esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Implementation Results
### Experiment Results
In particular, in the case of CHAM, all variables used in the encryption function could be allocated and used in registers, so the performance improvement of the GPU encryption kernel according to the proposed optimization method was very high. Table 7 shows the difference in speed depending on the size of the memory and the number of threads per block. All performance in the table was measured on the RTX 2070. The best performance for the optimization implementation of AES, CHAM, and LEA is 310 Gbps, 3.03 Tbps, and 2.47 Tbps, respectively. In the case of AES, the highest performance was confirmed when encrypting 512 MB data with 512 threads per block. In the case of CHAM and LEA, unlike AES, they do not use a table, thus it takes less time to access the memory and shows higher throughput. In this table, the performance was measured ignoring data copy time. Additionally, for GTX 1070, the highest performance of AES, CHAM, and LEA was 214 Gbps, 1.67 Tbps, and 1.54 Tbps, respectively, and, for GTX 970, the performance of 123 Gbps, 936 Gbps, and 868 Gbps was confirmed, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Platform** & **Algorithm** & **Basic** & **CTR Opt.** & **Memory Opt.** & **Async Opt.** & **Inline PTX** & **All** \\ \hline \multirow{3}{*}{GTX 970} & AES & 29 & 34 (+17\%) & 104 (+259\%) & 31 (+7\%) & 37 (+28\%) & 123 (+324\%) \\  & CHAM & 131 & 139 (+6\%) & 277 (+111\%) & 132 (+1\%) & 345 (+163\%) & 936 (+615\%) \\  & LEA & 122 & 124 (+2\%) & 348 (+185\%) & 122 & 271 (+122\%) & 868 (+611\%) \\ \hline \multirow{3}{*}{GTX 1070} & AES & 51 & 59 (+16\%) & 171 (+235\%) & 57 (+12\%) & 58 (+14\%) & 214 (+320\%) \\  & CHAM & 221 & 247 (+12\%) & 473 (+114\%) & 241 (+9\%) & 594 (+169\%) & 1671 (+656\%) \\  & LEA & 202 & 219 (+8\%) & 615 (+204\%) & 226 (+12\%) & 460 (+128\%) & 1546 (+665\%) \\ \hline \multirow{3}{*}{RTX 2070} & AES & 74 & 94 (+27\%) & 237 (+220\%) & 80 (+8\%) & 89 (+20\%) & 310 (+318\%) \\  & CHAM & 434 & 476 (+10\%) & 889 (+105\%) & 446 (+3\%) & 1342 (+209\%) & 3,033 (+598\%) \\ \cline{1-1}  & LEA & 381 & 431 (+13\%) & 985 (+159\%) & 395 (+4\%) & 753 (+97\%) & 2472 (+548\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Throughput by each optimization implementation (throughput is measured by Gbps). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **128 MB** & **256 MB** & **512 MB** & **1024 MB** \\ \hline \multirow{3}{*}{AES} & 256 threads & 300 & 307 & 296 & 280 \\  & 512 threads & 309 & 306 & **310** & 301 \\  & 1024 threads & 306 & 309 & 309 & 308 \\ \hline \multirow{3}{*}{CHAM} & 256 threads & 2886 & **3033** & 2952 & 2859 \\  & 512 threads & 2827 & 2851 & 2870 & 2844 \\  & 1024 threads & 2511 & 2539 & 2526 & 2501 \\ \hline \multirow{3}{*}{LEA} & 256 threads & 2437 & **2472** & 2412 & 2419 \\  & 512 threads & 2429 & 2447 & 2452 & 2450 \\ \cline{1-1}  & 1024 threads & 2238 & 2251 & 2256 & 2261 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Throughput by data size and the number of threads in RTX 2070 (Gbps).
# Smart IoMT Framework for Supporting UAV Systems with AI
## 3 Architecture of the System
The data obtained by the UAV is relayed to medical personnel, family members, and emergency workers, allowing them to take necessary action. The system can be designed as an application that may be accessed via a mobile device and the obtained health measurements can be readily accessed in the system proposed in this paper. After analysis of the data sent from the central base to the hospital or clinic, the UAVs can also play a role in post-analysis treatment by the transportation of medicines to these remote locations. The report for analysis is auto-generated and available on the webpage for patient access. The UAVs can also be directed to re-visit the patient after a set period for a regular checkup on the previous analysis and detect improvements in the patient health. The schematic diagram, as portrayed in Figure 3, is designed in a Fritzing environment. All the required connections between the microcontroller and other peripherals are given in the circuit diagram. All the sensors are connected to the GPIO pin of NodeMCU, which is powered via a USB cable. The next three phases make up the HMS-UAV architecture, as shown in Figure 4.
