{t}H_{m}^{o}(\text{g})_{\text{theor}}/\text{kJ}\cdot\text{mol}^{-1}=0.9893 \;\times\;\Delta_{t}H_{m}^{o}(\text{g},\;\text{AT})+3.7\text{ with }R^{2}=0.9991\) (the expanded uncertainty assessed to be \(\pm 3.5\;\text{kJ}\cdot\text{mol}^{-1}\)) [5]. \({}^{\text{c}}\) Difference between column 4 and 5 in this table. \({}^{\text{d}}\) Taken from Table 9, column 4.  
\end{table} 
Table 10: Results of calculation of enthalpies of formation of alkyl-quinolines and alkyl-pyridines calculated with the G4 method at \(T=298.15\) K (\(p^{\circ}=0.1\) MPa, in kJ\(\cdot\text{mol}^{-1}\)) \({}^{\text{a}}\). experimental results on \(\Delta_{\mathrm{f}}H_{m}^{o}\), as well as in the vaporization enthalpies and to recommend the experimental data evaluated in Table 2, Table 3, and Table 10 as the reliable benchmark properties for further thermochemical calculations in Section 3.5.
# Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis
## 5 The ALPR System
### Detection
#### 5.1.2 Connected-Component Analysis  
At this stage, a connected component analysis technique is used. An 8-pixels neighborhood method is used to find the candidate plate regions. In this method, the image is initially scanned, then a set of pixels that share the same values is created to form groups; the size of each group is at least 8 pixels. Once formed, the groups are separated from each other to create a single image that becomes a license plate candidate region. The location of these candidate regions should be known in the image source and preserved. Examples of candidate regions for an image are shown in Figure 5.
# Time Modulated Arrays: From their Origin to Their Utilization in Wireless Communication Systems
## 3 Challenges and Future Research Lines  
The interplay between TMA and digital communications still faces a number of challenges. Among them, we highlight the following ones:  
* The exploitation of TMA at transmission. Up to now, the applications of TMA in the area of digital communications mainly focus on receiving TMA. Hence, the performance of transmitting TMA from a signal processing outlook, and in different scenarios, is still an unexplored research field. We propose two areas which certainly deserve further exploration: (1) the performance analysis of transmitting TMA in multiuser scenarios; and (2) the feasibility of diversity transmission techniques with TMA.
* Performance with broadband signals. The TMA state of the art exclusively focuses on narrowband signals. However, communications nowadays must unavoidably deal with broadband signals. The higher the bandwidth, the higher the switching frequency in the TMA, the wider the bandwidth at the RF stage, and the higher the sampling rate at the ADC. On the other hand, an analysis of TMA behavior under frequency-selective fading still remains to be done.
* Beamforming design through the preprocessing of periodic pulses. More specifically, we propose a more accurate design of the periodic pulses in the frequency domain. The Fourier transform of a periodic pulse is a discrete spectrum with impulses at multiples of the time-modulation frequency and whose corresponding areas are \(2\pi\) times the associated exponential Fourier series coefficients. Therefore, a simpler design, by applying the Fourier series coefficients properties to preprocess conventional rectangular pulses before they are applied to the antenna elements, is possible.
# An Overview of Key Technologies in Physical Layer Security
## 3 Secure Multi-Antenna Techniques
### Zero-Forcing (ZF) Precoding  
Zero-forcing precoding, or null-steering, is a method of spatial signal processing in which multiple antennas at the transmitter can completely cancel out or null the multiple user interference signals in a wireless communication network. The ZF method is based on canceling out the interference at the intended receiver in multiple user communications. This can be done by using the eavesdropper's CSI to transmit a message orthogonal to the eavesdropper, which is equivalent to steering a null in the direction of the eavesdropper, illustrated in Figure 10b.  
The design problem of ZF precoding has been presented in [75]. The iterative algorithm presented in [75] was developed to obtain the optimal transmit and receive filters to cancel out the interference. The designed filters minimizes the mean-square error (MSE) between the legitimate parties whilst guaranteeing and maintaining a certain eavesdropper MSE level, subject to the power constraint. Nonetheless, the ZF precoding methods presented have been demonstrated to perform well or achieve the highest secrecy system capacity when full knowledge of CSI is available at the transmitter, which is a highly unlikely case. Therefore, with limited CSI at the transmitter, the performance of ZF precoding is very poor, which makes it less applicable.
