esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments  
### Datasets  
Our methodology is assessed using the NYUv2 dataset (NYUv2 dataset: [[https://cs.nyu.edu/~fergus/datasets/nyu_depth_v2.html](https://cs.nyu.edu/~fergus/datasets/nyu_depth_v2.html)]([https://cs.nyu.edu/~fergus/datasets/nyu_depth_v2.html](https://cs.nyu.edu/~fergus/datasets/nyu_depth_v2.html)), accessed on 1 January 2025) [43] and the KITTI Depth Completion (KITTI DC) dataset (KITTI DC dataset: [[https://www.cvlibs.net/datasets/kitti/eval_depth.php](https://www.cvlibs.net/datasets/kitti/eval_depth.php)]([https://www.cvlibs.net/datasets/kitti/eval_depth.php](https://www.cvlibs.net/datasets/kitti/eval_depth.php)), accessed on 1 January 2025) [44]. The NYUv2 dataset [43] consists of RGB and depth images collected from 464 indoor scenes captured by a Kinect sensor. By the official split, 249 scenes are allocated for training, while the remaining 215 are reserved for testing, with the test set comprising a total of 654 images. From the raw training data, 46,000 images are extracted. To address missing depth values, interpolation is performed using the cross-bilateral filter provided in the official toolkit. All the images are resized to 320 \(\times\) 240 and center-cropped to 304 \(\times\) 228. For each image, a random selection of 500 sparse depth samples is made. The model undergoes training for 25 epochs using the \(l_{1}\) loss function, with the learning rate decreasing by a factor of 0.2 every five epochs after the first 10 epochs. The training is conducted with a batch size of 24. To evaluate the model's few-shot learning capabilities, approximately 20 data points are extracted from each sequence, forming a 20-shot training set comprising around 5000 data points. The model is trained using the reduced dataset, while evaluation is carried out on the designated validation set. The KITTI Depth Completion (KITTI DC) dataset [44] contains over 90,000 synchronized RGB images and sparse LiDAR point clouds captured in outdoor driving scenarios. Following the standard protocol, we excluded the top 100 pixels where LiDAR projections are absent due to sensor mounting positions. All images were center-cropped to 1216 \(\times\) 240 patches for training consistency. The model was trained for 25 epochs with an initial learning rate that decayed by a factor of 0.4 every 5 epochs after the first 10 epochs, using a batch size of 25.
## 4 Cads
### CADS Evolution Process  
The optimization goal of CADS is:  
\[min\Delta P_{k,cut}\ =\ P_{k,fur}\ -\ maxP_{k,tra} \tag{40}\]  
In the formula, \(P_{k,fur}\) is the load of the distribution line. \(\Delta P_{k,cut}\) is the amount of loads shedding. \(P_{k,tra}\) is the amount of protected load. Finally, the CADS is carried out according to the following steps, which are briefly described as follows:  
1. Search for high-risk distribution lines and determine the defense order according to the difference between the threshold and the risk value. At the same time, find all available interconnection switches and section switches for the distribution line;
2. Transfer the load using an interconnection switch. This step attempts to use a interconnection switch to transfer all loads of the distribution line, if it can be achieved, go to step (6), otherwise go to step (3);  
\begin{table}
\begin{tabular}{c c c} \hline
**Constraints** & **Charging State** & **Discharging State** \\ \hline Island Power Balance & \(P_{load}\ =\ \sum\ P_{DG}^{t}-P_{ch}^{t}\) & \(P_{load}\ =\ P_{dis}^{t}\ +\ \sum\ P_{DG}^{t}\) \\ \hline Charge and discharge power constraints & \(P_{ch,min}^{t}\ <\ P_{ch}^{t}\ <\ P_{ch,max}^{t}\) & \(P_{dis,min}^{t}\ <\ P_{dis}^{t}\ <\ P_{dis,max}^{t}\) \\ \hline Island load demand & \(E_{load}\ =\ \sum\ E_{DGs}\) & \(E_{load}\ =\ \sum\ E_{DGs}\ +\ E_{ess}\) \\ \hline State of charge (SOC) constraints & \(E_{ess,min}\ <\ E_{ess}\ \leq\ E_{ess,max}\) & \(0\ <\ E_{ess}\ \leq\ E_{ess,critical}\) \\ \hline \end{tabular}
\end{table}
Table 1: Island constraint.  
