esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Rural Environment  
Figure 11 illustrates the performance of the communication chain using RF and NB-PLC links in a rural environment. The simulation parameters for this scenario are summarized in Table 3. The distance between the SM and DC is set to 1 km. For the NB-PLC link, the electric line is divided into three branches, as depicted in Figure 10. Each branch of the NB-PLC line has a length of 200 m. In the simulation setup, the RF path number is equal to 5, indicating the presence of multiple RF paths. The noise model utilized for both RF and NB-PLC links is AWGN.  
Figure 11 demonstrates that the RF link outperforms the NB-PLC link in terms of the BER. This is attributed to the rural environment being characterized by a low number  
Figure 11: RF vs. NB-PLC performances for a rural environment.  
Figure 10: Electrical topology network.  
of obstructions. With a bit error rate (BER) equal to \(10^{-3}\), the RF link exhibits a higher performance compared to the NB-PLC link, with a difference of approximately 15 dB, which means that to have the same BER = \(10^{-3}\), a higher SNR is required for NB-PLC with respect to RF. Therefore, transmitting data over the RF link is more suitable for rural environments.  
In order to provide extensive network coverage for nodes at greater distances, multi-hop operations are likely to be employed in rural areas, where the population is sparse, and the number of obstructions is low. Considering this scenario, the RF technology emerges as a suitable communication solution to simplify information exchange between the SM and DC within this type of environment.
# Ultra-High-Performance Concrete (UHPC) Piles and Splicing Options
## 2 Types of Driven Piles Used in Foundation  
Driven piles are generally categorized into timber, steel, concrete, and composite piles. These piles have been adopted in many pile foundation projects for bridges and buildings. Timber piles provide a green method of construction; however, their application has been limited as they experience significant deterioration due to the fungal decay of the wood; an example is shown in Figure 1a [18]. The use of steel piles always raises the issue of corrosion [19] affecting their performance during service. An example of steel pile corrosion observed in one of the bridges in Missouri is shown in Figure 1b [20]. On the other hand, although composite piles provide good durability and service performance, their application has been limited due to the high cost for their production and installation. Among all types of driven piles, concrete piles, specifically prestressed-precast concrete piles (PPCPs), are the most common ones used in foundation projects [21]. Recent studies indicate that PPCPs have a tendency to crack during driving due to the presence of large tensile stresses; they can also fail due to the compressive stress developed using a large driving hammer [22; 23]. Cracking can also occur in concrete piles due to the corrosion of embedded steel reinforcement bars and prestressing strands, and exposure to contaminated groundwater or seawater can initiate chemical deterioration that leads to cracking [24; 25]. An example of concrete deterioration is shown in Figure 1c [26]. To address these issues, Ultra-High-Performance Concrete (UHPC) piles have recently been developed. The research findings on the use of these piles are explained in the following sections.  
_Comparison of Various Available Driven Piles Used in Pile Foundation_  
Table 1 presents a summary of the advantages and disadvantages, as well as some applications, of various available driven piles used in pile foundation.
## 3 The Proposed Scheme
### Hot/Cold-Aware Data Placement Structure  
As can be seen in Figure 5, retention error is also seen that the number of errors occurs differently by page and layer. It can be seen that the middle and upper pages have a relatively high number of errors, whereas the lower page has a relatively low number of errors. When looking at the number of errors by layer, it is clear that the lower layer has the highest number of errors. The middle and upper pages of the lower layer are vulnerable to the retention error, as are the middle and upper pages of the middle and upper layers, as well as the lower layer.  
Retention error means an error that occurs when data is stored on the flash memory page and then read data after a certain period. This mostly happened with cold data, which is data that has been stored but has no access to it. Conversely, hot data is a relatively frequent update to the data in a short period after the data is stored on the page, and the probability of occurrence of retention error is low. If we arrange cold and hot data, we place hot data in a relatively vulnerable area to retention error.  