* **Phase 1--Data collection:** During this stage, the UAV gathers patient data from several BANs. The cores often have access to these details. This is explained further in Figure 5, where the architecture uses UAVs to reliably and efficiently gather data from the BANs. In the suggested architecture, the UAV arranges the cores into a star network topology and manages resources using a WUR approach. WUR and data transfer, respectively, employ data and control channels. * **Phase 2--Data Transmission:** This is when the UAV communicates the data it has gathered to data servers, medical servers, and doctors. * **Phase 3--Data processing:** During this stage, servers process patient data to make different health choices, including real-time prescription and diagnosis. This architecture is involved with a fog layer interface between the UAV and IoT sensors. The next step is data integration. After that, we generate the dataset. From the dataset, we classify the results of the sensor into normal and abnormal classes using a Decision Tree, where the accuracy is tested regarding the performance of the data extraction and analysis. It is challenging to build a beacon-based synchronizing technique to align the UAV's scheduling with BANs because the protocols being used for BANs typically adhere to the IEEE 802.15.6 standard, which uses beacon transmission for synchronization. One of the key causes is that each core must be operational for periodical beacon transmission to continue, regardless of whether they intend to deliver data. Figure 3: Circuit diagram of the sensors. Additionally, while the cores are in standby mode, the beacons could not be picked up, lengthening the time it takes for data to be sent. It is suggested to employ a wakeup-radio (WUR) based communication method that enables the UAV to effectively gather data from several BANs. The following two steps make up the suggested technique:
## 3 Materials and Methods
### Datasets
#### 3.1.2 KDEF Dataset  
KDEF contains 4900 colored images from 70 participants, evenly split between 35 females and 35 males, and every subtle nuance of emotion is captured in detail. The dataset includes 7 distinct emotional facial expressions--Afraid, Angry, Disgusted, Happy, Neutral, Sad, and Surprised--and each expression is captured from 5 different angles, ensuring that every nuance of facial expression is accounted for. In Figure 1, we offer a glimpse of the KDEF dataset, presenting samples of different facial expressions captured from a variety of angles: (a) full left profile, (b) full right profile, (c) half left profile, (d) half right profile, and (e) straight-on. Although many studies in FER focus solely on a subset of the KDEF dataset--namely, the 980 images that represent the seven emotions from a frontal view--we went above and beyond to use the 3920 images that represent the seven emotions from full and half left and right poses excluding the frontal view as well as the full dataset in our study. To test the accuracy and effectiveness of our model, we applied SAFEPA to the KDEF dataset subsets as well as the full dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Samples** & **Participants** & **Brief Description** & **Format** & **Number of Classes** & **Role in Our Work** \\ \hline JAFFE [28] & 213 & 10 & Front view & Gray scaled images & 7 facial expressions & Train and test FEAPAS & on FER. \\ \hline \multirow{3}{*}{KDEF [15]} & \multirow{3}{*}{4900} & \multirow{3}{*}{70} & Five different poses & & & Train and test SAE. \\  & & & including full & & Train and test Basic, & FEAPAS, SAFEPA, and \\  & & & left/right profiles & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{3}{*}{UNBC McMaster [11]} & 200 sequences & \multirow{3}{*}{25} & Very imbalanced [the images were captured from & Sequences of & 16 pain levels, but & Train and test basic, \\  & & & a frontal perspective or & & colored frames & & SAFEPA, and models in \\  & & & & partial view] & & & [17] for PA *. \\ \hline \multirow{3}{*}{BioVid [16]} & 6900 labeled & \multirow{3}{*}{69} & \multirow{3}{*}{Free range of head motions} & \multirow{3}{*}{Colored videos} & \multirow{3}{*}{5 levels of pain} & Unseen test of SAFEPA & \\  & & & & & & for PA *. \\ \hline \hline \end{tabular}  
* PA is an abbreviation for Pain Assessment.  
