esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Discussion  
Direct experimental comparisons are complicated by the fact that GAEN is focused on confirmed case interactions, whereas IntelliTrace emphasizes co-location within the same space. Due to the proprietary nature of GAEN, which typically restricts development to a single entity per nation, direct experimental comparisons with GAEN are inherently limited. Therefore, access to GAEN's API for developmental purposes is not universally available, limiting the capacity for empirical comparative studies. Furthermore, GAEN focuses on confirmed case interactions, whereas our approach emphasizes co-location within the same space.  
Consequently, we resorted to a literature-based performance assessment. Table 5 presents our findings and the development of Germany's official COVID-19 exposure notification app, the \"Corona-Warn-App\", as detailed in [28].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Precision** & **Recall** & **Accuracy** & **F1 Score** \\ \hline
96.31 & 96.60 & 97.50 & 94.94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of trajectory-based outdoor GPS contact tracing.  
Figure 8: Exposure detection performance of trajectory-based outdoor GPS contact tracing.  
Figure 7: Contact detection performance of trajectory-based outdoor GPS contact tracing according to \(R_{max}\) and \(S_{max}\).  
In the landscape of digital epidemiology, South Korea's use of QR code-based electronic entry logs (KI-Pass) has been instrumental in facilitating spatially informed responses to the pandemic [29]. Despite its utility, this system has presented challenges, including the inconvenience of manual QR code scanning by individuals, and a dearth of practical alternatives. Our IntelliTrace system offers an innovative and seamless alternative, circumventing the need for manual efforts, as evidenced by our performance comparison with the GAEN system, as outlined in Table 5.  
Wi-Fi adoption continues to grow globally, with installations in high-traffic public areas becoming increasingly common [30]. Nonetheless, the assumption of ubiquitous Wi-Fi availability in our system could restrict its use in areas without such infrastructure. Furthermore, the proposed method's efficacy is also contingent on the reception quality of BLE beacon signals, which are essential for the labeling of spatial information. There is a need for further research to refine the labeling process, ensuring that the data used to train and validate our system is as precise as possible.
# HORSIC+: An Efficient Post-Quantum Few-Time Signature Scheme
###### Abstract  
It is well known that conventional digital signature algorithms such as RSA and ECDSA are vulnerable to quantum computing attacks. Hash-based signature schemes are attractive as post-quantum signature schemes in that it is possible to calculate the quantitative security level and the security is proven. SPHINCS is a stateless hash-based signature scheme and introduces HORST few-time signature scheme which is an improvement of HORS. However, HORST as well as HORS suffers from pretty large signature sizes. HORSIC is proposed to reduce the signature size, yet does not provide in-depth security analysis. In this paper, we propose HORSIC+, which is an improvement of HORSIC. HORSIC+ differs from HORSIC in that HORSIC+ does not apply \(f\) as a plain function to the signature key, but uses a member of a function family. In addition, HORSIC+ uses the chaining function similar to W-OTS\({}^{+}\). These enable the strict security proof without the need for the used function family to be a permutation or collision resistant. HORSIC+ is existentially unforgeable under chosen message attacks, assuming a second-preimage resistant family of undetectable one-way functions and cryptographic hash functions in the random oracle model. HORSIC+ reduces the signature size by as much as 37.5% or 18.75% compared to HORS and by as much as 61.5% or 45.8% compared to HORST for the same security level.  
post-quantum signature; hash-based signature; few-time signature; second preimage resistant +
Footnote †: journal: Applied Sciences  
1
## 3 Results
### Simulation Results  
This section presents the simulation results of the proposed solution. As discussed earlier, the network area is visualized as a grid with discrete points.  