In Figure 7, we present a data placement structure we proposed. We assume that cold and hot data exist at a ratio of 50% for the sake of simple notation. We also assume that the SSD is made up of five flash memory chips and that one block is made up of four pages, as shown in Figure 7. Let us start with the lower layer, which is the most vulnerable to the retention error. We use hot data striping on the middle page, which has the highest number of retention errors. Furthermore, if new hot data arrives, it can be placed on an empty upper page. Then, let us consider the middle layer, where the retention error rate  
Figure 6: Example of differentiated data protection in SSD [12]. (**a**) Upper Layer; (**b**) Middle Layer; (**c**) Lower Layer.  
is lower than the lower layer but more than the upper layer. Similarly, we can see that hot data is organized on the middle page. However, since the retention error rate of the middle layer is lower than that of the lower layer, it can be seen that the number of hot data is relatively smaller than the lower layer. Finally, let us look at the upper layer that the number of errors is relatively the least shown. Hot data, like other layers, is placed first in the middle pages where the most errors occur. As the lower page of the upper layer is less vulnerable to the retention error, many cold data have been placed. Furthermore, because the number of errors is lower than in the middle layer, the number of hot data is lower. Additional overhead with the proposed policy will be discussed in Section 5.3.
## 3 Results and Discussion
### Determination of Mesh Number and Drain Time  
Additionally, high amounts of salt would generate a disadvantage in terms of dry bagasse production costs, in contrast to the aim of achieving the lowest possible production costs with the lowest energy expenditure. Therefore, these operating levels were discarded and adjusted, reducing both the concentration percentage of the osmotic agent and solution to sample ratio.  
New operating conditions were established: NaCl concentrations between 30 and 60% and solution-to-sample ratios between 3:1 and 5:1, which would allow the maximum use of levels of fiber, protein, and other components shown by the cassava bagasse [15; 32; 33].  
Results of the weight loss test indicated that the #30 sieve did not allow the solution passage, so it was discarded. Figure 4 illustrates the amount of water retained as a time function in the draining operation. The #10 sieve held 62 g of water, while the #20 sieve held 80 g. Therefore, the sieve selected for the draining operation was #10. Nevertheless, it was observed that sieve #10 lets some bagasse particles escape, which is why sieve #12, corresponding to the number of mesh in its order, was selected as the draining mesh. Furthermore, it was observed that, after 20 min in the draining stage, the amount of retained water did not vary significantly, so it was decided to define this time as the optimum for the operation.
# Cyber-Physical-Social Frameworks for Urban Big Data Systems: A Survey
## 4 Data Sources
### Physical Sensor Deployments
Similar initiatives have been adopted in Madrid [81], where pollen sensors have been mounted on public transport buses. Mobile sensors mounted on buses have also been deployed in a city in China [15], that monitor both urban physical environment and road conditions. The corresponding data are stored in databases and retrieved in XML format, containing the following fields: bus ID, bus line, temperature, humidity, speed, latitude, longitude, tested data, tested time and illumination data. Mobile sensing gives an opportunity for large scale environmental monitoring with limited number of sensors, however, it also needs a proper approach for managing the fast changes in the locations of data and data sources [62]. Another example can be found in the city of Barcelona and its multiple Smart City initiatives [75]. The city started to deploy sensors across the city in 2012, focusing on the areas of public transit, street lighting and waste management. Smart meters, smart bins, location sensors in public transport, sensors in the asphalt to detect parking spaces, air quality sensors, irrigation and water levels in public fountains are some of the sensors that have been successfully deployed. The sensor network relies on Sentilo [82], an open source data acquisition and sharing platform based on Web technologies. Intelligent Transportation Systems (ITS) applications involve a number of fixed sensor installations such as those in parking places (cameras at entrances, ultrasonic or infrared detectors in each parking space), road-side units providing traffic signal timing information, road size and pavement state [19].
