esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Total Phenol Content  
According to the results, the highest percentage increase in total phenol (YTP) between PEF and reference extracts was shown by the 25% EtOH solvent, after PEF with 100 us pulse duration. The 75% EtOH and 100% EtOH solvents also showed significant increases, while pure water and 50% EtOH led to lower increases.  
In detail, regarding the pulse duration of 10 us (Figure 1), the highest percentage increase in YTP between PEF and the reference sample obtained with the 25% EtOH solvent, was 59.00% (significant at \(p<0.05\)). Specifically, YTP for the PEF sample was 21.76 mg GAE g\({}^{-1}\) dw, while for the reference sample it was 13.68 GAE g\({}^{-1}\) dw. The 75% EtOH and 100% EtOH solvents showed significant (\(p<0.05\)) increases of 40.48% and 42.18%, respectively. The lowest increases presented with pure water and 50% EtOH were 15.65% and 14.00%, respectively. In these cases, PEF treated and reference samples showed no significant differences.  
The results of PEF treatment with a pulse duration of 100 us are presented in Figure 2. The highest (significant at \(p<0.05\)) percentage increase between PEF and the reference sample was achieved again with the 25% EtOH solvent (73.23%), and it was much higher (\(-24\%\)) compared to that of the pulse duration of 10 us. In addition, there were slightly higher increases concerning pure water and 50% EtOH (14.40% and 16.97%, respectively).  
Although the highest percentage increases in YTP between PEF and reference extracts, for both pulse durations, were achieved with the addition of only 25% EtOH, the highest extraction rate was observed for the PEF-treated samples using 75% EtOH as solvent (Figures 1 and 2). It seems the use of the PEF replenished part of the losses in the extraction rate when a solvent with low concentration of EtOH was used. Additionally, it was shown that longer-duration pulses appeared to deliver higher efficiency regarding the content in SOLL total polyphenols.
## 2 Point Cloud Data Acquisition and Its Pre-Processing  
### Point Cloud Data Acquisition  
The data acquisition device used in this paper is the ECCO 75 series 3D line laser sensor of Germany SmartRay Company (Wolfrathausausen, Germany), and its related parameters are shown in Table 1. The sensor uses laser triangulation: the laser line is first struck onto the object to be measured, which then reflects the beam back to the image sensor. The 3D sensor receives the offset of the reflected beam from the image sensor and, according to the constant change in the height of the object to be measured, determines the different positions of the reflected beam presented in the image, thus achieving the measurement of the object's contour.  
Using the 3D line laser sensor and its adapted SR_Starterkit 5.5.0.91 software, the data acquisition was carried out on the 60 model rail, setting the laser brightness at 75%, exposure time at 100 \(\upmu\)s, the number of image acquisition lines at 100, and the region of interest (ROI) at 1920 \(\times\) 1200 mm\({}^{2}\). The rail profile point cloud data acquisition and preparation process is shown in Figure 2.  
Figure 1: ISS-4 PCS + KD-ICP point cloud alignment method flowchart.  
The set of coordinate points under the sensor coordinate system for some randomly selected data from the initial 3D point cloud data is shown in Figure 3a, which mainly includes the X, Y, and Z coordinates of the point cloud data in 3D space. The 3D point cloud shape of the rail profile presented by connecting all the data in this coordinate point set sequentially is shown in Figure 3b.
# A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs
###### Abstract  
Routing security attacks in Vehicular Ad hoc Networks (VANETs) represent a challenging issue that may dramatically decrease the network performances and even cause hazardous damage in both lives and equipment. This study proposes a new approach named Multivariate Statistical Detection Scheme (MVSDS), capable of detecting routing security attacks in VANETs based on statistical techniques, namely the multivariate normality tests (MVN). Our detection approach consists of four main stages: first, we construct the input data by monitoring the network traffic in real time based on multiple metrics such as throughput, dropped packets ratio, and overhead traffic ratio. Secondly, we normalize the collected data by applying three different rescaling techniques, namely the Z-Score Normalization (ZSN), the Min-Max Normalization (MMN), and the Normalization by Decimal Scaling (NDS). The resulting data are modeled by a multivariate dataset sampled at different times used as an input by the detection step. The next step allows separating legitimate behavior from malicious one by continuously verifying the conformity of the dataset to the multivariate normality assumption by applying the Rao-Ali test combined with the Ryan-Joiner test. At the end of this step, the Ryan-Joiner correlation coefficient (R-J) is computed at various time windows. The measurement of this coefficient will allow identifying an attacker's presence whenever this coefficient falls below a threshold corresponding to the normal critical values. Realistic VANET scenarios are simulated using SUMO (Simulation of Urban Mobility) and NS-3 (network simulator). Our approach implemented in the Matlab environment offers a real time detection scheme that can identify anomalous behavior relying on multivariate data. The proposed scheme is validated in different scenarios under routing attacks, mainly the black hole attack. As far as we know, our proposed approach unprecedentedly employed multivariate normality tests to attack detection in VANETs. It can further be applied to any VANET routing protocol without making any additional changes in the routing algorithm.  