Figure 3: Power curve in island mode operation.  
Transfer the load by using another interconnection switch. If only one interconnection switch cannot transfer all the loads of the distribution line, this step attempts to use other interconnection switches to transfer the remaining loads of the distribution line. If it can be realized, go to step (6), otherwise go to step (4);
4. Look for other defense strategies to make up for the shortcomings of a single solution. If the power flow direction of DG and the tie-line are the same, the DG can be used to assist when the loads cannot be completely transferred by the tie-lines, and if there are still no transfer loads, the island mode is used. If the island can be achieved, go to step (5);
5. If it can achieve complete defense, go to step (7), otherwise go to step (6);
6. If the ability of the active defense plan is exceeded after the above process, part of the loads must be cut off in order to ensure the safety constraints of the distribution network;
7. If there are other high-risk lines, go to step (1), otherwise, go to step (8);
8. Get an active defense evolution plan and release dispatch instructions to the next moment until the end of the disaster;
9. Determine if the ice disaster is over. If it is not over, go to step (1), otherwise the process end.  
The evolution flow chart of the active defense strategy is shown in Figure 4.  
Figure 4: Evolution process of CADS.  
According to the coordinated active defense evolution process, it can deal with the group faults risk and reduce the impact of accidents in the ADN.
## 5 Experiment Evaluation
### Experiment Results
#### 5.2.2 Latency  
The impact of proposed framework is evaluated based on latency in service delivery. The sum of task completion time and network propagation is calculated to find the latency of service delivery. The Fog infrastructure brings the computational capacity closer to the data source. This results in the reduction of network delay. The effect of Fog computing would be less if the size of the data source is less. The proposed architecture increases the latency by adding the Blockchain feature. Figure 8 shows the comparison of the proposed architecture latency of service delivery. The users can disable the Blockchain feature in the proposed BFAN architecture. The processing overhead will be increased with the Blockchain feature. The interval of the subsequent task can be managed, which further improves the performance of the proposed BFAN framework.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 2 MANET Routing Protocols: Background and a Review of the Literature
### Related Work  
A summary of the related work on heterogeneous MANETs and their routing performance is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Scope** & **Routing** & **Proactive/** & **Heterogeneous** & **Simulation?** \\ \hline
[15] &
\begin{tabular}{c} Routing protocols and their \\ overheads \\ \end{tabular} & AODV, OLSR & Reactive & - & \(\surd\) \\ \hline
[26] &
\begin{tabular}{c} On-demand routing \\ \end{tabular} & AODV & Reactive & - & \(\surd\) \\ \hline
[29] &
\begin{tabular}{c} Periodic control message \\ exchanges \\ \end{tabular} & OLSR & Proactive & - & \(\surd\) \\ \hline
[28] &
\begin{tabular}{c} Proactive protocols \\ \end{tabular} & OLSR & Proactive & - & \(\surd\) \\ \hline
[32] & Hybrid protocols & Hybrid & Both & - & \(\surd\) \\ \hline
[31] &
\begin{tabular}{c} Scalability in hybrid protocols \\ \end{tabular} & Hybrid & Both & - & \(\surd\) \\ \hline
[7] &
\begin{tabular}{c} DYMO routing and path \\ accumulation \\ \end{tabular} & DYMO & Reactive & - & \(\surd\) \\ \hline
[17] &
\begin{tabular}{c} BATMAN protocol and its \\ message delivery \\ \end{tabular} & BATMAN & Reactive & - & \(\surd\) \\ \hline
[18] &
\begin{tabular}{c} Challenges in OLSR and BATMAN \\ \end{tabular} & OLSR, BATMAN & Reactive & - & \(\surd\) \\ \hline
[27] & AODV in heterogeneous MANETs & AODV & Reactive & - & \(\surd\) \\ \hline
[19] &
\begin{tabular}{c} OLSR and AODV in mobile ad hoc \\ networks \\ \end{tabular} & OLSR, AODV & Both & - & \(\surd\) \\ \hline
[22] &
\begin{tabular}{c} Surveys MANET routing protocols \\ \end{tabular} & General & Both & - & \(\surd\) \\ \hline
[24] &
\begin{tabular}{c} Reviews of energy-efficient routing \\ MANETs \\ \end{tabular} & General & Both & - & \(\surd\) \\ \hline
[23] &
\begin{tabular}{c} Hybrid routing protocols for \\ MANETs \\ \end{tabular} & Hybrid & Both & - & \(\surd\) \\ \hline
[30] & Routing protocols in MANETs & General & Both & - & \(\surd\) \\ \hline Our work &
\begin{tabular}{c} Performance comparison of AODV, OLSR, BATMAN and \\ DYMO in heterogenous networks \\ \end{tabular} & Both & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the related work on MANETs’ heterogenous routing protocols.
