esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Characteristics of Datasets  
Several email datasets have been reported in the literature. Two recent review articles by [PERSON] et al. [2] in 2019 and [PERSON] et al. [58] in 2018 have listed a number of datasets, which are shown in Table 3. As seen from the table, the datasets were developed during the years 1998 and 2007. Since 2007, no new email dataset has been reported in the literature. Furthermore, another article [59] in 2020 identifies that among the known datasets (as given in Table 3), SpamAssasin, Enron, and PU datasets are publicly available datasets, and other datasets are generally not available or easily accessible in the public domain.  
As far as the current study is concerned, three datasets, namely Enron [32], Lingspam [38], and PU [39], were used. The reasons for selecting these datasets are as follows:  
* Many recent studies utilized the Enron, Lingspam, and PU datasets. As shown in Table 2, the use of Enron dataset has been reported in several studies between the years 2020 and 2022 [11; 13; 15; 26; 27; 41]. Similarly, the use of Lingspam and PU has also been reported in two recent studies published in 2021 [11; 15]. This indicates that the datasets are still in active use in the research community.
* The emails in these datasets were generated between 2000 and 2010. This scenario characterizes the change in wordings and writing patterns in emails over a period of ten years [15]. Secondly, the Enron dataset is employed due to its bias towards spam class [15]. Thirdly, the LingSpam dataset is used due to its domain-specific ham mails, which are extracted from scholarly linguistic discussions [15]. Lastly, the PU dataset is used, since it is less frequently used by researchers, thus encouraging us to study the behavior of this dataset with respect to the aforementioned three algorithms and the proposed fuzzy-logic-based performance metric.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 8 Challenges and Opportunities  
### Latency and Bandwidth  
Latency and bandwidth are critical factors in the performance and user experience of the metaverse [240]. Latency refers to the delay between a user's action and the system's response, which is crucial for real-time interactions in virtual environments. High latency can result in lag, making movements and communications appear delayed or out of sync, thus disrupting the immersive experience [241]. Bandwidth, on the other hand, refers to the amount of data that can be transmitted over a network in a given period. Sufficient bandwidth is necessary to support the high data transfer rates required for high-resolution graphics, real-time interactions, and seamless streaming of virtual content [242]. Together, low latency and high bandwidth ensure smooth, responsive, and immersive experiences in the metaverse, allowing users to interact in real-time without interruptions or delays.  
Figure 10 illustrates the critical relationship between bandwidth and latency for various applications [243], focusing on a metaverse system.
## 1 Introduction
Charging systems described in [30; 32; 33; 34; 35] can operate with mains supply voltages of V\({}_{\text{inRMS}}\) ranging from 90 to 264 V and \(I_{\text{fine}}\) frequency from 47 to 63 Hz. Only the system described in [31] is a DC charging system, which can be supplied with a maximum DC voltage of 48 V and can deliver an output power of up to 100 W. All charging circuits feature the USB PD 3.1 charging standard and operate with mobile devices via a USB Type-C connector. The output power of the charging systems ranges from 100 [30; 31; 33] to 240 W [34]. The maximum output voltage of these systems can change in the range from 20 to 48 V. The operating parameters of selected charging system are summarized in Table 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of Charger** & **Power Supply** & **Input Frequency [Hz]** & **Input Voltage Range [V]** & **Maximum Output Power [W]** & **Output Voltage [V]** \\ \hline EVLONE140W [35] & AC & 47-63 & 250 & 140 & 5, 9, 12, 15, 20, 28 \\ RDR-1002 & AC & 47-63 & 90-265 & 140 & 5, 9, 12, 15, 20, 28 \\ InnoSwitch5-Pro [32] & AC & 47-63 & 90-264 & 100 & 5, 9, 12, 15, 20 \\ EVLVIPGAN100 PD [30] & DC & — & 5-48 & 100 & 5, 9, 12, 15, 20 \\ STEVAL-ISC004V1 [31] & DC & 47-63 & 100-240 & 100 & 5, 9, 12, 15, 20 \\ SSM522 [33] & AC & 47-63 & 90-264 & 240 & 5, 9, 12, 15, 20 \\ XDPS2222 [34] & AC & 47-63 & 90-264 & 240 & 5, 9, 12, 15, 20, 28, 40, 48 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Operating parameters of the selected charging system [30; 31; 32; 33; 34; 35]. The aim of this study is to analyze the performance of a dedicated charging system loaded with two selected mobile devices and a charging system operating with a resistive load varying over a wide range. The measurement set-up was designed and constructed. Using this set-up, some tests of the considered charging system were performed. The experimental results on the distortion of the input current drawn from the power grid were discussed. The results of an analysis of the battery SoCs used in two arbitrarily selected mobile devices on the parameters of the tested charging system were also presented. On the basis of the performed measurements, parameters characterizing the influence of the tested system on the power grid were computed and the obtained results were discussed. Section 2 presents the topology of the dedicated USB Power Delivery (PD) charging system selected for the study and describes its performance parameters. Section 3 presents the measurement set-up used, while Section 4 shows and discusses selected experimental results.