VANET; AODV; SUMO; NS-3; black hole attack; detection; multivariate normality +
Footnote †: journal: Information  
000  
_information_
## 6 Simulation Experiment and Analysis
### Simulation Results
#### 6.2.2 OFDM Signal Parameter Analysis Simulation  
Figure 8 presents the estimation performance graph with normalized MSE as the reference for various SNR conditions when the number of OFDM symbols used for simulation is 5. Figure 9 shows the estimation performance graph with normalized MSE as the reference for various SNR conditions when the number of OFDM symbols used for simulation is 10 (Note: due to the fact that \(N_{use}\), the number of subcarriers, and the oversampling factor are fully estimated correctly when the symbol count is high, their curves exceed the display range after the normalized MSE reaches zero, indicating that their curves or parameter points have disappeared from the graph.). By comparing Figures 8 and 9, it can be observed that the normalized MSE for parameter estimation changes due to the different number of OFDM symbols used in the simulation, i.e., the error in parameter estimation changes. Figure 9 exhibits a better normalized MSE index than Figure 8, and by observing the normalized MSE values in Figure 9, it can
###### Abstract  
This paper aims to automatically reconstruct 3D building models on a large scale using a new approach on the basis of half-spaces, while making no assumptions about the building layout and keeping the number of input parameters to a minimum. The proposed algorithm is performed in two stages. First, the airborne LiDAR data and buildings' outlines are preprocessed to generate buildings' base models and the corresponding half-spaces. In the second stage, the half-spaces are analysed and used for shaping the final 3D building model using 3D Boolean operations. In experiments, the proposed algorithm was applied on a large scale, and its' performance was inspected on a city level and on a single building level. Accurate reconstruction of buildings with various layouts were demonstrated and limitations were identified for large-scale applications. Finally, the proposed algorithm was validated on an ISPRS benchmark dataset, where a RMSE of 1.31 m and completeness of 98.9% were obtained.  
building model; reconstruction; half-space; LiDAR data; urban scale Article
# Analyzing the Performance of the S3 Object Storage API for HPC Workloads
## 3 Results
### MinIO Benchmarks in HPC Environment
#### 3.1.4 MinIO vs. REST vs. TCP/IP  
To understand performance limitations, we plot the throughput of various transfer modes using one node and four processes in Figure 6. The figure includes performance numbers from [23], where the base performance of REST/HTTP is analyzed by emulating a best-case client/server scenario: The throughput of the HTTP GET operation--representing read-only access to a storage server--is calculated for different file sizes. We conduct the same experiments on Mistral, using tmpfs as backend. Since the tests are accomplished against localhost, we can compare the obtained results with the IOR results of the single client MinIO setup described in Section 3.1.1, where MinIO is running in standalone mode with RAM as backend, and using 1 Node-4 PPN as well. We also include:  
* The IOR results of the direct lustre access using 1N-4 PPN.
* The TCP/IP throughput measured using iperf.
* For reference, the S3 Embedded results for a similar setup (1N-4 PPN), which we will describe in more detail later in Section 4.  