# Novel 3U Stand-Alone CubeSat Architecture for Autonomous Near Earth Asteroid Fly-By
## 4 Subsystems Analysis
### Power
GomSpace P31u and ISIS iEPS-A appeared as the most valuable solutions for this application. The former is characterized by a slightly higher mass (200 g vs. 184 g) and lower battery capacity (19.5 Wh vs. 22.5 Wh), but its lower volume (\(\sim\)0.2U vs. \(\sim\)0.23U) played a significant role for this compact application. Then for this work, the GomSpace P31u is selected, keeping in mind that the ISIS iEPS-A is an equally valuable alternative. Figure 8: Solar panels BOL available power in function of distance from the Sun and increasing incidence angle 0\({}^{\circ}\)–80\({}^{\circ}\) (from top to bottom).
# Inland Water Level Monitoring from Satellite Observations:
## 3 Review Results
### Study Regions  
As shown in Figure 10, lakes around the globe are the most common type of inland water bodies used for the evaluation of the performances of satellite missions for water level retrievals, while the use of rivers is limited only to 18 out of 48 publications. The return waveforms are contaminated over narrow rivers and small lakes due to the size of the radar footprint, and the extracted ranges are corrupted leading to inaccurate water level measurements [141]. In the near future, more researches are expected to be conducted on rivers benefiting from the technological achievements of state-of-the-art missions. Publications incorporating multiple types or continents in their applications are included in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Publication** & **Satellite Mission** & **Data** & **Retracker** & **Validation Data** & **Accuracy Metrics** \\ \hline \multirow{4}{*}{[115]} & \multirow{4}{*}{Sentinel-3, Jason-3: GDR-D, Sent-3: ESA Land_IPF_06.07\_V1.5} & \multirow{4}{*}{Jason-3: (Ocean, OCOG, SAMOSA)} & & \multicolumn{1}{c}{Absolute biases: Jason-3: \(-0.03\pm 0.04\) m (Ocean), \(-0.2\pm 0.03\) m (OCOG) and Sen-3: \(-0.01\pm 0.02\) m (SAMOSGA), \(0.29\pm 0.02\) m (OCOG) Lowest RMS \(=0.02\pm 0.07\) m (OCOG, RMS of f-3 \(>0.03\) m, \(-0.05\) m) \\ \hline \multirow{4}{*}{[71]} & \multirow{4}{*}{CryoSat-2, HY-2B, HY-2B, HY-2B, J-3: GDR-L2, Sen-3: SAR_IR_12.2} & \multirow{4}{*}{Jason-3, Sen-3A, CryoSat-2, Sen-6 (Ocean)} & & \multicolumn{1}{c}{RMSES: CryoSat-2 (0.05-0.30 m), HY-2B (0.04-0.23 m), HY-2C (0.07-0.26 m), J-3 (0.04-0.14 m), and \(\mathrm{R}\)-CryoSat-2 (0.04-0.13 m), Sen-6 (Ocean)} \\  & & & & \multicolumn{1}{c}{} \\  & & & & (0.07-0.09 m), HY-2B (0.34-0.97), \\  & & & & (0.18-0.99), Sen-3 (0.87-0.98) \\ \hline \multirow{4}{*}{[72]} & \multirow{4}{*}{Jason-2/-3, ERS-2, ENVISAT, CryoSat-2, SARAL, Sen-3: A CFoSat-2 (CROP, I  
the relevant categories on the heatmap. The most used study area is the Qinghai-Tibetan Plateau in China [58; 62; 64; 65; 66; 67; 73; 97; 102; 104; 105; 106; 111].