## 4 Results  
In this section, the accuracy of detecting the start of the anchoring phase using pose estimation is evaluated, and the shooting time measured by the Dartfish software is compared with the shooting time measured using the method proposed in this study. Additionally, the results of measuring the shooting time using only the bowstring and detecting the anchoring start point based on the arm angle are also included. A total of 58 video clips, each containing the full sequence of drawing, anchoring, aiming, and release, were used for the experiments. The clips included archers from various nationalities participating in different competitions. The experiments were conducted using a program implemented based on Python v3.8 in an environment with an Intel Core i7-13700K processor and 32 GB RAM (Intel, Santa Clara, CA, USA). The program achieved an average performance of over 30 FPS, confirming that it can effectively meet real-time processing requirements.
###### Abstract  
This study presents a swarm-control algorithm to overcome the limitations inherent to single-object systems. The leader-follower swarm-control method was selected for its ease of mathematical interpretation and theoretical potential for the unlimited expansion of followers. However, a known drawback of this method is the risk of swarm collapse when the leader breaks down. To address this, a fault-coping algorithm was developed and supplemented to the leader-follower swarm-control method, which enabled the detection and responsive handling of failures, thereby ensuring mission continuity. Comprehensive data, including voltage, current, thruster speed, position, and heading angle were acquired and analyzed using sensors on unmanned surface vehicles (USVs) to monitor potential failures. In the case of a failure, such as thruster malfunction, the nearest USV seamlessly takes charge of the mission under the guidance of the fault-coping algorithm. The leader-follower swarm-control and fault-coping algorithms were successfully validated through actual sea area tests, which confirmed their operational efficacy. This study affirms the well-formed nature of the USV swarm formation and demonstrates the effectiveness of the fault-coping algorithm in ensuring normal mission performance under the virtual failure scenarios applied to the leader USV.  
unmanned surface vehicle; swarm-control algorithm; leader-follower method; fault-coping algorithm Article
## 4 Results and Discussion  
Below are the results obtained from simulations of the two scenarios. The lossless systems are presented in terms of BER vs. SNR, and, for the lossy system, in terms of BER vs. transmit power (PTx). The BER vs. SNR behavior is analyzed considering parameters of bandwidth (BW), subcarrier space (SCS), and number of resource blocks (NRB). Then, BER vs. transmit power (PTx) in the lossy system is analyzed, taking into account the variation of parameters such as the angle elevation of the UE (angUE), heights of the UE (hUE), and the frequency range FR1.
## 2 Experiments
### Experimental Scenario  
To investigate the impact behavior of a primary barrier, the reasonable drop height of the impactor with the drop object was first investigated. A reasonable range was set for the load using wet-drop testing to determine the impact loading on the primary barrier, because excessive impact energy in a drop test may damage the experimental apparatus. To determine the reasonable load to be applied on the primary barrier, an equivalent relationship was assumed between the impact energy and potential energy in the wet drop test.  
In the wet drop test, the dynamic pressure was measured using a pressure gauge attached to the lower surface of the specimen to investigate the impact loads acting on the specimen. In this process, the impact energy was applied to both the large and small corrugations in contact with the water surface. The potential energy of the impactor at a certain height, which would then be applied to a corrugation, is equivalent to the impact energy acting on the corrugation during a wet drop test, so that  
Figure 3: (**a**) Custom-built structural impact test equipment and (**b**) fixing jig with large corrugation specimen.  
the relationship was used to calculate the drop height used in this study. By applying the method for the drop height selection to the four cases of the wet drop test [24], the drop heights of the four cases were calculated in accordance with the peak pressure. Table 2 lists the impact energy during the wet drop testing, drop velocity, and drop height during the drop testing, based on the experimental results of the wet drop testing.  
The experimental scenarios according to the impact energy, number of iterations, and types of specimen are listed in Table 3. L and S points in the scenarios in Table 3 represent the specimens with large and small corrugations, respectively. Numbers 123, 230, 282, and 412 represent the impact energy in joules at each drop height. The impact loading was repeatedly applied up to 10 times. However, when the corrugation was completely collapsed, the repeated impact test was terminated. To capture the photograph of the test specimen, after the first, fifth, and tenth repeated impact tests, the test specimen was detached from the impact test jig. In addition, the time interval between successive impacts was set to be within 5 min.  