# An Experimental Framework for 5G Wireless System Integration into Industry 4.0 Applications
## 3 Experimental Framework for 5G Integration
### 5G Hardware and Software Prototyping Tools
As a reference, the HMS 5G device [20] supports maximum 70 Mbit/s when a 5G virtual private network (VPN) connection is established. Our WMAGW prototype supports higher throughput in L2-tunnel mode as no extra link encryption is implemented (we rely fully on the 5G encryption for security), which results in a smaller communication protocol overhead than in the VPN case. Figure 8: PER statistics for different combinations of packet size and PIAT when transmitted between two WMAGW in L2-tunneling operational conditions. The described prototype tools have been successfully used in connection with different industrial production systems. The NTS has been used for recording several industrial control data traffic traces for various machines such as production cells, or quality inspection systems or in operational factories [9]. Based on those measurements, we could further validate the correct and reliable operation of the sniffer prototype. Data flow from protocols such as delay-tolerant industrial control protocols like OPC-UA or CIP, but also other more time-critical such as PROFINET, were successfully logged for several hours without detecting any disruption in the operation of the machinery [38]. To date, the WMAGW devices have been used to successfully operate, over 5G (also Wi-Fi and 4G), the control of an operational production line by providing over 5G wireless the connectivity between a factory manufacturing execution system (MES) controller and the different programmable logical controllers (PLC) present inside of each of the stations of the line [9; 39; 40].
## 5 Experiment Results and Discussion
### Discussion and Limitations
In summary, findings from this study verified that offering multiple input methods (e.g., touch gestures and indirect controls) and ensuring adaptability based on user interaction data and preferences can be beneficial in the context of _accessible usability_. Game developers should take these recommendations into consideration when developing games based on AR to contribute to digital inclusion and widen their audience. This can also benefit educators who use AR games as educational tools in addition to traditional learning methods. With diverse student needs accommodated, equitable access to educational content can be ensured. However, accessibility in AR-based serious games goes beyond providing alternative input methods. It requires a deeper understanding of how usability, user experience, and accessibility intersect. Designing flexible, user-centred interaction mechanisms ensures not only functional usability but also an enjoyable and satisfying experience for a diverse range of users. Having said that, one of the limitations of this study is related to recruitment of participants with diverse needs. Sample sizes of some groups of participants with disabilities and of different ages were not sufficient to draw conclusions for the entire population. This primarily refers to the sample of participants in the category of visual impairments, which partly results from technological limitations as the game engine in which serious games were developed (Unity) does not support text-to-speech in the Croatian language, and therefore people who are blind or have low vision and use a screen reader could not participate in the testing. While our study included 108 participants, a larger and more diverse sample could provide further insights into how individual differences (primarily age and disabilities) in combination with data about user interaction can influence conclusions about accessibility and appropriate adaptations in AR-based serious games. In addition to sample size, a study was conducted using two models of tablet devices which may be considered as another study limitation since they do not fully represent the range of devices used in real-world scenarios. Differences in screen size, processing power, and tracking accuracy could affect interaction performance.
## 3 Results
### Accelerated Degradation
#### 3.4.3 Change in Thermal Properties with Degradation Time  
Accelerated degradation conditions induced a more rapid decrease in T\({}_{\rm g}\) to 53.34 \(\pm\) 0.03 \({}^{\circ}\)C at day 28 with an 11.75 \(\pm\) 2.3% increase in crystallisation (Table 1). A general trend of decreasing T\({}_{\rm g}\) and T\({}_{\rm m}\) with an increase in crystallinity was observed, as marked by the red dashed line in the DSC thermograms in Figure 12. Crystallisation was noted to start from day 21 at 2.97 \(\pm\) 1% with a T\({}_{\rm m}\) of 162.17 \(\pm\) 0.4 \({}^{\circ}\)C, followed by an increase in crystallisation to 11.75 \(\pm\) 1.5% on day 28 with a decrease in the T\({}_{\rm m}\) to 159.22 \(\pm\) 0.5 \({}^{\circ}\)C.  
Figure 11: Effect of accelerated degradation on (a) mass loss and swelling (b) pH (n = 4, Ave \(\pm\) std. dev).  
Figure 12: DSC thermograms on the effect of accelerated degradation on the glass transition temperature.  