# Systematic Review and Meta-Analysis of Melatonin Quantification in Wine
## 2 Materials and Methods
### Statistical Analyses  
Meta-analyses were performed on all 15 the studies that reported melatonin concentrations in wines, excluding studies in which concentrations of melatonin isomers were presented [37; 38; 39].  
An online statistical tool, METANALYSISONLINE, supported by ELIXIR Hungary ([[https://metaanalysisonline.com/](https://metaanalysisonline.com/)]([https://metaanalysisonline.com/](https://metaanalysisonline.com/)), (accessed on 7 April 2025)), was used to perform meta-analyses and to generate forest plot layouts using Cochrane's RevMan ([[https://revman.cochrane.org/info](https://revman.cochrane.org/info)]([https://revman.cochrane.org/info](https://revman.cochrane.org/info))). As a forest plot is the most efficient tool for presenting heterogeneity and pooled results, we chose to generate only forest plots instead of funnel plots, and included [PERSON]'s test and omitted potential publication error for readers. Meta-analyses were calculated and expressed as means of melatonin concentrations with standard deviations and the number of wine samples.  
Studies were weighted to reflect their relative contribution to the overall summary effect, and the method used was inverse variance weighting. A random effects model was utilized to determine the most appropriate model for meta-analysis and panel data analysis, taking the variability between different studies into consideration. To estimate the between-study heterogeneity, the DerSimonian and Laird method was used.  
Heterogeneity in meta-analysis refers to the variations in different study outcomes. The classical measure of heterogeneity is [PERSON]'s Q test.  
The \(p\)-value associated with heterogeneity tests indicates the probability of observing the given level of heterogeneity if the true effect is the same across all studies. A low \(p\)-value (typically less than 0.05) suggests statistically significant heterogeneity, meaning there is evidence of variability in the true effects across studies beyond what would be expected by chance. High \(p\)-values suggest homogeneity or a lack of heterogeneity.  
The I\({}^{2}\), also known as Cochran's Q statistic, is a statistical measure that quantifies the degree of heterogeneity or inconsistency between the results of different studies. I\({}^{2}\) was calculated for each meta-analysis. If I\({}^{2}\) is more than 50%, then the heterogeneity is considerable, and if I\({}^{2}\) is lower than 50%, heterogeneity is insignificant.  
The random effects model with the inverse variance method was used to obtain the summarized raw means (MRAW) and the confidence interval, a measure of dispersion. The number of eligible studies was limited, and it may be difficult to assign reasonable values to the bias parameters and priors for quantitative bias analysis methods. The review authors were required to search all possible sources for study reports and results. Due to the fact that only 15 non-randomized records were included in the meta-analyses, it was difficult to accurately evaluate whether the results were influenced by publication bias.