The results show that for objects with size greater than 1 KB, MinIO performance--even in this ideal setup--is significantly lower than the base REST performance and that there is room for improvement, which we address in Section 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Benchmark**} & \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Unit**} & \multicolumn{2}{c}{**Lustre**} & \multicolumn{2}{c}{**MinIO**} & \multicolumn{2}{c}{**Local-Gw**} \\  & & & & **Disjoint-Gw** & **Local-Gw** & **\% of Lustre** \\ \hline \multirow{2}{*}{**md-workbench**} & rate & IOPS & 18337 & 37 & 425 & 2.3\% \\  & throughput & MiBPs & 34.100 & 0.100 & 0.800 & 2.3\% \\ \hline \multirow{8}{*}{**IO500**} & ior-easy-write & GiB/s & 18.671 & 0.153 & 0.286 & 1.5\% \\  & mdtest-easy-write & kIOPS & 5.892 & 0.088 & 0.132 & 2.2\% \\  & ior-hard-write & GiB/s & 0.014 & 0.003 & 0.006 & 45.7\% \\  & mdtest-hard-write & kIOPS & 5.071 & 0.036 & 0.076 & 1.5\% \\  & ior-easy-read & GiB/s & 11.475 & 0.693 & 2.071 & 18.1\% \\  & mdtest-easy-stat & kIOPS & 24.954 & 1.198 & 4.092 & 16.4\% \\  & ior-hard-read & GiB/s & 0.452 & 0.029 & 0.094 & 20.7\% \\  & mdtest-hard-stat & kIOPS & 18.296 & 1.281 & 3.968 & 21.7\% \\  & mdtest-easy-delete & kIOPS & 9.316 & 0.025 & 0.023 & 0.3\% \\  & mdtest-hard-read & kIOPS & 6.950 & 0.449 & 1.636 & 23.5\% \\  & mdtest-hard-delete & kIOPS & 4.863 & 0.029 & 0.025 & 0.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of MinIO Gateway on 4 nodes with 20 PPN.
## 4 The Proposed Distributed Image Codec
### The Encoder
#### 4.2.1 Non-Adaptive Block Classification  
The non-adaptive block classification scheme is designed for very low complexity encoding with minimum computation. In this scheme, the blocks are classified sequentially according to the order position in which they are acquired. A group of consecutive \(M\) blocks is referred to as a group of blocks (GOB). The first block in a GOB is a key block. The remaining \(M-1\) blocks are WZ blocks. Block sparsity is not considered in this scheme. It is similar to the concept of group of picture (GOP) used in traditional video coding. If \(M=1\), then every block is a key block and is encoded at the same rate. The average measurement rate decreases as \(M\) increases.
## 1 Introduction
Retaining the overlap of electron and hole wave functions at a reduced electric field is, therefore, a prerequisite in enhancing the EQE over structural engineering of the hetero-structured QDs. As mentioned above, many efforts have been made to understand the electric field-induced luminescence quenching as well as QD charging. Surface states coupled luminescence quenching is expected to be more severe at the interface with adjacent functional layers such as carrier transport and blocking layers. Therefore, interfacial charging related to the surface states and/or the energy level as well as the structural engineering within the QD emission layer can be a key parameter in determining the EQE of the QD LEDs. Compared with the huge success of QLEDs in the visible range, as discussed above, progress in the near-infrared (NIR) CQD LEDs is not so fast. NIR CQDs such as PbS and PbSe have attracted much attention due to their applications to biomedical imaging, telecommunications, and night vision [24; 25]. Contrary to CQD LEDs in the visible range, luminescence efficiency in the NIR CQD LEDs lags in the presence of self-quenching in which efficient charge injection causes transport-assisted trapping of mobile carriers within QDs [25; 26]. Various strategies to suppress self-quenching in CQDs, including organic ligand capping, insertion into the polymer or hybrid perovskite matrix, and shelling of the surface have led to enhanced EQE of up to \(\sim\)7.9% [26; 27]. Most of all, unlike CQD LEDs in the visible range, extensive studies of interfacial charging and electric field-induced luminescence quenching associated with PbS CQDs have not been conducted. Here, we report, for the first time, the temporal evolution of the electroluminescence (EL) intensity during light emission in a PbS CQD LED testbed device. We correlated the temporal evolution of the EL intensity to electric-field-induced interfacial charging between the emission and transport layers, and luminescence quenching. To articulate the temporal evolution, we investigated the spectral shift and the magnitude of the current simultaneously. Surprisingly, to the best of our knowledge, the effect of temporal charging over long time scales during light emission at the emission/transport layer interface on the EL intensity has not been reported in NIR CQD LEDs, despite the fact that the time scale associated with electric-field induced charging can extend up to tens of seconds depending on the charge injection into QDs and carrier trapping process on the QD surface.