Two performance factors of the DTN UAV network were estimated: delivery rate and average latency. Detailed results of the comparison between battery charge and battery swapping are shown in Table 6. The values of the delivery rate in the case of drone battery swapping are in the range of 0.166 and 0.646 for the routing protocols Epidemic, Spray and Wait, PRoPHET, MaxProp, and MaxDelivery. The best delivery rate is an impressive 0.973 for the TD-UAV Dijkstra protocol (Figure 14). The worst results of latency were  
Figure 13: Visual C++ graphical interface: results for triangular networks with battery swapping.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Mission Phase**} & **Experimental Mean Flight Time [s]** & \multirow{2}{*}{**Standard Deviation**} \\ \hline Take off + Climb (30 m) & 8.24 & 0.193 \\ Cruise\_segment (4000 m) & 304 & 0.352 \\ Transfer data & 120 & - \\ Descent + Landing (30 m) & 12.12 & 0.085 \\ Total flight on triangular cell & 1172 & 1.127 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Flight test results.  
The delivery rate in the case of drone battery charge is between 0.135 and 0.143 for the routing protocols Epidemic, Spray and Wait, PRoPHET, MaxProp, and MaxDelivery. The maximum delivery rate is 0.664 and was obtained for the TD-UAV Dijkstra protocol. The worst results for latency were obtained for PRoPHET, and the TD-UAV Dijkstra latency was the best.  
The comparison between average latency (Figure 15) was performed on the routes where the delivery was successful for all the algorithms (62 routes for the case of battery swapping and 76 in the case of battery recharge).
## 3 Results and Discussion  
Three factors and their impact on the inhibition zone size and its time stability were measured:  
1. Plasma exposure time;
2. Applied microwave power;
3. Initial cell concentration.  
Figure 1: Scheme of the experimental set up: 1—mass flow controller; 2—quartz capillary; 3—surfatron resonator; 4—MW antenna; 5—MW-coaxial cable; 6—plasma beam; 7—Petri dish with inoculated _Candida glabrata_ culture; 8—Petri dish holder.
# U-Net-Based Semi-Automatic Semantic Segmentation Using Adaptive Differential Evolution
## 1 Introduction
The boundary of one part of the bones should be closed, and any missing line parts should not be admitted. Moreover, it is difficult to write the lines in a single stroke in many cases because most bone parts, such as the spine, are complex. Therefore, it takes time to generate each patient's bone model, even when applying U-net data. This is the main reason why automatic diagnosis has not been widely used [28; 29; 30]. With the above in mind, in this paper, we propose a novel semi-automatic semantic segmentation method for U-net data. Specifically, our method proposes a semi-automatic GT generator and an optimal CT image generator. The proposed method overcomes this problem using rough handwritten lines as input by formulating and solving the bone boundary generation problem with CT images, reframing it as a clustering and combinatorial optimization problem. The proposed method only needs rough handwritten lines, so time savings for input image preparation are expected. However, most CT images are in the digital imaging and communications in medicine (DICOM) data format and contain many parameters, such as window width and center. When applying semantic segmentation, input images are generated by converting from the DICOM data format to the bitmap format. The created input images are generated differently according to their parameters and affect the difficulties of semantic segmentation. To the best of our knowledge, the optical parameter settings are not applied in U-net; instead, the parameters are related to each other. Therefore, we propose a DICOM parameters optimizer using a differential evolution algorithm (DE), a robust optimization algorithm of high dimensions, to enhance our proposed method. As far as we know, our proposed semi-automatic semantic segmentation using clustering and combinatorial optimization is the first challenge of its kind to enable automatic modeling for each patient. The main contributions of this study are summarized as follows:  
1. Our method enables a novel semi-automatic GT generation by combining clustering and combinatorial optimization methods. 2. Our method optimizes CT images using DICOM parameters based on the adaptive DE. 3. We evaluate our GTs' capability as input data for the U-net using femur, spine, and artificial bones. To facilitate further semi-automatic semantic segmentation using the U-net approach, we have made our implementation publicly available. Please visit: [[https://github.com/ISDL-academic/U-Net_base_bone_segmentation](https://github.com/ISDL-academic/U-Net_base_bone_segmentation)]([https://github.com/ISDL-academic/U-Net_base_bone_segmentation](https://github.com/ISDL-academic/U-Net_base_bone_segmentation)) (accessed on 25 September 2023).