## 1 Introduction
In addition, large deformations or the linking of meshes with different spatial resolution, often necessary in cardiac electromechanics, can be better handled with meshless methods than with FEM, as FEM-based connectivity does not need to be satisfied. For instance, authors in [PERSON] et al. (2019) have shown the potential of meshless methods for fluid-structure interaction (FSI) applications, which are extremely time-consuming for mesh-based methods. Meshless approaches have already been applied to cardiac modeling. For example, [PERSON] et al. [PERSON] et al. (2019) used an element-free Galerkin meshless method for modeling cardiac mechanics. On the other hand, [PERSON] et al. [PERSON] et al. (2019) developed meshless methods based on smoothed particle hydrodynamics (SPH) meshless technique for modeling cardiac mechanics. The same authors later [PERSON] et al. (2019) employed genetic algorithms to calibrate a SPH-based fully coupled electro-mechanical model of the heart with high-resolution imaging and invasive in vivo measurements from a healthy canine heart. Recently, [PERSON] and [PERSON] and [PERSON] (2020) proposed a meshfree mixed collocation method with interpolating trial functions to solve the monodomain equations for cardiac electrophysiology and the O'Hara ventricular cell model [PERSON] et al. (2020), which was applied to one of the CRT-EPiggy19 challenge dataset under healthy and LBBB conditions. In this manuscript, we present a benchmark analysis of a meshless SPH method with finite-element methods for the prediction of cardiac electrical patterns in CRT, based on a subset of the CRT-EPiggy19 dataset, including infarcted and non-infracted cases. A data assimilation strategy was designed to personalize the most relevant parameters of the electrophysiological simulations and identify the optimal CRT lead configuration.
## 3 Results and Discussion
### Time-Averaged Results
#### 3.1.4 Momentum Flux  
In order to perform a thorough investigation on the entrainment of fluid, the distribution of momentum flux was conducted. The momentum flux equation could be written as:  
\[M_{f}\ =\ \iint V\rho(V.n)dA\ \to\ M_{f}\ =\ \int_{-b/2}^{b/2}\rho u^{2}dy \tag{3}\]  
where \(b\) is the domain's height at the external region. Figure 12 depicts that for the free external domain at Re = 2680, the momentum flux distribution along the external domain was almost constant. Since, the potential of the main jet, compared to the volume of fluid at up, down, and sidewalls were little, it caused smaller entrainment. In contrast, by confining the external domain, the momentum flux increased, compared to the free external domain, because the volume of the surrounded fluid decreased, which led to the potential of the main jet to be able to cope with the small volume of the surrounded fluid. Moreover, when the fluidic oscillator was emitted to the confined domain at Re = 2680, it created two identical vortices at the end of the jet, which caused a reduction in the energy of the core jet by friction and shear layers. At Re = 6440 for the free external domain, the potential of the jet could overcome the volume of the environment fluid, so the momentum flux increased, then decrease at the end of the jet, which had a minimum potential. On the other hand, confinement of the domain led to creating vortices at sidewalls that did not allow the jet to oscillate. Subsequently, the momentum flux decreased due to both the effect of the confined geometry and strong vortices. At Re = 10,730, for the free external domain, the momentum flux increased, and then the entrained fluid reduced the potential of the flow, as well as the momentum flux. On the contrary, the confined geometry led to create strong vortices and a small volume of the surrounded fluid that was conducive to decreasing the momentum flux.  
Figure 11: Time-averaged velocity components and fluctuations along y/D\({}_{\rm h}\) = 1 for the fully confined case.
## 4 Results and Discussion  
### Tunnel Deformation  
The measured displacements of the tunnels by the pull-wire displacement sensors in prototype scale are plotted in Figure 7, where positive values of vertical displacement indicate tunnel heave. It can be seen that the heaves of both tunnels experience the same tendency. With the increasing excavation depth of the foundation pit, the tunnel heave gradually increases, and then reaches the maximum when excavating to the bottom of the foundation pit. The measured heave at left tunnel is 9.88 mm while that at right tunnel is 10.22 mm. At the same time, an insignificant difference in tunnel heaves were observed during the first three excavation stages, but for the fourth excavation stage, the tunnel heave undergoes a more significant increase than in the previous three excavation stages. The horizontal displacements are relatively small throughout the excavation activities, with the maximum displacements of 2.52 mm and 2.47 mm at the left and right tunnel, respectively. Unlike tunnel heave, horizontal displacements in the tunnel are more sensitive  
Figure 5: Photos of the sensors used in experimental tests. (**a**) earth pressure cell, (**b**) strain gauge.  
Figure 6: Arrangement of measuring sensors.  
to the changes in excavation depth. Although the effect of the first two excavation stages on the horizontal displacement of the tunnel is marginal, it undergoes a more pronounced shift in the third excavation stage (i.e., stage six). The deformation responses of the twin-tube tunnels at different stages are drawn and displayed in Figure 8. It reveals the fact that the latter two excavation stages produced an obvious increase in horizontal displacement, which account for 82.54% and 80.85% of the total horizontal displacement of the left and right tunnels. Moreover, both tunnels are observed to be significantly displaced toward the center of the foundation pit with excavation advancement.