\end{table}
Table 1: Summary of the three used datasets.  
Figure 1: Samples of KDEF dataset with different facial expressions from different angles. Images (AF01 DIFL, AF01 DIFR, AF01 DIFL, AF01 DIFR, AF01 DIS, AF01 HAFL, AF01 HAFR, AF01 HAHL, AF01 HAHR, AF01 HAS, AF01 NEFL, AF01 NEHR, AF01 NEHR, AF01 NES, AF01 SAFL, AF01 SAHL, AF01 SAHR, and AF01 SAS) from KDEF dataset. Reprinted/adapted with permission from Ref. [15].
# Perceptual Similarities between Artificial Reverberation Algorithms and Real Reverberation
## 4 Results
### Statistical Analysis  
#### 4.2.1 Non-Parametric Kruskal-Wallis One-Way ANOVA Test  
Listening test data were checked for normality and variance homogeneity using the Lilliefors test [59] and Bartlett test [60]. The results show that not all data conform to normal distribution and homogeneity of variance. In addition, because of the presence of outliers and in order to ensure robustness, a non-parametric Kruskal-Wallis one-way ANOVA test [61] with 95% confidence intervals [62] was run to determine whether there were significant differences between the seven reverberation algorithms. The results are presented in Table 12. The null hypothesis was that there were no significant differences between the seven reverberation algorithms. All \(p\)-values were less than the significance level of 0.05; thus, the null hypothesis is rejected, meaning that there are significant differences between the seven reverberation algorithms.  
Figure 21: The error bars of the mean score with standard error of the cello piece simulated by the reverberation algorithms under short (0.266 s), medium (0.95 s), and long (2.34 s) reverberation times: (a) 0.266 s, (b) 0.95 s, (c) 2.34 s.  
Figure 22: The error bars of the mean score with standard error of the drumbeat simulated by the reverberation algorithms under short (0.266 s), medium (0.95 s), and long (2.34 s) reverberation times: (a) 0.266 s, (b) 0.95 s, (c) 2.34 s.
# Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration
## 5 Experimental Results
### Experimental Results Analysis  
#### 5.2.1 Evaluation of Obstacle Reconstruction in the Scene  
The scenes shown in Figure 9a,b feature six square flowerbeds, each approximately 3 m on each side. An occupancy grid map measuring approximately 30 \(\times\) 30 m was constructed for this scene, with the mapping results presented in Figure 9c. In this map, the green areas represent ground grids, while the black areas indicate the locations of the flowerbeds. Although a few small black areas yielded inaccurate mapping results, the positions of all flowerbeds were successfully reconstructed. However, the shapes of the obstacles could not be accurately reconstructed, as shown in Figure 9e, where the vertices of the flowerbeds do not form perfect squares.  
As shown in Figure 9f, we calculated the errors between the positions of each landmark point in the map and their actual positions in the real world. Since the reconstructed obstacles are not perfect squares, some points exhibit larger errors; however, the maximum error is approximately 0.8 m. Most landmark points have errors within 0.4 m, with the minimum error being less than 0.1 m.  
To evaluate the effectiveness of our method for reconstructing small-sized obstacles, we examine the mapping results in the scenes shown in Figure 10a,b. The scene contains five circular columns, with the black areas in Figure 10c representing their positions in the map. Four landmark points are selected at the same positions on the circular bases of each column, as illustrated in Figure 10e. Except for the fourth column, which exhibits a relatively large reconstruction error, the remaining columns show satisfactory results. In Figure 10f, we assess the errors of the landmark points along the X-axis and Y-axis. It can be observed that, apart from a few points with significantly larger errors, most errors are concentrated within 0.3 m.  
In summary, our method demonstrates superior performance in reconstructing small-sized obstacles. The maps generated using our approach provide a more accurate reconstruction of obstacle positions. However, limitations remain in the reconstruction of obstacle shapes. Nevertheless, since our method is primarily designed to construct navigable grid maps, which often involve obstacle expansion during navigation tasks, the shape reconstruction errors remain within an acceptable range.  
Figure 8: The OGM visualization format proposed in this paper.  