## 3 Study Area
### Feature Engineering  
From the deformation characteristics reflected by the Lishanyuan landslide monitoring data, we found that the deformation process of the landslide showed an obvious correlation with the rainfall process. To further mine the association rules of this correlation, we needed to carry out further data mining on the monitoring data, for which feature engineering was first needed. Feature engineering refers to extracting more representative features from raw monitoring data to improve the effectiveness of mining tasks. For the monitoring data and deformation characteristics of the Lishanyuan landslide, we constructed features for both deformation and velocity. In terms of deformation, we focused more on the accelerated deformation process, so the deformation velocity was the most important feature. The deformation velocity (\(v_{DB01}\),\(v_{DB02}\)) of two GNSS monitoring stations was chosen as the main feature. In terms of rainfall, we paid attention not only to the short-term rainfall features, but also to the long-term rainfall features. We chose the cumulative rainfall of three hours \(q^{3h}\), six hours \(q^{6h}\), twelve hours \(q^{12h}\), 24 h \(q^{24h}\), three days \(q^{3d}\), and seven days \(q^{7d}\) as the characteristics reflecting rainfall.  
According to [PERSON] et al. [40] and [PERSON] et al. [41], the strength of correlation between features can be quantitatively determined by gray relation analysis. Therefore, we used the gray relation analysis algorithm to calculate the gray relation degree between various types of rainfall features and deformation velocity; the calculation results are shown in Table 1. From Table 1, we can see that the gray relation degree of all rainfall features and deformation velocity is greater than 0.9, which is much higher than the empirical threshold of 0.6. So, all of these rainfall features can be adopted.
## 3 Proposed Method
### Data Preprocessing  
We download the raw AIS data for the southeast and southwest coastal waters of the United States in the whole year of 2021 on the website ([[https://marinecadastre.gov/accessais/](https://marinecadastre.gov/accessais/)]([https://marinecadastre.gov/accessais/](https://marinecadastre.gov/accessais/)) (accessed on 31 December 2021). We conduct comprehensive data preprocessing on the raw data and use it to train, validate, and test the model. Specifically, raw AIS data have the issues of data errors and high latency, and the targets are to denoise and cut the track points with equal time intervals, which can improve the performance of deep learning models significantly.  
The main process includes six steps. Firstly, we separate the trajectory data of different ships by maritime mobile service identify (MMSI) number, the unique ID of a vessel, and sort the track points of each vessel by timestamps. Secondly, we delete the duplicate and anomalous data. After that, we set the time interval of trajectory points to 30 min and perform cubic spline interpolation [35] on possible track points. Fourthly, we cut the trajectory into different segments by judging that the distance between three consecutive trajectory points is less than 100 m. Fifthly, we calculate the value of speed and course for new interpolated trajectory points. Finally, we normalize longitude, latitude, speed, course, and sailing distance by the Min-Max Normalization method.
## 2 Theory and Methodology
### Theory  
The three-fold model proposed by [PERSON] in 2006 was used in this research to propose the optimization path. Space reproduction in rural settlements refers to the phased transformation of the spatial pattern caused by the joint effect of various internal and external factors [37; 38; 39; 40]. [PERSON] was the founder of the production of space. [PERSON], borrowing the conceptual triad from [PERSON], exceeded the materialist and idealist dualism of rural space and built a \"three-fold model\" describing modern rural space's characteristics and states [41; 42; 43]. His model rearranged the scattered elements of rural space and achieves a re-understanding of rural space through restructuring rather than reconstruction [44; 45]. The three-fold model is based on the framework of spatial practice (the perceived space), representations of space (the conceived space), and representational space (the lived experience), and suggests that rural space is a \"three-fold space\" that includes rural locality, representations of the rural, and lives of the rural [46].  
Rural locality refers to space form after rural space is produced or consumed, such as through the physical manifestation of architecture, landscape, indoor and outdoor space, and industrial composition [47].  
Representations of the rural rxcefer to the demands of capital, power, and politics, which drives changes in rural space and can be regarded as the space connotation [48].  
Lives of the rural are the expression and communication of subjects' daily activities and culture in rural space, thus reflecting the value and meaning of the space and being termed as \"space meaning\" [49; 50]. The logical line of the three-fold model is demonstrated in Figure 2.  