Owing to the limitation of the experimental facility for establishing a cryogenic environment, all the experiments were performed at room temperature. According to [PERSON] et al. (2010) [26], yield and tensile strength of stainless steel increased by 9% and 71% at cryogenic temperatures, respectively. Thus, the room temperature test condition in this study is more conservative than the cryogenic test condition.
## 5 Numerical Experiments
### Stability Analysis of the LNS  
To assess the stability of the LNS algorithm, this section first analyzes the smoothness of the convergence curve for LNS by comparing it with that for GA, and then evaluates the consistency of the results across multiple experiments conducted on different instances.  
The convergence curves for both LNS and GA are obtained using instances 45 and 50, as illustrated in Figure 5. The results clearly indicate a distinct advantage of LNS in the optimization process. The convergence curve of LNS is significantly smoother and exhibits a faster rate of descent compared to GA, particularly during the early stages. The LNS is able to quickly identify relatively optimal solutions and sustain a faster optimization rate in subsequent stages. In contrast, the GA convergence curve displays greater volatility, with a slower descent rate, typically requiring more generations to achieve meaningful improvements in the objective function. This suggests that the LNS possesses superior local search capabilities in the short term, allowing it to rapidly converge to better solutions, whereas the GA may become trapped in larger fluctuations within the search space, thus requiring more time for fine-tuning the solution. In later stages, the convergence curve of LNS stabilizes, with convergence occurring at approximately 160 iterations for instances 45 and 340 iterations for instance 50, demonstrating that the optimization process has reached stability and the objective value is approaching a local or global optimum. Conversely, the GA curve may exhibit oscillations, indicating that the algorithm is still exploring the solution space and has not yet reached a stable state. In conclusion, Figure 5 not only demonstrates that the LNS can achieve convergence during the iteration process, but also shows that the number of iterations required for convergence increases as the instance size grows.  
Furthermore, the consistency of the results obtained by LNS is examined using instances selected from large-scale and super-large-scale problems, as shown in Table 8. Each instance is run ten times. The first column lists the instance index, while the second and third columns show the minimum and maximum objective values from the ten runs. The fourth column presents the average objective value, and the final column displays the difference between the maximum and minimum values. The largest difference is 0.41%, while the average difference is only 0.27%, indicating that the optimal solution experiences minimal fluctuations, thereby demonstrating the stability of LNS.
## 1 Introduction  
Evaluation of the immediate postnatal cerebral oxygen metabolism and hemodynamics is essential for understanding extrauterine adaptation. In particular, the hemodynamic changes occurringimmediately after birth are explosive and can induce fetal distress and asphyxia. The physiological processes after birth remain unclear and include cerebral vasodilation, vasoconstriction, and oxygenation. Because the brain is the most sensitive organ system of the infant, it is important to assess its autoregulation after birth.  
Near-infrared spectroscopy (NIRS) using near-infrared light (700-900 nm) enables detection of changes in the oxygenation state of hemoglobin (Hb) and water content in biological tissue. Near-infrared light is safe and penetrates deeply in the body, and NIRS has recently been applied for functional evaluation of cerebral circulation and oxygenation state in neonates [1]. Several studies have reported on the measurement of cerebral oxygenation via the tissue oxygenation index or regional saturation of oxygen (rSO\({}_{2}\)) in neonates during the immediate transition after birth using NIRS [2; 3; 4; 5; 6; 7; 8]. Furthermore, NIRS can measure changes in venous Hb concentration (tHb) and cerebral blood volume (CBV), with some work showing a decrease in CBV in term neonates in the first 15 min after birth [9]. However, commonly used NIRS modalities, such as continuous wave spectroscopy, which only measures changes in the Hb concentration, and spatially resolved spectroscopy does not provide CBV but tissue oxygen saturation [1]. The absolute value of CBV is required for its use as a clinical parameter to manage circulation and determine oxygen use. Near-infrared time-resolved spectroscopy (TRS) is a unique method for calculating quantitative CBV and ScO\({}_{2}\) using a light absorption coefficient (\(\mu_{\mathrm{a}}\)) without inducing changes in light-absorbing materials, such as Hb, because the reduced scattering coefficient (\(\mu_{\mathrm{s}}\)') and \(\mu_{\mathrm{a}}\) can be determined by resolving photon diffusion equation (PDE). Our group has already reported on the measurement of the absolute value of CBV in preterm and term neonates, determining that it sheds light on their cerebral hemodynamic development [10]. However, there have been no reports on how the absolute value of CBV is altered and whether optical properties such as the differential pathlength factor (DPF) and \(\mu_{\mathrm{s}}\)' are affected during this adaptation period by certain factors such as the vernix, amniotic fluid, blood, and systemic hemodynamic changes in the immediate transition period.  