3.4.4. Change in Molecular Weight with Degradation (GPC)  
The molecular weight of the polymer decreased rapidly with time from day 3 to day 28 of in vitro accelerated degradation (Table 3). After in vitro degradation for 28 days, the Mn had decreased by 92.36% to 9585. The M\({}_{w}\) and peak molecular weight (M\({}_{\rm p}\)) decreased by 88.3% and 84.6%, respectively, from day 3 (M\({}_{\rm w}\) = 176,009, M\({}_{\rm p}\) = 137,686) to day 28 (M\({}_{\rm w}\) = 20,572, M\({}_{\rm p}\) = 21,201) (Supplementary Table S4). The molecular weight changes were characterised by a rapid decrease from Day 21 to Day 28 at accelerated degradation conditions.
# Resampling Imbalanced Network Intrusion Datasets to Identify Rare Attacks
## 8 Results and Discussion
### Resampling before and after Splitting
#### 8.2.2 Random Undersampling after Stratified Splitting
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Oversampling \%** & **Accuracy** & **Precision** & **Recall** & **F-Score** & **Macro Precision** \\ \hline
0.1 & Avg & 0.999 & 0.900 & 0.750 & 0.779 & 0.949 \\  & SD & NA & 0.213 & 0.273 & 0.226 & 0.106 \\  & Avg & 0.999 & 0.813 & 0.725 & 0.712 & 0.906 \\
0.2 & SD & NA & 0.203 & 0.343 & 0.260 & 0.101 \\
0.3 & Avg & 0.999 & 0.820 & 0.825 & 0.768 & 0.909 \\  & SD & NA & 0.130 & 0.275 & 0.147 & 0.065 \\
0.4 & Avg & 0.999 & 0.800 & 0.800 & 0.788 & 0.899 \\  & SD & NA & 0.322 & 0.331 & 0.316 & 0.161 \\
0.5 & Avg & 0.999 & 0.843 & 0.825 & 0.811 & 0.921 \\  & SD & NA & 0.253 & 0.275 & 0.238 & 0.126 \\  & Avg & 0.999 & 0.745 & 0.899 & 0.804 & 0.872 \\
0.6 & SD & NA & 0.091 & 0.135 & 0.068 & 0.045 \\
0.7 & Avg & 0.999 & 0.704 & 0.911 & 0.785 & 0.852 \\  & SD & NA & 0.121 & 0.147 & 0.107 & 0.060 \\  & Avg & 0.999 & 0.707 & 0.888 & 0.781 & 0.853 \\  & SD & NA & 0.094 & 0.157 & 0.099 & 0.047 \\
0.9 & Avg & 0.999 & 0.738 & 0.855 & 0.778 & 0.869 \\
0.9 & SD & NA & 0.138 & 0.131 & 0.087 & 0.069 \\  & Avg & 0.999 & 0.759 & 0.966 & 0.843 & 0.879 \\  & SD & NA & 0.131 & 0.071 & 0.087 & 0.065 \\  & Averages & 0.999 & 0.7833 & 0.845 & 0.7853 & 0.8915 \\ \hline \hline \end{tabular}
\end{table} 
Table 23: UWF-ZeekData22: privilege escalation—classification results for random undersampling after splitting. After analyzing the classification results using [PERSON]'s \(t\)-tests, for Random Undersampling After Stratified Splitting, it can be seen that an undersampling of 0.5 for the majority of the data after stratified splitting gives the best results when the oversampling is at 0.1, for four of the five datasets. All the UNSW-NB15 datasets performed better at an oversampling of 0.1 and one of the UWF-ZeekData22 datasets, privilege escalation, also performed better at 0.1. Credential access, however, performed better at 0.5. This means that, for four out of the five datasets, generating more synthetic minority class samples beyond 0.1 will not result in a better prediction by the model.