Figure 9: Mapping results for Scene 1. (**a,b**) The real environment photographs, (**c**) the visualization of the OGM mapping results, (**d**) the ground truth of landmark points, (**e**) the comparison between the sampled points and the ground truth, and (**f**) the errors in the x-axis and y-axis for mapping results.
## 1 Introduction
In the field of military science, the contribution rate is a crucial indicator for measuring the impact of military equipment or operations on overall combat capability or operational effectiveness. In this study, it refers to the degree of influence of different types of ELF magnetic fields on the submarine's total magnetic field when being detected [12]. Recent advancements in system engineering and big data have prompted many scholars to conduct systematic research on the contribution rates of multifactorial systems, providing new insights into the composition and control of the ELF magnetic fields of underwater platforms. Current research indicates that the understanding of system contribution rates and their assessment methods has matured, with extensive research conducted on the contribution rates of equipment to combat systems. Among these, the Analytic Hierarchy Process (AHP) has emerged as an effective tool for assessing system contribution rates, and it has been widely applied in various fields. The Analytic Hierarchy Process is a systematic analytical tool designed to address complex decision-making problems. Proposed by Professor [PERSON] in the 1970s, the AHP integrates both qualitative and quantitative analyses. The core concept of the AHP involves breaking down complex problems into more manageable components to simplify the decision-making process. This method constructs a hierarchical model that divides the decision-making problem into multiple levels, such as the target level, criterion level, and alternative level. Through pairwise comparisons and weight assignments, the AHP provides decision-makers with quantitative support [13]. The AHP has been widely applied in risk assessments [14], performance assessments [15], and evaluations of the contribution rates of weapon systems [16]. This study leverages the advantages of the Analytic Hierarchy Process in comprehensive effectiveness evaluation and multi-criteria decision-making to establish an AHP-based model for assessing the contribution rates of ELF magnetic fields [17]. From a system engineering perspective, this model is integrative. It combines factors from both the detection and detected entities. The model supports the acquisition of specific contributions. These contributions come from different types of ELF magnetic field characteristics. These characteristics are associated with submarines operating under various conditions. Additionally, they are relevant in different detection scenarios. The goal is to understand their impact on the overall ELF field. This provides a theoretical basis for the active control research of ELF magnetic fields in submarines [18].
## 4 Discussion
Finally, a new degassing phase (Figure 3A,B) started in April-May 2019, characterized by a high natural daily variation (NDV), expressed as normalized standard deviation in % [2] culminated at the end of June with values >40%. Figure 3: **(A)** Hourly CO\({}_{2}\) fluxes of STR02 in the period from October 2018 to July 2019. Changes in the style of shallow degassing (green horizontal line represents the background level) with a progressively increased variability of daily CO\({}_{2}\) fluxes have been observed in March and May, as evidenced by yellow and red lines. **(B)** Natural daily variations (NDV; [2]) of hourly CO\({}_{2}\) fluxes expressed as daily (24 measurements) normalized standard deviation in per cent [2]. The blue line is the weekly average value of NDV, which showed a significant increment from March 2019 up to early July. This new strong increase of NDV% marked the begin of a new degassing style, characterized by a great disequilibrium between input/output degassing in the shallow volcanic system due to the strong increased volatile pressure. In this very anomalous framework of outgassing from soils in the summit areas, on 3 July 2019, a sharp and deep input of magma and gas arrived at the summit craters, producing a high-energy paroxysm with a column of gas and ash with a height of about 5 km (Figure 4). Understanding the shallow processes by studying data patterns and trends is useful for assessing the actual risks due to potential explosive unrests. In particular, these kinds of studies have been previously performed in this [2] and other active volcanoes, using different geochemical [32; 33] and geophysical [34; 35] parameters, but with the same finalities. A common finding from these studies is that, during active and passive degassing, these natural systems never attain real equilibria following pseudo-random processes that can be interpreted using statistical approaches. The fluctuating CO\({}_{2}\) flux signal (natural daily deviation) recorded in 2019 at Stromboli, together with the other monitored parameters, could be the key for the prediction of paroxysmal events. Further related studies on Stromboli, compared with other similar volcanic systems, could improve our knowledge of shallow degassing processes and provide insights on their future behaviors.