In this study, we used TRS to examine the absolute value of CBV and ScO\({}_{2}\) during the immediate transition period in term neonates and the changes in optical properties such as DPF and \(\mu_{\mathrm{s}}\)'.
## 6 Discussion
Therefore, in a case of very restrictive tie point distribution, another option for distortion limitation would consist of changing the default parameters in image matching (i.e., number of tie points limited to 4000 for every image in PhotoScan(r) and image size reduced by 3 in images matching in MicMac(r)). A test have been conducted processing Flight 1 dataset with unlimited number of tie points in PhotoScan(r) and full size images in Micmac(r). As expected, both PhotoScan(r) and MicMac(r) show a higher density of tie points (Table 3 and Figure 10). The number of tie points and the density are still higher with MicMac(r). With this new configuration, the RMS reprojection error is reduced (more than divided by two with MicMac(r)--Table 3). Nevertheless, as depicted in Figure 11, bowl effects remain in the DEM reconstruction despite the higher tie point density. The results are very similar with PhotoScan(r), the vertical error with an unlimited number of tie points being 58 cm, against 63 cm with default parameters. With, the processing time is largely increased when using full size images, but the vertical error is decreased to 97 cm, against 138 cm with a \"reduced image size\" configuration. This suggests that number and density of tie points are not a key parameter compared to the tie points \"quality\" (accuracy, relevant distribution) in relation to the camera distortion model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \begin{tabular}{c} **PhotoScan** \\ **Restricted nb. of** \\ **tie points (<4000)** \\ \end{tabular} & \begin{tabular}{c} **MicMac Reduced** \\ **Image Size** \\ \end{tabular} & \
\begin{tabular}{c} **PhotoScan** \\ **Unrestricted nb.** \\ **of tie points** \\ \end{tabular} &
\begin{tabular}{c} **MicMac Full** \\ **Resolution Images** \\ \end{tabular} \\ \hline Number of tie points & 57,781 & 129,478 & 214,374 & 2,449,061 \\ \hline Mean density of tie & 5.1 & 22.5 & 20.0 & 469.2 \\ points in r = 1 m & & & & \\ \hline RMS reprojection & 0.83 & 0.68 & 0.55 & 0.26 \\ error (pix) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Impact of changing the default parameters in images alignment (for Flight 1) on the tie points sparse point cloud. Figure 10: Comparison of tie point density for different parametrization in image alignment: (**a**) PhotoScan—Flight 1, (**b**) PhotoScan (unrestricted nb. of tie points)—Flight 1, (**c**) MicMac—Flight 1 and (**d**) MicMac (full resolution images)—Flight 1. Distortion problems can be encountered in UAV monitoring of non-linear areas [13; 27]. We have not yet tested the applicability of Flight 2 and Flight 3 scenarios for surveys of non-linear landforms, but it is very likely that choosing a scenario that offers a great variety of viewing angles for each homologous point would improve the quality of the reconstruction. In practice, several processing strategies can be tested. The problem is that, in case of lack of GCPs, the validation points are also lacking. It is therefore complex to quantify the bowl effect and to what extent the tested strategy corrects it.
# On the Role of Seismic Damage Tolerance on Costs and Life Cycle of CLT Buildings
## 3 Methodology  
### Description of the Sample Building  
The two-story CLT building (Figure 3) is representative of a class of common buildings recently erected in many Italian regions; therefore, an extended search of technical data was made resulting in the acquisition of the design reports and drawings. The building accommodates school functions and thus belongs to the Class of Use III, after IBC 2018 [29]. The main consequences of this classification are the level of ground motion design intensity--increased return period of the actions compared with ordinary buildings--and the type of verifications at the Damage Limit State (DS) based on the critical region strength rather than inter-storey drift. The real building was designed to be built in a low-intensity seismic zone, where a peak ground acceleration on bedrock is equal to \(\mathrm{a_{g}}\) = 0.081 g at a Life Safety (LS) limit state.  
In the real design, the structure was made of 140 mm thick five-ply seismic-resistant CLT walls, where the thickness of the two vertical external layers was equal to 40 mm and those internal (both in vertical and horizontal directions) were 20 mm thick. The strength class of CLT corresponds to solid wood class C24, derived according to ETA-14/0349 [35].  
The construction technology followed the platform-type system, where vertical walls are interrupted inter-storey by horizontal CLT floors interposed between two consecutive vertical panels. The floors were composed of seven-layered CLT panels with strength class C24.  
It is worth noting that the comparative analysis has no relation with the real design and direction of the building; indeed, available technical material was used to refer to realistic building geometry, material properties and live and dead loads. The arrangement of the walls herein addressed is the same as provided by the real project and shown in Figure 3, while different solutions have been adopted for the connections.  
Figure 3: Views of the sample CLT building.