## 6 Multidrug Resistance of _A. baumannii_
### Mechanisms Responsible for A. buumannii Multidrug Resistance
Another interesting example is the mechanism responsible for colistin resistance. Positively charged colistin kills bacteria by interacting with the negatively charged lipid A and destabilization of the OM. Mutations in the lipid A biosynthesis genes, _lpxA_, _lpxC_, and _lpxD_, result in the complete loss of lipopolysaccharideaccharides, which in turn abolish interactions with colistin. The second mechanism depends on the PmrAB two-component system. Mutations in _pmrA_ or _pmrB_ lead to the activation of the _pmrC_gene located upstream of \(pmrAB\), encoding phosphoethanolamine transferase. Phosphoethanolamine transferred to lipid A decreases the negative charge of LOS, preventing colistin binding [62; 63]. Among several types of efflux pumps that confer multidrug resistance, the RND efflux systems (AdeABC, AdeFRH, AdelJK) are the most prevalent in _A. baumanniii_. The AdeABC pump, found in 80% of _A. baumanniii_ isolates, extrudes a wide range of antibiotics, including \(\beta\)-lactams, aminoglucosides, fluoroquinolones, tetracyclines-tigecycline, macrolides-lincosamides, and chloramphenicol [103; 177; 102].The expression of AdeABC is tightly regulated by the AdeRS two-component system which was found to control almost 600 other genes [192; 193]. Point mutations in the _adeR-adeS_ genes or the presence of an IS_Aba1_ insertion sequence upstream from the _adeABC_ operon result in the overexpression of the AdeABC pump and multidrug resistance [194]. The AdeFRH pump, when overexpressed, confers enhanced resistance to fluoroquinolones, tetracycline-tigecycline, chloramphenicol, clindamycin, trimethoprim, sulfamethoxazole, sodium dodecyl sulfate, and dyes such as ethidium bromide, safranin O, and acridine orange [195]. It was found that overexpression of AdeFGH is caused by mutation in the _adeL_ gene located upstream from the _adeFGH_ operon, as well as the encoding of an a-Lys-type transcriptional regulator. The AdelJK pump is produced in _A. baumanniii_ constitutively, and it is responsible for resistance to the same major drug classes as AdeABC, as well as antifolates and fusidic acid [115].
# Coffee-Leaf Diseases and Pests Detection Based on YOLO Models
## 1 Introduction
This meticulous refinement resulted in a more robust dataset, directly improving the reliability and generalization of the trained models. Furthermore, in addition to conventional accuracy metrics such as mAP, precision, recall, and F1-Score, the models were evaluated in terms of computational efficiency, including inference time and parameter count. The main contributions of the paper are:  
* The enhancement of the BRACOL dataset with markings reviewed by experts, ensuring higher quality data for training and evaluating the models. The dataset will be made available to the scientific community. * A comparative analysis of the performance of YOLOv8, YOLOv9, YOLOv10, and YOLOv11 in miner, rust, phoma, and cercospora disease detection in coffee leaves. * The identification of the most efficient model considering performance metrics (mAP, precision, recall, and F1-Score) and the shortest inference time in disease prediction. This paper is organized as follows, Section 2 deals with the Literature Review, where the main related works are described. Section 3 details the materials and methods used. The experimental results and discussion are presented in Section 4, and Section 5 contains the conclusions of the research.
## 2 Load Profile Development
### Historical Profile vs. Flattened Profile
As the penetration of EVs increase, it is expected that the nightly energy demand will continue to grow. The combined effects from high penetrations of ES applications, PV systems, and nightly EV charging will make the historical load profile flatter. A load profile is quantified by the term \"load factor\" (\(LF\)), which is measured by dividing average demand (\(L_{AVG}\)) by the peak demand (\(L_{PK}\)), as illustrated in (1). A typical historical load factor ranges between 0.50 and 0.75. Increased penetration of ES applications, PV systems, and EV charging systems can result in \(LF\) values in excess of 0.80. The new load profile, which reflects the effects of high penetrations of DSM and DER applications, is flatter, illustrated in Figure 2, and designated as Profile A in this research. To identify the appropriate capacity limit, the authors analyzed the effects of Profile A with varying peak energy demands (range: 75-1.20 per unit). Unlike the historical load profile, which is illustrated as Profile B in Figure 3, the \(LF\) for the flattened profile ranges between 0.835 and 0.913 depending on peak demand. \[LF=\frac{L_{AVG}}{L_{PK}} \tag{1}\]  
The historical load profile utilized in the comparison analysis possessed the typical characteristics historically experienced by substation power transformers [1; 2; 7]. The period between 12 am and 6 am illustrates the lowest demand, while the peak demand occurs near 6 pm. The peak demand for the profile was varied for the analysis so that aging comparison could be performed for varying loads. As with peak load augmentations with Profile A, increases in demand were performed by equally  
Figure 3: Historical cyclic load profile (Profile B) experienced by substation transformers. Figure 2: DER effects on historical load profile that result in a flattened load profile (Profile A). increasing the energy demand for all hours. The load factor for the profile utilized for the analyses ranged between 0.65 and 0.78 depending upon energy peak (range: 0.90-1.50 per unit).