# Force of Intermaximillary Late Elastics from Different Suppliers: A Comparative In Vitro Study
## 3 Results
### Initial Force
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Hours**} & \multirow{2}{*}{**Manufacturer**} & \multicolumn{2}{c}{**Mean**} & \multirow{2}{*}{**SD**} & \multirow{2}{*}{**Variance**} & \multirow{2}{*}{**Minimum**} & \multirow{2}{*}{**Maximum**} & \multirow{2}{*}{\(p\)**} \\  & & & **Initial Force (N)** & & & & & \\ \hline \multirow{6}{*}{0} & Dent & 1.301 & 0.222 & 0.049 & 0.860 & 1.990 & \\  & AO & 1.109 & 0.093 & 0.009 & 0.870 & 1.370 & \\  & 3M & 1.406 & 0.329 & 0.108 & 0.910 & 2.500 & \textless{}0.0001 \\  & OO & 1.550 & 0.218 & 0.047 & 1.100 & 2.060 & \\  & G\&H & 1.124 & 0.145 & 0.021 & 0.790 & 1.510 & \\ \hline \multirow{2}{*}{**Hours**} & \multirow{2}{*}{**Manufacturer**} & \multirow{2}{*}{**Force degradation (\%)**} & \multirow{2}{*}{**SD**} & \multirow{2}{*}{**Variance**} & \multirow{2}{*}{**Minimum**} & \multirow{2}{*}{**Maximum**} & \multirow{2}{*}{\(p\)**} \\  & Dent & 16.38 & 7.82 & 61.10 & \(-\)2.15 & 38.14 & \\  & AO & 18.76 & 8.31 & 69.04 & \(-\)6.19 & 42.98 & \\
2 & 3M & 10.05 & 4.45 & 19.82 & \(-\)4.46 & 20.69 & \textless{}0.0001 \\  & OO & 11.74 & 4.49 & 20.12 & 5.11 & 26.56 & \\  & G\&H & 17.74 & 7.05 & 49.75 & \(-\)0.94 & 35.14 & \\ \hline \multirow{6}{*}{8} & Dent & 22.83 & 8.37 & 70.00 & 1.94 & 44.76 & \\  & AO & 27.88 & 9.30 & 86.40 & 7.44 & 51.75 & \\  & 3M & 14.90 & 6.32 & 39.97 & \(-\)17.56 & 30.26 & \textless{}0.0001 \\  & OO & 16.25 & 5.15 & 26.54 & 4.79 & 30.05 & \\  & G\&H & 22.24 & 6.32 & 39.93 & 10.58 & 37.11 & \\ \hline \multirow{6}{*}{24} & Dent & 28.32 & 7.73 & 59.74 & 9.71 & 47.10 & \\  & AO & 33.20 & 9.52 & 90.60 & 13.08 & 54.89 & \\  & 3M & 20.43 & 6.25 & 39.03 & 5.60 & 39.67 & \textless{}0.0001 \\  & OO & 19.62 & 5.85 & 34.21 & 4.73 & 33.00 & \\  & G\&H & 25.58 & 6.29 & 39.63 & 12.24 & 43.30 & \\ \hline \multirow{6}{*}{48} & Dent & 30.78 & 7.91 & 62.63 & 11.83 & 47.74 & \\  & AO & 36.08 & 9.89 & 97.88 & 14.55 & 61.65 & \\ \cline{1-1}  & 3M & 24.79 & 7.63 & 58.29 & 6.40 & 45.80 & \textless{}0.0001 \\ \cline{1-1}  & OO & 21.23 & 6.06 & 36.75 & 3.38 & 35.82 & \\ \cline{1-1}  & G\&H & 27.49 & 6.56 & 43.09 & 12.63 & 45.36 & \\ \hline \hline \end{tabular} N = newton; SD = standard deviation; Dent = Dentaurum; AO = American Orthodontics; OO = Ortho Organizers; G&H = G&H Orthodontics.  
\end{table} 
Table 2: Mean initial forces in newtons and force degradation in percent at 2, 8, 24, and 48 h.
## 4 Discussion
### Determination of Drought Events  
This study utilizes a long-term drought index and remote sensing datasets to analyze the impact of drought events occurring outside the flux observation periods on the GPP of rubber forests. The study employs SPEI indices at different time scales to evaluate drought events, with the SPEI index reflecting the deviation of rainfall from potential evapotranspiration [58]. Consequently, there may be inconsistencies in drought index thresholds and suitable monitoring time scales among the four sites during the occurrence of drought stress [59]. Generally, a lower drought index correlates with a smaller GPP deviation and a more pronounced suppression of GPP. The SR site receives nearly 3000 mm of annual precipitation, while the other sites receive between 1600 and 2000 mm. From a precipitation perspective, the abundant rainfall at the SR site diminishes the likelihood of experiencing drought threats. During the rainy season and the cool dry season (which differs from the other three sites as this site also receives ample precipitation), GPP increased by 2%-5% during drought periods when SPEI was less than \(-1\). This may be because, when SPEI is less than \(-1\), the precipitation in this region still meets the evapotranspiration requirements of rubber forests, and the ample sunlight provides additional energy for photosynthesis. The SR site only exhibited a slight decrease of 5%-6% under drought conditions during the warm dry season, which is also less than the reductions observed at the other sites.  