# Hybrid Cooperative Spectrum Sharing Protocols Based on OMA and NOMA in Cognitive Radio Networks
## Appendix A  
**Proof of Theorem 1.** The outage probability of the point-to-point transmission without interference depends on the channel gain \(h_{i}\sim(0,\lambda_{i})\). Therefore, in the Rayleigh fading channel, \(p_{j}^{out}\) can be calculated as:  
\[\begin{split}& p_{j}^{out}=Pr\Big{(}R_{jx_{p}}^{\text{non-CR}}<R_{p} \Big{)}=Pr\Big{(}log_{2}\Big{(}1+|h_{i}|^{2}\rho_{P}\Big{)}<R_{P}\Big{)}\\ &=Pr\Big{(}|h_{i}|^{2}\rho_{P}<\varphi_{P}\Big{)}=\int_{0}^{ \frac{\varphi_{P}}{\varphi_{P}}}\frac{1}{\lambda_{i}^{e}}a=1-exp\Big{(}-\frac {\varphi_{P}}{\rho_{P}\lambda_{i}}\Big{)},\end{split} \tag{1}\]  
where \(\varphi_{P}=2^{R_{P}}-1,\rho_{P}=\frac{P_{P}}{\varphi^{2}}\) and \(j=\text{PT}\rightarrow\text{PR},\text{PT}\rightarrow\text{ST}\), _and_\(i=PP,PS\). \(\square\)
# Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny
## 4 Experiments and Results
### Experiment
#### 4.4.3 Object-Detection Results
Compared to YOLOv7-tiny, the proposed model demonstrates improved detection accuracy while maintaining computational efficiency, making it better suited for practical applications in resource-constrained UAV platforms. This is reflected in its ability to reduce missed and false detections, as demonstrated in the experimental results. Despite these advancements, the proposed model still has some limitations. Its performance in detecting tiny objects and densely overlapping targets remains suboptimal. These challenges are primarily attributed to the loss of fine-grained features during multi-scale downsampling and the difficulty in distinguishing overlapping objects in dense scenarios. Additionally, although the model achieves higher computational efficiency than larger models, its inference time and hardware requirements could still pose challenges for deployment on ultra-low-power platforms. To address these limitations, future research could focus on the following directions:  
* Lightweight optimization: Techniques such as quantization and knowledge distillation can reduce the model's computational and memory demands while maintaining high detection accuracy;
* Advanced feature fusion: Incorporating dynamic feature pyramids or context-aware attention modules could improve the model's capability to detect small objects and overlapping targets more effectively;
* Robust data augmentation: Developing strategies tailored to extreme conditions, such as low-light environments or high-motion scenarios, could enhance the model's generalization and robustness;
* Global context modeling: Integrating transformer-based modules could expand the model's receptive field, improving its ability to capture global context in complex and dense scenes. These future directions are designed to address the current limitations of the proposed model and overcome the challenges that it faces, thereby further optimizing its performance and expanding its applicability in UAV-based object-detection tasks.