# Cryobiotechnology of Plants: A Hot Topic Not Only for Gene Banks
## 4 Cryobiotechnology of _Malus_ (Apple)
Most protocols used for ultra-low temperature storage of dormant apple buds are based on the original outlines by [PERSON] [93; 94], involving: cutting of winter hardened twigs in segments containing the dormant bud, desiccation at a temperature from \(-\)4 \({}^{\circ}\)C to \(-\)5 \({}^{\circ}\)C of the segments until moisture content is reduced to about 30%, cooling in a programmable slow cooler to a final temperature of \(-\)30 \({}^{\circ}\)C, and placing the cooled segments in liquid nitrogen vapor or liquid nitrogen for long-term storage. For plant regeneration, recovered scions with dormant buds are grafted directly on pot-grown rootstocks, or it is also possible to excise shoot tips from cryopreserved buds and place them on culture medium [95]. Successful regeneration of cryopreserved buds is tightly linked with the cold hardening of these buds before cryopreservation, rather than with the phase of endodormancy [96]. Several papers in recent years focused on pathogen eradication in apples by using cryotherapy. For example, the vitrification cryotherapy technique was reported by [PERSON] et al. (2016) [97]. They infected apple cultivars and rootstocks and treated them with ultra-low temperatures to eliminate four different viruses (apple chlorotic leaf spot closterovirus (ACLSV), apple stem pitting virus (ASPV), apple stem grooving virus (ASGV), apple mosaic virus (ApMV)). Their therapy resulted in the production of virus-free plants for seven out of nine tested cultivars. In another study, the encapsulation-dehydration method was applied to in vitro-grown apple rootstocks M9 and M26 to eliminate ASPV and ASGV viruses. Cryotherapy was successful for ASPV but failed for ASGV. A probable explanation of this result was the different distribution of viruses in shoot tips. Although ASPV was not detected in the upper part of the apical dome and leaf primordia, ASGV was [89]. Results obtained two years later also showed that elimination of ASGV by cryotherapy had some limitations due to its localization in the shoot tip [58]. Finally, a recent study from the same research group examined the effect of another cryotherapy technique, i.e., droplet-vitrification, on the eradication of three latent apple viruses (ACLSV, ASGV, ASPV) from an infected 'Monalisa' apple cultivar. Cryotherapy was successful for the elimination of ASPV and ACLSV with 100% and 95% efficiencies, respectively. However, only 35% of regenerated plants were free of ASGV [59].
# Development of Cost and Schedule Data Integration Algorithm Based on Big Data Technology
## 5 Case Study of Proposed Algorithm
### Summary of Case Study
The database covers data at 31 different levels in the six dimensions and associated five core unit data (i.e., unit, unit cost); these two types of data are called independent variables. The database also involves cost and schedule estimation data (dependent variables) using the independent variables. The composition of the implemented database is summarized in Table 2. The variables and attributes are accumulated in each row item. Since this case study used Excel as a database, algorithms for data mining, analysis, and visualization (Module 5-7) were also developed in the Excel environment. Pivot Table is utilized to develop Module 5, as an OLAP tool offering functions for data mining and query interactively from multiple perspectives and LODs (see Figure 9). It was also used as a data analysis platform to analyze cost and schedule data (Module 6) and report it (Module 7). The rules for checking project status  
Figure 8: Part of Transformed Data into the 5W1H Data Schema using Module 2 for Data Parsing. concerning cost, schedule, productivity, quantity, labor, periodic payment, and material of construction projects were defined within the Excel environment. Users also can analyze the data for their purposes by querying the multiple dimension and levels. The implemented algorithm generated various types of reports focusing on integrated dimensions and levels accurately. Figure 10 represents two reports created from the algorithm--periodic payment for labor (a) and resource (b). The labor payment for concrete work on superstructures in each building for a fixed period was extracted accurately. The result is derived by using three dimensions: _WHEN_ L3 (Month), _WHERE_ L3 (Facility), and _WHAT_ L3 (Element). For resource payment, a combination of _WHEN_ L3 (Month), _WHERE_ L3 (Facility), _WHAT_ L3 (Element), and _WHO_ L1 (Contractor) correctly created a total payment to contractors for concrete work on superstructures of each building from March to June. The reports in a similar context but different focuses were quickly produced by controlling a set of variables based on fully integrated cost and schedule data.