During the abundant rainy seasons at the DR, XR, and SR sites, no significant losses (more than 3%) due to drought stress were observed in GPP deviations, regardless of whether SPEI03 or SPEI12 was below \(-1.0\). During the cool dry season with reduced precipitation at the DR, XR, and SR sites, a significant reduction in GPP was observed when using the same drought threshold, with the magnitude of decrease ranging between 10% and 29%. This season is a critical period for rubber forests, marked by new leaf emergence and rapid growth [60], during which the trees are particularly susceptible to drought stress. All four sites exhibited a significant decline in GPP due to drought stress. It is noteworthy that, at the DR and XR sites, the GPP deviations during the drought indicated by SPEI12 are substantially greater than those indicated by SPEI03 (Table 3), suggesting that the rubber forests in these two regions are more sensitive to rainfall from the previous rainy season in the cool dry season. From December to April, the moisture demands of rubber trees consistently exceed effective precipitation, with the cumulative difference between supply and demand reaching its maximum in April [25]. During this period, the transpired moisture in rubber forests is primarily derived from water stored in the soil from the previous rainy season. Thus, factors such as soil moisture and the differences between precipitation and evapotranspiration must be considered in determining drought events.
## 4 Results
### Security Analysis
#### 4.1.1 Brute Force Attack  
The architecture of passwordless authentication relies on hardware keys or bio-metric identification devices owned by users. These authentication factors offer high levels of security, making it challenging for attackers to obtain users' identity information through brute force attacks. This architecture significantly enhances system security. This study adopts the characteristics of a passwordless authentication architecture, eliminating the need for traditional password login methods. This approach effectively mitigates brute force attacks, as attackers cannot infiltrate the system by guessing or cracking passwords.
## 8 Toward a Theoretical Framework
### Security, Privacy, and Familiarity Affecting Trust in the 6 GCloHE System  
To date, no study investigated the approaches where the 6 GCloHE system security, privacy, and familiarity can affect trust and, in turn, how trust can affect risk perception and attitudes toward use in healthcare applications. No research considered ways in which risk perception can mediate to bolster the relationship between trust and users' attitudes toward using the 6 GCloHE system technology at the heart of communication for the transport layer in the IoHE architecture. This would be the 6G Tactile Internet that offers speed, accuracy, real time latency, energy conservation, and reliability among sensors and actuators close to a human body. The 6G Tactile Internet, 6G Wi-Fi, or 6G FAW can reduce healthcare costs and improve its quality by using different sensors to read a patient's health parameters, thus improving the patient's quality of life.  
Cloud computing is one of the enabling technologies for IoHE that offers significant benefits to healthcare organizations such as rapid elasticity, self-healing, and self-configuration. It is employed at the processing layer, also called the middleware layer, in the five-layer 6 GCIoHE architecture. In the IoHE, the multitude of sensors generates a massive amount of data that can be stored and analyzed with the help of a cloud infrastructure. Medical professionals can monitor a patient's health, whose information is collected through various sensors and is stored in the cloud. Even though the cloud provides many advantages, one of the key challenges is latency. The process of data collection and analysis by the sensors is performed through the Internet and should reach physicians. This process takes a significant amount of time, which is not suitable for emergency healthcare services. The ideal solution to reduce the time delay is the latency offered by 6G enabled Tactile Internet, 6G Wi-Fi, or 6G FAW networks. In this layer, state-of-the-art applications of 6 GCIoHE system-related technologies in the healthcare domain are presented.  
The business model concepts underlying the healthcare ecosystem have their precedents in technology-based businesses that closely mirror those seen in the ICT industry. As with healthcare today, ICT a generation ago was part of a classically maturing market with disruptive innovation. One must re-imagine business models to find ways to innovate at scale and with speed as a differentiator for improving healthcare business models and earning superior returns. Consequently, this study sheds insights on developing a novel framework to measure the causes and the effects of contingency factors and how they can influence users' attitudes toward using the 6 GCIoHE systems. Security, privacy, and familiarity factors are discussed below in more detail to pave the way for developing the theoretical framework.