# A Doppler Frequency-Offset Estimation Method Based on the Beam Pointing of LEO Satellites
## 5 Discussion
The stability of atomic clocks is superior to \(10^{-11}\), while that of ordinary oven-controlled crystal oscillators (OCXOs) is better than \(10^{-8}\). Most satellites use atomic clocks as clock sources. For a signal with an operating frequency of 2 GHz, the signal's frequency stability caused by either atomic clocks or ordinary OCXOs is less than 20 Hz. This value is much smaller than the satellite-to-ground Doppler frequency shift; thus, the influence of clock stability on the DFS-BP algorithm is negligible. (4) Influence of Radio Frequency (RF) Tolerance on the DFS-BP Algorithm  
Radio frequency (RF) tolerance generally refers to the permissible deviation range of characteristics such as frequency, power, phase, and amplitude-frequency when RF equipment is in operation. It is an important indicator for measuring the tolerance capability of RF components or systems relative to parameter fluctuations and their operational stability. Among RF characteristics like frequency, power, phase, and amplitude-frequency, frequency accuracy may impact the DFS-BP algorithm. Frequency accuracy is typically related to the precision grade of crystal oscillators or clocks, the operating temperature range, and manufacturing processes. For example, the frequency accuracy of atomic clocks is usually better than \(10^{-10}\), while that of ordinary oven-controlled crystal oscillators is typically better than \(10^{-7}\). For a 2 GHz signal, the maximum operating frequency error caused by the frequency's accuracy is 200 Hz, which translates to an estimation error of less than 1 Hz in the DFS-BP algorithm.
## Materials and Methods
### Characterization  
The size and morphology of St/AA submicrocapsules were analyzed by scanning electron microscopy (SEM) (SU8220, Hitachi, Tokyo, Japan). The mean particle diameters and zeta potentials of St/AA submicrocapsules were also determined using DLS (ELS8000, Photal Otsuka Electronics, Osaka, Japan) equipped with vertically polarized light supplied by a He-Ne laser, operated at 10 mW. DLS measurements were performed at room temperature. The dispersion stability of St/AA submicroparticles in the aqueous phase was evaluated from the time variation of backscattered light by using the TurbiscanTM Lab (Formulation LAB, Toulouse, France). All the measurements were repeated three times.
# An Elliptical-Shaped Dual-Band UWB Notch Antenna for Wireless Applications
## 4 Discussion and Outcomes
### Outcome of Ultrawideband Notch Antenna
#### 4.2.1 Vswr  
Figure 21 shows a comparison of the UWB notch antenna's simulated and measured VSWR. Strong rejection can be seen in the simulated VSWR plot between the WLAN band of 5-5.6 GHz and the ITU band of 7.1-8.5 GHz, with a fractional bandwidth of almost 120% (2.8-11.2 GHz). The measured VSWR plot demonstrates a high level of attenuation across the WLAN band (5.2-5.7 GHz) and the ITU band (7.2-8.5 GHz), with a fractional bandwidth of around 125%. (2.5-11 GHz). The difference in VSWR between simulated and measured values should be attributed mostly to the influence of SMA connector soldering and fabrication tolerance.
# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
### Combining Multiple Metrics and Data Analytics  
The large variability observed in metrics of the voice implies, for instance, that we should not expect to find any single metric whose quantity will discriminate accurately between normal and pathological voice, nor indeed between various types of normal phonation. Regardless of whether we are talking about the cepstral peak prominence (CPP), the spectrum balance (SB), or the EGG contact quotient (\(Q_{\rm ci}\))--to name but a few --any one of these, on its own, is unlikely to give reliable evidence as to whether or not a particular voice is fully functional [1]. Many researchers have found that, when taken in combination, a set of several metrics can be more informative [20]. This insight has resulted in a number of proposed indexes that combine several metrics, such as the Acoustic Voice Quality Index [21], the Gottingen hoaseness diagram [22], and also in combination displays, such as the Multi-Dimensional Voice Program (MDVP) [23]. Typically, though, the constituent metrics of such indices are themselves highly dependent on SPL and/or \(f_{\rm o}\). The AVQI, for instance, has six such components. One may also expect such indexes to exhibit considerable intra-subject variation over the voice field, and so the issue of appropriate sampling remains.  
The potential of combined metrics has lately triggered an avalanche of investigations into using machine-learning methods for classifying voices and vocal status on the basis of multiple \"features\". For an in-depth appraisal of such methods, see the separate article by [PERSON] et al. in this issue [24]. As [PERSON] et al. point out, the data sets hitherto used in such studies are rarely, if ever, calibrated for SPL and \(f_{\rm o}\). For an example of a novel method that combines several metrics _in voice maps_ using only statistical clustering, see the article by [PERSON] et al. in this issue [25].