New rural settlements that are impacted or produced by tourism feature the overlap between scenic spots, ecological areas, and residence. They have the dual function of tourism development and environmental protection. Diversified interest entities are also involved in the tourism business. Property rights here are thus very complex, resulting in sharper and sharper contradictions between people and people, and human and land [40]. The aforementioned conditions create a complicated transition process of the multi-dimensional environments in rural settlements, and the production of space theory and the three-fold model based on it can help to understand this process and establish the optimization path accordingly.
## 3 Computational Approach
### Computational Grid  
The computational grids and the computational domain with a schematic diagram of the inflow are shown in Figure 4. The zonal method [49] using two computational grids with different resolutions were employed to treat small fluid fluctuations induced by PA in the present LES. The computational grids consist of a C-type grid around the airfoil (zone1: blue and red) and a fine grid around PA (zone 2: green). The body force of the Suzen-Huang model was obtained in advance and mapped to zone 2. Equations (2)-(4) were solved for zones 1 and 2, and physical values were exchanged with each other at every time step.  
The distance from the airfoil surface to the outer boundary was \(25c\), and the width of the span-wise computational domain was \(0.2c\). The grid points of zone 1 and zone 2 were approximately \(1.8\times 10^{7}\) and \(2.0\times 10^{6}\), respectively, as shown in Table 1. The minimum grid spacing in the wall-normal direction was \(0.00012c\). The maximum grid sizes based on the wall unit were \((\Delta\xi^{+},\Delta\eta^{+},\Delta\zeta^{+})\lesssim(8,9,1)\) at the attached turbulent boundary layer region. The present grid resolution and the computational methods were validated in the previous study [23].  
Figure 4: Computational grids and domain. The grids were visualized for every four points.
## 9 Dispersion Compensation  
In order for an optical cavity to sustain a pulse, the temporal shape and duration of the pulse must remain stable as it circulates through the cavity. Since the Ti:sapphire crystal and other optical elements in the cavity are dispersive, the pulse is deformed as it passes through them, due to the wavelength dependence of the refractive index. Thus, dispersion compensation is imperative in order to sustain short pulses. In the following, we provide a concise survey of the basic principles of dispersion management and the most commonly used dispersion compensation devices of prism pairs and chirped mirrors. For a more detailed study of the topic, readers are referred to the literature [54; 55; 56].
# The Long-Term Consequences of Forest Fires on the Carbon Fluxes of a Tropical Forest in Africa
## Appendix A  
_Species List of the Study Area at Mt. Kilimanjaro_  
List of tree species found in the tropical forest at Mt. Kilimanjaro with maximum growth height and light requirement (tol = shade tolerant; intol = shade intolerant; med = medium light requirement). All tree species were grouped into six plant functional types (PFT). For details of the species grouping, see [32,33]. In addition, the fire tolerance is indicated for each species (1 = fire intolerant to 4 = fire tolerant).  
\begin{tabular}{c c c c} \hline \hline