## 5 Results and Discussion
### Results of 69-Bus Radial Distribution System
Figure 10: Total power losses and VSI min for DNR without and with DG at light load in 69-bus test system. Figure 9: Voltage stability index for different load levels DNR with DG in 69-bus test system. At light load in 69-bus test system, the total power loss obtained from the PSO algorithm (case 2) with DG is 29.3 kW compared to 36.5 kW without DG with 7.2 kW differences, with 13.52% TPL reduction. The VSI without DG is 0.8423 which is improved to be 0.9205 with DG integration. For IPSO algorithm (case 3), the total power loss is 24.5 kW with DG compared to 26.7 kW without DG with 2.2 kW differences, with 4.13% TPL reduction. The VSI without DG is 0.8718 which is improved to be 0.9476 with DG integration. Finally, For IEPSO algorithm (case 4), the total power loss is 18 kW with DG compared to 18.8 kW without DG with 0.8 kW differences, with a 1.5% TPL reduction. The VSI without DG is 0.9298 which is improved to be 0.9649 with DG integration. As for the last case, the total power loss obtained from the PSO algorithm (case 2) with DG is 108.1 kW compared to 145.5 kW without DG with 37.4 kW differences, with 16.28% TPL reduction, while, the VSI improved is by 3.74%. For IPSO algorithm (case 3), the total power loss is 62.7 kW with DG compared to 69 kW without DG with 6.3 kW differences, with 2.74% TPL reduction, while, The VSI is improved by 8.18%. Finally, For IEPSO algorithm (case 4), the total power loss is 54.5 kW with DG compared to 61.9 kW without DG with 7.4 kW differences, with 3.22% TPL reduction. The VSI is improved by 8.25%. Similarly, at heavy load in 69-bus test system, the total power loss obtained from the PSO algorithm (case 2) with DG is 237.3 kW compared to 267.5 kW without DG with 30.2 kW differences, with 5.27% TPL reduction. The VSI is improved by 5.75%. For IPSO algorithm (case 3), the total power loss is 138.3 kW with DG compared to 160 kW without DG with 21.7 kW differences, with 3.79% TPL reduction. The VSI is improved by 13.64%. For IEPSO algorithm (case 4), the total power loss is 118.9 kW with DG compared to 143.6 kW without DG with 24.7 kW differences, with 4.31% TPL reduction. The VSI is improved by 9.38%. From the results of both test systems, the following points can be summarized as below:  
1. [label=()]
2. the proposed IEPSO technique performed better results in terms of fitness function value and computational time as compared to PSO and IPSO techniques in terms of TPL reduction and VSI improvement as objective function as seen in Table 8.
3. the proposed IEPSO technique is effective in finding the optimal sizing of DG in distribution power systems. 4. the TPL reduction and VSI enhancement can be obtained after DG placement and sizing. 5. by increasing the load condition of the test systems, from light to heavy load, the TPL is found to be increased and VSI decreased. This is to say that as far as the system is having a heavier the load, the system is getting more unstable with the increment in losses. 6. the voltage profiles are improved when DGs are installed at the optimal size and bus locations.
## 3 Results and Discussion  
### Effect of Chitosan and Eugenol Concentration on Eugenol Emulsions  
The data showed that the droplet size of F1 emulsions tended to increase along with the increasing concentration of chitosan solution as the second layer in the eugenol encapsulation (Table 2). This result is in line with that of [PERSON] and [PERSON] [25], who investigated oil encapsulation using chitosan and alginate as a biopolymer. The increased droplet size indicated polymer attachment on the surface of the oil core. However, the eugenol emulsion F2 did not show the same phenomenon. The results of the ANOVA test indicated that the increase in droplet size, due to the chitosan concentration, was not significantly different. Furthermore, the [PERSON]'s test results showed that there were significant differences in droplet size between F2-chi 0.0% and F2-chi 0.067%.  
The eugenol emulsion of F2-chi 0.067% had the largest particle size and was significantly different from the other samples. This was probably due to the instability of the emulsion, which eased the aggregation. The instability of the emulsion was also shown by its PdI and zeta potential values. The PdI of F2-chi 0.067% was the highest among all the samples, whereas the zeta potential value was the lowest (Table 2). A high PdI value indicates instability of an emulsion [26].  
Overall, the data showed that F1 emulsions had PdI values closer to 0.2, which represented the homogeneity of the emulsions. The F1 emulsions also showed a higher zeta potential than F2, but they were not significantly different. The F1 emulsion-chi 0.0% and the F2 emulsion-chi 0.0% showed negative values of zeta potential due to the absence of chitosan. The values were close to 30 mV, which revealed the stability of the emulsions. Zeta potential is an indicator of the stability of an emulsion and is influenced by the electric charge of the interface [27; 28]. This phenomenon indicated that the formula F1 emulsion was more stable than the formula F2 emulsion. The difference between the F1 and F2 emulsion formulas was in the eugenol concentration. The ratios of the mass emulsifier (WPMD conjugates) and eugenol in F1 and F2 emulsions were 5:3 and 5:1.5, respectively. The higher eugenol concentration in this study produced a better emulsion. The effect of the clove oil content on droplet size and stability in nanoemulsions was investigated by [PERSON] et al. [29].