**Species** & **Max. Height (m)** & **Shade Tolerance** & **PFT** & **Fire Tolerance** \\ \hline _Agarista salicifolia_ & 20 & intol & 4 & 4 \\ _Alangium chinense_ & 25 & intol & 4 & 1 \\ _Albizia gummifera_ & 30 & tol & 2 & 1 \\ _Aningeria adolfi friedericii_ & 50 & tol & 1 & 1 \\ _Aphloia theiformis_ & 15 & med & 3 & 1 \\ _Bersama abyssinica_ & 20 & med & 3 & 1 \\ _Canthium oligocarpum ssp capturing_ & 10 & tol & 5 & 1 \\ _Caseria battiscombei_ & 30 & tol & 1 & 1 \\ _Celis durandii_ & 25 & med & 3 & 1 \\ _Clausen anistata_ & 20 & med & 3 & 1 \\ _Corrus volkensii_ & 30 & med & 3 & 1 \\ _Cyathe manminana_ & 15 & intol & 6 & 1 \\ _Dracena afromontana_ & 10 & tol & 5 & 1 \\ _Dracaena laxissima_ & 5 & tol & 5 & 1 \\ _Eckebergia capensis_ & 25 & tol & 2 & 1 \\ _Emhelia schimperi_ & 30 & tol & 2 & 1 \\ _Entandrophragma excelsum_ & 70 & tol & 1 & 1 \\ _Erica excelsa_ & 28 & intol & 4 & 4 \\ _Erythroccoca polyandra_ & 10 & tol & 5 & 1 \\ _Ficus sur_ & 25 & med & 3 & 1 \\ _Galiniera saxifraga_ & 15 & tol & 5 & 1 \\ _Garcinia tanstainensis_ & 40 & tol & 1 & 1 \\ _Garcinia volkensii_ & 20 & tol & 2 & 1 \\ _Hagenia abyssinica_ & 25 & intol & 4 & 3 \\ _Hallea rubrostipulata_ & 33 & med & 3 & 1 \\ _Heinsenia direvilleoides_ & 25 & tol & 2 & 1 \\ _Ilex mitis_ & 30 & tol & 2 & 1 \\ _Lasianthus kilianandscharicus_ & 10 & tol & 5 & 1 \\ _Lepidopteraichilia volkensii_ & 16 & med & 3 & 1 \\ _Leptonychia usamharensis_ & 15 & tol & 5 & 1 \\ _Macananga capensis sur kilimandscharica_ & 30 & med & 3 & 1 \\ _Maesa lanccolata_ & 20 & intol & 4 & 1 \\ _Maytenus acuminata_ & 15 & tol & 5 & 1 \\ _Morella salicifolia_ & 15 & intol & 4 & 4 \\ _Myrica salicifolia_ & 15 & intol & 4 & 4 \\ _Nevtoria buchanantii_ & 40 & tol & 1 & 1 \\ _Codea usamharensis_ & 45 & tol & 1 & 2 \\ _Olinia rocheleiana_ & 25 & med & 3 & 1 \\ _Pauridiantha paucintervis_ & 10 & tol & 5 & 1 \\ _Paveetta abyssinica_ & 15 & tol & 5 & 1 \\ \hline \hline \end{tabular}
## 5 Experimental Evaluation
### Energy Efficiency in NEURON  
Simulations are performed in order to assess the energy efficiency of NEURON mechanisms and their impact to the network lifetime. Network lifetime refers to the time period where all the nodes of the network (or a very high percentage of them) are operational. The network size is set to 1,000 while the initial energy of each node is set to 100,000 units. Simulations are terminated when the existing WSN is split into two or more isolated groups as nodes leave the WSN when their battery is exhausted.  
Figure 17: (a) Total number of messages exchanged for cluster formulation and (b) total number of messages exchanged for cluster formulation per node.  
Figure 18: (a) Route Request and (b) Route Response Message Cost.  
In Figure 19(a), the number of nodes that are alive while the number of cycles increases is shown for fixed and variable probability and different densities. The threshold where a CH switches to normal mode--when the KPI of the CH goes below it--is set to 50%. It is noticed that the network tends to extend its network lifetime as the number of alive nodes reduces steeply after a certain number of cycles. This means that the available power of the sensor nodes is almost the same and thus they run out of power in a few cycles. Furthermore, it is shown that the network lifetime is longer in the case of applying the variable probability and in case of more sparse networks. This is reasonable since fewer messages have to be exchanged in sparse networks for cluster formulation and maintenance. In Figure 19(b), the residual energy in the network is shown. The threshold where a CH switches to normal mode is set to 50% and 75% while the density is set to 15 and 30, respectively. The residual energy is higher in case of sparse networks and in the case where the threshold is set to 50%. A high threshold reduces the rotation in the CHs in the WSN and causes high energy consumption in each CH, causing them to run out of energy earlier than the other nodes. In this case, therefore, energy consumption is not homogeneously distributed among the sensor nodes. In Figure 19(c), the consumed energy is depicted in case of the fixed and the variable probability, while the density is set to 15 and the threshold where a CH transits to normal mode is set to 50%. It is clear that the application of the  
Figure 19: (a) Alive Nodes in the WSN, (b) Residual Energy in the WSN and (c) Consumed Energy in the WSN.  
autonomic mechanism in the election of clusters is more energy efficient compared to the application of a stable probability.